1.Wintrobe's Clinical Hematology, 11th Ed by John P. Greer (Editor), John Foerster (Editor), John N. Lukens (Editor) Publisher: Lippincott Williams & Wilkins Publishers; 11th edition (December 2003) By OkDoKeY

2. Wintrobe's Clinical Hematology CONTENTS Editors Contributors Dedication Preface Volume 1 Volume 2 Volume 1 Top PART I LABORATORY HEMATOLOGY 1 Examination of the Blood and Bone Marrow Sherrie L. Perkins 2 Clusters of Differentiation Frixos Paraskevas 3 Clinical Flow Cytometry Frixos Paraskevas 4 Cytogenetics Sheila N. J. Sait and Maria R. Baer 5 Molecular Biology and Hematology Rebecca L. Shattuck-Brandt and Stephen J. Brandt PART II NORMAL HEMATOLOGIC SYSTEM Section 1. Hematopoiesis 6 Origin and Development of Blood Cells Maurice C. Bondurant and Mark J. Koury Section 2. The Erythrocyte 7 Erythropoiesis Emmanuel N. Dessypris and Stephen T. Sawyer 8 The Mature Erythrocyte Marilyn J. Telen and Russel E. Kaufman 9 Destruction of Erythrocytes Bertil Glader Section 3. Granulocytes and Monocytes 10 Neutrophilic Leukocytes Keith M. Skubitz 11 The Human Eosinophil Paige Lacy, Allan B. Becker, and Redwan Moqbel 12 Basophilic Leukocytes: Mast Cells and Basophils A. Dean Befus and Judah A. Denburg 13 Mononuclear Phagocytes J. Brice Weinberg 14 Phagocytosis Frixos Paraskevas Section 4. Lymphocytes 15 Lymphocytes and Lymphatic Organs Frixos Paraskevas 16 B Lymphocytes Frixos Paraskevas 17 T Lymphocytes and Natural Killer Cells Frixos Paraskevas 18 Effector Mechanisms in Immunity Frixos Paraskevas Section 5. Hemostasis 19 Megakaryocytes and Platelets Kenneth Kaushansky and Gerald J. Roth 20 Platelet Function in Hemostasis and Thrombosis David C. Calverley and Lori J. Maness 21 Blood Coagulation and Fibrinolysis Kathleen Brummel-Ziedins, Thomas Orfeo, Nancy Swords Jenny, Stephen J. Everse, and Kenneth G. Mann 22 Endothelium: Angiogenesis and the Regulation of Hemostasis Paul J. Shami and George M. Rodgers 3. PART III THERAPEUTIC MODALITIES 23 Red Cell, Platelet, and White Cell Antigens Kathryn E. Webert, Howard H. W. Chan, James William Smith, Nancy M. Heddle, and John G. Kelton 24 Transfusion Medicine Susan A. Galel, James M. Malone, III, and Maurene K. Viele 25 Hematopoietic Stem Cell Transplantation Richard A. Nash 26 Gene Therapy for Hematologic Disorders, Human Immunodeficiency Virus Infection, and Cancer John F. Tisdale, Cynthia E. Dunbar, Jay N. Lozier, and Stacey A. Goodman PART IV DISORDERS OF RED CELLS Section 1. Introduction 27 Anemia: General Considerations Bertil Glader Section 2. Disorders of Iron Metabolism and Heme Synthesis 28 Iron Deficiency and Related Disorders Nancy C. Andrews 29 Sideroblastic Anemias Sylvia S. Bottomley 30 Hemochromatosis Corwin Q. Edwards 31 Porphyria Sylvia S. Bottomley Section 3. Hemolytic Anemia 32 Hereditary Spherocytosis and Other Anemias Due to Abnormalities of the Red Cell Membrane William C. Mentzer and Bertil Glader 33 Hereditary Hemolytic Anemias Due to Enzyme Disorders Bertil Glader 34 Mechanisms of Immune Destruction of Erythrocytes Charles J. Parker 35 Autoimmune Hemolytic Anemias Anne T. Neff 36 Alloimmune Hemolytic Disease of the Fetus and Newborn Anne F. Eder and Catherine S. Manno 37 Paroxysmal Nocturnal Hemoglobinuria Charles J. Parker and Russell E. Ware 38 Acquired Nonimmune Hemolytic Disorders Michael R. Jeng and Bertil Glader Section 4. Hereditary Disorders of Hemoglobin Structure and Synthesis 39 Abnormal Hemoglobins: General Principles John N. Lukens 40 Sickle Cell Anemia and Other Sickling Syndromes Winfred C. Wang 41 Unstable Hemoglobin Disease John N. Lukens 42 Thalassemias and Related Disorders: Quantitative Disorders of Hemoglobin Synthesis Caterina Borgna-Pignatti and Renzo Galanello Section 5. Other Red Cell Disorders 43 Megaloblastic Anemias: Disorders of Impaired DNA Synthesis Ralph Carmel 44 Acquired and Inherited Aplastic Anemia Syndromes Eva C. Guinan and Akiko Shimamura 45 Red Cell Aplasia Emmanuel N. Dessypris and Jeffrey M. Lipton 46 Congenital Dyserythropoietic Anemias Peter W. Marks and Bertil Glader 47 Anemias Secondary to Chronic Disease and Systemic Disorders Robert T. Means, Jr. 48 Anemias Unique to Pregnancy and the Perinatal Period Robert D. Christensen and Robin K. Ohls 49 Hemoglobins Associated with Cyanosis: Methemoglobinemia and Low-Affinity Hemoglobins John N. Lukens 50 Erythrocytosis Robert T. Means, Jr. Volume 2 Top 4. PART V DISORDERS OF HEMOSTASIS AND COAGULATION Section 1. Introduction 51 Diagnostic Approach to the Bleeding Disorders George M. Rodgers Section 2. Thrombocytopenia 52 Thrombocytopenia: Pathophysiology and Classification Shirley Parker Levine 53 Thrombocytopenia Caused by Immunologic Platelet Destruction Shirley Parker Levine 54 Thrombotic Thrombocytopenic Purpura and Other Forms of Nonimmunologic Platelet Destruction Shirley Parker Levine 55 Miscellaneous Causes of Thrombocytopenia Shirley Parker Levine Section 3. Other Disorders of Primary Hemostasis 56 Bleeding Disorders Caused by Vascular Abnormalities Matthew M. Rees and George M. Rodgers 57 Thrombocytosis Shirley Parker Levine 58 Qualitative Disorders of Platelet Function Shirley Parker Levine Section 4. Coagulation Disorders 59 Inherited Coagulation Disorders Kenneth D. Friedman and George M. Rodgers 60 Acquired Coagulation Disorders George M. Rodgers Section 5. Thrombosis 61 Thrombosis and Antithrombotic Therapy Steven R. Deitcher and George M. Rodgers PART VI NONMALIGNANT DISORDERS OF LEUKOCYTES, THE SPLEEN, AND/OR IMMUNOGLOBINS 62 Diagnostic Approach to Malignant and Nonmalignant Disorders of the Phagocytic and Immune Systems Thomas L. McCurley and John P. Greer 63 Neutropenia Raymond G. Watts 64 Qualitative Disorders of Leukocytes Keith M. Skubitz 65 Abnormalities of the Monocyte-Macrophage System: Lysosomal Storage Diseases Margaret M. McGovern and Robert J. Desnick 66 Langerhans Cell Histiocytosis H. Stacy Nicholson 67 Infectious Mononucleosis and Other Epstein-Barr VirusRelated Disorders Thomas G. Gross 68 Primary Immunodeficiency Syndromes Anthony R. Hayward 69 Acquired Immunodeficiency Syndrome Elaine M. Sloand and Jerome E. Groopman 70 Disorders of the Spleen Jeremy Goodman, Martin I. Newman, and William C. Chapman 5. PART VII HEMATOLOGIC MALIGNANCIES Section 1. General Aspects 71 Hematopoietic-Lymphoid Neoplasms: Principles of Diagnosis John B. Cousar 72 Complications of Hematopoietic Neoplasms Madan H. Jagasia and Edward R. Arrowsmith 73 Principles and Pharmacology of Chemotherapy Kenneth R. Hande 74 Immunotherapy Stanford J. Stewart 75 Supportive Care in Hematologic Malignancies Madhuri Vusirikala Section 2. Acute Leukemias 76 Molecular Genetics of Acute Leukemia Mary Ann Thompson 77 Classification and Differentiation of the Acute Leukemias David R. Head 78 Acute Lymphoblastic Leukemia in Adults Thai M. Cao and Steven E. Coutre 79 Acute Myeloid Leukemia in Adults John P. Greer, Maria R. Baer, and Marsha C. Kinney 80 Acute Lymphoblastic Leukemia in Children James A. Whitlock and Paul S. Gaynon 81 Acute Myelogenous Leukemia in Children Robert J. Arceci and Richard Aplenc 82 Acute Promyelocytic Leukemia Steven L. Soignet and Peter G. Maslak 83 Myelodysplastic Syndromes Alan F. List, Avery A. Sandberg, and Donald C. Doll Section 3. Myeloproliferative Disorders 84 Chronic Myeloid Leukemia Ian Rabinowitz and Richard S. Larson 85 Polycythemia Vera Robert T. Means, Jr. 86 Myelofibrosis Douglas A. Clark and Wilbur L. Williams 87 Systemic Mastocytosis Alexandra S. Worobec and Dean D. Metcalfe Section 4. Lymphoproliferative Disorders 88 Diagnosis and Classification of Non-Hodgkin Lymphomas Thomas L. McCurley and William R. Macon 89 Molecular Aspects of Non-Hodgkin Lymphomagenesis Andreas Rosenwald, Louis M. Staudt, Justus Georg Duyster, and Stephan W. Morris 90 Non-Hodgkin Lymphomas in Adults John P. Greer 91 Non-Hodgkin Lymphomas in Children John T. Sandlund and Frederick G. Behm 92 Chronic Lymphocytic Leukemia James B. Johnston 93 Hairy Cell Leukemia James B. Johnston 94 Cutaneous T-Cell Lymphomas: Mycosis Fungoides and Szary Syndrome John A. Zic, Monika G. Kiripolsky, Katherine S. Hamilton, and John P. Greer 95 Hodgkin Disease Richard S. Stein and David S. Morgan Section 5. Plasma Cell Dyscrasias 96 Practical Aspects of the Clinical Approach to Patients with Monoclonal Immunoglobulin Disorders Philip R. Greipp and Rafael Fonseca 97 Monoclonal Gammopathy of Undetermined Significance and Smoldering Multiple Myeloma Robert A. Kyle, S. Vincent Rajkumar, and John A. Lust 98 Multiple Myeloma Angela Dispenzieri, Martha Q. Lacy, and Philip R. Greipp 99 Immunoglobulin Light-Chain Amyloidosis (Primary Amyloidosis) Morie A. Gertz, Martha Q. Lacy, and Angela Dispenzieri 100 Waldenstrm Macroglobulinemia Rafael Fonseca and Thomas E. Witzig 101 Cryoglobulinemia, Heavy Chain Diseases, and Monoclonal GammopathyAssociated Disorders Angela Dispenzieri and Morie A. Gertz APPENDIX A: Normal Blood and Bone Marrow Values in Humans 6. APPENDIX B: Comparative Hematology Color Plate 2004 Lippincott Williams & Wilkins John P. Greer, John Foerster, John N. Lukens, George M. Rodgers, Frixos Paraskevas, and Bertil Glader Wintrobe's Clinical Hematology 7. Contributing Authors Nancy C. Andrews, MD, PhD Associate Professor, Department of Pediatrics, Harvard Medical School, Associate Investigator, Department of Medicine, Howard Hughes Medical Institute and Children's Hospital, Boston, Massachusetts Richard Aplenc, MD, MSCE Assistant Professor of Pediatrics, Department of Pediatrics, University of Pennsylvania School of Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania Robert J. Arceci, MD, PhD Director and King Fahd Professor of Pediatric Oncology, Department of Pediatric Oncology, Sidney Kimmel Comprehensive Cancer Center at John Hopkins, Baltimore, Maryland Edward R. Arrowsmith, MD, MPH Chattanooga Oncology and Hematology Associates, Chattanooga, Tennessee Maria R. Baer, MD Professor, Department of Medicine, Leukemia Section, University at Buffalo State University of New York School of Medicine and Biomedical Sciences, Roswell Park Cancer Institute, Buffalo, New York Allan B. Becker, MD, FRCPC Professor, Department of Pediatrics and Child Health, Section of Allergy and Clinical Immunology, University of Manitoba Faculty of Medicine, Health Sciences Centre, Winnipeg, Manitoba, Canada A. Dean Befus, PhD Professor and AstraZeneca Canada Inc.; Chair in Asthma Research, Department of Medicine, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada Frederick G. Behm Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee Maurice C. Bondurant, PhD Associate Professor, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee Caterina Borgna-Pignatti, MD Professor of Pediatrics, Department of Clinical and Experimental Medicine/Pediatrics, University of Ferrara, Ferrara, Italy Sylvia S. Bottomley, MD Professor of Medicine, Department of Medicine, Hematology/Oncology Section, University of Oklahoma College of Medicine and Department of Veterans Affairs Medical Center, Oklahoma City, Oklahoma Stephen J. Brandt, MD Associate Professor, Departments of Medicine, Cell and Developmental Biology, and Cancer Biology, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Kathleen Brummel-Ziedins, PhD Research Assistant Professor of Biochemistry, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont David C. Calverley, MD Assistant Professor of Medicine, Division of Hematology and Medical Oncology, University of Colorado Health Sciences Center School of Medicine, Denver, Colorado Thai M. Cao, MD Clinical Instructor of Medicine, Department of Medicine, Division of Bone Marrow Transplantation, Stanford University Medical Center, Stanford, California Ralph Carmel, MD Director of Research, Department of Medicine, New York Methodist Hospital, Brooklyn, New York, Professor of Medicine, Department of Medicine, Weill Medical College of Cornell University, New York, New York Howard H. W. Chan, MBChB, FRCPC Research Fellow, Transfusion Medicine, Departments of Hematology and Internal Medicine, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada William C. Chapman, MD Professor of Surgery; Chief, Section of Transplantation, Washington University School of Medicine, St. Louis, Missouri Robert D. Christensen, MD Professor and Chairman, Department of Pediatrics, University of South Florida College of Medicine, All Children's Hospital, St. Petersburg, Florida Douglas A. Clark, MD New Mexico Cancer Center, Albuquerque, New Mexico John B. Cousar, MD Professor of Pathology, Department of Pathology, University of Virginia Health System, Charlottesville, Virginia Steven E. Coutre, MD Assistant Professor of Medicine (Hematology), Department of Medicine, Stanford University School of Medicine, Stanford, California Steven R. Deitcher, MD Head, Section of Hematology and Coagulation Medicine, Department of Hematology and Medical Oncology, The Cleveland Clinic Foundation, Cleveland, Ohio Judah A. Denburg, MD Professor, Department of Medicine, McMaster University School of Medicine, Hamilton, Ontario, Canada Robert J. Desnick, PhD, MD Professor of Human Genetics and Pediatrics; Chairman, Department of Human Genetics, Mount Sinai School of Medicine of the City University of New York, New 8. York, New York Emmanuel N. Dessypris, MD, FACP Professor of Medicine, Medical College of Virginia, Virginia Commonwealth University School of Medicine, Chief of Medicine, H.H. McGuire Veterans Affairs Medical Center, Richmond, Virginia Angela Dispenzieri, MD Assistant Professor, Department of Medicine, Division of Hematology, Mayo Clinic, Rochester, Minnesota Donald C. Doll, MD Professor of Medicine, Departments of Hematology and Medical Oncology, Ellis Fischel Cancer Center, University of Missouri'Columbia School of Medicine, Columbia, Missouri Cynthia E. Dunbar, MD Section Chief, Molecular Hematopoiesis Section, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland Justus Georg Duyster, MD Internal Medicine III, Technical University of Munich, Munich, Germany Anne F. Eder, MD, PhD Assistant Professor, Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania Corwin Q. Edwards, MD Professor, Department of Medicine, Associate Director, Internal Medicine Training Program, University of Utah School of Medicine, Director of Graduate Medical Education, LDS Hospital, Salt Lake City, Utah Stephen J. Everse, PhD Assistant Professor, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont John Foerster, MD, FRCPC Professor of Medicine, Division of Hematology/Oncology, University of Manitoba Faculty of Medicine, Director of Research, St. Boniface General Hospital, Winnipeg, Manitoba, Canada Rafael Fonseca, MD Associate Professor of Medicine, Department of Hematology, Mayo Medical School, Mayo Clinic, Rochester, Minnesota Kenneth D. Friedman, MD Medical Director, The Blood Center of Southeastern Wisconsin, Inc., Milwaukee, Wisconsin Renzo Galanello, MD Professor of Pediatrics, Dip. di Scienze Biomediche e Biotecnologie, University of Cagliari-Ospedale Microcitemie, Cagliari, Italy Susan A. Galel, MD Associate Professor, Department of Pathology, Stanford University School of Medicine, Stanford, California Paul S. Gaynon, MD Professor of Pediatrics, Children's Hospital of Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, Californina Morie A. Gertz, MD Professor of Medicine, Division of Hematology, Mayo Medical School, Chair, Division of Hematology, Mayo Clinic, Rochester, Minnesota Bertil Glader, MD, PhD Professor of Pediatrics, Division of Hematology/Oncology, Stanford University School of Medicine, Stanford, California Jeremy Goodman, MD Surgery Resident, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri Stacey A. Goodman, MD Associate Professor of Medicine, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee John P. Greer, MD Professor of Medicine and Pediatrics, Departments of Medicine and Pediatrics, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Philip R. Greipp, MD Professor of Medicine, Department of Hematology, Mayo Medical School, Mayo Clinic, Rochester, Minnesota Jerome E. Groopman, MD Professor of Medicine, Department of Medicine, Harvard Medical School/Beth Israel Deaconess Medical Center, Boston, Massachusetts Thomas G. Gross, MD, PhD Associate Professor of Pediatrics, Department of Hematology/Oncology, Ohio State University College of Medicine and Public Health, Children's Hospital, Columbus, Ohio Eva C. Guinan, MD Associate Professor of Medicine, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Pediatric Hematology/Oncology, Children's Hospital Boston, Boston, Massachusetts Katherine S. Hamilton, MD Assistant Professor, Department of Pathology, Vanderbilt University School of Medicine, Nashville, Tennessee Kenneth R. Hande, MD Professor of Medicine and Pharmacology, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Anthony R. Hayward, MD, PhD Director, Division of Clinical Research, National Center for Research Resources, National Institutes of Health, Bethesda, Maryland David R. Head, MD 9. Professor, Department of Pathology, Vanderbilt University School of Medicine, Nashville, Tennessee Nancy M. Heddle, MSc, FCSMLSD Associate Professor, Department of Medicine, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada Madan H. Jagasia, MBBS Assistant Professor of Medicine, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Michael R. Jeng, MD Assistant Professor, Department of Pediatrics, Stanford University School of Medicine, Stanford, California Nancy Swords Jenny, PhD Research Assistant Professor, Department of Pathology, University of Vermont College of Medicine, Colchester, Vermont James B. Johnston, MBBCh, FRCPC Professor of Medicine, Department of Internal Medicine, Section of Hematology/Oncology, University of Manitoba Faculty of Medicine, Winnipeg, Manitoba, Canada Russel E. Kaufman, MD Professor and Director, The Wistar Institute, Philadelphia, Pennsylvania Kenneth Kaushansky, MD Professor and Chair, Department of Medicine, University of California, San Diego, School of Medicine, San Diego, California John G. Kelton, MD Dean and Vice-President, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada Marsha C. Kinney, MD Professor of Pathology, Department of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, Texas Monika G. Kiripolsky, BS Fourth-Year Medical Student, Department of Dermatology, Vanderbilt University School of Medicine, Nashville, Tennessee Mark J. Koury, MD Professor, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Robert A. Kyle, MD Professor of Medicine, Laboratory Medicine, and Pathology, Department of Internal Medicine, Mayo Medical School, Rochester, Minnesota Martha Q. Lacy, MD Assistant Professor of Medicine; Consultant, Department of Hematology, Mayo Clinic, Rochester, Minnesota Paige Lacy, PhD Assistant Professor, Department of Medicine, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada Richard S. Larson, MD, PhD Associate Professor, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, New Mexico Shirley Parker Levine, MD Professor of Medicine, Department of Medicine, Division of Hematology, Albert Einstein College of Medicine of Yeshiva University/Montefiore Medical Center, Bronx, New York Jeffrey M. Lipton, MD, PhD Professor of Pediatrics, Division of Pediatric Hematology/Oncology and Stem Cell Transplantation, Albert Einstein College of Medicine of Yeshiva University/Schneider Children's Hospital, New Hyde Park, New York Alan F. List, MD Professor of Medicine, University of South Florida College of Medicine, Director, Hematologic Malignancies Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida Jay N. Lozier, MD, PhD Consulting Hematologist, Department of Laboratory Medicine, Warren G. Magnuson Clinical Center, National Institutes of Health, Bethesda, Maryland John N. Lukens, MD Professor of Pediatrics, Emeritus, Division of Pediatric Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee John A. Lust, MD, PhD Associate Professor of Medicine, Department of Internal Medicine, Division of Hematology, Mayo Clinic, Rochester, Minnesota William R. Macon, MD Associate Professor of Pathology, Department of Laboratory Medicine and Pathology, Mayo Medical School, Consultant, Mayo Clinic, Rochester, Minnesota James M. Malone, III, MD Assistant Medical Director, Transfusion Service, Staff Physician, Departments of Pathology and Medicine (Hematology), Stanford University School of Medicine, Stanford, California Lori J. Maness, MD Instructor of Medicine, Division of Hematology and Medical Oncology, University of Colorado School of Medicine, Denver, Colorado Kenneth G. Mann, PhD Professor of Biochemistry and Medicine, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont Catherine S. Manno, MD Associate Professor of Pediatrics, Hematology Division, University of Pennsylvania School of Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania Peter W. Marks, MD, PhD Instructor in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachussetts Peter G. Maslak, MD 10. Associate Member, Department of Clinical Laboratories, Leukemia Service, Memorial Sloan-Kettering Cancer Center, New York, New York Thomas L. McCurley, MD Associate Professor, Department of Pathology, Vanderbilt University School of Medicine, Nashville, Tennessee Margaret M. McGovern, MD, PhD Associate Professor of Human Genetics and Pediatrics; Vice Chair, Department of Human Genetics, Mount Sinai School of Medicine of the City University of New York, New York, New York Robert T. Means, Jr., MD Professor of Medicine; Director, Department of Medicine, Hematology/Oncology Division, Medical University of South Carolina College of Medicine, Ralph H. Johnson Veterans Affairs Medical Center, Charleston, South Carolina William C. Mentzer, MD Professor, Department of Pediatrics, University of California, San Francisco, School of Medicine, San Francisco, California Dean D. Metcalfe, MD Chief, Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases/National Institutes of Health, National Institutes of Health Clinical Center, Bethesda, Maryland Redwan Moqbel, PhD Professor, Department of Medicine, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada David S. Morgan, MD Assistant Professor, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Stephan W. Morris, MD Professor, Departments of Pathology and Hematology-Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee Richard A. Nash, MD Associate Member, Fred Hutchinson Cancer Research Center, Associate Professor, University of Washington School of Medicine, Seattle, Washington Anne T. Neff, MD Assistant Professor, Department of Pathology and Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee Martin I. Newman, MD Fellow, Department of Surgery, New York Presbyterian Hospital, New York, New York H. Stacy Nicholson, MD, MPH Professor of Pediatrics, Department of Pediatric Hematology/Oncology, Oregon Health Sciences University School of Medicine, Portland, Oregon Robin K. Ohls, MD Associate Professor, Department of Pediatrics, University of New Mexico School of Medicine, Albuquerque, New Mexico Thomas Orfeo, PhD Research Associate, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont Frixos Paraskevas, MD Associate, Institute of Cell Biology, University of Manitoba Faculty of Medicine, Cancer Care Manitoba, Winnipeg, Manitoba, Canada Charles J. Parker, MD Professor, Department of Medicine, University of Utah School of Medicine, Veterans Affairs Medical Center, Salt Lake City, Utah Sherrie L. Perkins, MD, PhD Professor, Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah Ian Rabinowitz, MD Assistant Professor, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico Harvey A. Ragan, DVM Staff Pathologist, Department of Toxicology, Batelle, Pacific Northwest National Laboratory, Richland, Washington S. Vincent Rajkumar, MD Associate Professor of Medicine, Department of Hematology, Mayo Clinic, Rochester, Minnesota Matthew M. Rees, MD Rutherford Hospital, Rutherfordton, North Carolina George M. Rodgers, MD, PhD Professor of Medicine and Pathology, University of Utah School of Medicine, Health Sciences Center, Veterans Affairs Medical Center, Medical Director, Coagulation Laboratory, ARUP Laboratories, Salt Lake City, Utah Andreas Rosenwald, MD Institute of Pathology, University or Wrzburg, Wrzburg, Germany Gerald J. Roth, MD Professor, Department of Medicine, University of Washington School of Medicine, Seattle Veterans Affairs Hospital, Seattle, Washington Sheila N. J. Sait Clinical Cytogenetics Laboratory, Department of Pathology and Leukemia Section, Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York Avery A. Sandberg, MD, DSc Professor, Department of Pathology, University of Arizona College of Medicine, Phoenix, Arizona John T. Sandlund, MD Associate Professor of Pediatrics, Department of Hematology-Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee Stephen T. Sawyer, PhD 11. Professor, Department of Pharmacology and Toxicology, Virginia Commonwealth University School of Medicine, Richmond, Virginia Paul J. Shami, MD Associate Professor of Medicine, Division of Medical Oncology, University of Utah School of Medicine, Veterans Affairs Medical Center, Salt Lake City, Utah Rebecca L. Shattuck-Brandt, PhD, MEd Teacher, Science Department, University School of Nashville, Nashville, Tennessee Akiko Shimamura, MD, PhD Instructor in Pediatrics; Assistant in Medicine, Department of Pediatric Hematology/Oncology, Children's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts Keith M. Skubitz, MD Professor, Department of Medicine, Division of Hematology, Oncology, and Transplantation, Musculoskeletal Tumor Program, University of Minnesota Medical School'Minneapolis, Minneapolis, Minnesota Elaine M. Sloand, MD Assistant to the Director; Clinical Investigator, Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland James William Smith, BS, MLT Coordinator, Platelet Immunology Laboratory, Department of Medicine, McMaster University Faculty of Health Sciences, Canadian Blood Services, Hamilton, Ontario, Canada Steven L. Soignet, BS, MD Consultant, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York Louis M. Staudt, MD, PhD Chief, Lymphoid Malignancies Section, Metabolism Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland Richard S. Stein, MD Associate Professor of Medicine, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Stanford J. Stewart, MD Vice President, Clinical Research, Corixa Corporation, South San Francisco, California Marilyn J. Telen, MD Wellcome Professor of Medicine; Chief, Division of Hematology, Department of Medicine, Division of Hematology, Duke University Medical Center, Durham, North Carolina Mary Ann Thompson, MD, PhD Assistant Professor, Department of Pathology, Division of Hematopathology, Vanderbilt University School of Medicine, Nashville, Tennessee John F. Tisdale, MD Senior Investigator, Molecular and Clinical Hematology Branch, National Institute of Diabetes and Digestive and Kidney Disorders, Bethesda, Maryland Maurene K. Viele, MD Clinical Associate Professor, Department of Pathology, Stanford University School of Medicine, Stanford, California Madhuri Vusirikala, MD Assistant Professor, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, Tennessee Winfred C. Wang, MD Professor, Department of Pediatrics, University of Tennessee, Memphis, College of Medicine, Member, Department of Hematology/Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee Russell E. Ware, MD, PhD Professor, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina Raymond G. Watts, MD The Frederick W. Renneker, III Endowed Chair in Pediatric Education; Associate Professor, Department of Pediatrics, Division of Hematology/Oncology, University of Alabama School of Medicine, Birmingham, Alabama Kathryn E. Webert, BSc, MD, FRCPC Clinical Scholar, Department of Medicine, McMaster University Faculty of Health Sciences, Canadian Blood Services, Hamilton, Ontario, Canada J. Brice Weinberg, MD Professor, Department of Medicine, Duke University School of Medicine, Durham Veterans Administration Hospital, Durham, North Carolina James A. Whitlock, MD Associate Professor of Pediatrics; Director, Division of Pediatric Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee Wilbur L. Williams, MD Associate Professor, Department of Laboratory Medicine, New Mexico VA Heath Care System, Albuquerque, New Mexico Thomas E. Witzig, MD Professor of Medicine, Department of Hematology, Mayo Clinic, Rochester, Minnesota Alexandra S. Worobec, MD Adjunct Investigator, Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland John A. Zic, MD Assistant Professor of Medicine, Department of Medicine, Division of Dermatology, Vanderbilt University School of Medicine, Nashville Veterans Administration, Nashville, Tennessee 12. Dedication To Dr. Maxwell M. Wintrobe 13. EDITORS EDITED BY JOHN P. GREER, MD PROFESSOR OF MEDICINE AND PEDIATRICS DEPARTMENTS OF MEDICINE AND PEDIATRICS DIVISION OF HEMATOLOGY/ONCOLOGY VANDERBILT, UNIVERSITY SCHOOL OF MEDICINE NASHVILLE, TENNESSEE JOHN FOERSTER, MD, FRCPC PROFESSOR OF MEDICINE DIVISION OF HEMATOLOGY/ONCOLOGY UNIVERSITY OF MANITOBA FACULTY OF MEDICINE; DIRECTOR OF RESEARCH ST. BONIFACE GENERAL HOSPITAL, WINNIPEG MANITOBA, CANADA JOHN N. LUKENS, MD PROFESSOR OF PEDIATRICS, EMERITUS DIVISION OF PEDIATRIC HEMATOLOGY/ONCOLOGY VANDERBILT UNIVERSITY SCHOOL OF MEDICINE NASHVILLE, TENNESSEE GEORGE M. RODGERS, MD, PHD PROFESSOR OF MEDICINE AND PATHOLOGY UNIVERSITY OF UTAH SCHOOL OF MEDICINE HEALTH SCIENCES CENTER VETERANS AFFAIRS MEDICAL CENTER; MEDICAL DIRECTOR COAGULATION LABORATORY, ARUP LABORATORIES SALT LAKE CITY, UTAH FRIXOS PARASKEVAS, MD ASSOCIATE, INSTITUTE OF CELL BIOLOGY UNIVERSITY OF MANITOBA; FACULTY OF MEDICINE CANCER CARE MANITOBA, WINNIPEG MANITOBA, CANADA BERTIL GLADER, MD, PHD PROFESSOR OF PEDIATRICS DIVISION OF HEMATOLOGY/ONCOLOGY STANFORD UNIVERSITY SCHOOL OF MEDICINE STANFORD, CALIFORNIA Secondary Authors JONATHAN PINE Acquisitions Editor Silverchair Science + Communications ALYSON FORBES Developmental Editor Silverchair Science + Communications TANYA LAZAR Managing Editor Silverchair Science + Communications MARY ANN MCLAUGHLIN Supervising Editor Silverchair Science + Communications LUCINDA EWING Production Editor Silverchair Science + Communications JANE B. MCQUEEN Production Editor Silverchair Science + Communications BEN RIVERA Manufacturing Manager CHRISTINE JENNY Cover Designer 14. Preface Blut ist ein ganz besondrer Saft. Goethe, 1808 Maxwell M. Wintrobe often cited Goethe, Blood is a very special kind of fluid, and the Eleventh Edition of Wintrobe's Clinical Hematology is a testimony to Dr. Wintrobe's legacy and commitment to the field of hematology. This edition extends the chronicle of progress to 60 years since the first edition of the book. The first six editions were the sole work of Dr. Wintrobe. When he retired from the editorship, Dr. Wintrobe recruited five former fellows to take over the task: Jack Athens, Tom Bithell, Dane Boggs, John Foerster, and Richard Lee. John Foerster remains an editor from the original group, and John Lukens joined the editorship in the eighth edition. John Greer, Frixos Paraskevas, and George Rodgers contributed to the ninth edition and became editors of the tenth edition. Bert Glader is a welcome addition to the present edition. Of the present group of editors, three (Foerster, Lukens, and Rodgers) worked directly with Dr. Wintrobe, whereas the other three (Glader, Greer, and Paraskevas) have been associated with Wintrobe-trained individuals. Dr. Wintrobe recognized the work of predecessors and the foundation of clinical hematology in basic research. In Blood, Pure and Eloquent. A Story of Discovery, of People and of Ideas (1980), Dr. Wintrobe edited historical milestones in hematology and emphasized three lessons of history: 1. Research starts with an idea, which may take many directions before becoming a valid concept: The path of progress is anything but straight. It is rough and rocky and often seems to wander endlessly and in all directions; it has many blind alleys and is strewn with the debris of false hopes, of failures, and discouragement. The course of research has been likened to the flow of a stream that ultimately becomes a rushing torrent. 2. A sense of skepticism is warranted in the practice of medicine: What was held to be the truth yesterday may not be so regarded today, and tomorrow the story may again be somewhat different. 3. Perseverance is required to make progress: many look, but few see. It is the exceptional person who recognizes the unusual event or manifestation. Still fewer pursue it to a new understanding. Many may ask questions but few have the imagination, the energy, and the overpowering drive to persist in the search for an answer, especially when this must be done in the face of difficulties and failures and even in spite of scorn from their peers ( 1). Although his statements may seem pessimistic, Dr. Wintrobe optimistically recognized the importance of building on prior contributions and the relationship between clinical hematology and basic research. Hematology has many stories characterized initially by clinical observations that are now understood at a molecular genetic level (2,3). Sickle cell anemia, pernicious anemia, hemophilia, Burkitt lymphoma, acute promyelocytic leukemia, and chronic myeloid leukemia are among the most interesting topics in medicine. The speed of basic research to the clinical bedside was remarkable in the twentieth century, and it promises to be even faster and more widely applied in the future. The Eleventh Edition of Wintrobe's Clinical Hematology ushers in the twenty-first century with the same principles found in the prior editions and with the additional availability of the knowledge base through the Internet. The value of books has been questioned in this new era. This edition retains the historical perspective of Wintrobe's Clinical Hematology, with extensive references; brings together the body of information on hematology in a single source; and bridges topics to the Internet with Web links cited by many of the authors in their chapters. As with other multiauthored textbooks, there are occasional redundancies, which are important observations that allow a chapter to stand alone, and there are cross references to other chapters that indicate the interdependence of the topics. We appreciate each author's contribution to the book. We have brought together clinician educators, pathologists, and physician scientists to review their topics of expertise. All of the chapters except Dr. Wintrobe's introduction to the approach to hematologic problems either have been revised or are new with an emphasis on molecular aspects of hematology. This edition recognizes the transition from a morphologic classification of hematopoietic neoplasms to the World Health Organization's classification that incorporates molecular genetics. We appreciate the efforts of Jonathan Pine, Senior Executive Editor at Lippincott Williams & Wilkins; Alyson Forbes, Developmental Editor, and Tanya Lazar, Managing Editor at Lippincott; Mary Ann McLaughlin, Supervising Editor at Lippincott; and Lucinda Ewing and Jane McQueen, Production Editors at Silverchair Science + Communications. Their unique combination of persistence and kindness and their commitment to the principles of prior editions brought the project to completion. We hope the readers find the information they seek in the Eleventh Edition of Wintrobe's Clinical Hematology. Below, each of us acknowledges people who have assisted him in this endeavor. Debbie Saurette, my faithful secretary and colleague, has provided invaluable services in the completion of this edition. My wife, Gisela, and our children David, Steven, and Susan, physicians all, have been a constant source of support and inspiration. Special thanks go to my mentors, Dr. L. G. Israels, whose enthusiasm for hematology and his ability to combine effectively clinical service, teaching, and research, drew me to this specialty as a medical student; Dr. M. M. Wintrobe, who taught me in his own unique way and gave me the opportunity to contribute to several editions of this great textbook; Dr. B. Benacerraf, who nurtured my interests in immunology; and my colleagues at the Mayo Clinic and elsewhere who have contributed valuable chapters to this text. John Foerster I wish to thank Jennifer Lu, Kari Costa, Theresa McCann, and Annamarie Coelho for administrative help. I also wish to acknowledge the many outstanding authors I have had the privilege to work with in the preparation of this edition. Last, but most of all, I want to acknowledge the understanding and support of my wife, Lou Ann; my children, Laurie, Anders, and Eric; their families; and our friends. Bert Glader I wish to thank Billi Bean, my assistant and colleague; Patti Lee at the Eskind Library of the Vanderbilt University School of Medicine; my wife, Gay; and our children, Lesley, Adam, and Scott; my mentors, including Robert Collins, John Flexner, Stanley Graber, Sanford Krantz, and John Lukens; Ellen Benneyworth and other nurses; and our patients. John P. Greer My contribution to this edition could not have been made without the understanding and unselfish support of my wife, Cauley Lukens. She and our children have weathered long hours and aborted vacations with encouragement and grace. John N. Lukens I want to express my deeply felt gratitude and appreciation to my wife Maria for her support and unwavering encouragement throughout the period of writing and especially when deadline worries became unmanageable. Maria, as a pathologist, has also been the testing ground for fine-tuning of complex concepts, helping me to lift them from the unfathomable depths of technicality and into the light of understanding. I want to thank Ms. Lynne Savage for her expert secretarial assistance and perseverance when last copy was proved to be just another in a never-ending line of typing. Our librarian, Donna Pacholok, helped me navigate the complex connections with the Internet. My sincere thanks to several colleagues for providing literature assistance or photography from their own data: A. A. Anderson, G. G. Gao, J. E. Gretz, L.A. Herzenberg, H. Kogelberg, J. Lambris, D. Y. Mason, C. Morales, K. H. Roux, P. Nickerson, H. Seguchi, S. Shaw, and H. Zimmerman. Frixos Paraskevas I acknowledge Robyn LeMon and Sherry Hartline for typing assistance, my numerous contributors for their hard work and timely submissions, and my family and friends for their support. 15. George M. Rodgers REFERENCES Wintrobe MM. Blood, pure and eloquent. A story of discovery, of people, and of ideas. New York: McGraw-Hill, 1980:720. Wintrobe MM. Hematology, the blossoming of a science: a story of inspiration and effort. Philadelphia: Lea & Febiger, 1985. Lichtman MA, Spivak JL, Boxer LA, et al., eds. Hematology: landmark papers of the twentieth century. San Diego: Academic Press, 2000. 16. 1 Examination of the Blood and Bone Marrow Wintrobes Clinical Hematology 1 Sherrie L. Perkins Examination of the Blood and Bone Marrow SPECIMEN COLLECTION RELIABILITY OF TESTS CELL COUNTS Aperture-Impedance Counters Optical Method Counters Combined Impedance and Optical Counters RED BLOOD CELL ANALYTIC PARAMETERS Volume of Packed Red Cells (Hematocrit) Hemoglobin Concentration Red Cell Count Mean Corpuscular Volume Mean Corpuscular Hemoglobin Mean Corpuscular Hemoglobin Concentration Red Cell Distribution Width Automated Reticulocyte Counts LEUKOCYTE ANALYSIS White Blood Cell Counts Leukocyte Differentials PLATELET ANALYSIS ADVANTAGES AND SOURCES OF ERROR WITH AUTOMATED HEMATOLOGY ANALYZERS MORPHOLOGIC ANALYSIS OF BLOOD CELLS Preparation of Blood Smears Routine Staining of Blood Smears Examination of the Blood Smear Other Means of Examining Blood BONE MARROW EXAMINATION Bone Marrow Aspiration and Biopsy Staining and Evaluation of Bone Marrow Aspirates and Touch Preparations Examination of Bone Marrow Histologic Sections SPECIAL STAINS Cytochemical Stains Immunocytochemical Stains OTHER LABORATORY STUDIES Cytogenetic Analysis Molecular Genetics Electron Microscopy Erythrocyte Sedimentation Rate Plasma and Blood Viscosity Total Quantity of Blood REFERENCES Careful assessment of the blood elements is often the first step in assessment of hematologic function and diagnosis. Many hematologic disorders are defined by specific findings gleaned from blood tests. Examination of blood smears and hematologic parameters often yields important diagnostic information and allows broad differential diagnostic impressions to be formed, directing further, more specific testing. Careful examination of cellular morphology, in concert with quantification of the blood elements and evaluation of a variety of parameters relating to cellular size and shape, is required. This chapter introduces the fundamental concepts that underlie laboratory evaluation of the blood and outlines additional testing that may aid in evaluating a hematologic disorder, including special stains and bone marrow examination. Limitations of such tests are also addressed. Blood elements include erythrocytes, or red cells; leukocytes, or white cells; and platelets. Although detailed morphologic descriptions and functional characteristics of each of the cell types are included in subsequent chapters, basic features necessary for blood smear analyses are covered in this chapter. Red cells are the most numerous cells in the blood and are required for tissue respiration. Erythrocytes lack nuclei and contain hemoglobin, an iron-containing protein that acts in the transport of oxygen and carbon dioxide. White blood cells serve in immune function and include a variety of cell types that have specific functions and characteristic morphologic appearances. In contrast to red cells, white cells are nucleated. The five types of white blood cells seen normally in blood smears are neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Platelets are cytoplasmic fragments derived from megakaryocytes in the bone marrow that function in coagulation and hemostasis. Evaluation of the blood requires quantification of each of the cellular elements by either manual or automated methods. Automated methods, using properly calibrated equipment ( 1 ), are usually more precise than manual procedures. In addition, automated methods may provide additional data describing characteristics such as cell volume. However, the automated measurements describe average cell characteristics but do not adequately describe the scatter of individual values around the average. Hence, a bimodal population of small (microcytic) and large (macrocytic) red cells might register as a normal cell size. Therefore, a thorough examination of blood also requires microscopic evaluation of a stained blood film to complement the hematology analyzer data. SPECIMEN COLLECTION Proper specimen collection is required for reliable and accurate laboratory data to be obtained on any hematologic specimen. Before a specimen is obtained, careful thought as to what studies are needed will aid in proper handling of the material and prevent collection of inadequate or unusable specimens. Communication with laboratory personnel who will analyze the specimen is often helpful in ensuring that specimens will be handled properly and that the requested testing can be performed. A number of factors may affect hematologic measurements, and each specimen should be collected in a standardized manner to reduce variability. Factors such as patient activity, level of patient hydration, medications, sex, age, race, smoking, and anxiety may affect hematologic parameters significantly ( 2 , 3 and 4 ). Similarly, the age of the specimen may affect the quality of the data collected ( 5 ). Thus, data such as patient age, sex, and time of specimen collection should be noted. Correlative clinical information is also extremely important in evaluating hematologic specimens. For example, a patient who has had severe diarrhea or vomiting before admission may be sufficiently dehydrated to have an erroneous increase in red blood cell concentration. Most often, blood is collected by venipuncture into tubes containing anticoagulant. The three most commonly used anticoagulants are tripotassium or disodium salts of ethylenediaminetetraacetic acid (EDTA), trisodium citrate, and heparin. EDTA and disodium citrate act to remove calcium, which is essential for the initiation of coagulation, from the blood. Heparin acts by forming a complex with antithrombin III in the plasma to prevent the formation of thrombin. EDTA is the preferred anticoagulant for blood cell counts because it produces complete anticoagulation with minimal morphologic and physical effects on all types of blood cells ( 6 ). Heparin causes a bluish coloration of the background when a blood smear is stained with one of the Romanowsky dyes but does not affect cell size or shape. Heparin is most often used for prevention of red blood cell hemolysis, for osmotic fragility testing, and for functional and immunologic analysis of leukocytes. Heparin does not completely inhibit white blood cell or platelet clumping. Trisodium citrate is the preferred anticoagulant for platelet and coagulation studies. Other anticoagulants have been identified that give results similar to EDTA, such as argatroban ( 7 ), although none has achieved widespread use in normal clinical settings The concentration of the anticoagulant used may affect cell concentration measures if it is inappropriate for the volume of blood collected and may also distort cellular 17. morphology. Most often, blood is collected directly into commercially prepared negative-pressure vacuum tubes (Vacutainer tubes; Becton Dickinson, Franklin Lakes, NJ), which contain the correct concentration of anticoagulant when filled appropriately, thereby minimizing error ( 8 ). Anticoagulated blood may be stored at 4C for a 24-hour period without significantly altering cell counts or cellular morphology ( 5 ). However, it is preferable to perform hematologic analysis as soon as possible after the blood is obtained. RELIABILITY OF TESTS In addition to proper acquisition of specimens, data reliability requires precise and reproducible testing methods. Both manual and automated testing of hematologic specimens must be interpreted in light of test precision. This becomes especially important when evaluating the significance of small changes. All laboratory tests are evaluated with respect to both accuracy and reproducibility. Accuracy is the difference between the measured value and the true value, which implies that a true value is known. Clearly, this may present difficulties when dealing with biologic specimens. The National Committee for Clinical Laboratory Standards and the International Committee for Standards in Haematology have attempted to develop standards to assess the accuracy of hematologic examination ( 9 ) and automated blood cell analyzers ( 10 ). Automated instrumentation requires regular quality assurance evaluations and careful calibration to reach expected performance goals and ability to collect reproducible data ( 1 , 11 ). CELL COUNTS Cell counts are important parameters in evaluating the blood. Cell counts may be determined either manually or by automated hematology analyzers. Whether performed by manual or automated methodologies, the accuracy and precision of the counts depend on proper dilution of the blood sample and precise sample measurement. Blood must be precisely aliquoted and diluted, so that cells are evenly distributed within the sample to be analyzed. Because blood contains large numbers of cells, sample dilution is usually required for accurate analysis. The type of diluent is dependent on the cell type to be enumerated. Thus, red cell counts require dilution with an isotonic medium, whereas in white cell or platelet counts, a diluent that lyses the more numerous red cells is often used to simplify counting. The extent of dilution also depends on the cell type. In general, red cell counts need more dilution than is required for the less abundant white blood cells. Errors in cell counts are caused primarily by errors in sample measurement, dilution, or enumeration of cells. The highest degree of precision occurs when a very large number of cells can be evaluated. Clearly, automated methods are superior to manual methods for counting large numbers of cells and minimizing statistical error. Table 1.1 lists the comparable values of reproducibility for automated and manual (hemocytometer) counting methods. TABLE 1.1. Reproducibility of Blood Counting Procedures Two Coefficients of Variation Cell Type Counted Hemocytometer a (%) Automated Hematology Analyzer (%) Red cells 11.0 1.0 White cells 16.0 1.5 Platelets b 22.0 2.0 Reticulocytes 33.9 5.0 a Minimum error. Usual error. b Error may be greater with low (450 10 9 /L) platelet counts. Data derived from Bentley S, Johnson A, Bishop C. A parallel evaluation of four automated hematology analyzers. Am J Clin Pathol 1993;100:626632; and Wintrobe M. A simple and accurate hematocrit. J Lab Clin Med 1929;15:287289. Manual counts are carried out after appropriate dilution of the sample in a hemocytometer, a specially constructed counting chamber that contains a specific volume. Cells may then be counted with a microscope. Red blood cells, leukocytes, and platelets may be counted by this method ( 13 ). Due to the inherent imprecision of manual counts and the amount of technical time required, most cell counting is now performed by automated or semiautomated instruments. These machines increase the accuracy and speed of analysis by the clinical laboratory, particularly as test entry, sampling, sample dilution, and analysis are incorporated into single systems with minimal human manipulation ( 12 , 13 ). With increasing levels of automation, some hematology analyzers have now moved to complete automation, which can be coupled with other laboratory tests using the same tube of blood. There are a variety of different automated hematology analyzers available, dependent on the volume of samples to be tested and the needs of the physician group ordering testing. The analyzers range in price and workload capacity from those that would be appropriate for an individual physician's office or point-of-care facility to those needed in a busy reference laboratory with capacity for over 100 samples to be analyzed per hour ( 14 ). Most automated hematology analyzers perform a variety of hematologic measurements, such as hemoglobin concentration (Hb), red cell size, and leukocyte differentials. Newer instruments may also perform more specialized testing, such as reticulocyte counts ( 15 ). The ability of the new analyzers to perform accurate white cell differential counts, particularly those that can perform a five-part differential (enumerating neutrophils, lymphocytes, monocytes, eosinophils, and basophils), has been a significant technologic advance over the past 10 years. Automated methods for white cell counts and differentials use several distinct technical approaches ( 16 ), including those that measure changes in electrical impedance and those that use differences in light scatter or optical properties, either alone or in combination ( 17 ). Another recent advance in hematology analyzers is incorporation of argon laser technology, allowing integration of some flow cytometric data using specific fluorochrome stains, such as T-cell subsets (CD4:CD8) or CD34 positive cells, with routine hematologic analyses ( 18 ). Aperture-Impedance Counters This type of analyzer, which includes the Coulter (Beckman Coulter, Hialeah, FL), the Sysmex (Baxter Diagnostics, Waukegan, IL), and some Cell-Dyn (Abbott Diagnostics, Santa Clara, CA) instruments, enumerates cells in a small aperture by measuring changes in electrical resistance as the cell passes through the orifice ( Fig. 1.1). A constant current passes between two platinum electrodes on either side of the orifice. The diluent that suspends the cells is more electrically conductive than are the cells. Hence, as each cell passes through the orifice, there is a momentary decrease in electrical conductance so that an electrical impulse is generated and recorded electronically. The drop in voltage is proportional to cell size, allowing average cell size to be determined simultaneously ( 19 , 20 ). Figure 1.1. Impedance type of automated hematology analyzer. As the cells pass through the aperture, they alter the current flow between the electrodes, generating an electronic pulse. Each pulse is recorded electronically. The magnitude of the pulse is proportional to the cell's volume. Instruments using aperture-impedance technology require even cell suspensions so that cells pass individually through the electrical current. Distortion of the electrical pulses may occur when the cells do not pass through the center of the aperture or when more than one cell enters the aperture at a time. The data may be electronically adjusted to exclude distorted peaks, and both upper and lower limits of particle size can be set to exclude cellular clumps or debris. Using size limitation 18. parameters, the instrument can be used to count particles of different sizes, thereby allowing different blood elements to be enumerated ( 21 ). Most of the modern analyzers can also be set to flag abnormal or suspect results, allowing for identification of those samples that need further, manual evaluation ( 22 ). The Coulter-type counters are probably the most widely used example of hematology analyzers that use electrical impedance methods. Most models print data in numerical form as well as providing histograms of blood cell size ( Fig. 1.2). Newer models often combine impedance and optical methodologies (described below). Data generated include a three- or five-part white cell differential in addition to red cell counts, white cell counts, platelet counts, reticulocyte counts, hemoglobin, hematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), and mean platelet volume (MPV). This type of instrumentation fully analyzes up to 109 samples per hour, depending on the model used, and flags abnormal red and white cell populations, including blasts and atypical cells 23 . Figure 1.2. Histograms and printout generated by the Coulter STKR automated hematology analyzer. BA, basophil; EO, eosinophil; HCT, hematocrit; HGB, hemoglobin; LY, lymphocyte; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MO, monocyte; MPV, mean platelet volume; NE, neutrophil; PLT, platelet; RBC, red blood cell; RDW, red cell distribution width; WBC, white blood cell. Optical Method Counters The other method commonly used in hematology analyzers depends on the light scatter properties of blood cells ( 24 , 25 ). Some instruments that use this technology include the Technicon series (H6000, H*1, H*2, H*3) (Bayer Diagnostic Division, Tarrytown, NY) and the Cell-Dyn instruments. In these systems, diluted blood passes through a flow cell detector placed in the path of a narrowly focused beam of light (usually a laser) ( Fig. 1.3). When the blood cells pass through the counting chamber, they interrupt or alter the beam of light, thereby generating an electrical impulse that may be recorded. The pattern of light scattering using different angles of detection may also be used to determine cell size, volume, shape, and cell cytoplasmic complexity ( 17 , 19 ). Optical systems count red cells, white cells, and platelets with precision equivalent to that observed in electrical impedance methods ( 26 , 27 ). Similar to the impedance analyzers, many of the optical analyzers can process over 100 specimens per hour and have the capacity to flag abnormal parameters ( 26 ). Figure 1.3. Optical type of automated hematology analyzer. A suspension of cells is passed through a flow chamber and focused into a single cell sample stream. The cells pass through a chamber and interact with a laser light beam. The scatter of the laser light beam at different angles is recorded, generating signals that are converted to electronic signals giving information about cell size, structure, internal structure, and granularity. (Adapted from Cell-Dyn 3500 Operator's Manual. Santa Clara, CA: Abbott Diagnostics, 1993.) Combined Impedance and Optical Counters Some of the newer hematology analyzers have combined impedance and optical methods together within one instrument, thereby allowing for optimal use and integration of the data generated by each method. Often, these are high-volume instruments, appropriate for larger hospitals and reference laboratories, and may be more expensive than some of the single-approach models. Examples of combined impedance and optical method analyzers include the Beckman Coulter Gen-S (Hialeah, FL) and Cell-Dyn 4000. Many of these newer instruments also provide an automated reticulocyte count and have improved precision of automated differential counts so as to lower the need for manual reviews by a technician ( 28 ). RED BLOOD CELL ANALYTIC PARAMETERS Red blood cells are defined by three quantitative values: the volume of packed red cells or Hct, the Hb, and the red cell concentration per unit volume. Three additional indices describing average qualitative characteristics of the red cell population are also collected. These include mean MCV, MCH, and MCHC. All of these values are collected and calculated by automated counters, largely replacing many of the previously used manual or semiautomated methods of red blood cell characterization with certain exceptions as noted below. Volume of Packed Red Cells (Hematocrit) The volume of packed red cells, or Hct, is the proportion of the volume of a blood sample that is occupied by red blood cells. The Hct may be determined manually by centrifugation of blood at a given speed and time in a standardized glass tube with a uniform bore, as was originally described by Wintrobe ( 29 ). The height of the column of red cells compared with that of the total blood sample after centrifugation yields the Hct. Macro (using 3-mm test tubes) methods with low-speed centrifugation or micro methods using capillary tubes and high-speed centrifugation may be used. The manual method of measuring Hct has proved to be a simple and accurate method of assessing red cell status. It is easily performed with little specialized equipment, allowing it to be adapted for situations in which automated cell analysis is not readily available or for office use. However, several sources of error are inherent in the technique. The spun Hct measures the red cell concentration, not red cell mass. Therefore, patients in shock or with volume depletion may have normal or high Hct measurements due to hemoconcentration despite a decreased red cell mass. Technical sources of error in manual Hct determinations usually arise from inappropriate concentrations of anticoagulants ( 30 ), poor mixing of samples, or insufficient centrifugation ( 29 ). Another inherent error in manual Hct determinations arises from trapping of plasma in the red cell column. This may account for 1 to 3% of the volume in microcapillary tube methods, with macrotube methods trapping more plasma ( 31 , 32 ). In addition, it should be noted that abnormal red cells (e.g., sickle cells, microcytic cells, macrocytic cells, or spherocytes) often trap higher volumes of plasma due to increased cellular rigidity, possibly accounting for up to 6% of the red cell volume ( 31 ). Very high Hcts, as in polycythemia, 19. may also have excess plasma trapping. Manual Hct methods typically have a precision [coefficient of variation (CV)] of approximately 2% ( 31 ). Automated analyzers do not depend on centrifugation techniques to determine Hct, but instead calculate Hct by direct measurements of red cell number and red cell volume (Hct = red cell number/red cell volume). The automated Hct closely parallels manually obtained values, so that manual Hct methodology is used as the reference method for automated methods (with correction for the error induced by plasma trapping). Errors of automated Hct calculation are more common in patients with polycythemia ( 33 ) or abnormal plasma osmotic pressures ( 34 ). Manual methods of Hct determination may be preferable in these cases. The precision of most automated Hcts is less than 1% (CV) ( 28 ). Hemoglobin Concentration Hemoglobin is an intensely colored protein, which allows its measurement by a variety of colorimetric and spectrophotometric techniques. Hemoglobin is found in the blood in a variety of forms, including oxyhemoglobin, carboxyhemoglobin, methemoglobin, and other minor components. These may be converted to a single stable compound, cyanmethemoglobin, by mixing blood with Drabkin solution, which contains potassium ferricyanide and potassium cyanide ( 35 , 36 ). Sulfhemoglobin is not converted but is rarely present in significant amounts. The absorbance of the cyanhemoglobin is measured in a spectrophotometer at 540 nm to determine hemoglobin. Similar methods are used in both manual methods and automated cell analyzers. Hb is expressed in grams per deciliter (g/dl) of whole blood. The main errors in measurement arise from dilution errors or increased sample turbidity due to improperly lysed red cells, leukocytosis, or increased levels of lipid or protein in the plasma ( 37 , 38 , 39 and 40 ). Using automated methods, the precision for hemoglobin determinations is less than 1% (CV) ( 25 ). Red Cell Count Manual methods for counting red cells have proven to be very inaccurate, and automated counters provide a much more accurate reflection of red cell numbers ( 26 , 41 ). Both erythrocytes and leukocytes are counted in whole blood that has been diluted in an isotonic medium. As the number of red cells greatly exceeds the number of white cells (by a factor of 500 or more), the error introduced by counting both cell types is negligible. However, when marked leukocytosis is present, red cell counts and volume determinations may be erroneous unless corrected for white cell effects. The observed precision for red cell counts using automated hematology analyzers is less than 1% (CV) ( 28 ) compared with a minimal estimated value of 11% using manual methods ( 29 ). Mean Corpuscular Volume The average volume of the red blood cells is a useful red cell index that is used in classification of anemias and may provide insights into pathophysiology of red cell disorders ( 42 ). The MCV is usually measured directly with automated instruments but may also be calculated from the erythrocyte count and the Hct by means of the following formula ( 29 ). The MCV is measured in femtoliters (fl, or 10 -15 L). Using automated methods, this value is derived by dividing the summation of the red cell volumes by the erythrocyte count. The CV in most automated systems is approximately 1% ( 28 ). Agglutination of red blood cells, as in cold agglutinin disease, may result in a falsely elevated MCV ( 43 ). Most automated systems gate out MCVs above 360 fl, thereby excluding most red cell clumps, although this may falsely lower Hct determinations. In addition, severe hyperglycemia (glucose >600 mg/dl) may cause osmotic swelling of the red cells, leading to a falsely elevated MCV ( 34 , 44 ). The CV for automated MCV measurements is less than 1%, compared with approximately 10% for manual methods ( 32 ). Mean Corpuscular Hemoglobin MCH is a measure of the average hemoglobin content per red cell. It may be calculated manually or by automated methods using the following formula 29 . MCH is expressed in picograms (pg, or 10 -12 g). Thus, the MCH is a reflection of hemoglobin mass. In anemias in which hemoglobin synthesis is impaired, such as iron deficiency anemia, hemoglobin mass per red cell decreases with a resultant decrease in MCH. MCH measurements may be falsely elevated by hyperlipidemia ( 38 ), as increased plasma turbidity may erroneously elevate the hemoglobin measurement. Leukocytosis may also spuriously elevate MCV values ( 37 ). Centrifugation of the blood sample to eliminate the turbidity followed by manual hemoglobin determination allows correction of the MCH value. The CV for automated analysis of MCH is less than 1% in most modern analyzers, compared with approximately 10% for manual methods ( 28 , 32 ). Mean Corpuscular Hemoglobin Concentration The average concentration of hemoglobin in a given red cell volume or MCHC may be calculated by the following formula ( 29 ). The MCHC is expressed in grams of hemoglobin per deciliter of packed red blood cells. This represents measurement of Hb or the ratio of hemoglobin mass to the volume of red cells. With the exception of hereditary spherocytosis and some cases of homozygous sickle cell or hemoglobin C disease, MCHC values will not exceed 37 g/dl. This level is close to the solubility value for hemoglobin, and further increases in Hb may lead to crystallization. The accuracy of the MCHC determination is affected by factors that affect measurement of either Hct (plasma trapping or presence of abnormal red cells) or hemoglobin (hyperlipidemia, leukocytosis) ( 37 ). The CV for MCHC for automated methods ranges between 1.0 and 1.5% ( 28 ). As noted above, the MCV, MCH, and MCHC reflect average values and may not adequately describe blood samples when mixed populations of cells are present. For example, in sideroblastic anemias, a dimorphic red cell population of both hypochromic and normochromic cells may be present, yet the indices may be normochromic and normocytic. It is important to examine the blood smear as well as red cell histograms to detect such dimorphic populations. The MCV is an extremely useful value in classification of anemias ( 42 ), but the MCH and MCHC often do not add significant, clinically relevant information. However, the MCH and MCHC play an important role in laboratory quality control because these values will remain stable for a given specimen over time ( 19 ). Red Cell Distribution Width The RDW is a red cell measurement that quantitates red cell volume heterogeneity that is provided by the more modern automated hematology analyzers and reflects the range of red cell sizes measured within a sample ( 45 ). RDW has been proposed to be useful in early classification of anemias because it becomes abnormal earlier in nutritional deficiency anemias than any of the other red cell parameters, especially in cases of iron deficiency anemia ( 42 , 46 , 47 ). RDW is particularly useful when characterizing microcytic anemias, particularly distinguishing between iron deficiency anemia (high RDW, normal to low MCV) and uncomplicated heterozygous thalassemia (normal RDW, low MCV) ( 42 , 47 , 48 , 49 and 50 ). RDW is useful as a method for initial characterization of anemia, particularly microcytic anemias, although other tests are usually required to confirm the diagnosis ( 51 ). RDW is also useful in identifying red cell fragmentation, agglutination, or dimorphic cell populations (including patients who have had transfusions or have been recently treated for a nutritional deficiency) ( 47 , 52 ). Automated Reticulocyte Counts Determination of the numbers of reticulocytes or immature, nonnucleated red blood cells that contain RNA provides useful information about the bone marrow's 20. capacity to synthesize and release red cells in response to a physiologic challenge, such as anemia. In the past, reticulocyte counts were performed manually using supravital staining with methylene blue. Reticulocytes will stain precipitated RNA that appears as a dark blue meshwork or granules (at least two per cell) allowing reticulocytes to be identified and enumerated by manual counting methods ( 53 ). Normal values for reticulocytes in adults are 0.5 to 1.5%, although they may be 2.5 to 6.5% in newborns (falling to adult levels by the second week of life). Because there are relatively low numbers of reticulocytes, the CV for reticulocyte counting is relatively large (10 to 20%). To increase accuracy of reticulocyte counting, alternative methods using flow cytometry and staining with acridine orange or thioflavin allow for many more cells to be analyzed, thereby increasing accuracy and precision of counts ( 15 , 54 , 55 ). Stand-alone reticulocyte analyzers, such as the Sysmex R-2000 or ABX PENTRA 120 Retic (ABX Diagnostics, Montpellier, France), allow for determination of reticulocyte counts without requiring a full flow cytometer, affording increased accuracy over manual counts. Many of the newest automated hematology analyzers, such as the Coulter STKS, Coulter GenS or the Cell-Dyn 4000, have automated reticulocyte counting as part of the testing capabilities and allow reticulocyte counts to be included with routine complete blood count parameters. Comparisons of stand-alone instruments, integrated hematology analyzers, and flow cytometric methods show that these automated methods provide similar data with superior accuracy when compared to manual counting methods, with similar CVs of 5 to 8% ( 56 , 57 and 58 ). LEUKOCYTE ANALYSIS White Blood Cell Counts Leukocytes may be enumerated by either manual methods or automated hematology analyzers. Leukocytes are counted after dilution of blood in a diluent that lyses the red blood cells (usually acid or detergent). The much lower numbers of leukocytes present require less dilution of the blood than is needed for red blood cell counts (usually a 1:20 dilution, although it may be less in cases of leukocytopenia or more with leukocytosis). Manual counts are done using a hemocytometer or counting chamber. As with red cell counts, manual leukocyte counts have more inherent error, with CVs ranging from 6.5% in cases with normal or increased white cell counts to 15% in cases with decreased white cell counts. Automated methods characteristically yield CVs in the 1 to 3% range ( 26 , 28 ). Automated leukocyte counts may be falsely elevated in the presence of cryoglobulins or cryofibrinogen ( 59 ), aggregated platelets ( 60 ), and nucleated red blood cells or when there is incomplete lysis of red cells, requiring manual counting. Falsely low neutrophil counts have also been reported due to granulocyte agglutination secondary to surface immunoglobulin interactions ( 61 ). Leukocyte Differentials White cells are analyzed to find the percentage of each white blood cell type by doing a differential leukocyte count, providing important information in evaluation of the patient. Uniform standards for performing manual differential leukocyte counts on blood smears have been proposed by the National Committee for Clinical Laboratory Standards ( 62 ) to ensure reproducibility of results between laboratories. It is important to scan the smear at low power to ensure that all atypical cells and cellular distribution patterns are recognized. In wedge-pushed smears, leukocytes tend to aggregate in the feathered edge and side of the blood smear rather than in the center of the slide. Larger cells (blasts, monocytes) also tend to aggregate at the edges of the blood smear ( 63 ). Use of coverslip preparations and spinner systems tends to minimize this artifact of cell distribution. For wedge-push smears, it is recommended that a battlement pattern of smear scanning be used in which one counts fields in one direction, then changes direction and counts an equal number of fields before changing direction again to minimize distributional errors ( 41 ). In manual leukocyte counts, three main sources of error are encountered: distribution of cells on the slide, cell recognition errors, and statistical sampling errors ( 57 , 58 ). Poor blood smear preparation and staining are major contributors to cell recognition and cell distribution errors ( 63 ). Statistical errors are the main source of error inherent in manual counts, due to the small sample size in counts of 100 or 200 cells. The CV in manual counts is between 5 and 10% and is also highly dependent on the skill of the technician performing the differential. Accuracy may be improved by increasing the numbers of cells counted, but for practical purposes, most laboratories will do a differential on 100 white cells ( 64 ). Automated methods of differential counting tend to be more accurate because of the much larger numbers of cells evaluated, with CV of 3 to 5% ( 64 , 65 , 66 and 67 ). Automated methods of obtaining a leukocyte differential have been developed that markedly decrease the time and cost of performing routine examinations as well as increasing accuracy. However, automated analysis is incapable of accurately identifying and classifying all types of cells and is particularly insensitive to abnormal or immature cells. Therefore, most analyzers will identify possible abnormal white cell populations by flagging, indicating the need for examination by a skilled morphologist for confirmation ( 68 ). The automated instruments used for performing automated leukocyte differentials are of two general types: those that perform cell identification on the basis of pattern recognition using stained blood smear slides and automated microscopy, and flow-through systems that identify cells on the basis of size, cell complexity, or staining characteristics. Pattern recognition systems were first available in the early 1970s and included such instruments as the Hematrack, Coulter diff 3 and diff 4, Abbott\R ADC 500, and the Leukocyte Automatic Recognition Counter ( 69 , 70 ). This technology uses a blood film on a glass slide that was stained and loaded onto the instrument. A computer drives a microscopic mechanical stage until a dark staining area, corresponding to a leukocyte nucleus, is detected. Using data collected for each cell on cell size, nuclear and cytoplasmic coloration, and density, the computer matches the data patterns with specifications for each white cell type and identifies the cell. Most pattern recognition technology is hampered by many of the same limitations of accuracylimited numbers of cells counted, difficulties in classifying abnormal cell types, and cell distribution characteristicsas manual counts ( 71 ). Although the automated pattern recognition systems do decrease technician time, they are significantly slower than the flow-through methods. Hence, pattern recognition systems are now rarely used, and the instruments are no longer manufactured. Because of the ability to link the automated differential to the rest of the automated hematologic analysis, most recent methods use a flow-through system that generates a leukocyte differential as a part of the complete blood count ( 67 , 72 ). Flow-through systems collect and analyze data from large numbers of white blood cells to provide a differential count that has a high degree of precision when compared to manual methods. White blood cell determination depends on both cell size and cytochemical staining characteristics (Technicon H6000, H*1, H*2, H*3 series) ( 73 ) or on the basis of cell volume and internal complexity as measured by electrical impedance and light scatter characteristics [Coulter STKR and Gen-S series ( 58 , 74 ), Cell-Dyn 4000 ( 28 ), Sysmex NE-8000 ( 75 ), Bayer Advia 120 (Bayer Diagnostic Division, Tarrytown, NY) ( 28 ), and Cobas-Helios (Roche Diagnostic Systems, Inc., Branchburg, NJ) ( 27 ) systems]. Systems that use myeloperoxidase staining characteristics of cells perform cell counts on specimens via continuous-flow cytometric analysis of blood samples in which the red cells have been lysed and white cells fixed. The cells are suspended in diluent and passed through a flow cell in a continuous stream so that single cells are analyzed for cell size (dark field light scatter) and cytochemical characteristics of myeloperoxidase staining (bright field detector). The data are plotted as a scattergram reflecting cell size (light scatter) on the y-axis and myeloperoxidase staining intensity or activity on the x-axis ( Fig. 1.4), which gives rise to a six-part differential (neutrophils, lymphocytes, monocytes, eosinophils, basophils, and large unstained cells). Figure 1.4. Histograms and printout generated by the H*1 automated hematology analyzer. ANISO, anisocytosis; ATYP, atypical; BASO, basophils; CBC, complete blood count; CONC, concentration; DIFF, differential; EOS, eosinophils; HCT, hematocrit; HDW, reticulocyte hemoglobin distribution width; HGB, hemoglobin; L. SHIFT, left shift; LUC, large unstained cell; LYMP, lymphocyte; MACRO, macrocyte; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MICRO, microcyte; MONO, monocyte; MPV, mean platelet volume; NEUT, neutrophil; PEROX, peroxidase; PLT, platelet; RBC, red blood cell; RDW, red cell distribution width; VAR, variant; WBC, white blood cell. 21. The total white blood cell count as well as the neutrophil, lymphocyte, monocyte, and eosinophil counts are enumerated in the myeloperoxidase channel. Lymphocytes are characterized as small (low-scatter) unstained cells. Larger atypical lymphocytes, blasts, or circulating plasma cells fall into the large unstained cells channel. Neutrophils have stronger peroxidase staining and appear as larger cells. Eosinophils have very strong peroxidase activity but appear smaller than neutrophils because they tend to absorb some of their own light scatter. Monocytes have lower levels of peroxidase activity and are usually found between neutrophils and the large unstained cell areas. The system uses floating myeloperoxidase staining thresholds to bracket the neutrophil area, which helps adjust for individual sample differences in myeloperoxidase staining. To enumerate basophils, which are difficult to enumerate with automated flow-through techniques, the later models (Technicon H*1, H*2, and H*3) use a basophil-nuclear lobularity channel. For this determination, red blood cells and white blood cells are differentially lysed, leaving bare leukocyte nuclei, with the exception of basophils, which are resistant to lysis and can then be counted based on cell size. Light scatter data obtained from the leukocyte nuclei may also help identify blasts, which have a lower light scatter than do mature lymphocyte nuclei. The nuclear lobularity index is a measurement of the number of mononuclear and polynuclear cells that may help identify immature neutrophils or nucleated red blood cells when correlated with mean peroxidase activity and cell count data. These abnormal cell populations generate a flag, indicating a need for morphologic review of the peripheral smear. Studies using these systems have shown good ability to identify acute leukemias ( 76 , 77 and 78 ), myelodysplastic syndromes ( 79 ), and acute infection or inflammation ( 80 ). Analysis using this technique examines thousands of cells per sample, increasing statistical accuracy ( 64 , 66 ). The H*3 analyzers may perform 60 or more leukocyte differentials per hour. The remaining instruments use leukocyte volume determinations based on electrical impedance or coupled with light scatter data to generate a leukocyte differential. Initially, this type of methodology gave rise to a three-part differential that enumerated only neutrophils, monocytes, and lymphocytes, exemplified by the Coulter S-Plus series of analyzers. This count was based on white cells that had been lysed, with subsequent collapse of the cellular cytoplasm around the nucleus and cytoplasmic granules ( 81 ). The cells were divided into three distinct size populations: large cells (neutrophils), intermediate cells (monocytes), and small cells (lymphocytes). When clear-cut size populations were not discernible, the machine generated a flag to indicate that the peripheral smear needed to be reviewed. This type of technology is best at enumerating neutrophils and lymphocytes, with high levels of correlation between manual and instrument determinations, but was less effective on monocytic counts because of lower cell numbers. In addition, other cell populations, including eosinophils, basophils, atypical lymphocytes, blasts, immature granulocytes, and plasma cells, tended to fall into the monocytic region or granulocyte region and confounded the data. Depending on the patient population studied (i.e., the percentage of normal vs. abnormal samples), the proportion of false negatives (samples in which a true abnormal population was not detected by the analyzer) varied from 4 to 16% ( 82 , 83 ). This value is similar to those of the manual methods, in which the false negative rate is estimated to be 9% ( 71 ). The three-part differential is most useful as a screening tool. The need for more detailed white cell analysis has led to development of the improved white cell differential analysis by combination of impedance methods with conductivity or light scatter measurements. This modification has greatly improved the ability of later model analyzers to provide full, five-part, differential white blood cell counts. The most commonly used hematology analyzers of this later generation include the Coulter STKS or Gen-S, the Sysmex NE-8000 or NE-9000, and the Cell-Dyn 3500 or 4000, although new upgrades and models appear with great rapidity ( 28 , 84 ). The Coulter STKS and Gen-S use electronic impedance to measure volume, high-frequency electromagnetic fields to measure conductivity, and light scatter with a monochromatic laser to determine cell cytoplasmic complexity or granule content, analyzing up to 144 specimens per hour. These generate a three-dimensional scatter plot ( Fig. 1.2) that can separate the leukocytes into neutrophils, lymphocytes, monocytes, eosinophils, and basophils with flags for abnormal populations ( 58 , 84 ). The Sysmex NE-8000 uses electrical impedance and electromagnetic data to identify the monocytes, neutrophils, and lymphocytes, then identifies eosinophils and basophils based on a proprietary lysing agent ( 85 , 86 ). It may analyze up to 120 samples per hour. The Cell-Dyn 3000 identifies all of the leukocyte classes based on light scatter properties [small-angle forward light scatter, wide-angle light scatter, orthogonal light scatter, and depolarized light scatter ( 87 )]. The Cell-Dyn 3500 uses both impedance and laser light scatter at 0-, 10-, and 90-degree angles ( 90 , 91 ). When compared among themselves and with the Technicon H*1 or H*2, all of the automated hematology analyzers mentioned above had excellent accuracy and precision for typical clinical laboratory usage with slight differences between the different technologies but a marked improvement over manual methods. Most studies find a poor correlation value for basophil counts ( 88 ), probably reflecting the very low levels of these cells available for manual counts. The Cobas analyzer uses a flow cytometric and light scatter technology that allows somewhat improved detection of band neutrophils over other systems with similar accuracy and precision with regard to other white and red blood cell parameters ( 27 , 89 ). All of the above approaches appear to offer sensitive and efficient evaluation of leukocyte differentials, although instrument flags may require technician review for some cases ( 28 ). In addition to their use in providing a differential count of white blood cells, the flow-through techniques of automated cell counting also can provide reproducible and accurate absolute numbers of each cell type because they analyze large cell populations ( 28 ). Use of percentages (as in the leukocyte differential) may mask some cytopenias or excessive numbers of cells. Absolute counts are used to define some disease states, such as chronic lymphocytic leukemia and chronic myelomonocytic leukemia. Absolute neutrophil counts are often useful when monitoring bone marrow recovery after chemotherapy or bone marrow transplant ( 90 ). PLATELET ANALYSIS Platelets are anucleate cytoplasmic fragments that are 2 to 4 microns in diameter. As with the other blood components, they may be counted by either manual or automated methods. Manual methods involve dilution of blood samples and counting in a counting chamber or hemocytometer using phase contrast microscopy. Sources of error are similar to other manual counts and include dilution errors and low sample numbers. The CV, especially in patients with thrombocytopenia, may be greater than 15% ( 91 , 92 ). Platelets are counted in automated hematology analyzers after removal of red cells by sedimentation or centrifugation or using whole blood. Platelets are identified by light scatter, impedance characteristics, or both ( 91 , 93 ). These give highly reliable platelet counts with a CV of less than 2%. Falsely low platelet counts may be caused by the presence of platelet clumps or platelet agglutinins ( 60 ) or adsorption of platelets to leukocytes ( 94 , 95 ). Fragments of red or white blood cells may falsely elevate the automated platelet count, but this usually gives rise to an abnormal histogram that identifies the spurious result ( 96 , 97 ). Automated hematology analyzers also determine MPV, which has been correlated with several disease states ( 98 , 99 ). In general, MPV has an inverse relationship with platelet number, with larger platelet volumes seen in thrombocytopenic patients in whom platelets are decreased due to peripheral destruction (as in idiopathic thrombocytopenia purpura) ( 100 , 101 ). MPV is characteristically increased in hyperthyroidism ( 102 ) and myeloproliferative disorders ( 103 ). However, it should be noted that platelets tend to swell during the first 2 hours in EDTA anticoagulant, shrinking again with longer storage ( 104 , 105 ). Decreased MPV has been associated with megakaryocytic hypoplasia and cytotoxic drug therapy ( 101 , 106 ). Reticulated platelets are newly released platelets that retain residual RNA, analogous to red cell reticulocytes. Reticulated platelet counts give an estimate of thrombopoiesis and may be useful in distinguishing platelet destruction syndromes from hypoplastic platelet production ( 107 , 108 ). Reticulated platelets are usually detected by flow cytometric methods using thiazole orange dyes that bind to RNA ( 109 , 110 ). Normal values vary between 3 and 20% ( 109 ), and 2.5- to 4.5-fold increases in reticulated platelet counts are seen in the clinical setting of idiopathic thrombocytopenia purpura ( 111 , 112 ). Increased reticulated platelets may herald the return of platelet production after chemotherapy ( 113 ). Although automated hematology analyzers offering reticulated platelet counts are not yet available, it is anticipated that this test may be incorporated in newer models, similar to the reticulocyte count. ADVANTAGES AND SOURCES OF ERROR WITH AUTOMATED HEMATOLOGY ANALYZERS Clearly, the use of automated hematology analyzers has reduced laboratory costs and turnaround time coincident with improving the accuracy and reproducibility of blood counts. The CV for most of the parameters measured is in the range of 1 to 2%. This level of reproducibility is not achievable with the use of most manual techniques ( Table 1.1 and Table 1.2). TABLE 1.2. Reproducibility of Red Cell Indices Index Method Used % Error (2 Coefficients of Variation) 22. Hemoglobin concentration Spectrophotometric 1.02.0 Automated