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PR06

Using SAS® Enterprise Guide 4.0® to find a Correlation between Mortality and Co-morbidities for Prostate Disease Patients Jeong W. Park, University of Louisville, Louisville, KY

ABSTRACT The purpose of this paper is to examine correlation between mortality and co-morbidities for prostate disease patients and to describe the method using SAS® Enterprise Guide 4.0®. The National Inpatient Sample (NIS) data of prostate diseases used for this study were provided by the Agency for Healthcare Research and Quality. The main variables studied are mortality and diagnosis. Diagnosis Related Group (DRG) and diagnosis codes were compressed for the study. The compression coding will be demonstrated. Data visualization tools, including kernel density estimation, were used to explore the dataset. Chi-square test and logistic regression analysis were the statistical methods used. The results show a strong relationship among co-morbidities, mortality and age. In addition, the mortality of prostate disease patients is more related to co-morbidities rather than pure prostate diseases. Diseases of the circulatory system and the digestive system show relatively very high frequency with prostate diseases, but diseases of the respiratory system show a strong positive correlation with mortality of prostate disease patients.

INTRODUCTION The purpose of this study is to examine correlation between mortality and co-morbidities for prostate disease patients. Most prostate disease patients have many kinds of co-morbidities besides prostate diseases. Previous research on men who died of prostate cancer found that about 80% of patients with prostate cancer had one or more co-morbidities.1 Hypertension, diabetes and coronary artery diseases were the most common co-morbidities.1 Some of the co-morbidities could be the main factors to the increase in mortality rather than prostate diseases such as prostate cancer.

Prostate diseases in this study include prostate cancer and other prostate diseases such as prostatitis. The most common disease related to prostate is prostatitis, not prostate cancer. A prostatitis diagnosis was present in 4.5% of the male population.2 However, if prostate diseases can develop into prostate cancer, previous studies about prostate cancer are enough to demonstrate the necessity of studying prostate diseases.

Prostate cancer is the second leading cause of cancer-related death among males in the United States.3 This is the most direct reason for the increase in the mortality of prostate disease patients. Another main factor related to an increase in the incidence and mortality rates is race. Incidence and mortality rates have been consistently higher for African Americans as compared with Caucasian Americans.4 It is the purpose of this study to examine co-morbidities related to the mortality of prostate disease patients. Other social factors that influence life style will be excluded.

We will find co-morbidities and other factors related to mortality of prostate disease patients by researching statistical relationships among variables in the given data. It was very important to pre-process the National Inpatient Sample (NIS) data of prostate diseases for the chi-square and logistic regression analyses with SAS® statistical software 9.1. Thus, the pre-processing using SAS® Enterprise Guide 4.0® will be demonstrated in detail in the Methods section. The statistical results were also compared with the results that were reported from previous research.

Results not only show that co-morbidities are more related to mortality rather than pure prostate diseases, but also verify that age is the major factor related to prostate diseases and mortality in this statistical analysis. In addition, co-morbidities related to prostate diseases were diseases of the circulatory system and the digestive system. However, diseases of the respiratory system have a highly positive correlation with mortality.

METHODS The analysis used the National Inpatient Sample (NIS) data of prostate diseases that can be found at the Healthcare Cost and Utilization Project (HCUP) web site5. These data are provided by the Agency for Healthcare Research and Quality, Rockville, MD. Diagnosis and procedure variables are coded by the International Classification of Disease (ICD 9). The raw data included female patients even though the prostate is a part of the male reproductive system. It was an error of recording or surveying, with about 30% of patients recorded as female. So, all female data were deleted for this analysis because of uncertainty. Other variables still had missing values, but those observations were included in this study. However, the missing values were automatically deleted for chi-square and logistic regression analyses.

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After deleting female patients and missing data from the dataset, 13,187 out of 28,879 patient records were adopted for this study.

Table 1. Variables and description in NIS data

NIS data used in this study include all variables in Table 1. If data used in a statistical analysis were not collected and organized by the investigator who performs the analysis, the data usually must be pre-processed. The format of diagnosis codes in the NIS data is inadequate for this study. The diagnosis codes are recorded as ICD9 text codes without order. For this study, it was necessary to change the text codes to binary codes. The compressing process for the Diagnosis Related Group (DRG) and the diagnosis codes is demonstrated in the following. Statistical analyses and graphic presentation were performed with SAS® statistical software, Version 9.1, SAS Institute Inc., Cary, NC. Chi-square analysis, logistic regression, frequency analysis and data summary functions in SAS® statistical software were used for analysis. In addition, the filter function was mainly used for removing missing data and compressing DRG and ICD9 codes.

Compressing Process of DRG and Diagnosis codes

The goal of compressing DRG and diagnosis codes is to extract the main codes from the data and to switch the text codes to the binary codes by existence. The 10 most frequent DRG codes and the 20 most frequent ICD 9 codes of diagnosis were used for this analysis, and the other values were classified as “Other”.

Variable Value & Value Description AGE (Age in years at admission) Age in years (0~124 years old) LOS (Length of stay) Length of stay in days (0~365 days and up) TOTCHG (Total charges) Total charge rounded ($25~$1million) ATYPE (Admission type) 1-Elective, 2-Emergency, 3-Newborn, 4-Other, 5-Trauma Center, 6-Urgent DIED (Died during hospitalization) 0-Not died, 1-Died DRG (DRG in effect on discharge date) nnn-DRG value

DXn (Diagnosis) ICD9 codes for diagnosis ELECTIVE (Elective versus non-elective admission)

0-Non-elective, 1-Elective

FEMALE (Indicator of sex) 0-Male, 1-Female

PAY1 (Primary expected payer) 1-Medicare, 2-Medicaid, 3-Private insurance, 4-Sefl-pay, 5-No charge, 6-Other

PRn (Procedure) ICD9 codes for procedure

RACE (Race) 1-White, 2-Black, 3-Hispanic, 4-Asian or Pacific Islander, 5-Native American, 6-Other

ZIPInc_Qrtl (Median household income quartile for patient's ZIP Code)

1-Under $36,000, 2-$36,000~$44,999, 3-$45,000~$59,999, 4-$60,000 and up

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The first step of compressing DRG codes is to determine the ten most frequent DRG codes using “One-Way Frequency” analysis in SAS® Enterprise Guide 4.0®. 1. Describe > One-Way Frequencies 2. Select DRG as Analysis variable

3. Descending sort of output frequencies 4. Top ten DRG codes for the prostate disease patients

The DRG is re-coded by using “Compute Columns” in the “Filter and Query” menu. The rest of the DRG codes except the ten most frequent are recorded as “Other”. A new column for the re-coded DRG is automatically added into the original data. 1. Data > Filter and Query 2. Select all variables in the data

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3. Computed Columns > New > Recode a Column Replace the values 4. Recoded DRG column is created in the data.

Compressing diagnosis codes is more complicated than the DRG compressing process. Each patient has one DRG code, but some patients have more than one disease. So, more than one diagnosis code is recorded on a patient. Using “Filter and Query” and “Append Table” options, all diagnosis codes are listed by patient ID numbers. Then, using a frequency count, the 20 most frequent diagnosis codes are extracted. The 20 most frequent diagnoses recorded in ICD9 text codes are re-coded to binary codes in 20 columns by coding in SAS®. There are 15 diagnosis columns in the original NIS data. All diagnosis codes listed on those 15 columns are transferred onto a column by patient ID numbers. The first step of transferring is to make 15 data files for each diagnosis column arranged by patient ID numbers. Therefore, the following process has to be repeated 15 times. The name of the diagnosis column in each file should be changed to the same name, such as “DN”. 1. Data > Filter and Query 2. Select only KEY and DXn

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3. Uncheck “Read-only” in the Data menu Right-click on the DXn column and select Properties

4. Change the name “DXn” to “DX” (Make the names of 15 diagnosis columns as the same name)

Using the “Append Table” option in SAS® Enterprise Guide 4.0®, these 15 diagnosis columns in 15 data files are transferred to one diagnosis column, DN, in a new data file. The next step for compressing ICD9 diagnosis codes is the same as the first step for compressing the DRG codes. The 20 most frequent diagnosis codes are determined by using a frequency count, 1. Data > Append Table 2. Open 15 data files created in the previous process

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3. All diagnosis codes are listed by KEY (patient ID) on a column

4. Determine the 20 most frequent diagnosis codes

5. Re-code the DX by replacing the values 6. Recoded_DX column is created in the data.

7. Select Recode_DX as Analysis variable and KEY as Group analysis by for One-Way Frequency count

8. The frequency counts are sorted by KEY

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The extracted 20 diagnoses codes are recorded in binary codes in 20 columns by the following SAS coding. However, the same numbers of rows are created by the SAS coding due to the number of diagnosis codes observed in a patient.

data sasuser.recode_dx_2; set sasuser.freqfordx_2; if (recode_dx='4019') then c4019=1; else c4019=0; if (recode_dx='60000') then c60000=1; else c60000=0; if (recode_dx='5601') then c5601=1; else c5601=0; if (recode_dx='41401') then c41401=1; else c41401=0; if (recode_dx='25000') then c25000=1; else c25000=0; if (recode_dx='4280') then c4280=1; else c4280=0; if (recode_dx='42731') then c42731=1; else c42731=0; if (recode_dx='53081') then c53081=1; else c53081=0; if (recode_dx='5990') then c5990=1; else c5990=0; if (recode_dx='2724') then c2724=1; else c2724=0; if (recode_dx='2765') then c2765=1; else c2765=0; if (recode_dx='496') then c496=1; else c496=0; if (recode_dx='60001') then c60001=1; else c60001=0; if (recode_dx='V1582') then cV1582=1; else cV1582=0; if (recode_dx='2859') then c2859=1; else c2859=0; if (recode_dx='3051') then c3051=1; else c3051=0; if (recode_dx='2720') then c2720=1; else c2720=0; if (recode_dx='486') then c486=1; else c486=0; if (recode_dx='V4581') then c V4581=1; else c V4581=0; if (recode_dx='41400') then c41400=1; else c41400=0; if (recode_dx='Other') then cOther=1; else cOther=0; run;

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Thus, using the “Summary Statistics” option in SAS® Enterprise Guide 4.0®, binary diagnosis codes are arranged in a row by each patient ID number. These re-coded data sets are attached to the original NIS data by each patient ID number. The “Tables and Joins” option in the “Query and Filter” option is used to merge them, based on patient ID numbers. 1. Describe > Summary Statistics 2. Select 20 diagnoses as Analysis variables and KEY as

Classification variables for Summary Statistics

3. Check only “Maximum” in the Basic options 4. Create this output as a data file

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1. Data > Filter and Query 2. Add Tables > Open the original data files and the

summary statistics output data file

3. Join the two files based on KEY

Those compressed DRG and diagnosis codes are summarized in the following two tables.

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Table 2. 10 most frequent DRGs which prostate disease patients belong to DRG Description

88 Chronic obstructive pulmonary disease 89 Simple pneumonia & pleurisy age>17 w/ cardiac catheter

127 Heart failure & shock 148 Major small & large bowel procedures w/ cardiac catheter 180 Gastrointestinal obstruction w/ cardiac catheter 209 Major joint & limb reattachment procedures of lower extremity 320 Kidney & urinary tract infections age>17 w/ cardiac catheter 336 Transurethral prostatectomy w/ cardiac catheter 337 Transurethral prostatec tomy w/o cardiac catheter 430 Psychoses

Table 3. 20 most frequent diagnosis (co-morbidities) for prostate disease patients ICD9 Disease 285.9 Anemia, unspecified 401.9 Essential hypertension, unspecified

414.01 Coronary atherosclerosis of native coronary artery 428.0 Congestive heart failure, unspecified

427.31 Atrial fibrillation 414.00 Coronary atherosclerosis of unspecified type of vessel, native or graft 560.1 Paralytic ileus

530.81 Esophageal reflux 600.00 Hypertrophy (benign) of prostate without urinary obstruction

599 Urinary tract infection, site not specified 600.01 Hypertrophy (benign) of prostate with urinary obstruction

496 Chronic airway obstruction, not elsewhere classified 486 Pneumonia, organism unspecified 250 Diabetes mellitus without mention of complication

272.4 Other and unspecified hyperlipidemia 276.5 Volume depletion 272.0 Pure hypercholesterolemia 305.1 Tobacco use disorder

V15.82 History of tobacco use V45.81 Aortocoronary bypass status

RESULTS The analysis used the National Inpatient Sample (NIS) data of prostate diseases that can be found at the Healthcare Cost and Utilization Project (HCUP) web site5. These data are provided by the Agency for Healthcare Research and Quality, Rockville, MD. Diagnosis and procedure variables are coded by the International Classification of Disease (ICD 9).

Summary Statistics of Prostate Disease Patients

Prostate disease patients generally show the following tendency. Age is the major factor of prostate diseases found in the summary statistical analysis. The number of patients who have prostate diseases increased by age in Figure 1. Even though the graph shows a decline after 90 years of age, it is still relatively high for the ratio of the total population in society. The number of new-born babies who have prostate diseases is relatively high, unexpectedly.

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Figure 1. Number of patients by ages

As shown in Figure 2, the ratio for White and Black is a little higher than the ratio of the total population in the United States, but the ratio of prostate disease patients by race is quite close to the actual population ratio.

Figure 2. Number of patients by race

High percentages of prostate disease patients also have diseases of the circulatory system and the digestive system. The total percentages of two categories are about 34%, whereas patients who are in the hospital for prostate diseases as a primary disease are less than 11%. The next highest groups have diseases of the respiratory system, and diseases of the kidney and urinary tract. The percentage of each DRG is somewhat different by race, but the trend of high percentages in DRGs of the circulatory system, the digestive system and the respiratory system is similar.

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Figure 3. Prostate disease patients in simplified Diagnosis Related Group (DRG)

Mortality of all prostate disease patients is less than 1%. This ratio is almost the same whatever a patient’s race or income. The frequency of mortality changes with the frequency of the total number of patients. In the study of the relationship between mortality and other variables, only one interesting result was found. The mortality in the respiratory disease group is relatively high, about 24% of the total dead patients. The percentage of prostate disease patients in the respiratory disease group is about 12% of total patients, but the mortality is about twice as shown in Table 4.

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Table 4. Mortality of prostate disease patients by simplified Diagnosis Related Group (DRG)

DRG Not Died Died

Alcohol/Drug 0.18 0.00

Blood and Blood Forming Organs and Immunological 0.86 0.40

Circulatory Sys. 17.19 12.22

Digestive Sys. 17.12 12.22

Ear, Nose, Mouth and Throat 0.85 0.40

Endocrine, Nutritional, and Metabolic 2.47 2.40

Eye 0.58 0.00

Factors Influencing Health Status 2.17 1.00

Hepatobiliary Sys. and Pancreas 2.60 3.61

Human Immunodeficiency Virus Infections 0.19 0.80

Infectious and Parasitic 2.68 10.82

Injuries, Poisonings, and Toxic Effects 1.04 0.60

Kidney and Urinary Tract 8.76 9.42

Male Reproductive Sys. 11.27 1.00

Mental 3.50 0.20

Multiple Significant Trauma 1.90 7.62

Musculoskeletal Sys. and Connective Tissue 6.84 2.81

Myeloproliferative and Neoplasms 0.84 4.01

Nervous Sys. 4.56 6.41

Newborns and Other Neonates 0.47 0.20

Respiratory Sys. 12.06 23.65

Skin, Subcutaneous, Tissue, and Breast 1.88 0.20

Total 100 100

Relationship between Co-morbidities and Mortality

Table set 1 gives a summary of the information of the logistic regression model used in this study. A binary logistic regression model was used to relate the co-morbidities to death. The dependent variable, “Died”, has binary values “Not Died (0)” and “Died (1)”. All missing records in Died were deleted for the logistic regression. Thus, the number of observations used in the logistic regression analysis was 13,187 out of 13,187. About 3% of prostate disease patients in these observations died. The frequency of Died (1) is higher than that in the total observations mentioned in the summary statistics section. However, the mortality is still very low.

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Table Set 1. Logistic Regression Model Information

Model Information

Response Variable DIED (Died during hospitalization)

Number of Response Levels 2

Model binary logit

Optimization Technique Fisher's scoring

Number of Observations Read 13187

Number of Observations Used 13187

Response Profile

Ordered Value DIED Total Frequency

1 0 12775

2 1 412

Probability modeled is DIED=0.

The first table in Table Set 2 shows that the results of the logistic regression model satisfy its convergence criterion. Overall, the model is statistically significant because the Pr>ChiSq is less than 0.001. The c value in the third table suggests that the accuracy of mortality is 79% in this model.

Table Set 2. Summary of Logistic Regression Results

Model Convergence Status

Convergence criterion (GCONV=1E-8) satisfied.

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 517.8918 21 <.0001

Score 632.4119 21 <.0001

Wald 478.3123 21 <.0001

Association of Predicted Probabilities and Observed Responses

Percent Concordant 77.8 Somers' D 0.581

Percent Discordant 19.6 Gamma 0.597

Percent Tied 2.6 Tau-a 0.035

Pairs 5263300 c 0.791

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Co-morbidities of prostate diseases were classified by six body system groups; (1) Diseases of the Circulatory System, (2) Diseases of the Digestive System, (3) Diseases of the Genitourinary System, (4) Diseases of the Respiratory System, (5) Endocrine, Nutritional and Metabolic Diseases, and Immunity Disorders, and (6) Other. The most frequent diagnosis codes related to mortality of prostate disease patients in Table 5 will be explained in each system group that they belong to. The diagnosis codes that have Pr>ChiSq values less than 0.005 are statistically significant.

Table 5. Logistic regression analysis for 20 most frequent diagnosis codes with mortality

ICD9 Diagnosis Percent DF Wald Chi-Square

Pr > ChiSq

600.00 Hypertrophy (benign) of prostate without urinary obstruction 6.04 1 0.8563 0.3548

401.9 Unspecified Essential hypertension 5.34 1 23.3388 <.0001

560.1 Paralytic ileus 2.37 1 25.1574 <.0001

414.01 Coronary atherosclerosis of native coronary artery 2.04 1 7.8267 0.0051

250 Diabetes mellitus without mention of complication 1.85 1 4.2969 0.0382

428.0 Congestive heart failure, unspecified 1.76 1 59.8359 <.0001

427.31 Atrial fibrillation 1.73 1 13.9791 0.0002

600.01 Hypertrophy (benign) of prostate with urinary obstruction 1.56 1 11.4516 0.0007

272.4 Other and unspecified hyperlipidemia 1.50 1 2.2278 0.1355

496 Chronic airway obstruction, not elsewhere classified 1.40 1 16.3652 <.0001

530.81 Esophageal reflux 1.39 1 7.7598 0.0053

599 Urinary tract infection, site not specified 1.25 1 8.0829 0.0045

276.5 Volume depletion 1.17 1 32.4214 <.0001

272.0 Pure hypercholesterolemia 1.03 1 0.9247 0.3362

305.1 Tobacco use disorder 1.02 1 12.1986 0.0005

414.00 Coronary atherosclerosis of unspecified type of vessel, native or graft 0.99 1 0.1160 0.7334

285.9 Anemia, unspecified 0.97 1 0.2045 0.6511

V15.82 History of tobacco use 0.90 1 1.7948 0.1803

486 Pneumonia, organism unspecified 0.88 1 45.3952 <.0001

V45.81 Aortocoronary bypass status 0.85 1 2.5938 0.1073

Other Other co-morbidities 63.96 1 0.0012 0.9718

Diseases of the Circulatory System

Five out of the twenty most frequent diagnosis codes in Table 5 are involved in this group: unspecified essential hypertension (401.9), coronary atherosclerosis of native coronary artery (414.01), congestive heart failure unspecified (414.01), atrial fibrillation (427.31) and coronary atherosclerosis of unspecified type of vessel (414.00). As shown in Figure 3, approximately 17% of prostate disease patients belong to a Diagnosis Related Group (DRG) related to disease of the circulatory system. Only four diagnoses (except coronary atherosclerosis of unspecified type of vessel (414.00)) seem to be related to mortality. Especially, congestive heart failure (428.0) shows the highest positive relation with mortality in the logistic regression results in Table 6.

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Table 6. Odds Ratio Estimates

Effect (Not existed vs.

Existed) Diagnosis of Effect Point

Estimate 95% Wald

Confidence Limits

c4019_Max 0 vs. 1 Unspecified Essential hypertension 0.534 0.414 0.688

c60000_Max 0 vs. 1 Hypertrophy (benign) of prostate without urinary obstruction 0.867 0.641 1.173

c5601_Max 0 vs. 1 Paralytic ileus 2.028 1.539 2.674

c41401_Max 0 vs. 1 Coronary atherosclerosis of native coronary artery 0.600 0.42 0.858

c25000_Max 0 vs. 1 Diabetes mellitus without mention of complication 0.677 0.468 0.979

c4280_Max 0 vs. 1 Congestive heart failure, unspecified 2.663 2.078 3.413

c42731_Max 0 vs. 1 Atrial fibrillation 1.629 1.261 2.103

c53081_Max 0 vs. 1 Esophageal reflux 0.492 0.299 0.81

c5990_Max 0 vs. 1 Urinary tract infection, site not specified 1.515 1.138 2.017

c2724_Max 0 vs. 1 Other and unspecified hyperlipidemia 0.697 0.434 1.12

c2765_Max 0 vs. 1 Volume depletion 2.188 1.671 2.864

c496_Max 0 vs. 1 Chronic airway obstruction, not elsewhere classified 1.742 1.331 2.28

c60001_Max 0 vs. 1 Hypertrophy (benign) of prostate with urinary obstruction 0.443 0.276 0.71

cV1582_Max 0 vs. 1 History of tobacco use 0.715 0.437 1.168

c2859_Max 0 vs. 1 Anemia, unspecified 0.920 0.643 1.318

c3051_Max 0 vs. 1 Tobacco use disorder 0.337 0.183 0.62

c2720_Max 0 vs. 1 Pure hypercholesterolemia 0.774 0.46 1.304

c486_Max 0 vs. 1 Pneumonia, organism unspecified 2.585 1.961 3.408

cV4581_Max 0 vs. 1 Aortocoronary bypass status 0.576 0.294 1.127

c41400_Max 0 vs. 1 Coronary atherosclerosis of unspecified type of vessel, native or graft 0.912 0.536 1.55

cOther_Max 0 vs. 1 Other co-morbidities >999.999 <0.001 >999.999

Diseases of the Digestive System

In Figure 3, the number of patients in this group is as many as in diseases of the circulatory system group. However, only two diagnoses are in the twenty most frequent diagnosis codes. It is about 3.8% of total patients. According to the logistic regression results in Table 5, both paralytic ileus (560.1) and esophageal reflux (530.81) are related to mortality, but esophageal reflux (530.81) is not as related as paralytic ileus (560.1) comparatively by the odds ratio in Table 6.

Diseases of the Genitourinary System

As expected, about 20% of the prostate disease patients are involved in this group. However, correlation between mortality and pure prostate diseases such as Hypertrophy (benign) of prostate without urinary obstruction (600.00) in Table 5 is not related to mortality. Even though hypertrophy (benign) of prostate with urinary obstruction (600.01) is related to mortality, the odds ratio is the lowest.

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Diseases of the Respiratory System

Two diagnoses, chronic airway obstruction (496) and pneumonia (486), are involved in the top twenty frequent diagnoses in this group. Those frequencies are not high relatively, but as shown in Table 6, both of them have strong positive correlations with mortality.

Endocrine, Nutritional and Metabolic Diseases, and Immunity Disorders

In Figure 1, only 2.5% of prostate disease patients are involved in this group. However, four diagnoses (diabetes mellitus without mention of complication (250), volume depletion (276.5), pure hypercholesterolemia (272.0), other and unspecified hyperlipidemia (272.4)) in this group are marked on the twenty most frequent diagnoses. Diabetes mellitus without complication (250) and volume depletion (276.5) show correlation with mortality.

Other

It is interesting that about 10% of prostate disease patients have anemia (285.9) and 3.5% of prostate disease patients are involved in the mental disorders group because of smoking habits. Anemia dose not have a significant correlation with mortality, but tobacco use disorder (305.1) does.

CONCLUSION Fourteen out of the twenty most frequent co-morbidities for prostate disease patients are significantly related to mortality. Probably, it is the reason that age is the major factor of prostate diseases. Co-morbidities have correlation with age as well as mortality, because age is significantly related to mortality. This study for correlation between mortality and co-morbidities verifies that age is the major factor of prostate diseases. Previous research said that hypertension and benign prostatic hyperplasia (BPH) are common in elderly men; their prevalence increases significantly with age and an estimated 25% of men aged 60 years have both conditions.6

There are many studies showing that co-morbidities have a relationship with mortality as well as with age. Fowler et al. found that a greater proportion of men died from other causes, rather than from prostate cancer. They prospectively followed 276 prostate cancer patients for 7 years post-treatment and found that 33% of the group died of co-morbid disease, and 7% died of prostate cancer-related causes.7 Other researchers have found that from 39 to 49% of men diagnosed with prostate cancer die of other causes.2-8 Some investigators, however, have found that an equal or slightly greater proportion of men died due to prostate cancer. In a study of 514 prostate cancer patients in Sweden, Aus et al. found that 50% died from other causes and 50% died, directly or indirectly, from prostate cancer.9

In the present study, four diagnoses (unspecified essential hypertension, coronary atherosclerosis of native coronary artery, congestive heart failure and unspecified atrial fibrillation) seem to be related to mortality. Especially, congestive heart failure shows a high positive relation with mortality. Mona F. et al. found that 237 men (42.2%) died from prostate cancer, and 298 (53.1%) died with prostate cancer. Several serious/very serious conditions, specifically other cancers, heart disease (ischemic, organic, congestive heart failure), cerebral vascular accidents, and chronic obstructive pulmonary disease were more common among men who died of causes other than prostate cancer than among those who died from prostate cancer.10

According to the logistic regression results in Table 5, both paralytic ileus and esophageal reflux (530.81) are related to mortality. According to Quentin’s study, the strongest observed associations were with functional digestive disorders, dyspepsia, anxiety disorders, other soft tissue disorders and esophageal reflux.11

The mortality in the respiratory disease group is relatively high, about 24% of the total dead patients. The percentage of prostate disease patients in the respiratory disease group is about 12% of total patients, but the mortality is about twice.

An interesting result of this study is that diabetes is one of the frequent co-morbidities of prostate diseases that are related to mortality. However, Tavani A et al. found no association between diabetes and prostate cancer risk in their report,12 although their subject was limited to prostate cancer. Prostate cancer and other prostate disease have a lot of common factors. Heart failure is a common co-morbidity in patients with diabetes. The data of hospitalized patients with heart failure reveals a high prevalence of diabetes mellitus and mortality.13 Although the relationship between diabetes and prostate diseases is unreliable, the previous studies illustrate that diabetes is related to mortality.

The author can find a high frequency of anemia and tobacco use from other research reports. Anemia has been described as a common side effect of androgen deprivation therapy (ADT).14 Anemia in cancer patients is associated with treatment resistance, increased rates of local recurrence, decreased survival rates, and impaired quality of life. It is certainly well established that low hemoglobin (Hgb) levels significantly correlate with poor locoregional control and survival in patients treated with external beam radiation therapy (EBRT) for a variety of tumor sites.15 Tobacco use has been implicated as a leading cause of cancer death. The associations between smoking, prostate cancer incidence and survival have not demonstrated consistent outcomes.16 However, more recent studies suggest that a positive relationship may exist between smoking and death from prostate cancer.17

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Studies should be performed in more controlled and similar situations to compare with other research. Finally, the National Inpatient Sample (NIS) data include a lot of errors. It is too weak to derive a logical relation between prostate diseases and many variables in the data. More accurate data collection procedures are needed.

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