LOW RISK CHEST PAIN SEMINAR

Emily McLaren, PGY 3 7 February 2013

What are the history and physical characteristics of

patients presenting to an ED with chest pain that is low risk

for ACS?

Objectives

  What is the role of the H&P in identifying LRCPPTS?

  What is the role of classic cardiac risk factors in risk stratification of ACS?

  What clinical decision tools are available to aid in risk stratification of LRCPPTS?

What is low risk chest pain?

  Typical chest pain  Heberden 1768   A painful sensation in the breast accompanied

by a strangling sensation, anxiety, and occasional radiation of pain to the L arm

  Associated with exertion, relieved with rest   Atypical or low risk chest pain

  Everything else?

Why do we care?

  We miss about 2-5% of ACS   Most CP admissions are for non-cardiac

chest pain

  H+P, RF, and decision tools are available to aid in our decision making

Chest Pain History

  JAMA 2005   Literature review of prospective and

retrospective observational studies and systematic reviews

Chest Pain Characteristics

Chest Pain Radiation

  Typical CP: radiation to L neck, shoulder or arm

  ACS   To R arm, shoulder (PLR 4.7)   To both arms (PLR 4.1)   To L arm, shoulder (PLR 2.3)

Chest Pain Quality

  Typical CP: pressure, ache

  ACS   Same as prior MI (PLR 1.8)   Pressure (PLR 1.3)

  ACS   Sharp, stabbing (PLR 0.3)

Chest Pain Location

  Typical CP: substernal, L chest   Poorly studied   Poor predictive value

 Substernal CP  Region of infarction (exception: inferior AMI)

  ACS: Inframammary (PLR 0.8)

Area of Chest Pain

  Typical CP: diffuse

  ACS: < size of coin (PRL 0.6 with CI 0.3-1)   Everts et al, 822 pts  Non-AMI 11% vs AMI 7%

Chest Pain Severity

×

Time Course   ACS: crescendo pattern   Non-ACS: maximal intensity at onset

  Duration   Seconds – non-ACS   2-10 min – angina   10-30 min – unstable angina   > 30 min – AMI vs non-ACS (GI)   Recurrent, hrs-days – non-ACS

Palliative/Provocative Factors and Associated Symptoms

Palliative Factors

  Nitro   GI Cocktail   Rest ×

 Provocative Factors

  ACS   Exertion (PLR 2.4)

  Equivocal   Emotion   Stress

Provocative Factors   ACS

  Pleuritic (PLR 0.2)   Positional (PLR 0.3)   Reproducible (PLR 0.3)   Non-exertional (PLR 0.8)

  Lee et al (1985) – 22% of pts with sharp pain dx with ACS (13% pleuritic, 7% reproducible)

  Lee et al (1987) – 3 Ps + no hx of CAD, none dx with MI

Associated Symptoms

  ACS  Diaphoresis (PLR 2)  Nausea/vomiting (PLR 1.9)

  Disappears with multivariable testing

Conclusions

Conclusions

  Individual elements are assoc with increased or decreased risk of ACS

  No element of chest pain quality alone or in combination identify patients that can be safely discharged without further diagnostic testing

Limitations

  Characteristics treated as independent, rather than interdependent variables

  Quality is subjective   Only addresses CP, not other anginal

equivalents

Physical Exam

HIGH LIKELIHOOD

INTERMEDIATE LIKELIHOOD

LOW LIKELIHOOD

•  Pulmonary edema •  New or worsening MR •  S3 •  Hypotension •  Brady or tachycardia

•  Extracardiac vascular disease (bruit)

  Reproducible CP

  PE is usually normal in uncomplicated ACS

May point to non-ACS Dx

  Unequal pulses - dissection   Murmurs - endocarditis   Friction rub - pericarditis   Fever, rhonchi - pneumonia   Reproducible CP - MSK

Cardiac Risk Factors

Risk Factors

  Age, Male Gender, HTN, HLP, DM, smoking, and family history

  Framingham study: 2+ risk factors = higher lifetime risk of CAD

  Jayes et al, 1992   1743 pts   What to RF add to hx and EKG when

diagnosing ACS?

Jayes et al

  Han et al, 2007   Retrospective analysis of 10,806 patients

with suspected ACS   8.1% met end point: ACS within 30 days

(PCI, biomarkers, death)

Conclusions

Conclusions   Cardiac RF have limited value in diagnosing ACS in ED patients older than 40

Conclusions -LR 0 RF +LR 4+ RF

< 40 0.17 (0.04-0.66) 7.39 (3.09-17.67)

40-65 0.53 (0.4-0.71) 2.13 (1.66-2.73)

> 65 0.96 (0.74-1.23) 1.09 (0.64-1.62)

Limitations

  RF given equal weight   Verification bias

Clinical Decision Tools

Early Risk Scores

  Pozen et al, 1980: created a ‘mathematical predictive instrument’ to decrease CCU admissions

  Selker et al, 1998: ACI-TIPI   Goldman et al, 1988: < 7% risk of AMI   Limkakeng et al, 2001: < 4.9% risk of AMI

TIMI Risk Score

  Developed to categorize risk of death or ischemic events in pts with NSTEMI or UA

  Used as basis for MDM

  Chase et al, 2006   First prospective observational cohort to

validate TIMI in ED pts   1458 pts

Chase et al, 2006

Chase et al, 2006

TIMI Score 0 = 1.7% event rate

Chase et al

Similar Studies

  Pollack et al, 2006   3929 patients   TIMI 0 = 2.1% risk

Conclusions

  TIMI risk score does correlate with outcome   Identified large group of pts that are low risk

for primary outcome at 30 days   Cannot be used in isolation to determine

dispo

“Manchester” Modified TIMI

  Body et al, 2009   Pts with positive troponin or EKG changes

may only have TIMI = 1   Prospective cohort   796 pts

Body et al

TIMI < 3 = sensitivity of 96%

  Hess et al, 2010   Prospective observational study   1017 pts

Hess et al, 2010

  Than et al, 2011   3582 pts in 14 EDs, 9 countries   TIMI + biomarker panel at 0 and 2 hrs   2 hr TIMI 0 = 0.9% risk (9.8% of pts)

Than et al

  Aldous et al, 2012   1000 from ASPECT   Primary outcome in 36.2%   Also included high sensitivity Troponin T   2 hr TIMI 0 = 0.8% risk (12.3% pts)

GRACE   Global Registry of Acute Coronary Events   Prospective multinational observational study

of hospitalized pts with ACS   8 variables

  Looks at in-hospital and 6 month all-cause mortality

  Age   HR   SBP   Cr   Killup score

  ST segment depression

  Elevated biomarkers

  Cardiac arrest

  Lyon et al, 2006   Retrospective cohort   1000 pts   TIMI = GRACE

  Lee et al 2011   TIMI vs GRACE vs PURSUIT   Prospective cohort study   4723 pts

PURSUIT

Lee et al

TIMI = 0 in 39%

GRACE < 41 in 4.5%

Lee et al

  Kline et al, 2005   Prospective database of 8 variables from

14,796 pts   Attribute matching vs ACI-TIPI

Attributes matching

Kline et al

  Mitchell et al, 2006   1114 pts   Attributes matching vs. ACI-TIPI vs.

physician estimate

Mitchell et al

Sanchis Rule

  Sanchis et al, 2005   646 pts   Focuses on clinical history   Excludes EKG changes and (+) troponin   Primary end point at 1 year, secondary at 14

days

Chest Pain Score

Hospital Course and Results

  322 had exercise ST: (-) 190, (+) 52   216 pts early D/C   430 pts hospitalized

  227 cardiac cath  68 PCI  31 CABG

  Primary end point: 1 yr (6.7%), 14 days (5.4%)

Calculated risk score

  CP score > 10 1 point   > 2 pain episodes in 24 hrs 1 point   Age > 67 1 point   IDDM 2 points   Prior PCI 1 point

In pts with negative troponin and no EKG changes

Calculated Risk Score

Score 0 1 2 3 > 4

Event Rate 0% 3.1% 5.4% 17.6% 29.6%

Stress Results Event Rate

Negative Inconclusive Positive Not done

1.6% 3.9% 9.6% 10%

Score of 0 = 17.2% of pts

Limitations

  Complicated CP score   Subjective

Vancouver Rule

  Christenson et al, 2006   Prospective cohort, 769 pts   Screened 123 potential predictor variables   Clinical decision tool that is 98.8% sensitive

and allows for D/C of VLRCP pts within 2-3 hrs (32.5%)

Other similar studies

  Marsan et al (2005): age < 40, no CAD hx, normal EKG OR no CAD RF, normal initial biomarkers = ACS rate 0.14%, no CV events at 30 days

  Collin et al (2011): no events for same patients at 1 year

Limitations

  Outdated biomarkers   Detroit ≠ Vancouver

  Six et al, 2008   120 pts   Clinical questions

 Why do we admit to CCU?   Predictors of 90 day events?

Six et al

0-3: 2.5% risk (32.5% of pts)

4-6: 20.3% risk

7-10: 72.7% risk

Six et al

  Conclusion – can use HEART to determine early D/C vs. early intervention

  Limitations   Small study  Use CP hx

  PURSUIT vs TIMI vs GRACE vs FRISC vs HEART

  Uses c-statistic to claim HEART superiority

  Mahler et al, 2011   Prospective cohort   1070 CP Obs pts (TIMI < 2 and clinically low

risk)   Outcome

 HEART < 3: 0.6% events  HEART < 3 + 4-6 hr troponin: 0 events

  Fesmire et al, 2012   Retrospective study   2148 pts   Weighted HEART + 3 S’s

  Sex   Serial troponin and EKG  Decreased weight of RF, age and CAD hx   Increased weight on chest pain hx

Fesmire et al

HEARTS3 < 2 = 0 events

(14% vs 8%)

Fesmire et al

  Older troponin   Retrospective study

  Hess et al, 2012   Prospective observational cohort of 2,718

patients   12% met primary outcome (ACS,

revascularization, death) within 30 days   Identified patients with zero risk for 30 day ACS

Conclusions

  Developed a highly sensitive clinical decision tool to identify very low risk patients for ACS

Limitations

  Does not include pts at risk for ACS with non-chest pain CC

  Evaluation bias: not all patients underwent definitive testing

  What is typical chest pain?   Needs prospective multicenter validation

  Aldous et al, 2012   Post hoc analysis of ASPECT trial   Primary endpoint in 36.2%

Study Population

Results

Results

Conclusions

  Several elements of CP hx and multiple decision tools available to aid in dx of ACS

  Classic CAD risk factors less impt in acute setting

  Ultimately unlikely to change our clinical practice

References 1.  Swap and Nagurney. Value and Limitations of Chest Pain History in the Evaluation of Patient With Suspected Acute Coronary Syndrome.

JAMA. November 23/30. Vol 294. pp 2623-2629.

2.  Fesmire et al. Improving risk stratification in patients with chest pain: the Erlander HEARTS3 score. American Journal of Emergency Medicine. 2012. pp 1829-1837.

3.  Sanchis et al. New risk score for patients with acute chest pain, non-ST- segment deviation, and normal troponin concentrations: a comparison with the TIMI risk score. J Am Coll Cardiology 2005;46:443-449.

4.  Christenson et al. A clinical prediction rule for early discharge of patients with chest pain. Annals of Emergency Medicine. 2006;47:1-10.

5.  Backus et al. Chest pain in the emergency department: a multicenter validation of the HEART score. Crit Pathw Cardiol 2010;9:164-9.

6.  Mahler et al. Can the HEART score safely reduce stress testing and cardiac imaging in patients at low risk for major adverse cardiac events. Crit Pathw Cardiol 2011;10:128-33.

7.  Six et al. Chest pain in the emergency room: value of the HEART score. Neth Heart J 2008;16:191-6.

8.  Chase et al. Prospective validation of the thrombolysis in myocardial infarction risk score in emergency department chest pain population. Annals of Emergency Medicine 2006;48:252-9.

9.  Hess et al. Prospective validation of a modified thrombolysis in myocardial risk score in emergency department patients with chest pain and possible acute coronary syndrome. Acad Emergency Med 2010;17:368-75.

10.  Lee et al. Comparison of cardiac risk scores in ED patients with potential acute coronary syndrome. Crit Pathw Cardiol 2011;10:64-8.

11.  Lee et al. Clinical characteristics and natural history of patients with acute myocardial infarction sent home from the emergency room. Am J Cardiol. 1987;60:219-224.

12.  Hess et l. Development of a Clinical Prediction Rule for 30-Day Cardiac Events in Emergency Department Patients With Chest Pain And Possible Acute Coronary Syndrome. Annals of Emergency Medicine. Vol 59, N0 2. Feb 2012. pp 115-125

13.  Han et al. The Role of Cardiac Risk Factor Burden in Diagnosing Acute Coronary Syndromes in the Emergency Department Setting. Annals of Emergency Medicine. Vol 49, No 2. pp 145-152.

14.  Amsterdam et al. Testing of Low-Risk Patients Presenting to the Emergency Department With Chest Pain: A Scientific Statement From the American Heart Association. Circulation. 2010;122:1756-1776.

15.  Kline et al. Randomized Trial of Computerized Quantitative Pretest Probability in Low-Risk Chest Pain Patients: Effect on Safety and Resource Use. Annals of Emergency Medicine, June 2009. Vol 53, No 6. pp 727-735.

References 1.  Kline et al. Pretest probability assessment derived from attribute matching. BioMed Central. August 2005.

2.  Mitchell et al. Prospective Multicenter Study of Quantitative Pretest Probability Assessment to Exclude Acute Coronary Syndrome for Patients Evaluated in Emergency Department Chest Pain Units. Annals of Emergency Medicine. 2006;47:438-447.

3.  Selker et al. Use of the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) to Assist with Triage of Patients with Chest Pain or Other Symptoms Suggestive of Acute Cardiac Ischemia: A Multicenter Clinical Trial. Annals of Internal Medicine, Dec 1998. Vol 129, No 11.

4.  Marsan et a. Evaluation of a clinical decision rule for young adult patients with chest pain. Acad Emergency Medicine. 2005;12:26-31.

5.  Collin et al. Young patients with chest pain: 1-year outcomes. Am J Emerg Med. 2011;29:265-270.

6.  Goldman et al. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med. 1988;318:797-803.

7.  Limkakeng et al. Combination of Goldman risk and initial cardiac troponin I for emergency department chest pain patient risk stratification. Acad Emerg Med. 2001;8:696-702.

8.  Pollack et al. Application of the TIMI Risk Score for Unstable Angina and Non-ST elevation Acute Coronary Syndrome to an Unselected Emergency Department Chest Pain Population. Academic Emergency Medicine. 2006;13:13-18.

9.  Aldous et al. A 2-hour thrombolysis in myocardial infarction score outperforms other risk stratification tools in patients presenting with possible acute coronary syndromes: Comparison of chest pain risk stratification tools. American Heart Journal. 2012. Vol 164, No 4. pp 516-523.

10.  Than et al. A 2-h diagnostic protocol to assess patients with chest pain symptoms in the Asia-Pacific region (ASPECT): a prospective observational validation study. Lancet 2011;377:1077-84.

11.  Aldous et al. A new improved accelerated diagnostic protocol safely identifies low risk patients with chest pain in the emergency department. Academic Emergency Medicine. 2012;19:510-6.

12.  Eagle et al. A Validated Prediction Model for all Forms of Acute Coronary Syndrome: Estimating the Risk of 6-month Postdischarge Death in an International Registry. JAMA. June 9, 2004. Vol 291, No 22. pp 2727-2733.

13.  Pozen et al. The usefulness of a predictive instrument to reduce inappropriate admissions to the coronary care unit. Annals of Internal Medicine. 1980; 92:238-242.

14.  Body et al. Can a modified thrombolysis in myocardial infarction risk score outperform the original for risk stratifying emergency department patients with chest pain? Emerg Med J. 2009;26:95-99.

15.  Jayes et al. Do Patients’ Coronary Risk Factor Reports Predict Acute Ischemia in the Emergency Department? A multicenter study. J Clin Epidemiol. 1992. Vol 45, No 6. pp 612-626.