Feature

Playing by the Rules: Using Decision Rules Wisely Part 2, Nontraumatic Conditions

Emergency Medicine. 2018 December;50(12):1-14

References

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28. Chase M, Robey JL, Zogby KE, Sease KL, Shofer FS, Hollander JE. Prospective validation of the Thrombolysis in Myocardial Infarction risk score in the emergency department chest pain population. Ann Emerg Med. 2006;48(3):252-259.

29. Body R, Carley S, McDowell G, Ferguson J, Mackway-Jones K. 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.

30. Hess EP, Perry JJ, Calder LA, et al. Prospective validation of a modified Thrombolysis In Myocardial Infarction risk score in emergency department patients with chest pain and possible acute coronary syndrome. Acad Emerg Med. 2010;17(4):368-375.

31. Macdonald SPJ, Nagree Y, Fatovich DM, ad Brown SGA. Modified TIMI risk score cannot be used to identify low-risk chest pain in the emergency department: a multicenter validation study. Emerg Med J. 2014;31:281-285.

32. Hess EP, Agarwal D, Chandra S, et al. Diagnostic accuracy of the TIMI risk score in patients with chest pain in the emergency department: a meta-analysis. CMAJ. 2010;182(10):1039-1044.

33. Than, M, Cullen L, Aldous S, et al. 2-Hour accelerated diagnostic protocol to assess patients with chest pain symptoms using contemporary troponins as the only biomarker: the ADAPT trial. JACC. 2012;59(23):2091-2098.

34. Carlton EW, Khattab A, Greaves K. Identifying patients suitable for discharge after a single-presentation high-sensitivity Troponin result: a comparison of five established risk scores and two high-sensitivity assays. Ann Emerg Med. 2015;66(6):635-645.

35. Poldervaart JM, Langedijk M, Backus BE, et al. Comparison of the GRACE, HEART and TIMI score to predict major adverse cardiac events in chest pain patients at the emergency department. Int J Cardiol. 2017;227:656-661.

36. Nieuwets A, Poldervaart JM, Reitsma JB, et al. Medical consumption compared for TIMI and HEART score in chest pain patients at the emergency department: a retrospective cost analysis. BMJ Open. 2016;6:e010694.

37. Wells PS, Ginsberg JS, Anderson DR, et al. Use of a clinical model for safe management of patients with suspected pulmonary embolism. Ann Intern Med. 1998;129:997-1005.

38. Wells PS, Anderson DR, Rodger M, et al. Derivation of a simple clinical model to categorize patients’ probability of pulmonary embolism: increasing the model’s utility with the SimpliRED D-dimer. Thromb Haemost. 2000;83(3):416-420.

39. Wells PS, Anderson DR, Rodger M, et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and D-dimer. Ann Intern Med. 2001;135:98-107.

40. Wolf SJ, McCubbin TR, Feldhaus KM, Faragher JP, Adcock DM. Prospective validation of Wells’ criteria in the evaluation of patients with suspected pulmonary embolism. Ann Emerg Med. 2004;44:503-510.

41. Chagnon I, Bounameaux H, Aujesky D, et al. Comparison of two clinical prediction rules and implicit assessment among patients with suspected pulmonary embolism. Am J Med. 2002;113:269-275.

42. Christopher Study Investigators. Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. JAMA. 2006;295:172-179.

43. Kline JA, Mitchell AM, Kabrhel C, Richman PB, Courtney DM. Clinical criteria to prevent unnecessary diagnostic testing in emergency department patients with suspected pulmonary embolism. J Thromb Haemost. 2004;2:1247-1255.

44. Kline JA, Courtney DM, Kabrhel C, et al. Propsective multicenter evaluation of the pulmonary embolism rule-out criteria. J Thromb Haemost. 2008;6:772-780.

45. Penaloza A, Soulie C, Moumneh T, et al. Pulmonary embolism rule-out criteria (PERC) rule in European patients with low implicit clinical probability (PERCEPIC): a multicenter, prospective, observational study. Lancet Haematol. 2017;4:e615-e621.

46. Freund Y, Cachanado M, Aubry A, et al. Effect of the Pulmonary Embolism Rule-Out Criteria on subsequent thromboembolic events among low-risk emergency department patients. The PROPER randomized clinical trial. JAMA. 2018;319(6):559-566.

47. Hugli O, Righini M, Le Gal G, et al. The pulmonary embolism rule-out criteria (PERC) rule does not safely exclude pulmonary embolism. J Thromb Haemost. 2011;9:300-4.

48. Theunissen JMG, Scholing C, van Hasselt WE, van der Maten J, ter Avest E. A retrospective analysis of the combined use of PERC rule and Wells score to exclude pulmonary embolism in the Emergency Department. Emerg Med J. 2016;33:696-701.

49. Singh B, Parsaik AK, Aharwal D, Surana A, Mascarenhas SS, Chandra S. Diagnostic accuracy of Pulmonary Embolism Rule-Out Criteria: a systematic review and meta-analysis. Ann Emerg Med. 2012;59(6):517-520.

50. Shapiro NI, Wolfe RE, Moore RB, Smith E, Burdick E, Bates DW. Mortality in Emergency Department Sepsis (MEDS) score: a prospectively derived and validated clinical prediction rule. Crit Care Med. 2003;31(3):670-675.

51. Shapiro NI, Howell MD, Talmor D, Donnino M, Ngo L, Bates DW. Mortality in Emergency Department Sepsis (MEDS) score predicts 1-year mortality. Crit Care Med. 2007;35(1):192-198.

52. Sankoff JD, Goyal M, Gaieski DF, et al. Validation of the Mortality in Emergency Department Sepsis (MEDS) score in patients with the systemic inflammatory response syndrome (SIRS). Crit Care Med. 2008;36(2):421-26.

53. Jones AE, Saak K, Kline JA. Performance of the Mortality in Emergency Department Sepsis score for predicting hospital mortality among patients with severe sepsis and septic shock. Am J Emerg Med. 2008;26(6):689-692.

54. Carpenter CR., Keim SM, Upadhye S, Nguyen HB. Risk stratification of the potentially septic patient in the emergency department: the Mortality in the Emergency Department Sepsis (MEDS) score. J Emerg Med. 2009;37(3):319-327.

55. Hermans MAW, Leffers P, Jansen LM, Keulemans YC, Stassen PM. The value of the Mortality in Emergency Department Sepsis (MEDS) score, C reactive protein and lactate in predicting 28-day mortality of sepsis in a Dutch emergency department. Emerg Med J. 2012;29(4):295–300.

56. Fine MJ, Auble TE, Yealy DM, et al. A Prediction Rule to Identify Low-Risk Patients with Community Acquired Pneumonia. N Engl J Med. 1997;326(4):243-250.

57. Marrie TJ, Lau CY, Wheeler SL, et al. A controlled trial of a critical pathway for treatment of community-acquired pneumonia. JAMA. 2000;283(6):749-755. doi:10.1001/jama.283.6.749.

58. Carratalà J, Fernandez-Sabe N. Outpatient care compared with hospitalization for community-acquired pneumonia: a randomized trial in low-risk patients . Ann Intern Med. 2005;142:165-172. doi:10.7326/0003-4819-142-3-200502010-00006.

59. Chalmers JD, Akram AR, Hill AT. Increasing outpatient treatment of mild community-acquired pneumonia: Systematic review and meta-analysis. Eur Respir J. 2011;37(4):858-864. doi:10.1183/09031936.00065610.

60. Loke YK, Kwok CS, Niruban A, Myint PK. Value of severity scales in predicting mortality from community-acquired pneumonia: systematic review and meta-analysis. Thorax. 2010;65(10):884-890. doi:10.1136/thx.2009.134072.

61. Marti C, Garin N, Grosgurin O, et al. Prediction of severe community-acquired pneumonia: A systematic review and meta-analysis. Crit Care. 2012;16(4):R141. doi:10.1186/cc11447.

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63. Lim WS, Van Der Eerden MM, Laing R, et al. Defining community acquired pneumonia severity on presentation to hospital: An international derivation and validation study. Thorax. 2003;58(5):377-382. doi:10.1136/thorax.58.5.377.

64. Aujesky D, Auble TE, Yealy DM, et al. Prospective comparison of three validated prediction rules for prognosis in community-acquired pneumonia. Am J Med. 2005;118(4):384-392. doi:10.1016/j.amjmed.2005.01.006.

65. Capelastegui A, España PP, Quintana JM, et al. Validation of a predictive rule for the management of community-acquired pneumonia. Eur Respir J. 2006;27(1):151-157. doi:10.1183/09031936.06.00062505.

66. Ilg A, Moskowitz A, Konanki V, et al. Performance of the CURB-65 score in predicting critical care interventions in patients admitted with community-acquired pneumonia. Ann Emerg Med. 2018. doi:10.1016/j.annemergmed.2018.06.017.

67. Bauer TT, Ewig S, Marre R, Suttorp N, Welte T. CRB-65 predicts death from community-acquired pneumonia. J Intern Med. 2006;260(1):93-101. doi:10.1111/j.1365-2796.2006.01657.x.

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In an external validation study, the AIR and Alvarado scores were calculated on a series of 941 patients (aged 1 to 97 years) being evaluated for possible appendicitis; 201 patients were younger than 18.¹³ At a cutoff of greater than 4, the sensitivity and specificity were found to be 93% and 85% for the AIR and 90% and 55% for Alvarado.¹³ In a cohort of 182 patients (aged 4 to 75 years), a score of 4 or greater on the AIR and Alvarado was found to have comparable sensitivity to that of a senior surgical consultant for detecting appendicitis—with sensitivities of 94%, 93%, and 90% respectively.¹⁴ Subsequently, the original investigators undertook a large multicenter implementation study of the AIR at 24 hospitals of patients (aged 5 to 96 years) with suspected appendicitis. As compared to the pre-implementation group, using AIR to categorize patients as low risk resulted in significantly fewer imaging studies, admissions, and surgical explorations.¹⁵

Comment: The AIR has the benefit of recent prospective studies that assess performance of the rule in settings that mirror the practice environments of most EPs today. The classification of rebound tenderness as light, medium, or strong may be difficult to ascertain. Ultimately, reductions in imaging, admissions, and surgical explorations are important goals and EPs might benefit from using this rule to guide imaging.

CHEST

HEART Score

The increasingly popular HEART score, first developed by physicians in the Netherlands in 2008, seeks to risk-stratify patients presenting to the ED with suspected cardiac chest pain without ST-elevation myocardial infarction (STEMI). It scores patients 0 to 2 on 5 different characteristics (with a total scored of 10 possible points):

History: 2 points for highly suspicious, 1 point for moderately suspicious
EKG: 2 points for significant ST deviation, 1 point for nonspecific repolarization disturbance
Age: 2 points for age 65 years or greater, 1 point for age 45-64 years
Risk Factors: 2 points for 3 or more risk factors or history of atherosclerotic disease, 1 point for 1 to 2 risk factors
Troponin: 2 points for troponin value >3 times the normal limit, 1 point for value 1-3 times the normal limit.

The authors developed these 5 categories “based on clinical experience and current medical literature,” and then applied the rule to 122 chest pain patients in the ED, finding a higher incidence of major adverse coronary events (MACE) with increasing score: 2.5% for low risk score of 0-3, 20.3% for intermediate risk score of 4-6, and 72.7% for score 7 or higher.¹⁶ The score has been retrospectively and prospectively validated.^17,18 In a study of 2440 patients, the low risk group had a MACE of 1.7%, and the score had a c-statistic of 0.83, outperforming Thrombolysis in Myocardial Infarction (TIMI) and GRACE c-statistics of 0.75 and 0.70, respectively.¹⁸ In 2013, investigators calculated the HEART score on a multinational database of 2906 chest pain patients, finding a negative predictive value of 98.3% for MACE with HEART score less than or equal to 3.¹⁹

In the United States, Mahler et al have produced a series of 3 articles validating the HEART score and demonstrating its use in reducing cardiac testing and length of stay. In 1070 patients admitted to their observation unit, who were deemed low risk by physician assessment and TIMI <2, a score of less than or equal to 3 had a negative predictive value of 99.4% for MACE; the inclusion of serial troponins resulted in sensitivity of 100%, specificity of 83.1%, and negative predictive value of 100%.²⁰ The team then conducted a secondary analysis of chest pain patients enrolled in a large multicenter trial (MIDAS) and compared HEART score, the North American Chest Pain Rule, and unstructured clinical assessment.²¹ Both rules had high sensitivities, but the HEART score identified 20% of patients suitable for early discharge, as compared to 4% for the North American Chest Pain Rule.²¹ Finally, Mahler’s team performed a randomized control trial of 282 patients investigating whether the HEART score with serial troponins compared with usual care could safely reduce cardiac testing.²² The HEART pathway resulted in an absolute reduction of 12.1% in cardiac testing, and median reduction in length of stay by 12 hours, with no missed MACE in discharged patients.²²