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The Ability of the Acute Physiology and Chronic Health Evaluation (APACHE) IV Score to Predict Mortality in a Single Tertiary Hospital

이영석|박정은|박동일|박영선|설영훈|박연희|문재영|권인선|이주상|유상훈|최재우|전규락|정채욱|민나은
대한중환자의학회 (The Korean Society Of Critical Care Medicine)|2017.08|Acute and Critical Care|제32권 제3호|pp. 275-283

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참고문헌

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discriminationcalibrationintensive care unitsAPACHE IVtriage.

초록

Background: The Acute Physiology and Chronic Health Evaluation (APACHE) II model has been widely used in Korea. However, there have been few studies on the APACHE IV model in Korean intensive care units (ICUs). The aim of this study was to compare the ability of APACHE IV and APACHE II in predicting hospital mortality, and to investigate the ability of APACHE IV as a critical care triage criterion. Methods: The study was designed as a prospective cohort study. Measurements of discrimination and calibration were performed using the area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test respectively. We also calculated the standardized mortality ratio (SMR). Results: The APACHE IV score, the Charlson Comorbidity index (CCI) score, acute respiratory distress syndrome, and unplanned ICU admissions were independently associated with hospital mortality. The calibration, discrimination, and SMR of APACHE IV were good (H = 7.67, P = 0.465; C = 3.42, P = 0.905; AUROC = 0.759; SMR = 1.00). However, the explanatory power of an APACHE IV score >93 alone on hospital mortality was low at 44.1%. The explanatory power was increased to 53.8% when the hospital mortality was predicted using a model that considers APACHE IV >93 scores, medical admission, and risk factors for CCI >3 coincidentally. However, the discriminative ability of the prediction model was unsatisfactory (C index <0.70). Conclusions: The APACHE IV presented good discrimination, calibration, and SMR for hospital mortality.

Abstract

Background: The Acute Physiology and Chronic Health Evaluation (APACHE) II model has been widely used in Korea. However, there have been few studies on the APACHE IV model in Korean intensive care units (ICUs). The aim of this study was to compare the ability of APACHE IV and APACHE II in predicting hospital mortality, and to investigate the ability of APACHE IV as a critical care triage criterion. Methods: The study was designed as a prospective cohort study. Measurements of discrimination and calibration were performed using the area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test respectively. We also calculated the standardized mortality ratio (SMR). Results: The APACHE IV score, the Charlson Comorbidity index (CCI) score, acute respiratory distress syndrome, and unplanned ICU admissions were independently associated with hospital mortality. The calibration, discrimination, and SMR of APACHE IV were good (H = 7.67, P = 0.465; C = 3.42, P = 0.905; AUROC = 0.759; SMR = 1.00). However, the explanatory power of an APACHE IV score >93 alone on hospital mortality was low at 44.1%. The explanatory power was increased to 53.8% when the hospital mortality was predicted using a model that considers APACHE IV >93 scores, medical admission, and risk factors for CCI >3 coincidentally. However, the discriminative ability of the prediction model was unsatisfactory (C index <0.70). Conclusions: The APACHE IV presented good discrimination, calibration, and SMR for hospital mortality.

참고문헌 (36)

  1. Knaus WA, "APACHE II: a severity of disease classification system", Crit Care Med, 13, pp.818~829 (1985)
  2. Strand K, "Severity scoring in the ICU: a review", Acta Anaesthesiol Scand, 52, pp.467~478 (2008)
  3. Moreno RP, "SAPS 3: from evaluation of the patient to evaluation of the intensive care unit. Part 2: development of a prognostic model for hospital mortality at ICU admission", Intensive Care Med, 31, pp.1345~1355 (2005)
  4. Lemeshow S, "A method for predicting survival and mortality of ICU patients using objectively derived weights", Crit Care Med, 13, pp.519~525 (1985)
  5. Caldeira VM, "Criteria for patient admission to an intensive care unit and related mortality rates", Rev Assoc Med Bras (1992), 56, pp.528~534 (2010)
  6. Zimmerman JE, "Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients", Crit Care Med, 34, pp.1297~1310 (2006)
  7. Le Gall JR, "A simplified acute physiology score for ICU patients", Crit Care Med, 12, pp.975~977 (1984)
  8. Brinkman S, "External validation of Acute Physiology and Chronic Health Evaluation IV in Dutch intensive care units and comparison with Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II", J Crit Care, 26, pp.105.e11~105.e18 (2011)
  9. Keegan MT, "Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and influence of resuscitation status on model performance", Chest, 142, pp.851~858 (2012)
  10. Nassar Junior AP, "SAPS 3, APACHE IV or GRACE: which score to choose for acute coronary syndrome patients in intensive care units?", Sao Paulo Med J, 131, pp.173~178 (2013)
  11. Berger MM, "Evaluation of the consistency of Acute Physiology and Chronic Health Evaluation (APACHE II) scoring in a surgical intensive care unit", Crit Care Med, 20, pp.1681~1687 (1992)
  12. 김재열, "External Validation of the Acute Physiology and Chronic Health Evaluation II in Korean Intensive Care Units", Yonsei Medical Journal, 54(2), pp.425~431 (2013), 연세대학교의과대학[DOI]
  13. Gupta R, "Performance evaluation of APACHE II score for an Indian patient with respiratory problems", Indian J Med Res, 119, pp.119~273 (2004)
  14. Ludwigs U, "Predicting in-hospital mortality in acute myocardial infarction: impact of thrombolytic therapy on APACHE II performance", Scand Cardiovasc J, 34, pp.371~376 (2000)
  15. Knaus WA, "The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults", Chest, 100, pp.1619~1636 (1991)
  16. Sprung CL, "Triage of intensive care patients: identifying agreement and controversy", Intensive Care Med, 39, pp.1916~1924 (2013)
  17. Orsini J, "Factors influencing triage decisions in patients referred for ICU admission", J Clin Med Res, 5, pp.343~349 (2013)
  18. Orsini J, "Triage of patients consulted for ICU admission during times of ICU-bed shortage", J Clin Med Res, 6, pp.463~468 (2014)
  19. 이한나, "Validation of the APACHE IV model and its comparison with the APACHE II, SAPS 3, and Korean SAPS 3 models for the prediction of hospital mortality in a Korean surgical intensive care unit", Korean Journal of Anesthesiology, 67(2), pp.115~122 (2014), 대한마취통증의학회
  20. Sedloň P, "Comparison of the accuracy and correctness of mortality estimates for intensive care unit patients in internal clinics of the Czech Republic using APACHE II, APACHE IV, SAPS 3 and MPMoIII models", Med Glas (Zenica), 13, pp.82~89 (2016)
  21. Salluh JI, "ICU severity of illness scores:APACHE, SAPS and MPM", Curr Opin Crit Care, 20, pp.557~565 (2014)
  22. Costa e Silva VT, "Performance of the third-generation models of severity scoring systems (APACHE IV, SAPS 3 and MPM-III) in acute kidney injury critically ill patients", Nephrol Dial Transplant, 26, pp.3894~3901 (2011)
  23. Aminiahidashti H, "Comparison of APACHE II and SAPS II scoring systems in prediction of critically ill patients’ outcome", Emerg (Tehran), 5, pp.e4~ (2017)
  24. Daley J, "Predicting hospital-associated mortality for Medicare patients: a method for patients with stroke, pneumonia, acute myocardial infarction, and congestive heart failure", JAMA, 260, pp.3617~3624 (1988)
  25. Kopterides P, "General prognostic scores in outcome prediction for cancer patients admitted to the intensive care unit", Am J Crit Care, 20, pp.56~66 (2011)
  26. Gilani MT, "A comparison of Simplified Acute Physiology Score II, Acute Physiology and Chronic Health Evaluation II and Acute Physiology and Chronic Health Evaluation III scoring system in predicting mortality and length of stay at surgical intensive care unit", Niger Med J, 55, pp.144~147 (2014)
  27. Kramer AA, "Comparison of the Mortality Probability Admission Model III, National Quality Forum, and Acute Physiology and Chronic Health Evaluation IV hospital mortality models: implications for national benchmarking", Crit Care Med, 42, pp.544~553 (2014)
  28. Juneja D, "Comparison of newer scoring systems with the conventional scoring systems in general intensive care population", Minerva Anestesiol, 78, pp.194~200 (2012)
  29. Xing X, "Performance of three prognostic models in patients with cancer in need of intensive care in a medical center in China", PLoS One, 10, pp.e0131329~ (2015)
  30. Wong RS, "An external independent validation of APACHE IV in a Malaysian intensive care unit", Ann Acad Med Singapore, 44, pp.127~132 (2015)
  31. Bhattacharyya M, "APACHE IV: benchmarking in an Indian ICU", Crit Care, 13(Suppl 1), pp.P510~ (2009)
  32. Yamin S, "Predictive efficasy of APACHE IV at ICU.s of CHK", Pak J Chest Med, 17 (2011)
  33. Ayazoglu TA., "A comparison of APACHE II and APACHE IV scoring systems in predicting outcome in patients admitted with stroke to an intensive care unit", Anaesth Pain Intensive Care, 15, pp.7~12 (2011)
  34. Garrouste-Orgeas M, "Triaging patients to the ICU: a pilot study of factors influencing admission decisions and patient outcomes", Intensive Care Med, 29, pp.774~781 (2003)
  35. Adeniji KA, "The Simple Triage Scoring System (STSS) successfully predicts mortality and critical care resource utilization in H1N1 pandemic flu: a retrospective analysis", Crit Care, 15, pp.R39~ (2011)
  36. Ho KM, "Using patient admission characteristics alone to predict mortality of critically ill patients: a comparison of 3prognostic scores", J Crit Care, 31, pp.21~25 (2016)

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