Indian Journal of Critical Care Medicine

Register      Login

SEARCH WITHIN CONTENT

FIND ARTICLE

Volume / Issue

Online First

Archive
Related articles

VOLUME 24 , ISSUE 4 ( April, 2020 ) > List of Articles

Original Article

Evaluation and Validation of Four Scoring Systems: the APACHE IV, SAPS III, MPM0 II, and ICMM in Critically Ill Cancer Patients

Keywords : Acute physiology and chronic health evaluation IV, Cancer, Intensive care unit cancer mortality model, Intensive care unit mortality, Intensive care unit outcome, Mortality probability models II at 0 hours, Severity-of-illness scoring systems, Simplified acute physiology score 3

Citation Information : Evaluation and Validation of Four Scoring Systems: the APACHE IV, SAPS III, MPM0 II, and ICMM in Critically Ill Cancer Patients. Indian J Crit Care Med 2020; 24 (4):263-269.

DOI: 10.5005/jp-journals-10071-23407

License: CC BY-NC 4.0

Published Online: 01-04-2020

Copyright Statement:  Copyright © 2020; Jaypee Brothers Medical Publishers (P) Ltd.


Abstract

Background and aims: To evaluate and validate four severity-of-illness scores, acute physiology and chronic health evaluation IV (APACHE IV), simplified acute physiology score III (SAPS III), mortality probability models II at 0 hours (MPM0 II), and ICU cancer mortality model (ICMM), in a prospective cohort of critically ill cancer patients. Materials and methods: Single-center, prospective observational study performed in a 14-bedded combined medical–surgical ICU of a tertiary care cancer center of India, from July 2014 to November 2015. Score performance was judged by discrimination and calibration, using the area under receiver–operating characteristics (ROC) curve and Hosmer–Lemeshow goodness-of-fit test, respectively. Results: A total of 431 patients were included in the study. Intensive care unit (ICU) and hospital mortality were 37.4% and 41.1%, respectively. The area under ROC curve for APACHE IV, SAPS III, MPM0 II, and ICMM were 0.73, 0.70, 0.67, and 0.67, respectively. Calibration as calculated by Hosmer–Lemeshow analysis type C statistics for APACHE IV, SAPS III, MPM0 II, and ICMM shows good calibration with Chi-square values of 5.32, 9.285, 9.873, and 9.855 and p values of 0.723, 0.319, 0.274, and 0.275, respectively. Conclusion: All the four models had moderate discrimination and good calibration. However, none of the mortality prediction models could accurately discriminate between survivors and nonsurvivors in our patients.


PDF Share
  1. Brenner H. Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis. Lancet 2002;360(9340): 1131–1135. DOI: 10.1016/S0140-6736(02)11199-8.
  2. Staudinger T, Stoiser B, Müllner M, Locker GJ, Laczika K, Knapp S, et al. Outcome and prognostic factors in critically ill cancer patients admitted to the intensive care unit. Crit Care Med 2000;28(5): 1322–1328. DOI: 10.1097/00003246-200005000-00011.
  3. Pène F, Percheron S, Lemiale V, Viallon V, Claessens YE, Marqué S, et al. Temporal changes in management and outcome of septic shock in patients with malignancies in the intensive care unit. Crit Care Med 2008;36(3):690–696. DOI: 10.1097/CCM.0B013E318165314B.
  4. Larché J, Azoulay E, Fieux F, Mesnard L, Moreau D, Thiery G, et al. Improved survival of critically ill cancer patients with septic shock. Intensive Care Med 2003;29(10):1688–1695. DOI: 10.1007/s00134-003-1957-y.
  5. Legrand M, Max A, Peigne V, Mariotte E, Canet E, Debrumetz A, et al. Survival in neutropenic patients with severe sepsis or septic shock. Crit Care Med 2012;40(1):43–49. DOI: 10.1097/CCM.0b013e31822b50c2.
  6. Azoulay E, Soares M, Darmon M, Benoit D, Pastores S, Afessa B. Intensive care of the cancer patient: recent achievements and remaining challenges. Ann Intensive Care 2011;1(1):5. DOI: 10.1186/2110-5820-1-5.
  7. Schellongowski P, Benesch M, Lang T, Traunmüller F, Zauner C, Laczika K, et al. Comparison of three severity scores for critically ill cancer patients. Intensive Care Med 2004;30(3):430–436. DOI: 10.1007/s00134-003-2043-1.
  8. Vincent JL, Moreno R. Scoring systems in the critically ill. Crit Care 2010;14(2):207. DOI: 10.1186/cc8204.
  9. Groeger JS, Lemeshow S, Price K, Nierman DM, White PJr, Klar J. Multicenter outcome study of cancer patients admitted to the intensive care unit: a probability of mortality model. J Clin Oncol 1998;16(2):761–770. DOI: 10.1200/JCO.1998.16.2.761.
  10. Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, et al. 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 2005;31(10):1345–1355. DOI: 10.1007/s00134-005-2763-5.
  11. Zimmerman JE, Kramer A, McNair DS, Malila FM. Acute physiology and chronic health evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Crit Care Med 2006;34(5):1297–1310. DOI: 10.1097/01.CCM.0000215112.84523.F0.
  12. Soares M, Silva UVA, Teles JMM, Silva E, Caruso P, Lobo SMA, et al. Validation of four prognostic scores in patients with cancer admitted to Brazilian intensive care units: results from a prospective multicenter study. Intensive Care Med 2010;36(7):1188–1195. DOI: 10.1007/s00134-010-1807-7.
  13. Xing X, Gao Y, Wang H, Huang C, Qu S, Zhang H, et al. Performance of three prognostic models in patients with cancer in need of intensive care in a medical center in China. PLoS ONE 2015;10(6):e0131329. DOI: 10.1371/journal.pone.0131329.
  14. Middle East Critical Care. http://www.mecriticalcare.net/icu_scores/apacheIV.php [Last accessed on 20 December 2016].
  15. Google Play. https://play.google.com/store/apps/details?id=org.lacofi.saps3 [Last accessed on 20 December 2016].
  16. Intensive Care Network. http://intensivecarenetwork.com/Calculators/Files/Mpm.html [Last accessed on 20 December 2016].
  17. Soares M, Caruso P, Silva E, Teles JM, Lobo SM, Friedman G, et al. Characteristics and outcomes of patients with cancer requiring admission to intensive care units: a prospective multicenter study. Crit Care Med 2010;38(1):9–15. DOI: 10.1097/CCM.0b013e3181c0349e.
  18. Taccone FS, Artigas AA, Sprung CL, Moreno R, Sakr Y, Vincent J-L. Characteristics and outcomes of cancer patients in European ICUs. Crit Care 2009;13(1):R15. DOI: 10.1186/cc7713.
  19. Bos MM, de Keizer NF, Meynaar IA, Bakhshi-Raiez F, de Jonge E. Outcomes of cancer patients after unplanned admission to general intensive care units. Acta Oncol 2012;51(7):897–905. DOI: 10.3109/0284186X.2012.679311.
  20. Soares M, Fontes F, Dantas J, Gadelha D, Cariello P, Nardes F, et al. Performance of six severity-of-illness scores in cancer patients requiring admission to the intensive care unit: a prospective observational study. Crit Care 2004;8(4):R194–R203. DOI: 10.1186%2Fcc2870.
  21. Berghmans T, Paesmans M, Sculier JP. Is a specific oncological scoring system better at predicting the prognosis of cancer patients admitted for an acute medical complication in an intensive care unit than general gravity scores? Support Care Cancer 2004;12(4):234–239. DOI: 10.1007/s00520-003-0580-3.
  22. Keegan MT, Gajic OAB. Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and influence of resuscitation status on model performance. Chest 2012;142(4):851–858. DOI: 10.1378/chest.11- 2164.
  23. Bennett CE, Wright RS, Jentzer J, Gajic O, Murphree DH, Murphy JG, et al. Severity of illness assessment with application of the APACHE IV Predicted mortality and outcome trends analysis in an academic cardiac intensive care unit. J Crit Care 2019;50:242–246. DOI: 10.1016/j.jcrc.2018.12.012.
  24. Choi JW, Park YS, Lee YS, Park YH, Chung C, Park DI, et al. The ability of the acute physiology and chronic health evaluation (APACHE) IV score to predict mortality in a single tertiary hospital. Korean J Crit Care Med 2017;32(3):275–283. DOI: 10.4266/kjccm.2016.00990.
  25. Lee H, Shon YJ, Kim H, Paik H, Park HP. 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 J Anesthesiol 2014;67(2):115–122. DOI: 10.4097/kjae.2014.67.2.115.
  26. Basile-Filho A, Lago AF, Menegueti MG, Nicolini EA, Rodrigues LAB, Nunes RS, et al. The use of APACHE II, SOFA, SAPS 3, C-reactive protein/albumin ratio, and lactate to predict mortality of surgical critically ill patients: a retrospective cohort study. Medicine (Baltimore) 2019;98(26):e16204. DOI: 97/MD.0000000000016204.
  27. Xie J, Su B, Li C, Lin K, Li H, Hu Y, et al. A review of modeling methods for predicting in-hospital mortality of patients in intensive care unit. J Emerg Crit Care Med 2017;1(8):18. DOI: 10.21037/jeccm.2017.08.03.
  28. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2014;13:8–17. DOI: https://doi.org/10.1016/j.csbj.2014.11.005.
  29. Jaimes F, Farbiarz J, Alvarez D, Martinez C. Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room. Crit Care 2005;9(2):R150–R156. DOI: https://dx.doi.org/10.1186%2Fcc3054.
  30. Dybowski R, Gant V, Weller P, Chang R. Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet 1996;347(9009):1146–1150. DOI: 10.1016/s0140-6736(96)90609-1.
  31. Nilsson J, Ohlsson M, Thulin L, Hoglund P, Nashef SA, Brandt J. Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks. J Thorac Cardiovasc Surg 2006;132(1):12–19. DOI: 10.1016/j.jtcvs.2005.12.055.
  32. Lecuyer L, Chevret S, Thiery G, Darmon M, Schlemmer B, Azoulay E. The ICU trial: a new admission policy for cancer patients requiring mechanical ventilation. Crit Care Med 2007;35(3):808–814. DOI: 10.1097/01.CCM.0000256846.27192.7A.
  33. Aygencel G, Turkoglu M, Turkoz Sucak G, Benekli M. Prognostic factors in critically ill cancer patients admitted to the intensive care unit. J Crit Care 2014;29(4):618–626. DOI: 10.1016/j.jcrc.2014.01.014.
  34. Xia R, Wang D. Intensive care unit prognostic factors in critically ill patients with advanced solid tumors: a 3-year retrospective study. BMC Cancer 2016;16(1):188. DOI: 10.1186/s12885-016-2242-0.
PDF Share
PDF Share

© Jaypee Brothers Medical Publishers (P) LTD.