The online version of this article (doi:https://doi.org/10.1186/s13054-017-1930-8) contains supplementary material, which is available to authorized users.
Prognostic models—used in critical care medicine for mortality predictions, for benchmarking and for illness stratification in clinical trials—have been validated predominantly in high-income countries. These results may not be reproducible in low or middle-income countries (LMICs), not only because of different case-mix characteristics but also because of missing predictor variables. The study objective was to systematically review literature on the use of critical care prognostic models in LMICs and assess their ability to discriminate between survivors and non-survivors at hospital discharge of those admitted to intensive care units (ICUs), their calibration, their accuracy, and the manner in which missing values were handled.
The PubMed database was searched in March 2017 to identify research articles reporting the use and performance of prognostic models in the evaluation of mortality in ICUs in LMICs. Studies carried out in ICUs in high-income countries or paediatric ICUs and studies that evaluated disease-specific scoring systems, were limited to a specific disease or single prognostic factor, were published only as abstracts, editorials, letters and systematic and narrative reviews or were not in English were excluded.
Of the 2233 studies retrieved, 473 were searched and 50 articles reporting 119 models were included. Five articles described the development and evaluation of new models, whereas 114 articles externally validated Acute Physiology and Chronic Health Evaluation, the Simplified Acute Physiology Score and Mortality Probability Models or versions thereof. Missing values were only described in 34% of studies; exclusion and or imputation by normal values were used. Discrimination, calibration and accuracy were reported in 94.0%, 72.4% and 25% respectively. Good discrimination and calibration were reported in 88.9% and 58.3% respectively. However, only 10 evaluations that reported excellent discrimination also reported good calibration. Generalisability of the findings was limited by variability of inclusion and exclusion criteria, unavailability of post-ICU outcomes and missing value handling.
Robust interpretations regarding the applicability of prognostic models are currently hampered by poor adherence to reporting guidelines, especially when reporting missing value handling. Performance of mortality risk prediction models in LMIC ICUs is at best moderate, especially with limitations in calibration. This necessitates continued efforts to develop and validate LMIC models with readily available prognostic variables, perhaps aided by medical registries.
Additional file 1: A table presenting the search terms used. (XLSX 27 kb)13054_2017_1930_MOESM1_ESM.xlsx
Additional file 2: A table presenting the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies. (XLSX 41 kb)13054_2017_1930_MOESM2_ESM.xlsx
Juneja D, Singh O, Nasa P, et al. Comparison of newer scoring systems with the conventional scoring systems in general intensive care population. Minerva Anestesiol. 2012;78(2):194–200. https://www.minervamedica.it/en/journals/minerva-anestesiologica/article.php?cod=R02Y2012N02A0194. Accessed 4 Oct 2016. PubMed
Knaus WA, Zimmerman JE, Wagner DP, et al. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med. 1981;9(8):591–7. http://journals.lww.com/ccmjournal/Abstract/1981/08000/APACHE_acute_physiology_and_chronic_health.8.aspx. Accessed 4 Oct 2016. CrossRefPubMed
Le Gall J-R, Lemeshow S, Saulnier F, et al. A New Simplified Acute Physiology Score (SAPS II) based on a European/North American Multicenter Study. JAMA J Am Med Assoc. 1993;270(24):2957. https://doi.org/10.1001/jama.1993.03510240069035. CrossRef
Aggarwal AN, Sarkar P, Gupta D, et al. Performance of standard severity scoring systems for outcome prediction in patients admitted to a respiratory intensive care unit in North India. Respirology. 2006;11(2):196–204. https://doi.org/10.1111/j.1440-1843.2006.00828.x. CrossRefPubMed
Namendys-Silva SA, Silva-Medina MA, Vásquez-Barahona GM, et al. Application of a modified sequential organ failure assessment score to critically ill patients. Braz J Med Biol Res. 2013;46(2):186–93. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3854366/. Accessed 10 Aug 2016. CrossRefPubMedPubMedCentral
Haniffa R, De Silva AP, Weerathunga P, et al. Applicability of the APACHE II model to a lower middle income country. J Crit Care. 2017;42:178–83. http://www.jccjournal.org/article/S0883-9441(17)31025-0/fulltext. Accessed 13 July 2017. CrossRefPubMed
Knaus WA, Draper EA, Wagner DP, et al. Prognosis in acute organ-system failure. Ann Surg. 1985;202(6):685–93. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1250999/. Accessed 27 Sept 2016. CrossRefPubMedPubMedCentral
Cullen DJ, Civetta JM, Briggs BA, et al. Therapeutic intervention scoring system: a method for quantitative comparison of patient care. Crit Care Med. 1974;2(2):57–60. http://journals.lww.com/ccmjournal/Abstract/1974/03000/Therapeutic_intervention_scoring_system__a_method.1.aspx. Accessed 12 Dec 2016.
Lemeshow S, Teres D, Klar J, et al. Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA. 1993;270(20):2478–86. https://jamanetwork.com/journals/jama/article-abstract/409377?redirect=true. Accessed 12 Dec 2016. CrossRefPubMed
Vincent J-L, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22(7):707–10. https://doi.org/10.1007/BF01709751. CrossRefPubMed
Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidimiology. 2010;21(1):128–38. https://doi.org/10.1097/EDE.0b013e3181c30fb2. CrossRef
Haniffa R, De Silva AP, Iddagoda S, et al. A cross-sectional survey of critical care services in Sri Lanka: a lower middle-income country. J Crit Care. 2014;29(5):764–8. https://doi.org/10.1016/j.jcrc.2014.04.021. CrossRefPubMed
Haniffa R, De Silva AP. National Intensive Care Surveillance. A Survey Report on Intensive Care Units of the Government Hospitals in Sri Lanka. Colombo: National Intensive Care Surveillance Unit Division of Deputy Director General (Medical Services); 2012. ISBN 978-955-0505-25-8.
Adhikari NKJ, Rubenfeld GD. Worldwide demand for critical care. Curr Opin Crit Care. 2011;17(6):620–5. https://doi.org/10.1097/MCC.0b013e32834cd39c. CrossRefPubMed
Rivera-Fernández R, Vázquez-Mata G, Bravo M, et al. The Apache III prognostic system: customized mortality predictions for Spanish ICU patients. Intensive Care Med. 1998;24(6):574–81. https://link.springer.com/article/10.1007/s001340050618. Accessed 5 Oct 2016. CrossRefPubMed
World Bank. Low and middle income data. 2017. http://data.worldbank.org/income-level/low-and-middle-income?view=chart.. Accessed 13 July 2017.
Higgins JPT, Green S, editors. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration; 2011. Available from http://handbook-5-1.cochrane.org/.
Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS Checklist. PLoS Med. 2014;11(10):e1001744. https://doi.org/10.1371/journal.pmed.1001744. CrossRefPubMedPubMedCentral
Ridley S. Severity of illness scoring systems and performance appraisal. Anaesthesia. 1998;53(12):1185–94. https://doi.org/10.1046/j.1365-2044.1998.00615.x. CrossRefPubMed
Vincent JL. Severity of illness scoring system. In: Roberts PR, editor. Comprehensive Critical Care: Adult. 2012:875-84). Retrieved from https://med.uth.edu/anesthesiology/files/2015/05/Chapter-47-Severity-of-Illness-Scoring-Systems.pdf.
Yamin S, Vaswani AK, Afreedi M. Predictive efficasy of APACHE IV at ICUs of CHK. Pakistan J Chest Med. 2011;17(1):1-14. http://www.pjcm.net/index.php/pjcm/article/view/132/125. Accessed 18 May 2016.
Ahluwalia G, Pande JN, Sharma SK. Prognostic scoring for critically ill hospitalized patients. Indian J Chest Dis Allied Sci. 1974;41(4):201–6. http://www.ncbi.nlm.nih.gov/pubmed/10661007. Accessed 7 Nov 2015.
Eapen CE, Thomas K, Cherian AM, et al. Predictors of mortality in a medical intensive care unit. Natl Med J India. 1974;10(6):270–2. http://archive.nmji.in/approval/archive/Volume-10/issue-6/original-articles-2.pdf.
Naqvi IH, Mahmood K, Ziaullaha S, et al. Better prognostic marker in ICU—APACHE II, SOFA or SAP II! Pak J Med Sci . 2016;32(5):PMC5103123. https://doi.org/10.12669/pjms.325.10080.
Naved SA, Siddiqui S, Khan FH. APACHE-II score correlation with mortality and length of stay in an intensive care unit. J Coll Physicians Surg Pakistan. 2011;21(1):4–8. https://doi.org/01.2011/JCPSP.0408.
Nimgaonkar A, Karnad DR, Sudarshan S, et al. Prediction of mortality in an Indian intensive care unit. Comparison between APACHE II. Intensive Care Med. 2004;30(2):248–53. https://doi.org/10.1007/s00134-003-2105-4.
Turner JS, Potgieter PD, Linton DM. Systems for scoring severity of illness in intensive care. S Afr Med J. 1989;76(1):17–20. http://archive.samj.org.za/1989%20VOL%20LXXVI%20Jul-Dec/Articles/07%20July/1.7%20SYSTEMS%20FOR%20SCORING%20SEVERITY%20OF%20ILLNESS%20IN%20THE%20RSA.%20J.A.%20Frean,%20W.F.%20Carman,%20H.H.%20Crewe-Brown.pdf. Accessed 7 Nov 2015. PubMed
Faruq MO, Mahmud MR, Begum T, et al. Comparison of severity systems APACHE II and SAPS II in critically ill patients. Bangladesh Crit Care J. 2013;1(1):27–32. http://dx.doi.org/10.3329/bccj.v1i1.14362.
Khwannimit B, Geater A. A comparison of APACHE II and SAPS II scoring systems in predicting hospital mortality in Thai adult intensive care units. J Med Assoc Thai. 2007;90(4):643–52. http://www.jmatonline.com/index.php/jmat/article/view/8591. Accessed 7 Nov 2015. PubMed
Nassar AP, Mocelin AO, Nunes ALB, et al. Caution when using prognostic models: a prospective comparison of 3 recent prognostic models. J Crit Care. 2012;27(4):423.e1–7. https://doi.org/10.1016/j.jcrc.2011.08.016. CrossRef
Riviello ED, Kiviri W, Fowler RA, et al. Predicting mortality in low-income country ICUs: The Rwanda Mortality Probability Model (R-MPM). Lazzeri C, ed. PLoS One . 2016;11(5):e0155858. https://doi.org/10.1371/journal.pone.0155858.
Steyerberg EW, Bleeker SA, Moll HA, et al. Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J Clin Epidemiol. 2003;56(5):441–7. http://www.jclinepi.com/article/S0895-4356(03)00047-7/fulltext, Accessed 25 Oct 2017. CrossRefPubMed
Kiatboonsri S, Charoenpan P. The severity of disease measurements among Thai medical intensive care unit patients. Southeast Asian J Trop Med Public Health. 1995;26(1):57–65. http://www.tm.mahidol.ac.th/seameo/1995-26-1/1995-26-1-57.pdf. Accessed 6 Mar 2016. PubMed
Mohan A, Shrestha P, Guleria R, et al. Development of a mortality prediction formula due to sepsis/severe sepsis in a medical intensive care unit. Lung India. 1974;32(4):313–19. https://doi.org/10.4103/0970-2113.159533.
Zhao X-X, Su Y-Y, Wang M, et al. Evaluation of neuro-intensive care unit performance in China: predicting outcomes of Simplified Acute Physiology Score II or Glasgow Coma Scale. Chin Med J (Engl). 2013;126(6):1132–7. http://126.96.36.199:81/ch/reader/view_abstract.aspx?file_no=12-2886&flag=1. Accessed 7 Nov 2015.
Khwannimit B, Bhurayanontachai R. A comparison of the performance of Simplified Acute Physiology Score 3 with old standard severity scores and customized scores in a mixed medical-coronary care unit. Minerva Anestesiol. 2011;77(3):305–12. https://www.minervamedica.it/en/journals/minerva-anestesiologica/article.php?cod=R02Y2011N03A0305. Accessed 6 Mar 2016. PubMed
Xing X, Gao Y, Wang 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. https://doi.org/10.1371/journal.pone.0131329. CrossRefPubMedPubMedCentral
Silva Junior JM, Malbouisson LMS, Nuevo HL, et al. Aplicabilidade do escore fisiológico agudo simplificado (SAPS 3) em hospitais brasileiros. Rev Bras Anestesiol. 2010;60(1):20–31. https://doi.org/10.1590/S0034-70942010000100003. CrossRefPubMed
Steyerberg EW, Moons KGM, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. https://doi.org/10.1371/journal.pmed.1001381. CrossRefPubMedPubMedCentral
Abhinandan KS, Vedavathi R. Usefulness of Sequential Organ Failure Assessment (SOFA) and Acute Physiological and Chronic Health Evaluation II (APACHE II) score in analysing patients with multiple organ dysfunction syndrome in sepsis. J Evol Med Dent Sci. 2013;2(49):9591-605. https://jemds.com/data_pdf/dr%20abhinandan%20-.pdf. Accessed 1 Apr 2017.
Liu X, Shen Y, Li Z, et al. Prognostic significance of APACHE II score and plasma suPAR in Chinese patients with sepsis: a prospective observational study. BMC Anesthesiol. 2016;16:46. https://doi.org/10.1186/s12871-016-0212-3.
Nair R, Bhandary NM, D’Souza AD. Initial Sequential Organ Failure Assessment score versus Simplified Acute Physiology score to analyze multiple organ dysfunction in infectious diseases in intensive care unit. Indian J Crit Care Med. 2016;20(4):210–5. https://doi.org/10.4103/0972-5229.180041. CrossRefPubMedPubMedCentral
Celik S, Sahin D, Korkmaz C, et al. Potential risk factors for patient mortality during admission to the intensive care units. Saudi Med J. 2014;35(2):159–64. https://www.smj.org.sa/index.php/smj/article/view/2805. Accessed 6 Mar 2016. PubMed
Chang L, Horng C-F, Huang Y-CT, et al. Prognostic accuracy of Acute Physiology and Chronic Health Evaluation II scores in critically ill cancer patients. Am J Crit Care. 2006;15(1):47–53. http://ajcc.aacnjournals.org/content/15/1/47.long. Accessed 1 Apr 2017. PubMed
Chiavone PA, Rasslan S. Influence of time elapsed from end of emergency surgery until admission to intensive care unit, on Acute Physiology and Chronic Health Evaluation II (APACHE II) prediction and patient mortality rate. Sao Paulo Med J. 2005;123(4):167–74. https://doi.org//S1516-31802005000400003. CrossRefPubMed
Nouira S, Belghith M, Elatrous S, et al. Predictive value of severity scoring systems: comparison of four models in Tunisian adult intensive care units. Crit Care Med. 1998;26(5):852–9. http://journals.lww.com/ccmjournal/Abstract/1998/05000/Predictive_value_of_severity_scoring_systems_.16.aspx. Accessed 6 Mar 2016. CrossRefPubMed
Fadaizadeh L, Tamadon R, Saeedfar K, et al. Performance assessment of Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II in a referral respiratory intensive care unit in Iran. Acta Anaesthesiol Taiwanica. 2012;50(2):59–62. https://doi.org/10.1016/j.aat.2012.05.004. CrossRef
Ratanarat R, Thanakittiwirun M, Vilaichone W, et al. Prediction of mortality by using the standard scoring systems in a medical intensive care unit in Thailand. J Med Assoc Thai. 2005;88(7):949–55. https://pdfs.semanticscholar.org/426c/15599cf5b85adcb291dbae9e60408dbe743a.pdf. Accessed 7 Nov 2015. PubMed
Gilani MT, Razavi M, Azad A. 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. 2014;55(2):144–7. https://doi.org/10.4103/0300-1652.129651. CrossRefPubMedPubMedCentral
Gupta R, Arora VK. Performance evaluation of APACHE II score for an Indian patient with respiratory problems. Indian J Med Res. 2004;119(6):273–82. http://www.ijmr.in/CurrentTopicView.aspx?year=Indian%20J%20Med%20Res%20119,%20June%202004,%20pp%20273-282$Original%20Article. Accessed 7 Nov 2015. PubMed
Shrestha GS, Gurung R, Amatya R. Comparison of Acute Physiology, Age, Chronic Health Evaluation III score with initial Sequential Organ Failure Assessment score to predict ICU mortality. Nepal Med Coll J. 2011;13(1):50–4. http://nmcth.edu/images/gallery/Editorial/3EFLlgs_shrestha.pdf. Accessed 6 Mar 2016. PubMed
Haidri FR, Rizvi N, Motiani B. Role of APACHE score in predicting mortality in chest ICU. J Pak Med Assoc. 2011;61(6):589–92. http://jpma.org.pk/full_article_text.php?article_id=2828. Accessed 6 Mar 2016. PubMed
Halim DA, Murni TW, Redjeki IS. Comparison of APACHE II, SOFA, and Modified SOFA Scores in Predicting Mortality of Surgical Patients in Intensive Care Unit at Dr. Hasan Sadikin General Hospital. Crit Care Shock. 2009;12(4):157–69. http://criticalcareshock.org/files/Original-Comparison-of-Apache-II-SOFA-and-Modified-SOFA-Scores-in-Predicting-Mortality-of-Surgical-Patients-in-Intensive-Care-Unit-at-Dr.-Hasan-Sadikin-General-Hospital1.pdf.
Hamza A, Hammed L, Abulmagd M, et al. Evaluation of general ICU outcome prediction using different scoring systems. Med J Cairo Univ. 2009;77(1):27-35. http://medicaljournalofcairouniversity.net/Home/images/pdf/2009/march/35.pdf. Accessed 8 Nov 2016.
Sutheechet N. Assessment and comparison of the performance of SAPS II and MPM 24 II scoring systems in predicting hospital mortality in intensive care units. Bull Dep Med Serv Thail. 2009;34(11):641-50. http://www.dms.moph.go.th/dmsweb/dmsweb_v2_2/content/org/journal/data/2009-11_p641-650.pdf. Accessed 11 Aug 2016.
Hosseini M, Ramazani J. Comparison of acute physiology and chronic health evaluation II and Glasgow Coma Score in predicting the outcomes of Post Anesthesia Care Unit’s patients. Saudi J Anaesth. 1974;9(2):136–41. https://doi.org/10.4103/1658-354X.152839.
Teoh GS, Mah KK, Abd Majid S, et al. APACHE II: preliminary report on 100 intensive care unit cases in University Hospital, Kuala Lumpur. Med J Malaysia. 1991;46(1):72–81. http://www.e-mjm.org/1991/v46n1/APACHE_II.pdf. Accessed 6 Mar 2016. PubMed
Wilairatana P, Noan NS, Chinprasatsak S, et al. Scoring systems for predicting outcomes of critically ill patients in northeastern Thailand. Southeast Asian J Trop Med Public Health. 1995;26(1):66–72. http://www.tm.mahidol.ac.th/seameo/1995-26-1/1995-26-1-66.pdf. Accessed 7 Nov 2015. PubMed
- Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review
A. Pubudu De Silva
Arjen M. Dondorp
Nicolette F. De Keizer
- BioMed Central
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