Mortality prediction models in the adult critically ill: A scoping review

Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients.

[1]  F. Shann,et al.  Critical care outcome prediction equation (COPE) for adult intensive care. , 2008, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

[2]  C. Camargo,et al.  Addition of 25-hydroxyvitamin D levels to the Deyo-Charlson Comorbidity Index improves 90-day mortality prediction in critically ill patients , 2016, Journal of Intensive Care.

[3]  T. Perneger,et al.  Determinants of the calibration of SAPS II and SAPS 3 mortality scores in intensive care: a European multicenter study , 2017, Critical Care.

[4]  Michael J. Fine,et al.  How to derive and validate clinical prediction models for use in intensive care medicine , 2014, Intensive Care Medicine.

[5]  Zhiyi Wang,et al.  A multifactor model for predicting mortality in critically ill patients: A multicenter prospective cohort study , 2017, Journal of critical care.

[6]  Hua Min,et al.  A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients. , 2017, Journal of palliative medicine.

[7]  M. Suistomaa,et al.  Sampling rate causes bias in APACHE II and SAPS II scores , 2000, Intensive Care Medicine.

[8]  Michael Bailey,et al.  Risk prediction of hospital mortality for adult patients admitted to Australian and New Zealand intensive care units: development and validation of the Australian and New Zealand Risk of Death model. , 2013, Journal of critical care.

[9]  J. Zimmerman,et al.  Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients* , 2006, Critical care medicine.

[10]  Parisa Rashidi,et al.  DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning , 2018, Scientific Reports.

[11]  S Lemeshow,et al.  Mortality probability models for patients in the intensive care unit for 48 or 72 hours: A prospective, multicenter study , 1994, Critical care medicine.

[12]  Wayne Copes,et al.  Updated Mortality Probability Model(MPM-III) , 2005 .

[13]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study , 1993 .

[14]  Yvonne Vergouwe,et al.  A calibration hierarchy for risk models was defined: from utopia to empirical data. , 2016, Journal of clinical epidemiology.

[15]  P. Myrianthefs,et al.  A novel mortality prediction model for the current population in an adult intensive care unit , 2018, Heart & lung : the journal of critical care.

[16]  Hamid Mohamadlou,et al.  Using electronic health record collected clinical variables to predict medical intensive care unit mortality , 2016, Annals of medicine and surgery.

[17]  Ari Ercole,et al.  Optimal intensive care outcome prediction over time using machine learning , 2018, PloS one.

[18]  L. Goldman,et al.  The SUPPORT Prognostic Model: Objective Estimates of Survival for Seriously Ill Hospitalized Adults , 1995, Annals of Internal Medicine.

[19]  E. Y. Kim,et al.  A Simplified Mortality Score Using Delta Neutrophil Index and the Thrombotic Microangiopathy Score for Prognostication in Critically Ill Patients , 2018, Shock.

[20]  A. Moss,et al.  Derivation and Validation of a Prognostic Model to Predict 6‐Month Mortality in an Intensive Care Unit Population , 2017, Annals of the American Thoracic Society.

[21]  Gari D. Clifford,et al.  A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy* , 2013, Critical care medicine.

[22]  P. Pronovost,et al.  Long-term mortality and quality of life in sepsis: A systematic review* , 2010, Critical care medicine.

[23]  A. Mebazaa,et al.  Determinants of long-term outcome in ICU survivors: results from the FROG-ICU study , 2018, Critical Care.

[24]  D. Harrison,et al.  Development and validation of the new ICNARC model for prediction of acute hospital mortality in adult critical care , 2017, Journal of critical care.

[25]  A. Perner,et al.  Important methodological flaws in the recently published clinical prediction model the REMEMBER score , 2019, Critical Care.

[26]  S. Lemeshow,et al.  Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. , 1993, JAMA.

[27]  Sven Van Poucke,et al.  Establishment and Validation of GV-SAPS II Scoring System for Non-Diabetic Critically Ill Patients , 2016, PloS one.

[28]  H. Wunsch,et al.  International comparisons of intensive care: informing outcomes and improving standards , 2012, Current opinion in critical care.

[29]  John P A Ioannidis,et al.  Predicting death: an empirical evaluation of predictive tools for mortality. , 2011, Archives of internal medicine.

[30]  M. Collet,et al.  Development and internal validation of the Simplified Mortality Score for the Intensive Care Unit (SMS‐ICU) , 2018, Acta anaesthesiologica Scandinavica.

[31]  W. Knaus,et al.  The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. , 1991, Chest.

[32]  Y. Imanaka,et al.  An outcome prediction model for adult intensive care. , 2010, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

[33]  Matthew Knuiman,et al.  Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model , 2008, PloS one.

[34]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[35]  J. Vincent,et al.  Clinical review: Scoring systems in the critically ill , 2010, Critical care.

[36]  James R Carpenter,et al.  A new risk prediction model for critical care: The Intensive Care National Audit & Research Centre (ICNARC) model* , 2007, Critical care medicine.

[37]  Yvonne Vergouwe,et al.  Prognosis and prognostic research: Developing a prognostic model , 2009, BMJ : British Medical Journal.

[38]  Combination of APACHE Scoring Systems with Adductor Pollicis Muscle Thickness for the Prediction of Mortality in Patients Who Spend More Than One Day in the Intensive Care Unit , 2018, Critical care research and practice.

[39]  E. Steyerberg.  Clinical Prediction Models , 2008, Statistics for Biology and Health.

[40]  H. Flaatten,et al.  Severity scoring in the ICU: a review , 2008, Acta anaesthesiologica Scandinavica.

[41]  Peter Bauer,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 , 2005, Intensive Care Medicine.

[42]  G. Rubenfeld,et al.  Critical care and the global burden of critical illness in adults , 2010, The Lancet.

[43]  T. Holland-Letz,et al.  A laboratory-based risk score for medical intensive care patients , 2008, Clinical chemistry and laboratory medicine.

[44]  Sabina Hunziker,et al.  Red cell distribution width improves the simplified acute physiology score for risk prediction in unselected critically ill patients , 2012, Critical Care.

[45]  D. Bouch,et al.  Severity scoring systems in the critically ill , 2008 .

[46]  Simplification of the SAPS by selecting independant variables , 1991, Intensive Care Medicine.

[47]  V. Novack,et al.  Predicting Mortality in Low-Income Country ICUs: The Rwanda Mortality Probability Model (R-MPM) , 2016, PloS one.

[48]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. , 1993, JAMA.

[49]  Kung Chen,et al.  Development of a daily mortality probability prediction model from Intensive Care Unit patients using a discrete-time event history analysis , 2013, Comput. Methods Programs Biomed..

[50]  W. Knaus,et al.  APACHE II: a severity of disease classification system. , 1985 .

[51]  S. Lemeshow,et al.  Modeling the severity of illness of ICU patients. A systems update. , 1994, JAMA.

[52]  Karel Moons,et al.  PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration , 2019, Annals of Internal Medicine.

[53]  Jie Ma,et al.  A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. , 2019, Journal of clinical epidemiology.

[54]  T. Holland-Letz,et al.  Estimation of the Mortality Risk of Surgical Intensive Care Patients Based on Routine Laboratory Parameters , 2008, European Surgical Research.

[55]  F. Priestap,et al.  To develop a regional ICU mortality prediction model during the first 24 h of ICU admission utilizing MODS and NEMS with six other independent variables from the Critical Care Information System (CCIS) Ontario, Canada , 2016, Journal of Intensive Care.

[56]  M. Bailey,et al.  Assessing Contemporary Intensive Care Unit Outcome: Development and Validation of the Australian and New Zealand Risk of Death Admission Model , 2017, Anaesthesia and intensive care.

[57]  Y. Cohen,et al.  Accuracy of a composite score using daily SAPS II and LOD scores for predicting hospital mortality in ICU patients hospitalized for more than 72 h , 2001, Intensive Care Medicine.

[58]  A. Morabito,et al.  Scoring system for the selection of high-risk patients in the intensive care unit , 2006, Critical care medicine.

[59]  F. Sjöberg,et al.  In‐hospital vs. 30‐day mortality in the critically ill – a 2‐year Swedish intensive care cohort analysis , 2015, Acta anaesthesiologica Scandinavica.

[60]  A. Rapsang,et al.  Scoring systems in the intensive care unit: A compendium , 2014, Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine.

[61]  T. Rasekaba,et al.  Development and validation of the critical care outcome prediction equation, version 4. , 2013, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

[62]  A. Abu-Hanna,et al.  Prediction of long-term mortality in ICU patients: model validation and assessing the effect of using in-hospital versus long-term mortality on benchmarking , 2013, Intensive Care Medicine.

[63]  Barbara Kitchenham,et al.  Procedures for Performing Systematic Reviews , 2004 .

[64]  A. Hoeft,et al.  Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions A statement from the ESA-ESICM joint taskforce on perioperative outcome measures , 2015, European journal of anaesthesiology.

[65]  V. Gil-Guillén,et al.  Construction and internal validation of a new mortality risk score for patients admitted to the intensive care unit , 2016, International journal of clinical practice.

[66]  J. McGowan,et al.  PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation , 2018, Annals of Internal Medicine.

[67]  M. Leeflang,et al.  Search Filters for Finding Prognostic and Diagnostic Prediction Studies in Medline to Enhance Systematic Reviews , 2012, PloS one.