• Yannick Le Manach, Gary Collins, Reitze Rodseth, Christine Le Bihan-Benjamin, Bruce Biccard, Bruno Riou, P J Devereaux, and Paul Landais.
• From the Department of Anesthesia (Y.L.M.), Department of Clinical Epidemiology and Biostatistics (Y.L.M., P.J.D.), and Department of Medicine (P.J.D.), Faculty of Health Sciences, Michael DeGroote School of Medicine, McMaster University, Hamilton, Canada; Perioperative Research Group, Population Health Research Institute, Hamilton, Canada (Y.L.M., R.R.); Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom (G.C.); Perioperative Research Group, Department of Anaesthesia, University of KwaZulu-Natal, Pietermaritzburg, South Africa (R.R.); Department of Biostatistics and Medical Information, Necker University Hospital, Assistance Publique Hôpitaux de Paris, France (C.L.B.-B.); Perioperative Research Group, Department of Anaesthetics, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Pietermaritzburg, South Africa (B.B.); Department of Emergency Medicine and Surgery, CHU Pitié-Salpêtrière, Paris, France (B.R.); Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Perioperative Medicine and Surgical Research Unit, Hamilton, Ontario, Canada (P.J.D.); and Montpellier 1 University, Faculty of Medicine, Department of Biostatistics, Clinical Research and Medical Informatics, Nîmes University Hospital, Nîmes, France (P.L.).
• Anesthesiology. 2016 Mar 1; 124 (3): 570-9.

BackgroundAn accurate risk score able to predict in-hospital mortality in patients undergoing surgery may improve both risk communication and clinical decision making. The aim of the study was to develop and validate a surgical risk score based solely on preoperative information, for predicting in-hospital mortality.MethodsFrom January 1, 2010, to December 31, 2010, data related to all surgeries requiring anesthesia were collected from all centers (single hospital or hospitals group) in France performing more than 500 operations in the year on patients aged 18 yr or older (n = 5,507,834). International Statistical Classification of Diseases, 10th revision codes were used to summarize the medical history of patients. From these data, the authors developed a risk score by examining 29 preoperative factors (age, comorbidities, and surgery type) in 2,717,902 patients, and then validated the risk score in a separate cohort of 2,789,932 patients.ResultsIn the derivation cohort, there were 12,786 in-hospital deaths (0.47%; 95% CI, 0.46 to 0.48%), whereas in the validation cohort there were 14,933 in-hospital deaths (0.54%; 95% CI, 0.53 to 0.55%). Seventeen predictors were identified and included in the PreOperative Score to predict PostOperative Mortality (POSPOM). POSPOM showed good calibration and excellent discrimination for in-hospital mortality, with a c-statistic of 0.944 (95% CI, 0.943 to 0.945) in the development cohort and 0.929 (95% CI, 0.928 to 0.931) in the validation cohort.ConclusionThe authors have developed and validated POSPOM, a simple risk score for the prediction of in-hospital mortality in surgical patients.

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