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Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation
Yannick Le Manach1, Gary Collins, Reitze Rodseth
1From 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.).
A new surgical risk score, POSPOM, accurately predicts in-hospital mortality using only preoperative data. This tool aids clinical decisions and patient risk communication for surgical patients.
Area of Science:
- Medical Informatics
- Surgical Outcomes Research
- Predictive Analytics in Healthcare
Background:
- Accurate prediction of in-hospital mortality in surgical patients is crucial for risk communication and clinical decision-making.
- Existing risk assessment tools may not fully leverage preoperative information for surgical mortality prediction.
Purpose of the Study:
- To develop and validate a novel surgical risk score using solely preoperative data.
- To enhance the prediction of in-hospital mortality in patients undergoing surgery.
Main Methods:
- Utilized a large dataset of over 5.5 million surgeries across French centers from 2010-2010.
- Developed a risk score using 29 preoperative factors in a cohort of 2.7 million patients.
- Validated the risk score in a separate, independent cohort of 2.7 million patients.
Main Results:
- Identified 17 key predictors for in-hospital mortality.
- The developed risk score, POSPOM (PreOperative Score to predict PostOperative Mortality), demonstrated high predictive accuracy.
- POSPOM achieved excellent discrimination with c-statistics of 0.944 in the development cohort and 0.929 in the validation cohort.
Conclusions:
- POSPOM is a validated, simple risk score for predicting in-hospital mortality in surgical patients.
- The score relies exclusively on readily available preoperative information.
- POSPOM can be a valuable tool for improving risk assessment in surgical care.
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