Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation

Background:An 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. Methods:From 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. Results:In 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. Conclusion:The authors have developed and validated POSPOM, a simple risk score for the prediction of in-hospital mortality in surgical patients.

[1]  C. Lam,et al.  Validation of POSSUM scoring systems for audit of major hepatectomy , 2004, The British journal of surgery.

[2]  E. Mascha,et al.  Development and Validation of a Risk Quantification Index for 30-Day Postoperative Mortality and Morbidity in Noncardiac Surgical Patients , 2011, Anesthesiology.

[3]  G. Collins,et al.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement , 2015, BMC Medicine.

[4]  E Magi,et al.  ASA classification and perioperative variables as predictors of postoperative outcome. , 1997, British journal of anaesthesia.

[5]  R. Hirsch Validation samples. , 1991, Biometrics.

[6]  Paolo Pelosi,et al.  Mortality after surgery in Europe: a 7 day cohort study , 2012, The Lancet.

[7]  G. Baron,et al.  Prospective Evaluation of In-hospital Mortality with the P-POSSUM Scoring System in Patients Undergoing Major Digestive Surgery , 2012, World Journal of Surgery.

[8]  Robert M Mentzer,et al.  National Surgical Quality Improvement Program (NSQIP) Risk Factors Can Be Used to Validate American Society of Anesthesiologists Physical Status Classification (ASA PS) Levels , 2006, Annals of surgery.

[9]  R. Laskin,et al.  ASA physical status classification is not a good predictor of infection for total knee replacement and is influenced by the presence of comorbidities. , 2008, Acta orthopaedica Belgica.

[10]  E. Mascha,et al.  Impact of Present-on-admission Indicators on Risk-adjusted Hospital Mortality Measurement , 2013, Anesthesiology.

[11]  P. Albaladéjo,et al.  American Society of Anesthesiologists’ Physical Status system: a multicentre Francophone study to analyse reasons for classification disagreement , 2011, European journal of anaesthesiology.

[12]  Gary S Collins,et al.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.

[13]  G. P. Copeland,et al.  POSSUM: A scoring system for surgical audit , 1991, The British journal of surgery.

[14]  D G Altman,et al.  What do we mean by validating a prognostic model? , 2000, Statistics in medicine.

[15]  P. Höglund,et al.  Comparison of 19 pre-operative risk stratification models in open-heart surgery. , 2006, European heart journal.

[16]  Armin Schubert,et al.  Broadly Applicable Risk Stratification System for Predicting Duration of Hospitalization and Mortality , 2010, Anesthesiology.

[17]  B. Gersh,et al.  Cardiac Risk of Noncardiac Surgery Influence of Coronary Disease and Type of Surgery in 3368 Operations , 1997 .

[18]  K. Thennarasu,et al.  Evaluation of POSSUM and P-POSSUM scoring systems for predicting the mortality in elective neurosurgical patients , 2008, British journal of neurosurgery.

[19]  M. Irwin,et al.  The ASA Physical Status Classification: Inter-observer Consistency , 2002, Anaesthesia and intensive care.

[20]  E. Rackow Rehospitalizations among patients in the Medicare fee-for-service program. , 2009, The New England journal of medicine.

[21]  David R Flum,et al.  Blueprint for a new American College of Surgeons: National Surgical Quality Improvement Program. , 2008, Journal of the American College of Surgeons.

[22]  W. Berry,et al.  An estimation of the global volume of surgery: a modelling strategy based on available data , 2008, The Lancet.

[23]  U. Wolters,et al.  Risk factors, complications, and outcome in surgery: a multivariate analysis. , 1997, The European journal of surgery = Acta chirurgica.

[24]  W. Henderson,et al.  Association of perioperative β-blockade with mortality and cardiovascular morbidity following major noncardiac surgery. , 2013, JAMA.

[25]  K. Rowan,et al.  Risk Stratification Tools for Predicting Morbidity and Mortality in Adult Patients Undergoing Major Surgery: Qualitative Systematic Review , 2013, Anesthesiology.

[26]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .

[27]  G. Guyatt,et al.  Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery. , 2012, JAMA.

[28]  David M Kent,et al.  Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis , 2006, BMC medical research methodology.

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

[30]  C.J.H. Mann,et al.  Clinical Prediction Models: A Practical Approach to Development, Validation and Updating , 2009 .

[31]  C. Ko,et al.  Effect of Subjective Preoperative Variables on Risk-Adjusted Assessment of Hospital Morbidity and Mortality , 2009, Annals of surgery.

[32]  O. Jonasson,et al.  The patient safety in surgery study: background, study design, and patient populations. , 2007, Journal of the American College of Surgeons.

[33]  E. DeLong,et al.  Comparing risk-adjustment methods for provider profiling. , 1998, Statistics in medicine.

[34]  Feng Qian,et al.  The Surgical Mortality Probability Model: Derivation and Validation of a Simple Risk Prediction Rule for Noncardiac Surgery , 2012, Annals of surgery.

[35]  Ralph B D'Agostino,et al.  Presentation of multivariate data for clinical use: The Framingham Study risk score functions. , 2005, Statistics in medicine.

[36]  David A Harrison,et al.  Identification and characterisation of the high-risk surgical population in the United Kingdom , 2006, Critical care.

[37]  A. Keats The ASA classification of physical status--a recapitulation. , 1978, Anesthesiology.

[38]  Jonathan P Wanderer,et al.  Validation of a Risk Stratification Index and Risk Quantification Index for Predicting Patient Outcomes: In-hospital Mortality, 30-day Mortality, 1-year Mortality, and Length-of-stay , 2013, Anesthesiology.

[39]  Jeroen J. Bax,et al.  Postoperative Mortality in The Netherlands: A Population-based Analysis of Surgery-specific Risk in Adults , 2010 .

[40]  A. Bottle,et al.  Provider profiling models for acute coronary syndrome mortality using administrative data. , 2013, International journal of cardiology.

[41]  P. Landais,et al.  Computerized Medico-Economic Decision Making: An International Comparison , 2014 .

[42]  D. Watters,et al.  Preoperative Risk Stratification for Mortality and Major Morbidity in Major Colorectal Surgery , 2009, Diseases of the colon and rectum.

[43]  R. Califf,et al.  An independently derived and validated predictive model for selecting patients with myocardial infarction who are likely to benefit from tissue plasminogen activator compared with streptokinase. , 2002, The American journal of medicine.