BACKGROUND: The Preoperative Score to Predict Postoperative Mortality (POSPOM) based on preoperatively available data was presented by Le Manach et al. in 2016. This prognostic model considers the kind of surgical procedure, patients' age and 15 defined comorbidities to predict the risk of postoperative in-hospital mortality. Objective of the present study was to validate POSPOM for the German healthcare coding system (G-POSPOM). METHODS AND FINDINGS: All cases involving anaesthesia performed at the University Hospital Bonn between 2006 and 2017 were analysed retrospectively. Procedures codified according to the French Groupes Homogènes de Malades (GHM) were translated and adapted to the German Operationen- und Prozedurenschlüssel (OPS). Comorbidities were identified by the documented International Statistical Classification of Diseases (ICD-10) coding. POSPOM was calculated for the analysed patient collective using these data according to the method described by Le Manach et al. Performance of thereby adapted POSPOM was tested using c-statistic, Brier score and a calibration plot. Validation was performed using data from 199,780 surgical cases. With a mean age of 56.33 years (SD 18.59) and a proportion of 49.24% females, the overall cohort had a mean POSPOM value of 18.18 (SD 8.11). There were 4,066 in-hospital deaths, corresponding to an in-hospital mortality rate of 2.04% (95% CI 1.97 to 2.09%) in our sample. POSPOM showed a good performance with a c-statistic of 0.771 and a Brier score of 0.021. CONCLUSIONS: After adapting POSPOM to the German coding system, we were able to validate the score using patient data of a German university hospital. According to previous demonstration for French patient cohorts, we observed a good correlation of POSPOM with in-hospital mortality. Therefore, further adjustments of POSPOM considering also multicentre and transnational validation should be pursued based on this proof of concept.
BACKGROUND: The Preoperative Score to Predict Postoperative Mortality (POSPOM) based on preoperatively available data was presented by Le Manach et al. in 2016. This prognostic model considers the kind of surgical procedure, patients' age and 15 defined comorbidities to predict the risk of postoperative in-hospital mortality. Objective of the present study was to validate POSPOM for the German healthcare coding system (G-POSPOM). METHODS AND FINDINGS: All cases involving anaesthesia performed at the University Hospital Bonn between 2006 and 2017 were analysed retrospectively. Procedures codified according to the French Groupes Homogènes de Malades (GHM) were translated and adapted to the German Operationen- und Prozedurenschlüssel (OPS). Comorbidities were identified by the documented International Statistical Classification of Diseases (ICD-10) coding. POSPOM was calculated for the analysed patient collective using these data according to the method described by Le Manach et al. Performance of thereby adapted POSPOM was tested using c-statistic, Brier score and a calibration plot. Validation was performed using data from 199,780 surgical cases. With a mean age of 56.33 years (SD 18.59) and a proportion of 49.24% females, the overall cohort had a mean POSPOM value of 18.18 (SD 8.11). There were 4,066 in-hospital deaths, corresponding to an in-hospital mortality rate of 2.04% (95% CI 1.97 to 2.09%) in our sample. POSPOM showed a good performance with a c-statistic of 0.771 and a Brier score of 0.021. CONCLUSIONS: After adapting POSPOM to the German coding system, we were able to validate the score using patient data of a German university hospital. According to previous demonstration for French patient cohorts, we observed a good correlation of POSPOM with in-hospital mortality. Therefore, further adjustments of POSPOM considering also multicentre and transnational validation should be pursued based on this proof of concept.
Authors: T H Lee; E R Marcantonio; C M Mangione; E J Thomas; C A Polanczyk; E F Cook; D J Sugarbaker; M C Donaldson; R Poss; K K Ho; L E Ludwig; A Pedan; L Goldman Journal: Circulation Date: 1999-09-07 Impact factor: 29.690
Authors: Laurent G Glance; Stewart J Lustik; Edward L Hannan; Turner M Osler; Dana B Mukamel; Feng Qian; Andrew W Dick Journal: Ann Surg Date: 2012-04 Impact factor: 12.969
Authors: Yannick Le Manach; Gary Collins; Reitze Rodseth; Christine Le Bihan-Benjamin; Bruce Biccard; Bruno Riou; P J Devereaux; Paul Landais Journal: Anesthesiology Date: 2016-03 Impact factor: 7.892
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