Validation of the Preoperative Score to Predict Postoperative Mortality (POSPOM) in Germany

Abstract

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.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Patient flow chart.

Procedures without relevant anaesthesia include mainly patients of intensive care, patients who received electroconvulsion therapy or minor interventions as biopsies.

Fig 2. Receiver operating characteristic (ROC) curve.

The ROC curve shows the false positive rate on the X-axis and the true positive rate on the Y-axis. The optimum is an area under the curve (AUC) of 1.

Fig 3. Calibration plot with predicted and observed in-hospital mortality for POSPOM.

The bisectrix equals perfect calibration. Black dots represent the observed values. The blue line is the line of best fit using linear regression. Grey shade shows the confidence interval.

Fig 4. Distribution of the POSPOM scores in the cohort of Bonn University Hospital (n = 199,780) in relation to the observed in-hospital mortality rate at each POSPOM value between 0 and 40.

Fig 5. Distribution of the POSPOM scores in the cohort of Bonn University Hospital (orange bars, n = 199,780) compared to POSPOM validation cohort (grey bars, n = 2,789,932).

Fig 6. Observed in-hospital mortality rate at each POSPOM value between 0 and 40 in the cohort of Bonn University Hospital (red line, n = 199,780) compared to POSPOM validation cohort (grey line, n = 2,789,932).