ArticlePloS one2021
Validation of the Preoperative Score to Predict Postoperative Mortality (POSPOM) in Germany.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05892796 (Prediction of Morbidity and Mortality With Medical Pre-Operative Fitness Assessment), which is not on this map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Prediction of Morbidity and Mortality With Medical Pre-Operative Fitness Assessment
Who cites it
6 citing papers in PubMed.
- Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?Annals of gastroenterological surgery · 2026Review
- Article
- Risk Prediction Models for Peri-Operative Mortality in Patients Undergoing Major Vascular Surgery with Particular Focus on Ruptured Abdominal Aortic Aneurysms: A Scoping Review.Journal of clinical medicine · 2023Article
- Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.Scientific reports · 2023Article
- Validation of the PreOperative Score to predict Post-Operative Mortality (POSPOM) in Dutch non-cardiac surgery patients.BMC anesthesiology · 2022Article
- The level of postoperative care influences mortality prediction by the POSPOM score: A retrospective cohort analysis.PloS one · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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
findingsAll 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.
conclusionsAfter 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.
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