The level of postoperative care influences mortality prediction by the POSPOM score: A retrospective cohort analysis

Abstract

Background The Preoperative Score to Predict Postoperative Mortality (POSPOM) assesses the patients’ individual risk for postsurgical intrahospital death based on preoperative parameters. We hypothesized that mortality predicted by the POSPOM varies depending on the level of postoperative care. Methods All patients age over 18 years undergoing inpatient surgery or interventions involving anesthesia at a German university hospital between January 2006, and December 2017, were assessed for eligibility for this retrospective study. Endpoint was death in hospital following surgery. Adaptation of the POSPOM to the German coding system was performed as previously described. The whole cohort was divided according to the level of postoperative care (normal ward vs. intensive care unit (ICU) admission within 24 h vs. later than 24 h, respectively). Results 199,258 patients were finally included. Observed intrahospital mortality was 2.0% (4,053 deaths). 9.6% of patients were transferred to ICU following surgery, and mortality of those patients was increased already at low POSPOM values of 15. 17,165 patients were admitted to ICU within 24 h, and these patients were older, had more comorbidities, or underwent more invasive surgery, reflected by a higher median POSPOM score compared to the normal-ward group (29 vs. 17, p <0.001). Mortality in that cohort was significantly increased to 8.7% (p <0.001). 2,043 patients were admitted to ICU later than 24 h following surgery (therefore denoted unscheduled admission), and the median POSPOM value of that group was 23. Observed mortality in this cohort was highest (13.5%, p <0.001 vs. ICU admission <24 h cohort). Conclusion Increased mortality in patients transferred to high-care wards reflects the significance of, e.g., intra- or early postoperative events for the patients’ outcome. Therefore, scoring systems considering only preoperative variables such as the POSPOM reveal limitations to predict the individual benefit of postoperative ICU admission.

Preoperative cerebral oxygenation in high-risk noncardiac surgical patients: an observational study on postoperative mortality and complications

“…In addition to the method-specific confounders, there are other confounders of the operative and postoperative period which have an impact on the postoperative outcome. Thus, the difficult-to-stratify surgical impact factor as a relevant determinant of outcome [28] as well as the difficultto-measure variable of fluctuating quality of postoperative care [29,30]. Another issue is the heterogeneity of our study population, in contrast to the more homogenous collective in cardiac surgery.…”

Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables

“…Machine learning and other modeling techniques to support clinical decision making are receiving substantial attention in current research. Many investigations are focused on predicting events and outcomes, such as risk of complications, discharge, length of stay, or mortality [8,9,[29][30][31][32]. A limitation of some of the previous studies is that they only include demographic variables and/or parameters that are measured a limited number of times, leading to an inability to adjust predictions based on new information [11,29,32,33].…”

Anesthesia, You Run Fast!

“…However, we agree with the authors that, despite the results of this interesting study, intraoperative data have to be considered a valuable resource. Menzenbach et al 2 have clearly demonstrated that the preoperative parameters are not sufficient to accurately identify patients who could benefit from postoperative intensive care unit (ICU) admission. In a recent paper, in 5 postoperative complications, the best models were the combined ones, ie, those that unified intraoperative data with preoperative ones.…”