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Erschienen in:
10.01.2022 | Original Scientific Report
P-POSSUM and the NELA Score Overpredict Mortality for Laparoscopic Emergency Bowel Surgery: An Analysis of the NELA Database
Erschienen in: World Journal of Surgery | Ausgabe 3/2022
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Background
Risk stratification has become a key part of the care processes for patients having emergency bowel surgery. This study aimed to determine if operative approach influences risk-model performance, and risk-adjusted mortality rates in the United Kingdom.
Methods
A prospectively planned analysis was conducted using National Emergency Laparotomy Audit (NELA) data from December 2013 to November 2018. The risk-models investigated were P-POSSUM and the NELA Score, with model performance assessed in terms of discrimination and calibration. Risk-adjusted mortality was assessed using Standardised Mortality Ratios (SMR). Analysis was performed for the total cohort, and cases performed open, laparoscopically and converted to open. Sub-analysis was performed for cases with ≤ 20% predicted mortality.
Results
Data were available for 116 396 patients with P-POSSUM predicted mortality, and 46 935 patients with the NELA score. Both models displayed excellent discrimination with little variation between operative approaches (c-statistic: P-POSSUM 0.801–0.836; NELA Score 0.811–0.862). The NELA score was well calibrated across all deciles of risk, but P-POSSUM over-predicted risk beyond 20% mortality. Calibration plots for operative approach demonstrated that both models increasingly over-predicted mortality for laparoscopy, relative to open and converted to open surgery. SMRs calculated using both models consistently demonstrated that risk-adjusted mortality with laparoscopy was a third lower than open surgery.
Conclusion
Risk-adjusted mortality for emergency bowel surgery is lower for laparoscopy than open surgery, with P-POSSUM and NELA score both over-predicting mortality for laparoscopy. Operative approach should be considered in the development of future risk-models that rely on operative data.
