3 research outputs found
Development and internal validation of a clinical prediction model for serious complications after emergency laparotomy
Purpose
Emergency laparotomy (EL) is a common operation with high risk for postoperative complications, thereby requiring accurate risk stratification to manage vulnerable patients optimally. We developed and internally validated a predictive model of serious complications after EL.
Methods
Data for eleven carefully selected candidate predictors of 30-day postoperative complications (Clavien-Dindo gradeā>āā=ā3) were extracted from the HELAS cohort of EL patients in 11 centres in Greece and Cyprus. Logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) was applied for model development. Discrimination and calibration measures were estimated and clinical utility was explored with decision curve analysis (DCA). Reproducibility and heterogeneity were examined with Bootstrap-based internal validation and InternalāExternal Cross-Validation. The American College of Surgeons National Surgical Quality Improvement Programās (ACS-NSQIP) model was applied to the same cohort to establish a benchmark for the new model.
Results
From data on 633 eligible patients (175 complication events), the SErious complications After Laparotomy (SEAL) model was developed with 6 predictors (preoperative albumin, blood urea nitrogen, American Society of Anaesthesiology score, sepsis or septic shock, dependent functional status, and ascites). SEAL had good discriminative ability (optimism-corrected c-statistic: 0.80, 95% confidence interval [CI] 0.79ā0.81), calibration (optimism-corrected calibration slope: 1.01, 95% CI 0.99ā1.03) and overall fit (scaled Brier score: 25.1%, 95% CI 24.1ā26.1%). SEAL compared favourably with ACS-NSQIP in all metrics, including DCA across multiple risk thresholds.
Conclusion
SEAL is a simple and promising model for individualized risk predictions of serious complications after EL. Future external validations should appraise SEALās transportability across diverse settings
Prospective multicenter external validation of postoperative mortality prediction tools in patients undergoing emergency laparotomy
BACKGROUND
Accurate preoperative risk assessment in emergency laparotomy (EL) is valuable for informed decision-making and rational use of resources. Available risk prediction tools have not been validated adequately across diverse healthcare settings. Herein, we report a comparative external validation of 4 widely cited prognostic models.
METHODS
A multicenter cohort was prospectively composed of consecutive patients undergoing EL in 11 Greek hospitals from January 2020 to May 2021 using the National Emergency Laparotomy (NELA) audit inclusion criteria. 30-day mortality risk predictions were calculated using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), NELA, Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (P-POSSUM) and Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tools. Surgeonsā assessment of postoperative mortality using pre-defined cutoffs was recorded, and a surgeon-adjusted ACS-NSQIP prediction was calculated when the original modelās prediction was relatively low. Predictive performances were compared using scaled Brier scores, discrimination and calibration measures and plots, and decision curve analysis. Heterogeneity across hospitals was assessed by random-effects meta-analysis.
RESULTS
631 patients were included and 30-day mortality was 16.3%. The ACS-NSQIP and its surgeon-adjusted version had the highest scaled Brier scores. All models presented high discriminative ability, with concordance statistics ranging from 0.79 for P-POSSUM to 0.85 for NELA. However, except the surgeon-adjusted ACS-NSQIP (Hosmer-Lemeshow test p = 0.742), all other models were poorly calibrated (p < 0.001). Decision curve analysis revealed superior clinical utility of the ACS-NSQIP. Following recalibrations, predictive accuracy improved for all models but ACS-NSQIP retained the lead. Between-hospital heterogeneity was minimum for the ACS-NSQIP model and maximum for P-POSSUM.
CONCLUSION
The ACS-NSQIP tool was most accurate for mortality predictions after EL in a broad external validation cohort, demonstrating utility for facilitating preoperative risk management in the Greek healthcare system. Subjective surgeon assessments of patient prognosis may optimise ACS-NSQIP predictions.
Level of Evidence
Level II, Diagnostic test/criteri
Prospective multicenter external validation of postoperative mortality prediction tools in patients undergoing emergency laparotomy
BACKGROUND: Accurate preoperative risk assessment in emergency laparotomy (EL) is valuable for informed decision making and rational use of resources. Available risk prediction tools have not been validated adequately across diverse health care settings. Herein, we report a comparative external validation of four widely cited prognostic models. METHODS: A multicenter cohort was prospectively composed of consecutive patients undergoing EL in 11 Greek hospitals from January 2020 to May 2021 using the National Emergency Laparotomy Audit (NELA) inclusion criteria. Thirty-day mortality risk predictions were calculated using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), NELA, Portsmouth Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (P-POSSUM), and Predictive Optimal Trees in Emergency Surgery Risk tools. Surgeons' assessment of postoperative mortality using predefined cutoffs was recorded, and a surgeon-adjusted ACS-NSQIP prediction was calculated when the original model's prediction was relatively low. Predictive performances were compared using scaled Brier scores, discrimination and calibration measures and plots, and decision curve analysis. Heterogeneity across hospitals was assessed by random-effects meta-analysis. RESULTS: A total of 631 patients were included, and 30-day mortality was 16.3%. The ACS-NSQIP and its surgeon-adjusted version had the highest scaled Brier scores. All models presented high discriminative ability, with concordance statistics ranging from 0.79 for P-POSSUM to 0.85 for NELA. However, except the surgeon-adjusted ACS-NSQIP (Hosmer-Lemeshow test, p = 0.742), all other models were poorly calibrated ( p < 0.001). Decision curve analysis revealed superior clinical utility of the ACS-NSQIP. Following recalibrations, predictive accuracy improved for all models, but ACS-NSQIP retained the lead. Between-hospital heterogeneity was minimum for the ACS-NSQIP model and maximum for P-POSSUM. CONCLUSION: The ACS-NSQIP tool was most accurate for mortality predictions after EL in a broad external validation cohort, demonstrating utility for facilitating preoperative risk management in the Greek health care system. Subjective surgeon assessments of patient prognosis may optimize ACS-NSQIP predictions. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level II. Copyright Ā© 2023 Wolters Kluwer Health, Inc. All rights reserved