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External Validation of the VOCAL-Penn Cirrhosis Surgical Risk Score in 2 Large, Independent Health Systems.

ABSTRACT: Cirrhosis poses an increased risk of postoperative mortality, yet it remains challenging to accurately risk stratify patients in clinical practice. The VOCAL-Penn cirrhosis surgical risk score was recently developed and internally validated in the national Veterans Affairs health system; however, to date this score has not been evaluated in independent cohorts. The goal of this study was to compare the predictive performance of the VOCAL-Penn to the Mayo risk, Model for End-Stage Liver Disease (MELD), and MELD-sodium (MELD-Na) scores in 2 large health systems. We performed a retrospective cohort study of patients with cirrhosis undergoing surgical procedures of interest at the Beth Israel Deaconess Medical Center or University of Pennsylvania Health System from January 1, 2008, to October 1, 2015. The outcomes of interest were 30-day and 90-day postoperative mortality. Concordance statistics (C-statistics), calibration curves, Brier scores, and the index of prediction accuracy (IPA) were compared for each predictive model. A total of 855 surgical procedures were identified. The VOCAL-Penn score had the numerically highest C-statistic for 90-day postoperative mortality (eg, 0.82 versus 0.79 Mayo versus 0.78 MELD-Na versus 0.79 MELD), although differences were not statistically significant. Calibrations were excellent for the VOCAL-Penn, MELD, and MELD-Na; however, the Mayo score consistently overestimated risk. The VOCAL-Penn had the lowest Brier score and highest IPA at both time points, suggesting superior overall predictive model performance. In subgroup analyses of patients with higher MELD scores, the VOCAL-Penn had significantly higher C-statistics compared with the MELD and MELD-Na. The VOCAL-Penn score (www.vocalpennscore.com) has excellent discrimination and calibration for postoperative mortality among diverse patients with cirrhosis. Overall performance is superior to the Mayo, MELD, and MELD-Na scores. In contrast to the MELD/MELD-Na, the VOCAL-Penn retains excellent discrimination among patients with higher MELD scores.

SUBMITTER: Mahmud N

PROVIDER: S-EPMC8283779 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceAcute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, and few prediction models have been externally validated.ObjectiveTo internally and externally validate a machine learning risk score to detect AKI in hospitalized patients.Design, setting, and participantsThis diagnostic study included 495 971 adult hospital admissions at the University of Chicago (UC) from 2008 to 2016 (n = 48 463), at Loyola University Medical Center (LUMC) from 2007 to 2017 (n = 200 613), and at NorthShore University Health System (NUS) from 2006 to 2016 (n = 246 895) with serum creatinine (SCr) measurements. Patients with an SCr concentration at admission greater than 3.0 mg/dL, with a prior diagnostic code for chronic kidney disease stage 4 or higher, or who received kidney replacement therapy within 48 hours of admission were excluded. A simplified version of a previously published gradient boosted machine AKI prediction algorithm was used; it was validated internally among patients at UC and externally among patients at NUS and LUMC.Main outcomes and measuresPrediction of Kidney Disease Improving Global Outcomes SCr-defined stage 2 AKI within a 48-hour interval was the primary outcome. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC).ResultsThe study included 495 971 adult admissions (mean [SD] age, 63 [18] years; 87 689 [17.7%] African American; and 266 866 [53.8%] women) across 3 health systems. The development of stage 2 or higher AKI occurred in 15 664 of 48 463 patients (3.4%) in the UC cohort, 5711 of 200 613 (2.8%) in the LUMC cohort, and 3499 of 246 895 (1.4%) in the NUS cohort. In the UC cohort, 332 patients (0.7%) required kidney replacement therapy compared with 672 patients (0.3%) in the LUMC cohort and 440 patients (0.2%) in the NUS cohort. The AUCs for predicting at least stage 2 AKI in the next 48 hours were 0.86 (95% CI, 0.86-0.86) in the UC cohort, 0.85 (95% CI, 0.84-0.85) in the LUMC cohort, and 0.86 (95% CI, 0.86-0.86) in the NUS cohort. The AUCs for receipt of kidney replacement therapy within 48 hours were 0.96 (95% CI, 0.96-0.96) in the UC cohort, 0.95 (95% CI, 0.94-0.95) in the LUMC cohort, and 0.95 (95% CI, 0.94-0.95) in the NUS cohort. In time-to-event analysis, a probability cutoff of at least 0.057 predicted the onset of stage 2 AKI a median (IQR) of 27 (6.5-93) hours before the eventual doubling in SCr concentrations in the UC cohort, 34.5 (19-85) hours in the NUS cohort, and 39 (19-108) hours in the LUMC cohort.Conclusions and relevanceIn this study, the machine learning algorithm demonstrated excellent discrimination in both internal and external validation, supporting its generalizability and potential as a clinical decision support tool to improve AKI detection and outcomes.

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External Validation of the VOCAL-Penn Cirrhosis Surgical Risk Score in 2 Large, Independent Health Systems.

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