Eugene, N;
Oliver, CM;
Bassett, MG;
Poulton, TE;
Kuryba, A;
Johnston, C;
Anderson, ID;
... Walker, K; + view all
(2018)
Development and internal validation of a novel risk adjustment model for adult patients undergoing emergency laparotomy surgery: the National Emergency Laparotomy Audit risk model.
British Journal of Anaesthesia
, 121
(4)
pp. 739-748.
10.1016/j.bja.2018.06.026.
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Abstract
BACKGROUND: Among patients undergoing emergency laparotomy, 30-day postoperative mortality is around 10–15%. The risk of death among these patients, however, varies greatly because of their clinical characteristics. We developed a risk prediction model for 30-day postoperative mortality to enable better comparison of outcomes between hospitals. METHODS: We analysed data from the National Emergency Laparotomy Audit (NELA) on patients having an emergency laparotomy between December 2013 and November 2015. A prediction model was developed using multivariable logistic regression, with potential risk factors identified from existing prediction models, national guidelines, and clinical experts. Continuous risk factors were transformed if necessary to reflect their non-linear relationship with 30-day mortality. The performance of the model was assessed in terms of its calibration and discrimination. Interval validation was conducted using bootstrap resampling. RESULTS: There were 4458 (11.5%) deaths within 30-days among the 38 830 patients undergoing emergency laparotomy. Variables associated with death included (among others): age, blood pressure, heart rate, physiological variables, malignancy, and ASA physical status classification. The predicted risk of death among patients ranged from 1% to 50%. The model demonstrated excellent calibration and discrimination, with a C-statistic of 0.863 (95% confidence interval, 0.858–0.867). The model retained its high discrimination during internal validation, with a bootstrap derived C-statistic of 0.861. CONCLUSIONS: The NELA risk prediction model for emergency laparotomies discriminates well between low- and high-risk patients and is suitable for producing risk-adjusted provider mortality statistics.
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