Early prediction of in-hospital mortality in acute pancreatitis: a retrospective observational cohort study based on a large multicentre critical care database

Link to article at PubMed

BMJ Open. 2020 Dec 23;10(12):e041893. doi: 10.1136/bmjopen-2020-041893.


OBJECTIVE: To develop and validate a prediction model for predicting in-hospital mortality in patients with acute pancreatitis (AP).

DESIGN: A retrospective observational cohort study based on a large multicentre critical care database.

SETTING: All subject data were collected from the eICU Collaborative Research Database (eICU-CRD), which covers 200 859 intensive care unit admissions of 139 367 patients in 208 US hospitals between 2014 and 2015.

PARTICIPANTS: A total of 746 patients with AP were drawn from eICU-CRD. Due to loss to follow-up (four patients) or incomplete data (364 patients), 378 patients were enrolled in the primary cohort to establish a nomogram model and to conduct internal validation.

PRIMARY AND SECONDARY OUTCOME MEASURES: The outcome of the prediction model was in-hospital mortality. All risk factors found significant in the univariate analysis were considered for multivariate analysis to adjust for confounding factors. Then a nomogram model was established. The performance of the nomogram model was evaluated by the concordance index (C-index) and the calibration plot. The nomogram model was internally validated using the bootstrap resampling method. The predictive accuracy of the nomogram model was compared with that of Acute Physiology, Age, and Chronic Health Evaluation (APACHE) IV. Decision curve analysis (DCA) was performed to evaluate and compare the potential net benefit using of different predictive models.

RESULTS: The overall in-hospital mortality rate is 4.447%. Age, BUN (blood urea nitrogen) and lactate (ABL) were the independent risk factors determined by multivariate analysis. The C-index of nomogram model ABL (0.896 (95% CI 0.825 to 0.967)) was similar to that of APACHE IV (p=0.086), showing a comparable discriminating power. Calibration plot demonstrated good agreement between the predicted and the actual in-hospital mortality. DCA showed that the nomogram model ABL was clinically useful.

CONCLUSIONS: Nomogram model ABL, which used readily available data, exhibited high predictive value for predicting in-hospital mortality in AP.

PMID:33361165 | DOI:10.1136/bmjopen-2020-041893

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