Development and validation of a multivariable prediction model in open abdomen patients for entero-atmospheric fistula

Cristaudo, Adam T., Hitos, Kerry, Gunnarsson, Ronny, and Decosta, Alan (2022) Development and validation of a multivariable prediction model in open abdomen patients for entero-atmospheric fistula. ANZ Journal of Surgery, 92 (5). pp. 1079-1084.

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Background: Laparostomy or Open Abdomen (OA) has matured into an effective strategy in the management of abdominal catastrophe. Single prognostic factors have been identified in a previous systematic review regarding entero-atmospheric fistula (EAF). Unfortunately, no prognostic multivariable model for EAF exist. The aim was to develop and validate a multivariable prediction model from a retrospective cohort study involving three hospital’s databases.

Methods: Fifty-seven variables were evaluated to develop a multivariable model. Univariate and multivariable logistic regression analyses were performed for on a developmental data set from two hospitals. Receiver operator characteristics analysis with area under the curve (AUC) and 95% confidence intervals (CI) were performed on the developmental data set (internal validation) as well as on an additional validation data set from another hospital (external validation).

Results: Five-hundred and forty-eight patients managed with an OA. Two variables remained in the multivariable prediction model for EAF. The AUC for EAF on internal validation were 0.74 (95% CI: 0.58–0.86) and 0.79 (95% CI: 0.67–0.92) on external validation.

Conclusions: A multivariable prediction model for EAF was externally validated and an easy-to-use probability nomogram was constructed using the two predictor variables.

Item ID: 72760
Item Type: Article (Research - C1)
ISSN: 1445-2197
Copyright Information: © 2022 The Authors. ANZ Journal of Surgery published by John Wiley & Sons Australia, Ltd on behalf of Royal Australasian College of Surgeons. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Date Deposited: 30 Aug 2022 02:42
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320226 Surgery @ 100%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200105 Treatment of human diseases and conditions @ 100%
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