Development of a prediction model for clinically important outcomes of acute diverticulitis

Objective: Acute diverticulitis (AD) is a common disease with various outcomes. When AD is diagnosed in the emergency department (ED), the ED clinician must determine the patient's treatment strategy whether the patient can be discharged, needs to be admitted to the general ward, ICU, or needs surgical consultation. This study aimed to identify potential risk factors for clinically important outcomes (CIOs) and to develop a prediction model for ClOs in AD to aid clinical decision making in the ED. Methods: Retrospective data from between 2013 and 2017 in an ED in an urban setting were reviewed for adult AD. Potential risk factors were age, sex, past medical history, symptoms, physical exams, laboratory results, and imaging results. A CIO was defined as a case with one of the following outcomes: hospital death, ICU admission, surgery or invasive intervention, and admission for 7 or more days. The prediction model for CIOs was developed using potential risk factors. Model discrimination and calibration were assessed using the area under the curve (AUC) and 95% confidence intervals (as) and the Hosmer-Lemeshow (HL) test, respectively. Model validation was conducted using 500 random bootstrap samples. Results: Of the final 337 AD patients, 63 patients had CIOs. Six potential factors (age, abdominal pain (>= 3 days), anorexia, rebound tenderness, white blood cell count (> 15,000/mu l), C-reactive protein (> 10 mg/dL), and CT findings of a complication) were used for the final model. The AUC (95% CI) for CIOs was 0.875 (0.826-0.923), and chi(2) was 2.969 (p-value = 0.936) with the HL test. Validation using bootstrap samples resulted in an optimism-corrected AUC of 0.858 (0.856-0.861). Conclusion: A prediction model for clinically important outcomes of AD visiting a single ED showed good discrimination and calibration power with an acceptable range. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

The study aimed to develop a prediction model for clinically important outcomes of AD in the ED, to aid clinical decision making. Analysis of potential risk factors resulted in a model with good discrimination and calibration power for predicting CIOs in AD patients visiting the ED.