Selected article for: "age gender and AUC curve area"

Author: Sperger, John; Shah, Kushal S.; Lu, Minxin; Zhang, Xian; Ungaro, Ryan C.; Brenner, Erica J.; Agrawal, Manasi; Colombel, Jean-Frederic; Kappelman, Michael D.; Kosorok, Michael R.
Title: Development and validation of multivariable prediction models for adverse COVID-19 outcomes in IBD patients
  • Cord-id: cxzscr4k
  • Document date: 2021_1_20
  • ID: cxzscr4k
    Snippet: IMPORTANCE: Risk calculators can facilitate shared medical decision-making(1). Demographics, comorbidities, medication use, geographic region, and other factors may increase the risk for COVID-19-related complications among patients with IBD(2,3). OBJECTIVES: Develop an individualized prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with IBD. DESIGN, SETTING, AND PARTICIPANTS: This study developed and validated prognostic penalized logistic
    Document: IMPORTANCE: Risk calculators can facilitate shared medical decision-making(1). Demographics, comorbidities, medication use, geographic region, and other factors may increase the risk for COVID-19-related complications among patients with IBD(2,3). OBJECTIVES: Develop an individualized prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with IBD. DESIGN, SETTING, AND PARTICIPANTS: This study developed and validated prognostic penalized logistic regression models(4) using reports to Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) from March–October 2020. Model development was done using a training data set (85% of cases reported March 13 – September 15, 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported September 16–October 20, 2020. MAIN OUTCOMES AND MEASURES: 1. Hospitalization+: composite outcome of hospitalization, ICU admission, mechanical ventilation, or death. 2. ICU+: composite outcome of ICU admission, mechanical ventilation, or death. 3. Death. We assessed the resulting models’ discrimination using the area under the curve (AUC) of the receiver-operator characteristic (ROC) curves and reported the corresponding 95% confidence intervals (CIs). RESULTS: We included 2709 cases from 59 countries (mean age 41.2 years [s.d. 18], 50.2% male). A total of 633 (24%) were hospitalized, 137 (5%) were admitted to the ICU or intubated, and 69 (3%) died. 2009 patients comprised the training set and 700 the test set. The models demonstrated excellent discrimination, with a test set AUC (95% CI) of 0.79 (0.75, 0.83) for Hospitalization+, 0.88 (0.82, 0.95) for ICU+, and 0.94 (0.89, 0.99) for Death. Age, comorbidities, corticosteroid use, and male gender were associated with higher risk of death, while use of biologic therapies was associated with a lower risk. CONCLUSIONS AND RELEVANCE: Prognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of IBD patients. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with IBD patients. The tool numerically and visually summarizes the patient’s probabilities of adverse outcomes and associated CIs. Helping physicians identify their highest-risk patients will be important in the coming months as cases rise in the US and worldwide. This tool can also serve as a model for risk stratification in other chronic diseases.

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