ObjectivesDevelop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD).Design and settingThis study developed and validated prognostic penalised logistic regression models using reports to the international Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease voluntary registry from March to October 2020. Model development was done using a training data set (85% of cases reported 13 March–15 September 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported 16 September–20 October 2020).ParticipantsWe included 2709 cases from 59 countries (mean age 41.2 years (SD 18), 50.2% male). All submitted cases after removing duplicates were included.Primary and secondary outcome measuresCOVID-19 related: (1) Hospitalisation+: composite outcome of hospitalisation, ICU admission, mechanical ventilation or death; (2) Intensive Care Unit+ (ICU+): composite outcome of ICU admission, mechanical ventilation or death; (3) Death. We assessed the resulting models’ discrimination using the area under the curve of the receiver operator characteristic curves and reported the corresponding 95% CIs.ResultsOf the submitted cases, a total of 633 (24%) were hospitalised, 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 area under the curve (95% CI) of 0.79 (0.75 to 0.83) for Hospitalisation+, 0.88 (0.82 to 0.95) for ICU+ and 0.94 (0.89 to 0.99) for Death. Age, comorbidities, corticosteroid use and male gender were associated with a higher risk of death, while the use of biological therapies was associated with a lower risk.ConclusionsPrognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of patients with IBD. 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 patients with IBD.

This paper highlights the importance of developing accurate flood hazard maps to price insurance effectively and to communicate flood risk to interested parties. Risk-based insurance premiums can encourage insurance purchase and investment in cost effective mitigation measures. We undertake a study using light imaging detection and ranging (LIDAR) technology and depth damage curves to determine risk-based rates for residential structures in three counties in the state of North Carolina. We then compare these prices with current premiums charged to homeowners by the National Flood Insurance Program (NFIP) for 11,915 single-family residences. NFIP premiums are significantly higher than risk-based premiums for over 90 percent of the homes in each of the counties in our study. Risk-based prices are higher than NFIP premiums only in instances where buildings are predicted to suffer damage from more frequent, shallow floods that are currently not considered explicitly in NFIP premium calculations. Accurate flood maps are needed to determine cost-effective loss reduction measures and to address issues of affordability and fairness for homeowners currently living in flood-prone areas.

In the applied sciences, the ultimate goal is not just to acquire knowledge but to turn knowledge into action. The next wave for data disciplines may be experimental designs and analytical methods for closing the gap between the "real-world" situations faced by decision-makers and their idealized representations in optimization problems, and the health sciences are poised to be the discipline where these developments substantially improve lives. We discuss three recent trends in research-experimental designs and analytical methods for precision medicine and pragmatic trials; technological developments in sensors, wearables, and smartphones for measuring health data; and methods addressing algorithmic bias and model interpretability-and argue that these seemingly disparate trends point to a future where data-driven decision support tools are increasingly used to promote wellbeing.

Structured AbstractImportanceRisk calculators can facilitate shared medical decision-making1. Demographics, comorbidities, medication use, geographic region, and other factors may increase the risk for COVID-19-related complications among patients with IBD2,3.ObjectivesDevelop an individualized prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with IBD.Design, Setting, and ParticipantsThis study developed and validated prognostic penalized logistic regression models4 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 MeasuresCOVID-19 related:Hospitalization+: composite outcome of hospitalization, ICU admission, mechanical ventilation, or deathICU+: composite outcome of ICU admission, mechanical ventilation, or deathDeathWe 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).ResultsWe 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 RelevancePrognostic 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.Key PointsQuestionHow well can a multivariable risk model predict the risk of hospitalization, intensive care unit (ICU) stay, or death due to COVID-19 in patients with inflammatory bowel disease (IBD)?FindingsMultivariable prediction models developed using data from an international voluntary registry of IBD patients and available for use online (https://covidibd.org/) have very good discrimination for predicting hospitalization (Test set AUC 0.79) and excellent discrimination for ICU admission (Test set AUC 0.88) and death (Test set AUC 0.94). The models were developed with a training sample of 2009 cases and validated in an independent test sample of 700 cases comprised of a random sub-sample of cases and all cases entered in the registry during a one-month period after model development.MeaningThis risk prediction model may serve as an effective tool for healthcare providers to facilitate conversations about COVID-19-related risks with IBD patients.

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