BACKGROUND: Coronavirus disease 2019 (COVID-19) is sweeping the globe. Despite multiple case-series, actionable knowledge to tailor decision-making proactively is missing. RESEARCH QUESTION: Can a statistical model accurately predict infection with COVID-19? STUDY DESIGN AND METHODS: We developed a prospective registry of all patients tested for COVID-19 in Cleveland Clinic to create individualized risk prediction models. We focus here on the likelihood of a positive nasal or oropharyngeal COVID-19 test. A least absolute shrinkage and selection operator logistic regression algorithm was constructed that removed variables that were not contributing to the model's cross-validated concordance index. After external validation in a temporally and geographically distinct cohort, the statistical prediction model was illustrated as a nomogram and deployed in an online risk calculator. RESULTS: In the development cohort, 11,672 patients fulfilled study criteria, including 818 patients (7.0%) who tested positive for COVID-19; in the validation cohort, 2295 patients fulfilled criteria, including 290 patients who tested positive for COVID-19. Male, African American, older patients, and those with known COVID-19 exposure were at higher risk of being positive for COVID-19. Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine or who were on melatonin, paroxetine, or carvedilol. Our model had favorable discrimination (c-statistic ¼ 0.863 in the development cohort and 0.840 in the validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cutoff points. The calculator is freely available at https://riskcalc.org/COVID19. INTERPRETATION: Prediction of a COVID-19 positive test is possible and could help direct health-care resources. We demonstrate relevance of age, race, sex, and socioeconomic characteristics in COVID-19 susceptibility and suggest a potential modifying role of certain common vaccinations and drugs that have been identified in drug-repurposing studies.

Background Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. Objective To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. Design Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator.

Background-Cardiac resynchronization therapy (CRT) has significant non-response rates. We assessed whether machine learning could predict CRT response beyond current guidelines. Methods-We analyzed CRT patients from Cleveland Clinic and Johns Hopkins. A training cohort was created from all Johns Hopkins patients and an equal number of randomly sampled Cleveland Clinic patients. All remaining patients comprised the testing cohort. Response was defined as ≥10% increase in left ventricular (LV) ejection fraction. Machine learning models were developed to predict CRT response using different combinations of classification algorithms and clinical variable sets on the training cohort. The model with the highest area under curve (AUC) was evaluated on the testing cohort. Probability of response was used to predict survival free from a composite endpoint of death, heart transplant, or placement of LV assist device. Predictions were compared to current guidelines.

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Background: Careful assessment of the reasons for discontinuation of active surveillance (AS) is required for men with prostate cancer (PCa). Objective: Using Movember's Global Action Plan Prostate Cancer Active Surveillance initiative (GAP3) database, we report on reasons for AS discontinuation. Design, setting, and participants: We compared data from 10 296 men on AS from 21 centres across 12 countries. Outcome measurements and statistical analysis: Cumulative incidence methods were used to estimate the cumulative incidence rates of AS discontinuation. Results and limitations: During 5-yr follow-up, 27.5% (95% confidence interval [CI]: 26.4-28.6%) men showed signs of disease progression, 12.8% (95% CI: 12.0-13.6%) converted to active treatment without evidence of progression, 1.7% (95% CI: 1.5-2.0%) continued to watchful waiting, and 1.7% (95% CI: 1.4-2.1%) died from other causes. Of the 7049 men who remained on AS, 2339 had follow-up for >5 yr, 4561 had follow-up for <5 yr, and 149 were lost to follow-up. Cumulative incidence of progression was 27.5% (95% CI: 26.4-28.6%) at 5 yr and 38.2% (95% CI: 36.7-39.9%) at 10 yr. A limitation is that not all centres were included due to limited information on the reason for discontinuation and limited follow-up. Conclusions: Our descriptive analyses of current AS practices worldwide showed that 43.6% of men drop out of AS during 5-yr follow-up, mainly due to signs of disease progression. Improvements in selection tools for AS are thus needed to correctly allocate men with PCa to AS, which will also reduce discontinuation due to conversion to active treatment without evidence of disease progression. Patient summary: Our assessment of a worldwide database of men with prostate cancer (PCa) on active surveillance (AS) shows that 43.6% drop out of AS within 5 yr, mainly due to signs of disease progression. Better tools are needed to select and monitor men with PCa as part of AS.