A multivariable assessment quantifying effects of cohort-level factors associated with combined mortality and culling risk in cohorts of U.S. commercial feedlot cattle

Economic losses due to cattle mortality and culling have a substantial impact on the feedlot industry. Since criteria for culling may vary and may affect measures of cumulative mortality within cattle cohorts, it is important to assess both mortality and culling when evaluating cattle losses over time and among feedlots. To date, there are no published multivariable assessments of factors associated with combined mortality and culling risk. Our objective was to evaluate combined mortality and culling losses in feedlot cattle cohorts and quantify effects of commonly measured cohort-level risk factors (weight at feedlot arrival, gender, and month of feedlot arrival) using data routinely collected by commercial feedlots. We used retrospective data representing 8,904,965 animals in 54,416 cohorts from 16 U.S. feedlots from 2000 to 2007. The sum of mortality and culling counts for each cohort (given the number of cattle at risk) was used to generate the outcome of interest, the cumulative incidence of combined mortality and culling. Associations between this outcome variable and cohort-level risk factors were evaluated using a mixed effects multivariable negative binomial regression model with random effects for feedlot, year, month and week of arrival. Mean arrival weight of the cohort, gender, and arrival month and a three-way interaction (and corresponding two-way interactions) among arrival weight, gender and month were significantly (P < 0.05) associated with the outcome. Results showed that as the mean arrival weight of the cohort increased, mortality and culling risk decreased, but effects of arrival weight were modified both by the gender of the cohort and the month of feedlot arrival. There was a seasonal pattern in combined mortality and culling risk for light and middleweight male and female cohorts, with a significantly (P < 0.05) higher risk for cattle arriving at the feedlot in spring and summer (March–September) than in cattle arriving during fall, and winter months (November–February). Our results quantified effects of covariate patterns that have been heretofore difficult to fully evaluate in smaller scale studies; in addition, they illustrated the importance of utilizing multivariable approaches when quantifying risk factors in heterogeneous feedlot populations. Estimated effects from our model could be useful for managing financial risks associated with adverse health events based on data that are routinely available

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024