Clinical predictive models in equine medicine: A systematic review

Clinical predictive models use a patient's baseline demographic and clinical data to make predictions about patient outcomes and have the potential to aid clinical decision making. The extent of equine clinical predictive models is unknown in the literature. Using PubMed and Google Scholar, we systematically reviewed the predictive models currently described for use in equine patients. Models were eligible for inclusion if they were published in a peer-reviewed article as a multivariable model used to predict a clinical/laboratory/imaging outcome in an individual horse or herd. The agreement of at least two authors was required for model inclusion. We summarised the patient populations, model development methods, performance metric reporting, validation efforts, and, using the Predictive model Risk of Bias Assessment Tool (PROBAST), assessed the risk of bias and applicability concerns for these models. In addition, we summarised the index conditions for which models were developed and provided detailed information on included models. A total of 90 predictive models and 9 external validation studies were included in the final systematic review. A plurality of models (41%) was developed to predict outcomes associated with colic, for example, need for surgery or survival to discharge. All included models were at high risk of bias, defined as failing one or more PROBAST signalling questions, primarily for analysis-related reasons. Importantly, a high risk of bias does not necessarily mean that models are unusable, but that they require more careful consideration prior to clinical use. Concerns about applicability were low for the majority of models. Systematic reviews such as this can serve to increase veterinarians' awareness of predictive models, including evaluation of their performance and their use in different patient populations.