Systematic Selection of Key Logistic Regression Variables for Risk Prediction Analyses: A Five-Factor Maximum Model

General and Critical Review Format: The evolution of clinical practice and medical technology has yielded an increasing number of clinical measures and tests to assess a patient's progression and return to sport readiness after injury. The plethora of available tests may be burdensome to clinicians in the absence of evidence that demonstrates the utility of a given measurement. Objective: Thus, there is a critical need to identify a discrete number of metrics to capture during clinical assessment to effectively and concisely guide patient care. Data Sources: The data sources included Pubmed and PMC Pubmed Central articles on the topic. Therefore, we present a systematic approach to injury risk analyses and how this concept may be used in algorithms for risk analyses for primary anterior cruciate ligament (ACL) injury in healthy athletes and patients after ACL reconstruction. Main Results: In this article, we present the five-factor maximum model, which states that in any predictive model, a maximum of 5 variables will contribute in a meaningful manner to any risk factor analysis. Conclusions: We demonstrate how this model already exists for prevention of primary ACL injury, how this model may guide development of the second ACL injury risk analysis, and how the five-factor maximum model may be applied across the injury spectrum for development of the injury risk analysis.