A predictive model to identify women with injuries related to intimate partner violence.

PURPOSE: The diagnosis of intimate partner violence (IPV) is challenging. The authors conducted a cross-sectional study to develop a predictive model to identify IPV-related injuries and validate the model with an independent sample.
MATERIALS AND METHODS: The authors enrolled women older than 18 years seeking treatment for injuries. They randomized the sample into index and validation datasets. They used the index dataset to develop a predictive model; the validation set served as an independent sample for assessing the predictive model's goodness of fit. Study variables included risk of self-report of an IPV-related injury and demographic and socioeconomic variables. The outcome variable was self-reported injury etiology (IPV or other). The authors used multiple logistic regression techniques to develop a predictive model that they then applied to the validation dataset, and they measured goodness of fit with the Hosmer-Lemeshow test.
RESULTS: The sample was randomized into index (n = 201) and validation (n = 104) sets. For the index set, age, race and risk of IPV were associated with IPV-related injuries (P < .01). The accuracy of the model was 92 percent. Application of the model to the validation dataset resulted in excellent agreement between the observed and actual number of women with IPV-related injuries (accuracy: 93 percent). No statistically significant differences existed between the observed and predicted outcomes (P = .64).
CONCLUSIONS: A predictive model composed of age, race and risk of experiencing IPV accurately characterizes women likely to report IPV-related injuries. CLINICAL IMPLICATIONS: Once the clinician diagnoses IPV-related injury, he or she can intervene to prevent future IPV-related injuries.

RCT Entities:   Population Interventions Outcomes