Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA 91101, United States.
Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA 91101, United States.
J Am Med Inform Assoc. 2024 Oct 1;31(10):2173-2180. doi: 10.1093/jamia/ocae222.
Firearm injuries constitute a public health crisis. At the healthcare encounter level, they are, however, rare events.
To develop a predictive model to identify healthcare encounters of adult patients at increased risk of firearm injury to target screening and prevention efforts.
Electronic health records data from Kaiser Permanente Southern California (KPSC) were used to identify healthcare encounters of patients with fatal and non-fatal firearm injuries, as well as healthcare visits of a sample of matched controls during 2010-2018. More than 170 predictors, including diagnoses, healthcare utilization, and neighborhood characteristics were identified. Extreme gradient boosting (XGBoost) and a split sample design were used to train and test a model that predicted risk of firearm injury within the next 3 years at the encounter level.
A total of 3879 firearm injuries were identified among 5 288 529 KPSC adult members. Prevalence at the healthcare encounter level was 0.01%. The 15 most important predictors included demographics, healthcare utilization, and neighborhood-level socio-economic factors. The sensitivity and specificity of the final model were 0.83 and 0.56, respectively. A very high-risk group (top 1% of predicted risk) yielded a positive predictive value of 0.14% and sensitivity of 13%. This high-risk group potentially reduces screening burden by a factor of 11.7, compared to universal screening. Results for alternative probability cutoffs are presented.
Our model can support more targeted screening in healthcare settings, resulting in improved efficiency of firearm injury risk assessment and prevention efforts.
枪支伤害构成了公共卫生危机。然而，在医疗保健层面，此类事件较为少见。
开发一种预测模型，以识别接受治疗的成年患者中存在枪支伤害风险的患者，从而为目标筛查和预防工作提供参考。
使用 Kaiser Permanente Southern California (KPSC) 的电子健康记录数据，识别 2010 年至 2018 年期间发生致命和非致命枪支伤害的患者的医疗保健遭遇，以及该患者样本的匹配对照的医疗保健就诊情况。确定了 170 多个预测指标，包括诊断、医疗保健使用情况和社区特征。使用极端梯度提升（XGBoost）和拆分样本设计来训练和测试预测模型，该模型可在就诊层面预测未来 3 年内枪支伤害的风险。
在 5288529 名 KPSC 成年成员中，共确定了 3879 例枪支伤害。在医疗保健遭遇层面，其患病率为 0.01%。前 15 个最重要的预测指标包括人口统计学因素、医疗保健使用情况和社区层面的社会经济因素。最终模型的灵敏度和特异性分别为 0.83 和 0.56。高危组（预测风险最高的 1%）的阳性预测值为 0.14%，灵敏度为 13%。与普遍筛查相比，该高危组将筛查负担减少了 11.7 倍。还提供了替代概率截止值的结果。
我们的模型可以为医疗保健环境提供更有针对性的筛查，从而提高枪支伤害风险评估和预防工作的效率。
