Veterans Affairs Connecticut Healthcare System, West Haven, CT.
Yale School of Nursing, West Haven, CT.
J Acquir Immune Defic Syndr. 2022 Oct 1;91(2):168-174. doi: 10.1097/QAI.0000000000003030.
Older (older than 50 years) persons living with HIV (PWH) are at elevated risk for falls. We explored how well our algorithm for predicting falls in a general population of middle-aged Veterans (age 45-65 years) worked among older PWH who use antiretroviral therapy (ART) and whether model fit improved with inclusion of specific ART classes.
This analysis included 304,951 six-month person-intervals over a 15-year period (2001-2015) contributed by 26,373 older PWH from the Veterans Aging Cohort Study who were taking ART. Serious falls (those falls warranting a visit to a health care provider) were identified by external cause of injury codes and a machine-learning algorithm applied to radiology reports. Potential predictors included a fall within the past 12 months, demographics, body mass index, Veterans Aging Cohort Study Index 2.0 score, substance use, and measures of multimorbidity and polypharmacy. We assessed discrimination and calibration from application of the original coefficients (model derived from middle-aged Veterans) to older PWH and then reassessed by refitting the model using multivariable logistic regression with generalized estimating equations. We also explored whether model performance improved with indicators of ART classes.
With application of the original coefficients, discrimination was good (C-statistic 0.725; 95% CI: 0.719 to 0.730) but calibration was poor. After refitting the model, both discrimination (C-statistic 0.732; 95% CI: 0.727 to 0.734) and calibration were good. Including ART classes did not improve model performance.
After refitting their coefficients, the same variables predicted risk of serious falls among older PWH nearly and they had among middle-aged Veterans.
年龄较大（大于 50 岁）的艾滋病毒感染者（PWH）发生跌倒的风险较高。我们探讨了我们预测中年退伍军人（45-65 岁）一般人群中跌倒的算法在使用抗逆转录病毒疗法（ART）的老年 PWH 中的表现如何，以及是否通过纳入特定的 ART 类别来提高模型拟合度。
这项分析包括来自退伍军人老龄化队列研究的 26373 名接受 ART 的年龄较大的 PWH 在 15 年期间（2001-2015 年）贡献的 304951 个六个月的个体间隔。严重跌倒（需要去医疗保健提供者就诊的跌倒）通过伤害外部原因代码和应用于放射学报告的机器学习算法来识别。潜在的预测因素包括过去 12 个月内发生的跌倒、人口统计学、体重指数、退伍军人老龄化队列研究指数 2.0 评分、物质使用以及多种疾病和多药物治疗的衡量标准。我们评估了从应用于老年 PWH 的原始系数（从中年退伍军人得出的模型）中进行评估的区分度和校准度，然后通过使用广义估计方程的多变量逻辑回归重新拟合模型来重新评估。我们还探讨了是否通过指示 ART 类别来提高模型性能。
应用原始系数时，区分度较好（C 统计量为 0.725；95%CI：0.719 至 0.730），但校准度较差。重新拟合模型后，区分度（C 统计量为 0.732；95%CI：0.727 至 0.734）和校准度都很好。包括 ART 类别并不能提高模型性能。
重新拟合系数后，同样的变量几乎可以预测老年 PWH 发生严重跌倒的风险，也可以预测他们在中年退伍军人中的风险。
