Harvard Medical School, Boston, Massachusetts, USA.
Clin Infect Dis. 2010 Mar 1;50(5):779-86. doi: 10.1086/650537.
We sought to use data captured in the electronic health record (EHR) to develop and validate a prediction rule for virologic failure among patients being treated for infection with human immunodeficiency virus (HIV).
We used EHRs at 2 Boston tertiary care hospitals, Massachusetts General Hospital and Brigham and Women's Hospital, to identify HIV-infected patients who were virologically suppressed (HIV RNA level < or = 400 copies/mL) on antiretroviral therapy (ART) during the period from 1 January 2005 through 31 December 2006. We used a multivariable logistic model with data from Massachusetts General Hospital to derive a 1-year virologic failure prediction rule. The model was validated using data from Brigham and Women's Hospital.We then simplified the scoring scheme to develop a clinical prediction rule.
The 1-year virologic failure prediction model, using data from 712 patients from Massachusetts General Hospital, demonstrated good discrimination (C statistic, 0.78) and calibration (chi(2)= 6.6; P= .58). The validation model, based on 362 patients from Brigham and Women's Hospital, also showed good discrimination (C statistic, 0.79) and calibration (chi(2)= 1.9; P= .93). The clinical prediction rule included 7 predictors (suboptimal adherence, CD4 cell count < 100 cells/microL, drug and/or alcohol abuse, highly ART experienced, missed > or = 1 appointment, prior virologic failure, and suppressed < or = 12 months) and appropriately stratified patients in the validation data set into low-, medium-, and high-risk groups, with 1-year virologic failure rates of 3.0%, 13.0%, and 28.6%, respectively.
A risk score based on 7 variables available in the EHR predicts HIV virologic failure at 1 year and could be used for targeted interventions to improve outcomes in HIV infection.
我们试图利用电子健康记录（EHR）中捕获的数据，为接受人类免疫缺陷病毒（HIV）感染治疗的患者开发和验证病毒学失败的预测规则。
我们使用马萨诸塞州波士顿的两家三级保健医院，马萨诸塞州总医院和布莱根妇女医院的 EHR，来确定在 2005 年 1 月 1 日至 2006 年 12 月 31 日期间接受抗逆转录病毒治疗（ART）时病毒学抑制（HIV RNA 水平<或=400 拷贝/ml）的 HIV 感染患者。我们使用来自马萨诸塞州总医院的数据建立多变量逻辑模型，以获得 1 年病毒学失败预测规则。该模型使用来自布莱根妇女医院的数据进行验证。然后，我们简化评分方案以开发临床预测规则。
使用来自马萨诸塞州总医院的 712 名患者的数据建立的 1 年病毒学失败预测模型显示出良好的区分度（C 统计量，0.78）和校准度（卡方=6.6；P=0.58）。基于来自布莱根妇女医院的 362 名患者的验证模型也显示出良好的区分度（C 统计量，0.79）和校准度（卡方=1.9；P=0.93）。临床预测规则包括 7 个预测因素（治疗依从性差、CD4 细胞计数<100 个细胞/μl、药物和/或酒精滥用、有丰富的 ART 治疗经验、错过>或=1 次预约、既往病毒学失败和抑制<或=12 个月），并在验证数据集中适当分层患者为低、中、高危组，1 年病毒学失败率分别为 3.0%、13.0%和 28.6%。
基于 EHR 中可用的 7 个变量的风险评分可预测 HIV 病毒学失败 1 年，并可用于有针对性的干预措施，以改善 HIV 感染的结果。
