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马拉维利隆圭一家性传播感染诊所用于急性艾滋病毒感染检测的更新风险评分算法。

Updated Risk Score Algorithms for Acute HIV Infection Detection at a Sexually Transmitted Infections Clinic in Lilongwe, Malawi.

作者信息

Bell Griffin J, Chen Jane S, Maierhofer Courtney N, Matoga Mitch, Rutstein Sarah E, Lancaster Kathryn E, Chagomerana Maganizo B, Jere Edward, Mmodzi Pearson, Bonongwe Naomi, Mathiya Esther, Ndalama Beatrice, Hosseinipour Mina C, Emch Michael, Dennis Ann M, Cohen Myron S, Hoffman Irving F, Miller William C, Powers Kimberly A

机构信息

Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC.

Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC.

出版信息

J Acquir Immune Defic Syndr. 2024 Dec 15;97(5):450-459. doi: 10.1097/QAI.0000000000003519.

Abstract

BACKGROUND

Detection of acute (preseroconversion) HIV infection (AHI), the phase of highest transmission risk, requires resource-intensive RNA- or antigen-based detection methods that can be infeasible for routine use. Risk score algorithms can improve the efficiency of AHI detection by identifying persons at highest risk of AHI for prioritized RNA/antigen testing, but prior algorithms have not considered geospatial information, potential differences by sex, or current antibody testing paradigms.

METHODS

We used elastic net models to develop sex-stratified risk score algorithms in a case-control study of persons (136 with AHI, 250 without HIV) attending a sexually transmitted infections (STI) clinic in Lilongwe, Malawi, from 2015 to 2019. We designed algorithms for varying clinical contexts according to 3 levels of data availability: (1) routine demographic and clinical information, (2) behavioral and occupational data obtainable through patient interview, and (3) geospatial variables requiring external datasets or field data collection. We calculated sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to assess model performance and developed a web application to support implementation.

RESULTS

The highest performing AHI risk score algorithm for men (AUC = 0.74) contained 5 variables (condom use, body aches, fever, rash, genital sores/ulcers) from the first 2 levels of data availability. The highest performing algorithm for women (AUC = 0.81) contained 15 variables from all 3 levels of data availability. A risk score cut point of 0.26 had an AHI detection sensitivity of 93% and specificity of 27% for men, and a cut point of 0.15 had 97% sensitivity and 44% specificity for women. Additional models are available in the web application.

CONCLUSIONS

Risk score algorithms can facilitate efficient AHI detection in STI clinic settings, creating opportunities for HIV transmission prevention interventions during this critical period of elevated transmission risk.

摘要

背景

急性(血清转化期)HIV感染(AHI)是传播风险最高的阶段,其检测需要基于RNA或抗原的资源密集型检测方法,而这些方法可能不适用于常规检测。风险评分算法可以通过识别AHI风险最高的人群进行优先RNA/抗原检测,从而提高AHI检测的效率,但先前的算法未考虑地理空间信息、性别潜在差异或当前的抗体检测模式。

方法

在2015年至2019年于马拉维利隆圭一家性传播感染(STI)诊所就诊的人群(136例AHI患者,250例未感染HIV者)的病例对照研究中,我们使用弹性网络模型开发了按性别分层的风险评分算法。我们根据3个数据可用性级别为不同的临床环境设计算法:(1)常规人口统计学和临床信息,(2)通过患者访谈可获得的行为和职业数据,(3)需要外部数据集或现场数据收集的地理空间变量。我们计算了敏感性、特异性和受试者操作特征曲线下面积(AUC)以评估模型性能,并开发了一个网络应用程序以支持实施。

结果

男性表现最佳的AHI风险评分算法(AUC = 0.74)包含来自前两个数据可用性级别的5个变量(避孕套使用、身体疼痛、发热、皮疹、生殖器溃疡)。女性表现最佳的算法(AUC = 0.81)包含来自所有三个数据可用性级别的15个变量。风险评分切点为0.26时,男性AHI检测的敏感性为93%,特异性为27%;切点为0.15时,女性的敏感性为97%,特异性为44%。网络应用程序中还有其他模型。

结论

风险评分算法可促进在性传播感染诊所环境中高效检测AHI,为在这个传播风险升高的关键时期进行HIV传播预防干预创造机会。

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