Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol.

IF 4.4 Q1 HEALTH CARE SCIENCES & SERVICES

BMJ Health & Care Informatics Pub Date : 2025-08-22 DOI:10.1136/bmjhci-2024-101419

Amos Otieno Olwendo, Gideon Kikuvi, Simon Karanja

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Abstract

Introduction: This study seeks to determine incidence, comorbidities and drivers for new HIV infections to develop, test and validate a risk prediction model for screening for new cases of HIV.

Methods and analysis: The study has two components: a cross-sectional study to develop the prediction model using the HIV dataset from the Kenya AIDS and STI Control Programme and a 15-month prospective study for the validation of the model. Inferential analysis will be conducted using algorithms that perform best in disease prediction: Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron. Model sensitivity and specificity will be examined using the receiver operating characteristic curve, and performance will be evaluated using metrics: accuracy, precision, recall and F1 score.

Ethics and dissemination: The study obtained ethical approval (JKU/ISERC/02321/1421) from the Jomo Kenyatta University of Agriculture and Technology Ethical and Research Board and a research licence (NACOSTI/P/24/414749) from the National Commission for Science, Technology and Innovation.

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肯尼亚初级卫生保健机构中15岁及以上人群新发艾滋病毒感染筛查预测模型的开发和验证：一项研究方案。

本研究旨在确定新发HIV感染的发生率、合并症和驱动因素，以开发、测试和验证用于筛查新发HIV病例的风险预测模型。方法和分析：这项研究有两个组成部分：一项横断面研究，利用肯尼亚艾滋病和性传播感染控制规划的艾滋病毒数据集开发预测模型；另一项为期15个月的前瞻性研究，用于验证该模型。将使用在疾病预测中表现最好的算法进行推理分析：极端梯度增强（XGBoost）和多层感知器。模型的敏感性和特异性将使用接收者工作特征曲线进行检查，性能将使用指标进行评估：准确性、精密度、召回率和F1分数。伦理和传播：该研究获得了乔莫·肯雅塔农业技术大学伦理和研究委员会的伦理批准（JKU/ISERC/02321/1421），并获得了国家科学、技术和创新委员会的研究许可证（NACOSTI/P/24/414749）。

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