Shandhi Md Mobashir Hasan, Cho Peter J, Roghanizad Ali R, Singh Karnika, Wang Will, Enache Oana M, Stern Amanda, Sbahi Rami, Tatar Bilge, Fiscus Sean, Khoo Qi Xuan, Kuo Yvonne, Lu Xiao, Hsieh Joseph, Kalodzitsa Alena, Bahmani Amir, Alavi Arash, Ray Utsab, Snyder Michael P, Ginsburg Geoffrey S, Pasquale Dana K, Woods Christopher W, Shaw Ryan J, Dunn Jessilyn P
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.
NPJ Digit Med. 2022 Sep 1;5(1):130. doi: 10.1038/s41746-022-00672-z.
Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.
大规模监测检测有助于控制COVID-19等传染病的爆发。然而，诊断检测短缺在全球普遍存在，在美国，随着新的COVID-19变种和猴痘等新出现疾病的出现，这种短缺仍在持续，这表明前所未有的需要改进我们当前的大规模监测检测方法。通过将监测检测目标对准最有可能被感染的个体，从而提高检测阳性率（即监测组中的阳性百分比），捕获相同数量的阳性病例所需的检测次数就会减少。在此，我们利用CovIdentify研究（6765名参与者）和MyPHD研究（8580名参与者）的数据开发了一种智能检测分配（ITA）方法，包括来自1265名个体的智能手表数据，其中126人COVID-19检测呈阳性。我们严格的模型和参数搜索发现了用于监测连续数字生物标志物以提高COVID-19诊断检测阳性率的最佳时间段和综合指标。我们发现，静息心率（RHR）特征在感染过程中比步数特征更早地区分COVID-19阳性和阴性病例，分别早在诊断测试前10天和5天。我们还发现，与单独使用RHR特征相比，纳入步数特征可使受试者工作特征曲线下面积（AUC-ROC）增加7-11%，而与单独使用步数特征相比，纳入RHR特征可使ITA模型的精确召回率曲线（AUC-PR）的AUC提高38-50%。通过使用来自单一设备类型（Fitbit）的高分辨率（分钟级）数据，分别在交叉验证训练集和独立测试集上实现了最佳的AUC-ROC（分别为0.73±0.14和0.77）和AUC-PR（分别为0.55±0.21和0.24）。最后，我们表明，ITA在交叉验证训练集中使阳性率提高了6.5倍，在独立测试集中使阳性率提高了4.5倍，包括有症状和无症状（高达27%）个体。我们的研究结果表明，如果大规模部署且无需自我报告症状，ITA方法可以改善诊断检测资源的分配并减轻检测短缺的负担。
