Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, United States.
Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego, San Diego, CA, United States.
J Med Internet Res. 2020 Dec 16;22(12):e24478. doi: 10.2196/24478.
Assigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care.
The aim of this study was to develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling in and ruling out COVID-19 in potential patients. We compared the diagnostic performance of probabilistic, graphical, and machine learning models against a previously published benchmark model.
We integrated patient symptoms and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on 13 symptoms and estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19-compatible illness at the University of California San Diego Medical Center over the course of 14 days starting in March 2020.
We included 55 consecutive patients with fever (n=43, 78%) or cough (n=42, 77%) presenting for ambulatory (n=11, 20%) or hospital care (n=44, 80%). In total, 51% (n=28) were female and 49% (n=27) were aged <60 years. Common comorbidities included diabetes (n=12, 22%), hypertension (n=15, 27%), cancer (n=9, 16%), and cardiovascular disease (n=7, 13%). Of these, 69% (n=38) were confirmed via reverse transcription-polymerase chain reaction (RT-PCR) to be positive for SARS-CoV-2 infection, and 20% (n=11) had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS-CoV-2 infection and alternate diagnoses with sensitivities of 81.6%-84.2%, specificities of 58.8%-70.6%, and accuracies of 61.4%-71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices.
Decision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real-world settings.
在整个医疗保健连续体中，赋予 SARS-CoV-2 感染风险有意义的概率具有诊断挑战性。
本研究的目的是开发和临床验证一种适应性强、个性化的诊断模型，以帮助临床医生对疑似 COVID-19 患者进行确诊或排除。我们将概率、图形和机器学习模型的诊断性能与之前发表的基准模型进行了比较。
我们使用机器学习和贝叶斯推断整合了患者的症状和检测数据，以量化个体患者感染 SARS-CoV-2 的风险。我们根据 13 种症状和从已发表报告中估计的当地患病率、影像学和分子诊断性能，使用 100,000 个模拟患者资料对模型进行了训练。我们使用 2020 年 3 月开始的 14 天内在加利福尼亚大学圣地亚哥医疗中心就诊的出现 COVID-19 相似疾病的连续患者对这些模型进行了测试。
我们纳入了 55 名连续出现发热（n=43，78%）或咳嗽（n=42，77%）症状并接受门诊（n=11，20%）或住院治疗（n=44，80%）的患者。共有 51%（n=28）为女性，49%（n=27）年龄<60 岁。常见的合并症包括糖尿病（n=12，22%）、高血压（n=15，27%）、癌症（n=9，16%）和心血管疾病（n=7，13%）。其中，69%（n=38）通过逆转录-聚合酶链反应（RT-PCR）证实为 SARS-CoV-2 感染阳性，20%（n=11）重复进行了核酸检测为阴性并有其他诊断。贝叶斯推断网络、距离度量学习和集成模型对 SARS-CoV-2 感染和其他诊断的区分度为 81.6%-84.2%、特异性为 58.8%-70.6%、准确性为 61.4%-71.8%。在将影像学和实验室检测统计数据与贝叶斯推断网络的预测相结合后，模拟临床评估过程中每个步骤的诊断不确定性变化对位置、症状和诊断检测选择非常敏感。
纳入症状和可用检测结果的决策支持模型可以帮助提供者在实际环境中诊断 SARS-CoV-2 感染。
