Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting.
Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting.
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DOI：
10.3122/jabfm.2023.230126r1
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发表时间：
2023-10-11
期刊：
影响因子：
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通讯作者：
Handel A
中科院分区：
文献类型：
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作者：
Billings WZ;Cleven A;Dworaczyk J;Dale AP;Ebell M;McKay B;Handel A
增加远程医疗的使用可能会简化流感诊断并减少传播。然而，远程医疗诊断依赖于患者准确的症状报告。如果患者不同意临床医生对症状的看法，那么先前得出的诊断规则可能是不准确的。我们对一所大学学生健康中心的一项前瞻性、非随机队列研究进行了二次数据分析。报告上呼吸道疾病的患者被要求报告症状，他们的临床医生被要求报告相同的症状清单。我们检查了五种先前开发的流感临床决策规则（cdr）对两种症状报告的表现。将这些预测结果与PCR诊断结果进行比较。我们分析了症状报告之间的一致性，并使用这两组数据建立了新的预测模型。与临床医生报告的症状数据相比，患者报告的症状数据的CDR表现总是较低。由于症状报告的不一致，cdr常常导致对同一个体的不同预测。我们能够将新模型拟合到患者报告的数据中，这比以前导出的cdr表现略差。这些模型和基于临床报告数据的模型都存在校准问题。患者和临床医生经常不同意症状的存在，这导致使用临床医生数据构建的cdr应用于患者报告的症状时准确性降低。使用患者报告症状数据的预测模型比使用临床报告数据和文献中先前结果的模型表现更差。然而，差异是微小的，开发具有更多数据的新模型是可能的。
Increased use of telemedicine could potentially streamline influenza diagnosis and reduce transmission. However, telemedicine diagnoses are dependent on accurate symptom reporting by patients. If patients disagree with clinicians on symptoms, previously-derived diagnostic rules may be inaccurate. We performed a secondary data analysis of a prospective, non-randomized cohort study at a university student health center. Patients who reported an upper respiratory complaint were required to report symptoms, and their clinician was required to report the same list of symptoms. We examined the performance of five previously-developed clinical decision rules (CDRs) for influenza on both symptom reports. These predictions were compared against PCR diagnoses. We analyzed the agreement between symptom reports, and we built new predictive models using both sets of data. CDR performance was always lower for the patient-reported symptom data, compared to clinician-reported symptom data. CDRs often resulted in different predictions for the same individual, driven by disagreement in symptom reporting. We were able to fit new models to the patient-reported data, which performed slightly worse than previously-derived CDRs. These models and models built on clinician-reported data both suffered from calibration issues. Patients and clinicians frequently disagree about symptom presence, which leads to reduced accuracy when CDRs built with clinician data are applied to patient-reported symptoms. Predictive models using patient-reported symptom data performed worse than models using clinician-reported data and prior results in the literature. However, the differences are minor, and developing new models with more data may be possible.