Factor Analysis in Distinguishing Coronavirus Disease 2019 From Other Influenza-like Illness Using a Validated Patient-Reported Outcome Instrument FLU-PRO Plus: A Prospective Real-world Cohort Study.

BACKGROUND/OBJECTIVE: InFLUenza Patient-reported Outcome (FLU-PRO Plus) is a 34-item patient-reported outcome instrument designed to capture the intensity and frequency of viral respiratory symptoms. This study evaluates whether FLU-PRO Plus responses could discriminate between symptoms of coronavirus disease 2019 (COVID-19) and influenza-like illness (ILI) with no COVID diagnosis, as well as forecast disease progression. METHODS: FLU-PRO Plus was administered daily for 14 days. Exploratory factor analysis was used to reduce the FLU-PRO Plus responses on the first day to 3 factors interpreted as "symptom clusters." The 3 clusters were used to predict COVID-19 versus ILI diagnosis in logistic regression. Correlation between the clusters and quality of life (QoL) measures was used to assess concurrent validity. The timing of self-reported return to usual health in the 14-day period was estimated as a function of the clusters within COVID-19 and ILI groups. RESULTS: Three hundred fourteen patients completed day 1 FLU-PRO Plus, of which 65% had a COVID-19 diagnosis. Exploratory factor analysis identified 3 symptom clusters: (1)general Body, (2) tracheal/bronchial, and (3) nasopharyngeal. Higher nasopharyngeal scores were associated with higher odds of COVID-19 compared with ILI diagnosis [adjusted odds ratio = 1.61 (1.21, 2.12)]. Higher tracheal/bronchial scores were associated with lower odds of COVID-19 [0.58 (0.44, 0.77)]. The 3 symptom clusters were correlated with multiple QoL measures ( r = 0.14-0.56). Higher scores on the general body and tracheal/bronchial symptom clusters were associated with prolonged time to return to usual health [adjusted hazard ratios: 0.76 (0.64, 0.91), 0.80 (0.67, 0.96)]. CONCLUSION: Three symptom clusters identified from FLU-PRO Plus responses successfully discriminated patients with COVID-19 from non-COVID ILI and were associated with QoL and predicted symptom duration.

Development and Validation of a Healthcare Utilization-Based Algorithm to Identify Acute Exacerbations of Chronic Obstructive Pulmonary Disease.

Introduction: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are important events that may precipitate other adverse outcomes. Accurate AECOPD event identification in electronic administrative data is essential for improving population health surveillance and practice management. Objective: Develop codified algorithms to identify moderate and severe AECOPD in two US healthcare systems using administrative data and electronic medical records, and validate their performance by calculating positive predictive value (PPV) and negative predictive value (NPV). Methods: Data from two large regional integrated health systems were used. Eligible patients were identified using International Classification of Diseases (Ninth Edition) COPD diagnosis codes. Two algorithms were developed: one to identify potential moderate AECOPD by selecting outpatient/emergency visits associated with AECOPD-related codes and antibiotic/systemic steroid prescriptions; the other to identify potential severe AECOPD by selecting inpatient visits associated with corresponding codes. Algorithms were validated via patient chart review, adjudicated by a pulmonologist. To estimate PPV, 300 potential moderate AECOPD and 250 potential severe AECOPD events underwent review. To estimate NPV, 200 patients without any AECOPD identified by the algorithms (100 patients each without moderate or severe AECOPD) during the two years following the index date underwent review to identify AECOPD missed by the algorithm (false negatives). Results: The PPVs (95% confidence interval [CI]) for both moderate and severe AECOPD were high: 293/298 (98.3% [96.1-99.5]) and 216/225 (96.0% [92.5-98.2]), respectively. NPV was lower for moderate AECOPD (75.0% [65.3-83.1]) than for severe AECOPD (95.0% [88.7-98.4]). Results were consistent across both healthcare systems. Conclusion: This study developed healthcare utilization-based algorithms to identify moderate and severe AECOPD in two separate healthcare systems. PPV for both algorithms was high; NPV was lower for the moderate algorithm. Replication and consistency of results across two healthcare systems support the external validity of these findings.