Publication date: Aug 26, 2024

Individuals may experience a range of symptoms after the clearance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This condition is termed long COVID (LC) or Post-COVID-19 condition (PCC). Despite the appreciable number of symptoms documented to date, one key challenge remains in the robust characterization of LC outcomes. This review aimed to assess the properties, identify gaps, and provide recommendations for relevant descriptive and evaluative Patient-Reported Outcome Measurement (PROM) instruments that can be used to comprehensively characterize LC. To achieve this objective, we identified and reviewed descriptive and evaluative PROM instruments that have been developed and validated to date with people living with LC. Our review assessed their properties, identified gaps, and recommended PROMs suitable for characterizing LC. To ensure a comprehensive and robust characterization of LC, we next identified, reviewed, and selected (with the input of patient partners) PROMs associated with the most frequently reported LC symptoms. The evaluation criteria included psychometric evidence, mode of delivery, cost, and administration time. Traditional matrix mapping revealed Post-COVID Functional Status Scale (PCFS) as a choice instrument for capturing LC outcomes largely because of the comprehensive domains it covered, and the number of psychometric evidence reported in literatures. This instrument can be effectively paired with the Fatigue Severity Scale (FSS), Montreal Cognitive Assessment (MoCA), Patient Health Questionnaire (PHQ-9), Headache Impact Test (HIT), Pittsburgh Sleep Quality Index (PSQI), and DePaul Symptom Questionnaire (DSQ-PEM) to characterize fatigue, cognitive impairment, depression/anxiety, headache, sleeplessness, and post-exertional malaise respectively. Our paper identified appropriate PROM instruments that can effectively capture the diverse impacts of LC. By utilizing these validated instruments, we can better understand and manage LC.

Semantics

Original Article