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Psychometric Properties of the Symptoms of Infection with Coronavirus-19: A Patient-Reported Outcome Measure for COVID-19 Signs and Symptoms
Open Forum Infectious Diseases ; 9(Supplement 2):S452-S453, 2022.
Article in English | EMBASE | ID: covidwho-2189724
ABSTRACT
Background. The Symptoms of Infection with Coronavirus-19 (SIC), a patient reported outcome (PRO) measure, was developed to assess COVID-19 signs and symptoms. Qualitative and cross-sectional studies demonstrated its content validity and preliminary psychometric properties. This study provides additional evidence on the reliability, responsiveness, known-group validity, and meaningful change thresholds of the SIC using methods aligned with regulatory guidance and best practices. Methods. Data were from ENSEMBLE-2, a multicenter, randomized, doubleblind, placebo-controlled phase 3 trial to assess the efficacy and safety of Ad26.COV2.S for the prevention of SARS-CoV-2 infections in adults (aged 18+). The SIC was used in the trial to evaluate COVID-19 signs and symptoms and the Patient Global Impression of Severity (PGIS) was used as an anchor for validation. Intra-class correlations (ICCs) and Cronbach's alphas were computed to evaluate the test-retest reliability and internal consistency, and analyses of variance (ANOVAs) were performed to assess the known-group validity of the SIC. Responsiveness was evaluated using PGIS as an anchor variable and a 1- or 2-point improvement in PGIS was used to estimate the meaningful change thresholds of the SIC. Results. 183 participants with polymerase chain reaction (PCR) confirmed moderate to severe/critical COVID-19 were included (mean +/- SD age 51.5 +/- 14.8 y;female 44%;White 65%). ICCs showed strong test-retest reliabilities for most SIC domains (.60 and above). The internal consistency reliability of the SIC had a Cronbach's alpha > .70 for all but one domain (Neurological). Statistically significant differences (p values < 0.05) for the different PGIS severity levels were found for all but one domain (Sensory), supporting known-group validity. All domains showed responsiveness based on changes (improvement and worsening) in PGIS, supporting the ability of the SIC to detect changes in COVID-19 signs and symptoms. Based on mean changes in the PGIS, estimated meaningful change thresholds for SIC domains ranged from -.36 to -2.11. Conclusion. These results, based on data from ENSEMBLE-2, build upon prior cross-sectional analyses and provide additional supportive psychometric evidence on the SIC.
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Full text: Available Collection: Databases of international organizations Database: EMBASE Type of study: Prognostic study Language: English Journal: Open Forum Infectious Diseases Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: EMBASE Type of study: Prognostic study Language: English Journal: Open Forum Infectious Diseases Year: 2022 Document Type: Article