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Nevin Manimala Statistics

Putting the PASC Score to the Test: Clinical vs. Statistical Accuracy in Long COVID Diagnosis

J Gen Intern Med. 2025 Nov 17. doi: 10.1007/s11606-025-10042-6. Online ahead of print.

ABSTRACT

OBJECTIVE: To validate the RECOVER Post-Acute Sequelae of SARS-CoV-2 infection (PASC) score in a cohort of patients who develop long COVID (LC) or fully recover while iteratively improving the tool’s sensitivity and specificity.

METHODS: A cross-sectional study in 130 LC patients followed at LC clinics in Baltimore, MD, USA, who met the National Academies of Sciences, Engineering, and Medicine (NASEM) 2024 LC definition, and 60 SARS-CoV-2 exposed but fully recovered individuals. LC participants were required to have at least one neuropsychiatric symptom. Participants completed comprehensive surveys and questionnaires assessing symptoms based on published methods to determine PASC score. Using the NASEM 2024 LC definition as the “true” condition, we compared evaluation metrics for the RECOVER PASC score cutoff (PASC > 12) and the presence of individual/multiple symptoms. Evaluation metrics (e.g., sensitivity, specificity, F1) were calculated based on these classifications for the overall PASC score and symptom combinations.

RESULTS: The LC cohort (n = 130) had a mean age of 47.2 years and was predominantly female (72%), White (79%), and well-educated (77% > 16 years). Controls (n = 60) were similar demographically. LC diagnosis and PASC scores were significantly associated (χ2 = 102.99, P < 0.001). The PASC score showed excellent specificity (100%) and positive predictive value (PPV; 100%) albeit limited sensitivity (80%), missing 20% of participants with LC. We found that loss of smell/taste, post-exertional malaise, or lack of sexual desire or capacity demonstrated 94% sensitivity, 92% specificity, and 96% PPV, 87% NPV, and an F1 score of 0.949.

CONCLUSION: Validation of the RECOVER PASC supports its utility and highlights the need for ongoing refinement of the LC definition. We call for national efforts to develop readily implementable clinical tools for LC diagnosis.

PMID:41249654 | DOI:10.1007/s11606-025-10042-6

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