BACKGROUND
Post-acute sequelae of SARS-CoV-2 infection, more commonly known as long COVID, has emerged as a major health problem. The pathogenesis of long COVID is unknown, but among the leading hypotheses is viral persistence. We aimed to investigate whether the use of the SARS-CoV-2 antiviral nirmatrelvir-ritonavir improved long COVID symptoms.
METHODS
We conducted a double-blind, placebo-controlled, randomised trial involving adults who had developed persistent symptoms (≥12 weeks) associated with three major symptom phenotypes (cognitive, autonomic, or exercise) after acute SARS-CoV-2 infection at 69 US sites. Participants were eligible if they were 18 years or older and had a previous suspected, probable, or confirmed SARS-CoV-2 infection, as defined by the Pan American Health Organization. Eligible participants were also required to have either at least two moderate symptoms from the same phenotype or one severe phenotype-associated symptom, as identified with the Cluster Targeted COVID-19 Symptom Questions. Participants were randomly allocated in a double-blind manner in a 1:1:1 ratio using permuted blocks of size 30 to receive either 15 days of active intervention followed by 10 days of placebo (300 mg nirmatrelvir-100 mg ritonavir twice daily, then 100 mg ritonavir-placebo); 25 days of active intervention (300 mg nirmatrelvir-100 mg ritonavir twice daily); or 25 days of placebo-ritonavir (100 mg ritonavir-placebo). A clinically significant change in patient-reported outcomes at day 90 comprised the primary endpoint: Patient-Reported Outcomes Measurement Information System Cognitive Function Short Form 8a, Orthostatic Hypotension Questionnaire question 1, and a modified version of the DePaul Symptom Questionnaire Post-Exertional Malaise short form. Secondary outcomes were phenotype-specific performance measures. The study was registered at ClinicalTrials.gov (NCT05595369) and is complete.
FINDINGS
Between July 27, 2023, and Sept 6, 2024, 1207 individuals were screened. Of these, 964 were randomly allocated and 959 participants, excluding four participants who were later found ineligible and one who did not initiate treatment, were enrolled in the three phenotypes: 332 to cognitive, 334 to autonomic, and 332 to exercise. In the 959 participants in the mITT population, 643 (67%) self-reported as female, 314 (33%) were male, and two participants had a sex of unknown or undifferentiated; 750 (78%) were White; and 108 (11%) were Hispanic, Latino, or Spanish. The median age was 49 years (IQR 38-59). No statistically significant benefits were observed for any phenotype for primary endpoints. For the cognitive phenotype, adjusted differences compared to placebo were 3·2% (95% CI -10·4 to 16·8, p=0·65) for the 25-day regimen and -2·2% (-15·5 to 11·1, p=0·74) for the 15-day regimen. For the autonomic phenotype, adjusted differences were -6·4% (-18·5 to 5·7, p=0·30) for the 25-day regimen compared to placebo and -0·1% (-12·5 to 12·3, p=0·99) for the 15-day regimen compared to placebo. For exercise, adjusted differences were -7·8% (-19·5 to 3·8, p=0·19) for the 25-day regimen compared to placebo and 0·9% (-11·4 to 13·2, p=0·88) for the 15-day regimen compared to placebo. There were no differences in secondary endpoints, and no safety signals were observed; there were no deaths, and 52 serious adverse events occurred in 42 (4%) of 963 participants over the course of the study.
INTERPRETATION
Nirmatrelvir-ritonavir for 15 days or 25 days showed no evidence of benefit in long COVID in any of the three phenotypes studied. These findings suggest additional approaches to measuring the symptom burden and treating Long COVID are needed.
FUNDING
National Institutes of Health.
L. Baden, N. Shah, Sean T. H. Liu et al.· Lancet. Infectious Diseases...· 0 citations
Abstract Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking functional vascular changes that may precede tissue damage and cognitive decline. Deep learning-derived cerebral blood volume (DeepCBV) maps, synthesized from non-contrast MRI, offer a scalable alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that combines predictions from two separate three-dimensional convolutional neural networks: one trained only on structural MRI scans and another trained only on DeepCBV maps generated by a pre-trained three-dimensional patch-based deep learning model. Each model was trained and validated on 2851 scans (1507 females) from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using 1233 subjects. The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error of 3.95 years (R2 = 0.943), outperforming models trained on MRI (mean absolute error = 4.10) or DeepCBV alone (mean absolute error = 4.49). Saliency maps revealed complementary modality contributions: MRI emphasized white matter and cortical atrophy, while DeepCBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with known patterns of normal ageing. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (Clinical Dementia Rating Sum of Boxes ⍴ = 0.403; Mini-Mental State Examination ⍴ = −0.310). DeepCBV-based BrainAGE showed a particularly strong separation between stable versus progressive MCI (Mann–Whitney U = 2.177 × 104, P = 4.43 × 10−8), suggesting sensitivity to prodromal vascular changes that precede overt atrophy. Integrating structural MRI with deep learning-derived vascular measures substantially enhances BrainAGE estimation and improves sensitivity to MCI and Alzheimer’s disease progression, supporting its potential role in risk stratification, early detection and monitoring of therapeutic response. By enabling a functional-like assessment from routine MRI, this approach lowers barriers to multimodal evaluation and provides a clinically actionable biomarker for large-scale ageing and dementia studies.
Jordan Jomsky, Zongyu Li, Kay C. Igwe et al.· Brain Communications· 1 citation
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