Exercise capacity following SARS-CoV-2 infection is related to changes in cardiovascular and lung function in military personnel.

BACKGROUND: Since the COVID-19 pandemic, post-COVID syndrome (persistent symptoms/complications lasting >12 weeks) continues to pose medical and economic challenges. In military personnel, where optimal fitness is crucial, prolonged limitations affecting their ability to perform duties has occupational and psychological implications, impacting deployability and retention. Research investigating post-COVID syndrome exercise capacity and cardiopulmonary effects in military personnel is limited. METHODS: UK military personnel were recruited from the Defence Medical Services COVID-19 Recovery Service. Participants were separated into healthy controls without prior SARS-CoV-2 infection (group one), and participants with prolonged symptoms (>12 weeks) after mild-moderate (community-treated) and severe (hospitalised) COVID-19 illness (group 2 and 3, respectively). Participants underwent cardiac magnetic resonance imaging (CMR) and spectroscopy, echocardiography, pulmonary function testing and cardiopulmonary exercise testing (CPET). RESULTS: 113 participants were recruited. When compared in ordered groups (one to three), CPET showed stepwise decreases in peak work, work at VT1 and VO2 max (all p < 0.01). There were stepwise decreases in FVC (p = 0.002), FEV1 (p = 0.005), TLC (p = 0.002), VA (p < 0.001), and DLCO (p < 0.002), and a stepwise increase in A-a gradient (p < 0.001). CMR showed stepwise decreases in LV/RV volumes, stroke volumes and LV mass (LVEDVi/RVEDVi p < 0.001; LVSV p = 0.003; RVSV p = 0.001; LV mass index p = 0.049). CONCLUSION: In an active military population, post-COVID syndrome is linked to subclinical changes in maximal exercise capacity. Alongside disease specific changes, many of these findings share the phenotype of deconditioning following prolonged illness or bedrest. Partitioning of the relative contribution of pathological changes from COVID-19 and deconditioning is challenging in post-COVID syndrome recovery.

Optimal search strategies for identifying sound clinical prediction studies in EMBASE.

BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies.