Intermittent physiological monitoring and early warning scores (EWS) are limited in their ability to detect deteriorating patients in a timely manner. Wearable physiological sensors allow continuous remote monitoring and may be more timely and accurate in the identification of those at risk, compared with manual collection. This study aims to determine if wearable physiological sensors can be used for the early detection of postoperative deterioration, while being acceptable to patients and healthcare staff.
This is a prospective observational cohort study that will recruit adults undergoing major surgery in Benin, India, Ghana, Guatemala, Mexico, Nigeria, Rwanda and the UK. Participants will wear wearable physiological chest and limb sensors before, during and after surgery for up to 10 days or until discharge. In this ‘shadow-mode’ study, continuous physiological observations collected using the devices will not be made available to clinical teams. No changes in participant care will result. Standard of care clinical data will be collected contemporaneously. Continuous sensor data will be used to design algorithms to predict deterioration and specific complications in this population. Usability and feasibility testing, through focus groups, interviews and questionnaires, will be undertaken with healthcare professionals and people undergoing surgery.
Our stakeholder panel are directly involved in all aspects of this study, which will be conducted in accordance with the principles of the International Conference on Harmonisation Tripartite Guideline for Good Clinical Practice (ICH GCP) in addition to the principles of the ethics committee(s)/Institutional Review Boards (IRBs) who have reviewed and approved this study. Artificial intelligence (AI) prediction models will be reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis+Artificial Intelligence (TRIPOD+AI) and Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI) reporting guidelines frameworks.
Post-COVID-19 conditions (PCC) may include pulmonary sequelae, fatigue and other symptoms, but its mechanisms are not fully elucidated.
This study investigated the correlation between fatigue and the presence of pulmonary abnormalities in PCC patients with respiratory involvement 6–12 months after hospitalisation.
Cross-sectional study.
A tertiary hospital in Brazil.
315 patients, aged ≥18 years, were considered eligible based on SARS-CoV-2 infection confirmed by reverse transcription-PCR.
Pulmonary function tests (PFT), cardiopulmonary exercise tests (CPET), chest CT and hand grip were performed. The following scales were applied: Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, Euroqol 5 Dimensions quality of life (EQ-5D) and Hospital Anxiety and Depression Scale (HADS). Participants were divided between the fatigue group (FACIT-F≤30) and the non-fatigue group (FACIT-F>30). For the statistical analysis, the primary outcome was the difference in the diffusing capacity of the lungs for carbon monoxide (DLCO) between groups. Considered secondary outcomes were differences in PFT, CPET, chest CT, hand grip, EQ-5D and HADS.
The fatigue group had 81 patients (25.7%) against 234 (74.3%). PFT and CPET showed no significant difference in DLCO and oxygen consumption peak values between groups. The fatigue group had a lower workload (mean 55.3±21.3 watts vs 66.5±23.2 watts, p=0.003), higher breathing reserve (median 41.9% (33.8–52.5) vs 37.7% (28.9–47.1), p=0.028) and lower prevalence of ground glass opacity (60.8% vs 77.7%, p=0.003) and reticulation (36.7% vs 54.9%, p=0.005) in chest CT. The fatigue group had higher anxiety (57% vs 24%, p
Fatigue in patients with PCC 6–12 months after hospitalisation is relatively common and had weak correlation with pulmonary disorders. Our results suggested fatigue could be strongly related with peripheral disorders such as reduced musculoskeletal strength or psychosocial limitations.