A study published in BMCPulmMed reports that dyspnea (shortness of breath) following hospitalization for COVID-19 is common, persistent, and negatively impacts quality of life. LongCovid
). However, patients with dyspnea had a lower percent-predicted 6-min walk distance compared to patients without dyspnea. There was no significant difference in the troponin or B-type natriuretic peptide levels of patients with and without dyspnea at 12 months post-COVID-19 infection.Patient-reported, respiratory, and cardiac outcomes 12 months post-COVID-19 stratified by presence and absence of dyspnea.
Table 2 Associations of 12-month dyspnea score with 3-month patient-reported, respiratory, and cardiac outcomesThis prospective cohort shows that dyspnea is a frequent symptom following COVID-19, and that most patients with dyspnea do not experience a meaningful improvement in the severity of their symptoms in the first year following infection.
Mood may play a role in post-COVID dyspnea and can predict which patients are at risk for significant persistent dyspnea at 12 months. However, it is unclear whether dyspnea itself is driving these mood abnormalities, or whether the mood abnormality is instead contributing to the development of dyspnea.
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Post COVID-19 irritable bowel syndromeObjectives The long-term consequences of COVID-19 infection on the gastrointestinal tract remain unclear. Here, we aimed to evaluate the prevalence of gastrointestinal symptoms and post-COVID-19 disorders of gut–brain interaction after hospitalisation for SARS-CoV-2 infection. Design GI-COVID-19 is a prospective, multicentre, controlled study. Patients with and without COVID-19 diagnosis were evaluated on hospital admission and after 1, 6 and 12 months post hospitalisation. Gastrointestinal symptoms, anxiety and depression were assessed using validated questionnaires. Results The study included 2183 hospitalised patients. The primary analysis included a total of 883 patients (614 patients with COVID-19 and 269 controls) due to the exclusion of patients with pre-existing gastrointestinal symptoms and/or surgery. At enrolment, gastrointestinal symptoms were more frequent among patients with COVID-19 than in the control group (59.3% vs 39.7%, p|0.001). At the 12-month follow-up, constipation and hard stools were significantly more prevalent in controls than in patients with COVID-19 (16% vs 9.6%, p=0.019 and 17.7% vs 10.9%, p=0.011, respectively). Compared with controls, patients with COVID-19 reported higher rates of irritable bowel syndrome (IBS) according to Rome IV criteria: 0.5% versus 3.2%, p=0.045. Factors significantly associated with IBS diagnosis included history of allergies, chronic intake of proton pump inhibitors and presence of dyspnoea. At the 6-month follow-up, the rate of patients with COVID-19 fulfilling the criteria for depression was higher than among controls. Conclusion Compared with controls, hospitalised patients with COVID-19 had fewer problems of constipation and hard stools at 12 months after acute infection. Patients with COVID-19 had significantly higher rates of IBS than controls. Trial registration number [NCT04691895][1]. Data are available upon reasonable request. Data are available on reasonable request. All figures have associated ra
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Bayesian network modelling to identify on-ramps to childhood obesity - BMC MedicineBackground When tackling complex public health challenges such as childhood obesity, interventions focused on immediate causes, such as poor diet and physical inactivity, have had limited success, largely because upstream root causes remain unresolved. A priority is to develop new modelling frameworks to infer the causal structure of complex chronic disease networks, allowing disease “on-ramps” to be identified and targeted. Methods The system surrounding childhood obesity was modelled as a Bayesian network, using data from The Longitudinal Study of Australian Children. The existence and directions of the dependencies between factors represent possible causal pathways for childhood obesity and were encoded in directed acyclic graphs (DAGs). The posterior distribution of the DAGs was estimated using the Partition Markov chain Monte Carlo. Results We have implemented structure learning for each dataset at a single time point. For each wave and cohort, socio-economic status was central to the DAGs, implying that socio-economic status drives the system regarding childhood obesity. Furthermore, the causal pathway socio-economic status and/or parental high school levels → parental body mass index (BMI) → child’s BMI existed in over 99.99% of posterior DAG samples across all waves and cohorts. For children under the age of 8 years, the most influential proximate causal factors explaining child BMI were birth weight and parents’ BMI. After age 8 years, free time activity became an important driver of obesity, while the upstream factors influencing free time activity for boys compared with girls were different. Conclusions Childhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. The strong and independent causal relationship between birth weight and childhood BMI suggests a biological link. Our study imp
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An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems - Respiratory ResearchBackground We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. Methods This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. Results Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P | 0.827) with c-statistics ranged 0.849–0.856, calibration slopes 0.911–1.173, and Hosmer–Lemeshow P | 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P | 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. Conclusion The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.
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Stockport bar closes for the night to attend 'live' AUTOPSY eventEnigma bar in Stockport closed for the night to attend Post-Mortem Live
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Post Office branch in Glasgow closes after postmaster resignsCustomers are advised to use nearby branches
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Misleading claims about ginger and cancer - Full FactA social media post claims that ginger kills 91% of leukaemia cells and shrinks tumours. But this comes from a study about a substance in ginger, not ginger itself. Nor did it always kill that many cells. Nor did it actually shrink tumours.
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