Walking performance: correlation between energy cost of walking and walking participation. new statistical approach concerning outcome measurement.

Walking ability, though important for quality of life and participation in social and economic activities, can be adversely affected by neurological disorders, such as Spinal Cord Injury, Stroke, Multiple Sclerosis or Traumatic Brain Injury. The aim of this study is to evaluate if the energy cost of walking (CW), in a mixed group of chronic patients with neurological diseases almost 6 months after discharge from rehabilitation wards, can predict the walking performance and any walking restriction on community activities, as indicated by Walking Handicap Scale categories (WHS). One hundred and seven subjects were included in the study, 31 suffering from Stroke, 26 from Spinal Cord Injury and 50 from Multiple Sclerosis. The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability. This model generated a cut-off value of.40, which enabled us to classify correctly the cases with a percentage of 85.0%. Our research reveal that, in our subjects, CW is the only predictor of the walking performance of in the community, to be compared with the score of WHS. We have been also identifying a cut-off value of CW cost, which makes a distinction between those who can walk in the community and those who cannot do it. In particular, these values could be used to predict the ability to walk in the community when discharged from the rehabilitation units, and to adjust the rehabilitative treatment to improve the performance

Model to identify a cut-off value of Energy Cost of walk (CW).

<p>The CW is the energy cost per kilogram per unit of distance covered <b>(</b>mlO₂*Kg⁻¹*min⁻¹<b>)</b>. C is the criterion. J = sensitivity (C)+specificity (C). J finding the best cut-off point that is equivalent to measuring the J of Youden Index. Youden Index is the greatest vertical distance between ROC curve and the diagonal line.</p

Cardiorespiratory response to walk in multiple sclerosis patients

AbstractTo ascertain whether fatigue perception is linked to exertion dyspnea and/or to an impaired cardiorespiratory response during walk, 11 patients (8 females, age range 21–46 years) with multiple sclerosis (MS) and mild disability underwent the 6-min walk test. Ten healthy subjects (7 females, age range 25–49 years) were studied, as a control group. Patients did not differ from controls in spirometry, lung volumes and respiratory muscle strength. There was a significant difference in walk distance between patients and controls (P<0.001), but not in dyspnea perception. In patients, the walk distance significantly related to disability score (P<0.01), but not to fatigue. Compared to controls, patients had a significant decrease in oxygen pulse during walk (P<0.05) and a significant increase in the ventilatory equivalent of CO2 both at baseline and during walk (P<0.05). The relative contribution of both the tidal volume and of the ratio of inspiratory to total breathing cycle duration to the increase in minute ventilation during walk was significantly less in patients, as compared to controls (P<0.05). We conclude that in MS patients with mild disability, fatigue and exertion dyspnea are different sensations without any link and a peripheral limitation during walk can occur

Interactive dot diagram of cut-off point of the energy cost of walking.

<p>(CW = Cost of walking).</p

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use