Physical Activity with Sports Scientist (PASS) programme to promote physical activity among patients with non-communicable diseases: a pragmatic randomised controlled trial protocol.

Physical activity (PA) effectively prevents and treats non-communicable diseases in clinical settings. PA promotion needs to be more consistent, especially in busy primary care. Sports scientists have the potential to support PA promotion in primary care. The Physical Activity with Sports Scientist (PASS) programme is created to personalise PA promotion led by a sports scientist in a primary care clinic. A pragmatic randomised controlled trial with two parallel groups will be conducted at a family medicine clinic. Physically inactive participants aged 35-70 years who have type 2 diabetes mellitus, hypertension or dyslipidaemia will be invited. The control group (n=60) will receive usual care. The intervention group (n=60) will receive the PASS programme and usual care. The PASS programme will consist of a tailored PA prescription after the physician's consultation at the first visit and monthly phone follow-ups. The primary outcome is the proportion of participants who have achieved the PA goal defined as aerobic activity (≥150 min/week of moderate to vigorous-intensity PA), muscle-strengthening activity (≥2 days/week of moderate or greater intensity) and multicomponent PA (≥2 days/week of moderate or greater intensity). Secondary outcomes are body composition and physical fitness. The primary and secondary outcomes will be measured and compared between the control and intervention groups at visit 1 (month 0: baseline measurements), visit 2 (months 3-4: follow-up measurements), visit 3 (months 6-8: end-point measurements) and visit 4 (months 9-12: continuing measurements). The study protocol was registered with the Thai Clinical Trials Registry. Trial registration number: TCTR20240314001. [Abstract copyright: © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Understanding the potential impact of different drug properties on SARS-CoV-2 transmission and disease burden : a modelling analysis

Q1Q1Background The unprecedented public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods and Findings develop a mathematical model of SARS-CoV-2 transmission, COVID-19 disease and clinical care to explore the potential public-health impact of a range of different potential therapeutics, under a range of different scenarios varying: i) healthcare capacity, ii) epidemic trajectories; and iii) drug efficacy in the absence of supportive care. In each case, the outcome of interest was the number of COVID-19 deaths averted in scenarios with the therapeutic compared to scenarios without. We find the impact of drugs like dexamethasone (which are delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in highincome countries but only 8% in low-income countries (assuming R=1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalisation) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions There is a global asymmetry in who is likely to benefit from advances in the treatment of COVID-19 to date, which have been focussed on hospitalised-patients and predicated on an assumption of adequate access to supportive care. Therapeutics that can feasibly be delivered to those earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priorityRevista Internacional - Indexad

Clinical risk prediction of progression to severe dengue illness during the febrile phase in primary healthcare settings

To date, no specific antiviral treatment is available for dengue. The recognition of clinical progression to severe disease during the early phases of the illness and the decisions made at triage are key for the clinical management and often determine the outcome. Previous studies have been developed to explore the relationship between risk factors and disease severity and have identified the risk predictors of severe dengue illness. However, the results of these studies have been inconsistent due to studies with small sample sizes and the significant variation in predictors on different days of illness. In addition, previous risk prediction models for early progression to severe disease were based on hospitalised patient data, thus missing to investigate early predictors potentially capable to inform triage and decision making in outpatient settings, where infections are first seen. This thesis aims to fill this knowledge gap and investigates how early risk predictors, collected within the first four days since symptom onset, can be used in conjunction with clinical prediction models to estimate individualised risks of progression to severe disease in outpatient settings. This thesis provides evidence that platelet count, liver function test, and serum albumin can be used as risk predictors of progression to severe disease, backed both by a systematic review and meta-analysis of the existing literature, and by the analysis of clinical data from two large cohort studies conducted in Thailand and Vietnam. To investigate the development of the first signs of severe diseases and compensate for the infrequency of dengue shock syndrome, we investigate two clinical endpoints (i.e. dengue shock syndrome and a combined endpoint of dengue shock syndrome and/or moderate plasma leakage) using a variety of statistical models, including logistic regression and machine learning techniques, such as extreme gradient boosted tree and artificial neural network. The risk prediction models were developed following best practices, were optimised through internal and external validation, and using independent datasets from Thailand and Vietnam, two countries in dengue-endemic areas. We also characterised the temporal dynamics of clinical and laboratory parameters throughout disease progression and investigated the use and predictive power of sequential measurements for prediction, using recurrent neural networks. The use of statistical predictive models to inform triage and clinical management of dengue in the outpatient setting is promising and has the potential to support decision making in the early phases of disease in clinical practice. While further and extensive validation across healthcare contexts and populations is needed, this work lays the foundations for integrating evidence-based and data-driven methods into a decision support system in clinical practice, which in turn can contribute to informing decision making and optimising healthcare allocation and the optimal use of resources.Open Acces

The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality

BackgroundSymptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.MethodsWe analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of &amp;lt;72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach.ResultsWe included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84–0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV &amp;gt;90%).ConclusionSupervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account—this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.</jats:sec

Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam.

BackgroundIdentifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.MethodsWe developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set.FindingsThe final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76-0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98.InterpretationThe study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management

Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam

Background Identifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context. Methods We developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set. Findings The final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76–0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98. Interpretation The study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management