Mediterranean diet and psychological well-being intervention to reverse metabolic syndrome in Chile (CHILEMED trial).

Psychosocial status and lifestyle are key risk factors of non-communicable diseases (NCDs), which, in turn, are main drivers of healthcare costs and morbimortality worldwide, including Chile. Mediterranean diet (MedDiet) is one of the healthiest dietary patterns under study. However, its impact on high-risk conditions, such as metabolic syndrome (MetS), and NCDs outside the Mediterranean Basin remains mostly unexplored. Even though Central Chile has an environment, food production, and culinary traditions comparable to those present in Mediterranean countries, few studies -some with significant methodological limitations- have evaluated the effect of MedDiet on health and/or disease in Chilean subjects. Importantly, a Mediterranean lifestyle is a modus vivendi that integrates physical health with mental and social well-being. Psychological well-being (PWB) is associated with healthy behaviors, positive health outcomes, and longevity, thereby emerging as a novel healthcare goal. We report here an ongoing randomized controlled clinical trial in Chilean patients with MetS seeking to test whether (1) a PWB theory-based intervention facilitates induction to and increases long-term adherence to a locally adapted MedDiet, and (2) a MedDiet intervention -implemented alone or combined with well-being promotion- is more effective at reversing MetS compared to individuals following a low-fat diet without psychological support. The CHILEan MEDiterranean (CHILEMED) diet intervention study is a 1-year trial including patients with MetS living in Chile. Participants will be assigned randomly by a computer-generated random number sequence to one of the three intervention arms: a) low-fat diet as control group, b) MedDiet alone, and c) MedDiet plus well-being support. Patients will be followed-up by individual and/or group online nutritional sessions or phone cal as well as 6- and 12-month in-person re-assessment of medical history, medication use, food intake, PWB, anthropometrics/physical exam, and blood collection for laboratory analysis. The primary outcome of the trial will be the effect of the MedDiet -with or without PWB intervention- on overall reversal of MetS compared to low-fat diet alone. Based on a statistical superiority trial, expected impact, and patient loss, the estimated study sample is 339 subjects (113 individuals per arm in 3 equal-sized groups). Currently, we have enrolled 179 patients, predominantly women, evenly distributed by age (group means ranging from 45.7 to 48,9 years-old), 3/4 are obese with almost all of them showing abdominal obesity, 70% are hypertensive, whereas <10% exhibit diabetes. If findings turn out as expected (e.g., MedDiet -with or without PWB intervention- is better than the low-fat diet for reversion of MetS at 1-year follow-up), CHILEMED will provide further beneficial evidence of the MedDiet on NCD risk conditions beyond the Mediterranean region.

Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study.

BACKGROUND: Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower. METHODS: We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0-1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov, NCT04601415. FINDINGS: Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81-0·89), sensitivity of 84·8% (76·2-91·3), and specificity of 69·5% (66·4-72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81-0·89), sensitivity of 82·7% (72·7-90·2), and specificity of 79·9% (77·0-82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88-0·95), sensitivity of 91·9% (78·1-98·3), and specificity of 80·2% (75·5-84·3). INTERPRETATION: A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment. FUNDING: NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research.

Unanticipated demand of Physiotherapist-Deployed Airway Clearance during the COVID-19 Surge 2020 a single centre report.

Bronchial secretion management was not an anticipated clinical problem in patients intubated and ventilated with COVID-19. Yet 63 (62%) of our intubated and ventilated patients demonstrated a moderate or greater sputum load, as recorded by physiotherapists on 5 or more days of the patient's ICU stay. The efficacy of airway clearance in these patients was further compounded by ineffective or absent cough and increased secretion tenacity, dramatically increasing the workload of critical care physiotherapists. We provide data to support the modelling of critical care physiotherapy staffing for future COVID-19 surges.