Generalisability of and lessons learned from a mixed-methods study conducted in three low- and middle-income countries to identify care pathways for atrial fibrillation

BackgroundIdentifying existing care pathways is the first step for understanding how services can be improved to enable early diagnosis and effective follow-up care for non-communicable diseases (NCDs); however, evidence on how care pathways can and should be identified in low- and middle-income countries (LMICs) is lacking.ObjectiveTo describe generalisability and lessons learned from recruitment and data collection for the quantitative component of a mixed methods study designed to determine the care pathway for atrial fibrillation (AF) in Brazil, China and Sri Lanka.MethodsAdults (≥18 years) that spoke the local language and with an AF diagnosis were eligible. We excluded anyone with a hearing or cognitive impairment or ineligible address. Eligible participants were identified using electronic records in Brazil and China; in Sri Lanka, researchers attended the outpatient clinics to identify eligible participants. Data were collected using two quantitative questionnaires administered at least 2-months apart. A minimum sample size of 238 was required for each country.ResultsThe required sample size was met in Brazil (n = 267) and China (n = 298), but a large proportion of AF patients could not be contacted (47% and 27%, respectively) or refused to participate (36% and 38%, respectively). In Sri Lanka, recruitment was challenging, resulting in a reduced sample (n = 151). Mean age of participants from Brazil, China and Sri Lanka was 69 (SD = 11.3), 65 (SD = 12.8) and 58 (SD = 11.7), respectively. Females accounted for 49% of the Brazil sample, 62% in China and 70% in Sri Lanka.ConclusionsGeneralisability was an issue in Brazil and China, as was selection bias. Recruitment bias was highlighted in Sri Lanka. Additional or alternative recruitment methods may be required to ensure generalisability and reduce bias in future studies aimed at identifying NCD care pathways in LMICs

Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China

ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.ConclusionThe ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings

The impact of rate and rhythm control strategies on quality of life for patients with atrial fibrillation: a protocol for a systematic review

Abstract Background Atrial fibrillation (AF) is the most common heart arrhythmia globally and it adversely affects the quality of life (QoL). Available rate and rhythm control strategies equally reduce mortality but may impact QoL differently. A number of systematic reviews have focused on the impact of specific strategies on QoL, though a 2006 review synthesized the evidence on the effect of all strategies on QoL, allowing for a clinically important comparison between the types of strategies. Many trials have been published since the review undertook the search in 2005; therefore, an update is needed. This systematic review aims to provide an update to the 2006 review on the impact of all rate and rhythm control strategies on QoL in people with AF. Methods The following four databases and three clinical trial registries will be searched for primary studies: CENTRAL, MEDLINE, Embase, CINAHL, WHO International Clinical Trials Registry Platform, ClinicalTrials.gov, and ClinicalTrialsRegister.eu. No language restriction will be applied. The search will be limited to 2004 or later publication year to allow overlap with the search conducted by the 2006 review authors. Any randomized control trial that reports the QoL of adult (≥ 18 years) AF patients following an eligible rate or rhythm control intervention will be eligible for inclusion. Eligible interventions (and comparators) include pacing, atrioventricular node junction and bundle of HIS ablation, pharmacological therapy, radio frequency catheter ablation, cryoablation, pulmonary vein isolation, maze operation, pace maker implantation, and defibrillator implantation. Two reviewers will independently screen for eligible studies, extract the data using a piloted tool, and assess bias by QoL outcome using the RoB 2 tool. The suitability of conducting a meta-analysis will be assessed by the clinical and methodology similarities of included studies. If it is feasible, standardized mean differences will be pooled using a random-effects model and assessed appropriately. Discussion The findings from this review will allow for meaningful comparisons between various rate and rhythm control strategies regarding their impact on QoL. This review will be useful for a wide range of stakeholders and will be crucial for optimizing the overall wellbeing of AF patients. Systematic review registration PROSPERO CRD42021290542 </jats:sec

Cardiovascular Health and Atrial Fibrillation or Flutter: A Cross-Sectional Study from ELSA-Brasil.

BackgroundThe association between ideal cardiovascular health (ICVH) status and atrial fibrillation or flutter (AFF) diagnosis has been less studied compared to other cardiovascular diseases.ObjectiveTo analyze the association between AFF diagnosis and ICVH metrics and scores in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).MethodsThis study analyzed data from 13,141 participants with complete data. Electrocardiographic tracings were coded according to the Minnesota Coding System, in a centralized reading center. ICVH metrics (diet, physical activity, body mass index, smoking, blood pressure, fasting plasma glucose, and total cholesterol) and scores were calculated as proposed by the American Heart Association. Crude and adjusted binary logistic regression models were built to analyze the association of ICVH metrics and scores with AFF diagnosis. Significance level was set at 0.05.ResultsThe sample had a median age of 55 years and 54.4% were women. In adjusted models, ICVH scores were not significantly associated with prevalent AFF diagnosis (odds ratio [OR]:0.96; 95% confidence interval [95% CI]:0.80-1.16; p=0.70). Ideal blood pressure (OR:0.33; 95% CI:0.15-0.74; p=0.007) and total cholesterol (OR:1.88; 95% CI:1.19-2.98; p=0.007) profiles were significantly associated with AFF diagnosis.ConclusionsNo significant associations were identified between global ICVH scores and AFF diagnosis after multivariable adjustment in our analyses, at least partially due to the antagonistic associations of AFF with blood pressure and total cholesterol ICVH metrics. Our results suggest that estimating the prevention of AFF burden using global ICVH scores may not be adequate, and ICVH metrics should be considered in separate

Table_1_Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.docx

ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.ConclusionThe ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.</p

Health care professionals’ perceptions about atrial fibrillation care in the Brazilian public primary care system: a mixed-methods study

Abstract Background Atrial fibrillation (AF) negatively impacts health systems worldwide. We aimed to capture perceptions of and barriers and facilitators for AF care in Brazilian primary care units (PCUs) from the perspective of healthcare professionals (HCPs). Methods This mixed-methods, cross-sectional study utilised an exploratory sequential design, beginning with the quantitative data collection (up to 18 closed questions) immediately followed by a semi-structured interview. HCPs were recruited from 11 PCUs in the Sao Paulo region and included managers, physicians, pharmacists, nurses and community health agents. Descriptive statistics were used to present findings from the quantitative questionnaire and inductive analysis was used to identify themes from the qualitative data. Results One hundred seven HCPs were interviewed between September 2019 and May 2020. Three main themes were identified that encapsulated barriers and facilitators to AF care: access to care (appointments, equipment/tests and medication), HCP and patient roles (HCP/patient relationship and patient adherence) and the role of the organisation/system (infrastructure, training and protocols/guidelines). Findings from the qualitative analysis reinforced the quantitative findings, including a lack of AF-specific training for HCPs, protocols/guidelines on AF management, INR tests in the PCUs, patient knowledge of AF management and novel oral anticoagulants (NOACs) as key barriers to optimal AF care. Conclusions Development and implementation of AF-specific training for PCU HCPs are needed in Brazil, along with evidence-based protocols and guidelines, educational programmes for patients, better access to INR tests for patients taking warfarin and availability of NOACs. </jats:sec

Healthcare provider and patient perspectives on access to and management of atrial fibrillation in the Northern Province, Sri Lanka: a rapid evaluation of barriers and facilitators to care

Abstract Background Atrial fibrillation (AF) is the most common cardiac arrhythmia that affects 60 million people worldwide. Limited evidence on AF management exists from low- and middle-income countries and none from Sri Lanka. We aimed to investigate the existing AF care pathway and patients’ perception on AF management to identify barriers and enablers for optimal AF care in Northern Province, Sri Lanka. Methods A rapid evaluation was undertaken with use of qualitative methods. Local healthcare providers (HCPs) mapped the intended pathway of care for AF patients which was then explored and annotated through 12 iterative sessions with additional HCPs. Topics of inefficiencies identified from the finalised map were used to guide focus group discussions (FGDs) with AF patients. AF patients who were attending the anticoagulation clinic at the only tertiary hospital in Northern Province were recruited and invited to participate using purposive sampling. The topic guide was developed in collaboration with local clinicians and qualitative experts. FGDs were conducted in the native Tamil language and all sessions were recorded, transcribed verbatim and thematically analysed using a deductive approach. Results The mapped pathway revealed inefficiencies in referral, diagnosis and ongoing management. These were explored through three FGDs conducted with 25 AF patients aged 25 to 70 years. Two key themes that contributed to and resulted in delays in accessing care and ongoing management were health seeking behaviours and atomistic healthcare structures. Four cross-cutting sub-themes identified were decision making, paternalistic approach to care, cost impacts and lifestyle impacts. These are discussed across 10 unique categories with consideration of the local context. Conclusions Strengthening primary healthcare services, improving public health literacy regarding AF and building patient autonomy whilst understanding the importance of their daily life and family involvement may be advantageous in tackling the inefficiencies in the current AF care pathway in Sri Lanka

Cardiovascular Health and Atrial Fibrillation or Flutter: A Cross-Sectional Study from ELSA-Brasil

Abstract Background The association between ideal cardiovascular health (ICVH) status and atrial fibrillation or flutter (AFF) diagnosis has been less studied compared to other cardiovascular diseases. Objective To analyze the association between AFF diagnosis and ICVH metrics and scores in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Methods This study analyzed data from 13,141 participants with complete data. Electrocardiographic tracings were coded according to the Minnesota Coding System, in a centralized reading center. ICVH metrics (diet, physical activity, body mass index, smoking, blood pressure, fasting plasma glucose, and total cholesterol) and scores were calculated as proposed by the American Heart Association. Crude and adjusted binary logistic regression models were built to analyze the association of ICVH metrics and scores with AFF diagnosis. Significance level was set at 0.05. Results The sample had a median age of 55 years and 54.4% were women. In adjusted models, ICVH scores were not significantly associated with prevalent AFF diagnosis (odds ratio [OR]:0.96; 95% confidence interval [95% CI]:0.80-1.16; p=0.70). Ideal blood pressure (OR:0.33; 95% CI:0.15–0.74; p=0.007) and total cholesterol (OR:1.88; 95% CI:1.19–2.98; p=0.007) profiles were significantly associated with AFF diagnosis. Conclusions No significant associations were identified between global ICVH scores and AFF diagnosis after multivariable adjustment in our analyses, at least partially due to the antagonistic associations of AFF with blood pressure and total cholesterol ICVH metrics. Our results suggest that estimating the prevention of AFF burden using global ICVH scores may not be adequate, and ICVH metrics should be considered in separate