Iyabosola Busola Oronti

Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, is limited. Objectives: The aim of this work is to develop a binary-outcome mixed-effects Xgboost (BME) model that integrates random effects at the hospital level. To ascertain how well the model handles correlated data in cardiovascular outcomes, we aim to assess its performance and compare it to fixed-effects Xgboost and traditional logistic regression models. Methods: A total of 227,087 patients over 17 years of age, undergoing cardiac surgery from 42 UK hospitals between 1 January 2012 and 31 March 2019, were included. The dataset was split into two cohorts: training/validation (n = 157,196; 2012–2016) and holdout (n = 69,891; 2017–2019). The outcome variable was 30-day mortality with hospitals considered as the clustering variable. The logistic regression, mixed-effects logistic regression, Xgboost and binary-outcome mixed-effects Xgboost (BME) were fitted to both standardized and unstandardized datasets across a range of sample sizes and the estimated prediction power metrics were compared to identify the best approach. Results: The exploratory study found high variability in hospital-related mortality across datasets, which supported the adoption of the mixed-effects models. Unstandardized Xgboost BME demonstrated marked improvements in prediction power over the Xgboost model at small sample size ranges, but performance differences decreased as dataset sizes increased. Generalized linear models (glms) and generalized linear mixed-effects models (glmers) followed similar results, with the Xgboost models also excelling at greater sample sizes. Conclusions: These findings suggest that integrating mixed effects into machine learning models can enhance their performance on datasets where the sample size is small.

Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, are limited. Objectives: The aim of this work is to develop a binary outcome mixed effects Xgboost (BME) model that integrates random effects at the hospital level. To ascertain how well the model handles correlated data in cardiovascular outcomes, we aims to assess its performance and compare it to fixed effects Xgboost and traditional logistic regression models. Methods: A total of 227,087 patients over 17 years of age, undergoing cardiac surgery from 42 UK hospitals between 1 Jan 2012 and 31 Mar 2019 were included. The dataset was split into two cohorts: Training/Validation (n = 157196; 2012-2016) and Holdout (n = 69891; 2017-2019). The outcome variable was 30 days mortality with hospitals considered as clustering variable. The logistic regression, mixed effects logistic regression, Xgboost and binary outcome mixed effects Xgboost (BME) were fitted to both standardized and unstandardized datasets across a range of sample sizes and the estimated prediction power metrics were compared to identify the best approach. Results: The exploratory study found high variability in hospital-related mortality across datasets, which supported the adoption of mixed effects models. Unstandardized Xgboost BME demonstrated marked improvements in predictor power over the Xgboost model at small sample size ranges, but performance differences decreased as dataset sizes increased. Generalized linear models (glm) and generalized linear mixed-effects models (glmer) models followed similar results, with Xgboost models excelling also at greater sample sizes. Conclusions: These findings suggest that integrating mixed effects into machine learning models can enhance their applicability in clinical settings where sample size is small such as rare conditions.

Purpose: From previous studies, we had observed that the number of biomedical engineers and technicians (BMETs) skilled in the maintenance of medical devices (MDs) were limited in sub-Saharan Africa (SSA) and that a likely parallelism existed between BME maintenance-related tasks and house chores mainly carried out by women in daily life. Our objective in carrying out this study was to understand underlying local cultural and social factors and domestic traditions interfering with the inclusion of women in the field of clinical and biomedical engineering (BME), and to identify existing gaps in opportunities for women in Engineering, Science and Technology (EST) in this region. Methods: We carried out an online survey involving 51 respondents (70.6% women, 29.4% men), to investigate gender-biased and cultural perceptions of maintenance-related tasks among engineers/technologists/technicians in BME and other allied professions in SSA​. The survey was refined through focus groups and pilot tests to assess its validity, and then shared among specific associations of women in engineering and other bodies in EST, as well as some hospital communities and institutions. Results: The survey brought to light evidence of prejudices against women in the society and workplace and showed that the adoption of our parallelism hypothesis has potential for increasing medical device maintenance practices in SSA hospitals. Conclusions: There is an apparent need to further study the possible effects of this sociological model through leveraging local culture and domestic traditions in ways that could ultimately increase the leadership and numbers of women BMETs in SSA.

Purpose: This study evaluates the effects of non-invasive home telemonitoring methods of managing congestive heart failure (CHF) patients with particular focus on complexity of intervention, patient characteristics, patient severity, and key enabling technologies (KETs) adopted. Our goal was to capture all possible aspects of previously documented outcomes and provide updated and clearer evidence on mixed effects on common themes. Methods: Randomized controlled trials (RCTs) published between 1 January 2012 and 6 June 2019, focusing on home telemonitoring of patients with only CHF or CHF coexisting with other chronic disease(s), were retrieved from online resources (PubMed, Embase, MEDLINE(R), Your journals@Ovid, Elsevier, and ClinicalTrials.gov). The snowball sampling method and forward citation tracking on Google Scholar were also adopted to identify additional relevant studies. Retrieved studies were in a language known by the authors (i.e., English, Spanish or Italian). Quality assessment of individual studies for shortcomings in design, management, evaluation, and reporting was done using the Cochrane risk of bias (RoB) tool. Variables of interest were synthesized as differences in relative risk (RR), or as weighted mean differences (WMD). Outcomes were assigned as primary or secondary based on a principal judgement of clinical importance, and secondarily on highest recurrent counts in included studies. Results: In all, 28 RCTs involving 10,258 patients were included in the qualitative synthesis, out of which 24 were used for the quantitative synthesis. These studies focused on non-invasive telemonitoring practices for home monitoring of CHF patients, through the deployment of different kinds of electronic/mobile devices, with most having wireless communication capabilities. Moreover, studies focusing on implantable monitoring devices in terms of inputs, data and patient performance were also included. Brain natriuretic peptide (BNP) ((WMD = -27.75; 95% CI (-53.36, -2.14); p-value = 0.034), rehospitalization/hospitalization for heart failure (RR = 0.88; 95% CI (0.79, 0.98); p-value = 0.015), cardiovascular death/heart failure hospitalization (RR = 0.70; 95% CI (0.51, 0.97); p-value = 0.03), and six minute walk test (6MWT) (WMD = 25.61; 95% CI (9.22, 41.99); p-value = 0.002) significantly improved in the telemonitoring group, while the number of visits to a nurse (WMD = 1.42; 95% CI (0.33, 2.52); p-value = 0.011) increased considerably compared to usual care. Conclusion: Although there were limitations to the evidence provided in this review such as wide variations in certain variables (e.g., sample populations, RoB assessment, telemonitoring tools, follow-up periods), issues with allocation concealment and blinding of participants and personnel, and paucity of data for synthesizing particular outcomes of interest, overall, telemonitoring seems to offer much better results in the treatment of CHF patients compared to usual care. Registration and protocol: This systematic review and meta-analysis has been retrospectively registered in the Open Science Framework (OSF) repository with https://doi.org/10.17605/OSF.IO/NDXCP. All data related to this study, including the electronic supplementary data, can be found at this link: osf.io/57q3h.

Congestive heart failure (CHF) is one of the most debilitating cardiac disorders. It is a costly disease in terms of both lives and financial outlays, given the high rate of hospital re-admissions and mortality. Heart failure (HF) is notoriously difficult to identify on time, and is frequently accompanied by additional comorbidities that further complicate diagnosis. Many decision support systems (DSS) have been developed to facilitate diagnosis and to raise the standard of screening and monitoring operations, even for non-expert staff. This is confirmed in the literature by records of highly performing diagnosis-aid systems, which are unfortunately not very relevant to expert cardiologists. In order to assist cardiologists in predicting the trajectory of HF, we propose a deep learning-based system which predicts severity of disease progression by employing medical patient history. We tested the accuracy of four models on a labeled dataset, composed of 1037 records, to predict CHF severity and progression, achieving results comparable to studies based on much larger datasets, none of which used longitudinal multi-class prediction. The main contribution of this work is that it demonstrates that a fairly complicated approach can achieve good results on a medium size dataset, providing a reasonably accurate means of determining the evolution of CHF well in advance. This potentially constitutes a significant aid for healthcare managers and expert cardiologists in designing different therapies for medication, healthy lifestyle changes and quality of life (QoL) management, while also promoting allocation of resources with an evidence-based approach.

Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension- related NCDs delivered through artificial intelligence and machine learning techniques is clear.

Purpose: The aim of this paper is to present and validate a framework for assessing healthcare facilities in low-resource settings to collect evidence and inform policies on the harmonisation, regulation and contextualised design of medical devices. Methods: A literature review and focus groups with several experts of medical device design, clinical engineering, health technology assessment and management, allowed the creation of a protocol, comprising two parts: a semi-structured interview and electrical safety measures. Results: Three hospitals were assessed in Benin and three in Uganda. All the health centres resulted to be facing typical challenges for low-resource settings, including the lack of funding, expertise, a well-established maintenance program, spare parts and consumables, and unreliable power supplies. Conclusion: As there is a paucity of information regarding low-resource settings, the proposed framework can be used by clinical or biomedical engineers to assess and thereby propose actions for improving the conditions of healthcare settings.