Biosensors with left ventricular assist devices

Heart failure imposes a significant global health burden, standing as a primary contributor to mortality. Various indicators and physiological shifts within the body may hint at distinct cardiac conditions. Specific biosensors have the capability to identify these changes. Integrating or embedding these biosensors into mechanical circulatory support devices (MCSDs), such as left ventricular assist devices (LVADs), becomes crucial for monitoring alterations in biochemical and physiological factors subsequent to an MCSD implantation. Detecting abnormal changes early in the course of disease progression will allow for improved patient outcomes and prognosis following an MCSD implantation. The aim of this review is to explore the available biosensors that may be coupled or implanted alongside LVADs to monitor biomarkers and changes in physiological parameters. Different fabrication materials for the biosensors are discussed, including their advantages and disadvantages. This review also examines the feasibility of integrating feedback control mechanisms into LVAD systems using data from the biosensors. Challenges facing this emerging technology and future directions for research and development are outlined as well. The overarching goal is to provide an overview of how implanted biosensors may improve the performance and outcomes of LVADs through continuous monitoring and closed-loop control.

Catheterization for Congenital Heart Disease Adjustment for Risk Method II

BACKGROUND

Current metrics used to adjust for case mix complexity in congenital cardiac catheterization are becoming outdated due to the introduction of novel procedures, innovative technologies, and expanding patient subgroups. This study aims to develop a risk adjustment methodology introducing a novel, clinically meaningful adverse event outcome and incorporating a modern understanding of risk.

METHODS

Data from diagnostic only and interventional cases with defined case types were collected for patients ≤18 years of age and ≥2.5 kg at all Congenital Cardiac Catheterization Project on Outcomes participating centers. The derivation data set consisted of cases performed from 2014 to 2017, and the validation data set consisted of cases performed from 2019 to 2020. Severity level 3 adverse events were stratified into 3 tiers by clinical impact (3a/b/c); the study outcome was clinically meaningful adverse events, severity level ≥3b (3bc/4/5).

RESULTS

The derivation data set contained 15 224 cases, and the validation data set included 9462 cases. Clinically meaningful adverse event rates were 4.5% and 4.2% in the derivation and validation cohorts, respectively. The final risk adjustment model included age <30 days, Procedural Risk in Congenital Cardiac Catheterization risk category, and hemodynamic vulnerability score (C statistic, 0.70; Hosmer-Lemeshow P value, 0.83; Brier score, 0.042).

CONCLUSIONS

CHARM II (Congenital Heart Disease Adjustment for Risk Method II) risk adjustment methodology allows for equitable comparison of clinically meaningful adverse events among institutions and operators with varying patient populations and case mix complexity performing pediatric cardiac catheterization.

Large animal models to study effectiveness of therapy devices in the treatment of heart failure with preserved ejection fraction (HFpEF)

Our understanding of the complex pathophysiology of Heart failure with preserved ejection fraction (HFpEF) is limited by the lack of a robust in vivo model. Existing in-vivo models attempt to reproduce the four main phenotypes of HFpEF; ageing, obesity, diabetes mellitus and hypertension. To date, there is no in vivo model that represents all the haemodynamic characteristics of HFpEF, and only a few have proven to be reliable for the preclinical evaluation of potentially new therapeutic targets. HFpEF accounts for 50% of all the heart failure cases and its incidence is on the rise, posing a huge economic burden on the health system. Patients with HFpEF have limited therapeutic options available. The inadequate effectiveness of current pharmaceutical therapeutics for HFpEF has prompted the development of device-based treatments that target the hemodynamic changes to reduce the symptoms of HFpEF. However, despite the potential of device-based solutions to treat HFpEF, most of these therapies are still in the developmental stage and a relevant HFpEF in vivo model will surely expedite their development process. This review article outlines the major limitations of the current large in-vivo models in use while discussing how these designs have helped in the development of therapy devices for the treatment of HFpEF.

Enhancing Quality of Congenital Heart Care Within Resource-Limited Settings

Over 90% of the world's children with congenital heart disease (CHD) are born in the resources poor settings of low- to middle-income countries (LMICs). The shortfall in human and material resources and dysfunctional health systems leads to poor quality of care (QoC) which contributes substantially to suboptimal outcomes of patients with CHD in LMICs. Notwithstanding these challenges, it is possible to develop a quality improvement (QI) framework that can have a significant impact on outcomes and prevent a number of deaths. In this review, we examine the common barriers to implementing effective QI processes in LMICs. Using examples of successful QI initiatives in LMIC, we propose a broad framework that focuses on simple, yet effective measures involving cohesive efforts of all key participants guided and nurtured by a leadership that strongly values QoC.

Calcium channel blockers for preventing cardiomyopathy due to iron overload in people with transfusion-dependent beta thalassaemia

BACKGROUND

Beta-thalassaemia is an inherited blood disorder that reduces the production of haemoglobin. The most severe form requires recurrent blood transfusions, which can lead to iron overload. Cardiovascular dysfunction caused by iron overload is the leading cause of morbidity and mortality in people with transfusion-dependent beta-thalassaemia. Iron chelation therapy has reduced the severity of systemic iron overload, but removal of iron from the myocardium requires a very proactive preventive strategy. There is evidence that calcium channel blockers may reduce myocardial iron deposition. This is an update of a Cochrane Review first published in 2018.

OBJECTIVES

To assess the effects of calcium channel blockers plus standard iron chelation therapy, compared with standard iron chelation therapy (alone or with a placebo), on cardiomyopathy due to iron overload in people with transfusion-dependent beta thalassaemia.

SEARCH METHODS

We searched the Cochrane Haemoglobinopathies Trials Register, compiled from electronic database searches and handsearching of journals and conference abstract books, to 13 January 2022. We also searched ongoing trials databases and the reference lists of relevant articles and reviews.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) of calcium channel blockers combined with standard chelation therapy versus standard chelation therapy alone or combined with placebo in people with transfusion-dependent beta thalassaemia.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. We used GRADE to assess certainty of evidence.

MAIN RESULTS

We included six RCTs (five parallel-group trials and one cross-over trial) with 253 participants; there were 126 participants in the amlodipine arms and 127 in the control arms. The certainty of the evidence was low for most outcomes at 12 months; the evidence for liver iron concentration was of moderate certainty, and the evidence for adverse events was of very low certainty. Amlodipine plus standard iron chelation compared with standard iron chelation (alone or with placebo) may have little or no effect on cardiac T2* values at 12 months (mean difference (MD) 1.30 ms, 95% confidence interval (CI) -0.53 to 3.14; 4 trials, 191 participants; low-certainty evidence) and left ventricular ejection fraction (LVEF) at 12 months (MD 0.81%, 95% CI -0.92% to 2.54%; 3 trials, 136 participants; low-certainty evidence). Amlodipine plus standard iron chelation compared with standard iron chelation (alone or with placebo) may reduce myocardial iron concentration (MIC) after 12 months (MD -0.27 mg/g, 95% CI -0.46 to -0.08; 3 trials, 138 participants; low-certainty evidence). The results of our analysis suggest that amlodipine has little or no effect on heart T2*, MIC, or LVEF after six months, but the evidence is very uncertain. Amlodipine plus standard iron chelation compared with standard iron chelation (alone or with placebo) may increase liver T2* values after 12 months (MD 1.48 ms, 95% CI 0.27 to 2.69; 3 trials, 127 participants; low-certainty evidence), but may have little or no effect on serum ferritin at 12 months (MD 0.07 μg/mL, 95% CI -0.20 to 0.35; 4 trials, 187 participants; low-certainty evidence), and probably has little or no effect on liver iron concentration (LIC) after 12 months (MD -0.86 mg/g, 95% CI -4.39 to 2.66; 2 trials, 123 participants; moderate-certainty evidence). The results of our analysis suggest that amlodipine has little or no effect on serum ferritin, liver T2* values, or LIC after six months, but the evidence is very uncertain. The included trials did not report any serious adverse events at six or 12 months of intervention. The studies did report mild adverse effects such as oedema, dizziness, mild cutaneous allergy, joint swelling, and mild gastrointestinal symptoms. Amlodipine may be associated with a higher risk of oedema (risk ratio (RR) 5.54, 95% CI 1.24 to 24.76; 4 trials, 167 participants; very low-certainty evidence). We found no difference between the groups in the occurrence of other adverse events, but the evidence was very uncertain. No trials reported mortality, cardiac function assessments other than echocardiographic estimation of LVEF, electrocardiographic abnormalities, quality of life, compliance with treatment, or cost of interventions.

AUTHORS' CONCLUSIONS

The available evidence suggests that calcium channel blockers may reduce MIC and may increase liver T2* values in people with transfusion-dependent beta thalassaemia. Longer-term multicentre RCTs are needed to assess the efficacy and safety of calcium channel blockers for myocardial iron overload, especially in younger children. Future trials should also investigate the role of baseline MIC in the response to calcium channel blockers, and include a cost-effectiveness analysis.

Advocacy at the Eighth World Congress of Pediatric Cardiology and Cardiac Surgery

The Eighth World Congress of Pediatric Cardiology and Cardiac Surgery (WCPCCS) will be held in Washington DC, USA, from Saturday, 26 August, 2023 to Friday, 1 September, 2023, inclusive. The Eighth World Congress of Pediatric Cardiology and Cardiac Surgery will be the largest and most comprehensive scientific meeting dedicated to paediatric and congenital cardiac care ever held. At the time of the writing of this manuscript, The Eighth World Congress of Pediatric Cardiology and Cardiac Surgery has 5,037 registered attendees (and rising) from 117 countries, a truly diverse and international faculty of over 925 individuals from 89 countries, over 2,000 individual abstracts and poster presenters from 101 countries, and a Best Abstract Competition featuring 153 oral abstracts from 34 countries. For information about the Eighth World Congress of Pediatric Cardiology and Cardiac Surgery, please visit the following website: [www.WCPCCS2023.org]. The purpose of this manuscript is to review the activities related to global health and advocacy that will occur at the Eighth World Congress of Pediatric Cardiology and Cardiac Surgery.Acknowledging the need for urgent change, we wanted to take the opportunity to bring a common voice to the global community and issue the Washington DC WCPCCS Call to Action on Addressing the Global Burden of Pediatric and Congenital Heart Diseases. A copy of this Washington DC WCPCCS Call to Action is provided in the Appendix of this manuscript. This Washington DC WCPCCS Call to Action is an initiative aimed at increasing awareness of the global burden, promoting the development of sustainable care systems, and improving access to high quality and equitable healthcare for children with heart disease as well as adults with congenital heart disease worldwide.

The Impact of Maternal Preeclampsia and Hyperglycemia on the Cardiovascular Health of the Offspring: A Systematic Review and Meta-analysis

OBJECTIVES

The objective of this review was to assess the impact of maternal preeclampsia or hyperglycemia on the body composition and cardiovascular health in the offspring.

STUDY DESIGN

We conducted a systematic review utilizing PubMed, EBSCO, CINAHLPlus, Cochrane Library, and Web of Science to include all studies assessing the impact of preeclampsia/eclampsia and/or gestational/pregestational diabetes mellitus on the health of the offspring (children <10 years of age). The health measures included anthropometry, cardiac dimensions and function, and vascular function. We performed a meta-analysis using Review Manager software and computed net risk ratio (RR) with 95% confidence interval (CI) for dichotomous data and mean difference (MD) with 95% CI for continuous data.

RESULTS

There were 6,376 studies in total, of which 45 were included in the review and 40 in the meta-analysis. The results demonstrated higher birth weight (MD: 0.12 kg; 95% CI: 0.06-0.18) and systolic and diastolic blood pressure (BP; MD: 5.98 mm Hg; 95% CI: 5.64-6.32 and MD: 3.27 mm Hg; 95% CI: 0.65-5.89, respectively) in the offspring of mothers with gestational diabetes compared to controls. In contrast, the offspring of mothers with preeclampsia had lower birth weight (MD: -0.41 kg; 95% CI: -0.7 to -0.11); however, they had increased systolic (MD: 2.2 mm Hg; 95% CI: 1.28-3.12) and diastolic BP (MD: 1.41 mm Hg; 95% CI: 0.3-2.52) compared to controls. There is lack of data to conduct a meta-analysis of cardiac morphology, functional, and vascular imaging parameters.

CONCLUSION

These findings suggest that the in-utero milieu can have a permanent impact on the body composition and vascular health of the offspring. Future work warrants multicenter prospective studies to understand the mechanism and the actual effect of exposure to maternal hyperglycemia and high BP on the cardiovascular health of the offspring and long-term outcomes.

KEY POINTS

· Adverse in-utero exposures may have an impact on cardiovascular risk in children.. · Maternal hyperglycemia/preeclampsia lead to changes in birthweight and BP.. · Limited echocardiographic and vascular imaging data in these cohorts necessitates future work..

Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients' lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.

Nutritional Heart Disease and Cardiomyopathies: JACC Focus Seminar 4/4

This JACC Focus Seminar provides an overview of and highlights recently published research on cardiomyopathies and nutritional heart disease that have a higher prevalence in tropical regions. The development of tropical cardiomyopathies and nutritional cardiovascular disease (CVD) is complicated by high rates of poverty, fragmented health care systems, and suboptimal access to health care because of socioeconomic inequalities, leading to the fact that children, adolescents, and young adults are disproportionally affected. Such tropical cardiomyopathies and nutritional CVD that have not been prevalent in high-income countries in the past decades are now reemerging. When treating migrants or refugees, it is important for attending physicians to consider the burden of endemic diseases in the countries of origin and the likelihood that such patients might be affected. In this review, the authors propose an approach for adequate diagnostic work-up leading to appropriate care for those with suspected or confirmed tropical cardiomyopathies and nutritional CVD.

Early Postoperative Congenital Cardiac Catheterization Outcomes: A Multicenter Study

BACKGROUND

Early postoperative catheterizations (EPOCs) within 6 weeks after a congenital heart surgical procedure can treat residual lesions and provide important clinical information. However, EPOCs are often assumed to impose additional risk on a vulnerable patient population. This study aimed to describe the EPOC population, evaluate procedural safety, compare EPOC patients with procedure-matched non-EPOC patients, and determine risk factors for poor outcomes using data from the Congenital Cardiac Catheterization Project on Outcomes registry.

METHODS

In a retrospective cohort, demographic, clinical, and procedural characteristics were analyzed for diagnostic and interventional catheterizations performed in 13 participating institutions from January 2014 to December 2017, excluding patients after heart transplant. The primary outcome was a high-severity adverse event (AE). Three distinct analyses included (1) describing the full cohort and EPOC patients, (2) comparing EPOC patients with and without a high-severity AE, and (3) comparing EPOC patients with controls matched on case type.

RESULTS

This study included 17,776 catheterizations, with 1399 EPOCs. The high-severity AE rate was 6.4% overall, 8.9% in the EPOC cohort, and 8.4% in matched controls (P = .74). The association between EPOC status and high-severity AE was not significant in a multivariable model (P = .17). In EPOCs with a high-severity AE, median procedure duration was 30 minutes longer (P < .001), and median time from surgical procedure to catheterization was 3 days longer (P = .05).

CONCLUSIONS

EPOC was not associated with additional risk. Individual patient characteristics of size and hemodynamic vulnerability may serve as informative predictors. Timely catheterization may preempt further clinical deterioration, and intraprocedure duration optimization may correlate with improved outcomes.

Quality improvement initiative in paediatric echocardiography laboratory in a low- to middle-income country

AIMS

To determine the impact of a quality improvement (QI) initiative in the area of paediatric echocardiography (echo) in a low- to middle-income country (LMIC).Care for patients with congenital heart disease is challenging, especially in LMICs. Collaborative learning through QI projects is imperative to ensure improvement in delivery processes leading to better patient outcomes.

METHODS AND RESULTS

This QI initiative was taken by a team consisting of physicians and sonographers. Problems were identified, a key driver diagram (KDD) was created, and simple process re-engineering was done using interventions based on the KDD. Metrics (five process and one outcome) were assessed to determine the effectiveness of the QI project. The process metrics assessed were comprehensiveness of exam, timeliness of reporting, diagnostic accuracy and error, and sedation adverse event rates of transthoracic echocardiograms, while a novel comprehensive echo laboratory (lab) quality score was developed as an outcome metric. Data were collected quarterly and analysed in the post-implementation phase. Significant improvement was seen in comprehensive mean score (20.4-29.7), timeliness (40-95%), and diagnostic accuracy rate (91-100%), while a decrease was seen in the diagnostic error rate (7.5-3.5%) and the sedation adverse event rate (6.8-0%), pre- vs. post-implementation. The overall quality outcome score improved from 7 to 19 and the echo lab was able to achieve adequate quality.

CONCLUSION

This QI initiative produced improvement in all the processes, and the overall quality of the echo lab without any substantial increase in resources or cost.

Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol

INTRODUCTION

Rheumatic heart diseases (RHDs) contribute significant morbidity and mortality globally. To reduce the burden of RHD, timely initiation of secondary prophylaxis is important. The objectives of this study are to determine the frequency of subclinical RHD and to train a deep learning (DL) algorithm using waveform data from the digital auscultatory stethoscope (DAS) in predicting subclinical RHD.

METHODS AND ANALYSIS

We aim to recruit 1700 children from a group of schools serving the underprivileged over a 12-month period in Karachi (Pakistan). All consenting students within the age of 5-15 years with no underlying congenital heart disease will be eligible for the study. We will gather information regarding sociodemographics, anthropometric data, history of symptoms or diagnosis of rheumatic fever, phonocardiogram (PCG) and electrocardiography (ECG) data obtained from DAS. Handheld echocardiogram will be performed on each study participant to assess the presence of a mitral regurgitation (MR) jet (>1.5 cm), or the presence of aortic regurgitation (AR) in any view. If any of these findings are present, a confirmatory standard echocardiogram using the World Heart Federation (WHF) will be performed to confirm the diagnosis of subclinical RHD. The auscultatory data from digital stethoscope will be used to train the deep neural network for the automatic identification of patients with subclinical RHD. The proposed neural network will be trained in a supervised manner using labels from standard echocardiogram of the participants. Once trained, the neural network will be able to automatically classify the DAS data in one of the three major categories-patient with definite RHD, patient with borderline RHD and normal subject. The significance of the results will be confirmed by standard statistical methods for hypothesis testing.

ETHICS AND DISSEMINATION

Ethics approval has been taken from the Aga Khan University, Pakistan. Findings will be disseminated through scientific publications and to collaborators.

ARTICLE FOCUS

This study focuses on determining the frequency of subclinical RHD in school-going children in Karachi, Pakistan and developing a DL algorithm to screen for this condition using a digital stethoscope.

Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD. Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to generate the hierarchical Summary ROC (HSROC) curve. Results: We included 16 studies (1217 participants) that used ML algorithm to diagnose CHD. Neural networks were used in seven studies with overall sensitivity of 90.9% (95% CI 85.2-94.5%) and specificity was 92.7% (95% CI 86.4-96.2%). Other ML models included ensemble methods, deep learning and clustering techniques but did not have sufficient number of studies for a meta-analysis. Majority (n=11, 69%) of studies had a high risk of patient selection bias, unclear bias on index test (n=9, 56%) and flow and timing (n=12, 75%) while low risk of bias was reported for the reference standard (n=10, 62%). Conclusion: ML models such as neural networks have the potential to diagnose CHD accurately without the need for trained personnel. The heterogeneity of the diagnostic modalities used to train these models and the heterogeneity of the CHD diagnoses included between the studies is a major limitation.

Development of an international standard set of clinical and patient-reported outcomes for children and adults with congenital heart disease: a report from the International Consortium for Health Outcomes Measurement Congenital Heart Disease Working Group

AIMS

Congenital heart disease (CHD) is the most common congenital malformation. Despite the worldwide burden to patient wellbeing and health system resource utilization, tracking of long-term outcomes is lacking, limiting the delivery and measurement of high-value care. To begin transitioning to value-based healthcare in CHD, the International Consortium for Health Outcomes Measurement aligned an international collaborative of CHD experts, patient representatives, and other stakeholders to construct a standard set of outcomes and risk-adjustment variables that are meaningful to patients.

METHODS AND RESULTS

The primary aim was to identify a minimum standard set of outcomes to be used by health systems worldwide. The methodological process included four key steps: (i) develop a working group representative of all CHD stakeholders; (ii) conduct extensive literature reviews to identify scope, outcomes of interest, tools used to measure outcomes, and case-mix adjustment variables; (iii) create the outcome set using a series of multi-round Delphi processes; and (iv) disseminate set worldwide. The Working Group established a 15-item outcome set, incorporating physical, mental, social, and overall health outcomes accompanied by tools for measurement and case-mix adjustment variables. Patients with any CHD diagnoses of all ages are included. Following an open review process, over 80% of patients and providers surveyed agreed with the set in its final form.

CONCLUSION

This is the first international development of a stakeholder-informed standard set of outcomes for CHD. It can serve as a first step for a lifespan outcomes measurement approach to guide benchmarking and improvement among health systems.

A Framework for Improving Parent Satisfaction With the Inpatient Pediatric Admission Process: Experience From a Low-Resource Setting

The objective for this initiative was to reduce parental anxiety and thus improve their satisfaction related to hospital admission by ensuring that the child’s management plan and basic amenities were communicated to the parents within the first hour of arrival at the Children’s Hospital inpatient unit. The intervention for this project was developed based on the Theory of Change framework. Effectiveness of the intervention was assessed by comparing change in parent-reported anxiety scores and improvement in patient satisfaction scores at 24 hours post-admission as an indicator of experience. The frequency of delivery of each individual message was also tracked and compared at baseline and post-intervention. The results showed a significant reduction in parental anxiety levels within the first 24 hours of admission (3.5 to 3.2, P = .01). The parent satisfaction score was significantly improved from baseline to post-intervention (4.00 vs 4.82 respectively; P < .001). Comparison between the baseline and post-intervention demonstrated a statistically significant (P < .001) increase in frequency of messages delivered. This initiative highlighted that reengineering the current system using existing resources to standardize admission communication along with an adequate monitoring and feedback plan can help reduce parental anxiety, thus improving parent satisfaction with the services.

Machine Learning for Child and Adolescent Health: A Systematic Review

CONTEXT

In the last few decades, data acquisition and processing has seen tremendous amount of growth, thus sparking interest in machine learning (ML) within the health care system.

OBJECTIVE

Our aim for this review is to provide an evidence map of the current available evidence on ML in pediatrics and adolescent medicine and provide insight for future research.

DATA SOURCES

A literature search was conducted by using Medline, the Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature Plus, Web of Science Library, and EBSCO Dentistry & Oral Science Source.

STUDY SELECTION

Articles in which an ML model was assessed for the diagnosis, prediction, or management of any condition in children and adolescents (0-18 years) were included.

DATA EXTRACTION

Data were extracted for year of publication, geographical location, age range, number of participants, disease or condition under investigation, study methodology, reference standard, type, category, and performance of ML algorithms.

RESULTS

The review included 363 studies, with subspecialties such as psychiatry, neonatology, and neurology having the most literature. A majority of the studies were from high-income (82%; n = 296) and upper middle-income countries (15%; n = 56), whereas only 3% (n = 11) were from low middle-income countries. Neural networks and ensemble methods were most commonly tested in the 1990s, whereas deep learning and clustering emerged rapidly in the current decade.

LIMITATIONS

Only studies conducted in the English language could be used in this review.

CONCLUSIONS

The interest in ML has been growing across various subspecialties and countries, suggesting a potential role in health service delivery for children and adolescents in the years to come.

Challenges and Special Aspects of Pulmonary Hypertension in Middle- to Low-Income Regions: JACC State-of-the-Art Review

Challenges and special aspects related to the management and prognosis of pulmonary hypertension (PH) in middle- to low-income regions (MLIRs) range from late presentation to comorbidities, lack of resources and expertise, cost, and rare options of lung transplantation. Expert consensus recommendations addressing the specific challenges for prevention and therapy of PH in MLIRs with limited resources have been lacking. To date, 6 MLIR-PH registries containing mostly adult patients with PH exist. Importantly, the global prevalence of PH is much higher in MLIRs compared with high-income regions: group 2 PH (left heart disease), pulmonary arterial hypertension associated with unrepaired congenital heart disease, human immunodeficiency virus, or schistosomiasis are highly prevalent. This consensus statement provides selective, tailored modifications to the current PH guidelines to address the specific challenges faced in MLIRs, resulting in the first pragmatic and cost-effective consensus recommendations for PH care providers, patients, and their families.

Isothermal microcalorimetry minimal inhibitory concentration testing in extensively drug resistant Gram-negative bacilli: a multicentre study

OBJECTIVES

To evaluate the performance of an isothermal microcalorimetry (IMC) method for determining the MICs among extensively drug-resistant Gram-negative bacilli.

METHODS

A collection of 320 clinical isolates (n = 80 of each) of Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Sweden, Spain, Italy and the Netherlands were tested. The MICs were determined using the IMC device calScreener (Symcel, Stockholm, Sweden) and ISO-broth microdilution as the reference method. Essential agreement, categorical agreement, very major errors (VME), major errors (ME) and minor (mE) errors for each antibiotic were determined.

RESULTS

Data from 316 isolates were evaluated. Four errors (two ME, one VME, one mE) among 80 K. pneumoniae, six errors (four ME, one VME, one mE) among 79 E. coli, 15 errors (seven VME, three ME, five mE) among 77 P. aeruginosa and 18 errors (12 VME, two ME, four mE) among 80 A. baumannii were observed. Average essential agreement and categorical agreement of the IMC method were 96.6% (95% confidence interval, 94.2-99) and 97.1% (95% confidence interval, 95.4-98.5) respectively when the MICs were determined at the end of 18 hours. Categorical agreement of the IMC method for prediction of MIC by the end of 8 hours for colistin, meropenem, amikacin, ciprofloxacin and piperacillin/tazobactam were 95%, 91.4%, 94%, 95.2% and 93.7% respectively.

CONCLUSIONS

The IMC method could accurately determine the MICs among extensively drug-resistant clinical isolates of E. coli, K. pneumoniae, P. aeruginosa and A. baumannii isolates.

Use of Machine Learning Algorithms for Prediction of Fetal Risk using Cardiotocographic Data

Background

A major contributor to under-five mortality is the death of children in the 1^st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor.

Aim

The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses.

Methods

CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified.

Results

Determined by obstetricians' interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (>96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (>92%).

Conclusion

The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health-care workers in low- and middle-income countries can use this model to triage pregnant women in remote areas for early referral and further management.