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Fulltext Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Mastoi QU, Wah TY, Gopal Raj R, Iqbal U

Cardiol Res Pract, 2018;2018:2016282.
PMID: 29507812 DOI: 10.1155/2018/2016282

Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.
Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things

Iqbal U, Wah TY, Habib Ur Rehman M, Mujtaba G, Imran M, Shoaib M

J Med Syst, 2018 Nov 05;42(12):252.
PMID: 30397730 DOI: 10.1007/s10916-018-1107-2

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology-Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.
Fulltext Correlation Between Previous Caesarean Section and Adverse Maternal Outcomes Accordingly With Robson Classification: Systematic Review and Meta-Analysis

Jamshed S, Chien SC, Tanweer A, Asdary RN, Hardhantyo M, Greenfield D, et al.

Front Med (Lausanne), 2021;8:740000.
PMID: 35096855 DOI: 10.3389/fmed.2021.740000

Background: The increasing rates of Caesarean section (CS) beyond the WHO standards (10-15%) pose a significant global health concern. Objective: Systematic review and meta-analysis to identify an association between CS history and maternal adverse outcomes for the subsequent pregnancy and delivery among women classified in Robson classification (RC). Search Strategy: PubMed/Medline, EbscoHost, ProQuest, Embase, Web of Science, BIOSIS, MEDLINE, and Russian Science Citation Index databases were searched from 2008 to 2018. Selection Criteria: Based on Robson classification, studies reporting one or more of the 14 adverse maternal outcomes were considered eligible for this review. Data Collection: Study design data, interventions used, CS history, and adverse maternal outcomes were extracted. Main Results: From 4,084 studies, 28 (n = 1,524,695 women) met the inclusion criteria. RC group 5 showed the highest proportion among deliveries followed by RC10, RC7, and RC8 (67.71, 32.27, 0.02, and 0.001%). Among adverse maternal outcomes, hysterectomy had the highest association after preterm delivery OR = 3.39 (95% CI 1.56-7.36), followed by Severe Maternal Outcomes OR = 2.95 (95% CI 1.00-8.67). We identified over one and a half million pregnant women, of whom the majority were found to belong to RC group 5. Conclusions: Previous CS was observed to be associated with adverse maternal outcomes for the subsequent pregnancies. CS rates need to be monitored given the prospective risks which may occur for maternal and child health in subsequent births.
Fulltext Effects of mobile health interventions on health-related outcomes in older adults with type 2 diabetes: A systematic review and meta-analysis

Lee JJN, Abdul Aziz A, Chan ST, Raja Abdul Sahrizan RSFB, Ooi AYY, Teh YT, et al.

J Diabetes, 2023 Jan;15(1):47-57.
PMID: 36649940 DOI: 10.1111/1753-0407.13346

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic metabolic condition that is associated with multiple comorbidities. Apart from pharmacological approaches, patient self-management remains the gold standard of care for diabetes. Improving patients' self-management among the elderly with mobile health (mHealth) interventions is critical, especially in times of the COVID-19 pandemic. However, the extent of mHealth efficacy in managing T2DM in the older population remains unknown. Hence, the present review examined the effectiveness of mHealth interventions on cardiometabolic outcomes in older adults with T2DM.
METHODS: A systematic search from the inception till May 31, 2021, in the MEDLINE, Embase, and PubMed databases was conducted, and 16 randomized controlled trials were included in the analysis.
RESULTS: The results showed significant benefits on glycosylated hemoglobin (HbA1c) (mean difference -0.24%; 95% confidence interval [CI]: -0.44, -0.05; p = 0.01), postprandial blood glucose (-2.91 mmol/L; 95% CI: -4.78, -1.03; p = 0.002), and triglycerides (-0.09 mmol/L; 95% CI: -0.17, -0.02; p = 0.010), but not on low-density lipoprotein cholesterol (-0.06 mmol/L; 95% CI: -0.14, 0.02; p = 0.170), high-density lipoprotein cholesterol (0.05 mmol/L; 95% CI: -0.03, 0.13; p = 0.220), and blood pressure (systolic blood pressure -0.82 mm Hg; 95% CI: -4.65, 3.00; p = 0.670; diastolic blood pressure -1.71 mmHg; 95% CI: -3.71, 0.29; p = 0.090).
CONCLUSIONS: Among older adults with T2DM, mHealth interventions were associated with improved cardiometabolic outcomes versus usual care. Its efficacy can be improved in the future as the current stage of mHealth development is at its infancy. Addressing barriers such as technological frustrations may help strategize approaches to further increase the uptake and efficacy of mHealth interventions among older adults with T2DM.
Fulltext Prophylactic potential of honey and Nigella sativa L. against hospital and community-based SARS-CoV-2 spread: a structured summary of a study protocol for a randomised controlled trial

Ashraf S, Ashraf S, Akmal R, Ashraf M, Kalsoom L, Maqsood A, et al.

Trials, 2021 Sep 15;22(1):618.
PMID: 34526081 DOI: 10.1186/s13063-021-05510-3

OBJECTIVES: Considering the therapeutic potential of honey and Nigella sativa (HNS) in coronavirus disease 2019 (COVID-19) patients, the objective of the study is defined to evaluate the prophylactic role of HNS.
TRIAL DESIGN: The study is a randomized, placebo-controlled, adaptive clinical trial with parallel group design, superiority framework with an allocation ratio of 1:1 among experimental (HNS) and placebo group. An interim analysis will be done when half of the patients have been recruited to evaluate the need to adapt sample size, efficacy, and futility of the trial.
PARTICIPANTS: All asymptomatic patients with hospital or community based COVID-19 exposure will be screened if they have had 4 days exposure to a confirmed case. Non-pregnant adults with significant exposure level will be enrolled in the study High-risk exposure (<6 feet distance for >10min without face protection) Moderate exposure (<6 feet distance for >10min with face protection) Subjects with acute or chronic infection, COVID-19 vaccinated, and allergy to HNS will be excluded from the study. Recruitment will be done at Shaikh Zayed Post-Graduate Medical Institute, Ali Clinic and Doctors Lounge in Lahore (Pakistan).
INTERVENTION AND COMPARATOR: In this clinical study, patients will receive either raw natural honey (0.5 g) and encapsulated organic Nigella sativa seeds (40 mg) per kg body weight per day or empty capsule with and 30 ml of 5% dextrose water as a placebo for 14 days. Both the natural products will be certified for standardization by Government College University (Botany department). Furthermore, each patient will be given standard care therapy according to version 3.0 of the COVID-19 clinical management guidelines by the Ministry of National Health Services of Pakistan.
MAIN OUTCOMES: Primary outcome will be Incidence of COVID-19 cases within 14 days of randomisation. Secondary endpoints include incidence of COVID-19-related symptoms, hospitalizations, and deaths along with the severity of COVID-19-related symptoms till 14th day of randomization.
RANDOMISATION: Participants will be randomized into experimental and control groups (1:1 allocation ratio) via the lottery method. There will be stratification based on high risk and moderate risk exposure.
BLINDING (MASKING): Quadruple blinding will be ensured for the participants, care providers and outcome accessors. Data analysts will also be blinded to avoid conflict of interest. Site principal investigator will be responsible for ensuring masking.
NUMBERS TO BE RANDOMISED (SAMPLE SIZE): 1000 participants will be enrolled in the study with 1:1 allocation.
TRIAL STATUS: The final protocol version 1.4 was approved by institutional review board of Shaikh Zayed Post-Graduate Medical Complex on February 15, 2021. The trial recruitment was started on March 05, 2021, with a trial completion date of February 15, 2022.
TRIAL REGISTRATION: Clinical trial was registered on February 23, 2021, www.clinicaltrials.gov with registration ID NCT04767087 .
FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). With the intention of expediting dissemination of this trial, the conventional formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines.
Fulltext Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

Cromwell EA, Osborne JCP, Unnasch TR, Basáñez MG, Gass KM, Barbre KA, et al.

PLoS Negl Trop Dis, 2021 07;15(7):e0008824.
PMID: 34319976 DOI: 10.1371/journal.pntd.0008824

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.
Fulltext Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Burstein R, Henry NJ, Collison ML, Marczak LB, Sligar A, Watson S, et al.

Nature, 2019 Oct;574(7778):353-358.
PMID: 31619795 DOI: 10.1038/s41586-019-1545-0

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.