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Nida Salman Yazdani,1 Kaniz Amna Haider,2 Amna Khan,2 Syed Ali Jaffar Zaidi,2 Akbar Rajani,1 Imran Nisar,2 Fyezah Jehan,2 Zahra Hoodbhoy2 1Research and Programs, VITAL Pakistan Trust, Karachi, Pakistan; 2Department of Paediatrics and Child Health, the Aga Khan University, Karachi, PakistanCorrespondence: Zahra Hoodbhoy, Aga Khan University, Stadium Road PO Box 3500, Karachi, 74800, Pakistan, Tel +92-3343481179, Email zahra.hoodbhoy@aku.eduPurpose: To understand pregnant women’s experience with midwifery-led antenatal care services using the Respectful Maternity Care charter in primary health centers in Karachi, Pakistan.Methods: This cross-sectional study was at Rehri Goth and Ibrahim Hyderi, two peri-urban communities in Karachi, Pakistan, where women receive antenatal care services. All pregnant women in their third trimester who consented during the study period were included. The participants were asked about access to care, antenatal care experience, person-centered approach, and general satisfaction with the facility using a pre-designed questionnaire. These themes were mapped onto the universal Respectful Maternity Care charter. Descriptive statistics were used to summarize the findings in each of these themes. Multivariable logistic regression techniques to determine the relationship between the dependent and independent variables.Results: There were 904 women who agreed to participate in this study during January to December 2021. Majority of the women (94%, n=854) were satisfied with the operating hours and cleanliness. More than 90% of the women reported positive experiences regarding privacy, respectful treatment by midwives, and non-discriminatory care. However, 40% (n=362) of the women reported not receiving adequate information and informed consent before a medical procedure, while 65% (n=587) reported poor counseling for birth preparedness. Maternal age, women’s occupation, women’s education, and parity were found to be significantly associated with respect provided, satisfaction with counseling and the consent process.Conclusion: This study reported satisfaction of pregnant women with the facility’s ambiance, respect, and care; however, poor communication skills regarding consent and antenatal counseling were reported. The findings suggest the need for more efficient strategies, such as regular respectful maternity care and technical training to strengthen midwife-patient interactions and enhance overall satisfaction, thus improving maternal and newborn outcomes.Keywords: antenatal care, patient satisfaction, Respectful Maternity Care, Pakistan

Introduction: Estimating a reliable gestational age (GA) is essential in providing appropriate care during pregnancy. With advancements in data science, there are several publications on the use of artificial intelligence (AI) models to estimate GA using ultrasound (US) images. The aim of this meta-analysis is to assess the accuracy of AI models in assessing GA against US as the gold standard., Methods: A literature search was performed in PubMed, CINAHL, Wiley Cochrane Library, Scopus, and Web of Science databases. Studies that reported use of AI models for GA estimation with US as the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Mean error in GA was estimated using STATA version-17 and subgroup analysis on trimester of GA assessment, AI models, study design, and external validation was performed., Results: Out of the 1,039 studies screened, 17 were included in the review, and of these 10 studies were included in the meta-analysis. Five (29%) studies were from high-income countries (HICs), four (24%) from upper-middle-income countries (UMICs), one (6%) from low-and middle-income countries (LMIC), and the remaining seven studies (41%) used data across different income regions. The pooled mean error in GA estimation based on 2D images ( n  = 6) and blind sweep videos ( n  = 4) was 4.32 days (95% CI: 2.82, 5.83; l 2 : 97.95%) and 2.55 days (95% CI: -0.13, 5.23; l 2 : 100%), respectively. On subgroup analysis based on 2D images, the mean error in GA estimation in the first trimester was 7.00 days (95% CI: 6.08, 7.92), 2.35 days (95% CI: 1.03, 3.67) in the second, and 4.30 days (95% CI: 4.10, 4.50) in the third trimester. In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). Most studies exhibited an unclear or low risk of bias in various domains, including patient selection, index test, reference standard, flow and timings and applicability domain., Conclusion: Preliminary experience with AI models shows good accuracy in estimating GA. This holds tremendous potential for pregnancy dating, especially in resource-poor settings where trained interpreters may be limited., Systematic Review Registration: PROSPERO, identifier (CRD42022319966)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2025 Naz, Noorani, Jaffar Zaidi, Rahman, Sattar, Das and Hoodbhoy.)

Background: Despite progress in global neonatal mortality, South Asia continues to lag behind in reducing neonatal deaths. The Small Vulnerable Newborn (SVN) framework has been proposed to integrate preterm birth (PT), small for gestational age (SGA), and low birth weight. However, there is lack of data on the burden and risk factors of SVN in Pakistan, a country which has one of the highest neonatal deaths globally. This study aimed to estimate the incidence of SVN, and identify risk factors among pregnant women in Pakistan., Methods: This secondary analysis leverages data from PRISMA (Pregnancy Risk Infant Surveillance, and Measurement Alliance)-Pakistan. Women presenting ≤20 weeks gestation and, with birth weights recorded within 72 h post-delivery were analysed. Newborns were classified into categories of SVN. Multinomial and binomial regression models were used to examine associations between maternal characteristics and SVN categories, as well as neonatal mortality., Findings: The overall incidence of SVN was 46% (n = 771) with Term + SGA being the most common category (n = 461, 27.5%), followed by PT + AGA (n = 210, 12.5%) and PT + SGA (n = 41, 2.5%). Maternal undernutrition (MUAC <23 cm) increased the risk of SVN by 17% (aRR 1.17, 95% CI 1.05-1.31). SVN also emerged as a significant predictor of neonatal mortality, quadrupling the risk (aRR 4.52, 95% CI 2.42-8.46)., Interpretation: This study adds to the growing body of evidence on Pakistan's alarming burden of SVN, with every second newborn at risk. Identification and targeted interventions are imperative to mitigate adverse birth outcomes and optimize child growth and development., Funding: No funding was received for this secondary data analysis., Competing Interests: The authors declare no competing interests., (© 2025 The Author(s).)

Introduction: Adverse perinatal outcomes (APO) pose a significant global challenge, particularly in low- and middle-income countries (LMICs). This study aims to analyse two cohorts of high-risk pregnant women for APO to comprehend risk factors and improve prediction accuracy., Methods: We considered an LMIC and a high-income country (HIC) population to derive XGBoost classifiers to predict low birth weight (LBW) from a comprehensive set of maternal and fetal characteristics including socio-demographic, past and current pregnancy information, fetal biometry and fetoplacental Doppler measurements. Data were sourced from the FeDoC (Fetal Doppler Collaborative) study (Pakistan, LMIC) and theIMPACT (Improving Mothers for a Better PrenAtal Care Trial) study (Spain, HIC), and included 520 and 746 pregnancies assessed from 28 weeks gestation, respectively. The models were trained on varying subsets of the mentioned characteristics to evaluate their contribution in predicting LBW cases. For external validation, and to highlight potential differential risk factors for LBW, we investigated the generalisation of these models across cohorts. Models' performance was evaluated through the area under the curve (AUC), and their interpretability was assessed using SHapley Additive exPlanations., Results: In FeDoC, Doppler variables demonstrated the highest value at predicting LBW compared with biometry and maternal clinical data (AUC Doppler , 0.67; AUC Clinical , 0.65; AUC Biometry , 0.63), and its combination with maternal clinical data yielded the best prediction (AUC Clinical+Doppler , 0.71). In IMPACT, fetal biometry emerged as the most predictive set (AUC Biometry , 0.75; AUC Doppler , 0.70; AUC Clinical , 0.69) and its combination with Doppler and maternal clinical data achieved the highest accuracy (AUC Clinical+Biometry+Doppler , 0.81). External validation consistently indicated that biometry combined with Doppler data yielded the best prediction., Conclusions: Our findings provide new insights into the predictive role of different clinical and ultrasound descriptors in two populations at high risk for APO, highlighting that different approaches are required for different populations. However, Doppler data improves prediction capabilities in both settings, underscoring the value of standardising ultrasound data acquisition, as practiced in HIC, to enhance LBW prediction in LMIC. This alignment contributes to bridging the health equity gap., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University Hospital, Karachi, Pakistan, this study utilized a dataset comprising 7,465 intraoral images, including both primary and secondary dentitions. These images were meticulously annotated by two experienced dentists and further verified by senior dentists. A YOLOv5s model was trained on this dataset and integrated into a smartphone application, while a Detection Transformer was also fine-tuned for comparative purposes. Explainable AI techniques were employed to assess the AI's decision-making processes. A sample of 70 photographs was used to directly compare the application's performance with that of junior dentists. Results showed that the YOLOv5s-based smartphone application achieved a precision of 90.7%, sensitivity of 85.6%, and an F1 score of 88.0% in detecting dental decay. In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. This application holds potential for aiding in the evaluation of the caries index within populations, thus contributing to efforts aimed at reducing the disease burden at the community level., (© 2024. The Author(s).)

Background: Helicobacter pylori ( H. pylori ) infection is associated with various gastrointestinal diseases and may lead to gastric cancer. Currently, endoscopy is the gold standard modality used for diagnosing H. pylori infection, but it lacks objective indicators and requires expert interpretation. In the past few years, the use of artificial intelligence (AI) for diagnosing gastrointestinal pathologies has increased tremendously and may improve the diagnostic accuracy of endoscopy for H. pylori infection. This study aimed to evaluate the diagnostic accuracy of AI algorithms for detecting H . pylori infection using endoscopic images., Methods: Three investigators searched the PubMed, CINHAL and Cochrane databases for studies that compared AI algorithms with endoscopic histopathology for diagnosing H. pylori infection using endoscopic images. We assessed the methodological quality of studies using the QUADAS-2 tool and performed a meta-analysis to estimate the pooled sensitivity, specificity, and accuracy of AI for detecting H. pylori infection., Results: A total of 11 studies were identified that met our inclusion criteria. All were conducted in different countries based in Asia. Our meta-analysis showed that AI had high sensitivity (0.93, 95% confidence interval [CI] 0.90-0.95), specificity (0.92, 95%CI 0.89-0.94), and accuracy (0.92, 95%CI 0.90-0.94) for detecting H. pylori infection using endoscopic images. However, there was also high heterogeneity among the studies (Tau 2 =0.87, I 2 =76.10% for generalized effect size; Tau 2 =1.53, I 2 =80.72% for sensitivity; Tau 2 =0.57, I 2 =70.86% for specificity)., Conclusion: This systematic review and meta-analysis showed that AI had high diagnostic accuracy for detecting H. pylori infection using endoscopic images., Competing Interests: Conflict of Interest: None, (Copyright: © 2024 Hellenic Society of Gastroenterology.)

Introduction: Extraction of Doppler-based measurements from feto-placental Doppler images is crucial in identifying vulnerable new-borns prenatally. However, this process is time-consuming, operator dependent, and prone to errors., Methods: To address this, our study introduces an artificial intelligence (AI) enabled workflow for automating feto-placental Doppler measurements from four sites (i.e., Umbilical Artery (UA), Middle Cerebral Artery (MCA), Aortic Isthmus (AoI) and Left Ventricular Inflow and Outflow (LVIO)), involving classification and waveform delineation tasks. Derived from data from a low- and middle-income country, our approach's versatility was tested and validated using a dataset from a high-income country, showcasing its potential for standardized and accurate analysis across varied healthcare settings., Results: The classification of Doppler views was approached through three distinct blocks: (i) a Doppler velocity amplitude-based model with an accuracy of 94%, (ii) two Convolutional Neural Networks (CNN) with accuracies of 89.2% and 67.3%, and (iii) Doppler view- and dataset-dependent confidence models to detect misclassifications with an accuracy higher than 85%. The extraction of Doppler indices utilized Doppler-view dependent CNNs coupled with post-processing techniques. Results yielded a mean absolute percentage error of 6.1 ± 4.9% ( n  = 682), 1.8 ± 1.5% ( n  = 1,480), 4.7 ± 4.0% ( n  = 717), 3.5 ± 3.1% ( n  = 1,318) for the magnitude location of the systolic peak in LVIO, UA, AoI and MCA views, respectively., Conclusions: The developed models proved to be highly accurate in classifying Doppler views and extracting essential measurements from Doppler images. The integration of this AI-enabled workflow holds significant promise in reducing the manual workload and enhancing the efficiency of feto-placental Doppler image analysis, even for non-trained readers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Aguado, Jimenez-Perez, Chowdhury, Prats-Valero, Sánchez-Martínez, Hoodbhoy, Mohsin, Castellani, Testa, Crispi, Bijnens, Hasan and Bernardino.)

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit., Competing Interests: MPS is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc., KelaHealth, Inc, and Cohere-Med, Inc. MPS holds equity in Clinetic, Inc. MPS has received honorarium for a conference presentation from Roche. MPS is a board member of Machine Learning for Health Care, a non-profit that convenes an annual research conference. SB is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc. and Cohere-Med, Inc. SB holds equity in Clinetic, Inc., (Copyright: © 2024 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Globally, 36.5% of pregnancies are affected by anemia, particularly in low-and middle-income countries, posing significant risks to maternal and perinatal health. In rural Pakistan, 44.3% of pregnant women suffer from iron deficiency, contributing to the high prevalence of anemia. Limited accessibility to antenatal care exacerbates the challenge, necessitating innovative solutions. This study assessed a midwife-led continuity of care model, utilizing intravenous (IV) iron therapy for the management of anemia in Karachi, Pakistan., Methods: We performed a retrospective analysis of data from a prospective cohort study conducted in two primary healthcare facilities, which employed a community midwife (CMW)-led continuity of care model for antenatal care, including IV iron therapy. We extracted data from February 2021 to March 2022 for women who were diagnosed with anemia based on hemoglobin (Hb) levels, categorized as mild (10.0 to 10.9 g/dL), moderate (7.0 to 9.9 g/dL), or severe (less than 7.0 g/dL). Assessment occurred at the initial antenatal care (ANC) visit to establish baseline anemia severity, and approximately 2 weeks after intravenous (IV) iron therapy administration to evaluate post-treatment changes were considered., Results: We enrolled 114 pregnant women, where the majority presented with moderate (88.6%) anemia. After IV iron treatment, 48.5% improved to normal-mild levels, while 50% remained unchanged. Severe anemia affected 10.5% at baseline; 42% shifted to moderate and 50% to normal-mild post-treatment, with one remaining unchanged ( p  < 0.001). Among women enrolled in the first and second trimesters, severe anemia improved to normal-mild (50%) and moderate levels (50%) (pre-treatment: n  = 10, post-treatment: n  = 0), and moderate anemia decreased by 48% (pre-treatment: n  = 92, post-treatment: n  = 47)., Conclusion: Our midwife-led model of care demonstrated an improvement in iron levels among pregnant women. The model addressed the challenges of anemia prevalence in Pakistan and underscored the significance of empowering front-line healthcare providers, such as community midwives (CMWs) for managing these common conditions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be considered as a potential conflict of interest., (Copyright © 2024 Naz, Shahid, Noorani, Fatima, Jaffar, Kashif, Yazdani, Khan, Rizvi, Nisar, Jehan and Hoodbhoy.)

The advent of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. Consent was taken from all participants before initiating the questionnaire. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AI's impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n = 610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 ± 12.5 years. Most of the participants (78.7%, n = 480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n = 429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n = 405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Habib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

When integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System. We conducted 20 interviews with the team of engineers and scientists that led the multi-year effort to build the tool, integrate it into practice, and maintain the solution. This "Algorithm Journey Map" enumerates all social and technical activities throughout the AI solution's procurement, development, integration, and full lifecycle management. In addition to mapping the "who?" and "what?" of the adoption of the AI tool, we also show several 'lessons learned' throughout the algorithm journey maps including modeling assumptions, stakeholder inclusion, and organizational structure. In doing so, we identify generalizable insights about how to recognize and navigate barriers to AI/ML adoption in healthcare settings. We expect that this effort will further the development of best practices for operationalizing and sustaining ethical principles-in algorithmic systems., (© 2024. The Author(s).)

Integrating Artificial Intelligence (AI) in orthopaedic within lower-middle-income countries (LMICs) promises landmark improvement in patient care. Delving into specific use cases-fracture detection, spine imaging, bone tumour classification, and joint surgery optimisation-the review illuminates the areas where AI can significantly enhance orthopaedic practices. AI could play a pivotal role in improving diagnoses, enabling early detection, and ultimately enhancing patient outcomes- crucial in regions with constrained healthcare services. Challenges to the integration of AI include financial constraints, shortage of skilled professionals, data limitations, and cultural and ethical considerations. Emphasising AI's collaborative role, it can act as a complementary tool working in tandem with physicians, aiming to address gaps in healthcare access and education. We need continued research and a conscientious approach, envisioning AI as a catalyst for equitable, efficient, and accessible orthopaedic healthcare for patients in LMICs.

Background: Early pregnancy nutritional status can be associated with adverse birth outcomes such as small-for-gestational age (SGA) and low birth weight (LBW). BMI (Body Mass Index) and MUAC (Mid-upper arm circumference) are easy to use assessments and are indicative of the pre-pregnancy nutritional status if obtained in the first trimester. This study primarily assesses the association of maternal nutritional status using BMI and MUAC with SGA in a community-based cohort of Pakistani women. It also aims to determine the predictive ability of MUAC and BMI in predicting SGA. Secondarily, we assessed the association between maternal nutrition and large for gestational age (LGA) and LBW., Methods: This study is a secondary analysis of an ongoing pregnancy cohort "Pregnancy Risk Infant Surveillance and Measurement Alliance (PRISMA)"in Ibrahim Hyderi and Rehri Goth, Karachi. PRISMA participants who were enrolled between January 2021 to August 2022 were included given they had a gestational age < 14 weeks confirmed via ultrasound, MUAC and BMI measurements were available and birth weight was captured within 72 hours. Multivariable logistic regression was used to determine an association between maternal nutritional status and SGA. The PRISMA study was approved by the Aga Khan University Ethics Review Committee (2021-5920-15,518)., Results: Of 926 women included in the analysis, 26.6% (n = 247) had a low MUAC (< 23 cm) while 18.4% (n = 171) were underweight (BMI < 18.5 kg/m2). Nearly one third of low MUAC and underweight women delivered SGA infants (34.4 and 35.1% respectively). Underweight women and women with low MUAC had a statistically significant association with SGA (Underweight: OR 1.49, 95% CI 1.1,2.4; Low MUAC-OR 1.64, 95% CI 1.2,2.3) as well as LBW (Underweight: OR-1.63, 95% CI 1.1,2.4; Low MUAC-OR-1.63, 95% CI 1.2,2.3). ROC curves showed that MUAC and BMI had modest predictability for SGA (AUC < 0.7)., Conclusion: Maternal nutritional status as indicated by BMI and MUAC are strongly associated with adverse pregnancy outcomes including SGA, LGA and LBW. Although MUAC and BMI are widely used to determine maternal nutritional status, they have poor predictive ability for newborn size. Further research is needed to identify other tools or a combination of tools to better predict adverse birth outcomes in resource-limited settings and plan interventions., (© 2024. The Author(s).)

Background: The Masimo Total Hemoglobin SpHb® is a continuous and non-invasive handheld device to measure hemoglobin levels. Previous research has found that SpHb is able to accurately detect hemoglobin levels in adult patients with a similar degree of bias and standard deviation to point-of-care invasive method measurements. Generally, limited clinical evidence, lack of validation of Masimo at higher than and lower than hemoglobin threshold values, and scientific consensus supporting the use of Masimo for accurate hemoglobin testing for the diagnosis of anemia during pregnancy calls for further research., Methods and Analysis: The proposed prospective cohort will be nested within the ongoing Pregnancy Risk and Infant Surveillance and Measurement Alliance (PRISMA) Maternal and Newborn Health (MNH) study. Three study sites (located in Zambia, Kenya, and Pakistan) will participate and collect hemoglobin data at five time points (<20 weeks, 20 weeks, 28 weeks, 36 weeks' gestation, and six weeks postpartum). We will measure hemoglobin using a venous blood sample via hematology auto-analyzer complete blood count (gold standard) and the non-invasive device. The primary objective is to assess agreement between Masimo total hemoglobin and complete blood count and on a continuous scale using Intraclass Correlation Coefficient and Bland-Altman Analysis. The second objective is to assess agreement between the two measures on a binary scale using Positive Percentage Agreement and Negative Percentage Agreement, Cohen's Kappa, and McNemar Test. On an ordinal scale, agreement will be measured using Weighted Cohen's Kappa and Harrel's Concordance Index. Lastly, we will assess factors that might affect the accuracy of Masimo total hemoglobin using linear mixed models., Conclusions: The primary aim of this study is to assess the validity of the non-invasive Masimo device compared to the gold standard method of invasive hemoglobin measurements during pregnancy and postpartum periods for the diagnosis of anemia., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Farooq F et al.)

Low- middle-income countries, including Pakistan, are facing significant obstacles in their efforts to achieve the global targets for maternal, newborn, and child health (MNCH) defined by the Sustainable Development Goals. Barriers at the individual, health system, and contextual levels undermine healthcare access for pregnant women and children, disproportionately affecting those in low-resource settings. To address these challenges in the high-mortality, peri-urban areas of Karachi, VITAL Pakistan Trust and Aga Khan University launched a digital health intervention (DHI) to stimulate demand for health services and streamline care management for health workers at the primary care level. In this case study, we present a narrative review of the design, development, and deployment of the DHI, an Android-based application, in accordance with the Principles for Digital Development. We draw on the initial experience with implementation to reflect on how each of the nine Principles was considered during different phases of the project lifecycle, focusing on the lessons learned and challenges encountered during this process. By engaging with end-users and understanding the community, we were able to map existing relationships and workflows onto a digital platform to address major challenges hindering service delivery. Leveraging insights from field observations and user feedback, we collaborated with experts in healthcare and technology to develop the DHI, which has now scaled to 44 peri-urban settlements in Karachi. Our experience underscores the value of substantiated frameworks like the Principles. However, on-ground challenges reveal important caveats requiring further assessment. These include building community trust in new digital systems and ensuring the ethical use of health data, particularly in low digital and data literacy contexts. Based on this understanding, we share recommendations for conditions central to the effective integration and uptake of technology in healthcare, specifically within the context of digital health for MNCH., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ahmer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Pakistan has disproportionately high maternal and neonatal morbidity and mortality. There is a lack of detailed, population-representative data to provide evidence for risk factors, morbidities and mortality among pregnant women and their newborns. The Pregnancy Risk, Infant Surveillance and Measurement Alliance (PRISMA) is a multicountry open cohort that aims to collect high-dimensional, standardised data across five South Asian and African countries for estimating risk and developing innovative strategies to optimise pregnancy outcomes for mothers and their newborns. This study presents the baseline maternal and neonatal characteristics of the Pakistan site occurring prior to the launch of a multisite, harmonised protocol., Participants: PRISMA Pakistan study is being conducted at two periurban field sites in Karachi, Pakistan. These sites have primary healthcare clinics where pregnant women and their newborns are followed during the antenatal, intrapartum and postnatal periods up to 1 year after delivery. All encounters are captured electronically through a custom-built Android application. A total of 3731 pregnant women with a mean age of 26.6±5.8 years at the time of pregnancy with neonatal outcomes between January 2021 and August 2022 serve as a baseline for the PRISMA Pakistan study., Findings to Date: In this cohort, live births accounted for the majority of pregnancy outcomes (92%, n=3478), followed by miscarriages/abortions (5.5%, n=205) and stillbirths (2.6%, n=98). Twenty-two per cent of women (n=786) delivered at home. One out of every four neonates was low birth weight (<2500 g), and one out of every five was preterm (gestational age <37 weeks). The maternal mortality rate was 172/100 000 pregnancies, the neonatal mortality rate was 52/1000 live births and the stillbirth rate was 27/1000 births. The three most common causes of neonatal deaths obtained through verbal autopsy were perinatal asphyxia (39.6%), preterm births (19.8%) and infections (12.6%)., Future Plans: The PRISMA cohort will provide data-driven insights to prioritise and design interventions to improve maternal and neonatal outcomes in low-resource regions., Trial Registration Number: NCT05904145., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Timely identification of at-risk neonates (ARNs) in the community is essential to reduce mortality in low-resource settings. Tools such as American Academy of Pediatrics pulse oximetry (POx) and WHO Young Infants Clinical Signs (WHOS) have high specificity but low sensitivity to identify ARNs. Our aim was assessing the value of POx and WHOS independently, in combination and with machine learning (ML) from clinical features, to detect ARNs in a low/middle-income country., Methods: This prospective cohort study was conducted in a periurban community in Pakistan. Eligible live births were screened using WHOS and POx along with clinical information regarding pregnancy and delivery. The enrolled neonates were followed for 4 weeks of life to assess the vital status. The predictive value to identify ARNs, of POx, WHOS and an ML model using maternal and neonatal clinical features, was assessed., Results: Of 1336 neonates, 68 (5%) had adverse outcomes, that is, sepsis (n=40, 59%), critical congenital heart disease (n=2, 3%), severe persistent pulmonary hypertension (n=1), hospitalisation (n=8, 12%) and death (n=17, 25%) assessed at 4 weeks of life. Specificity of POx and WHOS to independently identify ARNs was 99%, with sensitivity of 19% and 63%,respectively. Combining both improved sensitivity to 70%, keeping specificity at 98%. An ML model using clinical variables had 44% specificity and 76% sensitivity. A staged assessment, where WHOS, POx and ML are sequentially used for triage, increased sensitivity to 85%, keeping specificity 75%. Using ML (when WHOS and POx negative) for community follow-up detected the majority of ARNs., Conclusion: Classic screening, combined with ML, can help maximise identifying ARNs and could be embedded in low-resource clinical settings, thereby improving outcome. Sequential use of classic assessment and clinical ML identifies the most ARNs in the community, still optimising follow-up clinical care., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

In low- and middle-income countries (LMICs), maternal and newborn mortality is high due to the high prevalence of home births. Understanding the reasons behind this behavior is essential for improving maternal and newborn outcomes. Therefore, a qualitative exploratory study was conducted in a peri-urban community in Karachi, Pakistan to understand the perceptions of pregnant women who delivered at home despite receiving antenatal care and the perceptions of their decision-makers regarding this behavior. In-depth interviews were conducted with 15 randomly sampled women who chose to deliver at home after receiving antenatal care at a health facility, as well as 15 family members who were purposively identified as decision-makers by the women themselves. Thematic analysis was performed to explore the perceptions, myths, and cultural beliefs about homebirths as well as women's decision-making power related to childbirth. The three main themes identified showed that traditional beliefs and practices, poverty and gender inequality, and poor healthcare systems significantly influence the preference for childbirth. Traditional beliefs and practices, including religious and cultural beliefs, played a role in perceiving childbirth as a natural process best managed at home. The presence of traditional birth attendants who provide personalized care and emotional support further reinforced this preference. Gender inequalities, including limited access to mobile phones and women's caregiving roles, were identified as barriers to seeking formal healthcare at the time of delivery. Additionally, poor experiences with the formal healthcare system, such as the poor attitude of formal healthcare workers and fear of medical interventions, also contributed to the decision to deliver at home. The study highlighted the complex interplay between traditional/religious beliefs, gender inequalities, and healthcare experiences in shaping the decision to deliver at home despite receiving ANC services in marginalized settings. Addressing these factors is necessary for promoting facility-based delivery and improving maternal and neonatal outcomes in LMICs., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Khalid et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: Data are essential for tracking and monitoring of progress on health-related sustainable development goals (SDGs). But the capacity to analyse subnational and granular data is limited in low and middle-income countries. Although Pakistan lags behind on achieving several health-related SDGs, its health information capacity is nascent. Through an exploratory qualitative approach, we aimed to understand the current landscape and perceptions on data in decision-making among stakeholders of the health data ecosystem in Pakistan., Design: We used an exploratory qualitative study design., Setting: This study was conducted at the Aga Khan University, Karachi, Pakistan., Participants: We conducted semistructured, in-depth interviews with multidisciplinary and multisectoral stakeholders from academia, hospital management, government, Non-governmental organisations and other relevant private entities till thematic saturation was achieved. Interviews were recorded and transcribed, followed by thematic analysis using NVivo., Results: Thematic analysis of 15 in-depth interviews revealed three major themes: (1) institutions are collecting data but face barriers to its effective utilisation for decision-making. These include lack of collection of needs-responsive data, lack of a gender/equity in data collection efforts, inadequate digitisation, data reliability and limited analytical ability; (2) there is openness and enthusiasm for sharing data for advancing health; however, multiple barriers hinder this including appropriate regulatory frameworks, platforms for sharing data, interoperability and defined win-win scenarios; (3) there is limited capacity in the area of both human capital and infrastructure, for being able to use data to advance health, but there is appetite to improve and invest in capacity in this area., Conclusions: Our study identified key areas of focus that can contribute to orient a national health data roadmap and ecosystem in Pakistan., Competing Interests: Competing interests: Dr ZS reports research grant support from the NIH-Fogarty International Center for the AKUPI-NCDs Research training program (Project Number 5D43TW011625-02) and UZIMA DS (U54TW012089); the NIHR, UK for the Centre for IMPACT (NIHR203248); Bill & Melinda Gates Foundation (INV 021944 & INV 050389); and Duke University. Dr SV MD, PhD, FACC, FAHA, FASPC Research support: U.S. Department of Veterans Affairs, National Institutes of Health, World Heart Federation, Tahir and Jooma Family; Honorarium: American College of Cardiology American College of Cardiology (Associate Editor for Innovations, acc.org); Associate Editor (Current Atherosclerosis Reports, Current Cardiology reports, Journal of Clinical Lipidology). Dr ZB MBBS, FRCPCH, FAAP, PhD Executive Director of NCD Child., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Environmental and psychosocial adversities negatively impact children's developmental outcomes. When these factors are experienced in early childhood-a sensitive period of development-the developing brain can be altered. While these associations have been drawn in high-income countries, it is necessary to understand child growth, neurodevelopment, and the role of environmental factors in developmental trajectories in low-income settings. The objective of this study is to longitudinally assess how demographic factors, maternal health, maternal development, and child health, are related to child development on a behavioural, cognitive, and neuroimaging level in low-socioeconomic communities., Methods and Analysis: Mother-child dyads will be identified in the peri-urban field sites of Rehri Goth and Ibrahim Hyderi, Karachi, Pakistan. Dyads will undergo yearly assessments for 4 years beginning when the child is 1 month, 3 months or 6 months of age (+≤30 days of age) (depending on group assignment). Maternal assessments include anthropometry, behavioural, cognitive, and developmental assessments (Edinburgh Postnatal Depression Scale; Parenting Stress Index; Maternal Autonomy Index; Hurt, Insult, Threaten, Scream Tool; Reynolds Intellectual Assessment Scales (RIAS)), and biological samples collection (breast milk, blood, stool, hair). Children's assessments include anthropometry, developmental assessments (Global Scales for Early Development (GSED); RIAS), MRI brain assessments, and biological sample collection (blood, stool, hair). Using cross-sectional and longitudinal data with statistical analysis tools, associations will be quantified between brain structure (MRI) and connectivity (resting state connectivity and diffusion tensor imaging), general cognitive skills (RIAS, GSED) and environmental influences (nutrition via biological samples, maternal mental health via questionnaires) through repeated measures analysis of variance tests and χ 2 tests. Quantile regression and cortical analyses will be conducted to understand how demographic factors are related to the associations found., Ethics and Dissemination: The study has received ethical approval from the Aga Khan University Ethics Review Committee. The study's findings will be disseminated through scientific publications and project summaries for the participants., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Competing Interests: ZH received support from the Bill & Melinda Gates Foundation, VITAL Pakistan Trust, and Islamic Development Bank. RC received support from the National Science Foundation (NSF 1845487), the National Institutes of Health (NIH U2RTW012121), Blue Cross and Blue Shield of Louisiana via a grant awarded to New York University (New York, NY, USA), and from Arizona State University (Tempe, AZ, USA) to attend the Public Health Technology Meeting in collaboration with New America Foundation. ZS received support from the Bill & Melinda Gates Foundation, Fogarty International Center of the National Institutes of Health (INV-021944, D43TW011625), the UK National Institute for Health and Care Research, Duke University (Durham, NC, USA), Certara, and the Population Health Research Institute. ZS also received support as an advisor for participation in the Heart Failure Publication Committee meeting from Cytokinetics. AW and AAB declare no competing interests. This work is part of a larger grant funded by Bill & Melinda Gates Foundation. The funding agency had no role in the development of this Comment.

A quasi-experimental study was conducted at the Aga Khan University, Karachi, Pakistan, to evaluate the outcomes of a series of workshops on 25 medical students' statistical knowledge and acceptance of RStudio. The knowledge in each of the five sessions was assessed using pre- and post- knowledge-based quizzes. In addition, the Student's Attitude Towards Statistics (SATS-36) and the Technology Acceptance Model were used. Data analysis on RStudio revealed a statistically significant improvement in knowledge in all five sessions (p<0.05). SATS-36 showed statistically significant improvement in Cognitive Competence (p<0.001). RStudio had commendable acceptance with relatively high scores of Attitudes (behavioural intention, median = 6.00 [5.20-7.00]) and Utility (perceived usefulness, median = 5.20 [4.10-6.20]). In conclusion, medical students had improved statistical knowledge and acceptance towards the novel statistical tool. Hence, further studies must evaluate the effectiveness of RStudio when integrated as part of the medical curriculum.

Background: Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease., Methods: We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360)., Findings: We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0-94.1) and specificity was 91.7% (95% CI: 87.4-94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies ( n = 56, 76.7%) had a high risk of selection bias while 74% ( n = 54) studies were low risk on reference standard and 67% ( n = 49) were low risk for flow and timing bias., Interpretation: The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis., Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display&#95;record.php?RecordID=288360], identifier [CRD42021288360]., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Parkash, Siddiqui, Jiwani, Rind, Padhani, Rizvi, Hoodbhoy and Das.)

Objective: Digital health interventions (DHIs) have the potential to improve access and quality of care in low-middle-income countries. The aim of this study was to assess the acceptability, usability and aesthetics of a DHI by frontline workers in peri-urban community settings in Karachi, Pakistan., Methods: A mixed-methods study was carried out in peri-urban field sites in Karachi, Pakistan, where maternal and childcare services are provided through front-line care providers using a DHI. These workers include community health workers, midwives, and physicians who were using the DHI for at least six months. For quantitative data, a questionnaire regarding the module design and interface, technical difficulty, and appropriate utilisation was assessed using a 5-point Likert scale. For qualitative data, focus group discussions (FGDs) based on experiences regarding operability, design, its effect on work efficiency and the provision of beneficial health services were conducted., Results: There were 93 respondents for the quantitative questionnaire who reported high satisfaction (>85%) with the DHI in many themes including content quality, aesthetics and ease of use. Participants were least satisfied with service quality (45% satisfaction only) due to issues related to data sync and network connections in these areas. During the FGDs, the workers stated that the DHI helped them with accessing previous data and providing quality health care services to the community., Conclusion: Although frontline workers reported a few technical difficulties while using the DHI, the majority reported that it was acceptable, had user-friendly features and was beneficial in their work processes., Competing Interests: Declaration of conflicting interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Community health workers (CHWs) in maternal, newborn, and child health (MNCH) programs play an important role in demographic surveillance activities; however, there is lack of literature regarding the community and CHWs' perceptions about these activities. The purpose of this study was to explore perceptions of married women of reproductive age (MWRA) regarding the role of CHWs involved in maternal and child surveillance and explore facilitators and barriers for CHWs involved in surveillance activities. A qualitative study was conducted in five peri-urban surveillance sites along the coastal belt of Bin Qasim Town, Karachi, Pakistan. In-depth interviews were conducted with 25 randomly selected MWRAs and 15 CHWs. A thematic analysis was performed to explore perceptions, barriers, and facilitators of the study participants about maternal and child surveillance activities. The results showed that MWRAs perceived surveillance CHWs as service providers with regards to standard counselling i.e. importance of antenatal care, nutrition, immunization, and distribution of iron and folic acid tablets to pregnant women, child growth assessment, and referral of sick children to the health facility. Trust in the CHWs was an enabler for MWRAs, whereas lack of incentives was cited as a barrier to share their health data. CHWs perceived themselves as a bridge in liaising community with the primary health care facility. They highlighted an enabling environment such as appreciation, supportive supervision, training, and utilization of digital data collection tools as facilitators for their work. Low health literacy of the communities, lack of provision of incentives by CHWs to the community, and facility-based experiences of the community were reported as barriers. Surveillance CHWs are an integral link between the health facility and MWRAs. Hence an enabling environment may lead to improved health service delivery, translating into meaningful impact for the mother and child., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Khalid et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Incomplete vital registration systems mean that causes of death during pregnancy and childbirth are poorly understood in low-income and middle-income countries. To inform global efforts to reduce maternal mortality, we compared physician review and computerised analysis of verbal autopsies (interpreting verbal autopsies [InterVA] software), to understand their agreement on maternal cause of death and circumstances of mortality categories (COMCATs) in the Community-Level Interventions for Pre-eclampsia (CLIP) cluster randomised trials., Methods: The CLIP trials took place in India, Pakistan, and Mozambique, enrolling pregnant women aged 12-49 years between Nov 1, 2014, and Feb 28, 2017. 69 330 pregnant women were enrolled in 44 clusters (36 008 in the 22 intervention clusters and 33 322 in the 22 control clusters). In this secondary analysis of maternal deaths in CLIP, we included women who died in any of the 22 intervention clusters or 22 control clusters. Trained staff administered the WHO 2012 verbal autopsy after maternal deaths. Two physicians (and a third for consensus, if needed) reviewed trial surveillance data and verbal autopsies, and, in intervention clusters, community health worker-led visit data. They determined cause of death according to the WHO International Classification of Diseases-Maternal Mortality (ICD-MM). Verbal autopsies were also analysed by InterVA computer models (versions 4 and 5) to generate cause of death. COMCAT analysis was provided by InterVA-5 and, in India, by physician review of Maternal Newborn Health Registry data. Causes of death and COMCATs assigned by physician review, Inter-VA-4, and InterVA-5 were compared, with agreement assessed with Cohen's κ coefficient., Findings: Of 61 988 pregnancies with successful follow-up in the CLIP trials, 143 maternal deaths were reported (16 deaths in India, 105 in Pakistan, and 22 in Mozambique). The maternal death rate was 231 (95% CI 193-268) per 100 000 identified pregnancies. Most deaths were attributed to direct maternal causes (rather than indirect or undetermined causes as per ICD-MM classification), with fair to good agreement between physician review and InterVA-4 (κ=0·56 [95% CI 0·43-0·66]) or InterVA-5 (κ=0·44 [0·30-0·57]), and InterVA-4 and InterVA-5 (κ=0·72 [0·60-0·84]). The top three causes of death were the same by physician review, InterVA-4, and InterVA-5 (ICD-MM categories obstetric haemorrhage, non-obstetric complications, and hypertensive disorders); however, attribution of individual patient deaths to obstetric haemorrhage varied more between methods (physician review, 38 [27%] deaths; InterVA-4, 69 [48%] deaths; and InterVA-5, 82 [57%] deaths), than did attribution to non-obstetric causes (physician review, 39 [27%] deaths; InterVA-4, 37 [26%] deaths; and InterVA-5, 28 [20%] deaths) or hypertensive disorders (physician review, 23 [16%] deaths; InterVA-4, 25 [17%] deaths; and InterVA-5, 24 [17%] deaths). Agreement for all nine ICD-MM categories was fair for physician review versus InterVA-4 (κ=0·48 [0·38-0·58]), poor for physician review versus InterVA-5 (κ=0·36 [0·27-0·46]), and good for InterVA-4 versus InterVA-5 (κ=0·69 [0·59-0·79]). The most commonly assigned COMCATs by InterVA-5 were emergencies (68 [48%] of 143 deaths) and health systems (62 [43%] deaths), and by physician review (India only) were health systems (seven [44%] of 16 deaths) and inevitability (five [31%] deaths); agreement between InterVA-5 and physician review (India data only) was poor (κ=0·04 [0·00-0·15])., Interpretation: Our findings indicate that InterVA-5 is less accurate than InterVA-4 at ascertaining causes and circumstances of maternal death, when compared with physician review. Our results suggest a need to improve the next iteration of InterVA, and for researchers and clinicians to preferentially use InterVA-4 when recording maternal deaths., Funding: University of British Columbia (grantee of the Bill & Melinda Gates Foundation)., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Community-based strategies to promote maternal health can help raise awareness of pregnancy danger signs and preparations for emergencies. The objective of this study was to assess change in birth preparedness and complication readiness (BPCR) and pregnant women's knowledge about pre-eclampsia as part of community engagement (CE) activities in rural Pakistan during the Community Level Interventions for Pre-eclampsia (CLIP) Trial., Methods: The CLIP Trial was a cluster randomized controlled trial that aimed to reduce maternal and perinatal morbidity and mortality using CE strategies alongside mobile health-supported care by community health care providers. CE activities engaged pregnant women at their homes and male stakeholders through village meetings in Hyderabad and Matiari in Sindh, Pakistan. These sessions covered pregnancy complications, particularly pre-eclampsia/eclampsia, BPCR and details of the CLIP intervention package. BPCR was assessed using questions related to transport arrangement, permission for care, emergency funds, and choice of facility birth attendant for delivery during quarterly household surveys. Outcomes were assessed via multilevel logistic regression with adjustment for relevant confounders with effects summarized as odds ratios and 95% confidence intervals., Results: There were 15 137 home-based CE sessions with pregnant women and families (n = 46 614) and 695 village meetings with male stakeholders (n = 7784) over two years. The composite outcomes for BPCR and pre-eclampsia knowledge did not differ significantly between trial arms. However, CE activities were associated with improved pre-eclampsia knowledge in some areas. Specifically, pregnant women in the intervention clusters were twice as likely to know that seizures could be a complication of pregnancy (odds ratio (OR) = 2.17, 95% confidence interval (CI)  = 1.11, 4.23) and 2.5 times more likely to know that high blood pressure is potentially life-threatening during pregnancy (OR = 2.52, 95% CI = 1.31, 4.83) vs control clusters., Conclusions: The findings suggested that a CE strategy for male and female community stakeholders increased some measures of knowledge regarding complications of pre-eclampsia in low-resource settings. However, the effect of this intervention on long-term health outcomes needs further study., Trial Registration: Clinical Trials.gov - INCT01911494., Competing Interests: Competing interests: The authors completed the ICMJE Unified Competing Interest form (available upon request from the corresponding author), and declare no conflicts of interest., (Copyright © 2021 by the Journal of Global Health. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hoodbhoy, Jiwani, Sattar, Salam, Hasan and Das.)

Universal primary education is critical for individual academic growth and overall adult productivity of nations. Estimates indicate that 25% of 59 million primary age out of school children drop out and early grade failure is one of the factors. An objective and feasible screening measure to identify at-risk children in the early grades can help to design appropriate interventions. The objective of this study was to use a Machine Learning algorithm to evaluate the power of Electroencephalogram (EEG) data collected at age 4 in predicting academic achievement at age 8 among rural children in Pakistan. Demographic and EEG data from 96 children of a cohort along with their academic achievement in grade 1-2 measured using an academic achievement test of Math and language at the age of 7-8 years was used to develop the machine learning algorithm. K- Nearest Neighbor (KNN) classifier was used on different model combinations of EEG, sociodemographic and home environment variables. KNN model was evaluated using 5 Stratified Folds based on the sensitivity and specificity. In the current dataset, 55% and 74% failed in the mathematics and language test respectively. On testing data across each fold, the mean sensitivity and specificity was calculated. Sensitivity was similar when EEG variables were combined with sociodemographic, and home environment (Math = 58.7%, Language = 66.3%) variables but specificity improved (Math = 43.4% to 50.6% and Language = 32% to 60%). The model requires further validation for EEG to be used as a screening measure with adequate sensitivity and specificity to identify children in their preschool age who may be at high risk of failure in early grades., Competing Interests: The funder, CSIMAC provided support in the form of salaries for authors [MR] and the salaries of the authors [SA, HS, AS, MN] were supported by Ephlux Private Limited. This commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objective: The aim of this study was to review primary indications for foetal echocardiography among pregnant women at a tertiary care center in a low resource setting., Methods: A retrospective record review was conducted of all women who have had a foetal echocardiogram between January 2015 and December 2016 at The Aga Khan University Hospital, Karachi, Pakistan. Information regarding maternal clinical characteristics, anomaly scan findings, indications for echocardiography and findings on foetal echocardiogram (FE) was collected., Results: The dataset included 1909 patients. Maternal comorbidities including maternal diabetes, (n=614, 47%) was the most common maternal indication, while multiple pregnancy (n=232, 38%) and intra-cardiac echogenic foci (n=168, 28%) was the most common foetal indication for referral. Major CHD was detected in 4.2% (n=81) of cases. In those with CHD, the most common indication for getting a FE was an abnormal 4-chamber (n=31, 38%) view on the screening obstetric ultrasound., Conclusions: Most of our patients were referred on the basis of indications that were not warranted based on international guidelines and very few had major CHD on FE. This emphasizes the need for justifying referrals for a resource intensive procedure such as FE.

Background: This study is aimed at reviewing the published literature on the prognostic role of serum procalcitonin (PCT) in COVID-19 cases., Data Retrieval: We systematically reviewed the literature available on PubMed, MEDLINE, LitCovid NLM, and WHO: to assess the utility of PCT in prognosis of coronavirus disease. Scrutiny for eligible studies comprising articles that have evaluated the prognostic utility of PCT and data compilation was undertaken by two separate investigators. Original articles in human subjects reporting the prognostic role of PCT in adult COVID-19 patients were included. The Quality in Prognosis Studies (QUIPS) tool was utilized to assess the strength of evidence. Results were reported as narrative syntheses., Results: Out of the total 426 citations, 52 articles passed through screening. The quality of evidence and methodology of included studies was overall acceptable. The total sample size of the studies comprised of 15,296 COVID-19-positive subjects. Majority of the studies were from China, i.e., 40 (77%). The PCT cut-off utilized was 0.05 ng/mL by 18 (35%) studies, followed by 0.5 ng/mL by 9 (17.5%). Eighty five percent ( n = 44) studies reported statistically significant association ( p value < 0.05) between PCT and severity., Conclusion: Procalcitonin appears as a promising prognostic biomarker of COVID-19 progression in conjunction with the clinical context., How to Cite This Article: Ahmed S, Jafri L, Hoodbhoy Z, Siddiqui I. Prognostic Value of Serum Procalcitonin in COVID-19 Patients: A Systematic Review. Indian J Crit Care Med 2021;25(1):77-84., Competing Interests: Source of support: Nil Conflict of interest: None, (Copyright © 2021; Jaypee Brothers Medical Publishers (P) Ltd.)

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., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2020.)

Episiotomy is a commonly performed procedure at the time of vaginal delivery to prevent perineal lacerations. A study was conducted to evaluate the complications of episiotomy. A sample size of 235 patients was taken. The complications were divided in two groups depending on the time of occurrence after delivery. Out of 235 patients, immediate complications were reported in 10(4.3%) patients. These included perineal tears, postpartum haemorrhage, extended episiotomy, perineal pain, inability to pass urine or stool and vaginal haematoma. Early complications including wound infection, gaping wound and resuturing of wound were reported in 21(8.9%) patients. On multivariate analysis, it was seen that age (19-29 years) was significantly associated with complications. Mediolateral episiotomy is a safe obstetrical surgical procedure in order to prevent third and fourth degree perineal tears and is not associated with increased incidence of complications.

The objective of this review was to assess the impact of lifestyle interventions (including dietary interventions, physical activity, behavioral therapy, or any combination of these interventions) to prevent and manage childhood and adolescent obesity. We conducted a comprehensive literature search across various databases and grey literature without any restrictions on publication, language, or publication status until February 2020. We included randomized controlled trials and quasi-experimental studies from both high income countries (HIC) and low-middle-income countries (LMICs). Participants were children and adolescents from 0 to 19 years of age. Studies conducted among hospitalized children and children with any pre-existing health conditions were excluded from this review. A total of 654 studies (1160 papers) that met the inclusion criteria were included in this review. A total of 359 studies targeted obesity prevention, 280 studies targeted obesity management, while 15 studies targeted both prevention and management. The majority of the studies (81%) were conducted in HICs, 10% of studies were conducted in upper middle income countries, while only 2% of the studies were conducted in LMICs. The most common setting for these interventions were communities and school settings. Evidence for the prevention of obesity among children and adolescents suggests that a combination of diet and exercise might reduce the BMI z -score (MD: -0.12; 95% CI: -0.18 to -0.06; 32 studies; 33,039 participants; I 2 93%; low quality evidence), body mass index (BMI) by 0.41 kg/m 2 (MD: -0.41 kg/m 2 ; 95% CI: -0.60 to -0.21; 35 studies; 47,499 participants; I 2 98%; low quality evidence), and body weight (MD: -1.59; 95% CI: -2.95 to -0.23; 17 studies; 35,023 participants; I 2 100%; low quality evidence). Behavioral therapy alone (MD: -0.07; 95% CI: -0.14 to -0.00; 19 studies; 8569 participants; I 2 76%; low quality evidence) and a combination of exercise and behavioral therapy (MD: -0.08; 95% CI: -0.16 to -0.00; 9 studies; 7334 participants; I 2 74%; low quality evidence) and diet in combination with exercise and behavioral therapy (MD: -0.13; 95% CI: -0.25 to -0.01; 5 studies; 1806 participants; I 2 62%; low quality evidence) might reduce BMI z -score when compared to the control group. Evidence for obesity management suggests that exercise only interventions probably reduce BMI z -score (MD: -0.13; 95% CI: -0.20 to -0.06; 12 studies; 1084 participants; I 2 0%; moderate quality evidence), and might reduce BMI (MD: -0.88; 95% CI: -1.265 to -0.50; 34 studies; 3846 participants; I 2 72%) and body weight (MD: -3.01; 95% CI: -5.56 to -0.47; 16 studies; 1701 participants; I 2 78%; low quality evidence) when compared to the control group. and the exercise along with behavioral therapy interventions (MD: -0.08; 95% CI: -0.16 to -0.00; 8 studies; 466 participants; I 2 49%; moderate quality evidence), diet along with behavioral therapy interventions (MD: -0.16; 95% CI: -0.26 to -0.07; 4 studies; 329 participants; I 2 0%; moderate quality evidence), and combination of diet, exercise and behavioral therapy (MD: -0.09; 95% CI: -0.14 to -0.05; 13 studies; 2995 participants; I 2 12%; moderate quality evidence) also probably decreases BMI z -score when compared to the control group. The existing evidence is most favorable for a combination of interventions, such as diet along with exercise and exercise along with behavioral therapy for obesity prevention and exercise alone, diet along with exercise, diet along with behavioral therapy, and a combination of diet, exercise, and behavioral therapy for obesity management. Despite the growing obesity epidemic in LMICs, there is a significant dearth of obesity prevention and management studies from these regions.

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., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The Majority (99%) of maternal deaths occur in low and middle-income countries. The three most important causes of maternal deaths in these regions are postpartum hemorrhage, pre-eclampsia and puerperal sepsis. There are several diagnostic criteria used to identify sepsis and one of the commonly used criteria is systematic inflammatory response syndrome (SIRS). However, these criteria require laboratory investigations that may not be feasible in resource-constrained settings. Therefore, this study aimed to develop a model based on risk factors and clinical signs and symptoms that can identify sepsis early among postpartum women., Methods: A case-control study was nested in an ongoing cohort of 4000 postpartum women who delivered or were admitted to the study hospital. According to standard criteria of SIRS, 100 women with sepsis (cases) and 498 women without sepsis (controls) were recruited from January to July 2017. Information related to the socio-demographic status, antenatal care and use of tobacco were obtained via interview while pregnancy and delivery related information, comorbid and clinical sign and symptoms were retrieved from the ongoing cohort. Multivariable logistic regression was performed and discriminative performance of the model was assessed using area under the curve (AUC) of the receiver operating characteristic (ROC)., Results: Multivariable analysis revealed that 1-4 antenatal visits (95% CI 0.01-0.62). , 3 or more vaginal examinations (95% CI 1.21-3.65), home delivery (95% CI 1.72-50.02), preterm delivery, diabetes in pregnancy (95% CI 1.93-20.23), lower abdominal pain (95% CI 1.15-3.42)) vaginal discharge (95% CI 2.97-20.21), SpO2 < 93% (95% CI 4.80-37.10) and blood glucose were significantly associated with sepsis. AUC was 0.84 (95% C.I 0.80-0.89) which indicated that risk factors and clinical sign and symptoms-based model has adequate ability to discriminate women with and without sepsis., Conclusion: This study developed a non-invasive tool that can identify postpartum women with sepsis as accurately as SIRS criteria with good discriminative ability. Once validated, this tool has the potential to be scaled up for community use by frontline health care workers.

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., Competing Interests: There are no conflicts of interest., (Copyright: © 2019 International Journal of Applied and Basic Medical Research.)

Introduction: Pre-eclampsia is a common disorder associated with serious maternal and fetal complications. It is associated with abnormal placentation, which significantly reduces flow, resulting in a relative hypoxic state. These pathophysiological changes lead to subtle macrovascular and cardiac structural and functional changes in the fetus. This can predispose the child with maternal history of pre-eclampsia to risk of premature cardiovascular disease., Methods and Analysis: The children will be identified from a cohort of women with pre-eclampsia. The study will be conducted at The Aga Khan University Hospital, Karachi. Inclusion criteria will be children who are between 2 and 5 years of age and have a maternal history of pre-eclampsia. The child's current weight, height and blood pressure will be recorded. A two-dimensional functional echocardiogram and vascular assessment will be performed to evaluate alterations in cardiac function as well as macrovascular remodelling in these children. Data will be presented as mean±SD, median (IQR) or percentages as appropriate. Independent t-test or Mann-Whitney U test will be used for testing of continuous variables (based on the assumption of normality). A p<0.05 will be used to determine statistical significance., Ethics and Dissemination: Ethical approval has been obtained from AKUH Ethics Review Committee. Findings will be disseminated through scientific publications and project summaries for the participants., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)