Feto-maternal outcome of second stage cesarean section in B. P. Koirala institute of health sciences: a retrospective study

Background: Cesarean section (CS) is a common surgical procedure performed in obstetrics. The rate of rise of CS can be attributed to the increase in safety of the procedure, enhanced surgical techniques, improved    antibiotics, increase in number of women requesting for CS. In general, caesarean delivery is associated with more severe maternal complications compared to vaginal deliveries. The stage of labour at which CS is undertaken has been shown to influence the rate/risk of complication. Methods: It was an observational and retrospective study that depended on some clinical records related to more than 37 weeks’ gestation. The study was conducted in BP Koirala institute of health sciences, Dharan Nepal from 2021 December to 2022 December. The neonatal as well as maternal outcomes have been evaluated for CS among those who were observing the second stage in their labor period. The test statistics used to analyse the data were descriptive statistics chi-square test. Results:  The total delivery was 16131 out of which there were 6748 cesarean deliveries. Out of 6748 CS 65 patients had cesarean in second stage of labour. The most common cause of CS in second stage of labor was arrest of descent and dilatation (40%), followed by meconium-stained liquor (15.38%), occipito-posterior position (12.30%), and obstructed labour (3.07%) Being the least cause. One patient had to undergo peri-partum hysterectomy and the most common complication of second stage CS was prolong foleys catheterization (15 patients), post-partum febrile illness (20 patients out of 65), followed by wound infection, PPH, blood transfusion. The neonatal admission for NICU were birth asphyxia and respiratory distress were 50% each. Conclusions: CS in the second stage of labor is correlated with considerably improved neonatal and maternal rate of morbidity along with expanded neonatal mortality. A proper judgment and skilled obstetrician are required to perform a second-stage CS. CS in the second stage of labor is a technically demanding procedure with an increased risk of maternal and neonatal morbidity compared to the CS in the first stage of labor

Non-invasive diagnosis of liver fibrosis in children with chronic hepatitis B by transient elastography

Background Chronic hepatitis B (CHB) is one of the most alarming global health problems. Children with CHB mostly remain asymptomatic but serious sequelae like cirrhosis and hepatocellular carcinoma may develop at any age. Liver biopsy, despite being the gold standard,  is not preferable for the diagnosis of liver fibrosis because it is invasive and painful. Transient elastography, a noninvasive marker for fibrosis, could play an important role in this disease. Objective To observe the role of transient elastography in the assessment of the progression  of liver damage  in children with chronic hepatitis B. Methods This cross-sectional study was conducted at The Department of Paediatric Gastroenterology and Nutrition of Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh. Based on the inclusion and exclusion criteria, there were a total of 55 cases of CHB. Besides proper clinical history, physical examination, and initial investigation, transient elastography was performed in all of the cases. Liver biopsy was taken in 20 patients with raised serum ALT level after taking proper consent. Elastographic findings were compared with clinical, biochemical, virological, and histological findings. Results The mean age was 11.46  (SD 3.6) years and 68.7% were male. Most (65.4%) of the patients were asymptomatic at presentation and biochemically normal. Liver stiffness measurements had positive but insignificant correlation with liver biopsy (r=0.43, P=0.06). Sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy for transient elastography were 80%, 53.3%, 36.3%, 88%, and 60% respectively. Areas under the  ROC curve were 0.76 (95%CI 0.47 to 1.0) for patients with significant fibrosis (F? 2). Using a cut off value of 8.05 kPa, patients with significant fibrosis were detected with a sensitivity, specificity of 80% and 53%, respectively. Findings of transient elastography were significantly associated with clinical findings like anaemia, jaundice, hepatosplenomegaly, stigmata of CLD and biochemical findings like  serum ALT, AST as well as  virological parameters. Conclusion  Transient elastography has a limited role in confirming a diagnosis of significant fibrosis. But because of good sensitivity, transient elastography can be used as an initial presumptive diagnostic tool for assessing significant hepatic fibrosis.  A cut off value of less than 8.05 in transient elastography can be used for exclusion of significant fibrosis

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Community-based lifestyle intervention for diabetes (Co-LID study) management in rural Nepal: study protocol for a clustered randomized controlled trial

Abstract Background Type 2 diabetes mellitus (T2DM) has increased globally; with a disproportionate burden in South and Southeast Asian countries, including Nepal. There is an urgent need for clinically and cost-effective culturally adapted T2DM management programs. In this study, we aim to assess the effectiveness of community based culturally appropriate lifestyle intervention in improving the management and care of people with T2DM. Methods We will conduct a cluster randomized control trial to evaluate the effectiveness of community based culturally appropriate lifestyle intervention in improving T2DM outcomes. The trial will be conducted in 30 randomly selected healthcare facilities from two purposively selected districts (Kavrepalanchowk and Nuwakot districts) of Bagmati province, Nepal. The selected healthcare facilities are being randomized into 15 interventions (n = 15) and usual care (n = 15) groups. Those in the intervention will receive group-based 12 an hour-long fortnightly session delivered over 6 months period. The intervention package includes 12 planned modules related to diabetes care, ongoing support, supervision and monitoring, follow-up from the trained community health workers, and educational materials on diabetes self-management. The participants in the usual care groups will receive pictorial brochure on diabetes management and they will continue receiving the usual care available from the local health facilities. The primary outcome is HbA1c level, and the secondary outcomes include quality of life, health care utilization, and practice of self-care behaviour, depression, oral health quality of life, and economic assessment of the intervention. Two points measurements will be collected by the trained research assistants at baseline and at the end of the intervention. Discussion This study will provide tested approaches for culturally adapting T2DM interventions in the Nepalese context. The findings will also have practice and policy implications for T2DM prevention and management in Nepal. Trial registration Australia and New Zealand Clinical Trial Registry (ACTRN12621000531819). Registered on May 6, 2021

Frontline Healthcare Workers’ Knowledge and Perception of COVID-19, and Willingness to Work during the Pandemic in Nepal

This study investigated the contextual factors associated with the knowledge, perceptions, and the willingness of frontline healthcare workers (FHWs) to work during the COVID-19 pandemic in Nepal among a total of 1051 FHWs. Multivariable logistic regression analysis was applied to identify independent associations between predictors and outcome variables. Of the total study subjects, 17.2% reported inadequate knowledge on COVID-19, 63.6% reported that they perceived the government response as unsatisfactory, and 35.9% showed an unwillingness to work during the pandemic. Our analyses demonstrated that FHWs at local public health facilities, pharmacists, Ayurvedic health workers (HWs), and those with chronic diseases were less likely, and male FHWs were more likely, to have adequate knowledge of COVID-19. Likewise, nurses/midwives, public health workers, FHWs from Karnali and Far-West provinces, and those who had adequate knowledge of COVID-19 were more likely to have satisfactory perceptions towards the government response. Further, FHWs—paramedics, nurse/midwives, public health workers, laboratory workers—FHWs from Karnali Province and Far-West Province, and those with satisfactory perceptions of government responses to COVID-19 were predictors of willingness to work during the COVID-19 pandemic. These results suggest that prompt actions are required to improve FHWs’ knowledge of COVID-19, address negative perceptions of government responses, and motivate them through specific measures to provide healthcare services during the pandemic

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)