Christian Medical College & Hospital

Prenatal diagnosis (PND) is an effective measure to prevent births with transfusion-dependent β-thalassemia (TDT) and other severe hemoglobinopathies. However, multicentric consolidated data from high-volume Indian referral laboratories is limited. The authors collated a multi-center experience of invasive PND for β-thalassemia and the major hemoglobinopathies, including center-wise volumes, affected-fetus rates, trends in primigravida referrals, and diagnostic challenges from seven Indian tertiary-care referral centers with a minimum of 10 y PND services. Retrospective data were contributed by SGRH-New Delhi, ICMR-NIIH-Mumbai, PGIMER-Chandigarh, AIIMS-New Delhi, NIMS-CDFD-Hyderabad, CMC-Vellore and SGPGI-Lucknow. Carrier evaluation was based on blood counts and Hb-HPLC. Fetal diagnosis utilized chorionic villus sampling or amniocentesis with molecular testing by ARMS-PCR, reverse dot-blot and/or Sanger sequencing as per local practices. Descriptive analyses summarized volumes, proportions of fetuses with biallelic HBB variants, and primigravida referral trends. Across centers, 6,780 PND procedures were performed over 2015–2024, with ICMR-NIIH and SGRH contributing 58.2

Obesity is a global health crisis affecting developing nations, including India. The management of obesity continues to evolve with newer drugs, metabolic and bariatric surgery and endoscopic interventions, requiring family physicians and specialists to adapt their clinical practice accordingly. There is an urgent need for a standardized algorithm to diagnose, stage, and treat obesity. The Endocrine Society of India (ESI) and the Obesity Surgeons Society of India (OSSI) appointed a steering committee to develop an evidence-based algorithm for managing patients with obesity in India. This was put to vote by 80 specialists (38 from OSSI and 42 from ESI) in a physical meeting. A proposed stage-wise algorithm based on Edmonton Obesity Staging System, Asian definition of obesity, and resources in India, received 100

A real diagnostic challenge is posed by transfusion-dependent anemia in young patients with obscure gastrointestinal (GI) bleeding. Such a presentation can have many etiologies. Although rare, small intestinal hemangiomas are benign vascular tumors that frequently elude timely diagnosis owing to their intramural growth pattern and deep submucosal sequestration. Recent literature has increasingly shown the benefit of systemic therapies, such as sirolimus, for managing complex vascular anomalies. We report the case of a 17-year-old girl with a history of easy fatigability, melena, and exertional dyspnea, requiring regular red blood cell transfusions for the past six years. Initial laboratory investigations were suggestive of severe iron deficiency anemia, with a hemoglobin level of 6.3 g/dL and a ferritin level of 5.2 ng/mL. The stool occult blood test was positive. However, repeated upper and lower GI endoscopies and imaging were non-contributory. A repeat computed tomography angiography (CTA) of the abdomen was carried out, which showed intramural polypoidal lesions in the distal ileum and descending colon. Diagnostic laparoscopy was confirmatory of multiple small intestinal hemangiomas. In view of the multifocal nature of the lesions and the prohibitive risk of extensive bowel resection, a conservative strategy utilizing systemic mTOR (mechanistic target of rapamycin) inhibition was adopted. The patient achieved transfusion independence within two months, with a sustained normalization of hematological indices and a favorable side-effect profile. This case highlights the diagnostic complexity of small intestinal hemangiomas. It reinforces the role of imaging and laparoscopy, as well as the importance of considering rare causes, in transfusion-dependent anemia with chronic GI bleeding. In multifocal involvement of hemangiomas, sirolimus therapy may be an effective and safer alternative to surgical intervention. This case calls for more research and awareness of such rare vascular anomalies to accelerate diagnosis and management.

Abstract Diarrheal disease remains a significant cause of morbidity and mortality in children under five years of age in low-and middle-income countries. Identifying the etiology of diarrheal episodes represents a significant challenge in contexts with limited access to laboratory diagnostic methods, where therapeutic decisions are often based solely on clinical criteria, such as the presence of blood in the stool recommended by the World Health Organization (WHO). In this scenario, identifying combinations of clinical signs and symptoms could contribute to more precise therapeutic decisions. In this study, we developed and internally validated a predictive model based exclusively on clinical variables to identify episodes attributable to Shigella spp. infection, using as a reference an etiological outcome defined by quantitative molecular methods (qPCR). Secondary data from the Malnutrition-Enteric Diseases (MAL-ED) cohort, a multicenter study conducted in eight countries (2009–2016), with longitudinal follow-up of 1,715 children, were used. Diarrheal episodes were reconstructed from a disease surveillance database. Subsequently, fecal samples were temporally linked to these episodes, allowing the incorporation of molecular etiological data, defining diagnostic positivity for Shigella spp. After eligibility criteria and data processing, a final analytical database was obtained with 3,342 episodes and nine clinical variables (age, sex, blood in stool, bowel movement frequency, diarrhea duration, dehydration, fever, vomiting, and hospitalization) selected after multicollinearity assessment. Five machine learning algorithms were evaluated, with performance estimated by internal validation. Logistic regression showed the best discrimination (AUC = 0.789) and good calibration (Brier score = 0.077). At a cutoff point of 0.46, the model achieved a sensitivity of 0.753 and a specificity of 0.708. In comparison, the WHO score showed inferior performance (AUC = 0.556; sensitivity = 0.147; specificity = 0.965). The high negative predictive value (0.96) highlights the model’s ability to exclude cases not attributable to Shigella spp., suggesting potential utility as a tool to support primary clinical diagnostic decision-making and rational use of antibiotics in resource-limited settings.