Saidu Teaching Hospital

Background: Hereditary spherocytosis (HS) is an autosomal dominant haemolytic anemia in which the erythrocyte’s membrane proteins are mutated. Clinical features of HS include hemolysis, anemia, splenomegaly, and jaundice. Protein C and S deficiency is also an autosomal dominant condition characterized by impaired inactivation of coagulation factors Va and VIIIa. In this condition, the patient is lifelong, susceptible to Venous thromboembolism (VTE), usually manifesting during adolescence or early adulthood. Case Summary: A male patient in his late teens was identified as having extensive deep vein thrombosis (DVT) affecting the left lower extremity subsequent to his complaints of acute pain and edema in the left leg. The thrombophilia assessment revealed normal homocysteine levels, negative results for antiphospholipid antibodies, and normal antithrombin III concentrations, alongside hereditary deficiencies in both protein C and protein S. Intriguingly, a peripheral blood smear analysis indicated a significant presence of microspherocytes, which aligned with the diagnosis of HS. Although HS represents a common form of congenital haemolytic anemia (with an incidence of approximately 1 in 2000 among Northern Europeans), it is generally not associated with thrombotic events. Conversely, deficiencies in protein C and S are known to predispose individuals to VTE, conditions that represent rare autosomal-dominant thrombophilias impacting roughly 0.1% to 0.2% of the general populace. The concurrent occurrence of hereditary HS and combined protein C/S deficiencies is rarely documented in the literature. In this discussion, we explore the etiological factors related to each condition, potential synergistic mechanisms (such as endothelial activation resulting from hemolysis and the attenuation of natural anticoagulants), as well as the implications for diagnosis and treatment. Conclusion: This case underscores the necessity of conducting a thorough evaluation for haemolytic anemia in instances where thrombophilia is identified, particularly as young individuals presenting with "unprovoked" venous thrombosis may harbor multiple inherited risk factors. Key words: Hereditary Spherocytosis, Protein C & S Deficiency, Deep Vain Thrombosis.

ABSTRACT Background and Aims Congenital heart disease (CHD) is a significant cause of infant mortality. In Pakistan, inadequate screening in healthcare settings and under‐reporting of CHD cases contribute to high mortality and morbidity rates. This study aimed to determine the prevalence and case fatality rate of CHD over a decade among neonates and post‐neonatal infants in Buner, Khyber Pakhtunkhwa (KP), Pakistan. Clarifying the epidemiological profile of CHD in this region is vital for strengthening early detection in newborns. Methods This retrospective, single‐center study was conducted at the District Headquarter Hospital, District Buner. Data related to various parameters were extracted from hospital records and presented as prevalence and case fatality rate (CFR). Time‐series analysis was performed to assess trends over the years. Results During the study period, a total of 733 cases of CHD were identified among 78,713 newborns. Of these cases, 415 (56.6%) were male infants, while 318 (43.4%) were female. The prevalence rate varied, with a peak in 2023 at 17.05 per 1000 and a trough in 2018 at 5.15 per 1000. Over the 11 years, the overall CFR was 36.7%, with the highest in 2021 at 68.1% and the lowest in both 2015 and 2018 at 27.7% each. The number of CHD cases increased significantly over time, rising from 47 (0.86%) in 2013 to 139 (1.7%) in 2023. Conclusion The data indicate a consistent rise in CHD cases, along with an increasing case fatality rate and prevalence over the years in District Buner, Pakistan. This study underscores the need for improved screening protocols for CHD and the adoption of electronic health records to enable timely reporting of CHD cases among neonates and post‐neonatal infants.

Objectives: To determine the frequency and identify the determinants of contrast-induced nephropathy (CIN) in patients undergoing coronary angiography and/or percutaneous coronary intervention (PCI) in a tertiary care hospital in rural Pakistan. Methodology: This prospective observational cohort study was conducted from May 15, 2025, to November 15, 2025, at Saidu Teaching Hospital, Swat. A total of 196 patients undergoing coronary angiography and/or PCI were enrolled. Baseline demographic, clinical, and procedural data were collected. Serum creatinine levels were measured at baseline, 48 hours, and 72 hours post-procedure. CIN was defined as an increase in serum creatinine >= 0.5 mg/dL or >= 25%from baseline within 48-72 hours. Statistical analysis included univariate tests and multivariable logistic regression using Firth's penalized likelihood method. Results: The incidence of CIN was 11.2% at 48 hours and increased to 13.8% after 72-hour follow-up. Among CIN patients, 59.3% were diabetic, 18.5% had hypotension, and 44.4% had exposure to nephrotoxic drugs. Univariate analysis showed significant associations of CIN with diabetes, hypotension, nephrotoxic drug use, procedure type, and contrast volume (p < 0.05). Multivariable analysis identified diabetes mellitus (adjusted odds ratio [aOR] = 3.85, 95% CI: 1.41-10.52, p = 0.009) and contrast volume >100 mL (aOR = 4.92, 95% CI: 1.88-12.85, p = 0.001) as independent predictors. Conclusion: CIN is a frequent complication following coronary procedures, particularly among diabetic patients and those exposed to higher contrast volumes. These findings underscore the importance of risk stratification and preventive strategies, including optimized hydration and contrast minimization, especially in resource-limited settings.

Background: Dengue fever remains a major public health challenge in endemic regions, where recovery duration varies considerably across patients due to a combination of clinical, demographic, and contextual factors. Although machine learning (ML) approaches have increasingly been applied to dengue related prediction tasks, many existing models operate as black boxes, limiting their interpretability and practical usefulness in healthcare settings. This study presents an Explainable Artificial Intelligence (XAI) based machine learning framework for analyzing dengue recovery duration using a multi-center clinical dataset collected from healthcare institutions across Khyber Pakhtunkhwa, Pakistan. Methods: Clinical records from 100 laboratory-confirmed dengue patients treated across multiple healthcare institutions were analyzed. The dataset included demographic, socio-economic, and clinical variables. Four machine learning models: Linear Regression, Decision Tree, Random Forest, and Neural Network, were developed and evaluated using 10-fold cross-validation. Explainability techniques, including Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), and Local Interpretable Model-Agnostic Explanations (LIME), were employed to investigate global and patient specific factors influencing recovery duration. Results: Among the evaluated models, Random Forest demonstrated the best overall predictive performance, achieving the lowest Root Mean Square Error (RMSE; 11.29 days) and Mean Absolute Error (MAE; 9.09 days), corresponding to a 40.4% reduction in prediction error compared with Linear Regression. Decision Tree also showed substantial improvement, reducing RMSE by 37%, whereas the Neural Network achieved a more modest improvement of 8.6%. Although all models exhibited relatively low coefficient of determination (R2) values (maximum R2 = 0.026), the explainability analyses consistently identified age and platelet count as the most influential predictors of recovery duration. Older age and lower platelet counts were generally associated with longer recovery periods, while hospital type, education level, and blood group also contributed to prediction outcomes. ICE and LIME analyses further revealed considerable patient level heterogeneity, indicating that recovery trajectories are shaped by complex interactions among clinical, demographic, and contextual factors rather than a single dominant predictor.

Colchicine is an anti-inflammatory drug that has emerged as a potential treatment option following myocardial infarction (MI). This meta-analysis assesses the efficacy and safety of colchicine in patients following an MI. A comprehensive literature search was performed using the Cochrane Library, ClinicalTrials.gov, Embase, and MEDLINE, covering studies from their inception to December 2025. RevMan was used to perform a random-effects meta-analysis, and forest plots were used to visualize pooled estimates. The Mantel-Haenszel method was applied to analyze dichotomous outcomes. Nine randomized control trials (RCTs) were included in this meta-analysis, including 13,834 patients. The analysis revealed that there was no statistically significant difference between colchicine and placebo groups in all-cause mortality [relative risk (RR)=0.93, confidence interval (CI)=0.78-1.11], cardiac death (RR=0.95, CI=0.73-1.22), MI (RR=0.82, CI=0.67-1.00), stroke (RR=0.64, CI=0.26-1.58), adverse events (RR=1.01, CI=0.85-1.21), serious adverse events (RR=0.96, CI=0.87-1.05), serious gastrointestinal adverse events (RR=1.35, CI=0.94-1.95), and serious infections (RR=1.00, CI=0.62-1.62). However, there was an increased risk of diarrhea in people with colchicine compared to the placebo (RR=2.12, CI=1.35-3.32). This meta-analysis demonstrates that colchicine following an MI does not have a significant impact on cardiovascular events or major adverse events other than an increased risk of diarrhea. There is a need for more comprehensive RCTs with longer follow-up times to fully comprehend the potential cardiovascular benefits of colchicine.