Objectives: To compare the risk of hypophosphatemia and associated outcomes following ferric carboxymaltose (FCM) versus ferric derisomaltose (FDI) in adults with iron deficiency anemia. Methods: We conducted a systematic review and meta-analysis of studies identified through database searches up to November 2025. Randomized and observational studies comparing FCM and FDI were included. The primary outcome was the incidence of hypophosphatemia within 35 to 42 days after infusion. Secondary outcomes included severe and persistent hypophosphatemia, patient-reported outcomes, and economic outcomes. Data from randomized trials were quantitatively pooled using a random-effects model to calculate risk ratios (RRs) with 95% confidence intervals (CIs), whereas findings from observational and economic studies were summarized narratively. Results: Twenty-one studies were included, comprising 3 randomized controlled trials (364 participants) and 18 observational or economic evaluation studies. In pooled randomized comparisons, FCM was associated with a higher risk of hypophosphatemia within 35 to 42 days than FDI (RR 8.37, 95% CI 5.10-13.75; P < .00001; I 2 = 0%). Severe hypophosphatemia occurred in 11.3% of patients receiving FCM and was not observed with FDI. Persistent hypophosphatemia beyond 5 to 8 weeks was more frequent with FCM. Fatigue scores improved more with FDI. Economic evaluations reported greater quality-adjusted life years and lower healthcare costs with FDI. Conclusion: Ferric carboxymaltose is associated with a higher risk of hypophosphatemia compared with ferric derisomaltose. Ferric derisomaltose demonstrates a more favorable safety profile and consistent economic findings.
Background: Pediatric oncology patients are highly susceptible to medication errors, because chemotherapeutic agents are high-alert drugs with a narrow therapeutic index requiring individualized, weight-based dosing. Prospective intervention-based evidence from resource-limited settings is scarce. This study evaluated the effect of clinical pharmacist interventions on medication errors in pediatric oncology inpatients. Methods: In a prospective pre-post study in the central teaching hospital of pediatrics in Baghdad, Iraq (September 2025-April 2026), 125 patients receiving high-alert medications were included. Errors were identified from medical records, graded by the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) index, and compared before and after clinical pharmacist intervention. Changes were analyzed with Wilcoxon signed-rank and exact McNemar tests, and baseline differences with Kruskal-Wallis and Mann-Whitney U tests ( P ≤ .05). Results: All 125 patients had ≥1 baseline error (median 4 [IQR 3-6]); dose errors were most common (52.8%) and a missing intravenous-flush order universal (100%). After pharmacist review, errors were intercepted and corrected, leaving 123 of 125 patients (98.4%) error-free (median 0; P < .001), with two residual errors. All errors linked to toxicity (14.4%) or therapeutic failure (1.6%), and all grade E and F events, were eliminated. Physicians accepted 541 of 567 (95.6%) recommendations. Conclusions: Embedding a clinical pharmacist substantially reduced medication errors, with near-universal physician acceptance. Because the same pharmacist identified and corrected the errors, these findings reflect real-time interception rather than durable prevention; sustainability warrants controlled evaluation. The results nonetheless support routine clinical pharmacist integration in pediatric oncology, particularly in resource-limited settings.
Background: Drug shortages significantly impact hospital pharmacy services, patient care, and operational efficiency. Although causes and prevalence are well documented, evidence on operational workflows and decision making processes in shortage management remains fragmented. Objective: This scoping review aimed to characterise the profile of drug shortages and map the operational workflow processes and decision making pathways used by hospital pharmacy personnel to manage drug shortages, including operational strategies, communication practices, and policy implementation. Methods: Eligible sources included primary empirical research, conference papers, conference abstracts or proceedings with sufficient methodological detail, and dissertations with accessible full text empirical findings involving hospital pharmacy settings. Only English language studies were included. Ovid MEDLINE, Scopus and Web of Science were searched from database inception to 23 February 2026. Extracted data included study characteristics, healthcare setting, shortage type, operational workflow processes, decision pathways, communication strategies and guiding policies. Results: Of 1053 records identified, 31 studies were included, predominantly from high income settings. Drug shortages commonly involved essential and injectable medicines and were associated with treatment delays and service disruption. Causes were multifactorial, including manufacturing failures, supply chain inefficiencies, economic constraints and external shocks. Management activities were synthesised into three interconnected phases: (1) detection and assessment, (2) response and strategies and (3) mitigation and evaluation. Key strategies included procurement optimisation, inventory control, therapeutic substitution, allocation prioritisation and interfacility redistribution, supported by communication systems. Conclusions: Drug shortage management is a complex process that extends beyond inventory control and requires coordinated clinical, operational and governance responses. The proposed workflow framework provides a structured but adaptable representation of the shortage management process that may inform responses across different healthcare settings. Future studies should evaluate the feasibility and applicability of the framework across diverse settings and examine the potential role of predictive analytics and inventory management supported by artificial intelligence (AI) in strengthening early warning capabilities and preparedness.
Background:Identifying reliable predictors of mortality and survival in people living with HIV (PLWH) is essential for personalized risk stratification and treatment optimization. This study employed machine learning (ML) and explainable artificial intelligence (XAI) to identify key prognostic factors and develop predictive models for overall survival and mortality in a large PLWH cohort. Methods:We analyzed data from 2139 patients from the AIDS Clinical Trials Group Study 175. For time-to-event analysis, we implemented Cox Proportional Hazards, Random Survival Forest, and Cox Elastic-Net models. For mortality prediction, we employed Elastic Net, Random Forest, and Bayesian Additive Regression Trees (BART). Models were trained on 70% of the data and validated on the remaining 30%. SHapley Additive exPlanations (SHAP) analysis was applied to the best-performing models for interpretability. Model performance was evaluated using C-index, AUC-ROC, accuracy, sensitivity, specificity, and calibration metrics with 95% confidence intervals (CI). No external validation was performed. Results:The Cox Proportional Hazards model demonstrated the best survival prediction performance (C-index: 0.758, 95% CI: 0.723-0.784; 1-year AUC: 0.885, 95% CI: 0.849-0.934), while Elastic Net was optimal for mortality prediction (AUC: 0.751, 95% CI: 0.712-0.793; accuracy: 0.793, 95% CI: 0.758-0.825). CD4 count at 20 weeks consistently emerged as the most influential predictor across all models, with mean absolute SHAP values of 1.745 for survival and 0.129 for mortality. Treatment-related variables, including off-treatment status and specific antiretroviral therapy (ART) regimens, were also consistently important predictors. Symptomatic status showed strong negative contributions (81% for survival, 81% for mortality), while non-White race paradoxically demonstrated protective effects (73% positive contributions). BART demonstrated comparable performance (AUC: 0.743, 95% CI: 0.695-0.785) with excellent calibration (calibration slope: 0.895). Conclusion:Machine learning models, particularly Elastic Net and Cox Proportional Hazards, effectively predict mortality and survival in PLWH, with immunological response, treatment-related factors, and clinical presentation emerging as the dominant predictors. These findings support the potential of ML and XAI for personalized risk stratification in HIV care, emphasizing the critical importance of optimizing ART regimens, ensuring treatment adherence, and addressing modifiable risk factors to improve long-term outcomes.
Purpose:Medication discrepancies at hospital discharge contribute to readmissions and adverse outcomes. Although national groups endorse pharmacist-led discharge medication reconciliation, implementation varies. We examined the effect of pharmacist-led discharge medication reconciliation on 30-day hospital readmission odds and financial outcomes after launching a transitions-of-care discharge service at our quaternary medical center. Methods:This retrospective observational study evaluated discharged in-patients who received pharmacist-led discharge medication reconciliation (study intervention) versus those who did not. Discharges from intensive care, oncology, and transplant services were excluded. Patient-encounter data and pharmacist study intervention were analyzed, with 30-day hospital readmission odds as the primary endpoint. Pharmacist documented clinical interventions conducted during the study intervention were also analyzed for secondary outcomes of patient and cost saving outcomes. Generalized estimating equations accounted for repeated encounters and assessed the influence of discharge medication reconciliation on 30-day hospital readmission odds, including interaction with case mix index. Results:Of 14 592 discharges, 12 367 (84.8%) met the inclusion criteria, and 8469 (68.0%) received pharmacist-led discharge medication reconciliation, which is the study intervention. In the fourth quarter, 3170 discharges were evaluated. Among patients who did not receive pharmacist-led discharge medication reconciliation (n = 871), a not receiving PL-DMR was associated with significantly greater 30-day hospital readmission odds (OR, 1.06; 95% CI 1.00-1.11; P = .036). Patients discharged from the Digestive Diseases Institute without pharmacist-led discharge medication reconciliation had higher odds of readmission (OR, 2.14; 95% CI 1.14-3.99; P = .017), although this association was not significant after accounting for interaction with case mix index in the intervention model (OR, 1.47; 95% CI 0.52-4.19; P = .467). Surgical patients who did not receive pharmacist-led discharge medication reconciliation also showed a non-significant increase in 30-day hospital readmission odds (OR, 2.73; 95% CI 0.90-8.29; P = 0.076). In 2024, 437 pharmacist documented clinical interventions during the process of discharge medication reconciliation contributed an estimated $240 448.77 in cost avoidance. Conclusion:Pharmacist-led discharge medication reconciliation mitigated complexity-related readmissions, resulting in cost savings. These findings support implementation of transitions-of-care services.
