Hasil untuk "Medical emergencies. Critical care. Intensive care. First aid"

Danica Anderson, Pallash A. Desai, Hung Nguyen et al.

Background: Renal calculi commonly present with flank pain or hematuria. Extracorporeal shockwave lithotripsy (ESWL) is a noninvasive treatment for kidney or proximal ureteral stones ≤10 mm, except in cases of obesity, pregnancy, coagulopathy, high stone attenuation on computerized tomography (CT), or abnormal renal anatomy. Rare complications include ureteral obstruction, urinary tract infection, and hemorrhage. This case report reviews a rare complication of active renal arterial hemorrhage after outpatient ESWL, requiring resuscitation and interventional radiology (IR). Case report: A 44-year-old Caucasian male presented to the emergency department (ED) with mild tachycardia, pallor, and significant right flank pain following an outpatient ESWL hours earlier. A point-of-care ultrasound demonstrated free fluid in the hepatorenal space. The initial non-contrast CT scan of the abdomen and pelvis revealed a large perinephric hematoma but possible extravasation. The CT of the abdomen and pelvis with contrast revealed active intrarenal arterial extravasation with extensive retroperitoneal hemorrhage and perinephric hematoma; his hemoglobin was 8.1 g/dL. The patient underwent emergent IR embolization and received five units of packed red blood cells during his stay in the intensive care unit (ICU). He was discharged on hospital day three. Why should an emergency medicine physician be aware of this?: Less than 1 % of cases report major vascular bleeding following an ESWL. Early recognition by the emergency medicine physician can help significantly decrease morbidity and mortality by initiating early multidisciplinary response through urology, IR, and intensive care.

Simone Garatti, Lucrezia Manieri, Alessandro Falsone et al.

The scenario approach provides a powerful data-driven framework for designing solutions under uncertainty with rigorous probabilistic robustness guarantees. Existing theory, however, primarily addresses assessing robustness with respect to a single appropriateness criterion for the solution based on a dataset, whereas many practical applications - including multi-agent decision problems - require the simultaneous consideration of multiple criteria and the assessment of their robustness based on multiple datasets, one per criterion. This paper develops a general scenario theory for multi-criteria data-driven decision making. A central innovation lies in the collective treatment of the risks associated with violations of individual criteria, which yields substantially more accurate robustness certificates than those derived from a naive application of standard results. In turn, this approach enables a sharper quantification of the robustness level with which all criteria are simultaneously satisfied. The proposed framework applies broadly to multi-criteria data-driven decision problems, providing a principled, scalable, and theoretically grounded methodology for design under uncertainty.

Johannes Cordier

Normal care units (NCU) placement affects health outcomes. NCUs in a hospital have different specialisations. There are patients that can potentially stay in multiple different NCUs. On a given day the NCUs are on different utilisation levels, which also affects health outcomes. Our approach uses instrumental variable causal forests, with emergency admission as an instrument, to estimate how the effect of NCU placement varies across patients and utilisation levels. The results show a clear trade-off between specialisation and utilization. Based on these findings, we design a minimax regret placement policy, using frequentist, Balke-Pearl and Manski bounds, that lowers mortality without capacity expansion. The policy reallocates patients according to their individualized average treatment effects, showing that data-driven patient placement can improve outcomes by using existing resources more efficiently.

Shuozhe Li, Jincheng Cao, Bodun Hu et al.

Reinforcement finetuning (RFT) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, we identify a critical trade-off: while unconstrained RFT achieves strong reasoning performance, it severely compromises model trustworthiness by amplifying hallucination and worsening calibration; conversely, RKL-constrained RFT preserves trustworthiness but limits reasoning gains due to its unbounded penalty on exploratory deviations. To resolve this tension, we introduce CARE-RFT (Confidence-Anchored Regularized Reinforcement Finetuning), a novel method that replaces standard reverse KL regularization with a skew reverse KL divergence. CARE-RFT provides a confidence-sensitive penalty: it is bounded for confident, consistently rewarded explorations to enable reasoning, while unbounded elsewhere to preserve calibration. Extensive experiments across multiple model scales and RFT algorithms show that CARE-RFT achieves a superior balance, matching the reasoning performance of unconstrained RFT while recovering the trustworthiness and calibration of the base model. Our work establishes that careful, confidence-aware regularization is key to building both capable and trustworthy reasoning models.

Louise Rose, Peter Spronk, Stacey Skoretz

Victoria R. Bortner, Emily Holbrook, Heather Henderson et al.

Introduction: Rates of sexually transmitted infections (STI), remain high in Hillsborough County, FL. As the emergency department (ED) is frequently used for STI diagnosis and treatment, a local hospital ED implemented a linkage-to-care program using a callback system to ensure that patients with chlamydia, gonorrhea, and/or syphilis received treatment. Our primary aim in this paper was to evaluate implementation of an ED-based STI treatment program by describing empiric, follow-up, and overall treatment rates in STI-positive patients by disease and sex. A secondary aim was to evaluate reasons for undertreatment during the acute-care encounter. Methods: We conducted this quality assurance project, including a retrospective chart review of electronic health records from 2019–2022, at an urban ED in Hillsborough County, Florida. During this period, we reviewed all records reflecting positive results for chlamydia, gonorrhea and/or syphilis to determine whether empiric treatment was administered in the ED or the patient required coordination for follow-up care. Patients who received empiric treatment or successful follow-up treatment were classified as treated, while those who did not receive successful follow-up treatment were classified as untreated. Results: A total of 1,170 patients were diagnosed with an STI at an urban, quaternary-care hospital in the county. Of these, 689 (58.9%) had chlamydia, 324 (27.7%) had gonorrhea, 133 (11.4%) had dual gonorrhea-chlamydia, and 24 (2.1%) had syphilis. Rates of STI empiric, follow-up, and overall treatment were 47.1%, 86.1%, and 92.6%, respectively. Empiric and overall treatment rates were highest for male patients (72.3% male, 33.4% female) and patients presenting with gonorrhea (67.6% gonorrhea, 63.9% chlamydia). Follow-up treatment rates were highest for female patients (87.1%) and patients presenting with gonorrhea (87.6%). Conclusion: Our findings emphasize both the successes and opportunities for improvement of a linkage-to-care protocol to provide treatment access for patients in the ED who test positive for sexually transmitted infections. Given the significant strain on the public health infrastructure in the United States and on our local Department of Health, ED-based linkage programs fill an important gap in healthcare delivery. Going forward, improving overall treatment rates in females and patients with chlamydia or syphilis is warranted.

Abdullah Mijwil, Wafa Jasim

Background: Diabetes mellitus is rapidly becoming one of the main health issues and problem in the 21st century among humans, the number of patients with diabetes mellitus is steadily increasing both in developed and developing countries. Diabetes mellitus is a major health problem that results in serious consequences on global health and its economy. This study aimed to assess the diabetic attitude regarding their nutritional status in Kirkuk city.Methods: A descriptive design study started from 12 November 2023 and ended at 28 March 2024 in three places in Kirkuk city in Iraq, with a non–random sample collection of (425) diabetic patients who were diagnosed with diabetes mellitus.Results: The mean age of the patients was 50.47 ± 16.24 years. Of them, 49.6% were men compared to 50.4% female. Additionally, 318 (74.8%) of participants had accompanying diseases, including diabetes. Conclusion: There was a high level of attitude among diabetic patients regarding their nutritional status in Kirkuk City.

Anita Saigal, Songyuan Xiao, Owais Siddique et al.

Abstract Background Long-COVID research to date focuses on outcomes in non-hospitalised vs. hospitalised survivors. However Emergency Department attendees (post-ED) presenting with acute COVID-19 may experience less supported recovery compared to people admitted and discharged from hospital (post-hospitalised group, PH). Objective We evaluated outcomes and predictors of specialty care referrals (SCR) in those with ongoing symptomatic Long-COVID, comparing post-ED and PH adults. Methods This prospective observational cohort study evaluates 800 PH and 484 post-ED adults from a single hospital in London, United Kingdom. Participants had either confirmed laboratory-positive SARS-CoV-2 infection or clinically suspected acute COVID-19 and were offered post-COVID clinical follow-up at approximately six weeks after their ED attendance or inpatient discharge, to assess ongoing symptoms and support recovery. Multiple logistic regression determined associations with specialist care referrals (SCR) to respiratory, cardiology, physiotherapy (including chest physiotherapy), and mental health services. Results Presence of at least one Long-COVID symptom was lower in adults attending ED services with acute COVID-19 compared to those hospitalised (70.1% post-ED vs. 79.5% PH adults, p < 0.001). Total number of Long-COVID symptoms was associated with increased SCR in all patients (adjusted odds ratio (aOR) = 1.26, 95%CI:1.16, 1.36, p < 0.001), with post-ED adults more likely to need a SCR overall (aOR = 1.82, 95%CI:1.19, 2.79, p = 0.006). Post-ED adults had higher SCR to both physiotherapy (aOR = 2.59, 95%CI:1.35, 4.96, p = 0.004) and mental health services (aOR = 3.84, 95%CI:2.00, 7.37, p < 0.001), with pre-existing mental illness linked to the latter (aOR = 4.08, 95%CI:1.07, 15.6, p = 0.04). Conclusions We demonstrate greater specialist care referrals to mental health and physiotherapy services in patients attending the ED and discharged with acute COVID-19, compared to those admitted, despite lower ongoing COVID-19 symptom burden. Total number of symptoms, pre-existing co-morbidity such as smoking status, cardiac co-morbidities, and mental health illnesses may predict those requiring healthcare input. This information may enable better post-COVID support for ED attendees, a distinct group who should not be neglected when preparing for future pandemics. Trial registration This study had HRA approval (20/HRA/4928).

Yiji Chen, Jianhua Wan, Wenqing Shu et al.

Abstract Background Patients with severe acute pancreatitis (SAP) frequently develop hypoxic acute respiratory failure (AHRF), with a mortality rate as high as 37%. However, the optimal partial pressure of oxygen (PaO2) for SAP patients remains unclear to date. This study aims to investigate whether partial pressure of oxygen is associated with mortality in SAP patients. Methods A retrospective cohort study was conducted on patients with severe acute pancreatitis (SAP) admitted to the First Affiliated Hospital of Nanchang University from 2015 to 2024. Propensity score matching (based on whether arterial oxygen partial pressure PaO2 ≥ 80 mmHg during the first 3 days after ICU admission, assigning patients to the liberal PaO2 group or conservative PaO2 group), univariate logistic regression analysis, Cox regression analysis, subgroup analysis, Kaplan–Meier (K–M curve) survival analysis, and sensitivity analysis were employed to thoroughly evaluate the association between PaO2 and mortality in SAP patients. The primary outcome was 28-day mortality. Results The study included 1585 patients. We found that higher PaO2 was associated with lower 28-day mortality rates. In logistic regression analysis after propensity score matching, the incidence rates of adverse outcomes such as persistent circulatory failure (OR 0.50; 95% CI 0.35–0.69; P < 0.001) and persistent multiple organ failure (OR 0.60; 95% CI 0.47–0.78; P < 0.001) significantly decreased. The K–M curve demonstrated significant reductions in 28-day mortality (P = 0.02), 90-day mortality (P = 0.0079), and overall mortality (P = 0.008) in the liberal PaO2 group, with all P values showing statistical significance. Subgroup analysis revealed that the association between higher PaO2 and mortality in SAP patients varied across different age groups, BMI values, SIRS and APACHE II scores, and smoking status. Sensitivity analysis demonstrated stable results after excluding specific populations. On the third day of ICU admission (P = 0.016), higher PaO2 correlated with improved outcomes compared to the conservative group, particularly when PaO2 stabilized around 100 mmHg. Conclusions Early maintenance of higher PaO2 (≥80 mmHg) during the initial ICU period was associated with lower mortality.

Kristyn Jeffries, Sara C. Sanders, Rachel Ekdahl et al.

Abstract Background Nearly 7,000 snakebite injuries are reported yearly in the United States, with almost one quarter of those in the pediatric population. Due to increased exposure to snakes, rural children may experience different clinical outcomes for snakebite injuries. The goal of this study was to examine differences in resource utilization of rural and urban pediatric patients with snakebite injuries. Methods This is a retrospective cross-sectional study of patients aged 21 years and under presenting with venomous snakebites in the United States from January 1, 2016, through March 31, 2023, using the Pediatric Hospital Information System database and ICD-10 codes indicating snakebites. Comparisons were conducted to evaluate demographic and clinical characteristics in association with resource utilization and complications between patients living in rural areas and patients living in urban areas. Results The study included 2,633 patients from 23 states. The median age was 9 years; 61% of patients were male. Most patients were in the South and over 70% resided in urban areas. 82% of the population was admitted to a hospital, with median length of stay 1.59 days. Compared to urban patients, rural patients were more likely to be admitted and receive antivenom but were less likely to have an intensive care unit admission and have abnormal coagulation studies. Conclusions Rural pediatric patients with snakebites had different resource utilization and clinical complications than urban patients.

Szeyi Chan, Jiachen Li, Siman Ao et al.

Older adults with mild cognitive impairment (MCI) often face challenges during meal preparation, such as forgetting ingredients, skipping steps, or leaving appliances on, which can compromise their safety and independence. Our study explores the design of context-aware assistive technologies for meal preparation using a user-centered iterative design process. Through three iterative phases of design and feedback, evolving from low-tech lightbox to a digital screen, we gained insights into managing diverse contexts and personalizing assistance through collaboration with older adults with MCI and their care partners. We concluded our findings in three key contexts--routine-based, real-time, and situational--that informed strategies for designing context-aware meal prep assistance tailored to users' needs. Our results provide actionable insights for creating technologies to assist meal preparation that are personalized for the unique lifestyles of older adults with MCI, situated in the complex and dynamic homebound context, and respecting the collaboration between older adults and their care partners.

Prashant Solanki, Nikolaus Vertovec, Yannik Schnitzer et al.

Recent approaches to leveraging deep learning for computing reachable sets of continuous-time dynamical systems have gained popularity over traditional level-set methods, as they overcome the curse of dimensionality. However, as with level-set methods, considerable care needs to be taken in limiting approximation errors, particularly since no guarantees are provided during training on the accuracy of the learned reachable set. To address this limitation, we introduce an epsilon-approximate Hamilton-Jacobi Partial Differential Equation (HJ-PDE), which establishes a relationship between training loss and accuracy of the true reachable set. To formally certify this approximation, we leverage Satisfiability Modulo Theories (SMT) solvers to bound the residual error of the HJ-based loss function across the domain of interest. Leveraging Counter Example Guided Inductive Synthesis (CEGIS), we close the loop around learning and verification, by fine-tuning the neural network on counterexamples found by the SMT solver, thus improving the accuracy of the learned reachable set. To the best of our knowledge, Certified Approximate Reachability (CARe) is the first approach to provide soundness guarantees on learned reachable sets of continuous dynamical systems.

Dmytro Leontiev, Abicumaran Uthamacumaran, Riya Nagar et al.

Ratios of common biomarkers and blood analytes are well established for early detection and predictive purposes. Early risk stratification in critical care is often limited by the delayed availability of complex severity scores. Complete blood count (CBC) parameters, available within hours of admission, may enable rapid prognostication. We conducted an exhaustive and systematic evaluation of CBC-derived ratios for mortality prediction to identify robust, accessible, and generalizable biomarkers. We generated all feasible two-parameter CBC ratios with unit checks and plausibility filters on more than 90,000 ICU admissions (MIMIC-IV). Discrimination was assessed via cross-validated and external AUC, calibration via isotonic regression, and clinical utility with decision-curve analysis. Retrospective validation was performed on eICU-CRD (n = 156530) participants. The ratio of Red Cell Distribution Width (RDW) to Mean Corpuscular Hemoglobin Concentration (MCHC), denoted RDW:MCHC, emerged as the top biomarker (AUC = 0.699 discovery; 0.662 validation), outperforming RDW and NLR. It achieved near-universal availability (99.9\% vs.\ 35.0\% for NLR), excellent calibration (Hosmer--Lemeshow $p = 1.0$; $\mathrm{ECE} < 0.001$), and preserved performance across diagnostic groups, with only modest attenuation in respiratory cases. Expressed as a logistic odds ratio, each one standard deviation increase in RDW:MCHC nearly quadrupled 30-day mortality odds (OR = 3.81, 95\% CI [3.70, 3.95]). Decision-curve analysis showed positive net benefit at high-risk triage thresholds. A simple, widely available CBC-derived feature (RDW:MCHC) provides consistent, externally validated signal for early mortality risk. While not a substitute for multivariable scores, it offers a pragmatic adjunct for rapid triage when full scoring is impractical.

Pak-Hei Yeung, Jayroop Ramesh, Pengfei Lyu et al.

This paper explores the transfer of knowledge from general vision models pretrained on 2D natural images to improve 3D medical image segmentation. We focus on the semi-supervised setting, where only a few labeled 3D medical images are available, along with a large set of unlabeled images. To tackle this, we propose a model-agnostic framework that progressively distills knowledge from a 2D pretrained model to a 3D segmentation model trained from scratch. Our approach, M&N, involves iterative co-training of the two models using pseudo-masks generated by each other, along with our proposed learning rate guided sampling that adaptively adjusts the proportion of labeled and unlabeled data in each training batch to align with the models' prediction accuracy and stability, minimizing the adverse effect caused by inaccurate pseudo-masks. Extensive experiments on multiple publicly available datasets demonstrate that M&N achieves state-of-the-art performance, outperforming thirteen existing semi-supervised segmentation approaches under all different settings. Importantly, ablation studies show that M&N remains model-agnostic, allowing seamless integration with different architectures. This ensures its adaptability as more advanced models emerge. The code is available at https://github.com/pakheiyeung/M-N.

Minjae Chung, Jong Bum Won, Ganghyun Kim et al.

Although Vision Transformers (ViTs) have recently demonstrated superior performance in medical imaging problems, they face explainability issues similar to previous architectures such as convolutional neural networks. Recent research efforts suggest that attention maps, which are part of decision-making process of ViTs can potentially address the explainability issue by identifying regions influencing predictions, especially in models pretrained with self-supervised learning. In this work, we compare the visual explanations of attention maps to other commonly used methods for medical imaging problems. To do so, we employ four distinct medical imaging datasets that involve the identification of (1) colonic polyps, (2) breast tumors, (3) esophageal inflammation, and (4) bone fractures and hardware implants. Through large-scale experiments on the aforementioned datasets using various supervised and self-supervised pretrained ViTs, we find that although attention maps show promise under certain conditions and generally surpass GradCAM in explainability, they are outperformed by transformer-specific interpretability methods. Our findings indicate that the efficacy of attention maps as a method of interpretability is context-dependent and may be limited as they do not consistently provide the comprehensive insights required for robust medical decision-making.

Xin Wang, Yin Guo, Jiamin Xia et al.

Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by source-target alignment, or the source-free setting, which typically resorts to implicit adaptation mechanisms such as pseudo-labeling and network distillation. This substantial divergence in methodological designs between the two settings reveals an inherent flaw: the lack of an explicit, structured construction of anatomical knowledge that naturally generalizes across domains and settings. To bridge this longstanding divide, we introduce a unified, semantically grounded framework that supports both source-accessible and source-free adaptation. Fundamentally distinct from all prior works, our framework's adaptability emerges naturally as a direct consequence of the model architecture, without relying on explicit cross-domain alignment strategies. Specifically, our model learns a domain-agnostic probabilistic manifold as a global space of anatomical regularities, mirroring how humans establish visual understanding. Thus, the structural content in each image can be interpreted as a canonical anatomy retrieved from the manifold and a spatial transformation capturing individual-specific geometry. This disentangled, interpretable formulation enables semantically meaningful prediction with intrinsic adaptability. Extensive experiments on challenging cardiac and abdominal datasets show that our framework achieves state-of-the-art results in both settings, with source-free performance closely approaching its source-accessible counterpart, a level of consistency rarely observed in prior works. The results provide a principled foundation for anatomically informed, interpretable, and unified solutions for domain adaptation in medical imaging. The code is available at https://github.com/wxdrizzle/remind

Yuntian Bo, Tao Zhou, Zechao Li et al.

Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal analysis is required. However, existing methods typically filter out domain-specific information to improve generalization, which inadvertently limits cross-domain performance and degrades source-domain accuracy. To address this, we present Contrastive Graph Modeling (C-Graph), a framework that leverages the structural consistency of medical images as a reliable domain-transferable prior. We represent image features as graphs, with pixels as nodes and semantic affinities as edges. A Structural Prior Graph (SPG) layer is proposed to capture and transfer target-category node dependencies and enable global structure modeling through explicit node interactions. Building upon SPG layers, we introduce a Subgraph Matching Decoding (SMD) mechanism that exploits semantic relations among nodes to guide prediction. Furthermore, we design a Confusion-minimizing Node Contrast (CNC) loss to mitigate node ambiguity and subgraph heterogeneity by contrastively enhancing node discriminability in the graph space. Our method significantly outperforms prior CD-FSMIS approaches across multiple cross-domain benchmarks, achieving state-of-the-art performance while simultaneously preserving strong segmentation accuracy on the source domain.

Xiao Shuang Ling, Wei Cheng Anthony Brian Tian, Goran Augustin et al.

Abstract Background Small bowel obstruction can occur during pregnancy, which, if missed, can lead to dire consequences for both the mother and foetus. Management of this condition usually requires surgical intervention. However, only a small number of patients are treated conservatively. Objective The objective was to review the literature to determine the feasibility of conservative management for small bowel obstruction. Methods A systematic search of the PubMed and Embase databases was performed using the keywords [small bowel obstruction AND pregnancy]. All original articles were then reviewed and included in this review if deemed suitable. Conclusion Conservative management of small bowel obstruction in pregnant women is feasible if the patient is clinically stable and after ruling out bowel ischaemia and closed-loop obstruction.

Fiona S. McEwen, Hania El Khatib, Kristin Hadfield et al.

Abstract Background Refugee children are at high risk of mental health problems but face barriers to accessing mental health services, a problem exacerbated by a shortage of mental health professionals. Having trained lay counsellors deliver therapy via telephone could overcome these barriers. This is the first study to explore feasibility and acceptability of telephone-delivered therapy with refugee children in a humanitarian setting. Methods An evidence-based intervention, Common Elements Treatment Approach, was adapted for telephone-delivery (t-CETA) and delivered by lay counsellors to Syrian refugee children in informal tented settlements in the Beqaa region of Lebanon. Following delivery of t-CETA, semi-structured interviews were conducted with counsellors (N = 3) and with children who received t-CETA (N = 11, 45% female, age 8–17 years) and their caregivers (N = 11, 100% female, age 29–56 years) (N = 25 interviews). Thematic content analysis was conducted separately for interviews with counsellors and interviews with families and results were synthesized. Results Three themes emerged from interviews with counsellors and four themes from interviews with families, with substantial overlap between them. Synthesized themes were: counselling over the phone both solves and creates practical and logistical challenges; t-CETA is adapted to potential cultural blocks; the relationship between the counsellor and the child and caregiver is extremely important; the family’s attitude to mental health influences their understanding of and engagement with counselling; and t-CETA works and is needed. Counselling over the phone overcame logistical barriers, such as poor transportation, and cultural barriers, such as stigma associated with attending mental health services. It provided a more flexible and accessible service and resulted in reductions in symptoms for many children. Challenges included access to phones and poor network coverage, finding an appropriate space, and communication challenges over the phone. Conclusions Despite some challenges, telephone-delivered therapy for children shows promising evidence of feasibility and acceptability in a humanitarian context and has the potential to increase access to mental health services by hard-to-reach populations. Approaches to addressing challenges of telephone-delivered therapy are discussed. Trial Registration ClinicalTrials.gov ID: NCT03887312; registered 22nd March 2019.

Maxwell Afetor, Samuel Adolf Bosoka, Williams Azumah Abanga et al.

Introduction: Burn injuries represent a significant public health challenge in Ghana, highlighting the need for an improved surveillance system to improve the quality of epidemiological data for an informed decision making. This study aimed to present the incidence, trends, and distribution of burns in the Volta region of Ghana from 2019 to 2023. Method: A retrospective secondary data analysis of burns data from the District Health Information Management System (DHIMS-2) was conducted from 2019 to 2023. Burn injuries were retrieved from the OPD morbidity report form whiles deaths from burns were retrieved from the cause of death report. Data was analysed descriptively with Microsoft Excel and Quantum Geographical Information System (QGIS), with results presented in tables and graphs. Results: A total of 4,441 cases of burn injuries were reported between 2019 and 2023 with 20 cases resulting in death. Nearly 59 % of burns involved females. About a third (33.9 %) of cases involved persons aged 0–4 years. The overall average incidence of burn injuries was 51 per 100,000 population, with the highest incidence of 80 per 100,000 population reported in 2019. Conclusion: Burns are an important cause of morbidity and mortality in the Volta Region of Ghana. There is however scanty data on the epidemiology of the condition in the region.