Diverse Patient Populations Research Articles

Use of Artificial Intelligence in Current Fight Against Antimicrobial Resistance.

Antimicrobial resistance (AMR) poses a significant global health threat, with projections indicating it could surpass cancer in mortality rates by 2050 if left unaddressed. Optimizing antimicrobial dosing is critical to mitigate resistance and improve clinical outcomes. Traditional approaches, including population pharmacokinetics (PK) models and Bayesian estimation, are limited by mechanistic hypothesis requirements and complexity. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions by leveraging large datasets to predict drug exposure accurately, refine sampling strategies, and enable real-time dose adjustments through therapeutic drug monitoring. This review highlights the role of ML models, in managing PK and pharmacodynamic variability across diverse patient populations. AI models often equal or outperform traditional methods in achieving therapeutic targets while minimizing toxicity, as demonstrated in some case studies involving ganciclovir, vancomycin, and daptomycin. Despite challenges such as data quality, interpretability, and integration with clinical workflows, AI's dynamic adaptability and precision underscore its potential. Future directions emphasize integrating multi-omics data, developing bedside decision-support tools, and expanding AI applications to broader drug categories and populations. Continued research and clinical validation are essential to harness AI's full potential in advancing precision medicine and combating AMR effectively.

Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive Care IV (MIMIC-IV) Database.

Accurate mortality risk prediction is crucial for effective cardiovascular risk management. Recent advancements in artificial intelligence (AI) have demonstrated potential in this specific medical field. Qwen-2 and Llama-3 are high-performance, open-source large language models (LLMs) available online. An artificial neural network (ANN) algorithm derived from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) registry, termed SWEDEHEART-AI, can predict patient prognosis following acute myocardial infarction (AMI). This study aims to evaluate the 3 models mentioned above in predicting 1-year all-cause mortality in critically ill patients with AMI. The Medical Information Mart for Intensive Care IV (MIMIC-IV) database is a publicly available data set in critical care medicine. We included 2758 patients who were first admitted for AMI and discharged alive. SWEDEHEART-AI calculated the mortality rate based on each patient's 21 clinical variables. Qwen-2 and Llama-3 analyzed the content of patients' discharge records and directly provided a 1-decimal value between 0 and 1 to represent 1-year death risk probabilities. The patients' actual mortality was verified using follow-up data. The predictive performance of the 3 models was assessed and compared using the Harrell C-statistic (C-index), the area under the receiver operating characteristic curve (AUROC), calibration plots, Kaplan-Meier curves, and decision curve analysis. SWEDEHEART-AI demonstrated strong discrimination in predicting 1-year all-cause mortality in patients with AMI, with a higher C-index than Qwen-2 and Llama-3 (C-index 0.72, 95% CI 0.69-0.74 vs C-index 0.65, 0.62-0.67 vs C-index 0.56, 95% CI 0.53-0.58, respectively; all P<.001 for both comparisons). SWEDEHEART-AI also showed high and consistent AUROC in the time-dependent ROC curve. The death rates calculated by SWEDEHEART-AI were positively correlated with actual mortality, and the 3 risk classes derived from this model showed clear differentiation in the Kaplan-Meier curve (P<.001). Calibration plots indicated that SWEDEHEART-AI tended to overestimate mortality risk, with an observed-to-expected ratio of 0.478. Compared with the LLMs, SWEDEHEART-AI demonstrated positive and greater net benefits at risk thresholds below 19%. SWEDEHEART-AI, a trained ANN model, demonstrated the best performance, with strong discrimination and clinical utility in predicting 1-year all-cause mortality in patients with AMI from an intensive care cohort. Among the LLMs, Qwen-2 outperformed Llama-3 and showed moderate predictive value. Qwen-2 and SWEDEHEART-AI exhibited comparable classification effectiveness. The future integration of LLMs into clinical decision support systems holds promise for accurate risk stratification in patients with AMI; however, further research is needed to optimize LLM performance and address calibration issues across diverse patient populations.

Shortcut learning leads to sex bias in deep learning models for photoacoustic tomography.

Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact on photoacoustic tomography (PAT), particularly concerning sex bias, remains underexplored. This study investigates this issue using peripheral artery disease (PAD) diagnosis as a specific clinical application. To examine the potential for sex bias due to shortcut learning in convolutional neural network (CNNs) and assess how such biases might affect diagnostic predictions, we created training and test datasets with varying PAD prevalence between sexes. Using these datasets, we explored (1) whether CNNs can classify the sex from imaging data, (2) how sex-specific prevalence shifts impact PAD diagnosis performance and underdiagnosis disparity between sexes, and (3) how similarly CNNs encode sex and PAD features. Our study with 147 individuals demonstrates that CNNs can classify the sex from calf muscle PAT images, achieving an AUROC of 0.75. For PAD diagnosis, models trained on data with imbalanced sex-specific disease prevalence experienced significant performance drops (up to 0.21 AUROC) when applied to balanced test sets. Additionally, greater imbalances in sex-specific prevalence within the training data exacerbated underdiagnosis disparities between sexes. Finally, we identify evidence of shortcut learning by demonstrating the effective reuse of learned feature representations between PAD diagnosis and sex classification tasks. CNN-based models trained on PAT data may engage in shortcut learning by leveraging sex-related features, leading to biased and unreliable diagnostic predictions. Addressing demographic-specific prevalence imbalances and preventing shortcut learning is critical for developing models in the medical field that are both accurate and equitable across diverse patient populations.

Machine Learning Prediction and Validation of Plasma Concentration-Time Profiles.

Recent research has increasingly focused on using machine learning for covariate selection in population pharmacokinetics (PPK) analysis. However, few studies have explored the prediction of plasma concentration profiles of drugs using nonlinear mixed-effect models combined with machine learning. This gap includes limited validation of prediction accuracy and applicability to diverse patient populations and dosing conditions. This study addresses these gaps by using remifentanil as a model drug and applying machine learning models to predict plasma concentration profiles based on virtual and real-world data. We created various training data sets for the virtual data by clustering based on the size and diversity of the test data set. Our results demonstrated high prediction accuracy for virtual and real-world data sets using Random Forest models. These results suggest that machine learning models are effective for large-scale data sets and real-world data with variable dosing times and amounts per patient. Considering the efficiency of machine learning, it offers a fit-for-purpose approach alongside traditional PPK methods, potentially enhancing future pharmacokinetic and pharmacodynamic studies.

AI-powered personalized patient dosimetry in CT

Abstract Advanced X-ray imaging techniques have increased the importance of patient dosimetry, requiring better dose assessment methods and improved dose management strategies to optimize imaging protocols, resulting in diagnostic information obtained with the lowest possible dose to the patient. This review explores the potential of artificial intelligence (AI) in CT dosimetry. Using machine learning models, trained with extensive data from personalized Monte Carlo dosimetry, AI significantly enhances the dosimetry process by providing accurate, rapid dose assessments. It automates organ segmentation from CT images, a critical and traditionally time-consuming step, enabling precise estimation of organ doses. AI's ability to predict dose distributions with high accuracy facilitates the seamless integration of personalized dosimetry into clinical workflows, promising improved patient safety and optimized radiation dose management. Additionally, AI-driven approaches offer substantial advancements over conventional methodologies, which often face challenges such as prolonged computation times and labor-intensive manual processes. Despite these advancements, challenges hinder the widespread adoption of AI in CT dosimetry. These include potential biases in training data, and the need for robust validation to ensure accuracy across diverse patient populations and imaging conditions. Addressing these challenges is essential for realizing the full potential of AI-powered dosimetry in clinical practice.

Management practices and surgical techniques for ab externo less invasive glaucoma surgery: a literature review and expert recommendations.

Subconjunctival bleb-forming devices (SBD) can provide greater intraocular pressure reductions than minimally invasive glaucoma surgery (MIGS) whilst remaining less invasive than traditional surgical techniques. However, variations in management practices and surgical techniques indicate the need for concise and clear guidance on these procedures in diverse patient populations. Here we describe current recommendations on the use of PRESERFLO MicroShunt, an ab externo SBD, according to a review of current literature and the opinions of 20 international glaucoma experts. A literature search was performed to return all publications relating to the PRESERFLO MicroShunt, which were then reviewed to extract information or guidance on patient selection, pre-operative patient preparation, peri-operative practices and techniques, and post-operative management. Alongside the literature search findings, participants in an expert panel meeting discussed their current practices relating to these same four aspects of PRESERFLO MicroShunt use. PRESERFLO MicroShunt can be considered for the majority of patients with medically uncontrolled open-angle glaucoma, as well as in patients with uveitic glaucoma, advanced glaucoma and high myopia, adults with congenital glaucoma, and in some cases normal tension glaucoma patients. Prior to surgery, steroid drops may be given for 2-4weeks if feasible, and acetazolamide may also be useful in patients with advanced glaucoma and high IOP. During surgery, a deep (8mm) and wide sub-Tenon pocket is essential to surgical success. Mitomycin C is generally used at a concentration of 0.4mg/mL for a minimum of 2-3min. Intracameral bevacizumab and/or dexamethasone may be considered to increase the chance of surgical success. Post-operatively, antibiotics should be given for 7days and steroid drops for 3-6months. Monitoring visits may be less frequent than in patients undergoing trabeculectomy. Management practices and surgical techniques for ab externo SBD vary, and surgeons must use their best clinical judgement based on the requirements of the individual patient. However, here we provide some recommendations for patient selection and pre-, peri- and post-surgical management based on the opinions of experts in the use of PRESERFLO MicroShunt, which we hope will prove useful in optimizing surgical outcomes.

Artificial Intelligence in Nephrology: Pioneering Precision with Multimodal Intelligence

Artificial intelligence (AI) is a rapidly advancing tool in healthcare, which might have significant implications in nephrology. Integrating AI, particularly through models like GPT-3 and GPT-4, has potential in medical education and diagnostics, achieving accuracy in clinical assessments. AI’s ability to analyze large, complex datasets from diverse modalities (electronic health records, imaging, and genetic data) might enable early detection, personalized treatment planning, and clinical decision-making. Key developments include AI-driven chronic kidney disease and acute kidney injury predictive models, which utilize machine learning algorithms to predict risk factors and disease onset, thereby allowing timely intervention. AI is enhancing non-invasive diagnostics like retinal imaging to detect kidney disease biomarkers, offering a promising and cost-effective approach to early disease detection. Despite these advancements, AI implementation in clinical practice faces challenges, including the need for robust data integration, model generalizability across diverse patient populations, and ethical and regulatory standards adherence. Maintaining transparency, explainability, and patient trust is crucial for AI’s successful deployment in nephrology. This article explores AI’s role in kidney care, covering its diagnostic applications, outcome prediction, and treatment, with references to recent studies that highlight its potential and current limitations.

Population Pharmacokinetics of Risperidone and Paliperidone in Schizophrenia: A Systematic Review

: Background: The primary treatment of schizophrenia is pharmacotherapy with antipsychotic agents, such as risperidone and paliperidone. Population pharmacokinetic (PopPK) modelling plays a crucial role in optimising therapy by predicting of plasma concentrations, therapeutic efficacy, and the risk of adverse effects using model informed precision dosing. Objectives: This systematic review examined the PopPK models of risperidone and paliperidone in patients diagnosed with schizophrenia based on the available scientific evidence. Methods: A systematic review of the health science databases was conducted. The inclusion criteria were original articles published in peer-reviewed journals, studies focusing on the development of original PopPK models of risperidone and paliperidone, and clinical studies. The exclusion criteria were full-text articles that could not be retrieved; studies not including subjects diagnosed with schizophrenia or schizoaffective disorders; and studies that did not investigate risperidone or paliperidone. Results: A total of 19 studies developing PopPK models were analysed, including one- or two-compartment PopPK model structures. Interindividual variability in the pharmacokinetic parameters was shown to be influenced by factors such as CYP2D6 activity, renal function, body mass index, and sex. Parameter estimation revealed high variability in clearance and volume of distribution. Conclusion: Numerous PopPK models for risperidone and paliperidone have been published with a detailed characterisation of absorption, metabolism, and elimination. Therefore, future research should focus on the external validation of these models to facilitate their integration into clinical practice and optimise individualised dosing, ultimately improving treatment efficacy and safety across diverse patient populations.

Predicting Agitation Events in the Emergency Department Through Artificial Intelligence

Agitation events are increasing in emergency departments (EDs), exacerbating safety risks for patients and clinicians. A wide range of clinical etiologies and behavioral patterns in the emergency setting make agitation prediction difficult in this setting. To develop, train, and validate an agitation-specific prediction model based on a large, diverse set of past ED visit data. This cohort study included electronic health record data collected from 9 ED sites within a large, urban health system in the Northeast US. All ED visits featuring patients aged 18 years or older from January 1, 2015, to December 31, 2022, were included in the analysis and modeling. Data analysis occurred between May 2023 and September 2024. Variables that served as potential exposures of interest, encompassing demographic information, patient history, initial vital signs, visit information, mode of arrival, and health services utilization. The primary outcome of agitation was defined as the presence of an intramuscular chemical sedation and/or violent physical restraint order during an ED visit. A clinical model was developed to identify risk factors that predict agitation development during an ED visit prior to symptom onset. Model performance was measured using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (PR-AUC). The final cohort comprised 3 048 780 visits. The cohort had a mean (SD) age of 50.2 (20.4) years, with 54.7% visits among female patients. The final artificial intelligence model used 50 predictors for the primary outcome of predicting agitation events. The model achieved an AUROC of 0.94 (95% CI, 0.93-0.94) and a PR-AUC of 0.41 (95% CI, 0.40-0.42) in cross-validation, indicating good discriminative ability. Calibration of the model was evaluated and demonstrated robustness across the range of predicted probabilities. The top predictors in the final model included factors such as number of past ED visits, initial vital signs, medical history, chief concern, and number of previous sedation and restraint events. Using a cross-sectional cohort of ED visits across 9 hospitals, the prediction model included factors for detecting risk of agitation that demonstrated high accuracy and applicability across diverse patient populations. These results suggest that clinical application of the model may enhance patient-centered care through preemptive deescalation and prevention of agitation.

The microbiota-gut-brain-axis theory: role of gut microbiota modulators (GMMs) in gastrointestinal, neurological, and mental health disorders.

The modulation of gut microbiota presents promising therapeutic possibilities for various health conditions, ranging from gastrointestinal infections to neurodegenerative and mental health disorders. Among the available interventions, gut microbiota modulators (GMMs) such as probiotics and prebiotics have demonstrated significant potential in infection prevention and neuroprotection. Despite these encouraging findings, the clinical application of GMMs remains challenging due to safety concerns and inconsistent effectiveness across diverse patient populations. These factors create substantial barriers to the widespread adoption of microbiota-based therapies in clinical practice. To overcome these challenges and fully leverage the therapeutic potential of microbiota modulation, this review explores the feasibility of repurposing GMMs for managing multiple health disorders. A broad spectrum of microbiota-targeted strategies is examined, including dietary modifications, fecal microbiota transplantation, bacteriophage therapy, microbiome engineering, and immune system modulation. A particularly innovative approach involves integrating GMMs with pharmaceutical delivery systems to enhance therapeutic efficacy while mitigating potential adverse effects. This integrative strategy underscores the pivotal role of the gut microbiome in health and disease, supporting the development of precision medicine tailored to individual patient needs. By combining GMMs with targeted delivery mechanisms, this approach not only improves treatment effectiveness but also addresses critical concerns regarding safety and patient variability. Furthermore, this review outlines future research directions within the rapidly evolving field of microbiota modulation, emphasizing the necessity of comprehensive clinical trials and long-term safety evaluations. By critically assessing both the challenges and opportunities associated with microbiota-based interventions, this study provides a strategic framework for translating experimental research into viable clinical applications. A holistic approach to gut microbiota modulation has the potential to redefine treatment paradigms, offering personalized therapeutic strategies for a wide range of disorders and advancing the broader field of precision medicine.

An Informatics Approach to Characterizing Rarely Documented Clinical Information in Electronic Health Records: Spiritual Care as an Exemplar.

Electronic health records (EHRs) contain valuable patient information, yet certain aspects of care remain infrequently documented and difficult to extract. Identifying these rarely documented elements requires advanced informatics approaches to uncover clinical documentation patterns that would otherwise remain inaccessible for research and quality improvement. This study developed and validated an informatics approach using natural language processing (NLP) to detect and characterize rarely documented elements in EHRs, using spiritual care documentation as an exemplar case. Using EHR data from a Midwestern US hospital (2010-2023), we fine-tuned Spiritual-BERT, an NLP model based on Bio-Clinical-BERT. The model was trained on 80% of a manually annotated, gold-standard corpus of EHR notes, and its performance was validated using the remaining 20% of the corpus, alongside 150 synthetic notes generated by GPT-4 and curated by clinical experts. We applied Spiritual-BERT to identify spiritual care documentation and analyzed patterns across diverse patient populations, provider roles, and clinical services. Spiritual-BERT demonstrated high accuracy in capturing spiritual care documentation (F1-scores: 0.938 internal validation, 0.832 external validation). Analysis of nearly 3.6 million EHR notes from 14,729 older adults revealed that 2% of clinical notes contained spiritual care references, while 73% of patients had spiritual care documented in at least one note. Significant variations were observed across provider types: chaplains documented spiritual care in 99.4% of their notes, compared to 1.7% for nurses and 1.2% for physicians. Documentation patterns also varied based on ethnicity, language, and medical diagnosis. This study demonstrates how advanced NLP techniques can effectively identify and characterize rarely documented elements in EHRs that would be challenging to detect through traditional methods. This approach revealed distinct documentation patterns across provider types, clinical settings, and patient characteristics, with promise for analyzing other under-documented clinical information.

Explore the Effect of Low-Intensity Focused Ultrasound on Pain Relief in Patients With Diabetic Peripheral Neuropathy: A Pilot Randomized Trial.

To evaluate the efficacy of low-intensity focused ultrasound (LIFU) in alleviating pain in patients with painful diabetic peripheral neuropathy (PDPN). Between November 2022 and January 2024, a total of 50 participants diagnosed with PDPN were randomly assigned to either LIFU therapy or placebo therapy. Pain levels were assessed using the numeric pain rating scale (NRS), while neuropathic symptoms and signs were evaluated using the neuropathy symptom score (NSS) and neuropathy deficit score (NDS), respectively. Motor and sensory nerve conduction velocities were measured via electromyography (EMG). After 7 days of treatment, both groups exhibited a reduction in NRS compared to baseline; however, the LIFU group demonstrated significantly lower NRS scores than the placebo group (3.96 ± 1.07 versus 2.76 ± 1.79, P < .05). Additionally, significant improvements in neuropathic symptoms, as measured by NSS, were observed in the LIFU group post-treatment. Notably, LIFU therapy did not alter the conduction velocities or amplitudes of the bilateral peroneal, tibial, and sural nerves. LIFU therapy was effective in reducing pain and improving neuropathic symptoms in patients with mild-to-moderate PDPN. Future studies should further investigate the long-term effectiveness and sustainability of this novel therapeutic approach across diverse patient populations.

Exploring gender differences in empathy development among medical students: a qualitative analysis of reflections on juvenile correctional school visits

ABSTRACT Empathy development is a critical component of medical education, shaping future physicians’ ability to provide compassionate, patient-centered care. This study investigates the reflections of male and female medical students on their experiences at a juvenile correctional school, focusing on how gender shapes their interpretations, emotional engagement, and learning outcomes. Using a qualitative analysis of reflective journals collected from 28 participants, the study employed thematic analysis to identify gender-based patterns in emotional engagement, interpretive framing, and learning outcomes. Reflexivity measures and rigorous coding processes were implemented to ensure credibility and minimize bias in data interpretation. Male students frequently adopted a practical form of empathy, characterized by task-oriented interactions and a sense of competitive camaraderie. In contrast, female students exhibited relational empathy, emphasizing emotional connections and nurturing behaviors. These findings underscore the value of integrating gender-sensitive empathy training into medical education, encouraging the development of both relational and practical approaches. By incorporating tailored reflection exercises and interactive strategies, medical education programs can better equip students to engage compassionately with diverse patient populations. This approach enriches the learning experience and fosters a more holistic understanding of patient care, ultimately leading to improved healthcare outcomes.

The Role of Robotics in Rehabilitation Therapy

Rehabilitation robotics is revolutionizing patient recovery by integrating advanced hardware and software to enhance therapeutic interventions. These robotic systems aid in motor function recovery by providing precise, repetitive movements that support neuroplasticity and motor learning. Exoskeletons, end-effector systems, and telemanipulators are among the primary robotic rehabilitation devices used to assist patients with neurological, orthopedic, and geriatric conditions. While robotics in rehabilitation offers advantages such as data-driven therapy customization, enhanced accessibility, and improved patient adherence, challenges like cost, patient engagement, and ethical concerns persist. The growing role of artificial intelligence (AI), sensor technology, and tele-rehabilitation is shaping the future of robotic-assisted therapy, offering personalized and efficient rehabilitation solutions. Continued research and technological advancements will further optimize robotic rehabilitation, making it more effective and accessible for diverse patient populations. Keywords: Rehabilitation robotics, robotic-assisted therapy, exoskeletons, motor recovery, AI in healthcare, tele-rehabilitation.

New Simple and Fast Digital Screening Method for Minimal Hepatic Encephalopathy in Cirrhotic Patients

ABSTRACTCovert hepatic encephalopathy (CHE) is a subtle yet significant neurological complication of liver diseases, especially in patients with cirrhosis. Although it lacks overt clinical signs, CHE severely impacts quality of life, increases accident risks, and has serious prognostic implications. It is characterized by neurocognitive symptoms detectable only through specialized neuropsychometric tests. CHE affects 30%–80% of cirrhotic patients and represents the early stage of hepatic encephalopathy, which is a predictor of mortality. Early diagnosis is essential to improve patient outcomes. The Psychometric Hepatic Encephalopathy Score (PHES) is the gold standard for diagnosing CHE, but it is time‐consuming and requires specialized training. Other tests, like the Animal Naming Test (ANT), are simpler and more practical for screening minimal hepatic encephalopathy (MHE), though they lack specificity. The Stroop test shows promise as a quicker and reliable diagnostic tool, but still has limitations. Recent innovations include a smartphone‐based self‐screening method developed by Dobbermann et al., combining three digital tests: the Tip Test, Number Connection Test, and Modified Stroop Test. This approach correlates well with PHES, is independent of language skills, and is accessible for a diverse patient population, including those with color vision deficiencies. This tool offers a rapid and reliable way to screen for CHE even in home settings, potentially improving early detection and intervention. In conclusion, CHE is an underrecognized but critical condition that requires greater clinical attention. Current diagnostic tools have limitations, highlighting the need for more effective, practical methods. A multidisciplinary approach involving hepatologists, neurologists, and neuropsychologists is crucial to improve the diagnosis and management of CHE, ultimately enhancing patient outcomes.

Socio-environmental factors associated with shrimp allergy and shrimp sensitization in a large and diverse patient population and cohort study from metropolitan Detroit

Socio-environmental factors associated with shrimp allergy and shrimp sensitization in a large and diverse patient population and cohort study from metropolitan Detroit

Practical implementation of automated insulin delivery systems in 2025: A French position statement update.

Practical implementation of automated insulin delivery systems in 2025: A French position statement update.

Uptake and user characteristics of MyChart within a Canadian community hospital with a diverse patient population: A comparative study.

Patient portals offer a convenient way to access health information and increase patient participation in healthcare. To promote broad accessibility and impact of portals, it is essential to understand uptake patterns across patient populations. This study described the characteristics of patient users of a portal called MyChart and compared them to non-users at a large community hospital. We descriptively analyzed (frequency, counts) patient health records to characterize MyChart users and their usage patterns during the first year of its launch from September 11, 2023, to September 112024. We summarized user demographics along with information about how they activated accounts, accessed MyChart, and utilized its features. Using chi-square and t-tests, we compared MyChart user demographics to non-users who visited the hospital in the same time period. A total of 61,306 patients activated MyChart during the first year it was available. On average, MyChart users were 53 years old, 62% female, 64% predicted to have White ethnicity, and preferred to receive healthcare in English (88%). MyChart users tended to be regular healthcare users, with an average of five annual visits prior to creating an account and logged onto the portal on average five times a month. MyChart users were slightly younger than non-users (an average age of 53.5 vs. 56.9 years) and visited the hospital more often (an average of 5.7 vs. 3.1 annual visits). Many patients activated MyChart during the first year of launch, and users closely resembled the broader patient population. To enhance adoption and potential benefits of patient portals, targeted interventions such as accessible educational information tailored to diverse patient groups (e.g., older adults, different ethnicities) could increase their usage.

Postoperative Pain Management With Ketorolac, Acetaminophen, and Gabapentin in Femoral Shaft Fractures: A Prospective Cohort Study.

Postoperative pain management in orthopaedic trauma surgery is critical. Amid the opioid crisis, nonopioid analgesia offers a promising alternative. We aim to evaluate the feasibility of a multimodal regimen, including intravenous ketorolac, oral acetaminophen, and oral gabapentin, in managing postoperative pain after femur shaft fractures treated with intramedullary nailing. We conducted a prospective cohort study to evaluate the postoperative pain of patients with isolated femur shaft fractures treated with intramedullary nailing. The experimental group consisted of 29 patients, while the control group consisted of 42 patients who received standard opioid-based pain management. The outcomes measured were the Visual Analog Scale (VAS) scores and the morphine milligram equivalents every 12 hours until 48 hours postoperatively. Patient demographics were comparable between the experimental and control groups, with a significant age difference (31 vs. 40; P = 0.002) but not in sex, body mass index, smoking status, or diabetes prevalence. We observed no significant differences in VAS scores at 12, 24, 36, and 48 hours after surgery. In addition, there was a marked reduction in opioid usage in the experimental group, with P-values < 0.001 at each time point. The study concludes that the coadministration of ketorolac, acetaminophen, and gabapentin is an effective opioid-sparing alternative for postoperative pain in isolated femur shaft fractures. Future research could benefit from a more diverse patient population in orthopaedic trauma to further validate our results and explore long-term outcomes. Prospective Cohort Study; Level 2.

Safety of Stimulants Across Patient Populations

The use of stimulant medications has expanded substantially beyond the traditional treatment of attention-deficit/hyperactivity disorder (ADHD) to encompass a variety of other clinical conditions. Understanding the safety of these medications is important as their use increases across diverse patient populations. To assess the safety of stimulant medications as reported in randomized clinical trials (RCTs) investigating methylphenidate, lisdexamfetamine, and other amphetamines. A comprehensive literature search was conducted from July 1, 2024, through February 28, 2025, using CINAHL, Embase, PubMed or MEDLINE, ScienceDirect, and Web of Science for studies published since 2000. Keywords included safety, adverse event, side effect, amphetamine, dextroamphetamine, stimulant, lisdexamfetamine, and methylphenidate. RCTs published between January 1, 2000, and December 13, 2024, were included. These trials investigated the safety of stimulants in various clinical conditions, including ADHD, depression, binge eating disorder, schizophrenia, Alzheimer disease, and stimulant use disorders as well as in healthy individuals. Trials not focused on safety or adverse events (AEs) of stimulants, nonoriginal research, nonhuman research, trials with concomitant prescriptions other than stimulants, and trials without a placebo group were excluded. Data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Independent reviewers extracted study data, and a random-effects model was used to pool results. Heterogeneity was assessed using the I2 statistic. The primary outcome was the risk ratio (RR) of developing any AE in participants taking stimulants vs placebo. A total of 93 RCTs were included after exclusions. The methodological quality assessment of the included trials showed overall low or unclear risk of bias. Trials with a duration of up to 52 weeks showed that stimulant medications were associated with an increased risk of overall AEs compared with placebo (RR, 1.34; 90% CI, 1.27-1.41), with high heterogeneity (I2 = 67%). Statistical significance of this finding was maintained when subgroups (ie, methylphenidate, lisdexamfetamine, and other amphetamines) were separately analyzed. This meta-analysis found an increased risk of overall AEs associated with stimulants compared with placebo. Future research could provide more standardized and consistent assessments of this outcome and may improve understanding about misuse risk.