A GPT-based EHR modeling system for unsupervised novel disease detection.

OBJECTIVE: To develop an Artificial Intelligence (AI)-based anomaly detection model as a complement of an "astute physician" in detecting novel disease cases in a hospital and preventing emerging outbreaks. METHODS: Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was designed and further trained using Empirical Risk Minimization (ERM), which can model a hospitalized patient's Electronic Health Records (EHR) and detect atypical patients. Methods and performance metrics, similar to the ones behind the recent Large Language Models (LLMs), were leveraged to capture the dynamic evolution of the patient's clinical variables and compute an Out-Of-Distribution (OOD) anomaly score. RESULTS: In a completely unsupervised setting, hospitalizations for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection could have been predicted by our GPT model at the beginning of the COVID-19 pandemic, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 92.2 %, using 31 extracted clinical variables and a 3-day detection window. Our GPT achieves individual patient-level anomaly detection and mortality prediction AUC of 78.3 % and 94.7 %, outperforming traditional linear models by 6.6 % and 9 %, respectively. Different types of clinical trajectories of a SARS-CoV-2 infection are captured by our model to make interpretable detections, while a trend of over-pessimistic outcome prediction yields a more effective detection pathway. Furthermore, our comprehensive GPT model can potentially assist clinicians with forecasting patient clinical variables and developing personalized treatment plans. CONCLUSION: This study demonstrates that an emerging outbreak can be accurately detected within a hospital, by using a GPT to model patient EHR time sequences and labeling them as anomalous when actual outcomes are not supported by the model. Such a GPT is also a comprehensive model with the functionality of generating future patient clinical variables, which can potentially assist clinicians in developing personalized treatment plans.

Outcomes of In-Office, High Resolution, Ultrasound Silicone Breast Implant Surveillance by Plastic Surgeons.

BACKGROUND: Implant rupture is a known complication in cosmetic breast surgery but access and cost barriers hinder proper implant surveillance. OBJECTIVES: This study aims to validate High Resolution Ultrasound (HRUS) for diagnosing silicone implant rupture, and secondarily, explore the predictors of implant rupture in a single-surgeon cohort. METHODS: A retrospective chart review identified patients who underwent HRUS of silicone breast implants that were placed by the senior author (WPA). HRUS sensitivity/specificity and predictive values were calculated. Multivariable logistic regression assessed predictors of implant rupture. Kaplan-Meier methods estimated 10 and 15-year implant survival. RESULTS: Among 254 patients (508 implants) included, 52 patients (104 implants) underwent operative exploration for various reasons; 20 implants had confirmed ruptures. Examining this operative cohort, HRUS had excellent positive and negative predictive value, respectively, 100% (95% CI 83.1-100%) and 97.6% (95% CI 91.6-99.3%); the sensitivity was 90.9% (95% CI 70.8-98.9%) and specificity was 100% (95% CI 95.6%-100%). The median age of the implant at the time of ultrasound was 119 months (10 years; IQR 79-152 months). After multivariable adjustment, the only the implant age was a significant predictor of implant rupture (p=0.04). Across the entire cohort, Kaplan-Meier methods estimated a 10-year and 15-year implant survival rate of 0.98 (95% CI 0.96-0.99) and 0.80 (95% CI 0.71-0.87), respectively. Subgroup analysis showed 10 and 15-year implant survival rates of 0.99 and 0.92, respectively, for surveillance-only HRUS patients. CONCLUSIONS: Plastic surgeon delivered office-based HRUS surveillance is a highly sensitive and specific method to assess silicone breast implant integrity.

Long-term Outcomes of Poly-4-Hydroxybutyrate (P4HB) in Aesthetic Breast Surgery.

BACKGROUND: Over the past decade, the use of poly-4-hydroxybutyrate (P4HB) in aesthetic breast surgery has grown in popularity. Published data on long-term outcomes, however, is scarce. OBJECTIVES: The purpose of this study was to assess long-term outcomes when using P4HB in various aesthetic breast procedures. METHODS: A retrospective review of all patients undergoing breast procedures with P4HB performed by the senior author (WPA) between July 2012 and March 2022. All patients with greater than one year of follow-up were evaluated, including demographics, surgical indications, complications, secondary procedures, and patient satisfaction. A Kruskal-Wallis test was performed to assess the stability of satisfaction over time. RESULTS: A total of 248 patients were evaluated. The mean follow-up length was 2.9 years (range, 1-9.3y). The most common surgical indication was breast ptosis, which comprised 68 percent (n=167) of patients. The overall complication rate was 8.0 percent (n=20), with an unplanned reoperation rate of 2.8 percent (n=7). Across the entire study population, the mean satisfaction score was 3.42 out of 4. When satisfaction scores were compared to follow-up length, Kruskal-Wallis test demonstrated no statistical differences (H = 18.2, p = 0.89, df = 2), suggesting stability of satisfaction over time. CONCLUSIONS: This study presents the senior author's entire experience with P4HB across a wide range of aesthetic breast procedures. With an average follow-up length of almost 3 years, P4HB appears to be both a safe and effective tool to support soft tissue in aesthetic breast surgery. Further, high patient satisfaction appears stable.

Development and implementation of an injury and illness surveillance system for team USA.

BACKGROUND: The purpose of this report is to provide insight and details regarding the development and implementation of an injury and illness surveillance (IIS) system for the United States Olympic and Paralympic Committee (USOPC). METHODS: The development and deployment of the IIS employed a multiphase approach. First, researchers determined variables to include in the IIS using the recommendations from the 2020 IOC consensus statement for reporting sport epidemiological data. Second, the hosting and deployment platforms were comprehensively evaluated for their suitability, ease of use, flexibility, and backend data structure (for both capture and aggregation). Third, focus groups consisting of the Sports Medicine department leadership and clinicians piloted the IIS system and revisions were made based on their feedback. Pilot testing of the IIS and follow-up focus groups were then conducted among all departmental clinicians to solicit additional feedback and drive further revisions. Finally, the IIS system was piloted among providers working during the 2023 Pan American and Parapan American Games to refine the system for future Games. After reviewing all potential software platform options (electronic medical record [EMR] system, athlete management systems, secure data collection platforms), Qualtrics (Qualtrics, Provo, UT, USA) was selected to host the IIS system. This choice was made due to the inability of the EMR and athlete-management systems to make frequent updates, modify existing questions, and provide the necessary form logic for the variety of scenarios in which the IIS system would be deployed. Feedback from the department's leadership and clinicians resulted in a number of changes, most notably being the ability to enter multiple diagnoses for a single injury event. Additionally, clinician feedback resulted in the creation of additional diagnostic codes not currently present in the OSIICS v14.0 diagnostic coding system, adding "non-sport" as an additional variable for injury setting, and developing a system for reporting return-to-sport date for time-loss injuries. DISCUSSION: A multi-stage process of extensive planning, stakeholder feedback, and ongoing updates is required in order to successfully develop and implement an IIS system within a National Olympic and Paralynpic Committee. This process can be used to inform the development and implementation of IIS systems in other sporting organizations.

High rates of respiratory illnesses upon arrival: lessons from Team USA at the Santiago 2023 Pan American and Parapan American Games.

OBJECTIVE: To describe the incidence and characteristics of injuries and illnesses among Team USA athletes competing at the Santiago 2023 Pan American Games (PAG) and Parapan American Games (PPAG), with a particular focus on the incidence of respiratory illnesses and on injuries for sports new to the Olympic and Paralympic programmes. METHODS: Illnesses and injuries occurring among the 870 Team USA athletes competing in the Santiago 2023 PAG or PPAG were documented within Team USA's Injury and Illness Surveillance system. Illness and injury incidence per 1000 athlete-days (ADs) and incidence ratios (IR) were calculated, both with 95% CIs. RESULTS: Illness (IR 2.5, 95% CI 1.6, 3.9) and injury (IR 1.8, 95% CI 1.3, 2.5) rates were greater during PPAG compared with PAG. Illness rates were higher in the pre-opening ceremony period compared with the competition period for both PAG (IR 2.7, 95% CI 1.1, 5.9) and PPAG (IR 1.9, 95% CI 0.9, 3.8). Respiratory illness was the most common illness with 3.2% and 8.9% of all Team USA athletes reporting a respiratory illness during the PAG and PPAG, respectively. Sports that are relatively new to the Olympic/Paralympic programmes exhibited the highest injury rates during the Games: breaking (250.0 (91.7, 544.2) per 1000 ADs), Para taekwondo (93.8 (19.3, 274.0) per 1000 ADs) and surfing (88.9 (24.2, 227.6) per 1000 ADs). CONCLUSION: Respiratory illness rates were the most common type of illness during both PAG and PPAG and were more likely to occur prior to competition starting. Our data have identified high injury risk populations (breaking, surfing, Para taekwondo) and timing (pre-opening ceremony period) for further risk factor analysis.

Centering the female athlete voice in a sports science research agenda: a modified Delphi survey with Team USA athletes.

OBJECTIVES: To co-construct a sports medicine and exercise science research and translational agenda with Team USA elite female athletes serving as the experts on their health, performance and well-being. METHODS: 40 Team USA female athletes across sports disciplines participated in an online, anonymous, modified Delphi survey by ranking topics on a Likert scale (1='strongly disagree' and 5='strongly agree') and providing qualitative justification regarding whether they believed having more information and research on each topic would support their athletic performance, health and well-being. After each Delphi round, quantitative rankings of topics and qualitative justifications were analysed, informing revisions to the list of topics for review in the subsequent round. Researchers provided athletes with a detailed report of findings and revisions following each round. RESULTS: The final list contained 14 ranked topics. The top five were menstrual cycle symptoms (4.58±0.74), recovery (4.58±0.59), birth control (4.55±0.89), mental health (4.50±0.55) and fueling and the menstrual cycle (4.43±0.74). New topics originating from athletes included recovery, menstrual cycle symptoms, fueling and the menstrual cycle, mental health and sports performance, team dynamics, and institutionalised sexism. CONCLUSION: This is the first study to co-construct a research and translational agenda with Team USA elite female athletes. The list of sports science research topics developed by focusing on elite female athletes' voices lays the foundation for future research and provides valuable insight into the specific needs of female athletes.

Internal Medicine Acting Internship Trends in Rotation Structure and Student Responsibilities: Results from a 2023 National Survey.

BACKGROUND: The acting internship (AI) in internal medicine plays a key role in the transition from medical school to residency. While there have been recent changes in medical education including a pass/fail USMLE Step 1 and increasing use of competency-based assessment, there has not been a large survey of the state of the AI in many years. OBJECTIVE: To assess the current landscape of the internal medicine AI and identify areas in need of standardization. DESIGN: This was a voluntary online survey of medical schools in the United States (U.S.). PARTICIPANTS: Course directors of the AI rotation at U.S. medical schools. MAIN MEASURES: Number of AI rotations required for graduation, length of AI rotation, types of services allowed for AI, clinical responsibilities of students, curricular components. KEY RESULTS: Response rate was 50.7% (71/140 LCME accredited schools). All responding institutions require at least one AI for graduation, with nearly all schools integrating students into resident teaching teams, and almost half also allowing AI students to work on hospitalist services. Students carry 3-4 patients per day on average with a maximum of 5-6 in most institutions. Students are responsible for most aspects of patient care including notes, orders, interprofessional communication, and transitions of care. Night call or night float responsibilities are infrequently required. The structured curriculum published by AAIM is used by only 41% of schools. CONCLUSIONS: The internal medicine AI continues to be a staple in the medical school experience, but there is variation in the structure, curriculum, and expectations on the rotation. Opportunities exist to improve standardization of the AI experience and expectations to better prepare medical students for the transition from medical school to residency.

GBasis: A Python library for evaluating functions, functionals, and integrals expressed with Gaussian basis functions.

GBasis is a free and open-source Python library for molecular property computations based on Gaussian basis functions in quantum chemistry. Specifically, GBasis allows one to evaluate functions expanded in Gaussian basis functions (including molecular orbitals, electron density, and reduced density matrices) and to compute functionals of Gaussian basis functions (overlap integrals, one-electron integrals, and two-electron integrals). Unique features of GBasis include supporting evaluation and analytical integration of arbitrary-order derivatives of the density (matrices), computation of a broad range of (screened) Coulomb interactions, and evaluation of overlap integrals of arbitrary numbers of Gaussians in arbitrarily high dimensions. For circumstances where the flexibility of GBasis is less important than high performance, a seamless Python interface to the Libcint C package is provided. GBasis is designed to be easy to use, maintain, and extend following many standards of sustainable software development, including code-quality assurance through continuous integration protocols, extensive testing, comprehensive documentation, up-to-date package management, and continuous delivery. This article marks the official release of the GBasis library, outlining its features, examples, and development.

Utility of Body Weight, Urine Color, and Thirst Perception (WUT) in Determining Hydration in Young Adults.

OBJECTIVE: The primary aim of this study was to assess the efficacy of the weight, urine, thirst (WUT) framework in predicting dehydration after a body water manipulation protocol, while concurrently determining the individual and interactive contributions of the model components. METHODS: The total study sample was 93 participants (female, n = 47), recruited from two institutions. Phase 1 involved collecting daily hydration measures from free-living participants (Study 1, 58 participants for 3 days; Study 2, 35 participants for 7 days). Phase 2 entailed a two-hour passive heating protocol, where participants from Study 2 were randomly assigned to one of three groups that manipulated total body water over 24-hours using passive heating and fluid restriction. During each Phase, participants provided urine samples, underwent body mass measurements, and completed questionnaires pertaining to thirst perception. Morning and 24-hour urine samples were assessed for color, osmolality, and specific gravity. Differences between intervention groups, based on the probability of hydration status, were examined (ANOVA) and ridge regression analysis assessed the relative importance of variables within the WUT model. RESULTS: The study revealed significant differences among the intervention groups for predicted probability of dehydration, as determined by changes in body mass (p = 0.001), urine color (p = 0.044), and thirst perception (p < 0.001). Binomial ridge regression indicated that change in body mass (58%) and thirst perception (26%) were the most influential predictors of dehydration. CONCLUSIONS: These data support use of an enhanced version of the WUT model, underscoring the significance of changes in body mass and thirst perception in the assessment of hydration status.

Sleep, Health Outcomes and Body Weight (SHOW) study: a measurement burst design study on sleep and risk factors for obesity in black emerging adults in North Carolina, USA.

INTRODUCTION: Black emerging adults (18-28 years) have the highest risk of short sleep duration and obesity. This increased risk may be partly explained by greater stress levels, which may result from race-related stress (racial discrimination and heightened race-related vigilance) or living in more disadvantaged home and neighbourhood environments. Insufficient sleep may also impact obesity risk via several weight-related mechanisms including energy balance, appetite and food reward, cortisol profiles and hydration status. This paper describes the rationale, design and methods for the Sleep, Health Outcomes and Body Weight (SHOW) study. This study aims to prospectively assess the effects of sleep, race-related stress and home/neighbourhood environments on weight-related mechanisms and obesity markers (body weight, waist circumference and fat mass) in 150 black emerging adults. METHODS AND ANALYSIS: The SHOW study follows a measurement burst design that includes 3, 7-day data collection bursts (baseline, 6-month and 12-month follow-ups). Sleep is measured with three methods: sleep diary, actigraphy and polysomnography. Energy balance over 7 days is based on resting and postprandial energy expenditure measured via indirect calorimetry, physical activity via accelerometry and self-reported and ad libitum energy intake methods. Self-reported methods and blood biomarkers assess fasting and postprandial appetite profiles and a behavioural-choice task measures food reward. Cortisol awakening response and diurnal cortisol profiles over 3 days are assessed via saliva samples and chronic cortisol exposure via a hair sample. Hydration markers are assessed with 24-hour urine collection over 3 days and fasting blood biomarkers. Race-related stress is self-reported over 7 days. Home and neighbourhood environments (via the Windshield Survey) is observer assessed. ETHICS AND DISSEMINATION: Ethics approval was granted by the University of North Carolina at Greensboro's Institutional Review Board. Study findings will be disseminated through peer-reviewed publications, presentations at scientific meetings and reports, briefs/infographics for lay and community audiences.

Better Guidelines and Policies: AAP's Partnership for Policy Implementation.

OBJECTIVES: In 2005, the American Academy of Pediatrics founded the Partnership for Policy Implementation (PPI). The PPI has collaborated with authors to improve the quality of clinical guidelines, technical reports, and policies that standardize care delivery, improve care quality and patient outcomes, and reduce variation and costs. METHODS: In this article, we describe how the PPI trained informaticians apply a variety of tools and techniques to these guidance documents, eliminating ambiguity in clinical recommendations and allowing guideline recommendations to be implemented by practicing clinicians and electronic health record (EHR) developers more easily. RESULTS: Since its inception, the PPI has participated in the development of 45 published and 27 in-progress clinical practice guidelines, policy statements, technical and clinical reports, and other projects endorsed by the American Academy of Pediatrics. The partnership has trained informaticians to apply a variety of tools and techniques to eliminate ambiguity or lack of decidability and can be implemented by practicing clinicians and EHR developers. CONCLUSIONS: With the increasing use of EHRs in pediatrics, the need for medical societies to improve the clarity, decidability, and actionability of their guidelines has become more important than ever.

The Health Equity Explorer: An open-source resource for distributed health equity visualization and research across common data models.

Introduction: There is an urgent need to address pervasive inequities in health and healthcare in the USA. Many areas of health inequity are well known, but there remain important unexplored areas, and for many populations in the USA, accessing data to visualize and monitor health equity is difficult. Methods: We describe the development and evaluation of an open-source, R-Shiny application, the "Health Equity Explorer (H2E)," designed to enable users to explore health equity data in a way that can be easily shared within and across common data models (CDMs). Results: We have developed a novel, scalable informatics tool to explore a wide variety of drivers of health, including patient-reported Social Determinants of Health (SDoH), using data in an OMOP CDM research data repository in a way that can be easily shared. We describe our development process, data schema, potential use cases, and pilot data for 705,686 people who attended our health system at least once since 2016. For this group, 996,382 unique observations for questions related to food and housing security were available for 324,630 patients (at least one answer for all 46% of patients) with 65,152 (20.1% of patients with at least one visit and answer) reporting food or housing insecurity at least once. Conclusions: H2E can be used to support dynamic and interactive explorations that include rich social and environmental data. The tool can support multiple CDMs and has the potential to support distributed health equity research and intervention on a national scale.

Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems.

BACKGROUND AND HYPOTHESIS: Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. STUDY DESIGN: Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. STUDY RESULTS: PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). CONCLUSIONS: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis.

Incidence of staff injury and illness at the Tokyo 2020 and Beijing 2022 Olympic and Paralympic Games.

Objective: To report epidemiological data regarding injury and illness among the Team USA staff during the Tokyo 2020 Summer Olympic and Paralympic Games and Beijing 2022 Winter Olympic and Paralympic Games. Methods: A retrospective review of all Team USA staff (total staff, N=1703 (62.5% female); total staff days (SD)=34 489) medical encounters during the Tokyo 2020 Games and Beijing 2022 Games was conducted. Details related to injury and illness were evaluated. Incidence with 95% CI per 1000 staff days were calculated. Results: A total of 32 illnesses (incidence [95% CI] 0.9 [0.6, 1.2]) and 23 injuries (incidence 0.7 [0.4, 0.9]) were sustained by the Team USA delegation staff members during the Tokyo 2020 Games and Beijing 2022 Games. Female staff reported more illnesses (illnesses proportion (IP) 2.9%; incidence 1.4 [0.8, 2.0]), while male staff incurred more injuries (IP 1.8%; incidence 0.9 [0.5, 1.3]). When stratified by physiological system, dermatological and infectious were the most common systems involved with illness (IP 0.5%; incidence 0.2 [0.1, 0.4]). Injuries to the upper limb were most common (IP 0.3%; incidence 0.3 [0.1, 0.5]). Conclusion: Injury and illness rates among the Team USA staff during the Tokyo 2020 Games and Beijing 2022 Games were low, but notable. Knowledge of injury and illness risks contributes to staffing decisions and prevention strategies for staff supporting athletes during competition.

Secondary School State Athletic Association Health and Safety Policy Development Processes.

BACKGROUND: The National Federation of State High School Associations provides recommendations regarding health and safety policies; however, policy development is governed at the state level. Given interstate differences in governance, the primary purpose was to describe processes that State High School Athletic Associations (SHSAAs) utilize to develop a new policy. The secondary objective was to determine what methods associations use to implement new policies. METHODS: A cross-sectional survey requested SHSAA (n = 51) representatives to report how athlete health and safety policies are introduced, revised, approved, and implemented within their state. The 22-question survey was developed to gather variables for the aims of the study. Descriptive statistics were calculated for each survey item. RESULTS: Of states who responded (n = 33), most reported a 2-committee (n = 24, 72.7%) process for developing and vetting policies, with initiation from the Sports Medicine Advisory Committee (n = 27, 81.8%), followed by an executive-level committee (n = 18, 66.7%). States reported total time from policy initiation to final approval ranged from 2 weeks to over 12 months. When a new policy was approved, most states indicated implementation began with an e-mail (n = 24, 72.7%) sent to Athletic Directors (n = 26, 78.8%). School principal or district superintendent were reported as the position in charge of compliance (36.4%, n = 12). CONCLUSIONS: Most SHSAAs use a 2-step process to write and review an athlete health and safety policy before approval. SHSAAs that require a longer policy development time could delay the implementation of important health measures. SHSAAs could consider additional communication methods to ensure information reaches all stakeholders.

Trajectories of self-management and independence in youth with spina bifida: Family-related predictors of growth.

The purpose of this study was to assess family-related predictors of self-management trajectories in youth with spina bifida (SB). Participants with SB completed the Adolescent/Young Adult Self-Management and Independence Scale (AMIS II) interview across four time points. Family functioning, family-related stress, and perceived family support were assessed by multiple reporters and multiple methods. Growth in AMIS II total self-management and the AMIS II subscales (Condition and Independent Living) were estimated using linear mixed effect models as a function of family factors, after controlling for socio-demographic, condition-related, and neuropsychological variables that had been found to be significant predictors of self-management in prior studies. Model fit and parsimony were assessed using Akaike's information criterion (AIC). This diverse community sample included 99 respondents aged 18-27 years old. About half were female (52.5%) and White (52.5%); 15.2% were Black, and 32.3% were Hispanic/Latino. Observed family cohesion at baseline was associated with all self-management scales at age 18 (all p < 0.05). Growth in self-management was associated with parent-reported number of family stress events. For growth in total self-management, the best model included age, race/ethnicity, family income, shunt status, lesion level, neuropsychological function, observed family cohesion, and an age-by-number of family stress events interaction effect. The study findings suggested that family factors were important predictors of self-management trajectories, even after controlling for socio-demographic, condition-related, and neuropsychological covariates. Risk and protective factors identified in families of youth with SB can inform family-focused interventions for self-management.

Subcutaneous Administration of a Monoclonal Antibody to Prevent Malaria.

BACKGROUND: Subcutaneous administration of the monoclonal antibody L9LS protected adults against controlled Plasmodium falciparum infection in a phase 1 trial. Whether a monoclonal antibody administered subcutaneously can protect children from P. falciparum infection in a region where this organism is endemic is unclear. METHODS: We conducted a phase 2 trial in Mali to assess the safety and efficacy of subcutaneous administration of L9LS in children 6 to 10 years of age over a 6-month malaria season. In part A of the trial, safety was assessed at three dose levels in adults, followed by assessment at two dose levels in children. In part B of the trial, children were randomly assigned, in a 1:1:1 ratio, to receive 150 mg of L9LS, 300 mg of L9LS, or placebo. The primary efficacy end point, assessed in a time-to-event analysis, was the first P. falciparum infection, as detected on blood smear performed at least every 2 weeks for 24 weeks. A secondary efficacy end point was the first episode of clinical malaria, as assessed in a time-to-event analysis. RESULTS: No safety concerns were identified in the dose-escalation part of the trial (part A). In part B, 225 children underwent randomization, with 75 children assigned to each group. No safety concerns were identified in part B. P. falciparum infection occurred in 36 participants (48%) in the 150-mg group, in 30 (40%) in the 300-mg group, and in 61 (81%) in the placebo group. The efficacy of L9LS against P. falciparum infection, as compared with placebo, was 66% (adjusted confidence interval [95% CI], 45 to 79) with the 150-mg dose and 70% (adjusted 95% CI, 50 to 82) with the 300-mg dose (P<0.001 for both comparisons). Efficacy against clinical malaria was 67% (adjusted 95% CI, 39 to 82) with the 150-mg dose and 77% (adjusted 95% CI, 55 to 89) with the 300-mg dose (P<0.001 for both comparisons). CONCLUSIONS: Subcutaneous administration of L9LS to children was protective against P. falciparum infection and clinical malaria over a period of 6 months. (Funded by the National Institute of Allergy and Infectious Diseases; ClinicalTrials.gov number, NCT05304611.).