Sample Size Of Study Research Articles

Artificial Intelligence for Prediction of Shunt Response in Idiopathic Normal Pressure Hydrocephalus: A Systematic Review

BackgroundIdiopathic normal pressure hydrocephalus (iNPH) is a reversible cause of dementia, typically treated with shunt surgery, although outcomes vary. Artificial intelligence (AI) advancements could improve predictions of shunt response (SR) by analyzing extensive data sets. MethodsWe conducted a systematic review to assess AI's effectiveness in predicting SR in iNPH. Studies using AI or machine learning (ML) algorithms for SR prediction were identified through searches in MEDLINE, EMBASE, and Web of Science up to September 2023, adhering to Synthesis Without Meta-Analysis reporting guidelines. ResultsOut of 3541 studies identified, 33 were assessed for eligibility, and 8 involving 479 patients were included. Study sample sizes varied from 28 to 132 patients. Common data inputs included imaging/radiomics (62.5%) and demographics (37.5%), with Support Vector Machine being the most frequently used ML algorithm (87.5%). Two studies compared multiple algorithms. Only four studies reported the Area Under the Curve (AUC) values, which ranged between 0.80 and 0.94. The results highlighted inconsistency in outcome measures, data heterogeneity, and potential biases in the models used. ConclusionsWhile AI shows promise for improving iNPH management, there is a need for standardized data and extensive validation of AI models to enhance their clinical utility. Future research should aim to develop robust and generalizable AI models for more effective diagnosis and management of iNPH.

Effect of an oncology care management program on ED visits in a rural Medicaid population.

175 Background: Optum’s Cancer Support Program (CSP) provides care management to members with cancer diagnoses facing healthcare barriers. CSP nurses educate members on symptom management, palliative care and advance care planning, address psychosocial needs and close gaps in care. Improving health equity and outcomes is a program priority. In 2023, Optum analyzed outpatient claims for 24,221 Medicaid members with cancer and found that rural members visit emergency departments (ED) 31% more than urban members. As part of a CSP quality and outcomes assessment, we aimed to understand CSP’s impact on ED utilization within a high-risk rural Medicaid population. Methods: Beginning 1/1/23, Medicaid members in rural zip codes with an active cancer diagnosis were stratified as high- or low-risk based on diagnosis and clinical factors. High-risk members were invited to participate in CSP and receive individualized telephonic care management. 2023 outpatient medical claims were evaluated, and rural ED visit rates were compared for high-risk CSP participants and non-participants for approximately 4 months (average CSP engagement) after CSP enrollment or identification (non-participants). Population demographics were tested via a Mood's median test; ED visits per thousand members per year (PTMPY)—engaged, non-engaged, and difference in means—was tested via a 10,000-iteration bootstrap. Results: The study population included 421 members stratified as high-risk. Of those, 38% (161) enrolled in CSP. Reasons for non-enrollment included inability to reach the member (61%) and member refused (39%). There were no statistically significant differences between the groups’ demographic composition (all variables had p ≥ 0.05). Under a 10,000-iteration bootstrap, there were 2,696 ED visits PTMPY, 80% CI [2,258, 3,150] in the participant group and 3,427 ED visits PTMPY, 80% CI [2,904, 3,974] in the non-participant group. The difference in means between the groups was 730 ED visits PTMPY, 80% CI [52, 1,421]. Conclusions: Rural Medicaid members enrolled in CSP experienced lower ED visit rates, suggesting that care management may be effective at reducing ED utilization within rural populations that lack access to wraparound oncology services. Limitations of this study include small sample size and potential enrollment bias. Further analysis is underway to increase the study sample size and identify other factors that may be impacted by care management, thereby improving healthcare equity.

Quantitative electroencephalography to assess post-stroke functional disability: A systematic review and meta-analysis

ObjectiveQuantitative electroencephalography (QEEG) is a non-invasive, reliable and easily accessible modality to assess neuronal activity. QEEG in acute stroke may predict short and long-term functional outcomes. The role of individual indices has not been studied in a meta-analysis. We aim to assess individual QEEG-derived indices to predict post-stroke disability. MethodsWe included studies (sample size ≥ 10) with stroke patients who underwent EEG and a follow-up outcome assessment was available either in the form of modified Rankin scale (mRS) or National Institute of Stroke scale (NIHSS) or Fugl-Meyer scale (FMA). QEEG indices analysed were delta-alpha ratio (DAR), delta-theta-alpha-beta ratio (DTABR), brain symmetry index (BSI) and pairwise derived brain symmetry (pdBSI). ResultsNine studies (8 had only ischemic stroke, and one had both ischemic and haemorrhagic stroke), including 482 participants were included for meta-analysis. Higher DAR was associated with worse mRS (n=300, Pearson's r 0.26, 95 % CI 0.21-0.31). Higher DTABR was associated with worse mRS (n=337, r=0.32, 95 % CI 0.26-0.39). Higher DAR was associated with higher NIHSS (n=161, r=0.42, 95 % CI0.24-0.6). Higher DTABR was associated with higher NIHSS (n=158, r=0.49, 95 % CI 0.31-0.67). ConclusionsQEEG-derived indices DAR and DTABR have the potential to assess post-stroke disability. Adding QEEG to the clinical and imaging biomarkers in the acute phase may help in better prediction of post-stroke recovery. RegistryPROSPERO 2022 CRD42022292281

Usefulness of Platelet-rich Fibrin in Open Fractures of Tibia: A Randomised Comparative Pilot Study

Background and Aims: Platelet-rich fibrin (PRF), rich in growth factors and cytokines, found useful for bone healing in animal studies, has paucity of literature on open fractures in humans. This randomised comparative interventional study aimed to find out the role of PRF in wound healing and fracture union in open fractures of tibia. Material and Methods: Random computerised allocation of 14 open tibial shaft fractures managed with an external fixator were done into PRF and non-PRF groups. Autologous PRF injected at fracture site, with PRF gel over open wounds in the PRF group, was compared with conventional standard treatment in the non-PRF group. Wound assessment on day 3, day 7 and weekly till complete wound healing and radiographs for fracture union at 3, 6, and 12 months were done in both groups. The statistical analysis was done using an unpaired t-test (wound size assessment) and chi-squared test (fracture union), using SPSS version 25. Results: Fractures union in PRF group 7/8 (87.5%) was better than 3/6 (50%) in non-PRF group. External fixators were removed 4 weeks earlier in PRF group. Wound healing time, though not statistically significant, was faster in PRF group (36.25±3.03 days) compared to non-PRF (42±7 days), even with larger wound size in PRF group (7.87±1.43 cm 2 ) than non-PRF group (7.5±1.5 cm 2 ). Conclusions: Results of injectable PRF at fracture site and PRF gel over the wound are encouraging, found to be safe, with faster wound healing and better fracture union in the small sample size of this pilot study.

Ocular and Periocular Tattoo Adverse Effects: A Review.

Ocular and periocular tattoos, involving ink application to the eyeball or surrounding skin, have gained popularity as forms of self-expression. However, this trend raises significant concerns about potential complications that can adversely affect ocular health and esthetics. Awareness of these risks is crucial for both patients and practitioners. A comprehensive literature review was conducted, focusing on studies discussing complications related to ocular and periocular tattooing. Relevant studies were identified through the MEDLINE, PubMed, and Ovid databases. The reviewed papers were evaluated based on study design, including blinding, sample size, control use, randomization, and objective endpoints, and classified according to the Oxford Center for Evidence-Based Medicine evidence hierarchy. The review identified a wide range of complications, including immediate issues like bleeding, infections (conjunctivitis, endophthalmitis), and allergic reactions. Delayed reactions included granuloma formation, often requiring further treatment. The most serious risk identified was potential visual impairment due to improper technique or ink placement. With the growing trend in ocular and periocular tattooing, there is an urgent need for increased awareness of associated risks. It is crucial to ensure that only qualified professionals perform these procedures, emphasizing the importance of understanding ocular anatomy. Developing strict regulatory guidelines and prioritizing research on the long-term effects of these tattoos are essential for patient safety. A collaborative approach among healthcare providers, regulatory bodies, and educational institutions is needed to mitigate risks and promote best practices in cosmetic tattooing.

Comparing fourth-year medical students’ procedural surgical skill learning outcomes between third-person point-of-view video lectures and first-person point-of-view immersive virtual reality: a pilot study of surgically implanting a subdermal drain on cadavers

The use of virtual reality (VR) training in medical school education has increased over the last decade partly due to coronavirus-19 (COVID-19) and due to technology advancements in this integrative area. As medical students are being trained in new pedagogical interventions, it is important to drill-down and focus on the areas of most translational concern for assessing their competency and proficiency. Thus, the present pilot study sought to evaluate a group of fourth-year medical students’ ability to learn a modified medical school curriculum on implementing a subdermal drain by first learning how to stitch on a silicone model (i.e., proper stitching only) and then being randomly assigned to either a third-person point-of-view (TP-POV) or first-person point-of-view (FP-POV) immersive VR group to learn how to conduct the surgical procedure (i.e., conceptually and procedurally). Then, the participants’ surgical procedural motor skills were assessed on a single attempt to demonstrate their competency and proficiency on a cadaver. The results showed that the pedagogical learning differences in FP-POV immersive VR improved the cleanliness and accuracy of the surgical dissection movement and drain placement with 100% proficiency in the first attempt at implementing this surgical technique on a cadaver. Additionally, there was statistically significant improvement in the dissection movement (p < 0.05*) and the tension test (p < 0.01**) in the FP-POV group compared to the TP-POV group, even with a small sample size in the present pilot study. These findings offer unique training opportunities to be considered when designing future VR curriculum with the suggested use of repeated-measures designs on the surgical procedural motor skills to assess learning curves and their potential translations in reducing operating room errors, costs, planning and scheduling times, and surgery overlaps, thereby positively contributing to a more efficient and safer OR surgical experience by staff and patients in the near future.

A comparison of some existing and novel methods for integrating historical models to improve estimation of coefficients in logistic regression

Abstract Model integration refers to the process of incorporating a fitted historical model into the estimation of a current study to increase statistical efficiency. Integration can be challenging when the current model includes new covariates, leading to potential model misspecification. We present and evaluate seven existing and novel model integration techniques, which employ both likelihood constraints and Bayesian informative priors. Using a simulation study of logistic regression, we quantify how efficiency—assessed by bias and variance—changes with the sample sizes of both historical and current studies and in response to violations to transportability assumptions. We also apply these methods to a case study in which the goal is to use novel predictors to update a risk prediction model for in-hospital mortality among pediatric extracorporeal membrane oxygenation patients. Our simulation study and case study suggest that (i) when historical sample size is small, accounting for this statistical uncertainty is more efficient; (ii) all methods lose efficiency when there exist differences between the historical and current data-generating mechanisms; (iii) additional shrinkage to zero can improve efficiency in higher-dimensional settings but at the cost of bias in estimation.

The effects of stretching on running economy – A systematic review with meta-analysis

Introduction While all running athletes aim to cover the prescribed distance as fast as possible, the physiological and anatomical profiles of athletes are completely different between the disciplines. While sprint athletes have to exert maximal force in the smallest possible time window, the long-distance runner strives for the most economical movement pattern possible (Barnes & Kilding, 2015). Therefore, defined as oxygen uptake at a velocity at a prescribed percentage of the vVO2max, running economy (RE) has been identified as a decisive performance parameter (Saunders et al., 2004). One identified parameter which moderates RE was Achilles tendon stiffness. While stretching frequently used in warm-ups, it also decreases tissue stiffness. Objective: While some authors recommended the avoidance of stretching prior to running (Wilson et al., 2010), others found no affected economy in response to stretching. To address this research gap, this study was conducted to pool available stretching effects on RE. Methods A systematic search was conducted using MEDLINE/PubMed, Web of Science and Scopus (inception to April, 2024), which was supplemented by a manual search in Google Scholar and reference lists of identified studies. To account for multiple dependent study outcomes, robust variance estimation meta-analysis model was chosen to quantify pooled effect sizes. Controlled acute and chronic studies were included if they were performed in young, healthy participants on RE-parameter (energy/O2-consumption at a prescribed velocities determined at a percentage of VO2max (vVO2). Studies without a control group, patients or without the metabolic parameters were excluded. The PEDro scale and GRADE criteria were applied to evaluate the risk of bias and certainty of evidence, respectively. Results A total of 16 studies were investigated, with 14 studies (6 static stretching, 6 dynamic stretching and 2 static/dynamic stretching) addressing acute effects with 169 participants and 33 effect sizes (ES) matched the inclusion criteria. With a PEDro score of 4.88, study quality was rated fair. Overall, there was a low certainty of evidence without confirming any effect of stretching on RE, with overall group effect of ES = 0.007, −0.15 to 0.17 [95% CI], p = 0.93, τ2 = 0, W2 = 0, static group effect of ES = –0,22, –0,40 to 0,70 [95% CI], p = 0.3, τ2 = 0.18, W2 = 0 and dynamic group effect of ES = –0,07, −0.22 to 0.09 [95% CI], p = 0.35, τ2 = 0, W2 = 0, determined at vVO2 65% - 90%. Discussion Currently, there is no evidence supporting previous recommendations to avoid stretching before running due to reduced RE. However, several methodological limitations, such as high heterogeneity in the velocity used in RE determination, small study sample sizes and comparatively short stretching durations without intensity quantification and a lack of pre-post comparisons bias the certainty of evidence. Accordingly, there is a demand for high quality research that controls underlying mechanisms. References Barnes, K. R., & Kilding, A. E. (2015). Running economy: Measurement, norms, and determining factors. Sports Medicine – Open, 1, Article 8. https://doi.org/10.1186/s40798-015-0007-y Saunders, P., Pyne, D., Telford, R., & Hawley, J. (2004). Factors affecting running economy in trained distance runners. Sports Medicine, 34, 465–485. https://doi.org/10.2165/00007256-200434070-00005 Wilson, J. M., Hornbuckle, L. M., Kim, J.-S., Ugrinowitsch, C., Lee, S.-R., Zourdos, M. C., Sommer, B., & Panton, L. B. (2010). Effects of atatic atretching on wnergy cost and running endurance performance. The Journal of Strength & Conditioning Research, 24(9), 2274-2279. https://doi.org/10.1519/JSC.0b013e3181b22ad6

MRI economics: Balancing sample size and scan duration in brain wide association studies.

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan time given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan time per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan time are broadly interchangeable up to 20-30 min of data. However, the returns of scan time diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed overhead costs associated with each participant (e.g., recruitment, non-imaging measures), prediction accuracy in many small-scale and some large-scale BWAS might benefit from longer scan time than typically assumed. These results generalize across phenotypic domains, scanners, acquisition protocols, racial groups, mental disorders, age groups, as well as resting-state and task-state functional connectivity. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations maximize sample size, at the expense of scan time, which can result in sub-optimal prediction accuracies and inefficient use of resources. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

Exploring the role of personality traits as mediators of mental health in Pakistani commuters.

To investigate how resilience mediates the relationship of personality factors with depression and aggression among daily commuters in an urban setting. The correlational study was conducted at Lahore Pakistan from October 2019 and March 2020, and comprised male subjects aged 20-49 years who were employed at private schools, offices and banks, and commuted on a daily basis. Data was collected using the International Personality Item Pool, Brief Resilience Scale, Siddiqui Shah Depression Scale and the Clinical Anger Scale. Data was analysed using analysis of moment structure version 24. Sample size for the current study is 187 (Male participants) were aged 19-29 years. Resilience had a significant negative mediating effect between depression (-0.14*) and neuroticism (-0.22*) personality traits, but was a non-significant mediator for other personality factors with respect to aggression and depression. Resilience was found to be beneficial in addressing aggression and depression among daily commuters.

A Systematic Review of Gut Microbiota Diversity: A Key Player in the Management and Prevention of Diabetes Mellitus.

Diabetes mellitus represents a significant global health challenge, characterized by impaired insulin production and action, leading to elevated blood glucose levels. This systematic review investigates the association between gut microbiota composition and diversity, along with the structural and functional characteristics of the gut microbiome, and their implications for the risk, prevention, and management of both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a comprehensive search across multiple databases yielded 16 studies that met the inclusion criteria. The findings highlight the potential of gut microbiota interventions, such as fecal microbiota transplantation and probiotic supplementation, in improving metabolic parameters and glycemic control. Notably, the review underscores the importance of dietary interventions and the role of specific microbial populations in influencing diabetes outcomes. Despite the promising results, the variability in study designs, sample sizes, and methodologies poses challenges for generalizability and interpretation. This review emphasizes the need for further research to elucidate the mechanisms underlying these associations and to explore personalized microbiome-based therapies in diabetes management. The insights gained could pave the way for innovative therapeutic strategies aimed at harnessing gut health to mitigate the burden of diabetes mellitus.

Correlating plasma protein profiles with symptomatology and treatment response in acute phase and early remission of major depressive disorder.

This study aimed to explore the relationship between plasma proteome and the clinical features of Major Depressive Disorder (MDD) during treatment of acute episode. In this longitudinal observational study, 26 patients hospitalized for moderate to severe MDD were analyzed. The study utilized Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) alongside clinical metrics, including symptomatology derived from the Montgomery-Åsberg Depression Rating Scale (MADRS). Plasma protein analysis was conducted at the onset of acute depression and 6 weeks into treatment. Analytical methods comprised of Linear Models for Microarray Data (LIMMA), Weighted Correlation Network Analysis (WGCNA), Generalized Linear Models, Random Forests, and The Database for Annotation, Visualization and Integrated Discovery (DAVID). Five distinct plasma protein modules were identified, correlating with specific biological processes, and uniquely associated with symptom presentation, the disorder's trajectory, and treatment response. A module rich in proteins related to adaptive immunity was correlated with the manifestation of somatic syndrome, treatment response, and inversely associated with achieving remission. A module associated with cell adhesion was linked to affective symptoms and avolition, and played a role in the initial episodes and treatment response. Another module, characterized by proteins involved in blood coagulation and lipid transport, exhibited negative correlations with a variety of MDD symptoms and was predominantly associated with the manifestation of psychotic symptoms. This research points to a complex interplay between the plasma proteome and MDD's clinical presentation, suggesting that somatic, affective, and psychotic symptoms may represent distinct endophenotypic manifestations of MDD. These insights hold potential for advancing targeted therapeutic strategies and diagnostic tools. The study's limited sample size and its naturalistic design, encompassing diverse treatment modalities, present methodological constraints. Furthermore, the analysis focused on peripheral blood proteins, with potential implications for interpretability.

Awareness, knowledge, and attitude towards artificial intelligence: Perspective of medical students in Ghana

The adoption of emerging technologies among university students has become increasingly prevalent in recent years. AI-assisted technologies are gradually permeating medical education and practice to improve healthcare delivery and reduce resource waste. This study aimed to investigate the awareness, use, and perception of AI among medical students in Emmanuel Quaye Archampong Library at the University of Ghana. Using a survey research design, data were collected from medical students at the University of Ghana Medical School. Structured questionnaires were administered to the respondents online using Google Forms. With a total of 1366 respondents, Krejcie and Morgan's published table was employed to select the study sample size of 302 medical students. Forty-eight (39.0%) medical students agreed that the use of AI-assisted technologies was voluntary without being coerced to use them. More than half of the respondents (50.4%) reported being moderately aware of AI-assisted technologies and adequately understanding the concept of AI. Grammarly and ChatGPT were predominantly used in medical studies, despite the lack of opportunities for training on AI-assisted technologies. It is recommended that regular training and guidance be provided to students to appropriately use AI-assisted technologies in research and learning.

Meta-analysis on Effectiveness of Palliative Care versus Conventional Care amongst Advanced Gynaecological Patients with Cancer and Caregivers

In cancer patients, physical and psychological issues are very common. There is a need for family support and high utilisation of healthcare resources commonly. Palliative care (PC) has grown in popularity to better fulfil of needs of patients and their families and potentially lowering hospital costs. Given that the majority of patients still die in hospitals, there is a need for an effective model of PC for advanced gynaecological cancer, as well as the wise allocation of scarce resources. The main aim of this study was to compare the efficacy of PC to conventional treatment for adults with cancer, including gynaecological cancers, and their caregivers. Four randomised controlled trials (RCTs) were identified by searching PubMed, PubMed Central, Clinical Key, Embase and other grey literature from a duration of 2011–2021. Cochrane criteria were used to calculate the risk of bias, and the Grade Profiler Guideline Development Tool was used to check the quality of the included studies. Standardised mean differences (SMDs), I2 value and forest plot were prepared by using Review Manager 4.0. A total of four RCTs were extracted by following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and found to have a high risk for bias and low to poor quality of evidence. Included study sample sizes ranged from 22 to 104 participants, including 670 people in total, including 289 patients with advanced cancer patients, including gynaecological cancer and 381 unpaid caregivers. Results also showed that PC enhances patients’ quality of life (SMD = 0.26; 95% confidence interval [CI] = −0.29–0.80; I2 = 76%), lowered symptom burden amongst patients (SMD = −0.75, 95% CI = −1.75–0.25; I2 = 89%), reduces patient depression (SMD = 0.08, 95% CI = −0.19–0.34; I2 = 0%) and decreases depression in unpaid caregivers (SMD = −0.16, 95% CI = −0.56–0.24; I2 = 59%). PC treatment increases patients’ quality of life and lowers their symptom burden. In comparison to conventional care, it also reduces depression among patients and caregivers. We believe that the findings should be viewed with care until more recent exclusive RCTs are available.

The Value of Small Samples to Groups and Teams Research: Accumulating Knowledge across Philosophies of Science

The large increase in the average sample size of team studies published in premier management journals over the past decade is concerning. A strict focus on large samples suppresses the study of many teams, particularly less prominent ones; for example, teams in small or medium-sized enterprises, teams in unique contexts, teams containing underrepresented minorities, and teams adopting new technologies. It also impedes our collective understanding of teams by devaluing work relying on philosophies of science that do not prioritize large samples. Large samples allow positivists to establish relationships between constructs. Yet, contextualized, rich data from as few as just one team can help constructivists uncover lived experiences or could be studied by critical realists to identify mechanisms underlying social systems of active agents. In this paper, we review how these three philosophies of science view sample size, addressing how and when small samples are beneficial. Importantly, research from all three traditions is necessary to build an in-depth, practical understanding of teams. We also describe five specific ways small sample research can contribute to team science and lay out four general recommendations for assessing the value of sample size in team research. Throughout, we maintain that scientific progress is collective and pluralistic. A sole reliance on large samples threatens this goal.

Protein-Based Predictive Biomarkers to Personalize Neoadjuvant Therapy for Bladder Cancer-A Systematic Review of the Current Status.

The clinical outcome of patients with muscle-invasive bladder cancer (MIBC) is poor despite the approval of neoadjuvant chemotherapy or immunotherapy to improve overall survival after cystectomy. MIBC subtypes, immune, transcriptome, metabolomic signatures, and mutation burden have the potential to predict treatment response but none have been incorporated into clinical practice, as tumor heterogeneity and lineage plasticity influence their efficacy. Using the PRISMA statement, we conducted a systematic review of the literature, involving 135 studies published within the last five years, to identify studies reporting on the prognostic value of protein-based biomarkers for response to neoadjuvant therapy in patients with MIBC. The studies were grouped based on biomarkers related to molecular subtypes, cancer stem cell, actin-cytoskeleton, epithelial-mesenchymal transition, apoptosis, and tumor-infiltrating immune cells. These studies show the potential of protein-based biomarkers, especially in the spatial context, to reduce the influence of tumor heterogeneity on a biomarker's prognostic capability. Nevertheless, currently, there is little consensus on the methodology, reagents, and the scoring systems to allow reliable assessment of the biomarkers of interest. Furthermore, the small sample size of several studies necessitates the validation of potential prognostic biomarkers in larger multicenter cohorts before their use for individualizing neoadjuvant therapy regimens for patients with MIBC.

Cross-EHR validation of antidepressant response algorithm and links with genetics of psychiatric traits.

Antidepressants are commonly prescribed medications in the United States, however, factors underlying response are poorly understood. Electronic health records (EHRs) provide a cost-effective way to create and test response algorithms on large, longitudinal cohorts. We describe a new antidepressant response algorithm, validation in two independent EHR databases, and genetic associations with antidepressant response. We deployed the algorithm in EHRs at Vanderbilt University Medical Center (VUMC), the All of Us Research Program, and the Mass General Brigham Healthcare System (MGB) and validated response outcomes with patient health questionnaire (PHQ) scores. In a meta-analysis across all sites, worse antidepressant response associated with higher PHQ-8 scores (beta = 0.20, p-value = 1.09 x 10 -18 ). We used polygenic scores to investigate the relationship between genetic liability of psychiatric disorders and response to first antidepressant trial across VUMC and MGB. After controlling for depression diagnosis, higher polygenic scores for depression, schizophrenia, bipolar, and cross-disorders associated with poorer response to the first antidepressant trial (depression: p-value = 2.84 x 10 -8 , OR = 1.07; schizophrenia: p-value = 5.93 x 10 -4 , OR = 1.05; bipolar: p-value = 1.99 x 10 -3 , OR = 1.04; cross-disorders: p-value = 1.03 x 10 -3 , OR = 1.05). Overall, we demonstrate our antidepressant response algorithm can be deployed across multiple EHR systems to increase sample size of genetic and epidemiologic studies of antidepressant response.

JointPRS: A Data-Adaptive Framework for Multi-Population Genetic Risk Prediction Incorporating Genetic Correlation.

Genetic prediction accuracy for non-European populations is hindered by the limited sample size of Genome-wide association studies (GWAS) data in these populations. Additionally, it is challenging to tune model parameters with a small tuning dataset for methods that require tuning data, which is often the case for non-European samples. To address these challenges, we propose JointPRS, a novel, data-adaptive framework that simultaneously models multiple populations using GWAS summary statistics. JointPRS incorporates genetic correlation structures into the prediction framework, enabling accurate performance even without individual-level tuning data. Additionally, it uniquely employs a data-adaptive approach, providing a robust solution when only a small tuning dataset is available. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in five continental populations (European (EUR); East Asian (EAS); African (AFR); South Asian (SAS); and Admixed American (AMR)) evaluated using the UK Biobank (UKBB) and All of Us (AoU), we demonstrate that JointPRS outperforms six other state-of-art methods across three different data scenarios (no tuning data, tuning and testing data from the same cohort, and tuning and testing data from different cohorts) for most traits in non-European populations, while maintaining model simplicity and computational efficiency.

A - 130 Effectiveness of Beta-Amyloid Immunotherapy and Monoclonal Antibodies in Patients with Early Onset Alzheimer’s between the Ages of 45–60 Years

Abstract Objective This comprehensive systematic review examines various therapeutic approaches for Alzheimer’s Disease (ad), with a specific emphasis on immunotherapy targeting beta-amyloid and tau pathology. Data Selection Data were sourced from peer-reviewed journals, databases (e.g., PubMed, PsychInfo, Scielo, Google Scholar), and relevant conference proceedings from inception to present. Search terms included variations of “Alzheimer’s disease,” “clinical trials,” “monoclonal antibodies,” “beta amyloid,” “immunotherapy”. Selection criteria emphasized relevance to ad therapy, randomized controlled trials, and clinical outcomes. A total of 24 studies from 2013 to 2023 were reviewed, meeting robust study design, sample size, and therapeutic relevance were chosen. Data extraction followed standardized guidelines for abstracting key information, including study design, participant characteristics, interventions, and outcome measures. Data Synthesis The review synthesized recent studies on Alzheimer’s disease (ad) therapy, particularly monoclonal antibodies targeting amyloid beta oligomers. Notable studies such as Avgerinos et al. (2021) and Bateman et al. (2022), showcased significant cognitive decline reductions in ad patients. Positive outcomes were also observed with gantenerumab, lecanemab, and donanemab. Additionally, alternative approaches such as Zanthoxylum caribaeum extract and herbal extracts were explored. The synthesis underscored the complexity of ad treatment and the necessity for continued multidisciplinary research. Conclusions This systematic review provides valuable insights into ad therapeutics, guiding future research directions and informing clinical practice to better meet the needs of ad patients and their caregivers. Despite significant advancements, challenges persist in developing effective ad therapies, underscoring the ongoing need for research, particularly in immunotherapy and natural compounds, to address the multifaceted nature of the disease and enhance patient outcomes.

Professional Quality of Life Among Mental Health Nurses: A Systematic Review and Meta-Analysis.

The professional quality of life (ProQOL) is increasingly applied to nurses and is a measure of the positive and negative aspects of their work. This systematic review and meta-analysis evaluated the level of compassion satisfaction, burnout and secondary traumatic stress (STS) in empirical studies. Electronic databases Scopus, PubMed and Web of Science were searched on 7th February 2024, and the review followed PRISMA guidelines. The pooled estimate of compassion satisfaction, burnout and secondary traumatic stress mean scores using a random-effects model for meta-analyses was undertaken. R statistical software and the dmetar program were used to execute the analysis. Heterogeneity was evaluated with the I2 statistics, while publication bias was evaluated using Egger's regression test. Twenty-four studies (sample size = 4274) were systematically reviewed and 18 studies (n = 3163) were incorporated into the meta-analysis. Burnout, secondary traumatic stress and compassion satisfaction are found to be at moderate levels in this research. The qualitative analysis informs that variables such as healthy lifestyle, work environment and psychological resilience contribute towards optimum ProQOL scores. The pooled mean estimate was 32.79 (95% CI = 29.57-36) for compassion satisfaction, 24.99 (95% CI = 23.75-26.23) for burnout and 21.99 (95% CI = 18.93-25.06) for secondary traumatic stress, respectively. Mental health nurse managers need to address the factors in the work environment and promote interventions to enhance coping with burnout and STS. Subgroup analyses of country-based economies & regions and years revealed significant results.