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Background:. Autologous fat grafting (AFG) has gained popularity in both cosmetic and reconstructive surgery, including hand surgery, due to its regenerative potential and dual benefits of enhancing aesthetics and function. This systematic review aims to evaluate the efficacy of AFG in treating various hand pathologies. Methods:. A systematic review was conducted following PRISMA guidelines. PubMed, Cochrane Library, and Embase databases were searched for studies published between January 2001 and October 2022 using the terms “(autologous fat grafting) AND (hand).” Of 224 studies identified, 14 met the inclusion criteria. These studies were analyzed based on study design, pathology treated, type of AFG used, and primary outcomes. Results:. The review included 14 studies: 9 prospective/retrospective studies, 3 case reports, and 2 randomized control trials. Digital ulcers related to systemic sclerosis and hand osteoarthritis were the most frequently treated pathologies. AFG was predominantly used to address symptoms/pain (71.4%), with aesthetics as a secondary benefit in 21.4% of cases. The studies reported improvements in pain, functionality, and patient satisfaction. However, the variability in techniques and outcomes limited the ability to perform a meta-analysis. Conclusions:. AFG shows promise as a minimally invasive treatment option for various hand pathologies, offering both symptomatic relief and aesthetic enhancement. Although initial findings are favorable, further high-quality studies are needed to standardize techniques and confirm long-term efficacy. The versatility and safety profile of AFG suggest it has the potential to become a widely adopted technique in hand surgery.

Pressure-volume (PV) loop analysis is a sophisticated invasive approach to quantifying load-dependent and independent measures of cardiac function. Biventricular (BV) PV loops allow left and right ventricular function to be quantified simultaneously and independently, which is important for conditions and certain physiologic states, such as ventricular decoupling or acute physiologic changes. BV PV loops can be performed in an entirely endovascular, percutaneous, and closed-chest setting. This technique is helpful in a survival animal model, as a percutaneous monitoring system during endovascular device experiments, or in cases where chest wall compliance is being tested or may be a confounder. In this article, we describe the end-to-end implementation of a completely endovascular, totally percutaneous, and closed-chest large animal model to obtain contemporaneous BV PV loops in 40 to 70 kg swine. We describe the associated surgical and technical challenges and our solutions to obtaining endovascular BV PV loops, closed-chest cardiac output, and stroke volume (including validation of the correction factor necessary for thermodilution), as well as how to perform endovascular inferior vena cava occlusion in this swine model. We also include techniques for data acquisition and analysis that are required for this method. : Clinical Relevance: This article describes the end-to-end implementation of a completely endovascular, percutaneous, and closed-chest large animal model for obtaining biventricular pressure-volume loops in swine. This will allow researchers to obtain contemporaneous, continuous left and right ventricular physiology through a minimally invasive procedure that animals can survive and does not alter chest wall compliance. This model may be used to better quantify the physiology during clinical scenarios of ventricular decoupling, during procedures such as transcatheter aortic valve repair or those that alter left ventricular (LV) function, or during the initiation of resuscitative endovascular balloon occlusion of the aorta or extracorporeal cardiopulmonary resuscitation when LV and right ventricular filling become misaligned.

Background: Understanding the demographics and scholarly merit among National Institutes of Health (NIH) award recipients can help guide future applicants in the field of dermatology. Objective: Herein, we aimed to explore gender gaps in NIH award recipient demographics and scholarly merits for dermatology-related projects. Methods: Projects funded by the National Institutes of Arthritis and Musculoskeletal and Skin Diseases between 2015 and 2019 related to dermatology were extracted using the NIH Research Portfolio Online Reporting Tools database. The number of publications and h-index of each award recipient were collected using the Scopus database. The 2019 Blue Ridge Institute for Medical Research report was used to determine the top 20 NIH-funded dermatology departments/divisions. Results: Between 2015 and 2019, there were 35.1% more unique male than unique female recipients. Award recipients had an average of 116.7 total publications and an average h-index of 37.1. However, men also had more than half as many publications and a >37% higher average h-index. Gender gaps in research merit, as well as number and type of awards, are reduced among recipients affiliated with a top 20 NIH-funded institution. Conclusion: A higher number of awards were granted to male recipients. Men had a higher mean publication number and h-index compared with their female counterparts. Increased support, resources, and mentorship opportunities to women in research may help mitigate these gaps.

Given demographic changes and increases in longevity, physicians will see increasing numbers of patients over the age of 65 years. As the population ages, adapting and optimizing patient care to better serve the needs of older patients should be a priority. The goal of this review is to shed light on potential barriers affecting treatment adherence and to improve clinical care for older adults with dermatologic conditions. We provide practical strategies to help overcome these barriers and provide suggestions to address the impact of functional limitations on topical medication adherence. Some less apparent risks of treatment, such as fall risks with topical treatments, are also discussed, along with issues such as cost of treatments and prescribing considerations for patients who reside in facilities.

Background: Hidradenitis suppurativa (HS) predominantly affects women of childbearing age, and sex hormones are thought to play a role in HS pathogenesis. However, there is a paucity of data regarding the pattern of HS perimenstrual flares, as well as patient responses to hormone-based therapies. Objective: We aimed to characterize the temporal pattern of perimenstrual flares, as well as factors associated with perimenstrual flares. We also sought to investigate responses to hormonal therapies in women with HS. Methods: An anonymous web-based questionnaire was distributed to various online HS support groups in May 2020. Results: A total of 283 participants met the inclusion criteria as adult women who self-reported having HS and menstrual cycles. The majority (176 of 282 women; 62.4%) reported HS worsening with menses, and 86.9% (153 of 176 women) noted that perimenstrual HS flares occurred always or often. Most women (138 of 175 women; 78.9%) reported that their HS flared in the week preceding menses. Perimenstrual HS flares were more likely in women with a family history of HS compared with those without (49.6% vs. 28.2%; p = .019). More than a third of respondents who had been treated with spironolactone reported improvement of HS symptoms, but more than a quarter of participants who used medroxyprogesterone acetate or used a hormonal intrauterine device reported worsened HS symptoms. Conclusion: Female patients with HS have high rates of perimenstrual HS flares, specifically during the week preceding the onset of menses. Additional investigations on the role of sex hormones in HS pathogenesis and the efficacy of hormone-based therapies are warranted.

Introduction: Effective defibrillation is essential to out-of-hospital cardiac arrest (OHCA) survival. International guidelines recommend initial defibrillation energies between 120 and 360 Joules, which has led to widespread practice variation. Leveraging this natural experiment, we aimed to explore the association between initial defibrillation dose and outcome following OHCA., Methods: The ESO Data Collaborative (2018-2022) was used for this nationwide, retrospective study of adult (18-80 years of age) non-traumatic OHCA patients who presented with an initially shockable ECG rhythm. We excluded patients if they had ROSC prior to initial defibrillation, a resuscitation-limiting advanced directive, or were residents in a healthcare institution. The primary exposure was initial defibrillation dose, defined as Joules per kilogram of body weight, and the primary outcome was return of spontaneous circulation (ROSC). We included survival to discharge as a secondary outcome. We used multivariable logistic regression modeling to assess the relationship between defibrillation dose and outcome., Results: We analyzed data from 21,121 patients. Of the 12,160 patients linked to a defibrillator manufacturer, 7,240 (59.5%) were treated using a biphasic truncated exponential (BTE) waveform and 4,920 (40.5%) were treated using a rectilinear biphasic (RLB) waveform. Defibrillation dose (per 1 J/kg increase) was not associated with ROSC (BTE aOR: 0.97 [0.92, 1.01], n = 7,240; RLB aOR: 1.00 [0.92, 1.09], n = 4,920; all aOR: 1.01 [0.98, 1.04], 21,121) or survival (BTE aOR: 0.98 [0.87, 1.10], n = 1,245; RLB aOR: 0.89 [0.70, 1.12], n = 775; all aOR: 1.00 [0.92, 1.08], n = 2,981)., Conclusions: Initial defibrillation dose was not associated with outcome in this nationwide cohort., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Objective: The administration of amiodarone or lidocaine is recommended during the resuscitation of out-of-hospital cardiac arrest (OHCA) patients presenting with defibrillation-refractory or recurrent ventricular fibrillation or ventricular tachycardia. Our objective was to use 'target trial emulation' methodology to compare the outcomes of patients who received amiodarone or lidocaine during resuscitation., Methods: Adult, non-traumatic OHCA patients in the ESO Data Collaborative 2018-2023 datasets who experienced OHCA prior to EMS arrival, presented with a shockable rhythm, and received amiodarone or lidocaine during resuscitation were evaluated for inclusion. We used propensity score matching (PSM) to investigate the association between antiarrhythmic and outcomes. Return of spontaneous circulation (ROSC) was the primary outcome. Secondary outcomes included the number of post-drug defibrillations and survival to hospital discharge., Results: After application of exclusion criteria, 23,263 patients from 1,707 EMS agencies were eligible for analysis. Prior to PSM, 6,010/20,284 (29.6%) of the patients who received amiodarone and 1,071/2,979 (35.9%) of the patients who received lidocaine achieved prehospital ROSC. Following PSM, lidocaine administration was associated with greater odds of prehospital ROSC (36.0 vs. 30.4%; aOR: 1.29 [1.16, 1.44], n = 2,976 matched pairs). Lidocaine administration was also associated with fewer post-drug defibrillations (median: 2 [0-4] vs. 2 [0-6], mean: 3.3 vs. 3.9, p < 0.01, n = 2,976 pairs), and greater odds of survival to discharge (35.1 vs. 25.7%; OR: 1.54 [1.19, 2.00], n = 538 pairs)., Conclusion: Our 'target trial emulation' suggested that lidocaine was associated with greater odds of prehospital ROSC in comparison to amiodarone when administered during resuscitation from shock refractory or recurrent VF/VT., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Purpose: To investigate the enduring disparities in adverse COVID-19 events between urban and rural communities in the United States, focusing on the effects of SARS-CoV-2 vaccination and therapeutic advances on patient outcomes., Methods: Using National COVID Cohort Collaborative (N3C) data from 2021 to 2023, this retrospective cohort study examined COVID-19 hospitalization, inpatient death, and other adverse events. Populations were categorized into urban, urban-adjacent rural (UAR), and nonurban-adjacent rural (NAR). Adjustments included demographics, variant-dominant waves, comorbidities, region, and SARS-CoV-2 treatment and vaccination. Statistical methods included Kaplan-Meier survival estimates, multivariable logistic, and Cox regression., Findings: The study included 3,018,646 patients, with rural residents constituting 506,204. These rural dwellers were older, had more comorbidities, and were less vaccinated than their urban counterparts. Adjusted analyses revealed higher hospitalization odds in UAR and NAR (aOR 1.07 [1.05-1.08] and 1.06 [1.03-1.08]), greater inpatient death hazard (aHR 1.30 [1.26-1.35] UAR and 1.37 [1.30-1.45] NAR), and greater risk of other adverse events compared to urban dwellers. Delta increased, while Omicron decreased, inpatient adverse events relative to pre-Delta, with rural disparities persisting throughout. Treatment effectiveness and vaccination were similarly protective across all cohorts, but dexamethasone post-ventilation was effective only in urban areas. Nirmatrelvir/ritonavir and molnupiravir better protected rural residents against hospitalization., Conclusions: Despite advancements in treatment and vaccinations, disparities in adverse COVID-19 outcomes persist between urban and rural communities. The effectiveness of some therapeutic agents appears to vary based on rurality, suggesting a nuanced relationship between treatment and geographic location while highlighting the need for targeted rural health care strategies., (© 2024 The Author(s). The Journal of Rural Health published by Wiley Periodicals LLC on behalf of National Rural Health Association.)

Objectives: The use of machine learning to identify patient 'clusters' using post-return of spontaneous circulation (ROSC) vital signs may facilitate the identification of patient subgroups at high risk of rearrest and mortality. Our objective was to use k-means clustering to identify post-ROSC vital sign clusters and determine whether these clusters were associated with rearrest and mortality., Methods: The ESO Data Collaborative 2018-2022 datasets were used for this study. We included adult, non-traumatic OHCA patients with >2 post-ROSC vital sign sets. Patients were excluded if they had an EMS-witnessed OHCA or were encountered during an interfacility transfer. Unsupervised ( k -means) clustering was performed using minimum, maximum, and delta (last minus first) systolic blood pressure (BP), heart rate, SpO 2 , shock index, and pulse pressure. The assessed outcomes were mortality and rearrest. To explore the association between rearrest, mortality, and cluster, multivariable logistic regression modeling was used., Results: Within our cohort of 12,320 patients, five clusters were identified. Patients in cluster 1 were hypertensive, patients in cluster 2 were normotensive, patients in cluster 3 were hypotensive and tachycardic ( n  = 2164; 17.6%), patients in cluster 4 were hypoxemic and exhibited increasing systolic BP, and patients in cluster 5 were severely hypoxemic and exhibited a declining systolic BP. The overall proportion of patients who experienced mortality stratified by cluster was 63.4% (c1), 68.1% (c2), 78.8% (c3), 84.8% (c4), and 86.6% (c5). In comparison to the cluster with the lowest mortality (c1), each other cluster was associated with greater odds of mortality and rearrest., Conclusions: Unsupervised k-means clustering yielded 5 post-ROSC vital sign clusters that were associated with rearrest and mortality.

Background: Timely identification of high-risk pediatric trauma patients and appropriate resource mobilization may lead to improved outcomes. We hypothesized that reverse shock index times the motor component of the Glasgow Coma Scale (GCS) (rSIM) would perform equivalently to reverse shock index times the total GCS (rSIG) in the prediction of mortality and the need for intervention following pediatric trauma., Methods: The 2017-2020 National Trauma Data Bank data sets were used. We included all patients 16 years or younger who had a documented prehospital and trauma bay systolic blood pressure, heart rate, and total GCS. We excluded all patients who arrived at the trauma center without vital signs and interfacility transport patients. Receiver operating characteristic curves were used to model the performance of each metric as a classifier with respect to our primary and secondary outcomes, and the area under the receiver operating characteristic curve (AUROC) was used for comparison. Our primary outcome was mortality before hospital discharge. Secondary outcomes included blood product administration or hemorrhage control intervention (surgery or angiography) <4 hours following hospital arrival and intensive care unit admission., Results: After application of exclusion criteria, 77,996 patients were included in our analysis. Reverse shock index times GCS-motor and rSIG performed equivalently as predictors of mortality in the 1- to 2- ( p = 0.05) and 3- to 5-year-old categories ( p = 0.28), but rSIM was statistically outperformed by rSIG in the 6- to 12- (AUROC, 0.96 vs. 0.95; p = 0.04) and 13- to 16-year-old age categories (AUROC, 0.96 vs. 0.95; p < 0.01). Reverse shock index times GCS-motor and rSIG also performed similarly with respect to prediction of secondary outcomes., Conclusion: Reverse shock index times GCS-total and rSIM are both outstanding predictors of mortality following pediatric trauma. Statistically significant differences in favor of rSIG were noted in some age groups. Because of the simplicity of calculation, rSIM may be a useful tool for pediatric trauma triage., Level of Evidence: Diagnostic Tests or Criteria; Level III., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Epinephrine and norepinephrine are the two most commonly used prehospital vasopressors in the United States. Prior studies have suggested that use of a post-ROSC epinephrine infusion may be associated with increased rearrest and mortality in comparison to use of norepinephrine. We used target trial emulation methodology to compare the rates of rearrest and mortality between the groups of OHCA patients receiving these vasopressors in the prehospital setting., Methods: Adult (18-80 years of age) non-traumatic OHCA patients in the 2018-2022 ESO Data Collaborative datasets with a documented post-ROSC norepinephrine or epinephrine infusion were included in this study. Logistic regression modeling was used to evaluate the association between vasopressor agent and outcome using two sets of covariables. The first set of covariables included standard Utstein factors, the dispatch to ROSC interval, the ROSC to vasopressor interval, and the follow-up interval. The second set added prehospital systolic blood pressure and SpO 2 values. Kaplan-Meier time-to-event analysis was also conducted and the vasopressor groups were compared using a multivariable Cox regression model., Results: Overall, 1,893 patients treated by 309 EMS agencies were eligible for analysis. 1,010 (53.4%) received an epinephrine infusion and 883 (46.7%) received a norepinephrine infusion as their initial vasopressor. Adjusted analyses did not discover an association between vasopressor agent and rearrest (aOR: 0.93 [0.72, 1.21]) or mortality (aOR: 1.00 [0.59, 1.69])., Conclusions: In this multi-agency target trial emulation, the use of a post-resuscitation epinephrine infusion was not associated with increased odds of rearrest in comparison to the use of a norepinephrine infusion., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ‘RPC is an employee of ESO. The aims of this investigation were reviewed by an independent committee before access to the data was granted. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the results reported in this paper.’., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data., Materials and Methods: Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets., Results: Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic., Discussion: Utilizing real-time public health metrics, including estimated R t from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas., Conclusion: Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not generalize to other types of models. In this article we proposed the MinPen framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using common assumptions. The MinPen framework utilizes a novel penalty based on the minimum function to simultaneously detect and exploit relationships between responses. An iterative algorithm is proposed as a solution to the non-convex optimization. Theoretical results such as high dimensional convergence rates, model selection consistency, and a framework for post selection inference are provided. We extend the proposed MinPen framework to other exponential family loss functions, with a specific focus on multiple binomial responses. Tuning parameter selection is also addressed. Finally, simulations and two data examples are presented to show the finite sample properties of this framework. Supplemental material providing proofs, additional simulations, code, and data sets are available online.

Background: Psychological stress is recognized as an important modifiable risk factor for cardiovascular disease (CVD). Despite its potential significance, few to no studies have evaluated the association between stress, stress mindset, and CVD risk factors among rural first responders. The objectives of this study were to identify relationships between general stress, stress mindset, and CVD risk factors., Methods: The study sample (n = 148) included those 18 years or older and who currently serve as a first responder, defined as either EMS, firefighter, or law enforcement. Questionnaires captured information on demographics, years of work experience as a first responder, multiple first responder occupations, general stress, stress mindset, and self-reported CVD risk factors. Data were analyzed using regression analyses., Results: Findings suggest that first responders with a stress-is-negative mindset have significantly higher general stress levels (β = 2.20, p = 0.01). Of note, general stress was not a significant predictor of CVD risk factors (AOR = 1.00, 95%CI = 0.93, 1.08) included in our study. However, a negative stress mindset was statistically significant predictor of CVD risk factors (AOR = 2.82, 95%CI = 1.29, 6.41), after adjusting for general stress and other potential confounders., Conclusions: Findings suggest that stress mindset is an independent predictor of stress and CVD risk factors among rural first responders. These results have the potential to inform educational and organization level interventions targeting stress appraisal for this vulnerable sub population of workers., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Introduction: Traumatic out-of-hospital cardiac arrest (tOHCA) has a mortality rate over 95%. Many current protocols dictate rapid intra-arrest transport of these patients. We hypothesized that on-scene advanced life support (ALS) would increase the odds of arriving at the emergency department with ROSC (ROSC at ED) in comparison to performance of no ALS or ALS en route., Methods: We utilized the 2018-2021 ESO Research Collaborative public use datasets for this study, which contain patient care records from ~2000 EMS agencies across the US. All OHCA patients with an etiology of "trauma" or "exsanguination" were screened (n=15,691). The time of advanced airway management, vascular access, and chest decompression was determined for each patient. Logistic regression modeling was used to evaluate the association of ALS intervention timing with ROSC at ED., Results: 4942 patients met inclusion criteria. 14.6% of patients had ROSC at ED. In comparison to no vascular access, on-scene (aOR: 2.14 [1.31, 3.49]) but not en route vascular access was associated with increased odds of having ROSC at ED arrival. In comparison to no chest decompression, neither en route nor on-scene chest decompression were associated with ROSC at ED arrival. Similarly, in comparison to no advanced airway management, neither en route nor on-scene advanced airway management were associated with ROSC at ED arrival. The odds of ROSC at ED decreased by 3% (aOR: 0.97 [0.94, 0.99]) for every 1-minute increase in time to vascular access and decreased by 5% (aOR: 0.95 [0.94, 0.99]) for every 1-minute increase in time to epinephrine., Conclusion: On-scene ALS interventions were associated with increased ROSC at ED in our study. These data suggest that initiating ALS prior to rapid transport to definitive care in the setting of tOHCA may increase the number of patients with a palpable pulse at ED arrival., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: Shock index (SI) predicts outcomes after trauma. Prior single-center work demonstrated that emergency medical services (EMSs) initial SI was the most accurate predictor of hospital outcomes in a rural environment. This study aimed to evaluate the predictive ability of SI in multiple rural trauma systems with prolonged transport times to a definitive care facility., Methods: This retrospective review was performed at four American College of Surgeons-verified level 1 trauma centers with large rural catchment basins. Adult trauma patients who were transferred and arrived >60 minutes from scene during 2018 were included. Patients who sustained blunt chest or abdominal trauma were analyzed. Subjects with missing data or severe head trauma (Abbreviated Injury Scale score, >2) were excluded. Poisson and binomial logistic regression were used to study the effect of SI and delta shock index (∆SI) on outcomes., Results: After applying the criteria, 789 patients were considered for analysis (502 scene patients and 287 transfers). The mean Injury Severity Score was 8 (interquartile range, 6) for scene and 8.9 (interquartile range, 5) for transfers. Initial EMSs SI was a significant predictor of the need for blood transfusion and intensive care unit care in both scene and transferred patients. An increase in ∆SI was predictive of the need for operative intervention ( p < 0.05). There were increased odds for mortality for every 0.1 change in EMSs SI; those changes were not deemed significant among both scene and transfer patients ( p < 0.1)., Conclusion: Providers must maintain a high level of clinical suspicion for patients who had an initially elevated SI. Emergency medical services SI is a significant predictor for use of blood and intensive care unit care, as well as mortality for scene patients. This highlights the importance of SI and ∆SI in rural trauma care., Level of Evidence: Prognostic and Epidemiological; Level IV., (Copyright © 2023 American Association for the Surgery of Trauma.)

Background: The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19., Methods: Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction., Results: Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes., Conclusions: This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model's components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Moradi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: The purpose of this study was to examine the relationship between test site availability and testing rate within the context of social determinants of health., Study Design: A retrospective ecological investigation was conducted using statewide COVID-19 testing data between March 2020 and December 2021., Methods: Ordinary least squares and geographically weighted regression were used to estimate state and ZIP code level associations between testing rate and testing sites per capita, adjusting for neighbourhood-level confounders., Results: The findings indicate that site availability is positively associated with the ZIP code level testing rate and that this association is amplified in communities of greater economic deprivation. In addition, economic deprivation is a key factor for consideration when examining ethnic differences in testing in medically underserved states., Conclusion: The study findings could be used to guide the delivery of testing facilities in resource-constrained states., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: While COVID-19 vaccines reduce adverse outcomes, post-vaccination SARS-CoV-2 infection remains problematic. We sought to identify community factors impacting risk for breakthrough infections (BTI) among fully vaccinated persons by rurality., Methods: We conducted a retrospective cohort study of US adults sampled between January 1 and December 20, 2021, from the National COVID Cohort Collaborative (N3C). Using Kaplan-Meier and Cox-Proportional Hazards models adjusted for demographic differences and comorbid conditions, we assessed impact of rurality, county vaccine hesitancy, and county vaccination rates on risk of BTI over 180 days following two mRNA COVID-19 vaccinations between January 1 and September 21, 2021. Additionally, Cox Proportional Hazards models assessed the risk of infection among adults without documented vaccinations. We secondarily assessed the odds of hospitalization and adverse COVID-19 events based on vaccination status using multivariable logistic regression during the study period., Results: Our study population included 566,128 vaccinated and 1,724,546 adults without documented vaccination. Among vaccinated persons, rurality was associated with an increased risk of BTI (adjusted hazard ratio [aHR] 1.53, 95% confidence interval [CI] 1.42-1.64, for urban-adjacent rural and 1.65, 1.42-1.91, for nonurban-adjacent rural) compared to urban dwellers. Compared to low vaccine-hesitant counties, higher risks of BTI were associated with medium (1.07, 1.02-1.12) and high (1.33, 1.23-1.43) vaccine-hesitant counties. Compared to counties with high vaccination rates, a higher risk of BTI was associated with dwelling in counties with low vaccination rates (1.34, 1.27-1.43) but not medium vaccination rates (1.00, 0.95-1.07). Community factors were also associated with higher odds of SARS-CoV-2 infection among persons without a documented vaccination. Vaccinated persons with SARS-CoV-2 infection during the study period had significantly lower odds of hospitalization and adverse events across all geographic areas and community exposures., Conclusions: Our findings suggest that community factors are associated with an increased risk of BTI, particularly in rural areas and counties with high vaccine hesitancy. Communities, such as those in rural and disproportionately vaccine hesitant areas, and certain groups at high risk for adverse breakthrough events, including immunosuppressed/compromised persons, should continue to receive public health focus, targeted interventions, and consistent guidance to help manage community spread as vaccination protection wanes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Anzalone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this editorial discussion we describe our experience developing and implementing predictive models during the pandemic response in the state of West Virginia. We provide insights the on the importance of communication and the dynamic environment that exists that impacts predictive modeling in situations such as those that we faced. It is our hope that this work brings insight to those who may experience similar challenges while working in public health policy., Competing Interests: Disclosure Statement No potential competing interest was reported by the authors.

Objectives: Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data., Materials and Methods: An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal component analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis., Results: The data set used in this analysis consists of 2 880 456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period., Discussion: An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on the progression of COVID-19 disease severity over time., Conclusions: The OS provides a framework that can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Introduction: Shock index (SI) and delta shock index (∆SI) predict mortality and blood transfusion in trauma patients. This study aimed to evaluate the predictive ability of SI and ∆SI in a rural environment with prolonged transport times and transfers from critical access hospitals or level IV trauma centers., Methods: We completed a retrospective database review at an American College of Surgeons verified level 1 trauma center for 2 years. Adult subjects analyzed sustained torso trauma. Subjects with missing data or severe head trauma were excluded. For analysis, poisson regression and binomial logistic regression were used to study the effect of time in transport and SI/∆SI on resource utilization and outcomes. p < 0.05 was considered significant., Results: Complete data were available on 549 scene patients and 127 transfers. Mean Injury Severity Score was 11 (interquartile range, 9.0) for scene and 13 (interquartile range, 6.5) for transfers. Initial emergency medical services SI was the most significant predictor for blood transfusion and intensive care unit care in both scene and transferred patients (p < 0.0001) compared with trauma center arrival SI or transferring center SI. A negative ∆SI was significantly associated with the need for transfusion and the number of units transfused. Longer transport time also had a significant relationship with increasing intensive care unit length of stay. Cohorts were analyzed separately., Conclusion: Providers must maintain a high level of clinical suspicion for patients who had an initially elevated SI. Emergency medical services SI was the greatest predictor of injury and need for resources. Enroute SI and ∆SI were less predictive as time from injury increased. This highlights the improvements in en route care but does not eliminate the need for high-level trauma intervention., Level of Evidence: Therapeutic/care management, level IV., (Copyright © 2021 American Association for the Surgery of Trauma.)

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt., Competing Interests: No authors have competing interests.

Scientists should not play a role in investigations nor should investigators play a role in the scientific analyses. One way to bridge the relationship between the forensic scientist and the police investigator is through an Intelligence Analyst (IA) who is part of the forensic services operation. The IA offers the ability to walk between the role of scientist and law enforcement, receiving data after completion of scientific analyses and translating the information into actionable intelligence. The additional bridging and translating services represent a paradigm shift with increased emphasis on investigative contributions from forensic analysis. Forensic intelligence incorporates forensic data early in an investigation in a holistic case approach that incorporates possible datasets and information that could be relevant to the investigation. We present a brief review of the value added when an IA provides the bridge between the forensic laboratory and police investigators to enhance the use of forensic evidence., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier B.V.)

Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a sudden debilitating condition affecting individuals during the most productive times of their lives. Treatment advances have reduced mortality rates but increased the number of survivors facing deficits in physical and neuropsychological function., Objective: This study examined associations between neuropsychological function and work productivity after aSAH., Methods: Fifty-two patients with aSAH, employed before hemorrhage, were recruited from an ongoing National Institutes of Health study. Work Limitations Questionnaire (WLQ), neuropsychological tests (executive function, psychomotor speed, attention and mental flexibility, memory), and Patient Assessment of Own Function were completed at 3 and 12 months after aSAH., Results: Subjects in this analysis reported some level of difficulty in work productivity at 3 and 12 months (35% and 30%, respectively) after hemorrhage. Lower WLQ scores in time management and mental/interpersonal subscales were associated with poorer performance in psychomotor function (r = .5, p = .04 and r = .42, p = .09). Poorer mental flexibility and working memory correlated with time management difficulty at 3 months (r = -.4, p = .09 and r = .54, p = .02). Patients performing poorly on story recall tests were more likely to report difficulty with job physical performance (r = -.42, p = .09) and completing work effectively (r = .61, p = .009). Poorer working memory performance was associated with lower scores on mental/interpersonal WLQ subscales (r = .45, p = .05) and overall health-related work productivity loss (r = .47, p = .04). WLQ areas also correlated with participants' perception of their neuropsychological function after aSAH., Conclusions: These results suggest that neuropsychological deficits impact work quality after hemorrhage and provide strong impetus for future studies so that domain-specific interventions can be implemented to improve outcomes that affect quality of life including work productivity.

This study of patients in the outpatient department at an urban hospital revealed that almost all could have reached a neighborhood center in less time and only a small number came to the hospital rather than a neighborhood center out of medical necessity. When the patients were asked about their willingness to obtain treatment at a neighborhood center, 48 per cent were willing, 52 per cent were not. These responses did not vary by demographic or medical characteristics but rather by the patients' stated priorities regarding medical care. Eighty per cent of those willing to change sites stressed convenience of access as a first priority compared with only 17 per ccent of those not willing to change. Emphasis on quality of care (45 per cent) or on familarity with the site (37 per cent) distinguished the group not willing to change. The findings suggest that successful efforts to persuade patients to utilize a neighborhood center must base their appeal on patients' individual priorities.

Permanent Total Parenteral Nutrition (TPN) is a life-saving but complicated procedure which has profound effects upon the lives of patients and their families, but there is a dearth of information on the the psychosocial consequences of this unique form of therapy. The authors worked with 19 consecutive TPN patients in hospital and after discharge and observed their reactions. It was found that the earliest stages were the most difficult, with anxiety, depression, fear, and negative body image predictable and universal experiences. Major adjustment problems centered around the loss of the basic function, eating. This artificial form of feeding forced multiple alterations in the patients' life styles. Their ability to cope with this intrusive procedure was related to the level of restitution of physical health, ego strength, and the family and hospital support systems. If, in addition to being a life-sustaining procedure, TPN is to restore the psychological stability of patients, all team members must be aware of the psychosocial factors involved.