Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature.

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.

Logistic burdens of cancer care: A qualitative study.

Cancer treatment often creates logistic conflicts with everyday life priorities; however, these challenges and how they are subjectively experienced have been largely unaddressed in cancer care. Our goal was to describe time and logistic requirements of cancer care and whether and how they interfered with daily life and well-being. We conducted interviews with 20 adults receiving cancer-directed treatment at a single academic cancer center. We focused on participants' perception of the time, effort, and energy-intensiveness of cancer care activities, organization of care requirements, and preferences in how to manage the logistic burdens of their cancer care. Participant interview transcripts were analyzed using an inductive thematic analysis approach. Burdens related to travel, appointment schedules, healthcare system navigation, and consequences for relationships had roots both at the system-level (e.g. labs that were chronically delayed, protocol-centered rather than patient-centered bureaucratic requirements) and in individual circumstances (e.g. greater stressors among those working and/or have young children versus those who are retired) that determined subjective burdensomeness, which was highest among patients who experienced multiple sources of burdens simultaneously. Our study illustrates how objective burdens of cancer care translate into subjective burden depending on patient circumstances, emphasizing that to study burdens of care, an exclusive focus on objective measures does not capture the complexity of these issues. The complex interplay between healthcare system factors and individual circumstances points to clinical opportunities, for example helping patients to find ways to meet work and childcare requirements while receiving care.

Machine Learning Identifies Higher Survival Profile In Extracorporeal Cardiopulmonary Resuscitation.

OBJECTIVES: Extracorporeal cardiopulmonary resuscitation (ECPR) has been shown to improve neurologically favorable survival in patients with refractory out-of-hospital cardiac arrest (OHCA) caused by shockable rhythms. Further refinement of patient selection is needed to focus this resource-intensive therapy on those patients likely to benefit. This study sought to create a selection model using machine learning (ML) tools for refractory cardiac arrest patients undergoing ECPR. DESIGN: Retrospective cohort study. SETTING: Cardiac ICU in a Quaternary Care Center. PATIENTS: Adults 18-75 years old with refractory OHCA caused by a shockable rhythm. METHODS: Three hundred seventy-six consecutive patients with refractory OHCA and a shockable presenting rhythm were analyzed, of which 301 underwent ECPR and cannulation for venoarterial extracorporeal membrane oxygenation. Clinical variables that were widely available at the time of cannulation were analyzed and ranked on their ability to predict neurologically favorable survival. INTERVENTIONS: ML was used to train supervised models and predict favorable neurologic outcomes of ECPR. The best-performing models were internally validated using a holdout test set. MEASUREMENTS AND MAIN RESULTS: Neurologically favorable survival occurred in 119 of 301 patients (40%) receiving ECPR. Rhythm at the time of cannulation, intermittent or sustained return of spontaneous circulation, arrest to extracorporeal membrane oxygenation perfusion time, and lactic acid levels were the most predictive of the 11 variables analyzed. All variables were integrated into a training model that yielded an in-sample area under the receiver-operating characteristic curve (AUC) of 0.89 and a misclassification rate of 0.19. Out-of-sample validation of the model yielded an AUC of 0.80 and a misclassification rate of 0.23, demonstrating acceptable prediction ability. CONCLUSIONS: ML can develop a tiered risk model to guide ECPR patient selection with tailored arrest profiles.

Effect of a Multicomponent Food Pantry Intervention in Client Subgroups.

Nutrition promotion programs may have varying effects and influence health disparities. SuperShelf promotes healthy choices in food pantries through inventory changes and nudge implementation (e.g., choice architecture). This secondary analysis of the SuperShelf cluster-randomized trial assessed whether the effect of SuperShelf on client diet quality differed by equity characteristics. English-, Spanish-, or Somali-speaking adult clients from 11 food pantries in Minnesota were included (N = 193). We measured change in diet quality by the Healthy Eating Index 2015 (HEI-2015; maximum score 100) using up to two 24 h dietary recalls from pre-intervention and post-intervention periods. We used linear mixed-effects models to determine whether the effect of SuperShelf on diet quality varied by self-reported gender, race/ethnicity, education, and employment status. In separate adjusted models, the interactions of SuperShelf and gender, education, or employment status were not significant. The interaction of SuperShelf and race/ethnicity was significant (p-interaction = 0.008), but pairwise comparisons in diet quality were non-significant in all racial/ethnic subgroups. SuperShelf did not have differential effects on diet quality by gender, race/ethnicity, education, or employment status, suggesting it does not worsen dietary disparities among food pantry clients, though more subgroup analyses are needed to explore potential racial/ethnic disparities in this context.

Unsupervised Machine Learning of the Combined Danish and Norwegian Knee Ligament Registers: Identification of 5 Distinct Patient Groups With Differing ACL Revision Rates.

BACKGROUND: Most clinical machine learning applications use a supervised learning approach using labeled variables. In contrast, unsupervised learning enables pattern detection without a prespecified outcome. PURPOSE/HYPOTHESIS: The purpose of this study was to apply unsupervised learning to the combined Danish and Norwegian knee ligament register (KLR) with the goal of detecting distinct subgroups. It was hypothesized that resulting groups would have differing rates of subsequent anterior cruciate ligament reconstruction (ACLR) revision. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: K-prototypes clustering was performed on the complete case KLR data. After performing the unsupervised learning analysis, the authors defined clinically relevant characteristics of each cluster using variable summaries, surgeons' domain knowledge, and Shapley Additive exPlanations analysis. RESULTS: Five clusters were identified. Cluster 1 (revision rate, 9.9%) patients were young (mean age, 22 years; SD, 6 years), received hamstring tendon (HT) autograft (91%), and had lower baseline Knee injury and Osteoarthritis Outcome Score (KOOS) Sport and Recreation (Sports) scores (mean, 25.0; SD, 15.6). Cluster 2 (revision rate, 6.9%) patients received HT autograft (89%) and had higher baseline KOOS Sports scores (mean, 67.2; SD, 16.5). Cluster 3 (revision rate, 4.7%) patients received bone-patellar tendon-bone (BPTB) or quadriceps tendon (QT) autograft (94%) and had higher baseline KOOS Sports scores (mean, 65.8; SD, 16.4). Cluster 4 (revision rate, 4.1%) patients received BPTB or QT autograft (88%) and had low baseline KOOS Sports scores (mean, 20.5; SD, 14.0). Cluster 5 (revision rate, 3.1%) patients were older (mean age, 42 years; SD, 7 years), received HT autograft (89%), and had low baseline KOOS Sports scores (mean, 23.4; SD, 17.6). CONCLUSION: Unsupervised learning identified 5 distinct KLR patient subgroups and each grouping was associated with a unique ACLR revision rate. Patients can be approximately classified into 1 of the 5 clusters based on only 3 variables: age, graft choice (HT, BPTB, or QT autograft), and preoperative KOOS Sports subscale score. If externally validated, the resulting groupings may enable quick risk stratification for future patients undergoing ACLR in the clinical setting. Patients in cluster 1 are considered high risk (9.9%), cluster 2 patients medium risk (6.9%), and patients in clusters 3 to 5 low risk (3.1%-4.7%) for revision ACLR.

External validation of the Norwegian anterior cruciate ligament reconstruction revision prediction model using patients from the STABILITY 1 Trial.

PURPOSE: A machine learning-based anterior cruciate ligament (ACL) revision prediction model has been developed using Norwegian Knee Ligament Register (NKLR) data, but lacks external validation outside Scandinavia. This study aimed to assess the external validity of the NKLR model (https://swastvedt.shinyapps.io/calculator_rev/) using the STABILITY 1 randomized clinical trial (RCT) data set. The hypothesis was that model performance would be similar. METHODS: The NKLR Cox Lasso model was selected for external validation owing to its superior performance in the original study. STABILITY 1 patients with all five predictors required by the Cox Lasso model were included. The STABILITY 1 RCT was a prospective study which randomized patients to receive either a hamstring tendon autograft (HT) alone or HT plus a lateral extra-articular tenodesis (LET). Since all patients in the STABILITY 1 trial received HT ± LET, three configurations were tested: 1: all patients coded as HT, 2: HT + LET group coded as bone-patellar tendon-bone (BPTB) autograft, 3: HT + LET group coded as unknown/other graft choice. Model performance was assessed via concordance and calibration. RESULTS: In total, 591/618 (95.6%) STABILITY 1 patients were eligible for inclusion, with 39 undergoing revisions within 2 years (6.6%). Model performance was best when patients receiving HT + LET were coded as BPTB. Concordance was similar to the original NKLR prediction model for 1- and 2-year revision prediction (STABILITY: 0.71; NKLR: 0.68-0.69). Concordance 95% confidence interval (CI) ranged from 0.63 to 0.79. The model was well calibrated for 1-year prediction while the 2-year prediction demonstrated evidence of miscalibration. CONCLUSION: When patients in STABILITY 1 who received HT + LET were coded as BPTB in the NKLR prediction model, concordance was similar to the index study. However, due to a wide 95% CI, the true performance of the prediction model with this Canadian and European cohort is unclear and a larger data set is required to definitively determine the external validity. Further, better calibration for 1-year predictions aligns with general prediction modelling challenges over longer periods. While not a large enough sample size to elicit the true accuracy and external validity of the prediction model when applied to North American patients, this analysis provides more support for the notion that HT plus LET performs similarly to BPTB reconstruction. In addition, despite the wide confidence interval, this study suggests optimism regarding the accuracy of the model when applied outside of Scandinavia. LEVEL OF EVIDENCE: Level 3, cohort study.

Performance of the pooled cohort equations in cancer survivors: the Atherosclerosis Risk in Communities study.

BACKGROUND: Cancer survivors may have elevated atherosclerotic cardiovascular disease (ASCVD) risk. Therefore, we tested how accurately the American College of Cardiology/American Heart Association 2013 pooled cohort equations (PCEs) predict 10-year ASCVD risk in cancer survivors. OBJECTIVES: To estimate the calibration and discrimination of the PCEs in cancer survivors compared to non-cancer participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS: We evaluated the PCEs' performance among 1244 cancer survivors and 3849 cancer-free participants who were free of ASCVD at the start of follow-up. Each cancer survivor was incidence-density matched with up to five controls by age, race, sex, and study center. Follow-up began at the first study visit at least 1 year after the diagnosis date of the cancer survivor and finished at the ASCVD event, death, or end of follow-up. Calibration and discrimination were assessed and compared between cancer survivors and cancer-free participants. RESULTS: Cancer survivors had higher PCE-predicted risk, at 26.1%, compared with 23.1% for cancer-free participants. There were 110 ASCVD events in cancer survivors and 332 ASCVD events in cancer-free participants. The PCEs overestimated ASCVD risk in cancer survivors and cancer-free participants by 45.6% and 47.4%, respectively, with poor discrimination in both groups (C-statistic for cancer survivors = 0.623; for cancer-free participants, C = 0.671). CONCLUSIONS: The PCEs overestimated ASCVD risk in all participants. The performance of the PCEs was similar in cancer survivors and cancer-free participants. IMPLICATIONS FOR CANCER SURVIVORS: Our findings suggest that ASCVD risk prediction tools tailored to survivors of adult cancers may not be needed.

A Cluster-Randomized Evaluation of the SuperShelf Intervention in Choice-Based Food Pantries.

BACKGROUND: Interventions in food pantry settings have the potential to improve health among clients at risk of diet-related disease. PURPOSE: This study evaluates whether a cluster-randomized, behavioral intervention in food pantries resulted in improved client outcomes. METHODS: Sixteen Minnesota food pantries were randomized to an intervention (n = 8) or control condition (n = 8). The intervention offered pantries technical assistance to improve healthy food supply and implement behavioral economics strategies to promote healthy food selection. A convenience sample of adult clients were enrolled (paired sample, 158 intervention, 159 control) and followed for 1 year. Additional clients were enrolled at follow-up to assess food selection (follow-up sample, 85 intervention, 102 control). Analysis was limited to data from 11 pantries (5 intervention, 6 control) due to COVID-19. Outcome measures included Healthy Eating Index-2015 (HEI-2015) total and subcomponent scores for 24-hr dietary recalls and client cart selections, and Life's Simple 7 (LS7) total and subcomponent scores. Multilevel mixed-effects models tested whether client outcomes differed by intervention condition. RESULTS: In adjusted models, there were no statistically significant differences by intervention condition in HEI-2015 or LS7 scores. Clients in intervention food pantries had improved Refined Grain subcomponent scores (p = .004); clients in control pantries had worsened Saturated Fat subcomponents scores (p = .019) and improved physical activity scores (p = .007). CONCLUSIONS: The intervention did not result in improved diet quality or cardiovascular health as measured by HEI-2015 or LS7. Coordinated efforts across settings are needed to address health risks facing this population.

Food pantries are an optimal setting to address health and diet quality among clients experiencing food insecurity. This study tests whether a food pantry intervention resulted in improved dietary and cardiovascular outcomes among clients. Sixteen Minnesota food pantries were randomized to either receive an intervention or a delayed intervention. The intervention offered food pantries technical assistance to improve healthy food supply and "nudge" clients toward healthy choices. Due to the COVID-19 pandemic, measures were completed 11 pantries (5 intervention, 6 control). Outcome measures included diet quality of food selected by clients, diet quality of food consumed by clients, and Life's Simple 7 measure of cardiovascular health. The intervention did not result in improved diet quality or cardiovascular health. Coordinated efforts across community settings are needed to address health risks facing this population.

Associations Between Psychosocial Needs, Carbohydrate-Counting Behavior, and App Satisfaction: A Randomized Crossover App Trial on 92 Adults With Diabetes.

To examine whether psychosocial needs in diabetes care are associated with carbohydrate counting and if carbohydrate counting is associated with satisfaction with diabetes applications' usability, a randomized crossover trial of 92 adults with type 1 or 2 diabetes requiring insulin therapy tested two top-rated diabetes applications, mySugr and OnTrack Diabetes. Survey responses on demographics, psychosocial needs (perceived competence, autonomy, and connectivity), carbohydrate-counting frequency, and application satisfaction were modeled using mixed-effect linear regressions to test associations. Participants ranged between 19 and 74 years old (mean, 54 years) and predominantly had type 2 diabetes (70%). Among the three tested domains of psychosocial needs, only competence-not autonomy or connectivity-was found to be associated with carbohydrate-counting frequency. No association between carbohydrate-counting behavior and application satisfaction was found. In conclusion, perceived competence in diabetes care is an important factor in carbohydrate counting; clinicians may improve adherence to carbohydrate counting with strategies designed to improve perceived competence. Carbohydrate-counting behavior is complex; its impact on patient satisfaction of diabetes application usability is multifactorial and warrants consideration of patient demographics such as sex as well as application features for automated carbohydrate counting.

Personalized statin treatment plan using counterfactual approach with multi-objective optimization over benefits and risks.

Background: Statins are a class of drugs that lower cholesterol levels in the blood by inhibiting an enzyme called 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase. High cholesterol levels can lead to plaque buildup in the arteries, which can cause Atherosclerotic Cardiovascular Disease(ASCVD). Statins can reduce the risk of ASCVD events by about 25-35% but they might be associated with symptoms such as muscle pain, liver damage, or diabetes. As a result, this leads to a strong reason to discontinue statin therapy, which increases the risk of cardiovascular events and mortality and becomes a public-health problem.To solve this problem, in the previous work, we proposed a framework to produce a proactive strategy, called a personalized statin treatment plan (PSTP) to minimize the risks of statin-associated symptoms and therapy discontinuation when prescribing statin. In our previous PSTP framework, three limitations remain, and they can influence PSTP usability: (1) Not taking the counterfactual predictions and confounding bias into account. (2) The balance between multiple drug-prescribing objectives (especially trade-off objectives), such as tradeoff between benefits and risks. (3) Evaluating PSTP in retrospective data. Objectives: This manuscript aimed to provide solutions for the three abovementioned problems to improve PSTP robustness to produce a proactive strategy for statin prescription that can maximize the benefits (low-density lipoprotein cholesterol (LDL-C) reduction) and minimize risks (statin-associated symptoms and therapy discontinuation) at the same time. Methods: We applied overlapping weighting counterfactual survival risk prediction (CP), multiple objective optimization (MOO), and clinical trial simulation (CTS) which consists of Random Arms, Clinical Guideline arms, PSTP Arms, and Practical Arms to improve the PSTP framework and usability. Results: In addition to highly balanced covariates, in the CTS, the revised PSTP showed improvements in lowering the SAS risks overall compared to other arms across all time points by at most 7.5% to at least 1.0% (Fig. 8(a)). It also has the better flexibility of identifying the optimal Statin across all time points within one year. Conclusion: We demonstrated feasibility of robust and trustworthy counterfactual survival risk prediction model. In CTS, we also demonstrated the PSTP with Pareto optimization can personalize optimal balance between Statin benefits and risks.

Myocardial extracellular volume fraction is positively associated with activated monocyte subsets among cART-treated persons living with HIV in South Africa.

BACKGROUND: Despite treatment with combination antiretroviral therapy (cART), persons living with HIV (PLWH) are at higher risk of cardiac structural abnormalities that may presage clinical heart failure, including myocardial fibrosis. This study assessed whether circulating cellular and soluble protein markers of immune activation cross-sectionally associate with myocardial fibrosis among cART-treated PLWH in South Africa. METHODS: Participants were enrolled in Khayelitsha township near Cape Town, SA. Cardiac magnetic resonance imaging was performed. Plasma protein biomarkers were measured using enzyme-linked immunoassays and monocyte phenotypes were evaluated using flow cytometry. Associations were assessed using multivariable linear and logistic regression. RESULTS: Among 69 cART-treated PLWH, mean (SD) age was 48 (10) years, 71% were female, and time since HIV diagnosis was 9 (6) years. Evidence of left ventricular fibrosis by late gadolinium enhancement was present in 74% of participants and mean (SD) extracellular volume fraction (ECV) was 30.9 (5.9)%. Degree of myocardial fibrosis/inflammation measured by ECV was positively associated with percentages of circulating non-classical and intermediate monocyte phenotypes reflecting inflammation and tissue injury. CONCLUSION: These data generate hypotheses on possible immune mechanisms of HIV-associated non-ischemic myocardial disease, specifically among cART-treated PLWH in sub-Saharan Africa, where the majority of the HIV burden exists globally.

An intersectional framework for counterfactual fairness in risk prediction.

Along with the increasing availability of health data has come the rise of data-driven models to inform decision making and policy. These models have the potential to benefit both patients and health care providers but can also exacerbate health inequities. Existing "algorithmic fairness" methods for measuring and correcting model bias fall short of what is needed for health policy in two key ways. First, methods typically focus on a single grouping along which discrimination may occur rather than considering multiple, intersecting groups. Second, in clinical applications, risk prediction is typically used to guide treatment, creating distinct statistical issues that invalidate most existing techniques. We present novel unfairness metrics that address both challenges. We also develop a complete framework of estimation and inference tools for our metrics, including the unfairness value ("u-value"), used to determine the relative extremity of unfairness, and standard errors and confidence intervals employing an alternative to the standard bootstrap. We demonstrate application of our framework to a COVID-19 risk prediction model deployed in a major Midwestern health system.

Human Milk Feeding and Direct Breastfeeding Improve Outcomes for Infants With Single Ventricle Congenital Heart Disease: Propensity Score-Matched Analysis of the NPC-QIC Registry.

Background Infants with single ventricle congenital heart disease undergo 3 staged surgeries/interventions, with risk for morbidity and mortality. We estimated the effect of human milk (HM) and direct breastfeeding on outcomes including necrotizing enterocolitis, infection-related complications, length of stay, and mortality. Methods and Results We analyzed the National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC) registry (2016-2021), examining HM/breastfeeding groups during stage 1 and stage 2 palliations. We calculated propensity scores for feeding exposures, then fitted Poisson and logistic regression models to compare outcomes between propensity-matched cohorts. Participants included 2491 infants (68 sites). Estimates for all outcomes were better in HM/breastfeeding groups. Infants fed exclusive HM before stage 1 palliation (S1P) had lower odds of preoperative necrotizing enterocolitis (odds ratio [OR], 0.37 [95% CI, 0.17-0.84]; P=0.017) and shorter S1P length of stay (rate ratio [RR], 0.87 [95% CI, 0.78-0.98]; P=0.027). During the S1P hospitalization, infants with high HM had lower odds of postoperative necrotizing enterocolitis (OR, 0.28 [95% CI, 0.15-0.50]; P<0.001) and sepsis (OR, 0.29 [95% CI, 0.13-0.65]; P=0.003), and shorter S1P length of stay (RR, 0.75 [95% CI, 0.66-0.86]; P<0.001). At stage 2 palliation, infants with any HM (RR, 0.82 [95% CI, 0.69-0.97]; P=0.018) and any breastfeeding (RR, 0.71 [95% CI, 0.57-0.89]; P=0.003) experienced shorter length of stay. Conclusions Infants with single ventricle congenital heart disease in high-HM and breastfeeding groups experienced multiple significantly better outcomes. Given our findings of improved health, strategies to increase the rates of HM/breastfeeding in these patients should be implemented. Future research should replicate these findings with granular feeding data and in broader congenital heart disease populations, and should examine mechanisms (eg, HM components, microbiome) by which HM/breastfeeding benefits these infants.

Early results of a natural experiment evaluating the effects of a local minimum wage policy on the diet-related health of low-wage workers, 2018-2020.

OBJECTIVE: The current study presents results of a midpoint analysis of an ongoing natural experiment evaluating the diet-related effects of the Minneapolis Minimum Wage Ordinance, which incrementally increases the minimum wage to $15/h. DESIGN: A difference-in-difference (DiD) analysis of measures collected among low-wage workers in two U.S. cities (one city with a wage increase policy and one comparison city). Measures included employment-related variables (hourly wage, hours worked and non-employment assessed by survey questions with wages verified by paystubs), BMI measured by study scales and stadiometers and diet-related mediators (food insecurity, Supplemental Nutrition Assistance Program (SNAP) participation and daily servings of fruits and vegetables, whole-grain rich foods and foods high in added sugars measured by survey questions). SETTING: Minneapolis, Minnesota and Raleigh, North Carolina. PARTICIPANTS: A cohort of 580 low-wage workers (268 in Minneapolis and 312 in Raleigh) who completed three annual study visits between 2018 and 2020. RESULTS: In DiD models adjusted for time-varying and non-time-varying confounders, there were no statistically significant differences in variables of interest in Minneapolis compared with Raleigh. Trends across both cities were evident, showing a steady increase in hourly wage, stable BMI, an overall decrease in food insecurity and non-linear trends in employment, hours worked, SNAP participation and dietary outcomes. CONCLUSION: There was no evidence of a beneficial or adverse effect of the Minimum Wage Ordinance on health-related variables during a period of economic and social change. The COVID-19 pandemic and other contextual factors likely contributed to the observed trends in both cities.

Renin-Angiotensin-Aldosterone System Blockade after AKI with or without Recovery among US Veterans with Diabetic Kidney Disease.

SIGNIFICANCE STATEMENT: Among patients with CKD, optimal use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers after AKI is uncertain. Despite these medications' ability to reduce risk of mortality and other adverse outcomes, there is concern that ACEi/ARB use may delay recovery of kidney function or precipitate recurrent AKI. Prior studies have provided conflicting data regarding the optimal timing of these medications after AKI and have not addressed the role of kidney recovery in determining appropriate timing. This study in US Veterans with diabetes mellitus and proteinuria demonstrated an association between ACEi/ARB use and lower mortality. This association was more pronounced with earlier post-AKI ACEi/ARB use and was not meaningfully affected by initiating ACEis/ARBs before versus after recovery from AKI. BACKGROUND: Optimal use of angiotensin-converting enzyme inhibitors (ACEis) or angiotensin II receptor blockers (ARBs) after AKI is uncertain. METHODS: Using data derived from electronic medical records, we sought to estimate the association between ACEi/ARB use after AKI and mortality in US military Veterans with indications for such treatment (diabetes and proteinuria) while accounting for AKI recovery. We used ACEi/ARB treatment after hospitalization with AKI (defined as serum creatinine ≥50% above baseline concentration) as a time-varying exposure in Cox models. The outcome was all-cause mortality. Recovery was defined as return to ≤110% of baseline creatinine. A secondary analysis focused on ACEi/ARB use relative to AKI recovery (before versus after). RESULTS: Among 54,735 Veterans with AKI, 31,146 deaths occurred over a median follow-up period of 2.3 years. Approximately 57% received an ACEi/ARB <3 months after hospitalization. In multivariate analysis with time-varying recovery, post-AKI ACEi/ARB use was associated with lower risk of mortality (adjusted hazard ratio [aHR], 0.74; 95% confidence interval [CI], 0.72 to 0.77). The association between ACEi/ARB use and mortality varied over time, with lower mortality risk associated with earlier initiation ( P for interaction with time <0.001). In secondary analysis, compared with those with neither recovery nor ACEi/ARB use, risk of mortality was lower in those with recovery without ACEi/ARB use (aHR, 0.90; 95% CI, 0.87 to 0.94), those without recovery with ACEi/ARB use (aHR, 0.69; 95% CI, 0.66 to 0.72), and those with ACEi/ARB use after recovery (aHR, 0.70; 95% CI, 0.67 to 0.73). CONCLUSIONS: This study demonstrated lower mortality associated with ACEi/ARB use in Veterans with diabetes, proteinuria, and AKI, regardless of recovery. Results favored earlier ACEi/ARB initiation.

Effects of personalized exercise prescriptions and social media delivered through mobile health on cancer survivors' physical activity and quality of life.

PURPOSE: This study aimed to examine the effects of a multi-component mobile health intervention (wearable, apps, and social media) on cancer survivors' (CS') physical activity (PA), quality of life, and PA determinants compared to exercise prescription only, social media only, and attention control conditions. METHODS: A total of 126 CS (age = 60.37 ± 7.41 years, mean ± SD) were recruited from the United States. The study duration was 6 months and participants were randomly placed into 4 groups. All participants received a Fitbit tracker and were instructed to install its companion app to monitor their daily PA. They (1) received previously established weekly personalized exercise prescriptions via email, (2) received weekly Facebook health education and interacted with one another, (3) received both Conditions 1 and 2, or (4) were part of the control condition, meaning they adopted usual care. CS PA daily steps, quality of life (i.e., physical health and mental health), and PA determinants (e.g., self-efficacy, social support) were measured at baseline, 3 months, and 6 months. RESULTS: The final sample size included 123 CS. The results revealed only the multi-component condition had greater improvements in PA daily steps than the control condition post-intervention (95% confidence interval (95%CI): 368-2951; p < 0.05). Similarly, those in the multi-component condition had significantly greater increased physical health than the control condition (95%CI: -0.41 to -0.01; p < 0.05) over time. In addition, the social media condition had significantly greater increased perceived social support than the control condition (95%CI: 0.01-0.93; p < 0.05). No other significant differences on outcomes were identified. CONCLUSION: The study findings suggest that the implementation of a multi-component mobile health intervention had positive effects on CS PA steps and physical health. Also, offering social media intervention has the potential to improve CS perceived social support.

Predicting physical distancing on recreational trails during COVID-19.

The emergence of Coronavirus 19 led to societal and behavioral changes, including intensified use of many public parks and trails for mental respite and leisure time physical activity. As visitors sought stress-relief in the great outdoors, they also encountered stressful situations as they navigated risk exposure. Recommendations to physically distance between parties was a key component to reduce risk, but compliance is unknown in the outdoor arena. This observational study of more than 10 000 trail user encounters documented distancing and enabled predictive analysis that revealed wider trails, smaller groups and signage led to greater distancing compliance. Managers and planners can integrate these findings immediately and in consideration of future trail designs to minimize risk exposure. Management implications: Select site features increase odds of distancing compliance and can inform management decisions and designs immediately and in addressing future use surges: wider trails, unpaved surfaces, and COVID-19 signage.As distancing compliance waned with time but signage increased compliance, innovative and dynamic signs may sustain compliance and multi-media communications should be considered.Both activity size and group type influence distancing so considering group size recommendations and activity separation are in order.

Cases and context: Mask-related behaviors among U.S. trail visitors during the COVID-19 pandemic.

The challenge of simultaneously providing outdoor recreation opportunities while protecting the public from SARS-CoV-2 virus and COVID-19 transmission, as well as future pandemics, remains foremost on managers' minds. Safe spaces and cultures are paramount for managers and visitors alike. Recommended protective measures against COVID-19 included physically distancing 1.8 m (six-feet) between parties and mask-wearing when distancing is not possible. Adoption of these protective measures is relatively unknown but essential to inform recreation management and planning through future health crises. Such adoption is likely influenced by both the pandemic context and site context, particularly related to visitor density. An observational study assessed mask-wearing behaviors among trail walkers on multiple trails in the United States from November 2020 through May 2021. Trained observers identified if walking groups were prepared to mask or had masks correctly worn as well as if encounters were compliant with the 1.8 m recommendations. Data collected across seven U.S. states enabled comparisons of mask-related behaviors across sites as well as considerations to: the influence of the pandemic context in terms of cases and vaccination rates, mask mandates, and trail density. Results from nearly 3000 encounters revealed significant variance in visible masks, low compliance of mask-wearing in encounters less than 1.8 m, significant influence of both COVID-19 cases and vaccination rates on mask wearing at half the sites, and no impact of state-level mask mandates when controlling for cases and vaccinations. Integrating public health data can inform predictions of compliant behaviors, or lack thereof, and needs exist to advance a safety culture.

Repurposing Non-pharmacological Interventions for Alzheimer's Diseases through Link Prediction on Biomedical Literature.

Recently, computational drug repurposing has emerged as a promising method for identifying new pharmaceutical interventions (PI) for Alzheimer's Disease (AD). Non-pharmaceutical interventions (NPI), such as Vitamin E and Music therapy, have great potential to improve cognitive function and slow the progression of AD, but have largely been unexplored. This study predicts novel NPIs for AD through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement domain knowledge graph, SuppKG, with semantic relations from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set and was used to generate the score tables of the link prediction task. Discovery patterns were applied to generate mechanism pathways for high scoring triples. Our ADInt had 162,213 nodes and 1,017,319 edges. The graph convolutional network model, R-GCN, performed best in both the Time Slicing test set (MR = 7.099, MRR = 0.5007, Hits@1 = 0.4112, Hits@3 = 0.5058, Hits@10 = 0.6804) and the Clinical Trials test set (MR = 1.731, MRR = 0.8582, Hits@1 = 0.7906, Hits@3 = 0.9033, Hits@10 = 0.9848). Among high scoring triples in the link prediction results, we found the plausible mechanism pathways of (Photodynamic therapy, PREVENTS, Alzheimer's Disease) and (Choerospondias axillaris, PREVENTS, Alzheimer's Disease) by discovery patterns and discussed them further. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover NPIs (dietary supplements (DS) and complementary and integrative health (CIH)) for AD. We used discovery patterns to find mechanisms for predicted triples to solve the poor interpretability of artificial neural networks. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.