Family and personal history of cancer in the All of Us research program for precision medicine.

The All of Us (AoU) Research Program is making available one of the largest and most diverse collections of health data in the US to researchers. Using the All of Us database, we evaluated family and personal histories of five common types of cancer in 89,453 individuals, comparing these data to 24,305 participants from the 2015 National Health Interview Survey (NHIS). Comparing datasets, we found similar family cancer history (33%) rates, but higher personal cancer history in the AoU dataset (9.2% in AoU vs. 5.11% in NHIS), Methodological (e.g. survey-versus telephone-based data collection) and demographic variability may explain these between-data differences, but more research is needed.

Comparative Safety and Effectiveness of Biologic Therapy for Crohn's Disease: A CA-IBD Cohort Study.

BACKGROUND & AIMS: We compared the safety and effectiveness of tumor necrosis factor α (TNF-α) antagonists vs vedolizumab vs ustekinumab in patients with Crohn's disease (CD) in a multicenter cohort (CA-IBD). METHODS: We created an electronic health record-based cohort of adult patients with CD who were initiating a new biologic agent (TNF-α antagonists, ustekinumab, vedolizumab) from 5 health systems in California between 2010 and 2017. We compared the risk of serious infections (safety) and all-cause hospitalization and inflammatory bowel disease-related surgery (effectiveness) between different biologic classes using propensity score (PS) matching. RESULTS: As compared with TNF-α antagonists (n = 1030), 2:1 PS-matched, ustekinumab-treated patients with CD (n = 515) experienced a lower risk of serious infections (hazard ratio [HR], 0.36; 95% CI, 0.20-0.64), without any difference in the risk of hospitalization (HR, 0.99; 95% CI, 0.89-1.21) or surgery (HR, 1.08; 95% CI, 0.69-1.70). Compared with vedolizumab (n = 221), 1:1 PS-matched, ustekinumab-treated patients with CD (n = 221) experienced a lower risk of serious infections (HR, 0.20; 95% CI, 0.07-0.60), without significant differences in risk of hospitalization (HR, 0.76; 95% CI, 0.54-1.07) or surgery (HR, 1.42; 95% CI, 0.54-3.72). Compared with TNF-α antagonists (n = 442), 2:1 PS-matched, vedolizumab-treated patients with CD (n = 221) had a similar risk of serious infections (HR, 1.53; 95% CI, 0.84-2.78), hospitalization (HR, 1.32; 95% CI, 0.98-1.77), and surgery (HR, 0.63; 95% CI, 0.27-1.47). High comorbidity burden, concomitant opiate use, and prior hospitalization were associated with serious infections and hospitalization in biologic-treated patients with CD. CONCLUSION: In a multicenter cohort of biologic-treated patients with CD, ustekinumab was associated with a lower risk of serious infections compared with TNF-α antagonists and vedolizumab, without any differences in risk of hospitalization or surgery. The risk of serious infections was similar for TNF-α antagonists vs vedolizumab.

Effectiveness and Safety of Biologic Therapy in Hispanic Vs Non-Hispanic Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study.

BACKGROUND & AIMS: There are limited data on outcomes of biologic therapy in Hispanic patients with inflammatory bowel diseases (IBDs). We compared risk of hospitalization, surgery, and serious infections in Hispanic vs non-Hispanic patients with IBD in a multicenter, electronic health record-based cohort of biologic-treated patients. METHODS: We identified adult patients with IBD who were new users of biologic agents (tumor necrosis factor α [TNF-α] antagonists, ustekinumab, vedolizumab) from 5 academic institutions in California between 2010 and 2017. We compared the risk of all-cause hospitalization, IBD-related surgery, and serious infections in Hispanic vs non-Hispanic patients using 1:4 propensity score matching and survival analysis. RESULTS: We compared 240 Hispanic patients (53% male; 45% with ulcerative colitis; 73% TNF-α antagonist-treated; 20% with prior biologic exposure) with 960 non-Hispanic patients (51% male; 44% with ulcerative colitis; 67% TNF-α antagonist-treated; 27% with prior biologic exposure). After propensity score matching, Hispanic patients were younger (37 ± 15 vs 40 ± 16 y; P = .02) and had a higher burden of comorbidities (Elixhauser index, >0; 37% vs 26%; P < .01), without any differences in patterns of medication use, burden of inflammation, and hospitalizations. Within 1 year of biologic initiation, Hispanic patients had higher rates of hospitalizations (31% vs 23%; adjusted hazard ratio [aHR], 1.32; 95% CI, 1.01-1.74) and IBD-related surgery (7.1% vs 4.6%; aHR, 2.00; 95% CI, 1.07-3.72), with a trend toward higher risk of serious infections (8.8% vs 4.9%; aHR, 1.74; 95% CI, 0.99-3.05). CONCLUSIONS: In a multicenter, propensity score-matched cohort of biologic-treated patients with IBD, Hispanic patients experienced higher rates of hospitalization, surgery, and serious infections. Future studies are needed to investigate the biological, social, and environmental drivers of these differences.

An Overview of Cancer in the First 315,000 All of Us Participants.

INTRODUCTION: The NIH All of Us Research Program will have the scale and scope to enable research for a wide range of diseases, including cancer. The program's focus on diversity and inclusion promises a better understanding of the unequal burden of cancer. Preliminary cancer ascertainment in the All of Us cohort from two data sources (self-reported versus electronic health records (EHR)) is considered. MATERIALS AND METHODS: This work was performed on data collected from the All of Us Research Program's 315,297 enrolled participants to date using the Researcher Workbench, where approved researchers can access and analyze All of Us data on cancer and other diseases. Cancer case ascertainment was performed using data from EHR and self-reported surveys across key factors. Distribution of cancer types and concordance of data sources by cancer site and demographics is analyzed. RESULTS AND DISCUSSION: Data collected from 315,297 participants resulted in 13,298 cancer cases detected in the survey (in 89,261 participants), 23,520 cancer cases detected in the EHR (in 203,813 participants), and 7,123 cancer cases detected across both sources (in 62,497 participants). Key differences in survey completion by race/ethnicity impacted the makeup of cohorts when compared to cancer in the EHR and national NCI SEER data. CONCLUSIONS: This study provides key insight into cancer detection in the All of Us Research Program and points to the existing strengths and limitations of All of Us as a platform for cancer research now and in the future.

Effect of Obesity on Risk of Hospitalization, Surgery, and Serious Infection in Biologic-Treated Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study.

INTRODUCTION: Obesity is variably associated with treatment response in biologic-treated patients with inflammatory bowel diseases (IBD). We evaluated the association between obesity and risk of hospitalization, surgery, or serious infections in patients with IBD in new users of biologic agents in a large, multicenter, electronic health record (EHR)-based cohort (CA-IBD). METHODS: We created an EHR-based cohort of adult patients with IBD who were new users of biologic agents (tumor necrosis factor [TNF-α] antagonists, ustekinumab, and vedolizumab) between January 1, 2010, and June 30, 2017, from 5 health systems in California. Patients were classified as those with normal body mass index (BMI), overweight, or obese based on the World Health Organization classification. We compared the risk of all-cause hospitalization, IBD-related surgery, or serious infections among patients with obesity vs those overweight vs those with normal BMI, using Cox proportional hazard analyses, adjusting for baseline demographic, disease, and treatment characteristics. RESULTS: Of 3,038 biologic-treated patients with IBD (69% with Crohn's disease and 76% on TNF-α antagonists), 28.2% (n = 858) were overweight, and 13.7% (n = 416) were obese. On a follow-up after biologic initiation, obesity was not associated with an increased risk of hospitalization (adjusted hazard ratio [aHR] vs normal BMI, 0.90; [95% confidence interval, 0.72-1.13]); IBD-related surgery (aHR, 0.62 [0.31-1.22]); or serious infection (aHR, 1.11 [0.73-1.71]). Similar results were observed on stratified analysis by disease phenotype (Crohn's disease vs ulcerative colitis) and index biologic therapy (TNF-α antagonists vs non-TNF-α antagonists). DISCUSSION: In a multicenter, EHR-based cohort of biologic-treated patients with IBD, obesity was not associated with hospitalization, surgery, or serious infections. Further studies examining the effect of visceral obesity on patient-reported and endoscopic outcomes are needed.

The All of Us Research Program: Data quality, utility, and diversity.

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease.

INTRODUCTION: Hospitalization is the primary driver of inflammatory bowel disease (IBD)-related healthcare costs and morbidity. Traditional prediction models have poor performance at identifying patients at highest risk of unplanned healthcare utilization. Identification of patients who are high-need and high-cost (HNHC) could reduce unplanned healthcare utilization and healthcare costs. METHODS: We conducted a retrospective cohort study in adult patients hospitalized with IBD using the Nationwide Readmissions Database (model derivation in the 2013 Nationwide Readmission Database and validation in the 2017 Nationwide Readmission Database). We built 2 tree-based algorithms (decision tree classifier and decision tree using gradient boosting framework [XGBoost]) and compared traditional logistic regression to identify patients at risk for becoming HNHC (patients in the highest decile of total days spent in hospital in a calendar year). RESULTS: Of 47,402 adult patients hospitalized with IBD, we identified 4,717 HNHC patients. The decision tree classifier model (length of stay, Charlson Comorbidity Index, procedure, Frailty Risk Score, and age) had a mean area under the receiver operating characteristic curve (AUC) of 0.78 ± 0.01 in the derivation data set and 0.78 ± 0.02 in the validation data set. XGBoost (length of stay, procedure, chronic pain, drug abuse, and diabetic complication) had a mean AUC of 0.79 ± 0.01 and 0.75 ± 0.02 in the derivation and validation data sets, respectively, compared with AUC 0.55 ± 0.01 and 0.56 ± 0.01 with traditional logistic regression (peptic ulcer disease, paresthesia, admission for osteomyelitis, renal failure, and lymphoma) in derivation and validation data sets, respectively. DISCUSSION: In hospitalized patients with IBD, simplified tree-based machine learning algorithms using administrative claims data can accurately predict patients at risk of progressing to HNHC.

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program.

PURPOSE: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research. DESIGN: Development and evaluation of machine learning models. METHODS: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall. RESULTS: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests). CONCLUSIONS: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.

Frailty Is Independently Associated with Mortality and Readmission in Hospitalized Patients with Inflammatory Bowel Diseases.

BACKGROUND & AIMS: Old age must be considered in weighing the risks of complications vs benefits of treatment for patients with inflammatory bowel diseases (IBD). We conducted a nationally representative cohort study to estimate the independent effects of frailty on burden, costs, and causes for hospitalization in patients with IBD. METHODS: We searched the Nationwide Readmissions Database to identify 47,402 patients with IBD, hospitalized from January through June 2013 and followed for readmission through December 31, 2013. Based on a validated hospital frailty risk scoring system, 15,507 patients were considered frail and 31,895 were considered non-frail at index admission. We evaluated the independent effect of frailty on longitudinal burden and costs of hospitalization, inpatient mortality, risk of readmission and surgery, and reasons for readmission. RESULTS: Over a median follow-up time of 10 months, adjusting for age, sex, income, comorbidity index, depression, obesity, severity, and indication for index hospitalization, frailty was independently associated with 57% higher risk of mortality (adjusted hazard ratio [aHR], 1.57; 95% CI, 1.34-1.83), 21% higher risk of all-cause readmission (adjusted hazard ratio [HR], 1.21; 95% CI, 1.17-1.25), and 22% higher risk of readmission for severe IBD (aHR, 1.22; 95% CI, 1.16-1.29). Frail patients with IBD spent more days in the hospital annually (median 9 days; interquartile range, 4-18 days vs median 5 days for non-frail patients; interquartile range, 3-10 days; P < .01) with higher costs of hospitalization ($17,791; interquartile range, $8368-$38,942 vs $10,924 for non-frail patients, interquartile range, $5571-$22,632; P < .01). Infections, rather than IBD, were the leading cause of hospitalization for frail patients. CONCLUSIONS: Frailty is independently associated with higher mortality and burden of hospitalization in patients with IBD; infections are the leading cause of hospitalization. Frailty should be considered in treatment approach, especially in older patients with IBD.

Burden and Outcomes of Fragmentation of Care in Hospitalized Patients With Inflammatory Bowel Diseases: A Nationally Representative Cohort.

BACKGROUND: Fragmentation of care (FoC) may adversely impact health care quality in patients with chronic diseases. We conducted a US nationally representative cohort study to evaluate the burden and outcomes of FoC in hospitalized patients with inflammatory bowel disease (IBD). METHODS: Using Nationwide Readmissions Database 2013, we created 2 cohorts of superutilizer patients with IBD with 2 hospitalizations (cohort 1: FoC, defined as readmission to nonindex hospital vs no FoC) or 3 hospitalizations (cohort 2: multiple episodes of fragmentation vs single episode of fragmentation vs no FoC) between January and June 2013, which were followed through December 2013. We evaluated burden, pattern, and outcomes of fragmentation (6-month risk of readmission, risk of surgery, and inpatient mortality). RESULTS: In cohort 1, of 6073 patients with IBD with 2 admissions within 6 months, 1394 (23%) experienced FoC. Fragmentation of care was associated with modestly higher risk of readmission within 6 months (31% vs 28%, P < 0.01; adjusted relative risk, 1.11 [1.01-1.21]), without differences in risk of surgery (2.8% vs 4.3%, P = 0.19) or in-hospital mortality (0.2% vs 0.5%, P = 0.22). In cohort 2, of 1717 patients with 3 hospitalizations within 6 months, the number of patients with multiple episodes of fragmentation was associated with higher risk of readmission compared with patients with single episode of fragmentation or no FoC (52% vs 49% vs 43%, P = 0.03). CONCLUSIONS: In a US cohort study, FoC is associated with a modestly higher risk of readmission, without higher risk of surgery or mortality in superutilizer patients with IBD. Future studies focusing on impact of outpatient care and postdischarge coordination are warranted in superutilizer patients.

Comprehensive characterization of hepatocyte-derived extracellular vesicles identifies direct miRNA-based regulation of hepatic stellate cells and DAMP-based hepatic macrophage IL-1ß and IL-17 upregulation in alcoholic hepatitis mice.

Extracellular vesicles (EVs) have been growingly recognized as biomarkers and mediators of alcoholic liver disease (ALD) in human and mice. Here we characterized hepatocyte-derived EVs (HC-EVs) and their cargo for their biological functions in a novel murine model that closely resembles liver pathology observed in patients with alcoholic hepatitis (AH), the most severe spectrum of ALD. The numbers of circulating EVs and HC-EVs were significantly increased by 10-fold in AH mice compared with control mice. The miRNA (miR)-seq analysis detected 20 upregulated and 4 downregulated miRNAs (P < 0.001-0.05) in AH-HC-EVs. Treatment of murine primary hepatic stellate cells (HSCs) with AH-HC-EVs induced α-SMA (P < 0.05) and Col1a1 (P < 0.001). Smad7 and Nr1d2 genes, which were downregulated in HSCs from the AH mice, were predicted targets of 20 miRs upregulated in AH-HC-EVs. Among them were miR-27a and miR-181 which upon transfection in HSCs, indeed repressed Nr1d2, the quiescent HSC marker. AH-HC-EVs were also enriched with organelle proteins and mitochondrial DNA (10-fold, P < 0.05) and upregulated IL-1ß and IL-17 production by hepatic macrophages (HMs) from AH mice in a TLR9-dependent manner. These results demonstrate HC-EV release is intensified in AH and suggest that AH-HC-EVs orchestrate liver fibrogenesis by directly targeting the quiescent HSC transcripts via a unique set of miRNAs and by amplifying HSC activation via DAMP-based induction of profibrogenic IL-1ß and IL-17 by HMs. KEY MESSAGES: • Circulating EVs and HC-EVs were increased in AH mice compared with control mice • AH-HC-EVs were enriched in miRNAs, organelle proteins, and mitochondrial DNA • AH-HC-EVs increased cytokine production by AH-HMs in a TLR9-dependent manner.

Promoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era.

OBJECTIVE: To evaluate informatics-enabled quality improvement (QI) strategies for promoting time spent on face-to-face communication between ophthalmologists and patients. METHODS: This prospective study involved deploying QI strategies during implementation of an enterprise-wide vendor electronic health record (EHR) in an outpatient academic ophthalmology department. Strategies included developing single sign-on capabilities, activating mobile- and tablet-based applications, EHR personalization training, creating novel workflows for team-based orders, and promoting problem-based charting to reduce documentation burden. Timing data were collected during 648 outpatient encounters. Outcomes included total time spent by the attending ophthalmologist on the patient, time spent on documentation, time spent on examination, and time spent talking with the patient. Metrics related to documentation efficiency, use of personalization features, use of team-based orders, and note length were also measured from the EHR efficiency portal and compared with averages for ophthalmologists nationwide using the same EHR. RESULTS: Time spent on exclusive face-to-face communication with patients initially decreased with EHR implementation (2.9 to 2.3 minutes, p = 0.005) but returned to the paper baseline by 6 months (2.8 minutes, p = 0.99). Observed participants outperformed national averages of ophthalmologists using the same vendor system on documentation time per appointment, number of customized note templates, number of customized order lists, utilization of team-based orders, note length, and time spent after-hours on EHR use. CONCLUSION: Informatics-enabled QI interventions can promote patient-centeredness and face-to-face communication in high-volume outpatient ophthalmology encounters. By employing an array of interventions, time spent exclusively talking with the patient returned to levels equivalent to paper charts by 6 months after EHR implementation. This was achieved without requiring EHR redesign, use of scribes, or excessive after-hours work. Documentation efficiency can be achieved using interventions promoting personalization and team-based workflows. Given their efficacy in preserving face-to-face physician-patient interactions, these strategies may help alleviate risk of physician burnout.

Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records.

PURPOSE: To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs). DESIGN: Development and evaluation of machine learning models. METHODS: Structured EHR data of 385 POAG patients from a single academic institution were incorporated into models using multivariable logistic regression, random forests, and artificial neural networks. Leave-one-out cross-validation was performed. Mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Youden index were calculated for each model to evaluate performance. Systemic variables driving predictions were identified and interpreted. RESULTS: Multivariable logistic regression was most effective at discriminating patients with progressive disease requiring surgery, with an AUC of 0.67. Higher mean systolic blood pressure was associated with significantly increased odds of needing glaucoma surgery (odds ratio [OR] = 1.09, P < .001). Ophthalmic medications (OR = 0.28, P < .001), non-opioid analgesic medications (OR = 0.21, P = .002), anti-hyperlipidemic medications (OR = 0.39, P = .004), macrolide antibiotics (OR = 0.40, P = .03), and calcium blockers (OR = 0.43, P = .03) were associated with decreased odds of needing glaucoma surgery. CONCLUSIONS: Existing systemic data in the EHR has some predictive value in identifying POAG patients at risk of progression to surgical intervention, even in the absence of eye-specific data. Blood pressure-related metrics and certain medication classes emerged as predictors of glaucoma progression. This approach provides an opportunity for future development of automated risk prediction within the EHR based on systemic data to assist with clinical decision-making.

Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice.

PURPOSE: To assess time requirements for patient encounters and estimate after-hours demands of paper-based clinical workflows in ophthalmology. DESIGN: Time-and-motion study with a structured survey. METHODS: This study was conducted in a single academic ophthalmology department. A convenience sample consisted of 7 attending ophthalmologists from 6 subspecialties observed during 414 patient encounters for the time-motion analysis and 12 attending ophthalmologists for the survey. Outcome measurements consisted of total time spent by attending ophthalmologists per patient and time spent on documentation, examination, and talking with patients. The survey assessed time requirements of documentation-related activities performed outside of scheduled clinic hours. RESULTS: Among the 7 attending ophthalmologists observed (6 men and 1 woman), mean ± SD age 43.9 ± 7.1 years, during encounters with 414 patients (57.8 ± 24.6 years of age), total time spent per patient was 8.1 ± 4.8 minutes, with 2.8 ± 1.4 minutes (38%) for documentation, 1.2 ± 0.9 minutes (17%) for examination, and 3.3 ± 3.1 minutes (37%) for talking with patients. New patient evaluations required significantly more time than routine follow-up visits and postoperative visits. Higher clinical volumes were associated with less time per patient. Survey results indicated that paper-based documentation was associated with minimal after-hours work on weeknights and weekends. CONCLUSIONS: Paper-based documentation takes up a substantial portion of the total time spent for patient care in outpatient ophthalmology clinics but is associated with minimal after-hours work. Understanding paper-based clinical workflows may help inform targeted strategies for improving electronic health record use in ophthalmology.

Evaluating and sharing global genetic ancestry in biomedical datasets.

Genetic ancestry is a critical co-factor to study phenotype-genotype associations using cohorts of human subjects. Most publicly available molecular datasets are, however, missing this information or only share self-reported race and ethnicity, representing a limitation to identify and repurpose datasets to investigate the contribution of ancestry to diseases and traits. We propose an analytical framework to enrich the metadata from publicly available cohorts with genetic ancestry information and a resulting diversity score at continental resolution, calculated directly from the data. We illustrate this framework using The Cancer Genome Atlas datasets searched through the DataMed Data Discovery Index. Data repositories and contributors can use this framework to provide genetic diversity measurements for controlled access datasets, minimizing the work involved in requesting a dataset that may ultimately prove inadequate for a researcher's purpose. With the increasing global scale of human genetics research, studies on disease risk and susceptibility would benefit greatly from the adequate estimation and sharing of genetic diversity in publicly available datasets following a framework such as the one presented.

Engaging heart failure patients from a clinical data research network: A survey on willingness to participate in different types of research.

The willing participation of patients in clinical research is a critical element in national efforts to collect health data for precision medicine and large cohort studies. However, recruiting patients is challenging. Clinical data research networks (CDRN) have primarily been used for observational studies, but may be able to enhance recruitment efforts. We need a better understanding of patient motivation and preferences for research participation and their interest in different types of research activities, particularly among those who are already represented in CDRNs. We surveyed a heart failure patient cohort constructed from EHRs in a CDRN to assess research participation. Results showed that CDRN recruitment is feasible. Respondents were most interested in completing a one-time survey and giving a blood sample one time. They were least interested in a study about weight control that require surgery. We found statistically significant associations between race and research activity interests.

A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries.

BACKGROUND: A predictive model that can identify patients who are at an increased risk for prolonged postanesthesia care unit (PACU) stay could help optimize resource utilization and case sequencing. Although previous studies identified some predictors, there is not a model that only utilizes various patients demographic and comorbidities, that are already known preoperatively, and that may affect PACU length of stay for outpatient procedures requiring the care of an anesthesiologist. METHODS: We collected data from 4151 patients at a single institution from 2014 to 2015. The data set was split into a training set (cases before 2015) and a test set (cases during 2015). Bootstrap samples were chosen (R = 1000 replicates) and a logistic regression model was built on the samples using a combined method of forward selection and backward elimination based on the Akaike Information Criterion. The trained model was applied to the test set. Model performance was evaluated with the area under the receiver operating characteristic (ROC) Curve (AUC) for discrimination and the Hosmer-Lemeshow (HL) test for goodness-of-fit. RESULTS: The final model had 5 predictor variables for prolonged PACU length of stay, which included the following: morbid obesity, hypertension, surgical specialty, primary anesthesia type, and scheduled case duration. The model had an AUC value of 0.754 (95% confidence interval 0.733-0.774) on the training set and 0.722 (95% confidence interval 0.698-0.747) on the test set, with no difference between the 2 ROC curves (P = .06). The model had good calibration for the data in both the training and test data set indicated by nonsignificant P values from the HL test (P = .211 and .719 for the training and test set, respectively). CONCLUSIONS: We developed a predictive model with excellent discrimination and goodness-of-fit that can help identify those at higher odds for extended PACU length of stay. This information may help optimize case-sequencing methodologies.

A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis.

Big data coupled with precision medicine has the potential to significantly improve our understanding and treatment of complex disorders, such as cancer, diabetes, depression, etc. However, the essential problem is that data are stuck in silos, and it is difficult to precisely identify which data would be relevant and useful for any particular type of analysis. While the process to acquire and access biomedical data requires significant effort, in many cases the data may not provide much insight to the problem at hand. Therefore, there is a need to be able to measure the utility/relevance of additional datasets for a particular biomedical research task without direct access to the data. Towards this, in this paper, we develop a privacy-preserving approach to create synthetic data that can provide a firstorder approximation of utility. We evaluate the proposed approach with several biomedical datasets in the context of regression and classification tasks and discuss how it can be incorporated into existing data management systems such as REDCap.

Extracellular vesicles released by hepatocytes from gastric infusion model of alcoholic liver disease contain a MicroRNA barcode that can be detected in blood.

Extracellular vesicles (EVs) released during cell stress, or demise, can contain a barcode of the cell origin, including specific microRNAs (miRNAs). Here, we tested the hypothesis that during early alcoholic steatohepatitis (ASH) development, hepatocytes (HCs) release EVs with an miRNA signature that can be measured in circulation. A time-course experiment showed that after 2 weeks of intragastric infusion, a time point that results in isolated steatosis, there was no increase of blood EVs. After 4 weeks of infusion, mice developed features of early ASH accompanied by a marked increase in the level of EVs in blood (P < 0.05), as well as in culture media of isolated HCs (P < 0.001) and hepatic macrophages (P < 0.001), with HCs being the predominant source of EVs. The transcriptome analysis of HC-EVs from ASH mice detected differentially expressed miRNAs, including nine significantly up-regulated and four significantly down-regulated miRNAs. Target prediction and pathway analyses of the up-regulated miRNAs identified 121 potential target genes involved in inflammatory and cancer pathways, such as nuclear factor kappa B, EGF, Wnt, and B-cell lymphoma 2. Three miRNAs, let7f, miR-29a, and miR-340, were increased in blood EVs from ASH mice (P < 0.05), but not in blood EVs from three other models of chronic liver injury, including bile duct ligation, nonalcoholic steatohepatitis, and obese mice, as well as EVs released from hepatocytes exposed to ethanol. Blood EV level (P < 0.01) and three miRNAs (P < 0.05) were significantly increased in patients with ambulatory mild ALD as compared to nonalcoholics. CONCLUSION: Damaged hepatocytes from ASH mice are a key EV source with a specific miRNA cargo, which are specific for ASH-related liver injury. These findings uncover EVs as a potentially novel diagnostic for ASH. (Hepatology 2017;65:475-490).

Biospecimen Sharing Among Hispanic Women in a Safety-Net Clinic: Implications for the Precision Medicine Initiative.

Biospecimen donation is key to the Precision Medicine Initiative, which pioneers a model for accelerating biomedical research through individualized care. Personalized medicine should be made available to medically underserved populations, including the large and growing US Hispanic population. We present results of a study of 140 Hispanic women who underwent a breast biopsy at a safety-net hospital and were randomly assigned to receive information and request for consent for biospecimen and data sharing by the patient's physician or a research assistant. Consent rates were high (97.1% and 92.9% in the physician and research assistant arms, respectively) and not different between groups (relative risk [RR] = 1.05, 95% confidence interval [CI] = 0.96 to 1.10). Consistent with a small but growing literature, we show that perceptions of Hispanics' unwillingness to participate in biospecimen sharing for research are not supported by data. Safety-net clinics and hospitals offer untapped possibilities for enhancing participation of underserved populations in the exciting Precision Medicine Initiative.