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.

Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network

White blood cell count (WBC) is unique among identified inflammatory predictors of chronic disease in that it is routinely measured in asymptomatic patients in the course of routine patient care. We led a genome-wide association analysis to identify variants associated with WBC levels in 13,923 subjects in the electronic Medical Records and Genomics (eMERGE) Network. We identified two regions of interest that were each unique to subjects of genetically determined ancestry to the African continent (AA) or to the European continent (EA). WBC varies among different ancestry groups. Despite being ancestry specific, these regions were identifiable in the combined analysis. In AA subjects, the region surrounding the Duffy antigen/chemokine receptor gene (DARC) on 1q21 exhibited significant association (p value = 6.71e-55). These results validate the previously reported association between WBC and of the regulatory variant rs2814778 in the promoter region, which causes the Duffy negative phenotype (Fy-/-). A second missense variant (rs12075) is responsible for the two principal antigens, Fya and Fyb of the Duffy blood group system. The two variants, consisting of four alleles, act in concert to produce five antigens and subsequent phenotypes. We were able to identify the marginal and novel interaction effects of these two variants on WBC. In the EA subjects, we identified significantly associated SNPs tagging three separate genes in the 17q21 region: (1) GSDMA, (2) MED24, and (3) PSMD3. Variants in this region have been reported to be associated with WBC, neutrophil count, and inflammatory diseases including asthma and Crohn's disease.

Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

BACKGROUND

The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.

METHODS

Our objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission.

RESULTS

Utilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224 mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts.

CONCLUSIONS

Experimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI.

Genetic variation in the HLA region is associated with susceptibility to herpes zoster

Herpes zoster, commonly referred to as shingles, is caused by the varicella zoster virus (VZV). VZV initially manifests as chicken pox, most commonly in childhood, can remain asymptomatically latent in nerve tissues for many years and often re-emerges as shingles. Although reactivation may be related to immune suppression, aging and female sex, most inter-individual variability in re-emergence risk has not been explained to date. We performed a genome-wide association analyses in 22 981 participants (2280 shingles cases) from the electronic Medical Records and Genomics Network. Using Cox survival and logistic regression, we identified a genomic region in the combined and European ancestry groups that has an age of onset effect reaching genome-wide significance (P>1.0 × 10⁻⁸). This region tags the non-coding gene HCP5 (HLA Complex P5) in the major histocompatibility complex. This gene is an endogenous retrovirus and likely influences viral activity through regulatory functions. Variants in this genetic region are known to be associated with delay in development of AIDS in people infected by HIV. Our study provides further suggestion that this region may have a critical role in viral suppression and could potentially harbor a clinically actionable variant for the shingles vaccine.

Genetic variation associated with circulating monocyte count in the eMERGE Network

With white blood cell count emerging as an important risk factor for chronic inflammatory diseases, genetic associations of differential leukocyte types, specifically monocyte count, are providing novel candidate genes and pathways to further investigate. Circulating monocytes play a critical role in vascular diseases such as in the formation of atherosclerotic plaque. We performed a joint and ancestry-stratified genome-wide association analyses to identify variants specifically associated with monocyte count in 11 014 subjects in the electronic Medical Records and Genomics Network. In the joint and European ancestry samples, we identified novel associations in the chromosome 16 interferon regulatory factor 8 (IRF8) gene (P-value = 2.78×10(-16), β = -0.22). Other monocyte associations include novel missense variants in the chemokine-binding protein 2 (CCBP2) gene (P-value = 1.88×10(-7), β = 0.30) and a region of replication found in ribophorin I (RPN1) (P-value = 2.63×10(-16), β = -0.23) on chromosome 3. The CCBP2 and RPN1 region is located near GATA binding protein2 gene that has been previously shown to be associated with coronary heart disease. On chromosome 9, we found a novel association in the prostaglandin reductase 1 gene (P-value = 2.29×10(-7), β = 0.16), which is downstream from lysophosphatidic acid receptor 1. This region has previously been shown to be associated with monocyte count. We also replicated monocyte associations of genome-wide significance (P-value = 5.68×10(-17), β = -0.23) at the integrin, alpha 4 gene on chromosome 2. The novel IRF8 results and further replications provide supporting evidence of genetic regions associated with monocyte count.

Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics

Computational phenotyping is the process of converting heterogeneous electronic health records (EHRs) into meaningful clinical concepts. Unsupervised phenotyping methods have the potential to leverage a vast amount of labeled EHR data for phenotype discovery. However, existing unsupervised phenotyping methods do not incorporate current medical knowledge and cannot directly handle missing, or noisy data. We propose Rubik, a constrained non-negative tensor factorization and completion method for phenotyping. Rubik incorporates 1) guidance constraints to align with existing medical knowledge, and 2) pairwise constraints for obtaining distinct, non-overlapping phenotypes. Rubik also has built-in tensor completion that can significantly alleviate the impact of noisy and missing data. We utilize the Alternating Direction Method of Multipliers (ADMM) framework to tensor factorization and completion, which can be easily scaled through parallel computing. We evaluate Rubik on two EHR datasets, one of which contains 647,118 records for 7,744 patients from an outpatient clinic, the other of which is a public dataset containing 1,018,614 CMS claims records for 472,645 patients. Our results show that Rubik can discover more meaningful and distinct phenotypes than the baselines. In particular, by using knowledge guidance constraints, Rubik can also discover sub-phenotypes for several major diseases. Rubik also runs around seven times faster than current state-of-the-art tensor methods. Finally, Rubik is scalable to large datasets containing millions of EHR records.

Mortality Risk Factors Among Patients With Cirrhosis and a Low Model for End-Stage Liver Disease Sodium Score (≤15): An Analysis of Liver Transplant Allocation Policy Using Aggregated Electronic Health Record Data

Although the Model for End-Stage Liver Disease sodium (MELD Na) score is now used for liver transplant allocation in the United States, mortality prediction may be underestimated by the score. Using aggregated electronic health record data from 7834 adult patients with cirrhosis, we determined whether the cause of cirrhosis or cirrhosis complications was associated with an increased risk of death among patients with a MELD Na score ≤15 and whether patients with the greatest risk of death could benefit from liver transplantation (LT). Over median follow-up of 2.3 years, 3715 patients had a maximum MELD Na score ≤15. Overall, 3.4% were waitlisted for LT. Severe hypoalbuminemia, hepatorenal syndrome, and hepatic hydrothorax conferred the greatest risk of death independent of MELD Na score with 1-year predicted mortality >14%. Approximately 10% possessed these risk factors. Of these high-risk patients, only 4% were waitlisted for LT, despite no difference in nonliver comorbidities between waitlisted patients and those not listed. In addition, risk factors for death among waitlisted patients were the same as those for patients not waitlisted, although the effect of malnutrition was significantly greater for waitlisted patients (hazard ratio 8.65 [95% CI 2.57-29.11] vs. 1.47 [95% CI 1.08-1.98]). Using the MELD Na score for allocation may continue to limit access to LT.

A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results

Background

Testing for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate.

Objective

To predict SARS-CoV-2 PCR positivity based on complete blood count components and patient sex.

Study design

A retrospective case-control design for collection of data and a logistic regression prediction model was used. Participants were emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing. 33 confirmed SARS-CoV-2 PCR positive and 357 negative patients at Stanford Health Care were used for model training. Validation cohorts consisted of emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing in Northern California (41 PCR positive, 495 PCR negative), Seattle, Washington (40 PCR positive, 306 PCR negative), Chicago, Illinois (245 PCR positive, 1015 PCR negative), and South Korea (9 PCR positive, 236 PCR negative).

Results

A decision support tool that utilizes components of complete blood count and patient sex for prediction of SARS-CoV-2 PCR positivity demonstrated a C-statistic of 78 %, an optimized sensitivity of 93 %, and generalizability to other emergency department populations. By restricting PCR testing to predicted positive patients in a hypothetical scenario of 1000 patients requiring testing but testing resources limited to 60 % of patients, this tool would allow a 33 % increase in properly allocated resources.

Conclusions

A prediction tool based on complete blood count results can better allocate SARS-CoV-2 testing and other health care resources such as personal protective equipment during a pandemic surge.

Building bridges across electronic health record systems through inferred phenotypic topics

OBJECTIVE

Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems.

METHODS

We adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC).

RESULTS

The method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems.

CONCLUSIONS

Phenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.

Disease Outcomes and Care Fragmentation Among Patients With Systemic Lupus Erythematosus

OBJECTIVE

To examine the impact of care fragmentation across multiple health care institutions on disease outcomes in patients with systemic lupus erythematosus (SLE).

METHODS

Using the Chicago HealthLNK Data Repository, an assembly of electronic health records from 6 institutions, we identified patients with SLE, using International Classification of Diseases, Ninth Revision (ICD-9) codes, whose care was delivered at more than 1 organization. We examined whether patients had severe infections or comorbidities (ICD-9 code defined) that indicated SLE-induced damage. T-tests and chi-square tests were used to examine differences between fragmentation groups. Logistic regression was used to assess factors contributing to the occurrence of disease outcomes.

RESULTS

We identified 4,276 patients with SLE. A total of 856 (20%) received care from more than 1 health care institution. African American patients and patients with public insurance were more likely to experience care fragmentation compared to white and private insurance patients (odds ratio [OR] 1.66, 95% confidence interval [95% CI] 1.44-1.97 and OR 1.63, 95% CI 1.42-1.95). We identified increased risk of infections (OR 1.57, 95% CI 1.30-1.88), cardiovascular disease (OR 1.51, 95% CI 1.23-1.86), end-stage renal disease (OR 1.34, 95% CI 1.05-1.70), nephritis (OR 1.28, 95% CI 1.07-1.54), and stroke (OR 1.28, 95% CI 1.01-1.62) among patients with fragmented care, adjusted for age, sex, race, insurance status, length of followup time, and total visit count.

CONCLUSION

In this cross-site cohort of SLE patients, care fragmentation is associated with increased risk of severe infection and comorbidities. These results suggest that improved health information exchange could positively impact outcomes for SLE patients.

Patients With Persistently Low MELD-Na Scores Continue to Be at Risk of Liver-related Death

BACKGROUND

The vast majority of patients with cirrhosis have low Model for End-Stage Liver Disease-Sodium (MELD-Na) scores; however, the ability for the MELD-Na score to predict patient outcomes at low scores is unclear.

METHODS

Adult patients in a multicenter, Chicago-wide database of medical records with International Classification of Disease, Ninth Edition codes of cirrhosis and without a history of hepatocellular carcinoma were included. Records were linked with the state death registry, and death certificates were manually reviewed. Deaths were classified as "liver-related," "non-liver-related," and "non-descript" as adjudicated by a panel comprised of a transplant surgeon, a hepatologist, and an internist. A sensitivity analysis was performed where patients with hepatocellular carcinoma were included.

RESULTS

Among 7922 identified patients, 3999 patients had MELD-Na scores that were never higher than 15. In total, 2137 (27%) patients died during the study period with higher mortality rates for the patients in the high MELD-Na group (19.4 (41.6%) versus 4.1 (12.6%) per 100 person-y, P < 0.001). The high MELD-Na group died of a liver-related cause in 1142 out of 1632 (70%) as compared to 240 out of 505 (47.5%) deaths in the low MELD-Na group. There was no difference in the distribution of subcategory of liver-related death between low and high MELD-Na groups. Among subclassification of liver-related deaths, the most common cause of death was "Infectious" in both groups.

CONCLUSIONS

Despite persistently low MELD-Na scores, patients with cirrhosis still experience high rates of liver-related mortality.

Black Patients With Cirrhosis Have Higher Mortality and Lower Transplant Rates: Results From a Metropolitan Cohort Study

BACKGROUND AND AIMS

Estimates of racial disparity in cirrhosis have been limited by lack of large-scale, longitudinal data, which track patients from diagnosis to death and/or transplant.

APPROACH AND RESULTS

We analyzed a large, metropolitan, population-based electronic health record data set from seven large health systems linked to the state death registry and the national transplant database. Multivariate competing risk analyses, adjusted for sex, age, insurance status, Elixhauser score, etiology of cirrhosis, HCC, portal hypertensive complication, and Model for End-Stage Liver Disease-Sodium (MELD-Na), examined the relationship between race, transplant, and cause of death as defined by blinded death certificate review. During the study period, 11,277 patients met inclusion criteria, of whom 2,498 (22.2%) identified as Black. Compared to White patients, Black patients had similar age, sex, MELD-Na, and proportion of alcohol-associated liver disease, but higher comorbidity burden, lower rates of private insurance, and lower rates of portal hypertensive complications. Compared to White patients, Black patients had the highest rate all-cause mortality and non-liver-related death and were less likely to be listed or transplanted (P < 0.001 for all). In multivariate competing risk analysis, Black patients had a 26% increased hazard of liver-related death (subdistribution HR, 1.26; 95% CI, [1.15-1.38]; P < 0.001).

CONCLUSIONS

Black patients with cirrhosis have discordant outcomes. Further research is needed to determine how to address these real disparities in the field of hepatology.

Evolving use of seizure medications after intracerebral hemorrhage: A multicenter study

OBJECTIVE

Prophylactic medications can be a source of preventable harm, potentially affecting large numbers of patients. Few data exist about how clinicians change prescribing practices in response to new data and revisions to guidelines about preventable harm from a prophylactic medication. We sought to determine the changes in prescribing practice of seizure medications for patients with intracerebral hemorrhage (ICH) across a metropolitan area before and after new outcomes data and revised prescribing guidelines were published.

METHODS

We conducted an observational study using electronic medical record data from 4 academic medical centers in a large US metropolitan area.

RESULTS

A total of 3,422 patients with ICH, diagnosed between 2007 and 2012, were included. In 2009, after a publication found an association of phenytoin with higher odds of dependence or death, the use of phenytoin declined from 9.6% in 2009 to 2.2% in 2012 (p < 0.00001). Conversely, the use of levetiracetam more than doubled, from 15.1% in 2007 to 35% in 2012 (p < 0.00001). Use of levetiracetam varied among the 4 institutions from 6.7% to 29.8% (p < 0.00001).

CONCLUSIONS

New data that led to revised prescribing guidelines for prophylactic seizure medications for patients with ICH were temporally associated with a significant decrease in use of the medication, potentially reducing adverse outcomes. However, a corresponding increase in the use of an alternative medication, levetiracetam, occurred despite limited knowledge about its potential effects on outcomes. Future guideline changes should anticipate and address alternatives.

An exploratory survey about using ChatGPT in education, healthcare, and research

Objective ChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare. Materials and Methods We surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with Fisher's Exact. Furthermore, we used qualitative methods to analyze and code discussions. Results We received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare. Discussion There was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.

An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records

We describe a two-stage analytical approach for characterizing morbidity profile dissimilarity among patient cohorts using electronic medical records. We capture morbidities using the International Statistical Classification of Diseases and Related Health Problems (ICD-9) codes. In the first stage of the approach separate logistic regression analyses for ICD-9 sections (e.g., "hypertensive disease" or "appendicitis") are conducted, and the odds ratios that describe adjusted differences in prevalence between two cohorts are displayed graphically. In the second stage, the results from ICD-9 section analyses are combined into a general morbidity dissimilarity index (MDI). For illustration, we examine nine cohorts of patients representing six phenotypes (or controls) derived from five institutions, each a participant in the electronic MEdical REcords and GEnomics (eMERGE) network. The phenotypes studied include type II diabetes and type II diabetes controls, peripheral arterial disease and peripheral arterial disease controls, normal cardiac conduction as measured by electrocardiography, and senile cataracts.

The National Heart Lung and Blood Institute Disparities Elimination through Coordinated Interventions to Prevent and Control Heart and Lung Disease Alliance

OBJECTIVE

To describe the National Heart Lung and Blood Institute (NHLBI) sponsored Disparities Elimination through Coordinated Interventions to Prevent and Control Heart and Lung Disease (DECIPHeR) Alliance to support late-stage implementation research aimed at reducing disparities in communities with high burdens of cardiovascular and/or pulmonary disease.

STUDY SETTING

NHBLI funded seven DECIPHeR studies and a Coordinating Center. Projects target high-risk diverse populations including racial and ethnic minorities, urban, rural, and low-income communities, disadvantaged children, and persons with serious mental illness. Two projects address multiple cardiovascular risk factors, three focus on hypertension, one on tobacco use, and one on pediatric asthma.

STUDY DESIGN

The initial phase supports planning activities for sustainable uptake of evidence-based interventions in targeted communities. The second phase tests late-stage evidence-based implementation strategies.

DATA COLLECTION/EXTRACTION METHODS

Not applicable.

PRINCIPAL FINDINGS

We provide an overview of the DECIPHeR Alliance and individual study designs, populations, and settings, implementation strategies, interventions, and outcomes. We describe the Alliance's organizational structure, designed to promote cross-center partnership and collaboration.

CONCLUSIONS

The DECIPHeR Alliance represents an ambitious national effort to develop sustainable implementation of interventions to achieve cardiovascular and pulmonary health equity.

Information technology in PBRNs: the Indiana University Medical Group Research Network (IUMG ResNet) experience

OBJECTIVE

Research in practice-based research networks (PBRNs) is hampered by difficulty managing, identifying, and enrolling potential subjects. Well-designed informatics applications can greatly improve these processes.

METHODS

We considered a literature review, discussion with PBRN researchers, and personal experience to outline important principles to apply when considering electronic data collection in a PBRN. We provide specific working examples of electronic means we use to improve data collection and patient enrollment.

RESULTS

Our PBRN has screened more than 18,000 patients and enrolled more than 6000 study subjects in 5 years. Less than 2% of potentially eligible patients are missed by our research assistants. We achieved this high rate of success through extensive integration of the ResNet infrastructure (research databases and personnel) with an electronic medical record (EMR) system and computerized provider order entry. We make extensive use of widely used standards for data storage, definition, and transmission to ensure data reusability. We successfully implemented a real-time means to identify follow-up patients.

CONCLUSION

Electronic data collection can greatly facilitate PBRN research, particularly by improving data management and identification of eligible patients. Key principles to ensure successful implementation include use of data standards and centralized electronic data management.

The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research

The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.

An exploratory survey about using ChatGPT in education, healthcare, and research

OBJECTIVE

ChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare.

MATERIALS AND METHODS

We surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with a Fisher's Exact. Furthermore, we used qualitative methods to analyze and code discussions.

RESULTS

We received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare.

DISCUSSION

There was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.

Liver-related mortality is similar among men and women with cirrhosis

BACKGROUND & AIMS

Sex-based differences are known to significantly contribute to outcomes in patients with chronic liver diseases; however, the role of patient sex in cirrhosis is unclear. We aimed to study the relationship between patient sex and cirrhosis.

METHODS

We analyzed a cohort of 20,045 patients with cirrhosis using a Chicago-wide electronic health record database that was linked with the United Network for Organ Sharing and cause of death data from the state death registry. Adjusted Cox survival analyses and competing risk analyses were performed to obtain subdistribution hazard ratios (HRs) for liver-related cause of death.

RESULTS

Female and male patients had similar age, racial distribution, insurance status, and comorbidity status by Elixhauser score. Females had higher rates of cholestatic liver disease (17.1% vs. 6.2%, p <0.001) and non-alcoholic steatohepatitis (29.8% vs. 21.2%, p <0.001) than males. They were less likely to have portal hypertensive complications and had lower peak MELD-Na scores during follow-up. Female sex was associated with a decreased hazard of all-cause mortality (adjusted HR 0.85; 95% CI 0.80-0.90). This effect was attenuated when liver-related mortality was examined (subdistribution HR 0.93; 95% CI 0.87-1.00). No significant difference was noted for women who were 'ever-listed' in competing risk analyses for either all-cause mortality (subdistribution HR 1.09; 95% CI 0.88-1.35) or liver-related death (subdistribution HR 1.12; 95% CI 0.87-1.43), despite lower rates of listing (7.5% vs. 9.8%; p <0.001) and transplant (3.5% vs. 5.2%; p <0.001).

CONCLUSIONS

In this longitudinal study of patients with cirrhosis, female sex was associated with a survival advantage likely driven by lower rates of non-liver-related death. Women were not at an increased risk of liver-related death despite lower rates of listing and transplantation.

LAY SUMMARY

Patient sex is an important contributor in many chronic diseases, including cirrhosis. Prior studies have suggested that female sex is associated with worse outcomes. We analyzed a cohort of 20,045 patients with cirrhosis using a Chicago-wide electronic health record database. Using multivariate competing risk analyses, we found that female sex in cirrhosis is actually associated with a lower risk of all-cause mortality and has no association with liver-related mortality. Our findings are novel because we show that women with cirrhosis have a similar risk of liver-related death as their male counterparts, despite lower rates of listing and transplantation.

The Hispanic Paradox in Patients With Liver Cirrhosis: Current Evidence From a Large Regional Retrospective Cohort Study

BACKGROUND

Despite lower socioeconomic status, Hispanics in the United States paradoxically maintain equal or higher average survival rates compared to non-Hispanic Whites (NHW).

METHODS

We used multivariable Cox regression to assess whether this "Hispanic paradox" applies to patients with liver cirrhosis using a retrospective cohort of twenty 121 patients in a Chicago-wide electronic health record database.

RESULTS

Our study population included 3279 (16%) Hispanics, 9150 (45%) NHW, 4432 (22%) African Americans, 529 (3%) Asians, and 2731 (14%) of other races/ethnic groups. Compared to Hispanics, NHW (hazard ratio [HR] 1.26; 95% confidence interval [CI], 1.16-1.37), African American (HR 1.26; 95% CI, 1.15-1.39), and other races/ethnic groups (HR 1.55; 95% CI, 1.40-1.71) had an increased risk of death despite adjustment for age, sex, insurance status, etiology of cirrhosis, and comorbidities. On stratified analyses, a mortality advantage for Hispanics compared to NHW was seen for alcohol cirrhosis (HR for NHW 1.35; 95% CI, 1.19-1.52), hepatitis B (HR for NHW 1.35; 95% CI, 0.98-1.87), hepatitis C (HR for NHW 1.21; 95% CI, 1.06-1.38), and nonalcoholic steatohepatitis (HR for NHW 1.14; 95% CI, 0.94-1.39). There was no advantage associated with Hispanic race over NHW in cases of hepatocellular carcinoma or cholestatic liver disease.

CONCLUSIONS

Hispanic patients with cirrhosis experience a survival advantage over many other racial groups despite adjustment for multiple covariates.

Delphi consensus on the feasibility of translating the ACEP clinical policies into computerized clinical decision support

Clinical practice guidelines are developed to reduce variations in clinical practice, with the goal of improving health care quality and cost. However, evidence-based practice guidelines face barriers to dissemination, implementation, usability, integration into practice, and use. The American College of Emergency Physicians (ACEP) clinical policies have been shown to be safe and effective and are even cited by other specialties. In spite of the benefits of the ACEP clinical policies, implementation of these clinical practice guidelines into physician practice continues to be a challenge. Translation of the ACEP clinical policies into real-time computerized clinical decision support systems could help address these barriers and improve clinician decision making at the point of care. The investigators convened an emergency medicine informatics expert panel and used a Delphi consensus process to assess the feasibility of translating the current ACEP clinical policies into clinical decision support content. This resulting consensus document will serve to identify limitations to implementation of the existing ACEP Clinical Policies so that future clinical practice guideline development will consider implementation into clinical decision support at all stages of guideline development.

Implementing an animated geographic information system to investigate factors associated with nosocomial infections: a novel approach

BACKGROUND

Two million Americans acquire an infection in the hospital each year, resulting in an estimated 88,000 patient deaths per year.

OBJECTIVE

Our objective was to describe our initial experience using an animated geographic information system (GIS) to investigate factors associated with nosocomial transmission of resistant organisms. We used a descriptive study at a university-affiliated, county, teaching hospital. We studied all patients and nursing staff on 4 adult, general medicine wards from June through August 2004.

RESULTS

We developed and implemented GIS software. GIS-generated animations demonstrated inappropriate patient placement for 19% of patients with methicillin-resistant Staphylococcus aureus and insufficient time for hand hygiene in 14% (6248) of health care provider-patient contacts.

CONCLUSION

Animated GIS can uncover previously hidden factors that contribute to the spread of nosocomial infections. This technology may become a useful adjunct for the prevention of nosocomial transmission of infectious agents.

Variability in comorbidites and health services use across homeless typologies: multicenter data linkage between healthcare and homeless systems

BACKGROUND

Homelessness is associated with substantial morbidity. Data linkages between homeless and health systems are important to understand unique needs across homeless populations, identify homeless individuals not registered in homeless databases, quantify the impact of housing services on health-system use, and motivate health systems and payers to contribute to housing solutions.

METHODS

We performed a cross-sectional survey including six health systems and two Homeless Management Information Systems (HMIS) in Cook County, Illinois. We performed privacy-preserving record linkage to identify homelessness through HMIS or ICD-10 codes captured in electronic medical records. We measured the prevalence of health conditions and health-services use across the following typologies: housing-service utilizers stratified by service provided (stable, stable plus unstable, unstable) and non-utilizers (i.e., homelessness identified through diagnosis codes-without receipt of housing services).

RESULTS

Among 11,447 homeless recipients of healthcare, nearly 1 in 5 were identified by ICD10 code alone without recorded homeless services (n = 2177; 19%). Almost half received homeless services that did not include stable housing (n = 5444; 48%), followed by stable housing (n = 3017; 26%), then receipt of both stable and unstable services (n = 809; 7%). Setting stable housing recipients as the referent group, we found a stepwise increase in behavioral-health conditions from stable housing to those known as homeless solely by health systems. Compared to those in stable housing, prevalence rate ratios (PRR) for those without homeless services were as follows: depression (PRR = 2.2; 95% CI 1.9 to 2.5), anxiety (PRR = 2.5; 95% CI 2.1 to 3.0), schizophrenia (PRR = 3.3; 95% CI 2.7 to 4.0), and alcohol-use disorder (PRR = 4.4; 95% CI 3.6 to 5.3). Homeless individuals who had not received housing services relied on emergency departments for healthcare-nearly 3 of 4 visited at least one and many (24%) visited multiple.

CONCLUSIONS

Differences in behavioral-health conditions and health-system use across homeless typologies highlight the particularly high burden among homeless who are disconnected from homeless services. Fragmented and high use of emergency departments for care should motivate health systems and payers to promote housing solutions, especially those that incorporate substance use and mental health treatment.