Bridging information gaps in menopause status classification through natural language processing

Objective

To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status.

Materials and methods

A rule-based NLP system was designed to capture evidence of a patient's menopause status including dates of a patient's last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. NLP-derived output was used in combination with structured EHR data to classify a patient's menopausal status. NLP processing and patient classification were performed on a cohort of 307 512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA).

Results

NLP was validated at 99.6% precision. Including the NLP-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81 173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167 804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data.

Discussion

By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis.

Conclusion

NLP can be used to identify concepts relevant to a patient's menopausal status in clinical notes. Adding NLP-derived data to an algorithm classifying a patient's menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.

Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study

PURPOSE

To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab.

DESIGN

Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network.

SUBJECTS

Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion).

METHODS

The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate.

MAIN OUTCOME MEASURES

Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure.

RESULTS

Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60).

CONCLUSIONS

There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents.

FINANCIAL DISCLOSURES

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study

Background

SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials.

Methods

Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis.

Findings

Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]).

Interpretation

In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD.

Funding

National Institutes of Health, United States Department of Veterans Affairs.

An NLP Framework for the Extraction of Concept Measurements from Radiology and Pathology Notes

Natural language processing (NLP) tools can automate the identification of cancer patients eligible for specific pathways. We developed and validated a cancer agnostic, rules-based NLP framework to extract the dimensions and measurements of several concepts from pathology and radiology reports. This framework was then efficiently and cost-effectively deployed to identify patients eligible for breast, lung, and prostate cancers clinical pathways.

Toward Real-World Reproducibility: Verifying Value Sets for Clinical Research

Standardized operational definitions are an important tool to improve reproducibility of research using secondary real-world healthcare data. This approach was leveraged for studies evaluating the effectiveness of AZD7442 as COVID-19 pre-exposure prophylaxis across multiple healthcare systems. Value sets were defined, grouped, and mapped. Results of this exercise were reviewed and recorded. Value sets were updated to reflect findings.

Development of a Natural Language Processing System to Identify Clinical Documentation of Electronic Cigarette Use

Electronic Nicotine Delivery Systems (ENDS) use has increased substantially in the United States since 2010. To date, there is limited evidence regarding the nature and extent of ENDS documentation in the clinical note. In this work we investigate the effectiveness of different approaches to identify a patient's documented ENDS use. We report on the development and validation of a natural language processing system to identify patients with explicit documentation of ENDS using a large national cohort of patients at the United States Department of Veterans Affairs.

Genetic risk and likelihood of prostate cancer detection on first biopsy by ancestry

Despite differences in prostate cancer risk across ancestry groups, relative performance of prostate cancer genetic risks scores (GRS) for positive biopsy prediction in different ancestry groups is unknown. This cross-sectional retrospective analysis examines the association between a polygenic hazard score (PHS290) and risk of prostate cancer diagnosis upon first biopsy in male Veterans using two-sided tests. Our analysis included 36,717 Veterans (10,297 of African ancestry). Unadjusted rates of positive first prostate biopsy increased with higher genetic risk (low risk: 34%, high risk: 58%; p < .001). Among men of African ancestry, higher genetic risk was associated with increased prostate cancer detection on first biopsy (OR 2.18, 95% CI 1.93-2.47), but the effect was stronger among men of European descent (OR 3.89, 95% CI 3.62-4.18). These findings suggest that incorporating genetic risk into prediction models could better personalize biopsy decisions, although further study is needed to achieve equitable genetic risk stratification among ancestry groups.

Association between prediagnostic prostate-specific antigen and prostate cancer probability in Black and non-Hispanic White men

BACKGROUND

Although Black men are more likely than non-Hispanic White men to develop and die from prostate cancer, limited data exist to guide prostate-specific antigen (PSA) screening protocols in Black men. This study investigated whether the risk for prostate cancer was higher than expected among self-identified Black than White veterans based on prebiopsy PSA level.

METHODS

Multivariable logistic regression models were estimated to predict the likelihood of prostate cancer diagnosis on first biopsy for 75,295 Black and 207,658 White male veterans. Self-identified race, age at first PSA test, prebiopsy PSA, age at first biopsy, smoking status, statin use, and socioeconomic factors were used as predictors. The adjusted predicted probabilities of cancer detection on first prostate biopsy from the logistic models at different PSA levels were calculated.

RESULTS

After controlling for PSA and other covariates, Black veterans were 50% more likely to receive a prostate cancer diagnosis on their first prostate biopsy than White veterans (odds ratio [OR], 1.50; 95% CI, 1.47-1.53; p < .001). At a PSA level of 4.0 ng/mL, the probability of prostate cancer for a Black man was 49% compared with 39% for a White man. This model indicated that Black veterans with a PSA of 4.0 ng/mL have an equivalent risk of prostate cancer as White veterans with a PSA of 13.4 ng/mL.

CONCLUSIONS

The findings indicate that, at any given PSA level, Black men are more likely to harbor prostate cancer than White men. Prospective studies are needed to better evaluate risks and benefits of PSA screening in Black men and other high-risk populations.

CYP2C19 Polymorphisms and Clinical Outcomes Following Percutaneous Coronary Intervention (PCI) in the Million Veterans Program

Background

CYP2C19 loss-of-function (LOF) alleles decrease the antiplatelet effect of clopidogrel following percutaneous coronary intervention (PCI) in patients presenting with acute coronary syndrome (ACS). The impact of genotype in stable ischemic heart disease (SIHD) is unclear.

Objectives

Determine the association of CYP2C19 genotype with major adverse cardiac events (MACE) after PCI for ACS or SIHD.

Methods

Million Veterans Program (MVP) participants age <65 years with a PCI documented in the VA Clinical Assessment, Reporting and Tracking (CART) Program between 1/1/2009 to 9/30/2017, treated with clopidogrel were included. Time to MACE defined as the composite of all-cause death, stroke or myocardial infarction within 12 months following PCI.

Results

Among 4,461 Veterans (mean age 59.1 ± 5.1 years, 18% Black); 44% had ACS, 56% had SIHD and 29% carried a CYP2C19 LOF allele. 301 patients (6.7%) experienced MACE while being treated with clopidogrel, 155 (7.9%) in the ACS group and 146 (5.9%) in the SIHD group. Overall, MACE was not significantly different between LOF carriers vs. noncarriers (adjusted hazard ratio [HR] 1.18, confidence interval [95%CI] 0.97-1.45, p=0.096). Among patients presenting with ACS, MACE risk in LOF carriers versus non-carriers was numerically higher (HR 1.30, 95%CI 0.98-1.73, p=0.067). There was no difference in MACE risk in patients with SIHD (HR 1.09, 95%CI 0.82-1.44; p=0.565).

Conclusions

CYP2C19 LOF carriers presenting with ACS treated with clopidogrel following PCI experienced a numerically greater elevated risk of MACE events. CYP2C19 LOF genotype is not associated with MACE among patients presenting with SIHD.

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

Objective

To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.

Design

Federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Setting

10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.

Participants

4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.

Exposure

The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.

Main outcomes measures

The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.

Results

4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.

Conclusions

Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Development and Validation of a Tool to Identify Patients Diagnosed With Castration-Resistant Prostate Cancer

PURPOSE

Several novel therapies for castration-resistant prostate cancer (CRPC) have been approved with randomized phase III studies with continuing observational research either planned or ongoing. Accurately identifying patients with CRPC in electronic health care data is critical for quality observational research, resource allocation, and quality improvement. Previous work in this area has relied on either structured laboratory results and medication data or natural language processing (NLP) methods. However, a computable phenotype using both structured data and NLP identifies these patients with more accuracy.

METHODS

The Corporate Data Warehouse (CDW) of the Veterans Health Administration (VHA) was used to collect PCa diagnoses, prostate-specific antigen test results, and information regarding patient characteristics and medication use. The final system used for validation and subsequent analysis combined the NLP system and an algorithm of structured laboratory and medication data to identify patients as being diagnosed with CRPC. Patients with both a documented diagnosis of CRPC and a documented diagnosis of metastatic PCa were classified as having mCRPC by this system.

RESULTS

Among 1.2 million veterans with PCa, the International Classification of Diseases (ICD)-10 diagnosis code for CRPC (Z19.2) identifies 3,791 patients from 2016 when the code was created until 2022, compared with the combined algorithm which identifies 14,103, 10,312 more than ICD-10 codes alone, from 2016 to 2022. The combined algorithm showed a sensitivity of 97.9% and a specificity of 99.2%.

CONCLUSION

ICD-10 codes proved to be insufficient for capturing CRPC in the VHA CDW data. Using both structured and unstructured data identified more than double the number of patients compared with ICD-10 codes alone. Application of this combined approach drastically improved identification of real-world patients and enables high-quality observational research in mCRPC.

A deep learning approach for medication disposition and corresponding attributes extraction

OBJECTIVE

This article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task.

METHODS

The dataset was prepared using Contextualized Medication Event Dataset (CMED), including 500 notes from 296 patients. Our system consisted of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC). These three components were built using transformer models with slightly different architecture and input text engineering. A zero-shot learning solution for CC was also explored.

RESULTS

Our best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively.

CONCLUSION

In this study, we implemented a deep learning-based NLP system and demonstrated that our approach of (1) utilizing special tokens helps our model to distinguish multiple medications mentions in the same context; (2) aggregating multiple events of a single medication into multiple labels improves our model's performance.

Agent Orange exposure and prostate cancer risk in the Million Veteran Program

Purpose

Exposure to Agent Orange, a known carcinogen, might increase risk of prostate cancer (PCa). We sought to investigate the association of Agent Orange exposure and PCa risk when accounting for race/ethnicity, family history, and genetic risk in a diverse population of US Vietnam War veterans.

Methods & Materials

This study utilized the Million Veteran Program (MVP), a national, population-based cohort study of United States military veterans conducted 2011-2021 with 590,750 male participants available for analysis. Agent Orange exposure was obtained using records from the Department of Veterans Affairs (VA) using the US government definition of Agent Orange exposure: active service in Vietnam while Agent Orange was in use. Only veterans who were on active duty (anywhere in the world) during the Vietnam War were included in this analysis (211,180 participants). Genetic risk was assessed via a previously validated polygenic hazard score calculated from genotype data. Age at diagnosis of any PCa, diagnosis of metastatic PCa, and death from PCa were assessed via Cox proportional hazards models.

Results

Exposure to Agent Orange was associated with increased PCa diagnosis (HR 1.04, 95% CI 1.01-1.06, p=0.003), primarily among Non-Hispanic White men (HR 1.09, 95% CI 1.06- 1.12, p<0.001). When accounting for race/ethnicity and family history, Agent Orange exposure remained an independent risk factor for PCa diagnosis (HR 1.06, 95% CI 1.04-1.09, p<0.05). Univariable associations of Agent Orange exposure with PCa metastasis (HR 1.08, 95% CI 0.99-1.17) and PCa death (HR 1.02, 95% CI 0.84-1.22) did not reach significance on multivariable analysis. Similar results were found when accounting for polygenic hazard score.

Conclusions

Among US Vietnam War veterans, Agent Orange exposure is an independent risk factor for PCa diagnosis, though associations with PCa metastasis or death are unclear when accounting for race/ethnicity, family history, and/or polygenic risk.

Actionable Genomic Alterations in Prostate Cancer Among Black and White United States Veterans

Black Veterans have higher a incidence of localized and metastatic prostate cancer compared to White Veterans yet are underrepresented in reports of frequencies of somatic and germline alterations. This retrospective analysis of somatic and putative germline alterations was conducted in a large cohort of Veterans with prostate cancer (N = 835 Black, 1613 White) who underwent next generation sequencing through the VA Precision Oncology Program, which facilitates molecular testing for Veterans with metastatic cancer. No differences were observed in gene alterations for FDA approved targetable therapies (13.5% in Black Veterans vs. 15.5% in White Veterans, P = .21), nor in any potentially actionable alterations (25.5% vs. 28.7%, P =.1). Black Veterans had higher rates of BRAF (5.5% vs. 2.6%, P < .001) alterations, White Veterans TMPRSS2 fusions (27.2% vs. 11.7%, P < .0001). Putative germline alteration rates were higher in White Veterans (12.0% vs. 6.1%, P < .0001). Racial disparities in outcome are unlikely attributable to acquired somatic alterations in actionable pathways.

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study

OBJECTIVE

Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics.

MATERIALS AND METHODS

Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics.

RESULTS

On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5.

CONCLUSIONS

Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Healthy lifestyle, Agent Orange exposure, and inherited PCa risk: An analysis of the Million Veteran Program

210

Background: Prostate cancer (PCa) risk is understood to be mostly unmodifiable and inherited, but there is evidence that environmental and behavioral factors may also contribute. A recent study of health professional cohorts suggests a healthy lifestyle can mitigate a high inherited risk of lethal PCa. It is unknown how modifiable factors affect PCa risk in more diverse populations. Our objective was to determine the effects of healthy lifestyle and Agent Orange exposure on PCa risk when accounting for race/ethnicity, family history, and genetic risk in a diverse population. Methods: The Million Veteran Program (MVP) is a national, population-based cohort study of United States military veterans conducted 2011-2021 with 590,750 male participants available for analysis. Healthy lifestyle was quantified as: A healthy lifestyle score (range 0-3) was calculated with a point assigned for each of the following at MVP enrollment: not a current smoker, body mass index (BMI) 30 and strenuous activity 2 days per week. Agent Orange exposure was obtained from VA records. Genetic risk was assessed via a polygenic hazard score using genotype data. Results: Healthy lifestyle was independently associated with reduced metastatic PCa (HR 0.82, 95% CI 0.77–0.87, p<0.001) and fatal PCa (HR 0.76, 95% CI 0.68–0.86, p<0.01) when accounting for family history, genetic risk, and race/ethnicity. The benefit of healthy lifestyle was also observed in Black participants on subset analysis. Agent Orange exposure was an independent factor for PCa diagnosis (HR 1.06, 95% CI 1.04-1.09). Conclusions: Adherence to a healthy lifestyle is associated with reduced risk of metastatic or fatal PCa, which offsets inherited risk.

Effectiveness and comparative effectiveness of evidence-based psychotherapies for posttraumatic stress disorder in clinical practice

BACKGROUND

While evidence-based psychotherapy (EBP) for posttraumatic stress disorder (PTSD) is a first-line treatment, its real-world effectiveness is unknown. We compared cognitive processing therapy (CPT) and prolonged exposure (PE) each to an individual psychotherapy comparator group, and CPT to PE in a large national healthcare system.

METHODS

We utilized effectiveness and comparative effectiveness emulated trials using retrospective cohort data from electronic medical records. Participants were veterans with PTSD initiating mental healthcare (N = 265 566). The primary outcome was PTSD symptoms measured by the PTSD Checklist (PCL) at baseline and 24-week follow-up. Emulated trials were comprised of 'person-trials,' representing 112 discrete 24-week periods of care (10/07-6/17) for each patient. Treatment group comparisons were made with generalized linear models, utilizing propensity score matching and inverse probability weights to account for confounding, selection, and non-adherence bias.

RESULTS

There were 636 CPT person-trials matched to 636 non-EBP person-trials. Completing ⩾8 CPT sessions was associated with a 6.4-point greater improvement on the PCL (95% CI 3.1-10.0). There were 272 PE person-trials matched to 272 non-EBP person-trials. Completing ⩾8 PE sessions was associated with a 9.7-point greater improvement on the PCL (95% CI 5.4-13.8). There were 232 PE person-trials matched to 232 CPT person-trials. Those completing ⩾8 PE sessions had slightly greater, but not statistically significant, improvement on the PCL (8.3-points; 95% CI 5.9-10.6) than those completing ⩾8 CPT sessions (7.0-points; 95% CI 5.5-8.5).

CONCLUSIONS

PTSD symptom improvement was similar and modest for both EBPs. Although EBPs are helpful, research to further improve PTSD care is critical.

Association of Prostate-Specific Antigen Screening Rates With Subsequent Metastatic Prostate Cancer Incidence at US Veterans Health Administration Facilities

Importance

There is controversy about the benefit of prostate-specific antigen (PSA) screening. Prostate-specific antigen screening rates have decreased since 2008 in the US, and the incidence of metastatic prostate cancer has increased. However, there is no direct epidemiologic evidence of a correlation between population PSA screening rates and subsequent metastatic prostate cancer rates.

Objective

To assess whether facility-level variation in PSA screening rates is associated with subsequent facility-level metastatic prostate cancer incidence.

Design, Setting, and Participants

This retrospective cohort used data for all men aged 40 years or older with an encounter at 128 facilities in the US Veterans Health Administration (VHA) from January 1, 2005, to December 31, 2019.

Exposures

Yearly facility-level PSA screening rates, defined as the proportion of men aged 40 years or older with a PSA test in each year, and long-term nonscreening rates, defined as the proportion of men aged 40 years or older without a PSA test in the prior 3 years, from January 1, 2005, to December 31, 2014.

Main Outcomes and Measures

The main outcomes were facility-level yearly counts of incident metastatic prostate cancer diagnoses and age-adjusted yearly metastatic prostate cancer incidence rates (per 100 000 men) 5 years after each PSA screening exposure year.

Results

The cohort included 4 678 412 men in 2005 and 5 371 701 men in 2019. Prostate-specific antigen screening rates decreased from 47.2% in 2005 to 37.0% in 2019, and metastatic prostate cancer incidence increased from 5.2 per 100 000 men in 2005 to 7.9 per 100 000 men in 2019. Higher facility-level PSA screening rates were associated with lower metastatic prostate cancer incidence 5 years later (incidence rate ratio [IRR], 0.91 per 10% increase in PSA screening rate; 95% CI, 0.87-0.96; P < .001). Higher long-term nonscreening rates were associated with higher metastatic prostate cancer incidence 5 years later (IRR, 1.11 per 10% increase in long-term nonscreening rate; 95% CI, 1.03-1.19; P = .01).

Conclusions and Relevance

From 2005 to 2019, PSA screening rates decreased in the national VHA system. Facilities with higher PSA screening rates had lower subsequent rates of metastatic prostate cancer. These data may be used to inform shared decision-making about the potential benefits of PSA screening among men who wish to reduce their risk of metastatic prostate cancer.

Association of Sex and Race With Incident Peripheral Artery Disease Among Veterans With Normal Ankle-Brachial Indices

IMPORTANCE

Reported risk of incident peripheral artery disease (PAD) by sex and race varies significantly and has not been reported in national cohorts among individuals free of baseline PAD.

OBJECTIVE

To evaluate the association of sex and race, as well as prevalent cardiovascular risk factors, with limb outcomes in a national cohort of people with normal baseline ankle-brachial indices (ABIs).

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was conducted using data from participants in the Veterans Affairs Birth Cohort Study (born 1945-1965), with follow-up data between January 1, 2000, and December 31, 2016. Baseline demographics were collected from 77 041 participants receiving care from the Veterans Health Administration with baseline ABIs of 0.90 to 1.40 and no history of PAD. Data were analyzed from October 2019 through September 2022.

EXPOSURES

Sex, race, diabetes, and smoking status.

MAIN OUTCOMES AND MEASURES

Incident PAD, defined as subsequent ABI less than 0.90, surgical or percutaneous revascularization, or nontraumatic amputation.

RESULTS

Of 77 041 participants with normal ABIs (73 822 [95.8%] men; mean [SD] age, 60.2 [5.9] years; 13 080 Black [18.2%] and 54 377 White [75.6%] among 71 911 participants with race and ethnicity data), there were 6692 incident PAD events over a median [IQR] of 3.9 [1.7-6.9] years. Incidence rates were lower for women than men (incidence rates [IRs] per 1000 person-years, 7.4 incidents [95% CI, 6.2-8.8 incidents] vs 19.2 incidents [95% CI, 18.7-19.6 incidents]), with a lower risk of incident PAD (adjusted hazard ratio [aHR], 0.49 [95% CI, 0.41-0.59]). IRs per 1000 person-years of incident PAD were similar for Black and White participants (18.9 incidents [95% CI, 17.9-20.1 incidents] vs 18.8 incidents [95% CI, 18.3-19.4]). Compared with White participants, Black participants had increased risk of total PAD (aHR, 1.09 [95% CI, 1.02-1.16]) and nontraumatic amputation (aHR, 1.20 [95% CI, 1.06-1.36]) but not surgical or percutaneous revascularization (aHR, 1.10 [95% CI, 0.98-1.23]) or subsequent ABI less than 0.90 (aHR, 1.04 [95% CI, 0.95-1.13]). Diabetes (aHR, 1.62 [95% CI, 1.53-1.72]) and smoking (eg, current vs never: aHR, 1.76 [95% CI, 1.64-1.89]) were associated with incident PAD. Incident PAD was rare among individuals without a history of smoking or diabetes (eg, among 632 women: IR per 1000 people-years, 2.1 incidents [95% CI, 1.0-4.5 incidents]) despite an otherwise-high-risk cardiovascular profile (eg, 527 women [83.4%] with hypertension).

CONCLUSIONS AND RELEVANCE

This study found that the risk of PAD was approximately 50% lower in women than men and less than 10% higher for Black vs White participants, while the risk of nontraumatic amputation was 20% higher among Black compared with White participants.

International cohort study indicates no association between alpha-1 blockers and susceptibility to COVID-19 in benign prostatic hyperplasia patients

Purpose: Alpha-1 blockers, often used to treat benign prostatic hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storm release. The proposed treatment based on this hypothesis currently lacks support from reliable real-world evidence, however. We leverage an international network of large-scale healthcare databases to generate comprehensive evidence in a transparent and reproducible manner.

Methods: In this international cohort study, we deployed electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We assessed association between alpha-1 blocker use and risks of three COVID-19 outcomes—diagnosis, hospitalization, and hospitalization requiring intensive services—using a prevalent-user active-comparator design. We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We pooled database-specific estimates through random effects meta-analysis.

Results: Our study overall included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH medications. We observed no significant difference in their risks for any of the COVID-19 outcomes, with our meta-analytic HR estimates being 1.02 (95% CI: 0.92–1.13) for diagnosis, 1.00 (95% CI: 0.89–1.13) for hospitalization, and 1.15 (95% CI: 0.71–1.88) for hospitalization requiring intensive services.

Conclusion: We found no evidence of the hypothesized reduction in risks of the COVID-19 outcomes from the prevalent-use of alpha-1 blockers—further research is needed to identify effective therapies for this novel disease.

Improvements to PTSD quality metrics with natural language processing

RATIONALE AIMS AND OBJECTIVES

As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) data, clinicians are asked to perform documentation using tools that facilitate data capture. These tools may not be available, feasible, or acceptable in all clinical scenarios. Alternative methods of assessment, including natural language processing (NLP) of clinical notes, may improve the completeness of quality measurement in real-world practice. Our objective was to measure the quality of care for a set of evidence-based practices using structured EMR data alone, and then supplement those measures with additional data derived from NLP.

METHOD

As a case example, we studied the quality of care for posttraumatic stress disorder (PTSD) in the United States Department of Veterans Affairs (VA) over a 20-year period. We measured two aspects of PTSD care, including delivery of evidence-based psychotherapy (EBP) and associated use of measurement-based care (MBC), using structured EMR data. We then recalculated these measures using additional data derived from NLP of clinical note text.

RESULTS

There were 2 098 389 VA patients with a diagnosis of PTSD between 2000 and 2019, 72% (n = 1 515 345) of whom had not previously received EBP for PTSD and were treated after a 2015 mandate to document EBP using templates that generate structured EMR data. Using structured EMR data, we determined that 3.2% (n = 48 004) of those patients met our EBP for PTSD quality standard between 2015 and 2019, and 48.1% (n = 23 088) received associated MBC. With the addition of NLP-derived data, estimates increased to 4.1% (n = 62 789) and 58.0% (n = 36 435), respectively.

CONCLUSION

Healthcare quality data can be significantly improved by supplementing structured EMR data with NLP-derived data. By using NLP, health systems may be able to fill the gaps in documentation when structured tools are not yet available or there are barriers to using them in clinical practice.

Alcohol-attributable deaths and years of potential life lost due to alcohol among veterans: Overall and between persons with minoritized and non-minoritized sexual orientations

BACKGROUND

Unhealthy alcohol use is disproportionally experienced by individuals with minoritized sexual orientations. Unlike the general US population, for whom the burden of alcohol as it relates to mortality is consistently monitored across time with national survey data, the impact of unhealthy alcohol use among veterans with minoritized sexual orientations, for whom addressing substance use is a national priority, is largely unknown.

METHODS

Using Alcohol Use Disorders Identification Test Consumption data from the Department of Veterans Affairs electronic health record and underlying cause of death from National Death Index from 2014 to 2018 we quantified alcohol consumption and related mortality among veterans with (n = 102,085) and without minoritized sexual orientations (n = 5300,521). Age adjusted rates of alcohol attributed deaths (AAD) per 100,000 persons and years of potential life lost (YPLL) were estimated by sexual orientation, sex, and sexual orientation stratified by sex.

RESULTS

Alcohol attributable deaths (n = 21,861) were higher among veterans with minoritized sexual orientations than veterans without after adjustment for age (486.5 deaths/100,000 versus 309.7 deaths/100,000, respectively). Veterans with minoritized sexual orientations also experienced more YPLL (13,772.8 years/100,000 versus 7618.9 years/100,000). Years of potential life lost per AAD was higher in women (33.2 years) than men (18.7 years).

CONCLUSIONS

Alcohol consumption results in substantial disability and death among veterans, particularly veterans with minoritized sexual orientations. Findings suggest need for increased alcohol-related services for all VA patients, and potential targeted approaches to for veterans with minoritized sexual orientations and women to offset risk for, and years of potential life lost from, alcohol attributable death.

Effect of Pharmacogenomic Testing for Drug-Gene Interactions on Medication Selection and Remission of Symptoms in Major Depressive Disorder: The PRIME Care Randomized Clinical Trial

IMPORTANCE

Selecting effective antidepressants for the treatment of major depressive disorder (MDD) is an imprecise practice, with remission rates of about 30% at the initial treatment.

OBJECTIVE

To determine whether pharmacogenomic testing affects antidepressant medication selection and whether such testing leads to better clinical outcomes.

DESIGN, SETTING, AND PARTICIPANTS

A pragmatic, randomized clinical trial that compared treatment guided by pharmacogenomic testing vs usual care. Participants included 676 clinicians and 1944 patients. Participants were enrolled from 22 Department of Veterans Affairs medical centers from July 2017 through February 2021, with follow-up ending November 2021. Eligible patients were those with MDD who were initiating or switching treatment with a single antidepressant. Exclusion criteria included an active substance use disorder, mania, psychosis, or concurrent treatment with a specified list of medications.

INTERVENTIONS

Results from a commercial pharmacogenomic test were given to clinicians in the pharmacogenomic-guided group (n = 966). The comparison group received usual care and access to pharmacogenomic results after 24 weeks (n = 978).

MAIN OUTCOMES AND MEASURES

The co-primary outcomes were the proportion of prescriptions with a predicted drug-gene interaction written in the 30 days after randomization and remission of depressive symptoms as measured by the Patient Health Questionnaire-9 (PHQ-9) (remission was defined as PHQ-9 ≤ 5). Remission was analyzed as a repeated measure across 24 weeks by blinded raters.

RESULTS

Among 1944 patients who were randomized (mean age, 48 years; 491 women [25%]), 1541 (79%) completed the 24-week assessment. The estimated risks for receiving an antidepressant with none, moderate, and substantial drug-gene interactions for the pharmacogenomic-guided group were 59.3%, 30.0%, and 10.7% compared with 25.7%, 54.6%, and 19.7% in the usual care group. The pharmacogenomic-guided group was more likely to receive a medication with a lower potential drug-gene interaction for no drug-gene vs moderate/substantial interaction (odds ratio [OR], 4.32 [95% CI, 3.47 to 5.39]; P < .001) and no/moderate vs substantial interaction (OR, 2.08 [95% CI, 1.52 to 2.84]; P = .005) (P < .001 for overall comparison). Remission rates over 24 weeks were higher among patients whose care was guided by pharmacogenomic testing than those in usual care (OR, 1.28 [95% CI, 1.05 to 1.57]; P = .02; risk difference, 2.8% [95% CI, 0.6% to 5.1%]) but were not significantly higher at week 24 when 130 patients in the pharmacogenomic-guided group and 126 patients in the usual care group were in remission (estimated risk difference, 1.5% [95% CI, -2.4% to 5.3%]; P = .45).

CONCLUSIONS AND RELEVANCE

Among patients with MDD, provision of pharmacogenomic testing for drug-gene interactions reduced prescription of medications with predicted drug-gene interactions compared with usual care. Provision of test results had small nonpersistent effects on symptom remission.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03170362.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P < 5 × 10-8). External replication in histology-defined NAFLD cohorts (7,397 cases and 56,785 controls) or radiologic imaging cohorts (n = 44,289) replicated 17 single-nucleotide polymorphisms (SNPs) (P < 6.5 × 10-4), of which 9 were new (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH and IFI30). Pleiotropy analysis showed that 61 of 77 multiancestry and all 17 replicated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.

A Population-Level Analysis of the Protective Effects of Androgen Deprivation Therapy Against COVID-19 Disease Incidence and Severity

Background

The incidence and severity of coronavirus disease 19 (COVID-19) is substantially higher in men. Sex hormones may be a potential mechanism for differences in COVID-19 outcome in men and women. We hypothesized that men treated with androgen deprivation therapy (ADT) have lower incidence and severity of COVID-19.

Methods

We conducted an observational study of male Veterans treated in the Veterans Health Administration from February 15th to July 15th, 2020. We developed a propensity score model to predict the likelihood to undergo Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing. We performed multivariable logistic regression modeling adjusted with inverse probability weighting to examine the relationship between ADT and COVID-19 incidence. We conducted logistic regression analysis among COVID-19 patients to test the association between ADT and COVID-19 severity.

Results

We identified a large cohort of 246,087 VA male patients who had been tested for SARS-CoV-2, of whom 3,057 men were exposed to ADT, and 36,096 men with cancer without ADT. Of these, 295 ADT patients and 2,427 cancer patients not on ADT had severe COVID-19 illness. In the primary, propensity-weighted comparison of ADT patients to cancer patients not on ADT, ADT was associated with decreased likelihood of testing positive for SARS-CoV-2 (adjusted OR, 0.88 [95% CI, 0.81–0.95]; p = 0.001). Furthermore, ADT was associated with fewer severe COVID-19 outcomes (OR 0.72 [95% CI 0.53–0.96]; p = 0.03).

Conclusion

ADT is associated with reduced incidence and severity of COVID-19 amongst male Veterans. Testosterone and androgen receptor signaling may confer increased risk for SARS-CoV-2 infection and contribute to severe COVID-19 pathophysiology in men.

Veterans receiving a second course of cognitive processing therapy or prolonged exposure therapy: is it better to switch or stay the same?

Cognitive processing therapy (CPT) and prolonged exposure therapy (PE) are effective psychotherapies for post-traumatic stress disorder (PTSD). However, these treatments also have high rates of dropout and non-response. Therefore, patients may need a second course of treatment. We compared outcomes for patients who switched between CPT/PE and those who repeated CPT/PE during a second course of treatment. We collected data from Iraq and Afghanistan war veterans (n = 2,958) who received a second course of CPT/PE in the Veterans Health Administration from 2001 to 2017 and had symptom outcomes (PTSD checklist; PCL). We measured the association between treatment sequence and change in PCL score over the second course of treatment using hierarchical Bayesian regression, adjusted for sociodemographic and clinical characteristics. All treatment sequences showed a significant reduction in PCL score over time (β = -4.80; HDI₉₅: -5.74, -3.86). Veterans who switched from CPT to PE had modestly greater PCL reductions during the second course than those who repeated CPT. However, no significant difference in PCL change during the second course was observed between veterans who repeated PE and those who switched from PE to CPT. Veterans participating in a second course of CPT/PE can benefit, and switching treatment may be slightly more beneficial following CPT.

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

Purpose

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.

Patients and Methods

We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.

Results

We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.

Conclusion

We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Clinician documentation of patient centered care in the electronic health record

Background

In this study we sought to explore the possibility of using patient centered care (PCC) documentation as a measure of the delivery of PCC in a health system.

Methods

We first selected 6 VA medical centers based on their scores for a measure of support for self-management subscale from a national patient satisfaction survey (the Survey for Healthcare Experience-Patients). We accessed clinical notes related to either smoking cessation or weight management consults. We then annotated this dataset of notes for documentation of PCC concepts including: patient goals, provider support for goal progress, social context, shared decision making, mention of caregivers, and use of the patient's voice. We examined the association of documentation of PCC with patients’ perception of support for self-management with regression analyses.

Results

Two health centers had < 50 notes related to either tobacco cessation or weight management consults and were removed from further analysis. The resulting dataset includes 477 notes related to 311 patients total from 4 medical centers. For a majority of patients (201 out of 311; 64.8%) at least one PCC concept was present in their clinical notes. The most common PCC concepts documented were patient goals (patients n = 126; 63% clinical notes n = 302; 63%), patient voice (patients n = 165, 82%; clinical notes n = 323, 68%), social context (patients n = 105, 52%; clinical notes n = 181, 38%), and provider support for goal progress (patients n = 124, 62%; clinical notes n = 191, 40%). Documentation of goals for weight loss notes was greater at health centers with higher satisfaction scores compared to low. No such relationship was found for notes related to tobacco cessation.

Conclusion

Providers document PCC concepts in their clinical notes. In this pilot study we explored the feasibility of using this data as a means to measure the degree to which care in a health center is patient centered. Practice Implications: clinical EHR notes are a rich source of information about PCC that could potentially be used to assess PCC over time and across systems with scalable technologies such as natural language processing.

Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python

Despite impressive success of machine learning algorithms in clinical natural language processing (cNLP), rule-based approaches still have a prominent role. In this paper, we introduce medspaCy, an extensible, open-source cNLP library based on spaCy framework that allows flexible integration of rule-based and machine learning-based algorithms adapted to clinical text. MedspaCy includes a variety of components that meet common cNLP needs such as context analysis and mapping to standard terminologies. By utilizing spaCy's clear and easy-to-use conventions, medspaCy enables development of custom pipelines that integrate easily with other spaCy-based modules. Our toolkit includes several core components and facilitates rapid development of pipelines for clinical text.

Factors associated with gene mutation testing in United States veterans with metastatic castration-resistant prostate cancer

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Background: Practice guidelines have been modified to recommend hereditary and tumor gene mutation testing in patients with metastatic castration-resistant prostate cancer (mCRPC) to identify patients for molecularly targeted therapies. Identifying appropriate candidates for testing can be challenging in electronic health records and claims data. In this study, we used natural language processing (NLP) algorithms to identify veterans with mCRPC, reported gene mutation testing rates and identified factors associated with testing. Methods: This is a retrospective observational cohort study using NLP to identify veterans diagnosed with mCRPC between 2016 and 2020. Patient and facility characteristics were reported descriptively. Chi-square and t-tests were used to determine whether differences were statistically significant at a significance level of 0.05 based on receipt of testing. Generalized linear mixed models with binomial error distributions and logit links accounting for clustering by facility were used to determine which factors were independently associated with testing. Results: 9,282 veterans were diagnosed with mCRPC between 2016 and 2020, as determined by NLP algorithms identifying diagnosis of metastatic disease and castration-resistant disease. Among these patients, 381 died within 45 days of their diagnosis, and were excluded from analysis. In the analytic cohort of 8,901 veterans, 1,282 (14%) patients received testing. Of these, 1,041 (81%) received tumor tissue testing and 292 (23%) received hereditary testing. In bivariate analyses, age, race, ethnicity, Commission on Cancer (COC) facility certification, and facility complexity rating differed between veterans who received the test versus who did not (mean age of 73 versus 77, p < 0.0001; 30% versus 24% Black, p < 0.0001; 93% versus 92% non-Hispanic, p = 0.04; 64% versus 63% COC-certified facility, p = 0.04; and 59% versus 52% most complex facility, p < 0.0001). In multivariate analyses, older age and lower facility complexity rating were associated with lower odds of testing (for every 10-year increase in age, adjusted odds ratio [aOR], 95% confidence interval [CI]: 0.54, 0.50-0.58; Mid-high and low complexity facilities compared to highest complexity facilities: aOR, 95% CI: 0.52, 0.32-0.85 and 0.39, 0.22-0.71, respectively). Conclusions: Gene mutation testing in veterans with mCRPC is underutilized. Older age and being seen in a lower complexity facility are independently associated with a lower odds of testing. Patient and facility barriers to testing should be identified to improve guideline concordant care.

Diagnosis codes overestimate the burden of prostate cancer cases

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Background: Identifying cancer cases within the electronic health record (EHR) or claims data can be challenging because diagnosis codes are often entered into patient records during routine screenings or as “rule out” diagnosis codes when the patient is referred to a procedure. To improve accuracy of prostate cancer (PCa) case ascertainment, we compared algorithms that used diagnoses codes to natural language processing (NLP) tools applied to clinical notes and pathology reports to identify Veterans with prostate cancer (PCa). Methods: This is a retrospective observational cohort study using VA EHR data to identify veterans diagnosed with PCa between 2000 and 2020. Using International Classification of Diseases (ICD-10 CM or ICD-9 CM) diagnosis and procedure codes, we identified veterans who may have PCa. We deployed validated NLP tools to identify the presence of Gleason score, metastatic PCa, and castration sensitivity to identify evidence of PCa within the notes. We conducted a descriptive analysis to compare the results of algorithms that relied exclusively on diagnosis codes compared to use of NLP tools. Results: From 2000 through 2020,1,031,296 veterans had one or more PCa diagnosis code. This number decreased by 11% for each additional PCa diagnosis code required. When we required 4 or more PCa diagnosis codes to be present, only 746,350 veterans had PCa. When we deployed NLP tools to identify mention of a Gleason score or an indicator of mPCa, only 685,847 Veterans had these indicators of PCa, a 35% decrease in the number of PCa cases with a single diagnosis code. Chart review of patients with their first PCa diagnosis codes in 2019 and 4 or more codes in their records illustrated no evidence of Gleason score or mPCa disease in their EHR. Analysis of their pathology reports revealed that these patients had prostatic intraepithelial neoplasia or atypical small acinar proliferation and had not yet developed prostate cancer. Conclusions: Accurate ascertainment of PCa using EHR and claims data requires using NLP tools and clinical notes combined with structured data sources such as diagnosis codes. Relying on ICD diagnosis codes alone will overestimate the burden of PCa up to 30%.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

Background

We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients.

Methods

We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date.

Results

Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations.

Conclusions

This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01505-z.

Identifying Posttraumatic Stress Disorder and Disparity Among Transgender Veterans Using Nationwide Veterans Health Administration Electronic Health Record Data

Purpose: The prevalence of posttraumatic stress disorder (PTSD) and other psychiatric disorders is high among military veterans and even higher among transgender veterans. Prior prevalence estimates have become outdated, and novel methods of estimation have since been developed but not used to estimate PTSD prevalence among transgender veterans. This study provides updated estimates of PTSD prevalence among transgender and cisgender veterans. Methods: We examined Veterans Health Administration (VHA) medical record data from October 1, 1999 to April 1, 2021 for 9995 transgender veterans and 29,985 cisgender veteran comparisons (1:3). We matched on age group at first VHA health care visit, sex assigned at birth, and year of first VHA visit. We employed both probabilistic and rule-based algorithms to estimate the prevalence of PTSD for transgender and cisgender veterans. Results: The prevalence of PTSD was 1.5-1.8 times higher among transgender veterans. Descriptive data suggest that the prevalence of depression, schizophrenia, bipolar disorder, alcohol and non-alcohol substance use disorders, current/former smoking status, and military sexual trauma was also elevated among transgender veterans. Conclusion: The PTSD and overall psychiatric burden observed among transgender veterans was significantly higher than that of their cisgender peers, especially among recent users of VHA care. These PTSD findings are consistent with prior literature and minority stress theory, and they were robust across probabilistic and two rule-based methods employed in this study. As such, enhanced and careful screening, outreach, and evidence-based practices are recommended to help reduce this disparity among transgender veterans.

Racial and Ethnic Disparities in Prostate Cancer Outcomes in the Veterans Affairs Health Care System

IMPORTANCE

Prostate cancer (PCa) disproportionately affects African American men, but research evaluating the extent of racial and ethnic disparities across the PCa continuum in equal-access settings remains limited at the national level. The US Department of Veterans Affairs (VA) Veterans Hospital Administration health care system offers a setting of relatively equal access to care in which to assess racial and ethnic disparities in self-identified African American (or Black) veterans and White veterans.

OBJECTIVE

To determine the extent of racial and ethnic disparities in the incidence of PCa, clinical stage, and outcomes between African American patients and White patients who received a diagnosis or were treated at a VA hospital.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included 7 889 984 veterans undergoing routine care in VA hospitals nationwide from 2005 through 2019 (incidence cohort). The age-adjusted incidence of localized and de novo metastatic PCa was estimated. Treatment response was evaluated, and PCa-specific outcomes were compared between African American veterans and White veterans. Residual disparity in PCa outcome, defined as the leftover racial and ethnic disparity in the outcomes despite equal response to treatment, was estimated.

EXPOSURES

Self-identified African American (or Black) and White race and ethnicity.

MAIN OUTCOMES AND MEASURES

Time to distant metastasis following PCa diagnosis was the primary outcome. Descriptive analyses were used to compare baseline demographics and clinic characteristics. Multivariable logistic regression was used to evaluate race and ethnicity association with pretreatment clinical variables. Multivariable Cox regression was used to estimate the risk of metastasis.

RESULTS

Data from 7 889 984 veterans from the incidence cohort were used to estimate incidence, whereas data from 92 269 veterans with localized PCa were used to assess treatment response. Among 92 269 veterans, African American men (n = 28 802 [31%]) were younger (median [IQR], 63 [58-68] vs 65 [62-71] years) and had higher prostate-specific antigen levels (>20 ng/mL) at the time of diagnosis compared with White men (n = 63 467; [69%]). Consistent with US population-level data, African American veterans displayed a nearly 2-fold greater incidence of localized and de novo metastatic PCa compared with White men across VA centers nationwide. Among veterans screened for PCa, African American men had a 29% increased risk of PCa detection on a diagnostic prostate biopsy compared with White (hazard ratio, 1.29; 95% CI, 1.27-1.31; P < .001). African American men who received definitive primary treatment of PCa experienced a lower risk of metastasis (hazard ratio, 0.89; 95% CI, 0.83-0.95; P < .001). However, African American men who received nondefinitive treatment classified as “other” were more likely to develop metastasis (adjusted hazard ratio, 1.29; 95% CI, 1.17-1.42; P < .001). Using the actual rate of metastasis from veterans who received definitive primary treatment, a persistent residual metastatic burden for African American men was observed across all National Comprehensive Cancer Network risk groups (low risk, 4 vs 2 per 100 000; intermediate risk, 13 vs 6 per 100 000; high risk, 19 vs 9 per 100 000).

CONCLUSIONS AND RELEVANCE

This cohort analysis found significant disparities in the incidence of localized and metastatic PCa between African American veterans and White veterans. This increased incidence is a major factor associated with the residual disparity in PCa metastasis observed in African American veterans compared with White veterans despite their nearly equal response to treatment.

Characteristics and outcomes of patients with COVID-19 with and without prevalent hypertension: a multinational cohort study

OBJECTIVE

To characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.

DESIGN AND SETTING

This is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.

PARTICIPANTS

Two non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days.

OUTCOMES

Demographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported.

RESULTS

We identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension.

CONCLUSIONS

COVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD's timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five "training" US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model's area under the curve (AUC) varied 0.633-0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570-0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

Estimating prevalence of PTSD among veterans with minoritized sexual orientations using electronic health record data

Objective: Questionnaire studies show people with minoritized sexual orientations (MSOs) face increased risk for conditions including posttraumatic stress disorder (PTSD). This study replicated Harrington et al.'s (2019) electronic health record probabilistic algorithm to evaluate lifetime PTSD prevalence in Veterans Health Administration (VHA)-using veterans. Method: In 115,853 MSO veterans and a 1:3 matched (on sex assigned at birth, and age at and year of first VHA visit) sample of non-MSO veterans. Each veteran was given a probability of "likely PTSD" (0.0-1.0) and thresholds (e.g., 0.7) applied to minimize false positive classifications. Results: Veterans with MSO were 2.35 times, CI [2.33, 2.38], more likely to have "likely PTSD" than veterans with non-MSO. The prevalence of "likely PTSD" using the rule-based International Classification of Diseases (ICD) approach was 40.8% among the MSO group compared to 22.0% among the non-MSO group after excluding those with bipolar or schizophrenia diagnoses and those with limited VHA engagement. Without those exclusions, prevalence was slightly higher in both groups (46.1% vs. 24.3%, respectively; prevalence ratio: 1.90). Despite increased prevalence of exposure to military sexual trauma (MST; MSO = 20.7%; non-MSO = 8.3%) and double "likely PTSD" among MSO veterans, they were less likely to have a service-connected PTSD disability than their matched non-MSO (MSO = 78.1%; non-MSO = 87.6%) comparators. Conclusions: VHA-using veterans with MSO were twice as likely to have "likely PTSD" and exposure to MST than veterans with non-MSO. Veterans with MSO were less likely to be service connected for PTSD than non-MSO counterparts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Sexual orientation-related disparities in health conditions that elevate COVID-19 severity

Purpose

The Veterans Health Administration (VA) is the largest single integrated healthcare system in the US and is likely the largest healthcare provider for people with minoritized sexual orientations (e.g., gay, lesbian, bisexual). The purpose of this study was to use electronic health record (EHR) data to replicate self-reported survey findings from the general US population and assess whether sexual orientation is associated with diagnosed physical health conditions that may elevate risk of COVID-19 severity among veterans who utilize the VA.

Methods

A retrospective analysis of VA EHR data from January 10, 1999–January 07, 2019 analyzed in 2021. Veterans with minoritized sexual orientations were included if they had documentation of a minoritized sexual orientation within clinical notes identified via natural language processing. Veterans without minoritized sexual orientation documentation comprised the comparison group. Adjusted prevalence and prevalence ratios (aPR) were calculated overall and by race/ethnicity while accounting for differences in distributions of sex assigned at birth, age, calendar year of first VA visit, volumes of healthcare utilization, and VA priority group.

Results

Data from 108,401 veterans with minoritized sexual orientation and 6,511,698 controls were analyzed. After adjustment, veterans with minoritized sexual orientations had a statistically significant elevated prevalence of 10 of the 11 conditions. Amongst the highest disparities observed were COPD (aPR:1.24 [95% confidence interval:1.23–1.26]), asthma (1.22 [1.20–1.24]), and stroke (1.26 [1.24–1.28]).

Conclusions

Findings largely corroborated patterns among the general US population. Further research is needed to determine if these disparities translate to poorer COVID-19 outcomes for individuals with minoritized sexual orientation.

Positive Predictive Value of COVID-19 ICD-10 Diagnosis Codes Across Calendar Time and Clinical Setting

Purpose

To estimate the positive predictive value (PPV) of International Classification of Diseases, Tenth Revision (ICD-10) code U07.1, COVID-19 virus identified, in the Department of Veterans of Affairs (VA).

Patients and Methods

Records of ICD-10 code U07.1 from inpatient, outpatient, and emergency/urgent care settings were extracted from VA medical record data from 4/01/2020 to 3/31/2021. A weighted, random sample of 1500 records from each quarter of the one-year observation period was reviewed by study personnel to confirm active COVID-19 infection at the time of diagnosis and classify reasons for false positive records. PPV was estimated overall and compared across clinical setting and quarters.

Results

We identified 664,406 records of U07.1. Among the 1500 reviewed, 237 were false positives (PPV: 84.2%, 95% CI: 82.4–86.0). PPV ranged from 77.7% in outpatient settings to 93.8% in inpatient settings and was 83.3% in quarter 1, 80.5% in quarter 2, 86.1% in quarter 3, and 83.6% in quarter 4. The most common reasons for false positive records were history of COVID-19 (44.3%) and orders for laboratory tests (21.5%).

Conclusion

The PPV of ICD-10 code U07.1 is low, especially in outpatient settings. Directed training may improve accuracy of coding to levels that are deemed adequate for future use in surveillance efforts.

Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain

BACKGROUND

We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.

METHODS

We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.

RESULTS

We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.

CONCLUSIONS

Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.

IMPACT

This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

ProjectFlow: a configurable workflow management application for point of care research

Objective

To best meet our point-of-care research (POC-R) needs, we developed ProjectFlow, a configurable, clinical research workflow management application. In this article, we describe ProjectFlow and how it is used to manage study processes for the Diuretic Comparison Project (DCP) and the Research Precision Oncology Program (RePOP).

Materials and methods

The Veterans Health Administration (VHA) is the largest integrated health care system in the United States. ProjectFlow is a flexible web-based workflow management tool specifically created to facilitate conduct of our clinical research initiatives within the VHA. The application was developed using the Grails web framework and allows researchers to create custom workflows using Business Process Model and Notation.

Results

As of January 2021, ProjectFlow has facilitated management of study recruitment, enrollment, randomization, and drug orders for over 10 000 patients for the DCP clinical trial. It has also helped us evaluate over 3800 patients for recruitment and enroll over 370 of them into RePOP for use in data sharing partnerships and predictive analytics aimed at optimizing cancer treatment in the VHA.

Discussion

The POC-R study design embeds research processes within day-to-day clinical care and leverages longitudinal electronic health record (EHR) data for study recruitment, monitoring, and outcome reporting. Software that allows flexibility in study workflow creation and integrates with enterprise EHR systems is critical to the success of POC-R.

Conclusions

We developed a flexible web-based informatics solution called ProjectFlow that supports custom research workflow configuration and has ability to integrate data from existing VHA EHR systems.

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom

BACKGROUND

A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity.

METHODS

We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status.

RESULTS

We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity.

CONCLUSION

We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study

Background

SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the “prediction model risk of bias assessment” criteria, and it has not been externally validated.

Objective

The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.

Methods

We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.

Results

The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.

Conclusions

Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10-6; IFNAR2, P = 9.8 × 10-11 and IL-10RB, P = 2.3 × 10-14) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.

Alpha-1 blockers and susceptibility to COVID-19 in benign prostate hyperplasia patients : an international cohort study

Alpha-1 blockers, often used to treat benign prostate hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storms release. We conducted a prevalent-user active-comparator cohort study to assess association between alpha-1 blocker use and risks of three COVID-19 outcomes: diagnosis, hospitalization, and hospitalization requiring intensive services. Our study included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH therapy during the period between November 2019 and January 2020, found in electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We found no differential risk for any of COVID-19 outcome, pointing to the need for further research on potential COVID-19 therapies.

Variation in Sexual Orientation Documentation in a National Electronic Health Record System

Purpose: The purpose of this study was to determine variation in sexual minority (SM) sexual orientation documentation within the electronic medical records of the Veterans Health Administration (VHA). Methods: Documentation of SM sexual orientation was retrospectively extracted from clinical notes and administrative data in the VHA from October 1, 1999 to July 1, 2019. The rate of documentation overall and by calendar year was calculated, and differences across patient, provider, and clinic characteristics were evaluated. Results: Approximately 1.4% of all VHA Veterans (n = 115,911) had at least one documentation of SM sexual orientation, including 79,455 men and 36,456 women. The rate of documentation increased from 81.01/100,000 in 2000 to 568.84/100,000 in 2018. The majority of documentations (58.7%) occurred in mental health settings by non-MD mental health/social work counselors, whereas only 9.6% occurred in primary care settings. Although 99% of these Veterans had a primary care visit, only 19% had SM status recorded in that setting. Conclusion: Documentation patterns of SM sexual orientation varied considerably in the VHA with notable gaps in primary care. Diverse approaches to culturally competent training for primary care clinicians and patient-facing collection strategies could facilitate documentation of sexual orientation.

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

Objectives

To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions.

Design

Retrospective cohort study.

Setting

The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe.

Participants

Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2.

Primary and secondary outcome measures

Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction.

Results

Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites.

Conclusions

Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.