Heart failure subtype after acute kidney injury.

INTRODUCTION: Acute kidney injury (AKI) is associated with increased risk of heart failure (HF). Determining the type of HF experienced by AKI survivors (heart failure with preserved or reduced ejection fraction, HFpEF or HFrEF) could suggest potential mechanisms underlying the association and opportunities for improving post-AKI care. METHODS: In this retrospective study of adults within the Vanderbilt University health system with a diagnosis of HF, we tested whether AKI events in the two years preceding incident HF associated more with HFpEF or HFrEF while controlling for known predictors. HF outcomes were defined by administrative codes and classified as HFpEF or HFrEF by echocardiogram data. We used multivariable logistic regression models to estimate the effects of AKI on the odds of incident HFpEF versus HFrEF. RESULTS: AKI (all stages) trended towards a preferential association with HFpEF in adjusted analyses (adjusted OR 0.80, 95% CI 0.63 - 1.01). Stage 1 AKI was associated with higher odds of HFpEF that was statistically significant (adjusted OR 0.62, 95% CI 0.43 - 0.88), whereas stages 2-3 AKI showed a trend toward HFrEF that did not reach statistical significance (adjusted OR 1.11, 95% CI 0.76 - 1.63). CONCLUSIONS: AKI as a binary outcome trended towards a preferential association with HFpEF. Stage 1 AKI was associated with higher odds of HFpEF, whereas stage 2-3 trended towards an association with HFrEF that did not meet statistical significance. Different mechanisms may predominate in incident HF following mild versus more severe AKI. Close follow-up with particular attention to volume status and cardiac function after discharge is warranted after even mild AKI.

Determinants of appropriate antibiotic and NSAID prescribing in unscheduled outpatient settings in the veterans health administration.

BACKGROUND: Despite efforts to enhance the quality of medication prescribing in outpatient settings, potentially inappropriate prescribing remains common, particularly in unscheduled settings where patients can present with infectious and pain-related complaints. Two of the most commonly prescribed medication classes in outpatient settings with frequent rates of potentially inappropriate prescribing include antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs). In the setting of persistent inappropriate prescribing, we sought to understand a diverse set of perspectives on the determinants of inappropriate prescribing of antibiotics and NSAIDs in the Veterans Health Administration. METHODS: We conducted a qualitative study guided by the Consolidated Framework for Implementation Research and Theory of Planned Behavior. Semi-structured interviews were conducted with clinicians, stakeholders, and Veterans from March 1, 2021 through December 31, 2021 within the Veteran Affairs Health System in unscheduled outpatient settings at the Tennessee Valley Healthcare System. Stakeholders included clinical operations leadership and methodological experts. Audio-recorded interviews were transcribed and de-identified. Data coding and analysis were conducted by experienced qualitative methodologists adhering to the Consolidated Criteria for Reporting Qualitative Studies guidelines. Analysis was conducted using an iterative inductive/deductive process. RESULTS: We conducted semi-structured interviews with 66 participants: clinicians (N = 25), stakeholders (N = 24), and Veterans (N = 17). We identified six themes contributing to potentially inappropriate prescribing of antibiotics and NSAIDs: 1) Perceived versus actual Veterans expectations about prescribing; 2) the influence of a time-pressured clinical environment on prescribing stewardship; 3) Limited clinician knowledge, awareness, and willingness to use evidence-based care; 4) Prescriber uncertainties about the Veteran condition at the time of the clinical encounter; 5) Limited communication; and 6) Technology barriers of the electronic health record and patient portal. CONCLUSIONS: The diverse perspectives on prescribing underscore the need for interventions that recognize the detrimental impact of high workload on prescribing stewardship and the need to design interventions with the end-user in mind. This study revealed actionable themes that could be addressed to improve guideline concordant prescribing to enhance the quality of prescribing and to reduce patient harm.

Genome-Wide Association Study of CKD Progression.

SIGNIFICANCE STATEMENT: Rapid progression of CKD is associated with poor clinical outcomes. Most previous studies looking for genetic factors associated with low eGFR have used cross-sectional data. The authors conducted a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD, focusing on longitudinal data. They identified three loci (two of them novel) associated with longitudinal eGFR decline. In addition to the known UMOD/PDILT locus, variants within BICC1 were associated with significant differences in longitudinal eGFR slope. Variants within HEATR4 also were associated with differences in eGFR decline, but only among Black/African American individuals without diabetes. These findings help characterize molecular mechanisms of eGFR decline in CKD and may inform new therapeutic approaches for progressive kidney disease. BACKGROUND: Rapid progression of CKD is associated with poor clinical outcomes. Despite extensive study of the genetics of cross-sectional eGFR, only a few loci associated with eGFR decline over time have been identified. METHODS: We performed a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD-defined by two outpatient eGFR measurements of <60 ml/min per 1.73 m 2 , obtained 90-365 days apart-from the Million Veteran Program and Vanderbilt University Medical Center's DNA biobank. The primary outcome was the annualized relative slope in outpatient eGFR. Analyses were stratified by ethnicity and diabetes status and meta-analyzed thereafter. RESULTS: In cross-ancestry meta-analysis, the strongest association was rs77924615, near UMOD / PDILT ; each copy of the G allele was associated with a 0.30%/yr faster eGFR decline ( P = 4.9×10 -27 ). We also observed an association within BICC1 (rs11592748), where every additional minor allele was associated with a 0.13%/yr slower eGFR decline ( P = 5.6×10 -9 ). Among participants without diabetes, the strongest association was the UMOD/PDILT variant rs36060036, associated with a 0.27%/yr faster eGFR decline per copy of the C allele ( P = 1.9×10 -17 ). Among Black participants, a significantly faster eGFR decline was associated with variant rs16996674 near APOL1 (R 2 =0.29 with the G1 high-risk genotype); among Black participants with diabetes, lead variant rs11624911 near HEATR4 also was associated with a significantly faster eGFR decline. We also nominally replicated loci with known associations with eGFR decline, near PRKAG2, FGF5, and C15ORF54. CONCLUSIONS: Three loci were significantly associated with longitudinal eGFR change at genome-wide significance. These findings help characterize molecular mechanisms of eGFR decline and may contribute to the development of new therapeutic approaches for progressive CKD.

Genetic Inhibition of APOL1 Pore-Forming Function Prevents APOL1-Mediated Kidney Disease.

SIGNIFICANCE STATEMENT: African Americans are at increased risk of CKD in part due to high-risk (HR) variants in the apolipoprotein L1 ( APOL1 ) gene, termed G1/G2. A different APOL1 variant, p.N264K , reduced the risk of CKD and ESKD among carriers of APOL1 HR variants to levels comparable with individuals with APOL1 low-risk variants in an analysis of 121,492 participants of African ancestry from the Million Veteran Program (MVP). Functional genetic studies in cell models showed that APOL1 p.N264K blocked APOL1 pore-forming function and ion channel conduction and reduced toxicity of APOL1 HR mutations. Pharmacologic inhibitors that mimic this mutation blocking APOL1 -mediated pore formation may be able to prevent and/or treat APOL1 -associated kidney disease. BACKGROUND: African Americans are at increased risk for nondiabetic CKD in part due to HR variants in the APOL1 gene. METHODS: We tested whether a different APOL1 variant, p.N264K , modified the association between APOL1 HR genotypes (two copies of G1/G2) and CKD in a cross-sectional analysis of 121,492 participants of African ancestry from the MVP. We replicated our findings in the Vanderbilt University Biobank ( n =14,386) and National Institutes of Health All of Us ( n =14,704). Primary outcome was CKD and secondary outcome was ESKD among nondiabetic patients. Primary analysis compared APOL1 HR genotypes with and without p.N264K . Secondary analyses included APOL1 low-risk genotypes and tested for interaction. In MVP, we performed sequential logistic regression models adjusting for demographics, comorbidities, medications, and ten principal components of ancestry. Functional genomic studies expressed APOL1 HR variants with and without APOL1 p.N264K in cell models. RESULTS: In the MVP cohort, 15,604 (12.8%) had two APOL1 HR variants, of which 582 (0.5%) also had APOL1 p.N264K . In MVP, 18,831 (15%) had CKD, 4177 (3%) had ESKD, and 34% had diabetes. MVP APOL1 HR, without p.N264K , was associated with increased odds of CKD (odds ratio [OR], 1.72; 95% confidence interval [CI], 1.60 to 1.85) and ESKD (OR, 3.94; 95% CI, 3.52 to 4.41). In MVP, APOL1 p.N264K mitigated the renal risk of APOL1 HR, in CKD (OR, 0.43; 95% CI, 0.28 to 0.65) and ESKD (OR, 0.19; CI 0.07 to 0.51). In the replication cohorts meta-analysis, APOL1 p.N264K mitigated the renal risk of APOL1 HR in CKD (OR, 0.40; 95% CI, 0.18 to 0.92) and ESKD (OR, 0.19; 95% CI, 0.05 to 0.79). In the mechanistic studies, APOL1 p.N264K blocked APOL1 pore-forming function and ion channel conduction and reduced toxicity of APOL1 HR variants. CONCLUSIONS: APOL1 p.N264K is associated with reduced risk of CKD and ESKD among carriers of APOL1 HR to levels comparable with individuals with APOL1 low-risk genotypes.

Factors Shaping the Implementation of Strategies to Prevent Acute Kidney Injury: A Qualitative Study.

Reducing the prevalence of acute kidney injury (AKI) is an important patient safety objective set forth by the National Quality Forum. Despite international guidelines to prevent AKI, there continues to be an inconsistent uptake of these interventions by cardiac teams across practice settings. The IMPROVE-AKI study was designed to test the effectiveness and implementation of AKI preventive strategies delivered through team-based coaching activities. Qualitative methods were used to identify factors that shaped sites' implementation of AKI prevention strategies. Semi-structured interviews were conducted with staff in a range of roles within the cardiac catheterization laboratories, including nurses, laboratory managers, and interventional cardiologists (N = 50) at multiple time points over the course of the study. Interview transcripts were qualitatively coded, and aggregated code reports were reviewed to construct main themes through memoing. In this paper, we report insights from semi-structured interviews regarding workflow, organizational culture, and leadership factors that impacted implementation of AKI prevention strategies.

Predictors, Disparities, and Facility-Level Variation: SGLT2 Inhibitor Prescription Among US Veterans With CKD.

RATIONALE & OBJECTIVE: Sodium/glucose cotransporter 2 (SGLT2) inhibitors are recommended for type 2 diabetes mellitus (T2DM) in patients with chronic kidney disease (CKD) or atherosclerotic cardiovascular disease (ASCVD). We evaluated factors associated with SGLT2 inhibitor prescription, disparities by race and sex, and facility-level variation in prescription patterns. STUDY DESIGN: Retrospective cohort. SETTING & PARTICIPANTS: A national sample of US veterans with comorbid T2DM, CKD, and ASCVD with a primary care visit between January 1 and December 31, 2020. EXPOSURE: Race, sex, and individual Veterans Affairs (VA) location. OUTCOME: SGLT2 inhibitor prescription. ANALYTICAL APPROACH: Multivariable logistic regression assessed associations of race and sex with SGLT2 inhibitor prescription. Facility-level variation in SGLT2i prescription was quantified by median rate ratios (MRR), which express the likelihood that 2 randomly selected facilities differ in their use of SGLT2 inhibitor among similar patients. RESULTS: Of 174,443 patients with CKD, T2DM, and ASCVD, 20,024 (11.5%) were prescribed an SGLT2 inhibitor. Lower odds of SGLT2 inhibitor prescription were seen in Black or African American patients compared with White patients (OR, 0.87 [95% CI, 0.83-0.91]) and among women compared with men (OR, 0.59 [95% CI 0.52-0.67]). The adjusted MRR for SGLT2 inhibitor prescription was 1.58 (95% CI 1.48-1.67) in the total cohort, indicating an unexplained 58% variation in treatment between VA facilities, independent of patient and facility characteristics. Facility-level variation was evaluated among Black or African American patients (MRR, 1.55 [95% CI 1.41-1.68]), White patients (MRR, 1.57 [95% CI 1.47-1.66]), women (MRR, 1.40 [95% CI 1.28-1.51]), and men (MRR, 1.57 [95% CI 1.48-1.67]). LIMITATIONS: Albuminuria was not assessed. CONCLUSIONS: Prescription for SGLT2 inhibitors was low among likely eligible patients, with evident disparities by sex and race and between individual VA facilities. Efforts are needed to study and address the reasons for these disparities to improve equitable adoption of these important medications.

Documented Adverse Drug Reactions and Discontinuation of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers in Chronic Kidney Disease.

INTRODUCTION: Angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) are frequently discontinued in patients with chronic kidney disease (CKD). Documented adverse drug reactions (ADRs) in medical records may provide insight into the reasons for treatment discontinuation. METHODS: In this retrospective cohort of US veterans from 2005 to 2019, we identified individuals with CKD and a current prescription for an ACEi or ARB (current user group) or a discontinued prescription within the preceding 5 years (discontinued group). Documented ADRs in structured datasets associated with an ACEi or ARB were categorized into 17 pre-specified groups. Logistic regression assessed associations of documented ADRs with treatment discontinuation. RESULTS: There were 882,441 (73.0%) individuals in the current user group and 326,794 (27.0%) in the discontinued group. There were 26,434 documented ADRs, with at least one documented ADR in 7,520 (0.9%) current users and 9,569 (2.9%) of the discontinued group. ADR presence was associated with treatment discontinuation, aOR 4.16 (95% CI: 4.03, 4.29). The most common documented ADRs were cough (37.3%), angioedema (14.2%), and allergic reaction (10.4%). ADRs related to angioedema (aOR 3.81, 95% CI: 3.47, 4.17), hyperkalemia (aOR 2.03, 95% CI: 1.84, 2.24), peripheral edema (aOR 1.53, 95% CI: 1.33, 1.77), or acute kidney injury (aOR 1.32, 95% CI: 1.15, 1.51) were associated with treatment discontinuation. CONCLUSION: ADRs leading to drug discontinuation were infrequently documented. ADR types were differentially associated with treatment discontinuation. An understanding of which ADRs lead to treatment discontinuation provides an opportunity to address them at a healthcare system level.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. METHODS: We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. RESULTS: Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n < 3000) and attributable to random noise and variability in estimating realized values in small samples. When learning effects were specified, synthetic datasets exhibited changes in the probability of an adverse outcomes as cases accrued for the treatment group impacted by learning and stable probabilities as cases accrued for the treatment group not affected by learning. CONCLUSIONS: Our framework extends clinical data simulation techniques beyond generation of patient features to incorporate hierarchical learning effects. This enables the complex simulation studies required to develop and rigorously test algorithms developed to disentangle treatment safety signals from the effects of experiential learning. By supporting such efforts, this work can help identify training opportunities, avoid unwarranted restriction of access to medical advances, and hasten treatment improvements.

Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury.

STUDY DESIGN: A 5-year longitudinal, retrospective, cohort study. OBJECTIVES: Develop a prediction model based on electronic health record (EHR) data to identify veterans with spinal cord injury/diseases (SCI/D) at highest risk for new pressure injuries (PIs). SETTING: Structured (coded) and text EHR data, for veterans with SCI/D treated in a VHA SCI/D Center between October 1, 2008, and September 30, 2013. METHODS: A total of 4709 veterans were available for analysis after randomly selecting 175 to act as a validation (gold standard) sample. Machine learning models were created using ten-fold cross validation and three techniques: (1) two-step logistic regression; (2) regression model employing adaptive LASSO; (3) and gradient boosting. Models based on each method were compared using area under the receiver-operating curve (AUC) analysis. RESULTS: The AUC value for the gradient boosting model was 0.62 (95% CI = 0.54-0.70), for the logistic regression model it was 0.67 (95% CI = 0.59-0.75), and for the adaptive LASSO model it was 0.72 (95% CI = 0.65-80). Based on these results, the adaptive LASSO model was chosen for interpretation. The strongest predictors of new PI cases were having fewer total days in the hospital in the year before the annual exam, higher vs. lower weight and most severe vs. less severe grade of injury based on the American Spinal Cord Injury Association (ASIA) Impairment Scale. CONCLUSIONS: While the analyses resulted in a potentially useful predictive model, clinical implications were limited because modifiable risk factors were absent in the models.

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.

Facility-Level Variation in Reported Statin-Associated Side Effects Among Patients with Atherosclerotic Cardiovascular Disease-Perspective from the Veterans Affair Healthcare System.

PURPOSE: Statin-associated side effects (SASEs) can limit statin adherence and present a potential barrier to optimal statin utilization. How standardized reporting of SASEs varies across medical facilities has not been well characterized. METHODS: We assessed facility-level variation in SASE reporting among patients with atherosclerotic cardiovascular disease receiving care across the Veterans Affairs (VA) healthcare system from October 1, 2014, to September 30, 2015. The facility rates for SASE reporting were expressed as cases per 1000 patients with ASCVD. Facility-level variation was determined using hierarchical regression analysis to calculate median rate ratios (MRR [95% confidence interval]) by first using an unadjusted model and then adjusting for patient, provider, and facility characteristics. RESULTS: Of the 1,248,158 patients with ASCVD included in our study across 130 facilities, 13.7% had at least one SASE reported. Individuals with a history of SASE were less likely to be on a statin at follow-up compared with those without SASE (72.0% vs 80.8%, p < 0.01). The median (interquartile range) facility rate of SASE reported was 140.5 (109.4-167.7) cases per 1000 patients with ASCVD. Significant facility-level variation in the rate of SASE reported was observed: MRR 1.38 (1.33-1.44) in the unadjusted model and MRR 1.56 (1.47-1.65) in the adjusted model. CONCLUSION: Significant facility-level variation in SASE reporting was found within the VA healthcare system suggesting room for improvement in standardized documentation of SASEs among medical facilities. This has the potential to lead to improvement in statin utilization.

Comparison of COVID-19 versus influenza on the incidence, features, and recovery from acute kidney injury in hospitalized United States Veterans.

Acute kidney injury is a common complication in patients hospitalized with SARSCoV-2 (COVID-19), with prior studies implicating multiple potential mechanisms of injury. Although COVID-19 is often compared to other respiratory viral illnesses, few formal comparisons of these viruses on kidney health exist. In this retrospective cohort study, we compared the incidence, features, and outcomes of acute kidney injury among Veterans hospitalized with COVID-19 or influenza and adjusted for baseline conditions using weighted comparisons. A total of 3402 hospitalizations for COVID-19 and 3680 hospitalizations for influenza admitted between October 1, 2019 and May 31, 2020 across 127 Veterans Administration hospitals nationally were studied using the electronic medical record. Acute kidney injury occurred more frequently among those with COVID-19 compared to those with influenza (40.9% versus 29.4%, weighted analysis) and was more severe. Patients with COVID-19 were more likely to require mechanical ventilation and vasopressors and experienced higher mortality. Proteinuria and hematuria were frequent in both groups but more common in COVID-19. Recovery of kidney function was less common in patients with COVID-19 and acute kidney injury but was similar among survivors. Thus, findings from this study confirm that acute kidney injury is more common and severe among patients hospitalized with COVID-19 compared to influenza, a finding that may be driven largely by illness severity. Hence, the combined impact of these two illnesses on kidney health may be significant and have important implications for resource allocation.

Renin-angiotensin aldosterone inhibitor use at hospital discharge among patients with moderate to severe acute kidney injury and its association with recurrent acute kidney injury and mortality.

Recurrent episodes of acute kidney injury (AKI) are common among AKI survivors. Renin-angiotensin aldosterone inhibitors (RAASi) are often indicated for these patients but may increase the risk for recurrent AKI. Here, we examined whether RAASi associates with a higher risk for recurrent AKI and mortality among survivors of moderate to severe AKI in a retrospective cohort of Veterans who survived Stage II or III AKI. The primary exposure was RAASi at hospital discharge and the primary endpoint was recurrent AKI within 12 months. Cox proportional hazards models were fit on a propensity score-weighted cohort to compare time to recurrent AKI and mortality by RAASi exposure. Among 96,983 patients, 40% were on RAASi at discharge. Compared to patients who continued RAASi use, those discontinuing use experienced no difference in risk for recurrent AKI but had a significantly higher risk of mortality [hazard ratio 1.33 (95% confidence interval1.26-1.41)]. No differences in recurrent AKI risk was observed for non-users started or not on RAASi compared to prevalent users who continued RAASi. Subgroup analyses among those with diabetes, chronic kidney disease, heart failure, and malignancy were similar with exception of a modest reduction in recurrent AKI risk among RAASi discontinuers with chronic kidney disease. Thus, RAASi use among survivors of moderate to severe AKI was associated with little to no difference in risk for recurrent AKI but was associated with improved survival. Reinitiating or starting RAASi among patients with strong indications is warranted but should be balanced with individual overall risk for recurrent AKI and with adequate monitoring.

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.

Long-Term Safety with Sling Mesh Implants for Stress Incontinence.

PURPOSE: We examined long-term risks and predictors of mesh erosion and reoperation following mid urethral sling procedure for stress urinary incontinence. MATERIALS AND METHODS: Women aged 18 years or older who received a mid urethral sling for stress urinary incontinence between 2008 and 2016 in outpatient surgical settings in New York State were included in our study. Those who underwent concomitant mesh pelvic organ prolapse repair were excluded. Primary outcomes were post-implantation time to erosion and reoperations. Kaplan-Meier analysis and Cox proportional hazard models were used to assess the risks of erosion diagnosis and reoperation. RESULTS: Our cohort included 36,195 women with a mean±SD age of 53.7±12.4 years. Estimated risks of erosions and reoperations at 7 years after sling procedures were 3.7% and 6.7%, respectively. Older age (≥65 vs <65: HR 0.83, 95% CI 0.70-0.99) and high volume facilities (high vs low: HR 0.79, 95% CI 0.68-0.92) were associated with a lower risk of erosion. History of hysterectomy was associated with a higher risk of erosion (HR 1.62, 95% CI 1.36-1.92). Predictors of reoperation included concurrent abdominal or native tissue transvaginal prolapse repair, previous hysterectomy and depression. CONCLUSIONS: One in 27 women had sling erosions and 1 in 15 had invasive reoperations at 7 years after sling procedures. The highest erosion cases were observed among younger White women treated at low volume facilities. Continued and vigilant surveillance of mesh in stress urinary incontinence repairs, the nature and burden of stress urinary incontinence recurrence, different types of re-treatment, patient reported outcomes and information about treating surgeons are crucial.

COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries.

OBJECTIVE: Patients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. METHODS: A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization. RESULTS: We studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%). CONCLUSION: Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.

Improving the prediction of long-term readmission and mortality using a novel biomarker panel.

OBJECTIVE: Several short-term readmission and mortality prediction models have been developed using clinical risk factors or biomarkers among patients undergoing coronary artery bypass graft (CABG) surgery. The use of biomarkers for long-term prediction of readmission and mortality is less well understood. Given the established association of cardiac biomarkers with short-term adverse outcomes, we hypothesized that 5-year prediction of readmission or mortality may be significantly improved using cardiac biomarkers. MATERIALS AND METHODS: Plasma biomarkers from 1149 patients discharged alive after isolated CABG surgery from eight medical centers were measured in a cohort from the Northern New England Cardiovascular Disease Study Group between 2004 and 2007. We assessed the added predictive value of a biomarker panel with a clinical model against the clinical model alone and compared the model discrimination using the area under the receiver operating characteristic (AUROC) curves. RESULTS: In our cohort, 461 (40%) patients were readmitted or died within 5 years. Long-term outcomes were predicted by applying the STS ASCERT clinical model with an AUROC of 0.69. The biomarker panel with the clinical model resulted in a significantly improved AUROC of 0.74 (p value <.0001). Across 5 years, the hazard ratio for patients in the second to fifth quintile predicted probabilities from the biomarker augmented STS ASCERT model ranged from 2.2 to 7.9 (p values <.001). CONCLUSIONS: We report that a panel of biomarkers significantly improved prediction of long-term readmission or mortality risk following CABG surgery. Our findings suggest biomarkers help clinical care teams better assess the long-term risk of readmission or mortality.

Registry-Based Prospective, Active Surveillance of Medical-Device Safety.

BACKGROUND: The process of assuring the safety of medical devices is constrained by reliance on voluntary reporting of adverse events. We evaluated a strategy of prospective, active surveillance of a national clinical registry to monitor the safety of an implantable vascular-closure device that had a suspected association with increased adverse events after percutaneous coronary intervention (PCI). METHODS: We used an integrated clinical-data surveillance system to conduct a prospective, propensity-matched analysis of the safety of the Mynx vascular-closure device, as compared with alternative approved vascular-closure devices, with data from the CathPCI Registry of the National Cardiovascular Data Registry. The primary outcome was any vascular complication, which was a composite of access-site bleeding, access-site hematoma, retroperitoneal bleeding, or any vascular complication requiring intervention. Secondary safety end points were access-site bleeding requiring treatment and postprocedural blood transfusion. RESULTS: We analyzed data from 73,124 patients who had received Mynx devices after PCI procedures with femoral access from January 1, 2011, to September 30, 2013. The Mynx device was associated with a significantly greater risk of any vascular complication than were alternative vascular-closure devices (absolute risk, 1.2% vs. 0.8%; relative risk, 1.59; 95% confidence interval [CI], 1.42 to 1.78; P<0.001); there was also a significantly greater risk of access-site bleeding (absolute risk, 0.4% vs. 0.3%; relative risk, 1.34; 95% CI, 1.10 to 1.62; P=0.001) and transfusion (absolute risk, 1.8% vs. 1.5%; relative risk, 1.23; 95% CI, 1.13 to 1.34; P<0.001). The initial alerts occurred within the first 12 months of monitoring. Relative risks were greater in three prespecified high-risk subgroups: patients with diabetes, those 70 years of age or older, and women. All safety alerts were confirmed in an independent sample of 48,992 patients from April 1, 2014, to September 30, 2015. CONCLUSIONS: A strategy of prospective, active surveillance of a clinical registry rapidly identified potential safety signals among recipients of an implantable vascular-closure device, with initial alerts occurring within the first 12 months of monitoring. (Funded by the Food and Drug Administration and others.).

Timing of Recovery From Moderate to Severe AKI and the Risk for Future Loss of Kidney Function.

RATIONALE & OBJECTIVE: The extent of recovery of kidney function following acute kidney injury (AKI) is known to be associated with future chronic kidney disease. Less is known about how the timing of recovery affects the rate of future loss of kidney function. STUDY DESIGN: We performed a retrospective cohort study examining the independent association between the timing of recovery from moderate to severe AKI and future loss of kidney function. SETTING & PARTICIPANTS: 47,903 adult US veterans with stage 2 or 3 AKI who recovered to within 120% of baseline creatinine level within 90 days of peak injury. EXPOSURE: The timing of recovery of kidney function from peak inpatient serum creatinine level grouped into 1 to 4, 5 to 10, 11 to 30, and 31 to 90 days. OUTCOME: A sustained 40% decline in estimated glomerular filtration rate below that calculated from the last serum creatinine level available during the 90-day recovery period or kidney failure (2 outpatient estimated glomerular filtration rates<15mL/min/1.73m2, dialysis procedures > 90 days apart, kidney transplantation, or registry within the US Renal Data System). ANALYTICAL APPROACH: Time to the primary outcome was examined using multivariable Cox proportional hazards regression. RESULTS: Among 47,903 patients, 29,316 (61%), 10,360 (22%), 4,520 (9%), and 3,707 (8%) recovered within 1 to 4, 5 to 10, 11 to 30, and 31 to 90 days, respectively. With a median follow-up of 42 months, unadjusted incidence rates for the kidney outcome were 2.01, 3.55, 3.86, and 3.68 events/100 person-years, respectively. Compared with 1 to 4 days, recovery within 5 to 10, 11 to 30, and 31 to 90 days was associated with increased rates of the primary outcome: adjusted HRs were 1.33 (95% CI, 1.24-1.43), 1.41 (95% CI, 1.28-1.54), and 1.58 (95% CI, 1.43-1.75), respectively. LIMITATIONS: Predominately male population, residual confounding, and inability to make causal inferences because of the retrospective observational study design. CONCLUSIONS: The timing of recovery provides an added dimension to AKI phenotyping and prognostic information regarding the future occurrence of loss of kidney function. Studies to identify effective interventions on the timing of recovery from AKI are warranted.