Rates of ICD-10 Code U09.9 Documentation and Clinical Characteristics of VA Patients With Post-COVID-19 Condition.

Importance: A significant proportion of SARS-CoV-2 infected individuals experience post-COVID-19 condition months after initial infection. Objective: To determine the rates, clinical setting, risk factors, and symptoms associated with the documentation of International Statistical Classification of Diseases Tenth Revision (ICD-10), code U09.9 for post-COVID-19 condition after acute infection. Design, Setting, and Participants: This retrospective cohort study was performed within the US Department of Veterans Affairs (VA) health care system. Veterans with a positive SARS-CoV-2 test result between October 1, 2021, the date ICD-10 code U09.9 was introduced, and January 31, 2023 (n = 388 980), and a randomly selected subsample of patients with the U09.9 code (n = 350) whose symptom prevalence was assessed by systematic medical record review, were included in the analysis. Exposure: Positive SARS-CoV-2 test result. Main Outcomes and Measures: Rates, clinical setting, risk factors, and symptoms associated with ICD-10 code U09.9 in the medical record. Results: Among the 388 980 persons with a positive SARS-CoV-2 test, the mean (SD) age was 61.4 (16.1) years; 87.3% were men. In terms of race and ethnicity, 0.8% were American Indian or Alaska Native, 1.4% were Asian, 20.7% were Black, 9.3% were Hispanic or Latino, 1.0% were Native Hawaiian or Other Pacific Islander; and 67.8% were White. Cumulative incidence of U09.9 documentation was 4.79% (95% CI, 4.73%-4.87%) at 6 months and 5.28% (95% CI, 5.21%-5.36%) at 12 months after infection. Factors independently associated with U09.9 documentation included older age, female sex, Hispanic or Latino ethnicity, comorbidity burden, and severe acute infection manifesting by symptoms, hospitalization, or ventilation. Primary vaccination (adjusted hazard ratio [AHR], 0.80 [95% CI, 0.78-0.83]) and booster vaccination (AHR, 0.66 [95% CI, 0.64-0.69]) were associated with a lower likelihood of U09.9 documentation. Marked differences by geographic region and facility in U09.9 code documentation may reflect local screening and care practices. Among the 350 patients undergoing systematic medical record review, the most common symptoms documented in the medical records among patients with the U09.9 code were shortness of breath (130 [37.1%]), fatigue or exhaustion (78 [22.3%]), cough (63 [18.0%]), reduced cognitive function or brain fog (22 [6.3%]), and change in smell and/or taste (20 [5.7%]). Conclusions and Relevance: In this cohort study of 388 980 veterans, documentation of ICD-10 code U09.9 had marked regional and facility-level variability. Strong risk factors for U09.9 documentation were identified, while vaccination appeared to be protective. Accurate and consistent documentation of U09.9 is needed to maximize its utility in tracking patients for clinical care and research. Future studies should examine the long-term trajectory of individuals with U09.9 documentation.

JAK inhibitors and black box warnings: what is the future for JAK inhibitors?

INTRODUCTION: Janus kinase inhibitors (JAKi) have dramatically improved the treatment of various autoimmune and myeloproliferative disorders. Recently, concern has arisen regarding their safety in patients with rheumatoid arthritis. AREAS COVERED: Here, we provide a comprehensive summary of the major current and emerging JAKi and their indications, address recent studies on comparative safety, and provide insight into their future and use. We emphasize that the application of the research findings on a case-by-case basis should consider a patient's age, comorbidities, disease for which JAKi is being considered, disease activity, the JAKi target(s), alternate treatment options available for the patient, and the planned duration of JAKi. EXPERT OPINION: Rheumatologists are used to prescribing therapies in which a risk-to-benefit assessment is required as well as to screening and monitoring the safety of medications. Thus, rheumatologists are already practiced in applying specific criteria to effectively screen and monitor patients who are candidates for JAKi therapy. Ongoing research will help to clarify any mechanisms underlying differential safety signals between JAK and other therapies, what the balance between risk and efficacy is, who the susceptible subpopulations are, and whether safety signals are shared between different JAKis and across indications.

Design and analysis of outcomes following SARS-CoV-2 infection in veterans.

BACKGROUND: Understanding how SARS-CoV-2 infection impacts long-term patient outcomes requires identification of comparable persons with and without infection. We report the design and implementation of a matching strategy employed by the Department of Veterans Affairs' (VA) COVID-19 Observational Research Collaboratory (CORC) to develop comparable cohorts of SARS-CoV-2 infected and uninfected persons for the purpose of inferring potential causative long-term adverse effects of SARS-CoV-2 infection in the Veteran population. METHODS: In a retrospective cohort study, we identified VA health care system patients who were and were not infected with SARS-CoV-2 on a rolling monthly basis. We generated matched cohorts within each month utilizing a combination of exact and time-varying propensity score matching based on electronic health record (EHR)-derived covariates that can be confounders or risk factors across a range of outcomes. RESULTS: From an initial pool of 126,689,864 person-months of observation, we generated final matched cohorts of 208,536 Veterans infected between March 2020-April 2021 and 3,014,091 uninfected Veterans. Matched cohorts were well-balanced on all 39 covariates used in matching after excluding patients for: no VA health care utilization; implausible age, weight, or height; living outside of the 50 states or Washington, D.C.; prior SARS-CoV-2 diagnosis per Medicare claims; or lack of a suitable match. Most Veterans in the matched cohort were male (88.3%), non-Hispanic (87.1%), white (67.2%), and living in urban areas (71.5%), with a mean age of 60.6, BMI of 31.3, Gagne comorbidity score of 1.4 and a mean of 2.3 CDC high-risk conditions. The most common diagnoses were hypertension (61.4%), diabetes (34.3%), major depression (32.2%), coronary heart disease (28.5%), PTSD (25.5%), anxiety (22.5%), and chronic kidney disease (22.5%). CONCLUSION: This successful creation of matched SARS-CoV-2 infected and uninfected patient cohorts from the largest integrated health system in the United States will support cohort studies of outcomes derived from EHRs and sample selection for qualitative interviews and patient surveys. These studies will increase our understanding of the long-term outcomes of Veterans who were infected with SARS-CoV-2.

Associations Between Ambient Air Pollution and Cognitive Abilities from Midlife to Early Old Age: Modification by APOE Genotype.

BACKGROUND: Fine particulate matter (PM2.5) and nitrogen dioxide (NO2) measures of ambient air pollution are associated with accelerated age-related cognitive impairment, and Alzheimer's disease and related dementias (ADRD). OBJECTIVE: We examined associations between air pollution, four cognitive factors, and the moderating role of apolipoprotein E (APOE) genotype in the understudied period of midlife. METHODS: Participants were ∼1,100 men in the Vietnam Era Twin Study of Aging. Baseline cognitive assessments were from 2003 to 2007. Measures included past (1993-1999) and recent (3 years prior to baseline assessment) PM2.5 and NO2 exposure, in-person assessment of episodic memory, executive function, verbal fluency, and processing speed, and APOE genotype. Average baseline age was 56 years with a 12-year follow-up. Analyses adjusted for health and lifestyle covariates. RESULTS: Performance in all cognitive domains declined from age 56 to 68. Higher PM2.5 exposures were associated with worse general verbal fluency. We found significant exposure-by-APOE genotype interactions for specific cognitive domains: PM2.5 with executive function and NO2 with episodic memory. Higher PM2.5 exposure was related to worse executive function in APOE ɛ4 carriers, but not in non-carriers. There were no associations with processing speed. CONCLUSION: These results indicate negative effects of ambient air pollution exposure on fluency alongside intriguing differential modifications of cognitive performance by APOE genotype. APOE ɛ4 carriers appeared more sensitive to environmental differences. The process by which air pollution and its interaction with genetic risk for ADRD affects risk for later life cognitive decline or progression to dementia may begin in midlife.

AMPREDICT PROsthetics-Predicting Prosthesis Mobility to Aid in Prosthetic Prescription and Rehabilitation Planning.

OBJECTIVE: To develop and validate a patient-specific multivariable prediction model that uses variables readily available in the electronic medical record to predict 12-month mobility at the time of initial post-amputation prosthetic prescription. The prediction model is designed for patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation because of complications of diabetes and/or peripheral artery disease. DESIGN: Multi-methodology cohort study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected their patient-reported mobility. SETTING: The VA Corporate Data Warehouse, the National Prosthetics Patient Database, participant mailings, and phone calls. PARTICIPANTS: Three-hundred fifty-seven veterans who underwent an incident dysvascular TT or TF amputation and received a qualifying lower limb prosthesis between March 1, 2018, and November 30, 2020 (N=357). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: The Amputee Single Item Mobility Measure (AMPSIMM) was divided into a 4-category outcome to predict wheelchair mobility (0-2), and household (3), basic community (4), or advanced community ambulation (5-6). RESULTS: Multinomial logistic lasso regression, a machine learning methodology designed to select variables that most contribute to prediction while controlling for overfitting, led to a final model including 23 predictors of the 4-category AMPSIMM outcome that effectively discriminates household ambulation from basic community ambulation and from advanced community ambulation-levels of key clinical importance when estimating future prosthetic demands. The overall model performance was modest as it did not discriminate wheelchair from household mobility as effectively. CONCLUSIONS: The AMPREDICT PROsthetics model can assist providers in estimating individual patients' future mobility at the time of prosthetic prescription, thereby aiding in the formulation of appropriate mobility goals, as well as facilitating the prescription of a prosthetic device that is most appropriate for anticipated functional goals.

Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use.

The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between "observed" pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs. PERSPECTIVE: This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.

Rates and Factors Associated With Documentation of Diagnostic Codes for Long COVID in the National Veterans Affairs Health Care System.

Importance: Some persons infected with SARS-CoV-2 experience symptoms or impairments many months after acute infection. Objectives: To determine the rates, clinical setting, and factors associated with documented receipt of COVID-19-related care 3 or more months after acute infection. Design, Setting, and Participants: This retrospective cohort study used data from the US Department of Veterans Affairs health care system. Participants included persons with a positive SARS-CoV-2 test between February 1, 2020, and April 30, 2021, who were still alive 3 months after infection and did not have evidence of reinfection. Data analysis was performed from February 2020 to December 2021. Exposures: Positive SARS-CoV-2 test. Main Outcomes and Measures: Rates and factors associated with documentation of COVID-19-related International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes (U07.1, Z86.16, U09.9, and J12.82) 3 or more months after acute infection (hereafter, long-COVID care), with follow-up extending to December 31, 2021. Results: Among 198 601 SARS-CoV-2-positive persons included in the study, the mean (SD) age was 60.4 (17.7) years, 176 942 individuals (89.1%) were male, 133 924 (67.4%) were White, 44 733 (22.5%) were Black, and 19 735 (9.9%) were Hispanic. During a mean (SD) follow-up of 13.5 (3.6) months, long-COVID care was documented in a wide variety of clinics, most commonly primary care and general internal medicine (18 634 of 56 310 encounters [33.1%]), pulmonary (7360 of 56 310 encounters [13.1%]), and geriatrics (5454 of 56 310 encounters [9.7%]). Long-COVID care was documented in 26 745 cohort members (13.5%), with great variability across geographical regions (range, 10.8%-18.1%) and medical centers (range, 3.0%-41.0%). Factors significantly associated with documented long-COVID care included older age, Black or American Indian/Alaska Native race, Hispanic ethnicity, geographical region, high Charlson Comorbidity Index score, having documented symptoms at the time of acute infection (adjusted odds ratio [AOR], 1.71; 95% CI, 1.65-1.78) and requiring hospitalization (AOR, 2.60; 95% CI, 2.51-2.69) or mechanical ventilation (AOR, 2.46; 95% CI, 2.26-2.69). Patients who were fully vaccinated at the time of infection were less likely to receive long-COVID care (AOR, 0.78; 95% CI, 0.68-0.90). Conclusions and Relevance: Long-COVID care was documented in a variety of clinical settings, with great variability across regions and medical centers and was documented more commonly in older persons, those with higher comorbidity burden, those with more severe acute COVID-19 presentation and those who were unvaccinated at the time of infection. These findings provide support and guidance for health care systems to develop systematic approaches to the evaluation and management of patients who may be experiencing long COVID.

Risk factors for suicide in the Vietnam-era twin registry.

BACKGROUND: The risk of suicide among Veterans is of major concern, particularly among those who experienced a combat deployment and/or have a history of PTSD. DESIGN AND METHODS: This was a retrospective cohort study of post-discharge suicide among Vietnam-era Veterans who are members of the Vietnam Era Twin (VET) Registry. The VET Registry is a national sample of male twins from all branches of the military, both of whom served on active duty between 1964 and 1975. Military service and demographic factors were available from the military records. Service in-theater was based on military records; combat exposure and PTSD symptoms were assessed in 1987 by questionnaire. Mortality follow-up, from discharge to 2016, is identified from Department of Veterans Affairs, Social Security Administration, and National Death Index records; suicide as a cause of death is based on the International Classification of Death diagnostic codes from the death certificate. Statistical analysis used Cox proportional hazards regression to estimate the association of Vietnam-theater service, combat exposure, and PTSD symptoms with suicide while adjusting for military service and demographic confounding factors. RESULTS: From the 14,401 twins in the VET Registry, there were 147 suicide deaths during follow-up. In adjusted analyses, twins who served in the Vietnam theater were at similar risk of post-discharge suicide compared with non-theater Veterans; there was no association between combat and suicide. An increase in severity of PTSD symptoms was significantly associated with an increased risk of suicide in adjusted analyses (hazard ratio = 1.13 per five-point increase in symptom score; 95% CI: 1.02-1.27). CONCLUSIONS: Service in the Vietnam theater is not associated with greater risk of suicide; however, PTSD symptom severity poses a degree of risk of suicide in Vietnam-era Veterans. Adequate screening for PTSD in Veterans may be promising to identify Veterans who are at increased risk of suicide.

Comparative venous thromboembolic safety of oral and transdermal postmenopausal hormone therapies among women Veterans.

OBJECTIVE: Hormone therapy (HT) is used by menopausal women to treat vasomotor symptoms. Venous thromboembolism (VTE) is an important risk of HT use, and more knowledge on the comparative safety of different estrogenic compounds is useful for women who use HT for these symptoms. The objective was to compare the risk of VTE among users of oral conjugated equine estrogen (CEE), oral estradiol (E2), and transdermal E2, in a cohort of women veterans. METHODS: This retrospective cohort study included all women veterans aged 40 to 89 years, using CEE or E2, without prior VTE, between 2003 and 2011. All incident VTE events were adjudicated. Time-to-event analyses using a time-varying HT exposure evaluated the relative VTE risk between estrogen subtypes, with adjustment for age, race, and body mass index, with stratification for prevalent versus incident use of HT. RESULTS: Among 51,571 users of HT (74.5% CEE, 12.6% oral, and 12.9% transdermal E2 at cohort entry), with a mean age of 54.0 years, the incidence of VTE was 1.9/1,000 person-years. Compared with CEE use, in the multivariable regression model, there was no difference in the risk of incident VTE associated with oral E2 use (hazard ratio 0.96, 95% CI 0.64-1.46) or with transdermal E2 use (hazard ratio 0.95, 95% CI 0.60-1.49). Results were unchanged when restricting to incident users of HT. CONCLUSIONS: Among women veterans, the risk of VTE was similar in users of oral CEE, oral E2, and transdermal E2. These findings do not confirm the previously observed greater safety of transdermal and oral E2 over CEE.

High amplification levels of MDM2 and CDK4 correlate with poor outcome in patients with dedifferentiated liposarcoma: A cytogenomic microarray analysis of 47 cases.

Dedifferentiated liposarcoma (DDLS) is characterized at the molecular level by amplification of genes within 12q13-15 including MDM2 and CDK4. However, other than FNCLCC grade, prognostic markers are limited. We aim to identify molecular prognostic markers for DDLS to help risk stratify patients. To this end, we studied 49 cases of DDLS in our institutional archives and performed cytogenomic microarray analysis on 47 cases. Gene copy numbers for 12 loci were evaluated and correlated with outcome data retrieved from our institutional electronic medical records. Using cut point analysis and comparison of Kaplan-Meier survival curves by log rank tests, high amplification levels of MDM2 (>38 copies) and CDK4 (>30 copies) correlated with decreased disease free survival (DFS) (P = .0168 and 0.0169 respectively) and disease specific survival (DSS) (P = .0082 and 0.0140 respectively). Additionally, MDM2 and CDK4 showed evidence of a synergistic effect so that each additional copy of one enhances the effect on prognosis of each additional copy of the other for decreased DFS (P = .0227, 0.1% hazard). High amplification of JUN (>16 copies) also correlated with decreased DFS (P = .0217), but not DSS. The presence of copy number alteration at 3q29 correlated with decreased DSS (P = .0192). The presence of >10 mitoses per 10 high power fields and FNCLCC grade 3 also correlated with decreased DFS (P = .0310 and 0.0254 respectively). MDM2 and CDK4 gene amplification levels, along with JUN amplification and copy alterations at 3q29, can be utilized for predicting outcome in patients with DDLS.

Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics.

Serum proteomics analysis may lead to the discovery of novel osteoporosis biomarkers. The Osteoporotic Fractures in Men (MrOS) study comprises men ≥65 years old in the US who have had repeated BMD measures and have been followed for incident fracture. High-throughput quantitative proteomic analysis was performed on baseline fasting serum samples from non-Hispanic white men using a multidimensional approach coupling liquid chromatography, ion-mobility separation, and mass spectrometry (LC-IMS-MS). We followed the participants for a mean of 4.6 years for changes in femoral neck bone mineral density (BMD) and for incident hip fracture. Change in BMD was determined from mixed effects regression models taking age and weight into account. Participants were categorized into three groups: BMD maintenance (no decline; estimated change ≥0 g/cm2 , n = 453); expected loss (estimated change 0 to 1 SD below the estimated mean change, -0.034 g/cm2 for femoral neck, n = 1184); and accelerated loss (estimated change ≥1 SD below mean change, n = 237). Differential abundance values of 3946 peptides were summarized by meta-analysis to determine differential abundance of each of 339 corresponding proteins for accelerated BMD loss versus maintenance. Using this meta-analytic standardized fold change at cutoffs of ≥1.1 or ≤0.9 (p < 0.10), 20 proteins were associated with accelerated BMD loss. Associations of those 20 proteins with incident hip fracture were tested using Cox proportional hazards models with age and BMI adjustment in 2473 men. Five proteins were associated with incident hip fracture (HR between 1.29 and 1.41 per SD increase in estimated protein abundance). Some proteins have been previously associated with fracture risk (eg, CD14 and SHBG), whereas others have roles in cellular senescence and aging (B2MG and TIMP1) and complement activation and innate immunity (CO7, CO9, CFAD). These findings may inform development of biomarkers for future research in bone biology and fracture prediction. © 2017 American Society for Bone and Mineral Research.

Estimating uncertainty in respondent-driven sampling using a tree bootstrap method.

Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.

Estimating Population Size Using the Network Scale Up Method.

We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. people named Michael, intravenous drug users). The Network Scale up Method (NSUM) is a tool for producing population size estimates using these indirect measures of respondents' networks. Killworth et al. (1998a,b) proposed maximum likelihood estimators of population size for a fixed effects model in which respondents' degrees or personal network sizes are treated as fixed. We extend this by treating personal network sizes as random effects, yielding principled statements of uncertainty. This allows us to generalize the model to account for variation in people's propensity to know people in particular subgroups (barrier effects), such as their tendency to know people like themselves, as well as their lack of awareness of or reluctance to acknowledge their contacts' group memberships (transmission bias). NSUM estimates also suffer from recall bias, in which respondents tend to underestimate the number of members of larger groups that they know, and conversely for smaller groups. We propose a data-driven adjustment method to deal with this. Our methods perform well in simulation studies, generating improved estimates and calibrated uncertainty intervals, as well as in back estimates of real sample data. We apply them to data from a study of HIV/AIDS prevalence in Curitiba, Brazil. Our results show that when transmission bias is present, external information about its likely extent can greatly improve the estimates. The methods are implemented in the NSUM R package.