Abstract
Background
Electric health record (EHR) discontinuity, that is, receiving care outside of a given EHR system, can lead to substantial information bias. We aimed to determine whether a ...previously described EHR-continuity prediction model can reduce the misclassification of 4 commonly used risk scores in pharmacoepidemiology.
Methods
The study cohort consists of patients aged ≥ 65 years identified in 2 US EHR systems linked with Medicare claims data from 2007 to 2017. We calculated 4 risk scores, CHAD2DS2-VASc, HAS-BLED, combined comorbidity score (CCS), claims-based frailty index (CFI) based on information recorded in the 365 days before cohort entry, and assessed their misclassification by comparing score values based on EHR data alone versus the linked EHR-claims data. CHAD2DS2-VASc and HAS-BLED were assessed in atrial fibrillation (AF) patients, whereas CCS and CFI were assessed in the general population.
Results
Our study cohort included 204 014 patients (26 537 with nonvalvular AF) in system 1 and 115 726 patients (15 529 with nonvalvular AF) in system 2. Comparing the low versus high predicted EHR continuity in system 1, the proportion of patients with misclassification of ≥2 categories improved from 55% to 16% for CHAD2DS2-VASc, from 55% to 12% for HAS-BLED, from 37% to 16% for CCS, and from 10% to 2% for CFI. A similar pattern was found in system 2.
Conclusions
Using a previously described prediction model to identify patients with high EHR continuity may significantly reduce misclassification for the commonly used risk scores in EHR-based comparative studies.
Apixaban and rivaroxaban are the most commonly prescribed direct oral anticoagulants for adults with atrial fibrillation, but head-to-head data comparing their safety and effectiveness are lacking.
...To compare the safety and effectiveness of apixaban versus rivaroxaban for patients with nonvalvular atrial fibrillation.
New-user, active-comparator, retrospective cohort study.
A U.S. nationwide commercial health care claims database from 28 December 2012 to 1 January 2019.
Adults newly prescribed apixaban (n = 59 172) or rivaroxaban (n = 40 706).
The primary effectiveness outcome was a composite of ischemic stroke or systemic embolism. The primary safety outcome was a composite of intracranial hemorrhage or gastrointestinal bleeding.
39 351 patients newly prescribed apixaban were propensity score matched to 39 351 patients newly prescribed rivaroxaban. Mean age was 69 years, 40% of patients were women, and mean follow-up was 288 days for new apixaban users and 291 days for new rivaroxaban users. The incidence rate of ischemic stroke or systemic embolism was 6.6 per 1000 person-years for adults prescribed apixaban compared with 8.0 per 1000 person-years for those prescribed rivaroxaban (hazard ratio HR, 0.82 95% CI, 0.68 to 0.98; rate difference, 1.4 fewer events per 1000 person-years CI, 0.0 to 2.7). Adults prescribed apixaban also had a lower rate of gastrointestinal bleeding or intracranial hemorrhage (12.9 per 1000 person-years) compared with those prescribed rivaroxaban (21.9 per 1000 person-years), corresponding to an HR of 0.58 (CI, 0.52 to 0.66) and a rate difference of 9.0 fewer events per 1000 person-years (CI, 6.9 to 11.1).
Unmeasured confounding, incomplete laboratory data.
In routine care, adults with atrial fibrillation prescribed apixaban had a lower rate of both ischemic stroke or systemic embolism and bleeding compared with those prescribed rivaroxaban.
Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital.
Nonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and ...measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates.
To emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time PICOT) and to quantify agreement in RCT-database study pairs.
New-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022.
Therapies for multiple clinical conditions were included.
Database study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference.
In these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement).
Real-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.
•Electronic health record (EHR) discontinuity can lead to misclassification bias.•Patients with high continuity in an EHR may have less misclassification.•We constructed an algorithm that identifies ...high EHR-continuity in oncology patients.
Electronic health record (EHR) discontinuity (missing out-of-network encounters) can lead to information bias. We sought to construct an algorithm that identifies high EHR-continuity among oncology patients.
Using a linked Medicare-EHR database and regression, we sought to 1) measure how often Medicare claims for outpatient encounters were substantiated by visits recorded in the EHR, and 2) predict continuity ratio, defined as the yearly proportion of outpatient encounters reported to Medicare that were captured by EHR data. The prediction model...s performance was evaluated with the coefficient of determination and Spearman...s correlation. We quantified variable misclassification by decile of continuity ratio using standardized difference and sensitivity.
A total of 79,678 subjects met all eligibility criteria. Predicted and observed continuity was highly correlated (σSpearman=0.86). On average across all variables measured, MSD was reduced by a factor of 1/7th and sensitivity was improved 35-fold comparing subjects in the highest vs. lowest decile of CR.
In the oncology population, restricting EHR-based study cohorts to subjects with high continuity may reduce misclassification without greatly impacting representativeness. Further work is needed to elucidate the best manner of implementing continuity prediction rules in cohort studies.
Treatment decisions for Coronavirus Disease 2019 (COVID-19) depend on disease severity, but the prescribing pattern by severity and drivers of therapeutic choices remain unclear.
The objectives of ...the study were to evaluate pharmacological treatment patterns by COVID-19 severity and identify the determinants of prescribing for COVID-19.
Using electronic health record data from a large Massachusetts-based healthcare system, we identified all patients aged ≥ 18 years hospitalized with laboratory-confirmed COVID-19 from 1 March to 24 May, 2020. We defined five levels of COVID-19 severity at hospital admission: (1) hospitalized but not requiring supplemental oxygen; (2-4) hospitalized and requiring oxygen ≤ 2, 3-4, and ≥ 5 L per minute, respectively; and (5) intubated or admitted to an intensive care unit. We assessed the medications used to treat COVID-19 or as supportive care during hospitalization.
Among 2821 patients hospitalized for COVID-19, we found inpatient mortality increased by severity from 5% for level 1 to 23% for level 5. As compared to patients with severity level 1, those with severity level 5 were 3.53 times (95% confidence interval 2.73-4.57) more likely to receive a medication used to treat COVID-19. Other predictors of treatment were fever, low oxygen saturation, presence of co-morbidities, and elevated inflammatory biomarkers. The use of most COVID-19 relevant medications has dropped substantially while the use of remdesivir and therapeutic anticoagulants has increased over the study period.
Careful consideration of disease severity and other determinants of COVID-19 drug use is necessary for appropriate conduct and interpretation of non-randomized studies evaluating outcomes of COVID-19 treatments.
Abstract
Background
Dementia severity is unavailable in administrative claims data. We examined whether a claims-based frailty index (CFI) can measure dementia severity in Medicare claims.
Methods
...This cross-sectional study included the National Health and Aging Trends Study Round 5 participants with possible or probable dementia whose Medicare claims were available. We estimated the Functional Assessment Staging Test (FAST) scale (range: 3 mild cognitive impairment to 7 severe dementia) using information from the survey. We calculated CFI (range: 0–1, higher scores indicating greater frailty) using Medicare claims 12 months prior to the participants’ interview date. We examined C-statistics to evaluate the ability of the CFI in identifying moderate-to-severe dementia (FAST stage 5–7) and determined the optimal CFI cut-point that maximized both sensitivity and specificity.
Results
Of the 814 participants with possible or probable dementia and measurable CFI, 686 (72.2%) patients were ≥75 years old, 448 (50.8%) were female, and 244 (25.9%) had FAST stage 5–7. The C-statistic of CFI to identify FAST stage 5–7 was 0.78 (95% confidence interval: 0.72–0.83), with a CFI cut-point of 0.280, achieving the maximum sensitivity of 76.9% and specificity of 62.8%. Participants with CFI ≥0.280 had a higher prevalence of disability (19.4% vs 58.3%) and dementia medication use (6.0% vs 22.8%) and higher risk of mortality (10.7% vs 26.3%) and nursing home admission (4.5% vs 10.6%) over 2 years than those with CFI <0.280.
Conclusions
Our study suggests that CFI can be useful in identifying moderate-to-severe dementia from administrative claims among older adults with dementia.
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