Citations

Background: Institutions that participate in PCORnet ® transform their local electronic health record (EHR) data into the PCORnet ® Common Data Model (CDM), which is then used to generate data extracts for PCORnet ® Studies. PCORnet ® Studies can also include institutions that do not participate in PCORnet, and for these organizations, the cost of instantiating a PCORnet ® CDM can be prohibitive. Fast Health care Interoperability Resources (FHIR) provides an alternative method of obtaining EHR data. Objective: To determine whether data obtained through FHIR might be a viable study solution for those sites that do not participate in PCORnet. ® This mixed-methods project had 2 objectives: (1) survey sites participating in PCORnet on the availability of FHIR (FHIR survey); (2) compare the coverage of a FHIR-based data extract using REDCap with one from the PCORnet ® CDM across 3 sites (FHIR extract). Methods: (1) FHIR survey: A series of questions were asked about the use of FHIR in a production capacity. (2) FHIR extract: REDCap FHIR and PCORnet ® CDM extracts were created based on study variables from 2 prior PCORnet ® Studies. Data were extracted for 40 patients and concordance measures were computed between the 2 sources. Results: (1) FHIR survey: Of responding organizations, 73% (n=49) reported that FHIR was deployed in a production capacity. (2) FHIR extract: Results were highly variable. Cohen kappa ranged from 0.01 to 0.76 for certain diagnoses, 0.24 to 0.84 for laboratory results, and 0.1 to 0.87 for medications. Conclusions: Despite differences in data, certain studies may be well-suited for FHIR-based extracts.

Introduction: Elevated lipoprotein(a) [Lp(a)] is associated with atherosclerotic cardiovascular disease (ASCVD), yet little is known about Lp(a) testing patterns in real-world practice. We hypothesized that Lp(a) testing occurs in a different population than calculated low density lipoprotein (LDL-C) testing alone, and that elevated Lp(a) is associated with greater lipid-lowering therapy (LLT) initiation and cardiovascular (CV) events. Methods: We used data from 11 health systems participating in the National Patient-Centered Clinical Research Network. We created two cohorts based on lab tests from Jan 1, 2015 to Dec 31, 2019: 1) the Lp(a) cohort, of adults with an Lp(a) test and 2) the LDL-C cohort, of 4:1 date- and site-matched adults with an LDL-C test, but no Lp(a) test. We used logistic regression to assess the relationship between Lp(a) levels (< 50, 50-100, and > 100mg/dL) and initiation of LLT within 3 months; and cox proportional hazards to evaluate Lp(a) levels and time to all-cause hospitalization and the composite of MI, revascularization and ischemic stroke hospitalization. Results: Overall, 20,551 patients (0.06% per year) had Lp(a) tests, with median Lp(a) value 17.5mg/dL (Q1, Q3: 7.0, 56.0). Compared with the LDL-C cohort (N=82,204), the Lp(a) cohort more frequently had ASCVD (24.3% vs. 8.5%) or multiple prior CV events (8.6% vs. 2.6%). Elevated Lp(a) was associated with greater odds of LLT initiation (Figure 1). Elevated Lp(a) was not significantly associated with all-cause hospitalization; however, Lp(a) > 100mg/dL was correlated with composite CV hospitalization compared with Lp(a) < 50mg/dL [adj HR (95% CI): 1.24 (1.06-1.44), p=0.006]. Conclusions: Lp(a) testing occurs more often in adults with ASCVD and multiple CV events, but testing is infrequent. Elevated Lp(a) was associated with LLT initiation, including niacin, despite lack of evidence of CV benefit. There was an association between elevated Lp(a) and CV hospitalization outcomes.

Background and Purpose — The Questionnaire for Verifying Stroke-Free Status (QVSFS) is an 8-item structured interview designed to identify stroke-free individuals. Previously, the QVSFS was validated with medical record review in a cohort with a low prevalence (7.1%) of stroke or transient ischemic attack (TIA). The objective of this study was to evaluate the validity of the QVSFS by comparing it with stroke status as determined by neurological history and examination in a population with a higher prevalence of stroke. Methods — A research assistant administered the QVSFS to outpatients from Veterans Administration stroke and general medicine clinics. Subjects were defined as QVSFS negative if responses to all 8 questions were negative. Questions requiring rephrasing or clarification were noted. Neurologists, blinded to QVSFS scores, interviewed and examined all subjects to determine stroke-free status, defined as no history or examination findings of previous stroke and/or TIA. Results — One hundred fifty-five subjects were examined; mean age was 70 years; 98.1% were male. Seventy-eight subjects were determined to be stroke free by the neurologist. The negative predictive value of the QVSFS was 0.96, with positive predictive value of 0.71. No question required rephrasing or clarification >5 times. Twenty-two subjects (14.2%) required rephrasing or clarification of at least 1 question. Conclusions — The QVSFS can effectively identify stroke-free individuals with a high degree of accuracy, even in a population with a large proportion of patients with prior stroke or TIA. Accuracy for identifying subjects with stroke and/or TIA is lower, but the QVSFS may still be useful as a screening tool in that regard.