Dataset Information

Real-world electronic health record identifies antimalarial underprescribing in patients with lupus nephritis.

ABSTRACT: Antimalarials (AMs) reduce disease activity and improve survival in patients with systemic lupus erythematosus (SLE), but studies have reported low AM prescribing frequencies. Using a real-world electronic health record cohort, we examined if patient or provider characteristics impacted AM prescribing. We identified 977 SLE cases, 94% of whom were ever prescribed an AM. Older patients and patients with SLE nephritis were less likely to be on AMs. Current age (odds ratio = 0.97, p < 0.01) and nephritis (odds ratio = 0.16, p < 0.01) were both significantly associated with ever AM use after adjustment for sex and race. Of the 244 SLE nephritis cases, only 63% were currently on AMs. SLE nephritis subjects who were currently prescribed AMs were more likely to be followed by a rheumatologist than a nephrologist and less likely to have undergone dialysis or renal transplant (both p < 0.001). Non-current versus current SLE nephritis AM users had higher serum creatinine (p < 0.001), higher urine protein (p = 0.05), and lower hemoglobin levels (p < 0.01). As AMs reduce disease damage and improve survival in patients with SLE, our results demonstrate an opportunity to target future efforts to improve prescribing rates among multi-specialty providers.

SUBMITTER: Xiong WW

PROVIDER: S-EPMC6609095 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Real-world electronic health record identifies antimalarial underprescribing in patients with lupus nephritis.

Xiong W W WW Boone J B JB Wheless L L Chung C P CP Crofford L J LJ Barnado A A

Lupus 20190612 8

Antimalarials (AMs) reduce disease activity and improve survival in patients with systemic lupus erythematosus (SLE), but studies have reported low AM prescribing frequencies. Using a real-world electronic health record cohort, we examined if patient or provider characteristics impacted AM prescribing. We identified 977 SLE cases, 94% of whom were ever prescribed an AM. Older patients and patients with SLE nephritis were less likely to be on AMs. Current age (odds ratio = 0.97, <i>p</i> < 0.01) ...[more]

PMID: 31189414

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Project description:IntroductionLupus nephritis (LN) treatment aims to control and prevent flares and irreversible kidney damage. Around 30% of patients are unresponsive to treatment; however, real-world LN treatment patterns have not been reported. Objectives of this retrospective cohort study (GSK 209758) were to quantify the time to switching/re-initiating induction therapy in patients with LN initiating immunosuppressant therapy and conversion from induction to maintenance immunosuppressant therapy, and to assess corticosteroid use.MethodsPatients with LN initiating induction or maintenance immunosuppressant therapy were identified using claims data. Patients were followed up from the index date (immunosuppressant initiation date) until treatment discontinuation, death, disenrollment, administrative censoring, or the end of follow-up period. The cumulative incidence of switching/re-initiating induction therapy and conversion to maintenance therapy was estimated using outpatient pharmacy claims and procedure codes. Corticosteroid use was estimated using pharmacy claims; a mean daily dose of ≥ 7.5 mg/day was considered high.ResultsIn total, 5000 patients with LN contributed 5516 treatment episodes (induction cohort, N = 372; maintenance cohort, N = 5144). In the induction cohort, the cumulative incidence (95% confidence interval) of switching between induction therapies was 24.6% (20.1-30.0) at 12 months, while 59.6% (52.4-66.1) of patients converted to maintenance therapy at 12 months. In the maintenance cohort, 21.2% (19.9-22.5) re-initiated induction therapy at 12 months. Oral corticosteroid use decreased during the follow-up in both cohorts, but 21.5% of patients remained on a high dose at 12 months in the induction cohort, while 15.8% in the maintenance cohort were taking a high dose at 24 months.ConclusionsAround a quarter of patients with LN initiating immunosuppressant therapy switched within 12 months, while a fifth re-initiated induction therapy within 12 months. Use of high corticosteroid doses were observed during 24 months of follow-up. These data suggest that many patients do not respond to existing standard LN therapies.

Project description:BackgroundSystemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data from the Northwestern Medicine Enterprise Data Warehouse (NMEDW).MethodsWe developed five algorithms: a rule-based algorithm using only structured data (baseline algorithm) and four algorithms using different NLP models. The first NLP model applied simple regular expression for keywords search combined with structured data. The other three NLP models were based on regularized logistic regression and used different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components (i.e. a curated list of CUIs, regular expression concepts, structured data) respectively. The baseline algorithm and the best performing NLP algorithm were externally validated on a dataset from Vanderbilt University Medical Center (VUMC).ResultsOur best performing NLP model incorporated features from both structured data, regular expression concepts, and mapped concept unique identifiers (CUIs) and showed improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.52 vs 0.93) datasets compared to the baseline lupus nephritis algorithm.ConclusionOur NLP MetaMap mixed model improved the F-measure greatly compared to the structured data only algorithm in both internal and external validation datasets. The NLP algorithms can serve as powerful tools to accurately identify lupus nephritis phenotype in EHR for clinical research and better targeted therapies.

Dataset Information

Real-world electronic health record identifies antimalarial underprescribing in patients with lupus nephritis.

Publications

Real-world electronic health record identifies antimalarial underprescribing in patients with lupus nephritis.

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