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

Comparison of late-onset and non-late-onset systemic lupus erythematosus individuals in a real-world electronic health record cohort

Objective: Late-onset systemic lupus erythematosus (LO-SLE) is defined as SLE diagnosed at age 50 years or later. Current studies on LO-SLE are small and have conflicting results.Methods: Using a large, electronic health record (EHR)-based cohort of SLE individuals, we compared demographics, disease characteristics, SLE-specific antibodies, and medication prescribing practices in LO (n = 123) vs. NLO-SLE (n = 402) individuals.Results: The median age (interquartile range) at SLE diagnosis was 60 (56-67) years for LO-SLE and 28 (20-38) years for NLO-SLE. Both groups were predominantly female (85% vs. 91%, p = 0.10). LO-SLE individuals were more likely to be White than NLO-SLE individuals (74% vs. 60%, p = 0.005) and less likely to have positive dsDNA (39% vs. 58%, p = 0.001) and RNP (17% vs. 32%, p = 0.02) with no differences in Smith, SSA, and SSB. Autoantibody positivity declined with increasing age at SLE diagnosis. LO-SLE individuals were less likely to develop SLE nephritis (9% vs. 29%, p < 0.001) and less likely to be prescribed multiple classes of SLE medications including antimalarials (90% vs. 95%, p = 0.04), azathioprine (17% vs. 31%, p = 0.002), mycophenolate mofetil (12% vs. 38%, p < 0.001), and belimumab (2% vs. 8%, p = 0.02).Conclusion: LO-SLE individuals may be less likely to fit an expected course for SLE with less frequent positive autoantibodies at diagnosis and lower rates of nephritis, even after adjusting for race. Understanding how age impacts SLE disease presentation could help reduce diagnostic delays in SLE.

Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record

OBJECTIVE

Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting.

METHODS

Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGAEX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs).

RESULTS

SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE.

CONCLUSION

We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.

Rates of Pneumocystis jirovecii pneumonia and prophylaxis prescribing patterns in a large electronic health record cohort of patients with systemic lupus erythematosus

Objective No guidelines exist for Pneumocystis jirovecii pneumonia (PJP) prophylaxis in patients with systemic lupus erythematosus (SLE). Limited data are available on incidence of PJP infection and use of PJP prophylaxis. Using a real-world, electronic health record (EHR) cohort, we investigated the frequency of PJP infections as well as patient and provider factors that impacted use and type of PJP prophylaxis. Methods  In a large, de-identified EHR, we identified possible SLE patients using a previously validated algorithm. PJP ICD-9 or ICD-10-CM billing codes and PJP keywords were used to identify possible PJP cases within this SLE cohort. We assessed for PJP prophylaxis prescribing in all SLE patients using keywords and reviewing medication lists for prophylactic agents. Chart review was used to confirm cases of SLE, PJP, and PJP prophylaxis and to obtain data on demographics, comorbidities, and immunosuppressants. Results Of 977 SLE patients, there were only four with confirmed PJP infection. Two of these patients had concurrent Acquired Immunodeficiency Syndrome, and none were on prophylaxis. Of 977 SLE patients, 132 (14%) were prescribed PJP prophylaxis. Of 617 SLE patients ever prescribed immunosuppressants, 128 (21%) were prescribed PJP prophylaxis. Sulfonamides were the most common prophylaxis prescribed (69%), and possible adverse events were documented in 22 out of 117 instances of being placed on a sulfonamide. Patients of younger age, Black race, nephritis, and renal transplant, and on chronic glucocorticoids were all more likely to have PJP prophylaxis prescribed. Patients who were on transplant induction medications, calcineurin/mTOR inhibitors, cyclophosphamide, and mycophenolate mofetil all were more likely to be prescribed PJP prophylaxis compared to other immunosuppressants. Conclusion PJP is a rare diagnosis among SLE patients, and prior studies may even overestimate its prevalence. PJP prophylaxis was less common in our cohort than previously described. Adverse events related to sulfonamides used for PJP prophylaxis were relatively rare with lower rates than previously reported. Our study demonstrates real-world PJP prophylaxis prescribing patterns in a large cohort of SLE patients.

Why lupus patients discontinue antimalarials in real life: A 50 years-experience from a reference centre

Background

Because of the efficacy and good safety profile of antimalarials in systemic lupus erythematosus (SLE), hydroxychloroquine (HCQ) is currently recommended in all SLE patients. However, patients’ compliance was reported as suboptimal. This study aims to elucidate the reasons for discontinuing antimalarials in a large series of SLE patients followed in a single centre during the last 50 years.

Material and methods

Among all patients diagnosed between 1968 and 2017 at our reference centre, retrospective data were obtained from electronic medical records of SLE patients consecutively visited during 2015–2017 and controlled for at least 1 year. Demographic, clinical, laboratory and therapeutic data at disease onset and during the follow-up in the whole cohort and differences between SLE patients discontinuing and continuing on antimalarials were analysed.

Results

Five-hundred thirty-nine patients followed during a median of 19 years were analysed. Median age at disease diagnosis was 29 years and 91.8% were women. Antimalarials were initiated by 521 (96.7%) patients and 18 (3.3%) cases did not start them mainly because of a quiescent or life-threatening SLE disease. In the 129 (24.7%) patients starting antimalarials with subsequent discontinuation, median treatment duration was 8.4 years. The main reason leading to treatment cessation was drug toxicity in 97 (18.6%) patients, of which macular toxicity was the most frequent adverse effect (n = 80; 15.3%). Treatment was stopped because of patient’s preference in 13 (2.5%) cases. The factors independently associated with antimalarial discontinuation were age at the end of follow-up (OR 1.130, 95% CI 1.005–1.269, p = 0.040), duration on antimalarials (OR 0.872, 95% CI 0.841–0.903, p < 0.001), presence of hepatitis C virus infection (HCV) (OR 13.948, 95% CI 1.321–147.324, p = 0.028) and anti-β2-glycoprotein 1 antibodies (OR 2.275, 95% CI 1.146–4.517, p = 0.019).

Conclusions

In our 50 years-experience, almost all SLE patients underwent antimalarials. These drugs are usually stopped because of adverse effects, particularly macular toxicity. After a long-term follow-up, patients’ compliance to antimalarials was considerably high in our SLE patients.

Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus Nephritis: Development and Validation of Computable Phenotypes

BACKGROUND AND OBJECTIVES

Performing adequately powered clinical trials in pediatric diseases, such as SLE, is challenging. Improved recruitment strategies are needed for identifying patients.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Electronic health record algorithms were developed and tested to identify children with SLE both with and without lupus nephritis. We used single-center electronic health record data to develop computable phenotypes composed of diagnosis, medication, procedure, and utilization codes. These were evaluated iteratively against a manually assembled database of patients with SLE. The highest-performing phenotypes were then evaluated across institutions in PEDSnet, a national health care systems network of >6.7 million children. Reviewers blinded to case status used standardized forms to review random samples of cases (n=350) and noncases (n=350).

RESULTS

Final algorithms consisted of both utilization and diagnostic criteria. For both, utilization criteria included two or more in-person visits with nephrology or rheumatology and ≥60 days follow-up. SLE diagnostic criteria included absence of neonatal lupus, one or more hydroxychloroquine exposures, and either three or more qualifying diagnosis codes separated by ≥30 days or one or more diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 100% (95% confidence interval [95% CI], 99 to 100), specificity was 92% (95% CI, 88 to 94), positive predictive value was 91% (95% CI, 87 to 94), and negative predictive value was 100% (95% CI, 99 to 100). Lupus nephritis diagnostic criteria included either three or more qualifying lupus nephritis diagnosis codes (or SLE codes on the same day as glomerular/kidney codes) separated by ≥30 days or one or more SLE diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 90% (95% CI, 85 to 94), specificity was 93% (95% CI, 89 to 97), positive predictive value was 94% (95% CI, 89 to 97), and negative predictive value was 90% (95% CI, 84 to 94). Algorithms identified 1508 children with SLE at PEDSnet institutions (537 with lupus nephritis), 809 of whom were seen in the past 12 months.

CONCLUSIONS

Electronic health record-based algorithms for SLE and lupus nephritis demonstrated excellent classification accuracy across PEDSnet institutions.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Low prevalence of bone mineral density testing in patients with systemic lupus erythematosus and glucocorticoid exposure

SummaryPatients with systemic lupus erythematosus (SLE) have an increased risk of developing osteoporosis and fractures due to systemic inflammation and glucocorticoids (GCs). Professional organizations recommend bone mineral density (BMD) testing in SLE patients on GCs, especially within 6 months of initiation. Using a validated algorithm, we identified SLE patients in an electronic health record cohort with long-term GC exposure (≥90 days). Our primary outcome was ever BMD testing. We assessed the impact of patient and provider factors on testing. We identified 693 SLE cases with long-term GC exposure, 41% of whom had BMD testing performed. Only 18% of patients had BMD testing within 6 months of GC initiation. In a logistic regression model for BMD testing, male sex (OR = 0.49, 95% CI 0.27 - 0.87, p = 0.01) was associated with being less likely to have BMD testing after adjusting for race and ethnicity. In contrast, older age (OR = 1.04, p < 0.001) and nephritis (OR = 1.83, p = 0.003) were associated with being more likely to have BMD testing after adjusting for race and ethnicity. Bone health in SLE patients remains an area in need of improvement with attention to patients who are younger and male.

Quality of care for patients with SLE: data from the American College of Rheumatology's RISE registry

OBJECTIVE

Although multiple national quality measures focus on the management and safety of rheumatoid arthritis, few measures address the care of patients with SLE. We applied a group of quality measures relevant to the care of SLE patients and used the ACR's RISE registry to assess nationwide variations in care.

METHODS

Data derived from RISE and included patients with ≥2 visits with SLE codes ≥30 days apart in 2017-2018. We calculated performance on 5 quality measures: renal disease screening; blood pressure assessment and management; hydroxychloroquine (HCQ) prescribing; safe dosing for HCQ; and prolonged glucocorticoid use at doses > 7.5 mg/day. We reported performance on these measures at the practice level. We used logistic regression to assess independent predictors of performance after adjusting for sociodemographic and utilization factors.

RESULTS

We included 27,567 unique patients from 186 practices; 91.7% were female, 48% white, with mean age 53.5±15.2 years. Few patients had adequate screening for the development of renal manifestations (39.5%). Although blood pressure assessment was common (94.4%), a meaningful fraction had untreated hypertension (17.7%). Many received HCQ (71.5%), but only 62% at doses ≤ 5.0 mg/kg/day. Some received at least moderate-dose steroids for ≥ 90 days (18.5%). We observed significant practice variation on every measure.

CONCLUSION

We found potential gaps in care for patients with SLE across the U.S. Although some performance variation may be explained by differences in disease severity, dramatic differences suggest that developing quality measures to address important health care processes in SLE may improve care.