Karandeep Singh

American College of Rheumatology guidelines for screening, treatment, and management of lupus nephritis

In the United States, approximately 35% of adults with Systemic Lupus Erythematosus (SLE) have clinical evidence of nephritis at the time of diagnosis; with an estimated total of 50–60% developing nephritis during the first 10 years of disease [1–4]. The prevalence of nephritis is significantly higher in African Americans and Hispanics than in Caucasians, and is higher in men than in women. Renal damage is more likely to develop in non-Caucasian groups [2–4]. Overall survival in patients with SLE is approximately 95% at 5 years after diagnosis and 92% at 10 years [5, 6]. The presence of lupus nephritis significantly reduces survival, to approximately 88% at 10 years, with even lower survival in African Americans [5, 6]. The American College of Rheumatology (ACR) last published guidelines for management of systemic lupus erythematosus (SLE) in 1999 [7]. That publication was designed primarily for education of primary care physicians and recommended therapeutic and management approaches for many manifestations of SLE. Recommendations for management of lupus nephritis (LN) consisted of pulse glucocorticoids followed by high dose daily glucocorticoids in addition to an immunosuppressive medication, with cyclophosphamide viewed as the most effective immunosuppressive medication for diffuse proliferative glomerulonephritis. Mycophenolate mofetil was not yet in use for lupus nephritis and was not mentioned. Since that time, many clinical trials of glucocorticoids-plus-immunosuppressive interventions have been published, some of which are high quality prospective trials, and some not only prospective but also randomized. Thus, the ACR determined that a new set of management recommendations was in order. A combination of extensive literature review and the opinions of highly qualified experts, including rheumatologists, nephrologists and pathologists, has been used to reach the recommendations. The management strategies discussed here apply to lupus nephritis in adults, particularly to those receiving care in the United States of America, and include interventions that were available in the United States as of April 2011. While these recommendations were developed using rigorous methodology, guidelines do have inherent limitations in informing individual patient care; hence the selection of the term “recommendations.” While they should not supplant clinical judgment or limit clinical judgment, they do provide expert advice to the practicing physician managing patients with lupus nephritis.

Implementation of a CKD Checklist for Primary Care Providers

BACKGROUND AND OBJECTIVES CKD is associated with significant morbidity, mortality, and financial burden. Practice guidelines outlining CKD management exist, but there is limited application of these guidelines. Interventions to improve CKD guideline adherence have been limited. This study evaluated a new CKD checklist (a tool outlining management guidelines for CKD) to determine whether implementation in an academic primary care clinic improved adherence to guidelines. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS During a 1-year period (August 2012-August 2013), a prospective study was conducted among 13 primary care providers (PCPs), four of whom were assigned to use a CKD checklist incorporated into the electronic medical record during visits with patients with CKD stages 1-4. All providers received education regarding CKD guidelines. The intervention and control groups consisted of 105 and 263 patients, respectively. Adherence to CKD management guidelines was measured. RESULTS A random-effects logistic regression analysis was performed to account for intra-group correlation by PCP assignment and adjusted for age and CKD stage. CKD care improved among patients whose PCPs were assigned to the checklist intervention compared with controls. Patients in the CKD checklist group were more likely than controls to have appropriate annual laboratory testing for albuminuria (odds ratio [OR], 7.9; 95% confidence interval [95% CI], 3.6 to 17.2), phosphate (OR, 3.5; 95% CI, 1.5 to 8.3), and parathyroid hormone (OR, 8.1; 95% CI, 4.8 to 13.7) (P<0.001 in all cases). Patients in the CKD checklist group had higher rates of achieving a hemoglobin A1c target<7% (OR, 2.7; 95% CI, 1.4 to 5.1), use of an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker (OR, 2.1; 95% CI, 1.0 to 4.2), documentation of avoidance of nonsteroidal anti-inflammatory drugs (OR, 41.7; 95% CI, 17.8 to 100.0), and vaccination for annual influenza (OR, 2.1; 95% CI, 1.1 to 4.0) and pneumococcus (OR, 4.7; 95% CI, 2.6 to 8.6) (P<0.001 in all cases). CONCLUSIONS Implementation of a CKD checklist significantly improved adherence to CKD management guidelines and delivery of CKD care.

Evaluating the feasibility of the KDIGO CKD referral recommendations

BackgroundIn 2012, the international nephrology organization Kidney Disease Improving Global Outcomes (KDIGO) released recommendations for nephrology referral for chronic kidney disease (CKD) patients. The feasibility of adhering to these recommendations is unknown.MethodsWe conducted a retrospective analysis of the primary care population at Brigham and Women’s Hospital (BWH). We translated referral recommendations based upon serum creatinine, estimated glomerular filtration rate (eGFR), and albuminuria into a set of computable criteria in order to project referral volume if the KDIGO referral recommendations were to be implemented. Using electronic health record data, we evaluated each patient using the computable criteria at the times that the patient made clinic visits in 2013. We then compared the projected referral volume with baseline nephrology clinic volume.ResultsOut of 56,461 primary care patients at BWH, we identified 5593 (9.9%) who had CKD based on albuminuria or estimated GFR. Referring patients identified by the computable criteria would have resulted in 2240 additional referrals to nephrology. In 2013, this would represent a 38.0% (2240/5892) increase in total nephrology patient volume and 67.3% (2240/3326) increase in new referral volume.ConclusionsThis is the first study to examine the projected impact of implementing the 2012 KDIGO referral recommendations. Given the large increase in the number of referrals, this study is suggestive that implementing the KDIGO referral guidelines may not be feasible under current practice models due to a supply-demand mismatch. We need to consider new strategies on how to deliver optimal care to CKD patients using the available workforce in the U.S. health care system.

Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan

OBJECTIVE To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0. MATERIALS AND METHODS With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort. RESULTS Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population. CONCLUSION The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.

Fibroblast Growth Factor 23 Associates with Death in Critically Ill Patients

BACKGROUND AND OBJECTIVES Dysregulated mineral metabolism is a common and potentially maladaptive feature of critical illness, especially in patients with AKI, but its association with death has not been comprehensively investigated. We sought to determine whether elevated plasma levels of the osteocyte-derived, vitamin D-regulating hormone, fibroblast growth factor 23 (FGF23), are prospectively associated with death in critically ill patients with AKI requiring RRT, and in a general cohort of critically ill patients with and without AKI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We measured plasma FGF23 and other mineral metabolite levels in two cohorts of critically ill patients (n=1527). We included 817 patients with AKI requiring RRT who enrolled in the ARF Trial Network (ATN) study, and 710 patients with and without AKI who enrolled in the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study. We hypothesized that higher FGF23 levels at enrollment are independently associated with higher 60-day mortality. RESULTS In the ATN study, patients in the highest compared with lowest quartiles of C-terminal (cFGF23) and intact FGF23 (iFGF23) had 3.84 (95% confidence interval, 2.31 to 6.41) and 2.08 (95% confidence interval, 1.03 to 4.21) fold higher odds of death, respectively, after adjustment for demographics, comorbidities, and severity of illness. In contrast, plasma/serum levels of parathyroid hormone, vitamin D metabolites, calcium, and phosphate were not associated with 60-day mortality. In the VALID study, patients in the highest compared with lowest quartiles of cFGF23 and iFGF23 had 3.52 (95% confidence interval, 1.96 to 6.33) and 1.93 (95% confidence interval, 1.12 to 3.33) fold higher adjusted odds of death. CONCLUSIONS Higher FGF23 levels are independently associated with greater mortality in critically ill patients.

Epidemiology and natural history of the cardiorenal syndromes in a cohort with echocardiography

BACKGROUND AND OBJECTIVES It is unknown whether echocardiographic parameters are independently associated with the cardiorenal syndrome. No direct comparison of the natural history of various cardiorenal syndrome types has been conducted. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Our retrospective cohort study enrolled adult patients with at least one transthoracic echocardiography between 2004 and 2014 at a single health care system. Information on comorbidities was extracted using condition-specific diagnostic codes. All-cause mortality was the primary outcome among patients with cardiorenal syndrome types 1-4. Myocardial infarction and stroke were the secondary outcomes. RESULTS In total, 30,681 patients were included, and 2512 (8%) developed at least one of the cardiorenal syndromes: 1707 patients developed an acute form of the syndrome (type 1 or 3), 128 patients developed type 2, and 677 patients developed type 4. In addition, 16% of patients with type 2 and 20% of patients with type 4 also developed an acute cardiorenal syndrome, whereas 14% of patients with acute cardiorenal progressed to CKD or chronic heart failure. Decreasing left ventricular ejection fraction, increasing pulmonary artery pressure, and higher right ventricular diameter were independently associated with higher incidence of a cardiorenal syndrome. Acute cardiorenal syndrome was associated with the highest risk of death compared with patients with CKD without cardiorenal syndrome (hazard ratio, 3.13; 95% confidence interval, 2.72 to 3.61; P<0.001). Patients with cardiorenal type 4 had better survival than patients with acute cardiorenal syndrome (hazard ratio, 0.48; 95% confidence interval, 0.37 to 0.61; P<0.001). Patients with acute cardiorenal syndrome and type 4 had increased risk of myocardial infarction and stroke compared with patients with CKD without cardiorenal syndrome. CONCLUSIONS Up to 19% of patients with a chronic form of cardiorenal syndrome will subsequently develop an acute syndrome. Development of acute or type 4 cardiorenal syndrome is independently associated with mortality, the acute form having the worst prognosis.

The relative benefits of claims and electronic health record data for predicting medication adherence trajectory

Background Healthcare providers are increasingly encouraged to improve their patients’ adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims. Methods In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi‐specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group‐based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence. Results Claims were highly predictive of patients in the worst adherence trajectory (C = 0.78), but EHR data also provided good predictions (C = 0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables. Conclusion EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource‐intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available.

Grade Groups Provides Improved Predictions of Pathologic and Early Oncologic Outcomes Compared with Gleason Score Risk Groups

Purpose: The GG (Grade Group) system was introduced in 2013. Data from academic centers suggest that GG better distinguishes between prostate cancer risk groups than the Gleason score (GS) risk groups. We compared the performance of the 2 systems to predict pathological/recurrence outcomes using data from the MUSIC (Michigan Urological Surgery Improvement Collaborative). Materials and Methods: Patients who underwent biopsy and radical prostatectomy in the MUSIC from March 2012 to June 2017 were classified according to GG and GS. Outcomes included the presence or absence of extraprostatic extension, seminal vesical invasion, positive lymph nodes, positive surgical margins and time to cancer recurrence (defined as postoperative prostate specific antigen 0.2 ng/ml or greater). Logistic and Cox regression models were used to compare the difference in outcomes. Results: A total of 8,052 patients were identified. When controlling for patient characteristics, significantly higher risks of extraprostatic extension, seminal vesical invasion and positive lymph nodes were observed for biopsy GG 3 vs 2 and for GG 5 vs 4 (p <0.001). Biopsy GGs 3, 4 and 5 also showed shorter time to biochemical recurrence than GGs 2, 3 and 4, respectively (p <0.001). GGs 3, 4 and 5 at radical prostatectomy were each associated with a greater probability of recurrence compared to the next lower GG (p <0.001). GG (vs GS) had better predictive power for extraprostatic extension, seminal vesical invasion, positive lymph nodes and biochemical recurrence. Conclusions: GG at biopsy and radical prostatectomy allows for better discrimination of recurrence-free survival between individual risk groups than GS risk groups with GGs 2, 3, 4 and 5 each incrementally associated with increased risk.

askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men

BACKGROUND Clinical registries provide physicians with a means for making data-driven decisions but few opportunities exist for patients to interact with registry data to help make decisions. OBJECTIVE We sought to develop a web-based system that uses a prostate cancer (CaP) registry to provide newly diagnosed men with a platform to view predicted treatment decisions based on patients with similar characteristics. DESIGN, SETTING, AND PARTICIPANTS The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a quality improvement consortium of urology practices that maintains a prospective registry of men with CaP. We used registry data from 45 MUSIC urology practices from 2015 to 2017 to develop and validate a random forest machine learning model. After fitting the random forest model to a derivation cohort consisting of a random two-thirds sample of patients after stratifying by practice location, we evaluated the model performance in a validation cohort consisting of the remaining one-third of patients using a multiclass area under the curve (AUC) measure and calibration plots. RESULTS AND LIMITATIONS We identified 7543 men diagnosed with CaP, of whom 45% underwent radical prostatectomy, 30% surveillance, 17% radiation therapy, 5.6% androgen deprivation, and 1.8% watchful waiting. The personalized prediction for patients in the validation cohort was highly accurate (AUC 0.81). CONCLUSIONS Using clinical registry data and machine learning methods, we created a web-based platform for patients that generates accurate predictions for most CaP treatments. PATIENT SUMMARY We have developed and tested a tool to help men newly diagnosed with prostate cancer to view predicted treatment decisions based on similar patients from our registry. We have made this tool available online for patients to use.

PNFBA-06 ASKMUSIC©: LEVERAGING A CLINICAL REGISTRY TO INFORM PATIENTS

INTRODUCTION AND OBJECTIVES: Clinical registries increasingly provide physicians with a means for making data-driven decisions; however, few opportunities exist for patients to interact with registry data to support their own decisions. Herein, we report a webbased system that uses a prostate cancer (CaP) registry to provide newly-diagnosed men with a platform to understand treatment decisions made by others with similar characteristics. METHODS: The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a consortium of 43 diverse urology practices that maintains a prospective registry of men with CaP. We developed a patient-facing, web-based tool that uses self-reported information and registry data to generate a personalized prediction of the likelihood of receiving a given treatment for CaP (Figure 1). The treatment predictions rely on registry data from 1/2011 to 12/2015 and were generated using a random forest machine learning model derived in a 2/3 random sample of the data. Predictive performance was measured in this derivation cohort (using 10-fold cross validation) and verified in the remaining data using multinomial area-under-the-curve (AUC) and calibration plots. RESULTS: Between the included dates, 11,456 men were diagnosed with CaP and 44.7% underwent prostatectomy, 22.0% surveillance, 19.5% radiation (RT), 8.8% androgen deprivation, and 3.6% watchful waiting (WW). The predictive model demonstrated consistent discrimination between treatments in the derivation and validation cohorts (AUCs 0.762 and 0.744, respectively). The predicted likelihood of receiving a given treatment was accurate for the most common treatment types in the derivation and validation cohorts although the model overpredicted the likelihood of receiving WW in both cohorts and RT in the validation cohort (Figure 2). CONCLUSIONS: With MUSIC registry data and machine learning methods, we were able to create a tool, designed for patients, that generates accurate predictions for most CaP treatments. As a newly diagnosed man considers treatment options, this tool will provide insight into choices made by similar men. Source of Funding: Blue Cross and Blue Shield of Michigan and grant 1T32-CA180984 from the National Cancer Institute.