Prevalence, Resource Utilization, and Economic Impact of Kidney Function and Proteinuria in Patients With Focal Segmental Glomerulosclerosis

ABSTRACT

Background: Among patients with focal segmental glomerulosclerosis (FSGS), proteinuria and kidney function decline may be associated with increased economic burden. This study aimed to provide current information on the epidemiology and economic burden of FSGS in the United States.

Methods: In this descriptive, noninterventional, retrospective cohort study, 9899 patients were identified between January 2016 and December 2020 in Optum de-identified Market Clarity Data based on International Classification of Diseases code or Optum proprietary natural language processing data. Descriptive statistics were reported for categorical and continuous variables. Prevalence estimates were standardized to the age, gender, and race/ethnicity distribution of the general US population using direct methods and data from the 2021 United States Census Bureau. Per-patient-per-month health care resource utilization and associated costs (2024 US $) were reported by proteinuria (≤ 1.5 g/g vs > 1.5 g/g or < 3.5 g/g vs ≥ 3.5 g/g) and chronic kidney disease stage (stage 1-5/kidney failure). The Fisher exact test was used for categorical health care resource utilization outcomes, and linear regression (mean) and the Jonckheere-Terpstra test (medians) were used for continuous health care resource utilization and cost outcomes.

Results: Estimated annual US prevalence (average for 2016-2020) of FSGS was 212.6 per 1 million. There was a consistent trend toward higher health care resource utilization and total costs with both chronic kidney disease progression (stage 1-5/kidney failure) and higher levels of proteinuria (≤ 1.5 g/g vs > 1.5 g/g or < 3 .5 g/g vs ≥ 3.5 g/g).

Conclusions: The observed prevalence of FSGS increased in the US and was highest among Black individuals. More advanced chronic kidney disease and higher levels of proteinuria were both associated with greater health care resource utilization and costs. Treatments that reduce proteinuria and slow kidney function decline have the potential to delay disease progression and to reduce the economic burden associated with FSGS.

Am J Manag Care. 2025;31

For author information and disclosures, see end of text.

Introduction

Focal segmental glomerulosclerosis (FSGS) is a histological pattern of glomerular damage that occurs after varying damage to several renal cells, including podocytes. This results in changes in morphology in the podocyte, including hypertrophy, foot process effacement, and detachment from the glomerular basement membrane. Persistent injury ultimately leads to proteinuria and a progressive decline in glomerular filtration rate (GFR).^1-3 The causes of FSGS are diverse, with onset occurring at any age.⁴ FSGS can be categorized into different subclasses, which include primary; genetic; secondary forms such as maladaptive, viral, and drug-induced; and FSGS of undetermined cause. Primary forms of FSGS do not have apparent causes and can occur spontaneously.² In differentiating primary and secondary FSGS, the Kidney Disease Improving Global Outcomes (KDIGO) 2021 clinical practice guidelines define primary FSGS as a clinical-pathologic syndrome where examination of a kidney biopsy under light microscopy reveals FSGS lesions, while electron microscopy demonstrates widespread effacement of foot processes; clinically, patients exhibit nephrotic syndrome.⁵ Secondary forms of FSGS occur in association with other diseases like HIV infection or the use of medications such as bisphosphonates, lithium, and anabolic steroids.^4,6

Ultimately, FSGS leads to kidney failure (KF).^1-3 Patients diagnosed with FSGS may have proteinuria levels in the nephrotic or subnephrotic range;³ and when proteinuria levels exceed 3.5 g/d with hypoalbuminemia (<30 g/L), patients are classified as having nephrotic syndrome.⁵ It is estimated that between 54% and 100% of patients with primary FSGS eventually develop nephrotic syndrome.⁵ Patients with primary FSGS who present with nephrotic-range proteinuria (nephrotic syndrome together with a spot urinary protein-creatinine ratio [UPCR] ≥ 3 g/g) have a poor prognosis without treatment and typically progress to KF over the course of 5 to 10 years.^2,5,7 Slowing disease progression is a primary goal of FSGS treatment, and the reduction in proteinuria is regarded as a critical part of disease management.^8,9

One study found that patients with primary FSGS and partial proteinuria remission had significantly improved survival compared with patients with no remission, and that partial remission was independently associated with a lower risk of progression to renal failure (HR, 0.48; 95% CI, 0.24-0.96).⁹

To date, there is no FDA-approved pharmacological treatment for FSGS. Treatment guidelines recommend that patients with primary FSGS receive high-dose corticosteroid therapy with prednisone.⁵ As patients relapse and receive subsequent courses of immunotherapy, the risk of chronic kidney disease (CKD), drug adverse effects, and toxicities increases.⁵ The risk of adverse effects of corticosteroids may also be higher in certain subgroups of patients, including those who are obese or have diabetes, osteoporosis, or psychiatric disorders.⁵ For patients with contraindications to corticosteroids or who are corticosteroid-resistant, calcineurin inhibitors (CNIs), including cyclosporine and tacrolimus, are recommended.⁵ At doses exceeding 5.5 mg/kg/day in adults, cyclosporine is associated with an increased risk of nephrotoxicity.⁵ Mycophenolate mofetil and high-dose dexamethasone, rituximab, and adrenocorticotropic hormone are alternative treatments for patients with primary FSGS who are unable to tolerate or who have contraindications to calcineurin inhibitors; however, there is a lack of quality evidence for any specific alternative treatments. Treatment guidelines for secondary FSGS recommend avoiding the use of immune suppressive therapies entirely.⁵

Further complicating matters, the incidence and prevalence data for FSGS are difficult to attain, in part due to global disparities in kidney biopsy access as well as racial and geographical variations, though estimates appear to be increasing globally.³ Ascertaining this information is critical for quantifying the impact of FSGS on the health care system, particularly as the health care resource utilization (HRU) and costs associated with FSGS have only been examined in a few studies.¹⁰

The objective of this study was to estimate the prevalence, and describe the characteristics, of patients with FSGS in the US population. Additionally, we wanted to estimate all-cause HRU and costs by baseline CKD status and baseline UPCR.

Methods

Study Design and Data Source

This study was a descriptive, noninterventional, retrospective cohort study utilizing Optum de-identified Market Clarity Data and proprietary natural language processing (NLP) data (Figure 1). The Optum de-identified Market Clarity Data link electronic health record (EHR) data from providers across the care continuum with historical, linked administrative claim data; pharmacy claims; and facility claims (with clinical information). These data include medications prescribed and administered. The dataset is fully Health Insurance Portability and Accountability Act–compliant and statistician-certified, and it contains de-identified data. Institutional review board approval was not required for this study.

The Optum NLP system was developed using vocabulary from the Unified Medical Language System that includes multiple medical dictionaries such as the Logical Observation Identifiers Names and Codes (LOINC), the Systemized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), and RxNorm, a listing of generic and branded drugs (among others). NLP concepts are identified and created based on broad topics such as medications; signs, disease and symptoms (SDS); measurements, and observations, for example. The data are harvested from the notes fields within the electronic medical records provided to Optum from over 50 large health care systems throughout the United States. The data used for the development of each NLP concept are de-identified, and accuracy is verified through a series of quality-assurance steps prior to release for use. Each NLP concept included in the data is associated with a unique subject record and a date of observation, allowing longitudinal tracking of concepts over time.

The study and identification period for the prevalence cohort were from January 1, 2007, through March 31, 2021. Patients in the HRU/cost cohort were identified from July 1, 2007, through September 30, 2020, to allow for a 6-month baseline and follow-up period. The index date was the first FSGS International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code or NLP term within the identification period. During the pre-ICD-10 timeframe (prior to 2015), patients were identified using NLP terms, due to the limited granularity of the ICD-9 diagnosis code (582.1; chronic glomerulonephritis with lesion of membranous glomerulonephritis) for FSGS.

Study Population

For the overall FSGS prevalence cohort calculation, patients were eligible if they had at least 2 SDS term entries with focal segmental glomerulosclerosis or segmental glomerulosclerosis and/or FSGS-associated ICD-10 diagnosis codes (N03.1, N04.1, N05.1, N06.1, N07.1) at least 30 days apart and within 180 days (Figure 2). Patients with negation terms (eg, deny, failed, ignore, n/a, negative, question, reject, rule out, uncertain, unspecified) in relation to FSGS SDS terms were excluded from the study. For the HRU and cost cohort calculations, the patients had to meet the above diagnostic criteria as well as have at least 6 months of preindex continuous enrollment (baseline), at least 6 months of postindex continuous enrollment (follow-up), and linkage to the Optum Market Clarity claims data. To focus on FSGS attributable costs of care, patients were excluded if they had evidence of cancer, pregnancy, or a COVID-19 diagnosis pre- or post-index (eAppendix Figure 1 [eAppendices available at ajmc.com]). Proteinuria data were presented as UPCR (g/g). Patients with KF (defined as eGFR of < 15 mL/min/1.73 m², a diagnosis code for CKD stage 5, or the presence of a claim for dialysis or kidney transplant) during the baseline period were excluded from the proteinuria-
based stratifications.

Study Outcomes

Information on baseline demographic and clinical characteristics included age, gender, region, race/ethnicity, insurance type, post-index activity (months), Charlson Comorbidity Index (CCI), and occurrence of at least 1 cardiovascular (CV) event in the baseline period. Additionally, baseline eGFR, UPCR, and CKD staging by eGFR were collated; they are reported in Table 1. Crude and standardized prevalence were estimated from January 1, 2016, to December 31, 2020, to capture estimates that are most reflective of up-to-date ICD-10 implementation and the potential advances in NLP technology. HRU assessed during the study period included the proportion of patients with at least 1 inpatient admission, emergency department (ED) visits, outpatient visits and prescription(s), and the mean per-patient-per-month (PPPM) number of admissions, visits, or claims. Utilization-associated costs were also calculated. To examine the impact of baseline UPCR on HRU and costs, 2 cutoffs (1.5 g/g and 3.5 g/g) were used, and outcomes were evaluated across up to 1.5 g/g vs more than 1.5 g/g and across less than 3.5 g/g vs at least 3.5 g/g. If a patient did not have a UPCR value, but did have a urine protein and urine creatinine value on the same day, UPCR was calculated. Additionally, outcomes were evaluated by CKD stage.

Statistical Analyses

Baseline demographics and clinical characteristics were analyzed descriptively, while categorical variables were summarized using frequencies and percentages. Means, medians, percentiles, and standard deviations (SD) were reported for continuous variables, and missing data were considered as a separate category. The standardized annual and across-the-study prevalence and 95% confidence intervals (95% CIs) were estimated using direct methods to standardize to the age, gender, and race/ethnicity distribution of the general US population using data from the 2021 US Census Bureau. Average annual standardized prevalence over the study period was estimated by summing the annual prevalence for each year and dividing it by the number of years. All costs were adjusted to 2024 US dollars using the Consumer Price Index and presented as PPPM values. For categorical HRU outcomes, comparisons were assessed using the Fisher exact test. For differences in continuous HRU and cost outcomes by proteinuria level and by CKD stage, a linear regression was conducted to analyze differences in means, and the Jonckheere-Terpstra test was conducted to analyze differences in medians across groups.

Results

Baseline Demographics and Clinical Characteristics

A total of 9899 patients met the inclusion criteria for the prevalence cohort. The mean (SD) age was 47.9 (18.6) years; 56.9% of patients were male; 58.4% were White, and 26.8% were Black. Median (interquartile range [IQR]) baseline eGFR in the full cohort was 35.2 (16.9-66.5) mL/min/1.73 m². Six thousand forty-two patients (61.1%) were at CKD stages 3 to 5, and the median (IQR) baseline UPCR was 2.4 (0.8-5.1) g/g.

A total of 1082 adult patients (≥ 18 years of age), met the criteria for the HRU/cost analysis with mean (SD) age of 50.7 (15.8); 57.1% of the patients were male, 59.1% were White, and 27.2% were Black. Median (IQR) baseline eGFR was 36.0 (18.7-61.1) mL/min/1.73 m². In total, 728 patients (67.3%) were at CKD stages 3 to 5, and the median (IQR) baseline UPCR was 2.2 (0.8-5.4) g/g.

Additional information on baseline demographics and clinical characteristics is provided in Table 1.

Prevalence

Average annualized standardized prevalence (2016-2020) was 212.6 per million, and estimated standardized prevalence of FSGS increased over the study period (Table 2). Prevalence (95% CI) increased every year throughout the study period, starting at 158.0 (153.3-162.8) per million in 2016 to 260.1 (252.9-267.2) per million in 2020. Prevalence rates (95% CI) in 2020 were the highest in the Black population at 686.2 (652.0-720.9) per million compared to the White population, which was 211.3 (204.0-218.6) per million.

HRU by Baseline CKD Stage

Generally, a higher CKD stage was associated with higher HRU (Figure 3; eAppendix Table 1). A significant trend was observed, showing increases both in inpatient admissions (stage 1: 22.1%; stage 5/KF: 55.0%) and ED visits (stage 1: 41.2%; stage 5/KF: 65.9%) associated with disease severity and higher CKD stages (P < .001) (Figure 3A). This trend was also true for the PPPM mean number of inpatient admissions (stage 1: 0.10; stage 5: 0.20; P = .01), ED visits (stage 1: 0.10; stage 5: 0.30; P < .01), and outpatient visits (stage 1: 2.30; stage 5: 11.10; P < .001), where patients with more severe disease had higher utilization (Figure 3B). Because nearly all patients had at least 1 outpatient visit and pharmacy claim, the trend was less clear, though still statistically different, for pharmacy claims (stage 1: 3.90; stage 5: 4.90; P = .01). Median inpatient length of stay (LOS) was significantly higher with higher CKD stage (stage 1: 0.10 days; stage 5/KF: 0.50 days; P = .01) (eAppendix Table 1).

HRU by Baseline UPCR

There was a consistent trend toward higher HRU with higher levels of proteinuria when comparing up to 1.5 g/g vs more than 1.5 g/g or less than 3.5 g/g vs at least 3.5 g/g (Figure 4; eAppendix Table 2). Proteinuria exceeding 1.5 g/g was associated with a significantly higher proportion of patients with at least 1 inpatient visit (49.4% vs 15.2%; P < .001) or pharmacy claim (96.4% vs 88.6%; P = .02) compared with up to 1.5 g/g (Figure 4A). Proteinuria of at least 3.5 g/g was similarly associated with a significantly higher proportion of patients with at least 1 inpatient visit (56.7% vs 24.7%; P < .001) or pharmacy claim (97.9% vs 90.8%; P = .02) with a trend toward a higher proportion of patients with ED visits (59.8% vs 48.9%; P = .10) compared with levels less than 3.5 g/g (Figure 4B).

A trend for higher mean PPPM number of visits/claims was observed consistently across all HRU categories, reaching significance for ED visits (0.2 vs 0.1; P = .01) and outpatient visits (4.8 vs 2.3; P < .001) when comparing baseline proteinuria of up to 1.5 g/g vs more than 1.5 g/g (Figure 4C). The mean PPPM number of ED visits (0.2 vs 0.1; P = .02) and outpatient visits (5.6 vs 2.9; P < .001) were also significantly higher when comparing baseline proteinuria of less than 3.5 g/g vs at least 3.5 g/g (Figure 4D).

Costs by Baseline CKD Stage

Like utilization, there was a consistent trend for costs to increase as baseline disease severity increased. PPPM total costs were significantly higher at higher baseline CKD stage, increasing from $2431 at stage 1 to $14,666 at stage 5 (P < .001) (Figure 5, eAppendix Table 3). This trend was also true for inpatient admissions (stage 1: $2717; stage 5: $5262; P = .01), outpatient visits (stage 1: $1322; stage 5: $10,041; P < .001) and ED visits (stage 1: $79; stage 5: $353; P < .01), but not for pharmacy claims costs (stage 1: $536; stage 5: $1570; P = .12).

Costs by Baseline UPCR

Mean total costs PPPM increased with higher baseline UPCR levels (Figures 6A and B; eAppendix Table 4). Patients with baseline UPCR up to 1.5 g/g had significantly lower mean total costs PPPM vs those with UPCR exceeding 1.5 g/g vs ($2338 vs $6975; P < .001) and and those with UPCR less than 3.5 g/g had lower costs than patients with UPCR of at least 3.5 g/g (mean: $3386 vs $8391; P < .001). Total cost differences at both UPCR thresholds were most likely attributable to outpatient visits, $1463 vs $3370 PPPM for UPCR up to 1.5 g/g vs exceeding 1.5 g/g and $1756 vs $4214 PPPM for UPCR of less than 3.5 g/g vs at least 3.5 g/g, respectively (Figure 6A, B). Proteinuria up to 1.5 g/g was associated with significantly lower pharmacy costs than proteinuria of exceeding 1.5 g/g ($454 vs $1097; P = .04).

Discussion

Our study revealed a progressive increase in the estimated prevalence of FSGS in the US between 2016 and 2020. This aligns with other studies that have reported a rise in the prevalence and frequency of FSGS as a cause of KF. This finding is consistent with the literature from 2017 on FSGS prevalence from Olmstead County, Minnesota, which demonstrated a 17% rise in prevalence every 5 years over a 19-year period.¹¹

The progressive increase in prevalence in the current analysis was observed in all races, though it was more pronounced among Black individuals. By the end of the study period, the prevalence of FSGS was approximately 3-fold higher among Black individuals compared with White individuals. Other studies have shown that the incidence of FSGS is also increasing,^11,12 particularly among the Black population.¹³ APOL1 genetic variants in Black individuals may account for the observed higher prevalence.^14,15 Across all patients, increased disease state awareness and greater availability and use of diagnostic kidney biopsies may have contributed to the observed increase in the prevalence of FSGS.¹³

Our study also demonstrated that the HRU and costs associated with FSGS significantly increased with greater disease severity and with higher levels of proteinuria. Advanced CKD stage was associated with higher HRU and costs (with patients in CKD stage 5/KF incurring significantly higher costs) at $14,666 PPPM compared to $2431 PPPM at CKD stage 1 (P < 0.001). These findings highlight the potential for reducing the economic burden associated with FSGS by increasing patient adherence on treatments that can slow the rate of eGFR decline and progression to KF.

We also assessed the impact of proteinuria on costs and HRU using 2 clinically meaningful proteinuria thresholds: 1.5 g/g and 3.5 g/g.^5,16 At both thresholds, higher levels of proteinuria were associated with significantly higher costs and HRU. For the UPCR threshold of at least 3.5 g/g, the mean total costs PPPM were approximately 40% higher than for those patients with a UPCR of less than 3.5 g/g and about 33% higher for patients with a UPCR of more than 1.5 g/g vs up to 1.5 g/g. This finding is supported by the results reported in the study by Kalantar-Zadeh et al,¹⁰ which demonstrated that the nephrotic range proteinuria, defined as a UPCR of above 3000mg/g or albumin/creatinine ratio of above 2000mg/g, was associated with higher costs ($70,481 vs $36,099 per-patient-per year [PPPY]; P < .001).

Limited information is available on the economic burden of FSGS; 1 recent study has assessed this burden using US claims data. Using Optum Clinformatics Data to compare patients with FSGS to matched non-FSGS controls, the study demonstrated that mean total medical costs, primarily driven by outpatient costs, were higher for patients with FSGS ($59,753 vs $8431 PPPY; P < .001). Furthermore, costs associated with nephrotic-range proteinuria and the out-of-pocket costs for outpatient, inpatient, and prescriptions for FSGS patients were higher than those of the control group (P < .0001 for all).¹⁰ Our study demonstrated a similar substantial economic burden of FSGS and showed that these costs were driven by outpatient visits and associated costs, representing approximately 60% of the total costs across all CKD stages.

Although our study did not analyze the effects of proteinuria relapse on HRU and costs, we did observe that elevated proteinuria levels, decreased eGFR, and KF are associated with higher costs. Further studies are warranted.

Limitations

This analysis is subject to limitations due to its reliance on secondary databases. These limitations include potential miscoding errors and incomplete or missing data. The results of this study may not be generalizable to other payer populations, as patient encounters at facilities not part of the integrated data network captured by Optum are not recorded in the database. Notably, our study was unable to differentiate between primary FSGS and other forms of FSGS. As diagnosis of FSGS is typically based on renal biopsy results, conventional single sections may underestimate the number of sclerotic glomeruli, which can lead to an underestimation of the frequency of FSGS lesions in the entire kidney and result in underdiagnosis.¹⁷ As access to biopsy data was limited in this study, this underestimation may directly impact the frequency and accuracy of ICD-10 codes and/or NLP terms being used to classify FSGS patients.

In terms of pricing data, Optum applies standard pricing algorithms to account for variations in pricing across health plans and provider contracts. The resulting cost information is designed to reflect allowed payments for all provider services across regions. However, it is important to note that the resulting cost information only reflects allowed payments for all provider services across regions and does not include out-of-pocket costs incurred by patients.

Conclusion

We report an increasing prevalence of FSGS in the US, particularly among Black individuals. More advanced CKD and higher levels of proteinuria are associated with higher HRU and costs. The timely implementation of treatments that reduce proteinuria and slow the rate of kidney function decline could potentially alleviate the health care utilization associated with, and economic burden of, FSGS.

Author Affiliations: Travere Therapeutics, Inc (MEB, KW, WG), San Diego, CA; Notting Hill Consulting LLC (KMT), University of Illinois Chicago (EVL), Chicago, IL; Advocate Christ Medical Center (EVL), Oak Lawn, IL; VJA Consulting (RML), Walnut Creek, CA; C. Martin Bunke Consulting (CMB), MtPleasant, SC; Genesis Research (AR, MVM,* DO,* DTA), Hoboken, NJ; Genesis Research (DC*), Newcastle upon Tyne, UK; Putnam (DC), Newcastle upon Tyne, UK; Department of Nephrology, Ochsner Health (JCQV), New Orleans, LA; Ochsner Medical Center (JCQV), New Orleans, LA.

*MVM, DO, and DC were employees of Genesis Research at the time this research was conducted.

Source of Funding: This supplement was sponsored by Travere Therapeutics, Inc. Travere Therapeutics, Inc, funded this study and approved submission of this report for publication. MEB is the Managing Director of BenofitConsulting, which received consulting fees from Travere Therapeutics, Inc. KMT has a consulting contract with Travere Therapeutics, Inc, and does not have any equity interest in Travere Therapeutics, Inc. EVL received consulting fees from Travere Therapeutics, Inc. RL received consulting fees from Travere Therapeutics, Inc. CMB is a consultant for Travere Therapeutics, Inc. KW is a stockholder and former employee of Travere Therapeutics, Inc. WG is a stockholder and an employee of Travere Therapeutics, Inc.AR, MVM, DO, and DTA are employees of Genesis Research, which received compensation from Travere Therapeutics, Inc. DC was an employee of Genesis Research (Newcastle upon Tyne, UK) at the time the study was conducted. JCQV received consulting fees from Travere Therapeutics, Inc.

Author Disclosures: The Optum Market Clarity Dataset is fully Health Insurance Portability and Accountability Act-compliant as it contains de-identified data. Therefore, institutional review board approval was not required. Dr Bensink reports being employed by BenofitConsulting, which receives consulting fees from Travere Therapeutics, Inc. Dr Lerma reports consultancies or paid advisory boards, receipt of honoraria, and receipt of lecture fees for speaking at the invitation of Travere Therapeutics, Inc. He also reports royalties from published books. Dr Bunke reports he was an employee of Travere Therapeutics, Inc, for 3 years and is now a consultant for Travere Therapeutics, Inc, and has stock shares. Dr Wang and Mr Gong report being employees and stock owners of Travere Therapeutics, Inc. Mr Rava and Mr Oliveri report being employees of Genesis Research, which receives consulting fees from Travere Therapeutics, Inc. Dr Amari reports having received payment for involvement in the preparation of the manuscript. Dr Cork reports he was an employee of Genesis Research, which received funding from Travere Therapeutics, at the time of this study. Dr Velez reports having received honoraria from Calliditas; Mallinckrodt; Travere Therapeutics, Inc; and Vera. Dr Thakker, Dr Lieblich, and Mr Murphy report no conflicts of interest with the subject matter of this supplement.

Authorship Information: Concept and design (MEB, KMT, EVL, RML, KW, WG, AR, DO, DTA, DC, JCQV); acquisition of data (MEB); analysis and interpretation of data (MEB, KMT, EVL, RML, KW, WG, AR, MVM, DO, DTA, JCQV); drafting of the manuscript (EVL, AR, DO, DC); critical revision of the manuscript for important intellectual content (MEB, KMT, EVL, RML, CMB, KW, WG, AR, MVM, DO, DTA, DC, JCQV); statistical analysis (KW, WG, AR, MVM, DO); obtaining funding (MEB); supervision (MEB, EVL, DTA, DC).

Address Correspondence to: Mark Bensink, PhD. HEOR Lead for Sparsentan, Travere Therapeutics, Inc; 3611 Valley Centre Dr Suite 300; San Diego, CA 92130. Mark.Bensink@travere.com

Posted history: This manuscript was previously posted to Research Square https://assets-eu.researchsquare.com/files/rs-3754889/v1/4fe39ed4-9286-4857-9bef-c73a152290b9.pdf?c=1720074690.

Data Sharing Statement: De-identified data were used under Travere license agreement with Optum Market Clarity and are not publicly available. Data are available with permission from Optum Market Clarity.

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