Economic and clinical burden in patients with primary hyperoxaluria treated with dialysis: A real- world evidence study in the United States

doi:10.21203/rs.3.rs-3100611/v1

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Economic and clinical burden in patients with primary hyperoxaluria treated with dialysis: A real- world evidence study in the United States

https://doi.org/10.21203/rs.3.rs-3100611/v1

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Background: Primary hyperoxaluria (PH) is a family of rare genetic disorders characterized by dysregulation of glyoxylate metabolism. PH is associated with nephrocalcinosis and chronic kidney disease, which often requires dialysis treatment and organ transplant as the disease progresses to end-stage kidney disease. Limited patient data resulting from low incidence rates and diagnostic challenges associated with PH have prevented adequate characterization of the economic and clinical burden of the disorder. This study examined demographic characteristics, clinical markers, and healthcare utilization of dialysis-treated patients with PH.

Methods: This real-world retrospective study analyzed data from the TriNetX Dataworks USA Network from October 1, 2018, to February 2, 2021. The study cohort comprised of patients with a PH diagnosis who initiated dialysis during the study period. Demographics, clinical markers, and healthcare utilization were captured during the baseline period before initiating the first dialysis and during follow-up. Clinical disease progression was assessed via eGFR measurements and the number of kidney stone events (KSE).

Results: A total of 47 patients with PH were eligible for this analysis, of whom just 46.8% received a clinical diagnosis of PH before initiating dialysis. The mean age of the cohort was 58.7 years, and the majority of patients were female (53.2%), White (85.1%), and non-Hispanic/Latino (78.7%). The mean value of the earliest eGFR measurement was 27.2 mL/min, and 42.6% of patients had documented acute renal insufficiency during the baseline period. KSEs were highest in the baseline period immediately preceding dialysis initiation. Healthcare costs within the 12 months preceding and the 12 months following dialysis initiation were a mean of $150,346 and $217,784, respectively.

Conclusions: A delayed diagnosis was common among patients with PH, with over half of the cohort receiving their diagnosis after commencing dialysis. Patients with PH are burdened with high healthcare costs and complicated care pathways up to and following the first instance of dialysis. Future research focusing on strategies to improve health outcomes and address the financial burden associated with PH is warranted.

Primary hyperoxaluria

healthcare resource utilization

dialysis

disease burden

rare disease

genetic diagnosis

health outcomes

Primary hyperoxaluria (PH) is a family of rare genetic disorders with heterogenous clinical presentation, causative mutations, and disease prognosis across the three main subtypes (PH1, PH2, and PH3).¹ Based on clinical diagnosis rates, PH has been estimated to affect 1–3 individuals per 1,000,000 globally.^1,2 Whole genome sequencing has indicated that PH may affect as many as 1 in 38,600 individuals, suggesting that delayed or missed diagnosis of PH may be common.^3–5

PH is poorly characterized due to the limited availability of evidence-based patient data. PH is caused by the dysregulation of glyoxylate metabolism within the liver, leading to excessive oxalate production and the subsequent formation of calcium oxalate kidney stones (urolithiasis), oxalate deposition in kidneys (nephrocalcinosis), and chronic kidney disease (CKD).^1,2 Although clinically heterogenous, many patients with PH are at risk of CKD progression to end-stage kidney disease (ESKD) as the disease advances, particularly those diagnosed with PH 1.^6,7 Further markers of disease progression often include extra-renal deposition of calcium oxalate crystals leading to damage of the skin, bone, retina, and cardiovascular systems.¹

Initial management strategies for PH involve increasing the volume of daily fluid intake and, for patients with PH1, assessing response to pyridoxine treatment. Organ transplants are typically reserved for PH with progression of CKD to ESKD,⁸ and initiation of dialysis may be introduced for patients with continually advancing disease. Lumasiran, an RNA interference therapy, recently became the first FDA-approved drug for the treatment of PH 1 in November 2020.⁹ Phase III clinical trials have demonstrated substantial reductions in harmful levels of oxalate for both adults and children.^10,11 However, organ transplantation, most often dual liver/kidney transplantation, remains the sole curative option for PH.^12,13 Elevated anxiety and fear surrounding organ transplantation, kidney failure, and oxalosis are associated with poor quality of life among patients with PH.¹⁴ In addition, recent analyses have suggested that the direct healthcare costs for patients with PH may substantially exceed those of patients without PH.¹⁵ The overall economic and clinical burden for the management of PH, however, is currently uncertain due to the paucity of real-world clinical data regarding care pathways and related health outcomes.

Large-scale, real-world, observational studies of PH have been challenging due to the low prevalence rate, inadequate characterization, and diagnostic challenges. Indeed, most existing literature reflects case studies, which may not accurately represent a general population of patients with PH.^6,7,16 This study therefore aimed to investigate real-world practices and to characterize the economic and clinical disease burden in patients with PH in the United States (US). This study investigated demographics, clinical characteristics, healthcare resource utilization (HCRU), and healthcare costs both before and after the initiation of dialysis in patients with a clinical diagnosis of PH.

Study design

This real-world, retrospective observational study investigated patients with PH who received their first treatment with dialysis during the study period (October 1, 2018, to February 2, 2021). Data were extracted from electronic medical records (EMRs) accessed via the TriNetX Dataworks USA Network on February 2, 2021. EMR data were used to describe demographic and clinical patient characteristics, as well as the economic and clinical burden of healthcare among these patients both prior to and following the initiation of dialysis.

The index date was defined as the date of first dialysis treatment documented within the EMR. The full baseline period was defined as the 0–60 months immediately preceding the index date. This baseline period was sub-divided into the following baseline groups: 1) 6-months, 2) 12-months, 3) 24-months, 4) 36-months, 5) 48-months, and 6) 60-months. The 6-month baseline group was defined as the 6 months immediately preceding the index date. Similarly, the 12-, 24, 36-, 48- and 60-month baseline groups were defined as the corresponding period of time immediately preceding the index date. To qualify for inclusion within each baseline group, patients required at least one EMR entry during the associated timeframe. All patients included in the study were required to have at least 6 months of baseline data. The follow-up period was defined as the time from the index date to the earliest occurrence of any one of the following: the final EMR patient record, data extraction, or patient death. The study design is illustrated in Fig. 1.

Patient selection

Patients with a diagnosis of PH during the study period were identified via International Classification of Diseases codes (ICD-9-CM 271.8 and ICD-10-CM E72.53). Eligible patients must have been treated at a healthcare organization (HCO) and were required to have at least one encounter associated with a PH diagnosis on or after October 1, 2018, at least one encounter related to hemodialysis or peritoneal dialysis, and at least one encounter that took place 6 or more months before the index date. Patients were excluded if they enrolled into a clinical trial at any time during the study period.

Data source

The TriNetX Dataworks USA Network provided access to EMR data for approximately 65 million patients across 44 HCOs within the US, including academic medical centers, integrated delivery networks, specialty hospitals, and large specialty physician practices. This study utilized data from HCOs which contributed both inpatient and outpatient data to TriNetX, to accurately capture the continuum of healthcare. TriNetX is compliant with the Health Insurance Portability and Accountability Act (HIPAA), the US federal law which protects the privacy and security of healthcare data, as well as additional data privacy regulations applicable to each individual contributing HCO. HCRU and healthcare costs for services captured in the EMR were imputed using the 2020 CMS-generated summary level Medicare Fee-For-Service (FFS) claims data.¹⁷

Study outcomes

Demographic and clinical characteristics

Patient demographics (age, sex, race, ethnicity) were collected on the index date. Baseline characteristics were captured for the full baseline period and the 6-month baseline period. Baseline characteristics included body mass index (BMI), the Liu Comorbidity Index Score and contributing comorbidities,¹⁸ and other renal disease (acute renal insufficiency, urinary tract infection, hydronephrosis, pyelonephritis, nephrocalcinosis). BMI was determined using the BMI Logical Observation Identifiers Names Code (LOINC 39156-5) and ICD-10-CM codes (Z68.1-Z68.45), and categorized according to prespecified thresholds (underweight: <18.5; normal: 18.5 to < 25.0 overweight: 25.0 to < 30; obese: ≥30). For patients with multiple BMI values recorded within any given baseline group, the most recent value prior to the index date was used.

Clinical measures of disease progression

Disease progression was measured by estimated glomerular filtration rate (eGFR) and the frequency and mean number of kidney stone events (KSEs) per patient, overall and per year. Patients with an eGFR value of ≥ 90 mL/min for the most recent measurement prior to the index date were excluded from the eGFR analysis. KSEs were captured from unique encounter dates with a kidney stone diagnosis.

Sub-analyses were conducted to investigate patients with PH, treated with dialysis, who also received a kidney and/or liver transplant during the baseline or follow-up period. Among transplant recipients, those who experienced a transplant rejection or failure were identified via diagnosis coding. Time from index date to transplant to rejection or failure was captured.

Healthcare resource utilization and costs

HCRU data were captured for outpatient, emergency department (ED), and inpatient visits throughout the full baseline period and follow-up period. Provider and institutional cost components derived for each encounter identified in the EMR were used to estimate the total healthcare cost. Adjustment factors (specific to physician, hospital-based outpatient department, and inpatient stays) were applied to patients of < 65 years, as a proxy for commercially insured patients. Costs (US dollars) were adjusted from 2018 to 2021 using the medical care component of the Consumer Price Index.¹⁹

Statistical analysis

Categorical variables are reported as frequency (N) and percentage (%), while continuous variables are reported as mean and standard deviation (SD). eGFR values were calculated using the Modification of Diet in Renal Disease (MDRD) Study equation.²⁰ KSEs are reported by total number and normalized to per-person per-year. Kaplan-Meier curves are used to present time to transplant failure or rejection, according to type of transplant. Duration of inpatient stays are reported as median (min; max). Analyses were conducted using SAS 9.4 (Cary, NC).

Ethics

All methods were carried out in accordance with relevant guidelines and regulations under the ‘Ethics approval and consent to participate’ statement provided. The study was exempted from Institutional Review Board (IRB) approval and informed consent by a previous waiver from the Western IRB as all patient data obtained from TriNetX was de-identified and obtained retrospectively from health records.

Patient population and demographics

Of 5,011 patients with PH assessed for eligibility, this study observed 47 individuals with a diagnosis of PH on or after October 1, 2018 (Supplemental Figure S1). A clinical diagnosis of PH was received on or after the index date for 53.2% of patients, while the remaining 46.8% received a PH diagnosis prior to the index date. At the index date, the mean age (SD) of the cohort was 58.7 (14.8) years and 53.2% was female. White patients and Black or African American patients comprised most of the cohort (85.1% and 8.5%, respectively). Most patients were non-Hispanic/Latino or Hispanic/Latino (78.7% and 4.3%, respectively) (Table 1).

Table 1

Demographics at index date for patients with primary hyperoxaluria treated with dialysis
Variable	Patients with PH (N = 47)
Age, mean years (SD)	58.7 (14.8)
Female sex, n (%)	25 (53.2)
Race, n (%)
White	40 (85.1)
Black or African American	4 (8.5)
Other	0 (0.0)
Unknown	3 (6.4)
Ethnicity, n (%)
Hispanic/Latino	2 (4.3)
Non-Hispanic/Latino	37 (78.7)
Unknown	8 (17.0)
Transplant type, n (%)
Kidney	18 (38.3)
Liver	0 (0.0)
Both	5 (10.6)

PH: primary hyperoxaluria, SD: standard deviation.

All patients (N = 47) had the required ≥ 6 months of documented baseline data during the 6-month baseline immediately prior to the index date. A large proportion of these patients had baseline data available for ≥ 12-months (N = 42), ≥ 24-months (N = 35), ≥ 36-months (N = 32), ≥ 48-months (N = 31), and ≥ 60-months (N = 27) (Supplemental Table S1).

Baseline clinical characteristics

During the full baseline period, a BMI measurement was available for 74.5% of the cohort, with patients most often classified as overweight (25.5%) or obese (29.8%). The mean (SD) Liu Comorbidity Index score was 4.9 (5.0), and diabetes, dysrhythmia, and congestive heart failure were the most common associated conditions (48.9%, 29.8%, and 34.0% respectively). Other common comorbidities included acute renal failure and urinary tract infections (42.6% and 39.8%, respectively) (Table 2).

During the 6-month baseline, a BMI record was available for 66.0% of all patients, with the most common classifications recorded as overweight (23.4%) or obese (25.5%). The mean (SD) Liu Comorbidity Index score was 2.9 (3.6), and the most frequent conditions were diabetes, dysrhythmia, and congestive heart failure (38.3%, 21.3%, and 19.1%, respectively). Acute renal failure and urinary tract infections remained common (27.7% and 10.6%, respectively).

Table 2

Clinical characteristics for patients with primary hyperoxaluria treated with dialysis
Variable	6-month baseline (N = 47)	Full baseline (N = 47)	Follow-up (N = 47)
BMI Category ^a, n (%)	31 (66.0)	35 (74.5)	33 (70.3%)
Underweight	2 (6.5)	2 (5.7)	3 (9.1)
Normal	6 (19.4)	7 (20.0)	8 (24.2)
Overweight	11 (35.5)	12 (34.3)	10 (30.3)
Obese	12 (38.7)	14 (40.0)	12 (36.4)
Liu Comorbidity Index Score, mean (SD)	2.9 (3.6)	4.9 (5.0)	7.0 (5.5)
Liu Comorbidity Index conditions^b, n (%)
Diabetes	18 (38.3)	23 (48.9)	29 (61.7)
Dysrhythmia	10 (21.3)	16 (34.0)	18 (38.3)
Congestive heart failure	9 (19.1)	12 (25.5)	19 (40.4)
Other cardiac disease	9 (19.1)	15 (31.9)	19 (40.4)
Peripheral vascular disease	6 (12.8)	11 (23.4)	18 (38.3)
Other renal diseases, n (%)
Acute renal insufficiency	13 (27.7)	20 (42.6)	27 (57.4)
Urinary tract infection	5 (10.6)	14 (39.8)	16 (34.0)
Hydronephrosis	3 (6.4)	4 (8.5)	9 (19.1)
Pyelonephritis	2 (4.3)	2 (4.3)	4 (8.5)
Nephrocalcinosis	0 (0.0)	0 (0.0)	0 (0.0)
^a BMI category percentages were calculated using the total number of patients with a documented BMI reading within the given time period as the denominator, ^btop 5

Clinical measures of disease progression

eGFR measurements were available for 43 patients during the full baseline period, with the earliest mean (SD) value recorded of 27.2 (32.4) mL/ min. The most recent mean eGFR measurement prior to the index date during the full baseline was 24.9 (33.9) mL/ min. During the 6-month baseline period, the earliest and most recent eGFR measurements were 18.3 (15.7) and 17.5 (18.9) mL/min, respectively. Following the initiation of dialysis, the mean first eGFR measurement recorded during the follow-up period was 13.5 mL/ min (Fig. 2).

During the full baseline period, a total of 19 (40.4%) patients experienced at least one KSE, with a mean (SD) number of KSEs reaching 4.2 (15.0) per patient. The frequency of KSEs per patient per year increased as the patient neared their index date. During the 6-month baseline period, 10 (21.3%) patients reported at least one KSE, with a mean of 1.0 (3.5) KSEs per patient (Table 3).

Table 3

Kidney stone events among patients with primary hyperoxaluria treated with dialysis
	Full baseline (N = 47)	6-month baseline (N = 47)	12-month baseline (N = 42)	24-month baseline (N = 35)	36-month baseline (N = 32)	48-month baseline (N = 31)	60-month baseline (N = 27)
Patients with ≥ 1 KSE, N (%)	19 (40.4)	10 (21.3)	11 (26.2)	10 (28.6)	9 (28.1)	11 (35.5)	10 (37.0)
KSE per patients, mean (SD)	4.2 (15.0)	1.0 (3.5)	1.9 (6.4)	2.5 (8.0)	3.4 (10.2)	4.9 (14.5)	5.5 (15.4)
KSEs per patient per year, mean	n/a	2.0	1.9	1.3	1.1	1.2	1.1
KSE: kidney stone events, SD: standard deviation

A total of 48.9% of patients received either a kidney transplant (N = 18) or dual liver/kidney transplant (N = 5). Two dual transplants occurred prior to the index and three occurred afterwards. Liver transplants alone were not recorded for any patients. Overall, 12 (52.2%) patients received a PH diagnosis prior to an organ transplant. Transplant recipients had a mean (SD) age of 54.7 (16.5) years at the index date and 43.5% were female (Supplementary Table S2). Transplant patients were followed-up for a mean (SD) of 35.3 (23.2) months, during which the most common comorbidities experienced were renal disease (100.0%), mild liver disease (47.8%), and diabetes with chronic complications (Supplemental Table S3). Transplant failure or rejection was observed for 65.2% (N = 15) of transplant patients. A total of 14 (77.8%) kidney transplants and one (20.0%) dual transplant was rejected or failed. The mean (SD) time to transplant rejection or failure was 310 (479) days after the index date (Fig. 3).

Healthcare resource utilization and costs during the baseline and follow-up periods

During the full baseline period, 89.4% of patients had at least one outpatient visit, with 70.2% having at least one outpatient office visit. Emergency department (ED) visits were recorded for 51.1% of patients and inpatient visits for 72.3% of patients during the full baseline period. During the 6-month baseline period, 80.9% of patients had at least one outpatient visit, with 53.2% having at least one outpatient office visit. At least one ED and inpatient visit were recorded for 17.0% and 55.3%, respectively (Fig. 4). The median (min; max) duration of inpatient stay was 5.5 (1.0; 1549.0) days across the full baseline period and 8.0 (1.0; 72.0) days during the 6-month baseline period. During follow-up, 91.5% of patients had at least one outpatient visit with at least one outpatient office visit for 78.7%. ED and inpatient visits were recorded for 57.4% and 72.3%, respectively (Fig. 4). The median (min; max) duration of inpatient stay for patients during the follow-up period was 4 days (1.0; 724.0).

The mean (SD) overall unadjusted medical costs associated with PH were $150,346 ($222,789) per person during the 12-month baseline period and $217,784 ($515,809) during the first 12-months of follow-up (Table 4). The highest costs were associated with inpatient admissions during both time frames ($176,877 [$227,942] and $208,156 [$259,932], respectively).

Table 4

Healthcare costs for patients with primary hyperoxaluria treated with dialysis
	12-month baseline (N = 42)		12-month follow-up (N = 47)
	Patients, N (%)	Cost, mean $ (SD)	Patients, N (%)	Cost, mean $, (SD)
Overall Healthcare	42 (100)	150,346 (222,789)	47 (100)	217,784 (515,809)
Visit type
Outpatient	34 (81.0)	28,109 (69,957)	40 (85.1)	82,941 (348,482)
ED Outpatient	14 (33.3)	1,659 (1,302)	12 (25.5)	2,483 (2,585)
Inpatient	26 (61.9)	176,877 (227,942)	31 (66.0)	208,156 (259,932)
ED: emergency department, SD: standard deviation

Real-world studies of key health outcomes and care practices among patients with PH are crucial for understanding the economic and clinical burden of PH within the US. This retrospective, observational analysis investigated real-world patterns of HCRU and associated healthcare costs, as well as clinical measurements of disease progression among patients with PH who initiated dialysis treatment.

Healthcare costs associated with PH management were substantial, exceeding $200,000 per patient within the first year following the initiation of dialysis. Previous retrospective analyses of claims data from the IQVIA PharMetrics Plus Database, however, reported substantially lower annual healthcare costs of just $22,549 among patients with PH of any clinical stage.¹⁵ This may be due, in part, to differences in disease severity as this claims study reported notably lower rates of inpatient admissions (4%), kidney transplants (2.2%), and liver transplants (1.3%) indicating a lower disease burden compared with patients in the current study. The use of EMR data may also have led to the higher estimated costs reported in the current study, as EMRs allow for capturing data which may not be billed.

Clinical progression of disease was indicated in this study by the increasing volume of KSEs per patient, as the date of dialysis initiation approached. In addition, many patients had documented oxalate-related comorbidities such as diabetes, as well as high BMI values during the baseline period, both of which are associated with an increased risk of kidney stones. These findings may indicate progressive kidney damage among the cohort, likely due to PH-associated oxalate overproduction, and highlight the potential risk of subsequent kidney failure. Among patients who required an organ transplant, comorbidities such as mild liver disease and diabetes with chronic complications were prevalent and substantially exceeded rates among the general US population (1.7% and 11.3%, respectively).^21–23

Although PH is considered a rare disorder, recent genomic sequencing data has suggested that the prevalence rate is far higher than reported, possibly indicating under- or delayed diagnosis. In the current study, considerable diagnosis delays were observed, with over half of all patients receiving a clinical PH diagnosis after initiating dialysis. Early diagnosis of PH is crucial for managing clinical outcomes among patients, as delayed diagnosis is associated with a higher risk of progression to ESKD and transplant failure.^12,24,25 Indeed, of transplant recipients in the current study, approximately 65% experienced transplant failure during the follow-up period. Several previous studies support these findings. Observational analyses of the International Primary Hyperoxaluria Registry showed that among patients with a documented kidney or dual kidney/liver transplant (N = 58), 19% received a clinical PH diagnosis after their first kidney transplant. Patients with delayed diagnoses were subsequently more likely to have early transplant failure, although long-term failure rates were not significantly impacted by timely diagnosis.¹² Similarly, a retrospective analysis of the Scientific Registry of Transplant Recipients database showed that 8.3% of patients with PH who received a dual liver/kidney transplant (N = 181) and 21.4% of those who received sequential liver/kidney transplants (N = 20) did not receive pretransplant dialysis. Overall, the cumulative 5-year kidney and liver survival was 78.4% and 86.8% among dual transplant recipients compared with 85.0% and 88.2% of sequential transplant recipients.¹³ Furthermore, a retrospective analysis of the Rare Kidney Stone Consortium PH Registry demonstrated that of patients with PH (N = 409), 32 were diagnosed after progression to ESKD. Interestingly, however, delayed diagnosis was not attributed to rapid onset of disease, as time from first symptoms to diagnosis was shorter for patients without than those with ESKD at diagnosis (median: 1.4 versus 3.5 years). Among patients without ESKD, 20% developed ESKD after diagnosis during a median follow-up of 3 years, however, 10-year renal survival remained high, according to PH subtype I, II, or III (79%, 100%, or 95%, respectively).⁶

The high proportion of transplant failure or rejection in this study substantially exceeds that shown in the Organ Procurement and Transplantation Network (OPTN) and Scientific Registry of Transplant Recipients (SRTR) 2021 annual data report in which the 12-month acute kidney transplant rejection was observed in 5.3–9.3% of a general patient population.²⁶ One potential explanation for this difference is the high rate of comorbidities experienced by this study cohort, although further confirmation would be needed to establish causality. Another cause may be the limitations of identification of patients with PH and the resulting small dataset obtained for this analysis. Due to poor characterization and diagnosis of PH, patients who successfully receive a diagnosis may be more likely to demonstrate severe symptoms or advanced disease. Thus, patients who received a transplant in this study may have had poorer overall health compared with the general population of transplant recipients during this time.

The strengths of the study included the use of a real-word clinical dataset, the descriptive observation of a relatively large population of patients with PH, as well as the ability to observe real-world care journeys within the cohort. The study also had limitations. First, EMR data is susceptible to data entry errors, coding limitations, and missing data. Second, the ICD-10-CM code for PH was introduced on October 1, 2018, meaning underdiagnosis prior to dialysis initiation may have been overestimated. This code also does not distinguish between PH subtypes.²⁷ Third, most dialysis encounters in the US occur at private dialysis centers, which are not captured in the TriNetX database.²⁸ Identification and evaluation of patients undergoing dialysis was thus likely limited, leading to an underestimation of the volume of patients with PH and dialysis-related study measures. Similarly, certain lab data are not documented in the EMR and may have been subject to under-reporting. Fourth, KSEs may be underestimated as patients may not seek medical attention for all KSEs experienced. Fifth, patients were assumed to be covered by Medicare FFS or commercial insurance and other insurance types or specific benefit plan designs (e.g., coverage limits or benefit maximums) were not accounted for. Services documented in the EMR were assumed covered. Finally, only service costs within contributing HCO were imputed, and may have been overestimated if more than one encounter ID was created for a single encounter. Costs may also be underestimated as care received outside the contributing HCOs, such as prescription drug filling at retail pharmacies or services provided by home health aides, were not captured in the EMR.

This retrospective, observational study indicates that patients with PH are burdened with substantial healthcare costs and highlights the need to improve the understanding and management of complicated care pathways both preceding and following the initiation of dialysis. This study emphasizes the systematic delay in the diagnosis of PH. Finally, this study suggests a substantial economic burden for people living with PH, with high rates of HCRU and associated costs, even after organ transplantation.

BMI: body mass index, CCI: Charlson comorbidity index, CKD: chronic kidney disease, ED: emergency department, eGFR: estimated glomerular filtration rate, EMR: electronic medical records, ESKD: end-stage kidney disease, FFS: fee for service, HCO: healthcare organization, HCRU: healthcare resource utilization, ICD: international classification of disease, KSE: kidney stone events, LOINC: Logical Observation Identifiers Names Code, PH: primary hyperoxaluria, MDRD: modification of diet in renal disease, SD: standard deviation

Ethics approval and consent to participate

TriNetX, LLC is compliant with the Health Insurance Portability and Accountability Act (HIPAA), the US federal law which protects the privacy and security of healthcare data, and any additional data privacy regulations applicable to the contributing HCO. TriNetX is certified to the ISO 27001:2013 standard and maintains an Information Security Management System (ISMS) to ensure the protection of the healthcare data it has access to and to meet the requirements of the HIPAA Security Rule. Any data displayed on the TriNetX Platform in aggregate form, or any patient level data provided in a data set generated by the TriNetX Platform, only contains de-identified data as per the de-identification standard defined in Section §164.514(a) of the HIPAA Privacy Rule. The process by which the data is de-identified is attested to through a formal determination by a qualified expert as defined in Section §164.514(b)(1) of the HIPAA Privacy Rule. This formal determination by a qualified expert, refreshed in December 2020, supersedes the need for TriNetX’s previous waiver from the Western Institutional Review Board (IRB). Because this study used only de-identified patient records and did not involve the collection, use, or transmittal of individually identifiable data, this study was exempted from IRB approval.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

This study has no competing interests.

Funding

This study was funded by Dicerna Pharmaceuticals Inc., a Novo Nordisk Company.

Authors contributions

E.F-E., J.K., H.C., and J.S.B. contributed to the design, data collection, and analysis of this study. All authors approved the submitted draft of this manuscript and agreed to be personally accountable for the accuracy and integrity of the work.

Acknowledgements

Medical writing support was provided by Jessamine Winer-Jones, PhD, and Courtney N. Thompson, MSHS, CCRP, employees of TriNetX, LLC, and Rebecca Watkin, PhD, and employee of CHB Wordsmith, Inc. Funding for this support was provided by Dicerna Pharmaceuticals Inc., a Novo Nordisk Company. Aparna Raghavan, an employee of Novo Nordisk Inc., critically reviewed the manuscript.

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Economic and clinical burden in patients with primary hyperoxaluria treated with dialysis: A real- world evidence study in the United States

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Abstract

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BACKGROUND

METHODS

Study design

Patient selection

Data source

Study outcomes

Demographic and clinical characteristics

Clinical measures of disease progression

Healthcare resource utilization and costs

Statistical analysis

Ethics

RESULTS

Patient population and demographics

Baseline clinical characteristics

Clinical measures of disease progression

Healthcare resource utilization and costs during the baseline and follow-up periods

DISCUSSION

CONCLUSION

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Ethics approval and consent to participate

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Availability of data and materials

Competing interests

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References

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