Bleeding Risk Prediction in Patients Treated with Antithrombotic Drugs According to the Anatomic Site of Bleeding, Indication for Treatment, and Time Since Treatment Initiation

Background  Reasons for the relatively poor performance of bleeding prediction models are not well understood but may relate to differences in predictors for various anatomical sites of bleeding. Methods  We pooled individual participant data from four randomized controlled trials of antithrombotic therapy in patients with coronary and peripheral artery diseases, embolic stroke of undetermined source (ESUS), or atrial fibrillation. We examined discrimination and calibration of models for any major bleeding, major gastrointestinal (GI) bleeding, and intracranial hemorrhage (ICH), according to the time since initiation of antithrombotic therapy, and indication for antithrombotic therapy. Results  Of 57,813 patients included, 1,948 (3.37%) experienced major bleeding, including 717 (1.24%) major GI bleeding and 274 (0.47%) ICH. The model derived to predict major bleeding at 1 year from any site (c-index, 0.69, 95% confidence interval [CI], 0.68-0.71) performed similarly when applied to predict major GI bleeding (0.71, 0.69-0.74), but less well to predict ICH (0.64, 0.61-0.69). Models derived to predict GI bleeding (0.75, 0.74-0.78) and ICH (0.72, 0.70-0.79) performed better than the general major bleeding model. Discrimination declined over time since the initiation of antithrombotic treatment, stabilizing at approximately 2 years for any major bleeding and major GI bleeding and 1 year for ICH. Discrimination was best for the model predicting ICH in the ESUS population (0.82, 0.78-0.92) and worst for the model predicting any major bleeding in the coronary and peripheral artery disease population (0.66, 0.65-0.69). Conclusion  Performance of risk prediction models for major bleeding is affected by site of bleeding, time since initiation of antithrombotic therapy, and indication for antithrombotic therapy.

An evolutionary algorithm for the direct optimization of covariate balance between nonrandomized populations

Matching reduces confounding bias in comparing the outcomes of nonrandomized patient populations by removing systematic differences between them. Under very basic assumptions, propensity score (PS) matching can be shown to eliminate bias entirely in estimating the average treatment effect on the treated. In practice, misspecification of the PS model leads to deviations from theory and matching quality is ultimately judged by the observed post-matching balance in baseline covariates. Since covariate balance is the ultimate arbiter of successful matching, we argue for an approach to matching in which the success criterion is explicitly specified and describe an evolutionary algorithm to directly optimize an arbitrary metric of covariate balance. We demonstrate the performance of the proposed method using a simulated dataset of 275,000 patients and 10 matching covariates. We further apply the method to match 250 patients from a recently completed clinical trial to a pool of more than 160,000 patients identified from electronic health records on 101 covariates. In all cases, we find that the proposed method outperforms PS matching as measured by the specified balance criterion. We additionally find that the evolutionary approach can perform comparably to another popular direct optimization technique based on linear integer programming, while having the additional advantage of supporting arbitrary balance metrics. We demonstrate how the chosen balance metric impacts the statistical properties of the resulting matched populations, emphasizing the potential impact of using nonlinear balance functions in constructing an external control arm. We release our implementation of the considered algorithms in Python.

Risk of chronic kidney disease in patients with acute kidney injury following a major surgery: a US claims database analysis

Background

Acute kidney injury (AKI) is a common complication after major surgery. This study assessed the risk of developing or worsening of chronic kidney disease (CKD) and other clinical outcomes in patients experiencing AKI after major surgery.

Methods

This retrospective observational study used Optum's de-identified Clinformatics Data Mart Database to investigate cardiorenal outcomes in adult patients at the first AKI event following major surgery. The primary outcome was CKD stage ≥3; secondary outcomes included myocardial infarction (MI), stroke, heart failure, all-cause hospitalization, end-stage kidney disease, need for dialysis or kidney transplant and composite measures. Follow-up was up to 3 years. Additionally, the effect of intercurrent events on the risk of clinical outcomes was assessed.

Results

Of the included patients (N = 31 252), most were male (61.9%) and White (68.9%), with a median age of 72 years (interquartile range 64-79). The event rates were 25.5 events/100 patient-years (PY) for CKD stage ≥3, 3.1 events/100 PY for end-stage kidney disease, 3.0 events/100 PY for dialysis and 0.1 events/100 PY for kidney transplants. Additionally, there were 6.9 events/100 PY for MI, 8.7 events/100 PY for stroke and 49.8 events/100 PY for all-cause hospitalization during follow-up. Patients with AKI relapses as intercurrent events were more likely to develop CKD stage ≥3 than those with just one AKI event after major surgery.

Conclusion

This analysis demonstrated that patients experiencing AKI following major surgery are at high risk of developing severe CKD or worsening of pre-existing CKD and other cardiorenal clinical outcomes such as MI and stroke.

Identification of International Society on Thrombosis and Haemostasis major and clinically relevant non-major bleed events from electronic health records: a novel algorithm to enhance data utilisation from real-world sources

Introduction

In randomised controlled trials (RCTs), bleeding outcomes are often assessed using definitions provided by the International Society on Thrombosis and Haemostasis (ISTH). Information relating to bleeding events in real-world evidence (RWE) sources are not identified using these definitions. To assist with accurate comparisons between clinical trials and real-world studies, algorithms are required for the identification of ISTH-defined bleeding events in RWE sources.

Objectives

To present a novel algorithm to identify ISTH-defined major and clinically-relevant non-major (CRNM) bleeding events in a US Electronic Health Record (EHR) database.

Methods

The ISTH definition for major bleeding was divided into three subclauses: fatal bleeds, critical organ bleeds and symptomatic bleeds associated with haemoglobin reductions. Data elements from EHRs required to identify patients fulfilling these subclauses (algorithm components) were defined according to International Classification of Diseases, 9th and 10th Revisions, Clinical Modification disease codes that describe key bleeding events. Other data providing context to bleeding severity included in the algorithm were: 'interaction type' (diagnosis in the inpatient or outpatient setting), 'position' (primary/discharge or secondary diagnosis), haemoglobin values from laboratory tests, blood transfusion codes and mortality data.

Results

In the final algorithm, the components were combined to align with the subclauses of ISTH definitions for major and CRNM bleeds. A matrix was proposed to guide identification of ISTH bleeding events in the EHR database. The matrix categorises bleeding events by combining data from algorithm components, including: diagnosis codes, 'interaction type', 'position', decreases in haemoglobin concentrations (≥ 2 g/dL over 48 hours) and mortality.

Conclusions

The novel algorithm proposed here identifies ISTH major and CRNM bleeding events that are commonly investigated in RCTs in a real-world EHR data source. This algorithm could facilitate comparison between the frequency of bleeding outcomes recorded in clinical trials and RWE. Validation of algorithm performance is in progress.

Clinical characteristics and disease outcomes in non-diabetic chronic kidney disease: retrospective analysis of a US healthcare claims database

BACKGROUND

The observational, real-world evidence FLIEDER study aimed to describe patient clinical characteristics and investigate clinical outcomes in non-diabetic patients with chronic kidney disease (CKD) using data collected from routine clinical practice in the United States.

METHODS

Between 1 January, 2008-31 December, 2018, individuals aged ≥ 18 years, with non-diabetic, stage 3-4 CKD were indexed in the Optum^® Clinformatics^® Data Mart US healthcare claims database using International Classification of Diseases-9/10 codes for CKD or by laboratory values (estimated glomerular filtration rate [eGFR] 15-59 mL/min/1.73 m²). The primary outcomes were hospitalization for heart failure, a composite kidney outcome of end-stage kidney disease/kidney failure/need for dialysis and worsening of CKD stage from baseline. The effects of the intercurrent events of a sustained post-baseline decline in eGFR ≥ 30%, ≥ 40%, and ≥ 57% on the subsequent risk of the primary outcomes were also assessed.

RESULTS

In the main study cohort (N = 504,924), median age was 75.0 years, and 60.5% were female. Most patients (94.7%) had stage 3 CKD at index. Incidence rates for hospitalization for heart failure, the composite kidney outcome, and worsening of CKD stage from baseline were 4.0, 10.3, and 4.4 events/100 patient-years, respectively. The intercurrent event analysis demonstrated that a relative decline in kidney function from baseline significantly increased the risk of cardiorenal events.

CONCLUSIONS

This real-world study highlights that patients with non-diabetic CKD are at high risk of serious adverse clinical outcomes, and that this risk is amplified in patients who experienced greater post-baseline eGFR decline.

Worsening of kidney function in patients with atrial fibrillation and chronic kidney disease: evidence from the real-world CALLIPER study

OBJECTIVE

Evidence is needed on the impact of anticoagulation therapy on kidney function in patients with atrial fibrillation (AF). The objective of this analysis, which is part of the CALLIPER study, was to investigate the risk of worsening kidney function with rivaroxaban 15 mg once daily compared with warfarin in patients with AF and moderate-to-severe chronic kidney disease (CKD) in routine clinical practice in the United States.

METHODS

CALLIPER was an observational, retrospective, new-user cohort study. Adult patients with AF in the US IBM Watson MarketScan databases who newly initiated anticoagulation with rivaroxaban 15 mg once daily or warfarin between January 2013 and December 2017 were included. Comparative analysis was performed using Cox proportional hazards regression after adjustment for potential confounding by the stabilized inverse probability of treatment weighting approach and propensity score matching. One of the main study outcomes was worsening kidney function (composite of progression to CKD stage 5, kidney failure, or need for dialysis), besides traditional AF-related outcomes.

RESULTS

The cohort included 7368 patients: 5903 (80.1%) initiating warfarin and 1465 (19.9%) initiating rivaroxaban 15 mg once daily. Rivaroxaban 15 mg was associated with a significant 47% reduction in the risk of worsening kidney function versus warfarin (hazard ratio 0.53; 95% confidence interval 0.35-0.78). Similar results were observed in the subgroup of patients with type 2 diabetes.

CONCLUSIONS

Rivaroxaban 15 mg may be associated with a lower risk of worsening kidney function as compared with warfarin in the atrial fibrillation population with moderate-to-severe CKD.

TRIAL REGISTRATION NUMBER

NCT03359876.

Real-world data-driven risk prediction of hospitalization for heart failure in non-diabetic CKD

Background

Heart failure (HF) is a major cause of cardiovascular morbidity and mortality. Despite recent advances in diagnosis and management of HF, the prognosis remains poor. HF and chronic kidney disease (CKD) are interlinked chronic health conditions. The availability of large volume of patient data and modern analytic techniques opens new opportunities for identification of individuals at elevated risk of HF.

Purpose

Develop risk prediction model for HF hospitalizations (HHF) in patients with non-diabetic CKD by applying data-driven computational intelligence techniques to a US population-based administrative claims database.

Methods

Individual-level data from the US Optum Clinformatics Data Mart for years 2008–2018 were analysed. To be eligible for inclusion, adult individuals were required to have non-diabetic CKD stage 3 or 4 (index event) and one year continuous insurance coverage prior to the index date (baseline period). Selection criteria and the main clinical outcome, hospitalisation for heart failure (HHF), were identified by using laboratory tests results and/or specific codes from common clinical coding systems. Risk prediction model for HHF was built on patient data in the baseline period composed to more than 6,000 variables. Computational intelligence method based on ant colony optimization was used to develop a time-to-first-event risk prediction model for HHF.

Results

Of the 64 million individuals in the database, 504,924 satisfied the selection criteria. Median age was 75 years, 60% were female. Among most common baseline comorbidities were hypertension (85%) and hyperlipidaemia (68%). Coronary artery disease, HF, atrial fibrillation and peripheral artery disease were recorded in 24%, 16%, 15% and 14% of individuals. Over a median follow-up of 744 days, 53,282 (11%) patients had recorded HHF, the corresponding incidence rate was 3.95 events/100 patient-years.

The developed risk prediction model for HHF in non-diabetic CKD contained 20 risk factors. The five strongest risk factors were history of HF, intake of loop diuretics, severely increased albuminuria, atrial fibrillation or flutter and CKD 4 as observed “yes/no” in the baseline period. Fig. 1 depicts the final risk prediction model. To assess model performance, all patients in the cohort were stratified into five HHF risk groups. For each group, a Kaplan-Meier curve was built based on the HHF outcome data in the database. Fig. 2 shows clear separation between the curves, demonstrating high performance of the developed risk prediction model.

Conclusion

Despite many existing scores to predict HHF, their use is limited. Some scores rely on availability of rarely collected information, some are applicable for specific patient populations only. Risk prediction model for HHF in non-diabetic CKD is presented, which contains risk factors routinely collected by healthcare providers. Therefore, it might be applicable for HHF risk estimation in various settings.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Bayer AG Forest plot of HHF risk prediction modelKaplan-Meier plot of risk strata

MO526REAL-WORLD EVIDENCE ON CLINICAL OUTCOMES IN NON-DIABETIC CKD

Background and Aims

Chronic kidney disease (CKD) represents a global public health problem, with significant morbidity and mortality due to cardiovascular disease during CKD progression and due to kidney failure. Although non-diabetic CKD accounts for up to 70% of the global CKD burden, its clinical consequences are poorly understood, and data are needed to help identify individuals at high risk of adverse outcomes. This analysis uses real-world evidence to provide insights into clinical characteristics, care and outcomes in individuals with non-diabetic CKD in routine clinical practice.

Method

Individual-level data from the US administrative claims database, Optum Clinformatics Data Mart, from January 1, 2008 to December 31, 2018 were analysed. Adults with non-diabetic CKD stage 3 or 4 and ≥365 days continuous insurance coverage were included and followed until insurance disenrollment, end of data availability or death. Individuals with diabetes mellitus, CKD stage 5 or end-stage kidney disease (ESKD) prior to the index date, or who experienced kidney failure (acute or unspecified), kidney transplant or dialysis in the baseline period, were excluded from the analysis. Study outcomes, captured in the database, were defined using common clinical coding systems. Primary outcomes were hospitalisation for heart failure (HHF), a kidney composite of ESKD/kidney failure/need for dialysis, and worsening of CKD stage from baseline. Individual CKD stage was assigned based on estimated glomerular filtration rate (eGFR) values (priority) or the respective International Classification of Diseases code at index and during follow-up. Further prespecified kidney outcomes included individual components of the kidney composite, acute kidney injury, and absolute and relative change in eGFR from baseline. Event-based outcomes were assessed by time-to-first-event analysis. Summary statistics for time-course analysis of metric outcomes were generated on a quarterly basis.

Results

In total, 504,924 of 64 million individuals in the Optum Clinformatics Data Mart satisfied the selection criteria. Over a median follow-up of 744 (interquartile range 328–1432) days, the incidence rates of primary outcomes of HHF, the kidney composite and worsening of CKD stage from baseline were 3.95, 10.33 and 4.38 events/100 patient-years (PY), respectively.

The incidence rates of the components of the kidney composite outcome, namely ESKD/need for dialysis, kidney failure (acute and unspecified) and need for dialysis were 1.78, 9.53 and 0.49 events/100 PY, respectively. Kidney failure events were driven mainly by acute kidney injury, with an incidence of 8.61 events/100 PY.

In individuals with at least one available eGFR value at baseline and one value during follow-up (n=295,174), the incidence rates of relative decreases in eGFR of ≥30%, ≥40% and ≥57% from baseline were 1.98, 0.97 and 0.30 events/100 PY, respectively; in this cohort, more rapid eGFR decline was associated with increased risk of HHF and the kidney composite outcome. In individuals with a baseline eGFR value and at least one follow-up eGFR value and an available urine albumin-to-creatinine ratio (n=25,824), time-course analysis of eGFR showed that eGFR decline mostly occurred in individuals with moderately-to-severely increased albuminuria (≥30 mg/g).

Conclusion

This analysis generates real-world evidence on clinical outcomes in a cohort of individuals with non-diabetic CKD treated in routine clinical practice in the US. Despite known limitations of claims databases (e.g. low availability of some laboratory data, limited individual follow-up time and tactical coding), individuals with moderate-to-severe non-diabetic CKD are shown to be at high risk of serious clinical outcomes. This highlights the high unmet medical need, and urgency for new treatments and targeted interventions for patients with non-diabetic CKD.

MO511BASELINE CHARACTERISTICS OF A NON-DIABETIC CKD COHORT IN A US CLAIMS DATABASE

Background and Aims

Chronic kidney disease (CKD) is a major global health problem, affecting an estimated 850 million people worldwide. Non-diabetic CKD accounts for up to 70% of the CKD burden, which includes morbidity and mortality due to CKD progression to kidney failure and due to cardiovascular disease. This analysis uses real-world evidence to provide insights into the baseline clinical characteristics of individuals with non-diabetic CKD treated in routine clinical practice.

Method

The Optum Clinformatics Data Mart was used to identify individuals with non-diabetic CKD (enrolled in the database between January 1, 2008 and December 31, 2018), based on common diagnosis, procedure and laboratory codes. To be eligible for inclusion, individuals were required to have CKD stage 3 or 4, as identified by estimated glomerular filtration rate (eGFR) 15–59 ml/min/1.73 m2 and/or by an International Classification of Diseases (ICD) code, and confirmed by a second eGFR value or ICD code 90–365 days apart (index date). Individuals had to be ≥18 years old at index and have 365 days of continuous insurance coverage prior to the index event (baseline period). Those with diabetes mellitus, CKD stage 5 or end-stage kidney disease prior to the index date, or who experienced kidney failure (acute or unspecified), kidney transplant or dialysis at baseline, were excluded from the analysis. Patient demographics, clinical characteristics, comorbidities and medications were assessed at baseline.

Results

Of the 64 million individuals in the Optum Clinformatics Data Mart during the analysed time period, 504,924 satisfied the selection criteria. Median (interquartile range) age was 75 (68–81) years, 60% were female, 63% were white and 10% were black. The proportions of individuals with CKD stage 3 and 4 at index were 95% and 5%, respectively. At baseline, eGFR values were available for 62% of individuals; median (interquartile range) eGFR was 53 (47–57) ml/min/1.73 m2. A urine albumin-to-creatinine ratio was recorded in 6% of individuals, of whom 73%, 21% and 6% had normal-to-mildly increased (<30 mg/g), moderately increased (≥30 to ≤300 mg/g) and severely increased (>300 mg/g) albuminuria, respectively. The most common baseline comorbidities were hypertension (85% of individuals), hyperlipidaemia (68%), hypothyroidism (26%), anaemia (25%), pulmonary disease (24%) and coronary artery disease (24%). Heart failure, atrial fibrillation and peripheral artery disease were recorded in 16%, 15% and 14% of individuals, respectively. The most frequently used medication classes at baseline were statins (47% of individuals), beta blockers (44%), nonsteroidal anti-inflammatory drugs (36%) and angiotensin-converting enzyme inhibitors (34%). Angiotensin receptor blockers and mineralocorticoid receptor antagonists were used by 21% and 4% of individuals, respectively. The speciality of the diagnosing provider was reported on 26% of claims for the index event, the most common being family or internal medicine, followed by nephrology.

Conclusion

This analysis contributes to the characterisation of a real-world population with non-diabetic CKD treated in routine clinical practice in the US. A large cohort of individuals with moderate-to-severe CKD was identified. The majority were elderly with multiple serious cardiovascular and pulmonary comorbidities and frequent use of nonsteroidal anti-inflammatory drugs. Overall, the analysis highlights the urgent need for improving early diagnosis, prevention and effective treatment of CKD.

P4746Worsening of renal function in atrial fibrillation patients with stage 3 or 4 chronic kidney disease treated with warfarin or rivaroxaban - evidence from the real-world CALLIPER study in the US claims

Background

Anticoagulation therapy with vitamin K antagonists (e.g. warfarin) has recently been shown to contribute to the accelerated vascular calcification and worsening of renal function. Therefore, it is compelling to investigate the impact of different oral anticoagulants (OACs) on kidney function in non-valvular atrial fibrillation (NVAF) patients. Common co-morbidities in these patients are chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM), which might be presented at the OAC therapy initiation.

Purpose

The overall objective of the CALLIPER study was to evaluate the effectiveness and safety of the reduced dose rivaroxaban (15 mg once daily) as compared to warfarin in NVAF patients with renal dysfunction in real-world setting. In particular, we evaluated the risk of worsening of renal function in NVAF patients with CKD stage 3 and 4 at baseline (1 year prior to the cohort entry). Additionally, a sub-group analysis of patients with T2DM was performed. We defined worsening of renal function as progression to CKD stage 5, kidney failure or need for dialysis.

Methods

Individual level data of warfarin- and rivaroxaban-naïve NVAF patients from the MarketScan database for the years 2012 through 2017 were used. Patients with moderate-to-severe CKD (stage 3 and 4) were included in the study cohort and were followed until progression to CKD 5, kidney failure or dialysis, OAC discontinuation/switch, insurance disenrollment or end of data availability. A comparative analysis evaluating the hazard ratios (HRs) with the corresponding 95% confidence intervals (CIs) under warfarin or rivaroxaban treatment was performed using Cox regression. A stabilized inverse probability of treatment weighting was used to adjust for imbalances in baseline patient characteristics.

Results

We identified 5,906 warfarin- and 1,466 rivaroxaban-naïve patients with NVAF and CKD stage 3 and 4, of which 60% were male, median (25–75% range) age=79 (71- 84) years, CHADS2 score=2.67 (2.00- 3.50), CHA2DS2-VASc score=4.43 (3.40–5.62), modified HAS-BLED score=3.00 (2.40 - 3.65). T2DM was present in more than 50% of patients (Table), namely, in 3,160 warfarin- and 746 rivaroxaban-users. Hazard ratios and 95% CI for worsening of renal function were evaluated at 0.53 (0.35; 0.78) in the main cohort and 0.50 (0.30; 0.83) in the T2DM sub-group, meaning that rivaroxaban was associated with a significant 47% and 50% risk reduction of this outcome in NVAF patients with CKD stage 3 and 4 with and without T2DM, respectively.

Conclusion

The reduced dose of rivaroxaban has appeared to lower significantly the risk of worsening of renal function versus warfarin in NVAF patients with CKD stage 3 and 4 present at the OAC therapy initiation. The conclusion holds true for the patients with the co-morbid T2DM. This evidence was generated by the CALLIPER study using one of the largest US administrative claims database.

Acknowledgement/Funding

CI Coleman has received research grants from Bayer AG

P4749Renal function worsening in factor-xa inhibitors vs phenprocoumon in patients with non-valvular atrial fibrillation and renal disease - insights from the RELOADED study

Background

Data on the effectiveness and safety of Factor-Xa non-vitamin-K oral anticoagulants in patients with non-valvular atrial fibrillation (NVAF) and renal disease is scarce. Among others, our study aimed to investigate the risk of renal function worsening in new users of NOACs vs. phenprocoumon with renal disease.

Methods

We conducted a new user cohort study (one year washout period) in patients with NVAF overall and additionally with renal disease defined by either an extended list of ICD-10 codes (definition 1) or chronic kidney disease (CKD) stages 3 or 4 (definition 2). German claims data between January 1st, 2013 and June 30th, 2017 were utilized and a multiple Cox-regression was performed to calculate confounder-adjusted hazard ratios (HRs) for the risk of end stage renal disease (ESRD)/dialysis and acute kidney injury in new users of NOACs (rivaroxaban, apixaban and edoxaban) vs. new users of phenprocoumon.

Results

In the overall population 22,339 patients initiating rivaroxaban, 16,201 patients initiating apixaban, 2,828 patients initiating edoxaban and 23,552 patients initiating phenprocoumon were included. NOAC patients with renal disease (definition 1) initiating reduced doses comprised 2,121 initiators of rivaroxaban, 2,507 of apixaban and 292 of edoxaban. 7,289 patients of phenprocoumon were identified. Patients with CKD (definition 2) initiating reduced doses of Factor-Xa inhibitors comprised 1,216 initiators of rivaroxaban, 1,522 of apixaban, 166 of edoxaban and 3,513 of phenprocoumon. In the confounder-adjusted analysis, a beneficial effect for both, rivaroxaban and apixaban over phenprocoumon was seen for the risk of ESRD/dialysis for all populations (overall, renal definition 1 and renal definition 2). In addition, in the CKD population we found a statistically significant risk reduction related to acute kidney injury only for rivaroxaban initiators (44%). There was not sufficient data to conduct the analyses for edoxaban.

Figure 1

Conclusion

This is the first observational retrospective database study evaluating the effect of renal function worsening in Germany. Results indicate a beneficial effect for both, reduced doses of rivaroxaban and apixaban related to renal function worsening over time when compared to phenprocoumon. This effect was more pronounced for the risk reduction with rivaroxaban related to ESRD /dialysis and specifically also related to a significant risk reduction for AKI.

Acknowledgement/Funding

The study was funded by Bayer AG

P4795Comparative safety of factor-xa inhibitors vs phenprocoumon in patients with non-valvular atrial fibrillation and renal disease - insights from the RELOADED study

Background

Data on safety of Factor-Xa inhibitors and phenprocoumon in patients with non-valvular atrial fibrillation (NVAF) and renal disease is scarce. Among others, our study aimed to investigate the safety risks of fatal bleeding and intracranial haemorrhage (ICH) in new users of Factor-Xa inhibitors vs. phenprocoumon, the vitamin-K antagonist (VKA) of choice in Germany.

Methods

We conducted a new user cohort study (one year washout period) in patients with NVAF and renal disease. German claims data between January 1st, 2013 and June 30th, 2017 were utilized and a multiple Cox-regression was performed to calculate confounder-adjusted hazard ratios (HRs) for the risk of fatal bleeding and ICH in Factor-Xa inhibitors and phenprocoumon initiators. Additionally, a propensity score matching and an inverse probability of treatment weight analysis were performed as sensitivity analyses. Cases of fatal bleeding were defined as hospitalization with a primary hospital discharge diagnoses for bleeding with documented death as reason for hospital discharge or within 30 days after hospital discharge.

Results

The overall population comprised 23,552 phenprocoumon initiators, 22,338 rivaroxaban initiators and 16,201 apixaban initiators, where the number of patients with renal disease initiating these agents were 7,289 for phenprocoumon, 5,121 patients for rivaroxaban 15mg or 20mg and 4,750 patients for apixaban 2.5mg or 5mg, respectively. In the confounder-adjusted analysis, a beneficial effect for rivaroxaban and apixaban over phenprocoumon was observed for the risk of ICH and fatal bleeding (figure 1) for both the overall and renal disease population. Hazard ratios for rivaroxaban and the risk of ICH were calculated as 0.57 (0.43; 0.75) for the overall population and 0.62 (0.37; 1.01) for the renal disease population where hazard ratios for apixaban were calculated as 0.43 (0.31; 0.60) for the overall population and 0.41 (0.23; 0.74) for the renal disease population, respectively. There was not sufficient data to conduct the analyses for edoxaban.

Figure 1

Conclusion

This large retrospective database study conducted in Germany confirms the safety profile of rivaroxaban and apixaban over VKA in patients overall and specifically in patients with renal disease when assessing the risk of ICH and fatal bleeding. Our study adds evidence in a relevant subgroup of patients where anticoagulation is often challenging.

Acknowledgement/Funding

This study was funded by Bayer AG

P4781Comparative effcomparative effectiveness and safety of factor-xa inhibitors vs phenprocoumon in patients with non-valvular atrial fibrillation and malignant diseases, insights from the RELOADED study

Background

Data on safety and effectiveness of Factor-Xa inhibitors and phenprocoumon in patients with non-valvular atrial fibrillation (NVAF) and malignant diseases is scarce. Among others, our study aimed to investigate the safety and effectiveness in new users of Factor-Xa inhibitors vs. phenprocoumon, the vitamin-K antagonist (VKA) of choice in Germany.

Methods

We conducted a new user cohort study (one year washout period) in patients with NVAF and malignant diseases. German claims data between January 1st, 2013 and June 30th, 2017 were utilized and a multiple Cox-regression was performed to calculate confounder-adjusted hazard ratios (HRs) for the risk of ischemic stroke (IS)/systemic embolism (SE), intracranial haemorrhage (ICH) as well as renal function worsening, defined by end stage renal disease (ESRD) or dialysis and acute kidney injury (AKI) in Factor-Xa inhibitors and phenprocoumon initiators. Diagnoses of malignant diseases were assessed over the one-year baseline period.

Results

The population comprised 3,779 phenprocoumon initiators, 3,386 rivaroxaban initiators, 2,697 apixaban initiators and 434 edoxaban initiators. In the confounder-adjusted analysis, no difference related to the risk of IS/SE was found for rivaroxaban and edoxaban vs. phenprocoumon, where apixaban showed a numerically increased risk for stroke (figure 1). Point estimates related to the risk of ICH showed the expected beneficial effects for both, rivaroxaban and apixaban. A strong beneficial effect was observed for rivaroxaban when assessing the risk of renal function worsening. Hazard ratios related to the risk of ESRD/dialysis and AKI were 0.27 (0.10; 0.69) and 0.64 (0.38; 1.06), respectively. For apixaban, only the ESRD/dialysis showed a reduction in risk when compared to phenprocoumon, HR 0.42 (0.19; 0.94).

Conclusion

This retrospective database study conducted in Germany adds evidence on the effectiveness and safety profile of Factor-Xa inhibitors over VKA in patients with NVAF and malignant diseases, a critical subgroup of patients where anticoagulation is challenging. However, apixaban showed a numerically increased risk for IS/SE compared to phenprocoumon. Both, rivaroxaban and apixaban showed a risk reduction for renal function worsening within the study period of 63% and 48%, respectively compared to phenprocoumon. Only rivaroxaban showed a risk reduction of 36% for AKI.

Acknowledgement/Funding

The study was funded by Bayer AG

Cross design analysis of randomized and observational data - application to continuation rates for a contraceptive intra uterine device containing Levonorgestrel in adolescents and adults

Background

To combine results from a randomized controlled study (RCT) and an observational study (OS) to evaluate discontinuation rate of a levonorgestrel-containing intrauterine contraceptive device (LNG IUD) in a real-life setting.

Methods

We included 253 parous and nulliparous women aged 21–40 years from our own phase II RCT. A total of 1607 women of all ages (including adolescents, < 20 years) were recruited from an OS. We applied the cross design synthesis (CDS) method recommended by the United States General Accounting Office. This method combines the different strengths of RCTs and OSs into one single estimate.

Results

Combined continuation rates for parous vs nulliparous women could be estimated more precisely as well as overall continuation rates after one (86.6%) and two years (78.5%), irrespective of age and parity.

Conclusion

Cross design synthesis allowed more precise estimation of continuation rates of an intrauterine device.

Agreement between coding schemas used to identify bleeding-related hospitalizations in claims analyses of nonvalvular atrial fibrillation patients

BACKGROUND

Schemas to identify bleeding-related hospitalizations in claims data differ in billing codes used and coding positions allowed. We assessed agreement across bleeding-related hospitalization coding schemas for claims analyses of nonvalvular atrial fibrillation (NVAF) patients on oral anticoagulation (OAC).

HYPOTHESIS

We hypothesized that prior coding schemas used to identify bleeding-related hospitalizations in claim database studies would provide varying levels of agreement in incidence rates.

METHODS

Within MarketScan data, we identified adults, newly started on OAC for NVAF from January 2012 to June 2015. Billing code schemas developed by Cunningham et al., the US Food and Drug Administration (FDA) Mini-Sentinel program, and Yao et al. were used to identify bleeding-related hospitalizations as a surrogate for major bleeding. Bleeds were subcategorized as intracranial hemorrhage (ICH), gastrointestinal (GI), or other. Schema agreement was assessed by comparing incidence, rates of events/100 person-years (PYs), and Cohen's kappa statistic.

RESULTS

We identified 151 738 new-users of OAC with NVAF (CHA2DS2-VASc score = 3, [interquartile range = 2-4] and median HAS-BLED score = 3 [interquartile range = 2-3]). The Cunningham, FDA Mini-Sentinel, and Yao schemas identified any bleeding-related hospitalizations in 1.87% (95% confidence interval [CI]: 1.81-1.94), 2.65% (95% CI: 2.57-2.74), and 4.66% (95% CI: 4.55-4.76) of patients (corresponding rates = 3.45, 4.90, and 8.65 events/100 PYs). Kappa agreement across schemas was weak-to-moderate (κ = 0.47-0.66) for any bleeding hospitalization. Near-perfect agreement (κ = 0.99) was observed with the FDA Mini-Sentinel and Yao schemas for ICH-related hospitalizations, but agreement was weak when comparing Cunningham to FDA Mini-Sentinel or Yao (κ = 0.52-0.53). FDA Mini-Sentinel and Yao agreement was moderate (κ = 0.62) for GI bleeding, but agreement was weak when comparing Cunningham to FDA Mini-Sentinel or Yao (κ = 0.44-0.56). For other bleeds, agreement across schemas was minimal (κ = 0.14-0.38).

CONCLUSIONS

We observed varying levels of agreement among 3 bleeding-related hospitalizations schemas in NVAF patients.

Novel genetic matching methods for handling population stratification in genome-wide association studies

Background

A usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure.

Results

We assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer’s disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach.

Conclusions

Matching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0521-4) contains supplementary material, which is available to authorized users.

Rare variant testing of imputed data: an analysis pipeline typified

Important methodological advancements in rare variant association testing have been made recently, among them collapsing tests, kernel methods and the variable threshold (VT) technique. Typically, rare variants from a region of interest are tested for association as a group ('bin'). Rare variant studies are already routinely performed as whole-exome sequencing studies. As an alternative approach, we propose a pipeline for rare variant analysis of imputed data and develop respective quality control criteria. We provide suggestions for the choice and construction of analysis bins in whole-genome application and support the analysis with implementations of standard burden tests (COLL, CMAT) in our INTERSNP-RARE software. In addition, three rare variant regression tests (REG, FRACREG and COLLREG) are implemented. All tests are accompanied with the VT approach which optimizes the definition of 'rareness'. We integrate kernel tests as implemented in SKAT/SKAT-O into the suggested strategies. Then, we apply our analysis scheme to a genome-wide association study of Alzheimer's disease. Further, we show that our pipeline leads to valid significance testing procedures with controlled type I error rates. Strong association signals surrounding the known APOE locus demonstrate statistical power. In addition, we highlight several suggestive rare variant association findings for follow-up studies, including genomic regions overlapping MCPH1, MED18 and NOTCH3. In summary, we describe and support a straightforward and cost-efficient rare variant analysis pipeline for imputed data and demonstrate its feasibility and validity. The strategy can complement rare variant studies with next generation sequencing data.

METAINTER: meta-analysis of multiple regression models in genome-wide association studies

MOTIVATION

Meta-analysis of summary statistics is an essential approach to guarantee the success of genome-wide association studies (GWAS). Application of the fixed or random effects model to single-marker association tests is a standard practice. More complex methods of meta-analysis involving multiple parameters have not been used frequently, a gap that could be explained by the lack of a respective meta-analysis pipeline. Meta-analysis based on combining p-values can be applied to any association test. However, to be powerful, meta-analysis methods for high-dimensional models should incorporate additional information such as study-specific properties of parameter estimates, their effect directions, standard errors and covariance structure.

RESULTS

We modified 'method for the synthesis of linear regression slopes' recently proposed in the educational sciences to the case of multiple logistic regression, and implemented it in a meta-analysis tool called METAINTER. The software handles models with an arbitrary number of parameters, and can directly be applied to analyze the results of single-SNP tests, global haplotype tests, tests for and under gene-gene or gene-environment interaction. Via simulations for two-single nucleotide polymorphisms (SNP) models we have shown that the proposed meta-analysis method has correct type I error rate. Moreover, power estimates come close to that of the joint analysis of the entire sample. We conducted a real data analysis of six GWAS of type 2 diabetes, available from dbGaP (http://www.ncbi.nlm.nih.gov/gap). For each study, a genome-wide interaction analysis of all SNP pairs was performed by logistic regression tests. The results were then meta-analyzed with METAINTER.

AVAILABILITY

The software is freely available and distributed under the conditions specified on http://metainter.meb.uni-bonn.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of the progression of systolic blood pressure using imputation of missing phenotype values

We present a genome-wide association study of a quantitative trait, "progression of systolic blood pressure in time," in which 142 unrelated individuals of the Genetic Analysis Workshop 18 real genotype data were analyzed. Information on systolic blood pressure and other phenotypic covariates was missing at certain time points for a considerable part of the sample. We observed that the dropout process causing missingness is not independent of the initial systolic blood pressure; that is, the data is not missing completely at random. However, after the adjustment for age, the impact of systolic blood pressure on dropouts was no longer significant. Therefore, we decided to impute missing phenotype values by using information from individuals with complete phenotypic data. Progression of systolic blood pressure (∆SBP/∆t) was defined based on the imputed phenotypes and analyzed in a genome-wide fashion. We also conducted an exhaustive genome-wide search for interaction between single-nucleotide polymorphisms (7.14 × 10(10) tests) under an allelic model. The suggested data imputation and the association analysis strategy proved to be valid in the sense that there was no evidence of genome-wide inflation or increased type I error in general. Furthermore, we detected 2 single-nucleotide polymorphisms (SNPs) that met the criterion for genome-wide significance (p≤5 × 10(-8)), which was also confirmed via Monte-Carlo simulation. In view of the rather small sample size, however, the results have to be followed-up in larger studies.

A one-degree-of-freedom test for supra-multiplicativity of SNP effects

Deviation from multiplicativity of genetic risk factors is biologically plausible and might explain why Genome-wide association studies (GWAS) so far could unravel only a portion of disease heritability. Still, evidence for SNP-SNP epistasis has rarely been reported, suggesting that 2-SNP models are overly simplistic. In this context, it was recently proposed that the genetic architecture of complex diseases could follow limiting pathway models. These models are defined by a critical risk allele load and imply multiple high-dimensional interactions. Here, we present a computationally efficient one-degree-of-freedom "supra-multiplicativity-test" (SMT) for SNP sets of size 2 to 500 that is designed to detect risk alleles whose joint effect is fortified when they occur together in the same individual. Via a simulation study we show that the SMT is powerful in the presence of threshold models, even when only about 30-45% of the model SNPs are available. In addition, we demonstrate that the SMT outperforms standard interaction analysis under recessive models involving just a few SNPs. We apply our test to 10 consensus Alzheimer's disease (AD) susceptibility SNPs that were previously identified by GWAS and obtain evidence for supra-multiplicativity ([Formula: see text]) that is not attributable to either two-way or three-way interaction.