Methods of competing risks analysis of end-stage renal disease and mortality among people with diabetes

<p>Abstract</p> <p>Background</p> <p>When a patient experiences an event other than the one of interest in the study, usually the probability of experiencing the event of interest is altered. By contrast, disease-free survival time analysis by standard methods, such as the Kaplan-Meier method and the standard Cox model, does not distinguish different causes in the presence of competing risks. Alternative approaches use the cumulative incidence estimator by the Cox models on cause-specific and on subdistribution hazards models. We applied cause-specific and subdistribution hazards models to a diabetes dataset with two competing risks (end-stage renal disease (ESRD) or death without ESRD) to measure the relative effects of covariates and cumulative incidence functions.</p> <p>Results</p> <p>In this study, the cumulative incidence curve of the risk of ESRD by the cause-specific hazards model was revealed to be higher than the curves generated by the subdistribution hazards model. However, the cumulative incidence curves of risk of death without ESRD based on those three models were very similar.</p> <p>Conclusions</p> <p>In analysis of competing risk data, it is important to present both the results of the event of interest and the results of competing risks. We recommend using either the cause-specific hazards model or the subdistribution hazards model for a dominant risk. However, for a minor risk, we do not recommend the subdistribution hazards model and a cause-specific hazards model is more appropriate. Focusing the interpretation on one or a few causes and ignoring the other causes is always associated with a risk of overlooking important features which may influence our interpretation.</p

Additive and Multiplicative Hazards Regression Models In Competing Risks Analysis: Application To The Canadian Heart Health Survey

Background: In survival analysis, an event whose occurrence influences the occurrence of another event is termed a competing risk event. The Cox hazards model is applicable in standard survival analysis with a single event. To correctly assess covariate effects in competing risks analysis, the Fine & Gray (F-G) subdistribution hazards and the Cox cause-specific hazards models are appropriate. Equally, additive hazards models can be used to examine the covariate effects in a competing risks framework. Objectives: (i) To examine the additive and multiplicative hazards models in the competing risks setting by applying the said models to the Canadian Heart Health Survey data; (ii) To determine the risk factors for cardiovascular disease using the competing risks approach; (iii) To compare the risk factors identified by the additive and multiplicative hazards models in the context of competing risks. Methods: The observational Canadian Heart Health Survey database collected between 1986 and 1995 is the baseline data used in this study. Two competing outcomes, cardiovascular disease (CVD) and non-CVD-related deaths, are analyzed with the Cox cause-specific and the F-G multiplicative hazards models. Similarly, the additive hazards models of Aalen and that of Lin & Ying (L-Y) are modeled for the outcomes using the competing risks approach. Results: There were 13,996 eligible subjects in my data, and 7,071 (50.5%) of them were women. After a median follow-up time of 15 years (interquartile range = 5.52 years), a total of 1,536 deaths were observed, and 549 (35.7%) of these were CVD related deaths. Factors like male gender, old age, and alcohol abstinence significantly increased the risk of CVD mortality in the additive and multiplicative hazards models. Former alcohol users compared to current alcohol users have a 53% (P-value= 0.002) and a 55% (P-value= 0.001) increased risk of CVD mortality in the Cox cause-specific and the F-G models, respectively. In the L-Y additive model, former alcohol users compared to current users increased CVD mortality by adding 16 new cases per 10,000 person-years (P-value = 0.008). Conclusion: The results from my study suggest that covariate effects in the Cox cause-specific and the F-G subdistribution hazards models may be identical in terms of magnitude and direction. The numerical results from the multiplicative and the additive hazards models give different interpretation of the covariate effects, and using both the additive and multiplicative models together would boost understanding of the data

Prognostic Modeling in the Presence of Competing Risks: an Application to Cardiovascular and Cancer Mortality in Breast Cancer Survivors

Currently, there are an estimated 2.8 million breast cancer survivors in the United States. Due to modern screening practices and raised awareness, the majority of these cases will be diagnosed in the early stages of disease where highly effective treatment options are available, leading a large proportion of these patients to fail from causes other than breast cancer. The primary cause of death in the United States today is cardiovascular disease, which can be delayed or prevented with interventions such as lifestyle modifications or medications. In order to identify individuals who may be at high risk for a cardiovascular event or cardiovascular mortality, a number of prognostic models have been developed. The majority of these models were developed on populations free of comorbid conditions, utilizing statistical methods that did not account for the competing risks of death from other causes, therefore it is unclear whether they will be generalizable to a cancer population remaining at an increased risk of death from cancer and other causes. Consequently, the purpose of this work is multi-fold. We will first summarize the major statistical methods available for analyzing competing risk data and include a simulation study comparing them. This will be used to inform the interpretation of the real data analysis, which will be conducted on a large, contemporary cohort of breast cancer survivors. For these women, we will categorize the major causes of death, hypothesizing that it will include cardiovascular failure. Next, we will evaluate the existing cardiovascular disease risk models in our population of cancer survivors, and then propose a new model to simultaneously predict a survivor's risk of death due to her breast cancer or due to cardiovascular disease, while accounting for additional competing causes of death. Lastly, model predicted outcomes will be calculated for the cohort, and evaluation methods will be applied to determine the clinical utility of such a model

Monosomal Karyotype at the Time of Diagnosis or Transplantation Predicts Outcomes of Allogeneic Hematopoietic Cell Transplantation in Myelodysplastic Syndrome

AbstractVarious cytogenetic risk scoring systems may determine prognosis for patients with myelodysplastic syndromes (MDS). We evaluated 4 different risk scoring systems in predicting outcome after allogeneic hematopoietic cell transplantation (alloHCT). We classified 124 patients with MDS using the International Prognostic Scoring System (IPSS), the revised International Prognostic Scoring System (R-IPSS), Armand's transplantation-specific cytogenetic grouping, and monosomal karyotype (MK) both at the time of diagnosis and at alloHCT. After adjusting for other important factors, MK at diagnosis (compared with no MK) was associated with poor 3-year disease-free survival (DFS) (27% [95% confidence interval, 12% to 42%] versus 39% [95% confidence interval, 28% to 50%], P = .02) and overall survival (OS) (29% [95% confidence interval, 14% to 44%] versus 47% [95% confidence interval, 36% to 59%], P = .02). OS but not DFS was affected by MK at alloHCT. MK frequency was uncommon in low-score R-IPPS and IPSS. Although IPSS and R-IPSS discriminated good/very good groups from poor/very poor groups, patients with intermediate-risk scores had the worst outcomes and, therefore, these scores did not show a progressive linear discriminating trend. Cytogenetic risk score change between diagnosis and alloHCT was uncommon and did not influence OS. MK cytogenetics in MDS are associated with poor survival, suggesting the need for alternative or intensified approaches to their treatment

The degree of retinopathy is equally predictive for renal and macrovascular outcomes in the ACCORD Trial

AIMS: Diabetic retinopathy (DR) is associated with a higher risk of renal and cardiovascular events. We sought to compare the risk for renal versus cardiovascular (CV) outcomes, stratified by retinopathy severity. METHODS: ACCORD was a randomized trial of people with type 2 diabetes, at high-risk for CV disease. A subgroup (n=3,369 from 71 clinics) had stereoscopic fundus photographs graded centrally. Participants were stratified at baseline to moderate/severe DR or no/mild DR and were monitored for renal and CV outcomes at follow-up visits over 4 years. The composite renal outcome was composed of serum creatinine doubling, macroalbuminuria, or end-stage renal disease. The composite CV outcome was the ACCORD trial primary outcome. Competing risk techniques were used to estimate the relative risk (RR) of renal versus CV composite outcomes within each DR stratum. RESULTS: The hazards ratio for doubling of serum creatinine and incident CV event in the moderate/severe DR versus no/mild DR strata were: 2.31 (95% CI: 1.25-4.26) and 1.98 (95% CI: 1.49-2.62), respectively. The RR of the two composite outcomes was highly similar in the no/mild DR stratum (adjusted RR at 4 years for CV versus renal events=0.96, 95% CI: 0.72-1.28) and the moderate/severe DR stratum (adjusted RR=0.92, 95% CI: 0.64-1.31). CONCLUSIONS: Thus, in people with type 2 diabetes at high risk for cardiovascular disease, incident CV versus renal events was similar, irrespective of the severity of the DR. Further evaluation of the specificity of DR for microvascular versus macrovascular events in other populations is warranted

Allogeneic Transplantation Provides Durable Remission in a Subset of DLBCL Patients Relapsing after Autologous Transplantation

For diffuse large B-cell lymphoma (DLBCL) patients progressing after autologous haematopoietic cell transplantation (autoHCT), allogeneic HCT (alloHCT) is often considered, although limited information is available to guide patient selection. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we identified 503 patients who underwent alloHCT after disease progression/relapse following a prior autoHCT. The 3-year probabilities of non-relapse mortality, progression/relapse, progression-free survival (PFS) and overall survival (OS) were 30, 38, 31 and 37% respectively. Factors associated with inferior PFS on multivariate analysis included Karnofsky performance status (KPS) <80, chemoresistance, autoHCT to alloHCT interval <1-year and myeloablative conditioning. Factors associated with worse OS on multivariate analysis included KPS<80, chemoresistance and myeloablative conditioning. Three adverse prognostic factors were used to construct a prognostic model for PFS, including KPS<80 (4 points), autoHCT to alloHCT interval <1-year (2 points) and chemoresistant disease at alloHCT (5 points). This CIBMTR prognostic model classified patients into four groups: low-risk (0 points), intermediate-risk (2-5 points), high-risk (6-9 points) or very high-risk (11 points), predicting 3-year PFS of 40, 32, 11 and 6%, respectively, with 3-year OS probabilities of 43, 39, 19 and 11% respectively. In conclusion, the CIBMTR prognostic model identifies a subgroup of DLBCL patients experiencing long-term survival with alloHCT after a failed prior autoHCT

Allogeneic transplantation provides durable remission in a subset of DLBCL patients relapsing after autologous transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/133605/1/bjh14046.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/133605/2/bjh14046_am.pd

Myeloablative vs Reduced-Intensity Conditioning Allogeneic Hematopoietic Cell Transplantation for Chronic Myeloid Leukemia

Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment of chronic myeloid leukemia (CML). Optimal conditioning intensity for allo-HCT for CML in the era of tyrosine kinase inhibitors (TKIs) is unknown. Using the Center for International Blood and Marrow Transplant Research database, we sought to determine whether reduced-intensity/nonmyeloablative conditioning (RIC) allo-HCT and myeloablative conditioning (MAC) result in similar outcomes in CML patients. We evaluated 1395 CML allo-HCT recipients between the ages of 18 and 60 years. The disease status at transplant was divided into the following categories: chronic phase 1, chronic phase 2 or greater, and accelerated phase. Patients in blast phase at transplant and alternative donor transplants were excluded. The primary outcome was overall survival (OS) after allo-HCT. MAC (n = 1204) and RIC allo-HCT recipients (n = 191) from 2007 to 2014 were included. Patient, disease, and transplantation characteristics were similar, with a few exceptions. Multivariable analysis showed no significant difference in OS between MAC and RIC groups. In addition, leukemia-free survival and nonrelapse mortality did not differ significantly between the 2 groups. Compared with MAC, the RIC group had a higher risk of early relapse after allo-HCT (hazard ratio [HR], 1.85; P = .001). The cumulative incidence of chronic graft-versus-host disease (cGVHD) was lower with RIC than with MAC (HR, 0.77; P = .02). RIC provides similar survival and lower cGVHD compared with MAC and therefore may be a reasonable alternative to MAC for CML patients in the TKI era

Reduced-Intensity Allografting as First Transplantation Approach in Relapsed/Refractory Grades One and Two Follicular Lymphoma Provides Improved Outcomes in Long-Term Survivors

Comparison of long-term outcomes in patients with refractory/relapsed grade 1-2 follicular lymphoma (FL) after allogeneic (allo-HCT) vs. autologous hematopoietic cell transplantation (auto-HCT) in the rituximab-era