Prediction of Mortality Incidence in Patients with Chronic Kidney Disease Based on Influential Prognostic Factors with Competing Risks Approach

BACKGROUND

Chronic Kidney Disease (CKD) is a disease in which the kidney's functionality declines gradually. The aim of this study was to identify significant laboratory prognostic factors on death due to CKD in a clinical complex.

MATERIALS AND METHODS

A retrospective study including 109 patients with the end-stage renal disease treated at Iran Helal pharmaceutical and the clinical complex was conducted between 2014-2018. The survival time was set as the time interval between starting dialysis until death due to CKD. Also, the transplantation was considered as competing risk, which was occurred for a few patients. A three-parameter Gompertz model was used that considers both the event of interest and the competing event simultaneously.

RESULTS

Death due to CKD occurred in 29 (26.6%) of the patients and 19(17.4%) with transplantation. Serum uric acid was a significant prognostic factor that decreased the hazard of mortality by 21%. Serum phosphorus and age by increasing the risk of death, were poor prognoses for the event of interest. Serum uric acid and phosphorus 6.9-9.9 (mg/dl) were associated with 72% and 4.05- fold increased hazard of transplant, respectively. The 4-year cumulative incidence of death and transplant was 48.4% and 29.2%, respectively.

CONCLUSION

We have deduced that high serum phosphorus levels and increased levels of age were associated with worse outcomes. High serum uric acid level was related to better survival, which could be explained by having a better protein-rich diet alongside the high albumin level.

Inference about time-dependent prognostic accuracy measures in the presence of competing risks

BACKGROUND

Evaluating a candidate marker or developing a model for predicting risk of future conditions is one of the major goals in medicine. However, model development and assessment for a time-to-event outcome may be complicated in the presence of competing risks. In this manuscript, we propose a local and a global estimators of cause-specific AUC for right-censored survival times in the presence of competing risks.

METHODS

The local estimator - cause-specific weighted mean rank (cWMR) - is a local average of time-specific observed cause-specific AUCs within a neighborhood of given time t. The global estimator - cause-specific fractional polynomials (cFPL) - is based on modelling the cause-specific AUC as a function of t through fractional polynomials.

RESULTS

We investigated the performance of the proposed cWMR and cFPL estimators through simulation studies and real-life data analysis. The estimators perform well in small samples, have minimal bias and appropriate coverage.

CONCLUSIONS

The local estimator cWMR and the global estimator cFPL will provide computationally efficient options for assessing the prognostic accuracy of markers for time-to-event outcome in the presence of competing risks in many practical settings.

Cause-Specific Risk Factors of Death in Individuals with Diabetes: A Competing Risks Modeling

BACKGROUND

Diabetes is on the rise worldwide.

OBJECTIVES

This study aimed to evaluate the risk factors of various causes of death in people with type 2 diabetes (T2D).

METHODS

In this cohort study on 2638 people with T2D, we applied cause-specific and sub-distribution hazards models to assess the impact of various factors on the risk of death. Moreover, we plotted a cumulative incidence curve to summarize cumulative failure rates over time.

RESULTS

About 75% of individuals with T2D died from cardiovascular disease (CVD) and cerebrovascular accidents (CVA). Death from CVD was associated with the increased risk of hypertension (hazard ratio (HR) = 1.83, 95% CI: 1.37 - 2.46), hypercholesterolemia (HR = 1.58, 95% CI: 1.17 - 2.14), and diabetes duration. The risk of death from CVA was related to hypertension (HR = 2.76, 95% CI: 1.67 - 4.55) and hyperglycemia (HR = 4.34, 95% CI: 1.75 - 10.79). The CVA risk in patients with diabetes duration of 10 - 20 years was higher than the risk in patients with diabetes duration > 20 years (diabetes duration of ≤ 10 years as the reference category). Diabetes duration of longer than 20 years was associated with a higher risk of death from cancer (HR = 2.65, 95% CI: 1.05 - 6.68). The risk of death from foot infection and diabetic nephropathy increased in patients with longer diabetes duration after adjustment for sex, age, and body mass index.

CONCLUSIONS

Regardless of the cause, death rates in people with T2D increase over time and risk factors have different impacts on death from each cause. This should be acknowledged in risk management in individuals with T2D.

Evaluating center performance in the competing risks setting: Application to outcomes of wait-listed end-stage renal disease patients

It is often of interest to compare centers or healthcare providers on quality of care delivered. We consider the setting where evaluation of center performance on multiple competing events is of interest. We propose estimating center effects through cause-specific proportional hazards frailty models that allow correlation among a center's cause-specific effects. Estimation of our model proceeds via penalized partial likelihood and is implemented in R. To evaluate center performance, we also propose a directly standardized excess cumulative incidence (ECI) measure. Therefore, based on our proposed methods, practitioners can evaluate centers either through the cause-specific hazards or the cumulative incidence functions. We demonstrate, through simulations, the advantages of the proposed methods to detect outlying centers, by comparing the proposed methods and existing methods which assume uncorrelated random center effects. In addition, we develop a Correlation Score Test to test the null hypothesis that the competing event processes within a center are correlated. Using data from the Scientific Registry of Transplant Recipients, we apply our method to evaluate the performance of Organ Procurement Organizations on two competing risks: (i) receipt of a kidney transplant and (ii) death on the wait-list.

The estimation and modelling of cause-specific cumulative incidence functions using time-dependent weights

Competing risks occur in survival analysis when an individual is at risk of more than one type of event and the occurrence of one event precludes the occurrence of any other event. A measure of interest with competing risks data is the cause-specific cumulative incidence function (CIF) which gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event is experienced first. The user written command, stcompet, calculates non-parametric estimates of the cause-specific CIF and the official Stata command, stcrreg, fits the Fine and Gray model for competing risks data. Geskus (2011) has recently shown that some of the key measures in competing risks can be estimated in standard software by restructuring the data and incorporating weights. This has a number of advantages as any tools developed for standard survival analysis can then be used for the analysis of competing risks data. This paper describes the stcrprep command that restructures the data and calculates the appropriate weights. After using stcrprep a number of standard Stata survival analysis commands can then be used for the analysis of competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF and stcox will fit the Fine and Gray proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, the use of stcrprep opens up new opportunities for competing risk models. This is illustrated by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF.

The Effect of GVHD on Long-term Outcomes after Peripheral Blood Allogeneic Stem Cell Transplantation from an HLA-identical Sibling in Adult Acute Lymphocytic Leukemia: A Landmark Analysis Approach in Competing Risks

Allogeneic Hematopoietic stem cell transplantation (HSCT) is the most effective therapy to prevent relapse in acute lymphocytic leukemia (ALL). This benefit is affected by non-relapse mortality (NRM) due to complications such as graft versus host disease (GVHD). A new approach in analyzing time-dependent covariates in competing risks is landmark analysis. So, the aim of this study is to evaluate the effect of acute and chronic GVHD on long-term outcomes, relapse and NRM, after allogeneic HSCT in adult ALL using landmark analysis. This study was conducted on 252 ALL patients who were allogeneic transplanted from an HLA-identical sibling with peripheral blood (PB) as the source of stem cell from 2004 to 2012 and were followed-up until 2013. In the first 100 days after transplant, a landmark analysis on days +10, +11, +12, +17, +24, and +31 was applied to assess the effect of acute GVHD on early relapse and NRM. Similarly, for patients alive and event-free at day +100 after transplant, a landmark analysis at time points day +101, months +4, +5, +6, +9, and +12 was applied to evaluate the effect of chronic GVHD on late relapse and NRM. Five-year LFS and OS were 35.0% (95% CI: 29.1, 42.2%) and 37.5% (95% CI: 31.3, 45.0%), respectively. Five-year cumulative incidence of relapse was 44.5% (95% CI: 37.9, 51.0%) while this was 20.4% (95% CI: 15.4, 26.0%) for NRM. The landmark analysis in the first 100 days after transplant showed that the grade III/IV of aGVHD has a lower risk of relapse but higher risk of NRM after adjustment for the EBMT risk score. For patients alive at day +100, cGVHD had no significant effect on relapse. Limited cGVHD had lower risk of NRM and after 6 month post-transplant the risk of NRM decreased and there were not important difference between the groups of cGVHD. Using advanced models enables us to estimate the effects more precisely and ultimately make inference more accurately.

Comparison of competing risks models based on cumulative incidence function in analyzing time to cardiovascular diseases

BACKGROUND

Competing risks arise when the subject is exposed to more than one cause of failure. Data consists of the time that the subject failed and an indicator of which risk caused the subject to fail.

METHODS

With three approaches consisting of Fine and Gray, binomial, and pseudo-value, all of which are directly based on cumulative incidence function, cardiovascular disease data of the Isfahan Cohort Study were analyzed. Validity of proportionality assumption for these approaches is the basis for selecting appropriate models. Such as for the Fine and Gray model, establishing proportionality assumption is necessary. In the binomial approach, a parametric, non-parametric, or semi-parametric model was offered according to validity of assumption. However, pseudo-value approaches do not need to establish proportionality.

RESULTS

Following fitting the models to data, slight differences in parameters and variances estimates were seen among models. This showed that semi-parametric multiplicative model and the two models based on pseudo-value approach could be used for fitting this kind of data.

CONCLUSION

We would recommend considering the use of competing risk models instead of normal survival methods when subjects are exposed to more than one cause of failure.