Prediction model for cardiovascular events or all-cause mortality in incident dialysis patients

Serum zinc level is associated with aortic arch calcification in incident dialysis patients

BACKGROUND

Aortic calcification is a predictor of cardiovascular events. Several studies have shown an association between zinc deficiency and aortic calcification in patients with chronic kidney disease (CKD). We therefore investigated the associations between serum zinc levels and aortic arch calcifications in incident patients on dialysis.

METHODS

We analyzed data from 773 patients who started dialysis at our hospital between January 2013 and December 2023. Aortic arch calcification was graded 0-3 on chest X-ray, as follows: grade 0, no visible calcification; grade 1, < 50% calcification in the arch; grade 2, 50% calcification; or grade 3, circumferential calcification. We defined grades 2-3 as severe calcification. We stratified patients into tertiles of serum zinc levels.

RESULTS

Median serum zinc levels were 51, 47 and 44 μg/dL in patients with grade 0, 1 and 2-3 aortic arch calcification, respectively (p < 0.001). In multivariate analysis, low serum zinc level was independently associated with aortic arch calcification (OR 3.12, 95% CI 1.84-5.27; p < 0.001), particularly with severe aortic arch calcification (OR 6.91, 95% CI 3.11-15.40; p < 0.001). Adding serum zinc level to a model with established risk factors for aortic arch calcification ameliorated net reclassification (0.308; p < 0.001) and integrated discrimination improvement (0.018; p = 0.0074). More robust findings for net reclassification improvement (0.427; p < 0.001) and integrated discrimination improvement (0.035; p < 0.001) were observed with severe aortic arch calcifications.

CONCLUSION

Low serum zinc level was independently associated with aortic arch calcification, and in particular, with severe aortic arch calcifications, among patients who started dialysis.

The hidden interplay between sex and adverse outcomes in incident dialysis patients: the role of aortic calcification

Background

Research on the sex disparity in the prognosis of chronic kidney disease (CKD), particularly among those who are newly initiating dialysis, is limited and inconclusive. This study aimed to investigate the associations between sex, and all-cause mortality and major cardiovascular adverse events (MACE), with a particular focus on the presence of aortic calcification (AC).

Methods

We conducted a post hoc analysis of 1459 incident dialysis patients included in this prospective cohort study. The primary outcome of interest was all-cause mortality, and the secondary endpoint was a composite of MACE.

Results

During a median follow-up period of 3.55 years, 362 (269 male and 93 female) patients died and 477 (342 male and 135 female) patients developed MACE. The risks for all-cause mortality [hazard ratio (HR) 0.61, 95% confidence interval (CI) 0.47-0.79] and MACE (HR 0.74, 95% CI 0.60-0.93) were lower in females than in males. This finding was robust across multiple sensitivity analyses and most subgroups. Moreover, the associations between sex and adverse outcomes were significantly modified by AC status at dialysis initiation (P for interaction <.05). Specifically, among patients without AC, females exhibited lower risks for all-cause mortality (HR 0.45, 95% CI 0.29-0.69; P < .001) and MACE (HR 0.67, 95% CI 0.49-0.93; P = .015), whereas no differences were observed for all-cause mortality (HR 0.82, 95% CI 0.59-1.15; P = .256) or MACE (HR 0.80, 95% CI 0.59-1.10; P = .174) among patients with AC.

Conclusions

In patients with renal failure receiving dialysis, AC abolished the survival and cardiovascular protection observed in female versus male patients. This finding supports the need for greater awareness of the AC burden in female dialysis patients.

Mortality Risk Prediction Models for People With Kidney Failure: A Systematic Review

Importance

People with kidney failure have a high risk of death and poor quality of life. Mortality risk prediction models may help them decide which form of treatment they prefer.

Objective

To systematically review the quality of existing mortality prediction models for people with kidney failure and assess whether they can be applied in clinical practice.

Evidence Review

MEDLINE, Embase, and the Cochrane Library were searched for studies published between January 1, 2004, and September 30, 2024. Studies were included if they created or evaluated mortality prediction models for people who developed kidney failure, whether treated or not treated with kidney replacement with hemodialysis or peritoneal dialysis. Studies including exclusively kidney transplant recipients were excluded. Two reviewers independently extracted data and graded each study at low, high, or unclear risk of bias and applicability using recommended checklists and tools. Reviewers used the Prediction Model Risk of Bias Assessment Tool and followed prespecified questions about study design, prediction framework, modeling algorithm, performance evaluation, and model deployment. Analyses were completed between January and October 2024.

Findings

A total of 7184 unique abstracts were screened for eligibility. Of these, 77 were selected for full-text review, and 50 studies that created all-cause mortality prediction models were included, with 2 963 157 total participants, who had a median (range) age of 64 (52-81) years. Studies had a median (range) proportion of women of 42% (2%-54%). Included studies were at high risk of bias due to inadequate selection of study population (27 studies [54%]), shortcomings in methods of measurement of predictors (15 [30%]) and outcome (12 [24%]), and flaws in the analysis strategy (50 [100%]). Concerns for applicability were also high, as study participants (31 [62%]), predictors (17 [34%]), and outcome (5 [10%]) did not fit the intended target clinical setting. One study (2%) reported decision curve analysis, and 15 (30%) included a tool to enhance model usability.

Conclusions and Relevance

According to this systematic review of 50 studies, published mortality prediction models were at high risk of bias and had applicability concerns for clinical practice. New mortality prediction models are needed to inform treatment decisions in people with kidney failure.

Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study

BACKGROUND

The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.

METHODS

Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA).

RESULTS

The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets.

CONCLUSIONS

The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients.

Development of a prognostic risk score to predict early mortality in incident elderly Japanese hemodialysis patients

BACKGROUND

Information of short-term prognosis after hemodialysis (HD) introduction is important for elderly patients with chronic kidney disease (CKD) and their families choosing a modality of renal replacement therapy. Therefore, we developed a risk score to predict early mortality in incident elderly Japanese hemodialysis patients.

MATERIALS AND METHODS

We analyzed data of incident elderly HD patients from a nationwide cohort study of the Japanese Society for Dialysis Therapy Renal Data Registry (JRDR) to develop a prognostic risk score. Candidate risk factors for early death within 1 year was evaluated using multivariate logistic regression analysis. The risk score was developed by summing up points derived from parameter estimate values of independent risk factors. The association between risk score and early death was tested using Cox proportional hazards models. This risk score was validated twice by using an internal validation cohort derived from the JRDR and an external validation cohort collected for this study.

RESULTS

Using the development cohort (n = 2,000), nine risk factors were retained in the risk score: older age (>85), yes = 2, no = 0; sex, male = 2, female = 0; lower body mass index (<20), yes = 2, no = 0; cancer, yes = 1, no = 0; dementia, yes = 3, no = 0; lower creatinine (<6.5 mg/dL), yes = 1, no = 0; lower albumin (<3.0 g/dL), yes = 3, no = 0; normal or high calcium (≥8.5 mg/dL), yes = 1, no = 0; and higher C reactive protein (>2.0 mg/dL), yes = 2, no = 0. In the internal and external validation cohorts (n = 739, 140, respectively), the medium- and high-risk groups (total score, 6 to 10 and 11 or more, respectively) showed significantly higher risk of early death than the low-risk group (total score, 0 to 5) (p<0.001).

CONCLUSION

We developed a prognostic risk score predicting early death within 1 year in incident elderly Japanese HD patients, which may help detect elderly patients with a high-risk of early death after HD introduction.

Risk prediction models for cardiovascular events in hemodialysis patients: A systematic review

OBJECTIVE

To perform a systematic review of risk prediction models for cardiovascular (CV) events in hemodialysis (HD) patients, and provide a reference for the application and optimization of related prediction models.

METHODS

PubMed, The Cochrane Library, Web of Science, and Embase databases were searched from inception to 1 February 2023. Two authors independently conducted the literature search, selection, and screening. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to evaluate the risk of bias and applicability of the included literature.

RESULTS

A total of nine studies containing 12 models were included, with performance measured by the area under the receiver operating characteristic curve (AUC) lying between 0.70 and 0.88. Age, diabetes mellitus (DM), C-reactive protein (CRP), and albumin (ALB) were the most commonly identified predictors of CV events in HD patients. While the included models demonstrated good applicability, there were still certain risks of bias, primarily related to inadequate handling of missing data and transformation of continuous variables, as well as a lack of model performance validation.

CONCLUSION

The included models showed good overall predictive performance and can assist healthcare professionals in the early identification of high-risk individuals for CV events in HD patients. In the future, the modeling methods should be improved, or the existing models should undergo external validation to provide better guidance for clinical practice.

Improving the Prediction of Death from Cardiovascular Causes with Multiple Risk Markers

Background

Traditional risk factors including demographics, blood pressure, cholesterol, and diabetes status are successfully able to predict a proportion of cardiovascular disease (CVD) events. Whether including additional routinely measured factors improves CVD prediction is unclear. To determine whether a comprehensive risk factor list, including clinical blood measures, blood counts, anthropometric measures, and lifestyle factors, improves prediction of CVD deaths beyond traditional factors.

Methods

The analysis comprised of 21,982 participants aged 40 years and older (mean age=59.4 years at baseline) from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2016 survey cycles. Data were linked with the National Death Index mortality data through 2019 and split into 80:20 training and testing sets. Relative to the traditional risk factors (age, sex, race/ethnicity, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, antihypertensive medications, and diabetes), we compared models with an additional 22 clinical blood biomarkers, 20 complete blood counts, 7 anthropometric measures, 51 dietary factors, 13 cardiovascular health-related questions, and all 113 predictors together. To build prediction models for CVD mortality, we performed Cox proportional hazards regression, elastic-net (ENET) penalized Cox regression, and random survival forest, and compared classification using C-index and net reclassification improvement.

Results

During follow-up (median, 9.3 years), 3,075 participants died; 30.9% (1,372/3,075) deaths were from cardiovascular causes. In Cox proportional hazards models with traditional risk factors (C-index=0.850), CVD mortality classification improved with incorporation of clinical blood biomarkers (C-index=0.867), blood counts (C-index=0.861), and all predictors (C-index=0.871). Net CVD mortality reclassification improved 13.2% by adding clinical blood biomarkers and 12.2% by adding all predictors. Results for ENET-penalized Cox regression and random survival forest were similar. No improvement was observed in separate models for anthropometric measures, dietary nutrient intake, or cardiovascular health-related questions.

Conclusions

The addition of clinical blood biomarkers and blood counts substantially improves CVD mortality prediction, beyond traditional risk factors. These biomarkers may serve as an important clinical and public health screening tool for the prevention of CVD deaths.

Development of a prediction model to estimate the 5-year risk of cardiovascular events and all-cause mortality in haemodialysis patients: a retrospective study

Background

Cardiovascular disease (CVD) is a major cause of mortality in patients on haemodialysis. The development of a prediction model for CVD risk is necessary to help make clinical decisions for haemodialysis patients. This retrospective study aimed to develop a prediction model for the 5-year risk of CV events and all-cause mortality in haemodialysis patients in China.

Methods

We retrospectively enrolled 398 haemodialysis patients who underwent dialysis at the dialysis facility of the General Hospital of Northern Theater Command in June 2016 and were followed up for 5 years. The composite outcome was defined as CV events and/or all-cause death. Multivariable logistic regression with backwards stepwise selection was used to develop our new prediction model.

Results

Seven predictors were included in the final model: age, male sex, diabetes, history of CV events, no arteriovenous fistula at dialysis initiation, a monocyte/lymphocyte ratio greater than 0.43 and a serum uric acid level less than 436 mmol/L. Discrimination and calibration were satisfactory, with a C-statistic above 0.80. The predictors lay nearly on the 45-degree line for agreement with the outcome in the calibration plot. A simple clinical score was constructed to provide the probability of 5-year CV events or all-cause mortality. Bootstrapping validation showed that the new model also has similar discrimination and calibration. Compared with the Framingham risk score (FRS) and a similar model, our model showed better performance.

Conclusion

This prognostic model can be used to predict the long-term risk of CV events and all-cause mortality in haemodialysis patients. An MLR greater than 0.43 is an important prognostic factor.

Physiological Mechanisms of Hypertension and Cardiovascular Disease in End-Stage Kidney Disease

PURPOSE OF REVIEW

In this article, we summarize recent advances in understanding hypertension and cardiovascular disease in patients with end-stage kidney disease.

RECENT FINDINGS

Factors such as anemia, valvular and vascular calcification, vasoconstrictors, uremic toxins, hypoglycemia, carbamylated proteins, oxidative stress, and inflammation have all been associated with the progression of cardiovascular disease in end-stage kidney disease but the causality of these mechanisms has not been proven. The high risk of cardiovascular mortality has not improved as in the general population despite many advancements in cardiovascular care over the last two decades. Mechanisms that increase hypertension risk in these patients are centered on the control of extracellular fluid volume; however, over-correction of volume with dialysis can increase risks of intradialytic hypotension and death in these patients. This review presents both recent and classic work that increases our understanding of hypertension and cardiovascular disease in end-stage kidney disease.

Building clinical risk score systems for predicting the all-cause and expanded cardiovascular-specific mortality of patients with type 2 diabetes

AIM

To develop and validate risk score systems by examining the effects of glycaemic and blood pressure variabilities on the all-cause and expanded cardiovascular-specific mortality of people with type 2 diabetes.

MATERIALS AND METHODS

This retrospective cohort study consisted of 9692 patients aged 30-85 years, diagnosed with type 2 diabetes and enrolled in a managed care programme of a medical centre from 2002 to 2016. All the patients were randomly allocated into two groups, namely, training and validation sets (2:1 ratio), and followed up until death or August 2019. Cox's proportional hazard regression was performed to develop all-cause and expanded cardiovascular-specific mortality prediction models. The performance of the prediction model was assessed by using the area under the receiver operating characteristic curve (AUROC).

RESULTS

Overall, 2036 deaths were identified after a mean of 8.6 years of follow-up. The AUROC-measured prediction accuracies of 3-, 5-, 10- and 15-year all-cause mortalities based on a model containing the identified traditional risk factors, biomarkers and variabilities in fasting plasma glucose, HbA1c and blood pressure in the validation set were 0.79 (0.76-0.83), 0.78 (0.76-0.81), 0.80 (0.78-0.82) and 0.80 (0.78-0.82), respectively. The corresponding values of the expanded cardiovascular-specific mortalities were 0.85 (0.80-0.90), 0.83 (0.79-0.86), 0.80 (0.77-0.83) and 0.79 (0.77-0.82), respectively.

CONCLUSIONS

Our prediction models considering glycaemic and blood pressure variabilities had good prediction accuracy for the expanded cardiovascular-specific and all-cause mortalities of patients with type 2 diabetes.