Nomogram for predicting 1-, 5-, and 10-year survival in hemodialysis (HD) patients: a single center retrospective study

A novel nomogram for predicting mortality risk in young and middle-aged patients undergoing maintenance hemodialysis: a retrospective study

Objectives

The annual growth in the population of maintenance hemodialysis (MHD) patients is accompanied by a trend towards younger age groups among new cases. Despite the escalating mortality risk observed in MHD patients, there remains a dearth of research focused on young and middle-aged individuals in this cohort, leading to a deficiency in specialized predictive instruments for this demographic. This research seeks to explore the critical determinants impacting mortality risk in young and middle-aged MHD patients and to construct a prediction model accordingly.

Methods

This study involved 127 young and middle-aged patients undergoing MHD in the Blood Purification Center of Chaohu Hospital of Anhui Medical University from January 2019 to January 2022. The follow-up period for each patient ended either at the time of death or on January 31, 2024. Participants were monitored to determine their survival status and categorized into two groups: those who survived (98 patients) and those who deceased (29 patients). Clinical data were gathered for analysis. Logistic regression was utilized to pinpoint independent risk factors for mortality among these patients. Subsequently, a nomogram was established to predict mortality risk. The efficacy of this model was assessed through the area under the receiver operating characteristic curve (AUC-ROC), alongside a calibration curve and the Hosmer-Lemeshow test to examine its fit. Additionally, decision curve analysis (DCA) was conducted to ascertain the clinical relevance of the predictive model.

Results

The study incorporated 127 young and middle-aged patients undergoing MHD, with a mortality rate recorded at 22.83% (29 cases). A logistic regression analysis revealed that age, hemoglobin (HB), serum magnesium (Mg), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-albumin ratio (PAR) were significant independent predictors of mortality among these patients. Utilizing these variables, a nomogram was developed to predict mortality risk, achieving an AUC of 0.899 (95% CI: 0.833-0.966). The model exhibited a specificity of 83.67% and a sensitivity of 82.76%, demonstrating substantial discriminative ability. The model's robustness was confirmed through internal validation with 1,000 bootstrap samples, yielding an AUC of 0.894 (95% CI: 0.806-0.949). The calibration curve closely aligned with the ideal curve, and the Hosmer-Lemeshow goodness-of-fit test yielded a χ 2 value of 6.312 with a p-value of 0.612, verifying the model's high calibration accuracy. Additionally, the DCA indicated that the model provides a net benefit across a wide range of decision thresholds from 0 to 0.99, underscoring its clinical utility.

Conclusion

The nomogram developed from variables including age, HB levels, serum Mg, NLR, and PAR exhibits high levels of discrimination and calibration. This model effectively predicts mortality risk among young and middle-aged patients undergoing MHD, proving its clinical relevance.

Predictors of survival and functioning of arteriovenous fistula in patients on hemodialysis during a one-year follow-up

INTRODUCTION

The mortality rate of hemodialysis patients is extremely high and it is significantly affected by vascular access dysfunction. Our research aimed to determine predictive parameters of arteriovenous fistula functioning and survival in a one-year follow-up period.

METHODS

The research was organized as a prospective, one-year study, which included 120 dialysis patients who were followed for one year. We recorded the demographic and gender structure, clinical parameters, and laboratory findings significant for the survival and functioning of arteriovenous fistulas. Laboratory findings are presented as the mean values of the analysis at the beginning and the end of the one-year control period.

RESULTS

Univariable regression analysis confirmed the predictive significance of anastomosis positioning, type of vascular access, length of hemodialysis treatment, hemoglobin, Kt/V index values, and creatinine concentration for one-year survival, but multivariable regression analysis confirmed predictive significance only for length of treatment. Univariable regression analysis revealed significant predictors of vascular access function for the length of hemodialysis treatment, diastolic blood pressure, leukocytes, platelets, hemoglobin, creation of an arteriovenous fistula by a nephrologist, starting hemodialysis with a fistula and not with a central venous catheter, multivariable regression analysis confirmed predictive significance for the length of dialysis treatment and creation of an arteriovenous fistula by a nephrologist.

CONCLUSION

A prognostically important parameter for the one-year survival of a patient on hemodialysis is the length of dialysis treatment. In contrast, predictive parameters for the functioning of an arteriovenous fistula are the length of dialysis and the creation of a fistula by a nephrologist.

Construction and Evaluation of a Predictive Nomogram for Identifying Premature Failure of Arteriovenous Fistulas in Elderly Diabetic Patients

Background

This research aimed to identify risk factors contributing to premature maturation of arteriovenous fistulas (AVF) in elderly diabetic patients and develop a clinical prediction model.

Methods

We conducted a retrospective review of 548 geriatric diabetic patients who underwent AVF creation for maintenance hemodialysis (MHD) at Baoding No 1 Central Hospital between January 2011 and December 2023. Patients were divided into mature (386) and immature (162) groups based on AVF maturation status. Univariate logistic regression analysis and the least absolute shrinkage and selection operator were used to identify independent risk factors, including D-dimer levels, low-density lipoprotein cholesterol levels, internal radial meridian, radial artery plaque presence, and cephalic vein indwelling needle use history. A predictive nomogram was developed specifically for immature AVF in elderly diabetic patients. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

Among elderly patients with diabetes mellitus, the incidence of immature AVF was 29.56%, affecting 162 of 548 individuals. The five-variable model demonstrated an AUROC value of 0.922, with a 95% confidence interval (CI) of 0.870 to 0.947 in the training dataset, and an AUROC of 0.912, accompanied by a 95% CI of 0.880 to 0.935 in the internal validation dataset. The calibration curve, derived from 1000 bootstrap samples, showed good agreement between predicted and observed outcomes. Additionally, both the DCA and CIC exhibited favorable clinical utility and net benefits.

Conclusions

The nomogram prediction model, based on independent risk factors, serves as a valuable tool for accurate prognosis and has potential to aid in establishing and preserving hemodialysis access in elderly diabetic patients, ultimately optimizing their healthcare outcomes.

Prognostic nomogram for the patency of wrist autologous arteriovenous fistula in first year

Autologous arteriovenous fistula (AVF) is preferred in hemodialysis patients. Maintaining its patency is a critical problem. This study aimed to create a nomogram model for predicting 1-year primary patency of AVF. Consequently, a total of 414 patients were retrospectively enrolled and randomly allocated to training and validation cohorts. Risk factors were identified by multivariable logistic regression and used to create a nomogram model. Performance of the model was evaluated by receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and calibration curve. The results suggested that diameter of cephalic vein, low-density lipoprotein, glycosylated hemoglobin (%), and C-reactive protein were risk factors which could predict the patency of AVF. Area under ROC curves for training and validation cohorts were 0.771 and 0.794, respectively. Calibration ability was satisfactory in both cohorts. Therefore, present nomogram model could predict the 1-year primary patency of AVF.

COVID-19 increases mortality in hemodialysis patients: exploring links with inflammation and telomere attrition

BACKGROUND AND OBJECTIVE

An increased risk of mortality and hospitalization was consistently demonstrated in hemodialysis (HD) patients affected by pandemic coronavirus infection (COVID-19). In this study, we analyzed parameters that may impact mortality in COVID-19 HD patients, including neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP), COVID-19 disease status and telomere length in peripheral blood cells (TL).

MATERIALS AND METHODS

A total of 130 chronic hemodialysis patients were enrolled and followed up for 18 months. Patients were categorized into groups based on their COVID-19 disease history and subsequent data about their survival status at the end of the study. Routine laboratory parameters were assessed using standard automated methods and TL was determined using the modified Cawthon method. Survival predictors were analyzed using Kaplan-Meier analysis.

RESULTS

Deceased patients (30%) were older with higher body mass index (BMI), higher levels of LDH, NLR index, CRP and lower TL and lymphocytes count compared to survivors. Kaplan-Meier survival analysis showed six parameters were significant mortality predictors in the following order of significance: COVID-19 history, 2-years cardiovascular mortality risk score, NLR, TL, CRP, LDH. Using binary logistic regression analysis Summary risk score, a combination of these six parameters revealed as the best predictor of patient's survival in this group of parameters (log rank 25.4, p < 0.001).

CONCLUSION

Compared to the general population, the mortality rate among HD patients persists at a higher level despite advancements in HD technology and patient care. The situation has been exacerbated by COVID-19, by significant increase in mortality rate among these patients.

Causal factors of cardiovascular disease in end-stage renal disease with maintenance hemodialysis: a longitudinal and Mendelian randomization study

Background

The risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD (n = 218,792,101,866 cases), end-stage renal disease (ESRD, n = 16,405, 326 cases), diabetes (n = 202,046, 9,889 cases), creatinine (n = 7,810), and uric acid (UA, n = 109,029) were obtained from the large-open GWAS project. The inverse-variance weighted MR was used as the main analysis to estimate the causal associations, and several sensitivity analyses were performed to assess pleiotropy and exclude variants with potential pleiotropic effects.

Results

The AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant (p < 0.05) discrimination between the Kaplan-Meier curves of each age, diabetes, ipth, and creatinine subclassification. MR analysis indicated that diabetes had a causal role in risk of CVD (β = 0.088, p < 0.0001) and ESRD (β = 0.26, p = 0.007). In turn, ESRD was found to have a causal role in risk of diabetes (β = 0.027, p = 0.013). Additionally, creatinine exhibited a causal role in the risk of ESRD (β = 4.42, p = 0.01).

Conclusions

The results showed that old age, diabetes, and low level of ipth, creatinine, and UA were important risk factors for CVD in hemodialysis patients, and diabetes played an important bridging role in the link between ESRD and CVD.

Development of a risk prediction nomogram for sarcopenia in hemodialysis patients

Background

Sarcopenia is associated with various adverse outcomes in hemodialysis patients. However, current tools for assessing and diagnosing sarcopenia have limited applicability. In this study, we aimed to develop a simple and reliable nomogram to predict the risk of sarcopenia in hemodialysis patients that could assist physicians identify high-risk patients early.

Methods

A total of 615 patients undergoing hemodialysis at the First Affiliated Hospital College of Medicine Zhejiang University between March to June 2021 were included. They were randomly divided into either the development cohort (n = 369) or the validation cohort (n = 246). Multivariable logistic regression analysis was used to screen statistically significant variables for constructing the risk prediction nomogram for Sarcopenia. The line plots were drawn to evaluate the effectiveness of the nomogram in three aspects, namely differentiation, calibration, and clinical net benefit, and were further validated by the Bootstrap method.

Results

The study finally included five clinical factors to construct the nomogram, including age, C-reactive protein, serum phosphorus, body mass index, and mid-upper arm muscle circumference, and constructed a nomogram. The area under the ROC curve of the line chart model was 0.869, with a sensitivity and specificity of 77% sensitivity and 83%, the Youden index was 0.60, and the internal verification C-statistic was 0.783.

Conclusions

This study developed and validated a nomogram model to predict the risk of sarcopenia in hemodialysis patients, which can be used for early identification and timely intervention in high-risk groups.

Development and validation of a nomogram for predicting the 6-months survival rate of patients undergoing incident hemodialysis in China

Background

The all-cause mortality of patients undergoing hemodialysis (HD) is higher than in the general population. The first 6 months after dialysis are important for new patients. The aim of this study was to develop and validate a nomogram for predicting the 6-month survival rate of HD patients.

Methods

A prediction model was constructed using a training cohort of 679 HD patients. Multivariate Cox regression analyses were performed to identify predictive factors. The identified factors were used to establish a nomogram. The performance of the nomogram was assessed using the C-index and calibration plots. The nomogram was validated by performing discrimination and calibration tests on an additional cohort of 173 HD patients.

Results

During a follow-up period of six months, 47 and 16 deaths occurred in the training cohort and validation cohort, respectively, representing a mortality rate of 7.3% and 9.2%, respectively. The nomogram comprised five commonly available predictors: age, temporary dialysis catheter, intradialytic hypotension, use of ACEi or ARB, and use of loop diuretics. The nomogram showed good discrimination in the training cohort [C-index 0.775(0.693–0.857)] and validation cohort [C-index 0.758(0.677–0.836)], as well as good calibration, indicating that the performance of the nomogram was good. The total score point was then divided into two risk classifications: low risk (0–90 points) and high risk (≥ 91 points). Further analysis showed that all-cause mortality was significantly different between the high-risk group and the low-risk group.

Conclusions

The constructed nomogram accurately predicted the 6-month survival rate of HD patients, and thus it can be used in clinical decision-making.

Development and validation of a novel nomogram to predict overall survival of patients with moderate to severe chronic kidney disease

Introduction

The risk of death significantly increased from stage 3 chronic kidney disease (CKD) onward. We aimed to construct a novel nomogram to predict the overall survival (OS) of patients afflicted with CKD from stage 3–5.

Methods

A total of 882 patients with stage 3–5 CKD were enrolled from the NHANES 2001–2004 survey. Data sets from the 2003–2004 survey population were used to develop a nomogram that would predict the risk of OS. The 2001–2002 survey population was used to validate the nomogram. Least absolute shrinkage and selection operator (Lasso) regression was conducted to screen the significant predictors relative to all-cause death. The multivariate Cox regression based on the screened factors was applied to effectively construct the nomogram. The performance of the nomogram was evaluated according to the C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve with 1000 bootstraps resample. Kaplan–Meier’s curves were used for testing the discrimination of the prediction model.

Results

Five variables (age, urinary albumin-to-creatinine ratio (UACR), potassium, cystatin C (Cys C), and homocysteine) were screened by the Lasso regression. The nomogram was constructed using these factors, as well as the CKD stage. The included factors (age, CKD stage, UACR, potassium, Cys C, and homocysteine) were all significantly related to the death of CKD patients, according to the multivariate Cox regression analysis. The internal validation showed that this nomogram demonstrates good discrimination and calibration (adjusted C-index: 0.70; AUC of 3-, 5-, and 10-year OS were 0.75, 0.78, and 0.77, respectively). External validation also demonstrated exceedingly similar results (C-index: 0.72, 95% CI: 0.69–0.76; AUC of 3-, 5-, and 10-year OS were 0.76, 0.79, and 0.80, respectively).

Conclusions

This study effectively constructed a novel nomogram that incorporates CKD stage, age, UACR, potassium, Cys C, and homocysteine, which can be conveniently used to facilitate the individualized prediction of survival probability in patients with stage 3–5 CKD. It displays valuable potential for clinical application.