Association of the modified creatinine index with muscle strength and mortality in patients undergoing hemodialysis

Modified creatinine index for predicting prognosis in hemodialysis patients: a systematic review and meta-analysis

BACKGROUND

Currently, several studies have explored the association between the modified creatinine index (mCI) and prognosis in patients on hemodialysis (HD). However, some of their results are contradictory. Therefore, this study was conducted to comprehensively assess the role of mCI in predicting prognosis in HD patients through meta-analysis.

METHODS

We searched and screened literature from PubMed, Embase, Web of Science, and Cochrane databases from their establishment until March 2024. Relevant data were extracted. The statistical analysis was performed using Stata 15.0, RevMan 5.4, and Meta DiSc 1.4 software.

RESULTS

The results showed a positive association between mCI and nutritional status in HD patients (BMI r = 0.19, 95% CI: 0.1-0.28, p = .000; albumin r = 0.36, 95% CI: 0.33-0.39, p = .000; normalized protein catabolic rate (nPCR) r = 0.25, 95% CI: 0.13-0.38, p = .000). In addition, mCI in deceased HD patients was significantly lower than that in HD survivors (SMD = -0.94, 95% CI: -1.46 to -0.42, p = .000). A low mCI was associated with an increased risk of all-cause death in HD patients (HR = 1.95, 95% CI: 1.57-2.42, p = .000). In addition, a low mCI was significantly associated with decreased overall survival (OS) in HD patients (HR = 3.01, 95% CI: 2.44-3.70, p = .000). mCI showed moderate diagnostic accuracy for sarcopenia in both male and female HD patients (male AUC = 0.7891; female AUC = 0.759).

CONCLUSIONS

The mCI can be used as a prognostic marker for HD patients, and monitoring mCI may help to optimize the management of HD and improve overall prognosis in patients.

Association of the modified creatinine index with quality of life in haemodialysis patients

Aims/Background The evaluation of health-related quality of life in patients undergoing maintenance haemodialysis has garnered increasing attention. The modified creatinine index, a surrogate marker for muscle mass, has been linked to various clinical outcomes. However, the relationship between modified creatinine index and health-related quality of life in maintenance haemodialysis patients remains unclear. This study aims to elucidate the association between modified creatinine index and health-related quality of life in individuals receiving maintenance haemodialysis. Methods This cross-sectional study included 217 maintenance haemodialysis patients. Health-related quality of life was assessed using the Kidney Disease Quality of Life Instrument. Collected data included general patient information, laboratory results, and haemodialysis-related parameters. The modified creatinine index was calculated based on gender, age, single-pool Kt/V (spKt/V), and pre-dialysis serum creatinine levels. Multiple linear regression models and smooth curve fitting were used to investigate the relationship between modified creatinine index and health-related quality of life. Subgroup analyses and interaction tests were performed to identify potential effect modifiers. Results The 217 maintenance haemodialysis patients had a mean age of 53.66±13.15 years and a median dialysis vintage of 39 (25-84) months; 120 (55.30%) were male. The mean health-related quality of life score was 55.76±10.33, and the mean modified creatinine index was 22.72±2.95 mg/kg/day. After adjusting for confounding factors, an increase in modified creatinine index was associated with an improvement in health-related quality of life (β=0.55, 95% CI: 0.04, 1.06, p = 0.033). No nonlinear relationship was identified between modified creatinine index and health-related quality of life by smooth curve fitting. Subgroup and interaction analyses indicated that the relationship between modified creatinine index and health-related quality of life was stable and not significantly influenced by age, gender, dialysis vintage, diabetes status, or body mass index (p > 0.05). Conclusion Modified creatinine index is positively correlated with health-related quality of life in maintenance haemodialysis patients, suggesting its potential utility in evaluating patient quality of life. Modified creatinine index could be clinically useful to improve the predictability of health-related quality of life in maintenance haemodialysis patients.

Blood biomarkers for sarcopenia: A systematic review and meta-analysis of diagnostic test accuracy studies

Biomarkers are emerging as a potential tool for screening or diagnosing sarcopenia. We aimed to summarize the current evidence on the diagnostic test accuracy of biomarkers for sarcopenia. We comprehensively searched Ovid MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials up to January 2023 and only included diagnostic test accuracy studies. We identified 32 studies with 23,840 participants (women, 58.26%) that assessed a total of 30 biomarkers. The serum creatinine to cystatin C ratio (Cr/CysC) demonstrated a pooled sensitivity ranging from 51% (95% confidence interval [CI] 44-59%) to 86% (95% CI 70-95%) and a pooled specificity ranged from 55% (95% CI 38-70%) to 76% (95% CI 63-86%) for diagnosing sarcopenia defined by five different diagnostic criteria (11 studies, 7240 participants). The aspartate aminotransferase to alanine aminotransferase ratio demonstrated a pooled sensitivity of 62% (95% CI 56-67%) and a pooled specificity of 66% (95% CI 60-72%) (3 studies, 11,146 participants). The other 28 blood biomarkers exhibited low-to-moderate diagnostic accuracy for sarcopenia regardless of the reference standards. In conclusion, none of these biomarkers are optimal for screening or diagnosing sarcopenia. Well-designed studies are needed to explore and validate novel biomarkers for sarcopenia.

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

Creatinine index: a retrospective cohort study in an urban Australian dialysis context

AIM

This study aimed to described the relationship between the CI and mortality in an Australian context.

INTRODUCTION

Maintenance haemodialysis is a catabolic state associated with a significant decrease in lean body mass (LBM) and protein energy wasting. LBM can be derived or estimated from creatinine kinetic modelling, specifically the creatinine index (CI). This has been demonstrated in cohort studies to predict mortality.

METHODS

One hundred seventy-nine patients undergoing haemodialysis in 2015 were included in this cohort. They were followed for 5 years with pertinent clinical data collected to calculate the CI as of December 2015. For analysis, patients were split into a high and low CI group based on the median (18.32 mg/kg/day). The primary outcome of interest was all-cause mortality, and secondary outcomes included myocardial infarction, stroke and transplantation.

RESULTS

During follow-up, 69 (76.7%) patients in the low CI group and 28 (31.5%) patients in the high CI group died (P < 0.001). The relative risk (RR) of mortality within the low compared with the high CI group was 2.43 (95% confidence interval, 1.75-3.38). Fully adjusted Cox proportional hazards modelling demonstrated a hazard ratio (HR) of 0.498 (95% CI, 0.292-0.848) for survival in the high CI group. Lower CI was associated with increased risk of stroke (RR, 5.43 [95% CI, 1.24-23.84]), whereas transplant was more likely in the high CI group (RR, 6.4 [95% confidence interval, 1.96-20.88]).

CONCLUSIONS

In a single-centre Australian haemodialysis cohort, the CI was strongly associated with mortality and stroke risk. The CI is an accurate and simple method to identify patients with low LBM at risk for significant morbidity and mortality.

Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis

BACKGROUND

Muscle mass is important in determining patients' nutritional status. However, measurement of muscle mass requires special equipment that is inconvenient for clinical use. We aimed to develop and validate a nomogram model for predicting low muscle mass in patients undergoing hemodialysis (HD).

METHODS

A total of 346 patients undergoing HD were enrolled and randomly divided into a 70% training set and a 30% validation set. The training set was used to develop the nomogram model, and the validation set was used to validate the developed model. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, a calibration curve, and the Hosmer-Lemeshow test. A decision curve analysis (DCA) was used to evaluate the clinical practicality of the nomogram model.

RESULTS

Age, sex, body mass index (BMI), handgrip strength (HGS), and gait speed (GS) were included in the nomogram for predicting low skeletal muscle mass index (LSMI). The diagnostic nomogram model exhibited good discrimination with an area under the ROC curve (AUC) of 0.906 (95% CI, 0.862-0.940) in the training set and 0.917 (95% CI, 0.846-0.962) in the validation set. The calibration analysis also showed excellent results. The nomogram demonstrated a high net benefit in the clinical decision curve for both sets.

CONCLUSIONS

The prediction model included age, sex, BMI, HGS, and GS, and it can successfully predict the presence of LSMI in patients undergoing HD. This nomogram provides an accurate visual tool for medical staff for prediction, early intervention, and graded management.

Serum creatinine-to-cystatin C ratio as an indicator of sarcopenia in hemodialysis patients

BACKGROUND AND AIMS

In hemodialysis patients, sarcopenia is common and related to morbidity and mortality. In non-dialysis patients, the serum creatinine-to-cystatin C (Cre/Cys-C) ratio is a marker of sarcopenia. Its clinical utility in hemodialysis populations, however, is still unknown. Our study aimed to determine whether sarcopenia could be detected using the Cre/Cys-C ratio in hemodialysis patients.

METHODS

This retrospective cross-sectional study included 85 hemodialysis patients whose handgrip strength (HGS) and bioimpedance analysis-estimated skeletal muscle index (SMI) were assessed. Sarcopenia was diagnosed as a combination of reduced muscle strength (women: HGS <18 kg; men: HGS <28 kg) and decreased muscle mass volume (women: SMI <5.7 kg/m²; men: SMI <7.0 kg/m²).

RESULTS

Sarcopenia was observed in 33 (38.8%) patients. Patients with sarcopenia had a significantly lower Cre/Cys-C ratio than those without (1.3 ± 0.2 vs 1.7 ± 0.3, respectively; p < 0.0001). The Cre/Cys-C ratio was independently associated with HGS (β = 0.303, p = 0.011) and SMI (β = 0.376, p = 0.0007). After adjustment for sex and age, the C-statistic of the Cre/Cys-C ratio that predicted sarcopenia was 0.898 (95% CI [0.827, 0.969], p < 0.0001). Moreover, as Cre/Cys-C ratios increased, the risk of sarcopenia significantly decreased (adjusted OR: 0.665 for each 0.1 increase in the Cre/Cys-C ratio) (95% CI [0.501, 0.857], p = 0.0002).

CONCLUSION

The Cre/Cys-C ratio may be a helpful prediction tool for sarcopenia in patients receiving hemodialysis.