Revisiting serum creatinine as an indicator of muscle mass and a predictor of mortality among patients on hemodialysis

Association of muscle mass and radiodensity assessed by chest CT with all-cause and cardiovascular mortality in hemodialysis patients

PURPOSE

This study investigates the prognostic value of skeletal muscle index (SMI) and skeletal muscle radiodensity (SMD) measured by chest CT in relation to all-cause and cardiovascular disease (CVD) mortality among hemodialysis (HD) patients.

METHODS

A retrospective study was conducted from January 2015 to December 2021 involving HD patients at a dialysis center. Chest CT scans at the twelfth thoracic vertebra level (T12) were analyzed to assess SMI and SMD. Sex-specific cut-off values for two metrics were determined using maximally selected rank statistics. Hazard ratios (HRs) were calculated to evaluate the associations of SMI and SMD with mortality. The discrimination of prognostic models was also compared.

RESULTS

The study included 603 patients with a median age of 58 years. Of these, 187 (31.0%) patients with SMI < 30.00 cm2/m2 (male) or < 25.04 cm2/m2 (female) and 192 (31.8%) patients with SMD < 32.25 HU (male) or < 30.64 HU (female) were categorized as lower SMI and SMD, respectively. Over a median follow-up of 3.8 years, 144 deaths occurred. Multivariate Cox regression analysis showed that lower SMI and SMD were independently associated with all-cause mortality (SMI: HR = 1.47, 95% CI 1.03-2.10; SMD: HR = 1.75, 95% CI 1.20-2.54) and CVD mortality (SMI: HR = 1.74, 95% CI 1.03-2.94; SMD: HR = 1.72, 95% CI 1.02-2.95). Adding SMI and SMD to the established risk model improved the C-index from 0.82 to 0.87 (P < 0.001). Decision curve analysis showed that the prognostic model incorporating both SMI and SMD offered the highest net benefit for predicting all-cause mortality.

CONCLUSIONS

Muscle metrics derived from CT scans at T12 level provide valuable prognostic information which could enhance the role of chest CT in muscle assessment among HD patients.

Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study

BACKGROUND

Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.

OBJECTIVE

This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia.

METHODS

We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process.

RESULTS

Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy.

CONCLUSION

We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia.

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.

Impact of Abdominal Fat Distribution on Mortality and Its Changes Over Time in Patients Undergoing Hemodialysis: A Prospective Cohort Study

OBJECTIVE

Measures of fat distribution and visceral fat accumulation maintain a direct association with mortality in the general population. However, among patients undergoing hemodialysis (HD), there are few reports of this association. This study aimed to investigate the impact of computed tomography (CT)-measured abdominal fat levels, including the visceral fat area (VFA) and subcutaneous fat area (SFA), on all-cause mortality in patients undergoing HD and investigate whether there are sex-specific particularities regarding the associations between the abovementioned parameters.

METHODS

A total of 258 participants were selected from the population of patients undergoing stable HD. The baseline characteristics were collected by records and interviews. The following variables were assessed at baseline and every year: body mass index, abdominal circumference, VFA, and SFA. Abdominal circumference and body fat distribution were assessed at the level of the umbilicus via CT. All CT scans were performed on a nondialysis day with the subject in a supine position. The primary end point was the 5-year all-cause mortality.

RESULTS

This prospective cohort study revealed that age, cardiothoracic ratio, %VFA (VFA/[VFA + SFA]), and albumin were independent predictors of death via multivariable analyses. Regarding the %VFA, its area under the curve (0.599), which did not suffice to predict mortality, was higher than that of VFA, SFA, and body mass index. Also, the effect was recognized mainly in male patients. The %VFA of patients who survived for 60 months increased over time.

CONCLUSION

These data suggest that patients (especially men) with a high VFA-to-abdominal fat ratio have a high risk of death. Thus, more attention should be paid to such patients.

Simplified discriminant parameters for sarcopenia among patients undergoing haemodialysis

BACKGROUND

Patients with end-stage renal disease (ESRD) are at an increased risk of developing sarcopenia, which can lead to various adverse health outcomes. Although the diagnosis of sarcopenia is essential for clinical management, it is not feasible in routine clinical practice for populations undergoing haemodialysis because it is time-consuming and resources are limited. Serum creatinine levels in patients with ESRD have been gaining attention as a screening parameter for sarcopenia because serum creatinine is a routinely measured byproduct of skeletal muscle metabolism. This study aimed to evaluate the discriminative ability of the creatinine-derived index for sarcopenia in patients undergoing haemodialysis.

METHODS

We diagnosed sarcopenia according to the Asian Working Group for Sarcopenia (AWGS) 2 criteria in 356 clinically stable outpatients with ESRD enrolled from three dialysis facilities. We adopted the modified creatinine index as a simplified discriminant parameter for sarcopenia in addition to the calf circumference, SARC-F score, and combination of both (i.e. SARC-CalF score), which are recommended by the AWGS. Receiver operating characteristic analysis and logistic regression analysis were conducted to evaluate the discriminative ability of the modified creatinine index for sarcopenia.

RESULTS

Of the study participants, 142 (39.9%) were diagnosed with sarcopenia. The areas under the curve of the modified creatinine index against sarcopenia in the male and female participants were 0.77 (95% confidence interval [CI]: 0.71 to 0.83) and 0.77 (95% CI: 0.69 to 0.85), respectively. All simplified discriminant parameters were significantly associated with sarcopenia, even after adjusting for patient characteristics and centre. In the comparison of the odds ratios for sarcopenia for 1-standard deviation change in the simplified discriminant parameters, the odds ratio of the modified creatinine index was 1.92 (95% CI: 1.15 to 3.19), which was lower than that of the calf circumference (odds ratio: 6.58, 95% CI: 3.32 to 13.0) and similar to that of the SARC-F (odds ratio: 1.57, 95% CI: 1.14 to 2.16) and SARC-CalF scores (odds ratio: 2.36, 95% CI: 1.60 to 3.47).

CONCLUSIONS

This study revealed a strong association between the creatinine-derived index and sarcopenia in patients undergoing haemodialysis. The modified creatinine index was equal or superior to those of SARC-F and SARC-CalF score in discriminability for sarcopenia. However, the ability of the calf circumference to discriminate sarcopenia is extremely high, and further study is needed to determine whether it can be used to detect deterioration of muscle mass and function over time.