Nomogram to Predict the Risk of Protein Energy Wasting in Patients undergoing hemodialysis
Description
SPSS software (IBM, Chicago, IL) and R software (version 3.6.1; http://www.Rproject.org) were used for statistical analyses. Missing data were handled by single imputation. Means ± SDs or median with interquartile range were used for normally and non-normally descriptive statistics of continuous variables, respectively. Chi-square test for comparison of constituent ratios and independent-Sample Test was used to compare between PEW risk and without PEW risk among the primary group and validation group. Skewed data among the groups were analyzed using Kruskal - Wallis test. Multivariable logistic regression analysis of risk factors began with the following clinical candidate predictors: age, gender, waist circumference, hipline, Kt/V, fat mass, BMI, albumin, prealbumin, Scr, total cholesterol, upper arm muscle circumference, FGF23 and Klotho. Backward step-wise selection was applied by using the likelihood ratio test with Akaike’s information criterion as the stopping rule. To provide the clinician with a quantitative tool to predict individual probability of PEW, we built the nomogram on the basis of multivariable logistic analysis in the primary group and validation populations to determine the nomogram predicted probability of PEW. Calibration curves were plotted to assess the calibration of the nomogram, accompanied with the Hosmer-Lemeshow test. To quantify the discrimination performance of the nomogram, Harrell s C-index was measured. The PEW nomogram was subjected to bootstrapping validation (1,000 bootstrap re-samples) to calculate a relatively corrected C-index. Throughout the study, P<0.05 was taken as the minimum level of statistical significance.