Study design and subjects
The DOPPS is an international prospective cohort study of in-center adult HD patients including more than 20 countries in the world, which described in previous published papers[21, 22]. The China DOPPS was carries out in the 3 largest cities from the metropolitan areas in China (Beijing, Shanghai and Guangzhou). We randomly selected 15 dialysis facilities in each city and included an average of 30 patients at each facility. Finally, there were 1427 patients participated in China DOPPS5.
Of the 1427 patients, 134 patients were excluded from the present analysis as they didn’t answer the questions of self-reported physical function in the patient questionnaire survey. Baseline demographic and clinical data were collected at the start of participation in DOPPS5.
2 items to assess physical function
In China DOPPS5, there were 2 items about physical function as a part of SF-12v2 in patient questionnaire survey. The 2 items were ‘does your health now limit you in moderate activities, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf? If so, how much?’ and ’does your health now limit you in climbing several flights of stairs? If so, how much?’. Both the 2 items had 3 responses options: 1, ‘Yes, limited a lot’; 2, ‘Yes, limited a little’; 3, ‘No, not limited at all’. Patients completed the questionnaire soon after entered study.
Outcomes
The primary end-point event was all-cause mortality. The secondary end-point event was the first hospitalization with any causes during the follow-up period. Hospitalization event was defined as an inpatient hospitalization with an overnight stay, which was registered in the DOPPS survey. Observation or outpatient records were not included in this analysis.
Statistics Analysis
Continuous variables were represented as Mean±SD or median (25th, 75th) according to the results of normality test. Categorical variables were expressed as percentage. We stratified data according to moderate activity limited level and climbing stairs limited level, respectively. Differences in mean or median among groups were evaluated by using analysis of variance or non-parametric test. And categorical data were compared by using chi-square test.
Based on clinical experience and suggestions from previous published articles, we adjusted for many covariates, which were possibly associated with patient’s physical function or outcomes. Covariates in this study included patient’s demographic data (age, sex, dialysis vintage, body mass index (BMI) and whether urine output >200ml/day), clinical characteristics (serum hemoglobin (Hgb) and serum albumin (Alb)), dialysis prescriptions (single-pooled Kt/V, dialysis frequency (whether less than 3 times per week), vascular access type) and comorbidities (diabetes, coronary artery disease, congestive heart failure, other cardiovascular disease, cerebrovascular disease, hepatitis B and C, cancer (non-skin), peripheral vascular disease, lung disease and hypertension).
Survival curves were produced by using the Kaplan-Meier method and estimated by using log-rank test. Association between moderate activity limited level, climbing stairs limited level and all-cause mortality were analyzed using COX regression models. All COX models accounted for facility clustering effects by using the robust sandwich covariance estimate. Survival time for COX models of all-cause mortality was the time from study entry to the end of study or to death. And failure time for COX models of hospitalization was the time from study entry to the end of study or to the first hospitalization. We made 1 unadjusted model and 3 adjusted models for each endpoint. The adjusted covariates included in model 1: age, gender, vintage; model 2: model 1 variables plus BMI, Hgb, Alb, spKt/V, urine output, vascular access type; model 3: model 2 variables plus comorbidities (diabetes, coronary artery diseases, congestive heart failure, other cardiovascular diseases, cerebrovascular disease, hypertension, peripheral vascular disease, hepatitis B and C, lung disease, cancer).
We also used the generalized linear mixed models (the GLIMMIX procedure) to analyze the associations between the 2 items of physical function and patients’ baseline characteristics, as moderate activities limited level and climb stairs limited level were ordinal categorical variables. In these models, we used robust variance estimation to account for facility clustering effects. Adjusted odds ratio (OR) and 95% conference interval (CI) were calculated for each variable.
We performed MI procedure to impute missing data, and continuous and categorical variables were imputed by fully conditional specification regression and logistic regression, respectively. After 20 steps of imputation, 20 data sets were combined for the final analysis of Cox regression model and generalized linear mixed model. Percentages of missing for most variables were <10%, except for single-pooled Kt/V (36.2%). P value < 0.05 was seen as statistically significant. All statistical analyses were performed with SAS, version 9.4 (SAS institute, Cary, NC; USA).