Study population
We recruited 267 prevalent PD patients from a single dialysis unit between 2015 and 2016. Patients with expected survival of less than 3 months, or those who were planned to receive kidney transplantation in 3 months, were excluded. The study was approved by the Joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee. All study procedures were in compliance with the Declaration of Helsinki.
Data collection
After written informed consent, clinical and laboratory data were obtained by chart review. Clinical data comprises of patient’s age, gender, body weight, height, primary diagnosis of renal disease, concomitant chronic medical illnesses including diabetes mellitus, ischemic heart disease, cerebrovascular accident, peripheral vascular disease, chronic hepatitis B and C infection, chronic lung disease, malignancy and immunological diseases. Laboratory data comprises of serum albumin level, serum cholesterol (including total cholesterol, high density cholesterol, low density cholesterol) level, dialysis adequacy by Kt/V, residual renal function by measuring the residual glomerular filtration rate from urine. The Charlson Comorbidity Index (CCI) was used to assess the comorbidity load [15]. The Comprehensive Malnutrition Inflammation Score (MIS) [16] and Subjective Global Assessment (SGA) [17] were used to assess nutritional state.
Assessment of depression
The Patient Health Questionnaire (PHQ-9) was used for depression screening and classification of depression severity in our study population [18]. PHQ-9 is a questionnaire comprised of 9 questions corresponding to the 9 criteria for defining depression according to Diagnostic and Statistical Manual Fourth Edition (DSM-IV). Each question was scored from 0 point (i.e. not at all) to 3 points (i.e. nearly every day) according to severity. Overall score was then calculated and patients were divided into different severity of depression, grading from none, mild, moderate, moderately severe, and severe according to the PHQ-9 score of 0 to 4, 5 to 9, 10 to 14, 15 to 19, and ≥20, respectively [18].
Assessment of frailty
We used a validated Chinese questionnaire that consisted of 30 yes/no questions (Supplementary Figure 1) [6,13]. The questions involve assessment of subjective assessment of personal health, psychological state, physical state in terms of number of hospital or doctor visit and medication needs to be taken, body weight, need of assistance in different aspects of daily living and mobility. A total score was calculated and the degree of frailty was divided into nil (score 5 or below), mild (score 6-8), moderate (score 9-11), or severe (score 12 or above).
Outcome measures
After the baseline assessment, all patients were followed for 1 year. Primary outcomes included the number of hospitalization, total duration of hospitalization, number of peritonitis and all-cause mortality. Peritonitis was diagnosed by the International Society of Peritoneal Dialysis (ISPD) Peritonitis Recommendations published in 2016 [21]. The peritonitis rate was defined by the number of peritonitis episode during the 1-year observation period. Technique survival was added post hoc as a secondary outcome measure; technique failure was defined as transfer to long-term hemodialysis. Censoring events for technique survival include kidney transplant, recovery of renal function, loss to follow up, and transfer to other dialysis centers.
Study population
We recruited 267 prevalent PD patients from a single dialysis unit between 2015 and 2016. Patients with expected survival of less than 3 months, or those who were planned to receive kidney transplantation in 3 months, were excluded. The study was approved by the Joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee. All study procedures were in compliance with the Declaration of Helsinki.
Data collection
After written informed consent, clinical and laboratory data were obtained by chart review. Clinical data comprises of patient’s age, gender, body weight, height, primary diagnosis of renal disease, concomitant chronic medical illnesses including diabetes mellitus, ischemic heart disease, cerebrovascular accident, peripheral vascular disease, chronic hepatitis B and C infection, chronic lung disease, malignancy and immunological diseases. Laboratory data comprises of serum albumin level, serum cholesterol (including total cholesterol, high density cholesterol, low density cholesterol) level, dialysis adequacy by Kt/V, residual renal function by measuring the residual glomerular filtration rate from urine. The Charlson Comorbidity Index (CCI) was used to assess the comorbidity load [15]. The Comprehensive Malnutrition Inflammation Score (MIS) [16] and Subjective Global Assessment (SGA) [17] were used to assess nutritional state.
Assessment of depression
The Patient Health Questionnaire (PHQ-9) was used for depression screening and classification of depression severity in our study population [18]. PHQ-9 is a questionnaire comprised of 9 questions corresponding to the 9 criteria for defining depression according to Diagnostic and Statistical Manual Fourth Edition (DSM-IV). Each question was scored from 0 point (i.e. not at all) to 3 points (i.e. nearly every day) according to severity. Overall score was then calculated and patients were divided into different severity of depression, grading from none, mild, moderate, moderately severe, and severe according to the PHQ-9 score of 0 to 4, 5 to 9, 10 to 14, 15 to 19, and ≥20, respectively [18].
Assessment of frailty
We used a validated Chinese questionnaire that consisted of 30 yes/no questions (Supplementary Figure 1) [6,13]. The questions involve assessment of subjective assessment of personal health, psychological state, physical state in terms of number of hospital or doctor visit and medication needs to be taken, body weight, need of assistance in different aspects of daily living and mobility. A total score was calculated and the degree of frailty was divided into nil (score 5 or below), mild (score 6-8), moderate (score 9-11), or severe (score 12 or above).
Outcome measures
After the baseline assessment, all patients were followed for 1 year. Primary outcomes included the number of hospitalization, total duration of hospitalization, number of peritonitis and all-cause mortality. Peritonitis was diagnosed by the International Society of Peritoneal Dialysis (ISPD) Peritonitis Recommendations published in 2016 [21]. The peritonitis rate was defined by the number of peritonitis episode during the 1-year observation period. Technique survival was added post hoc as a secondary outcome measure; technique failure was defined as transfer to long-term hemodialysis. Censoring events for technique survival include kidney transplant, recovery of renal function, loss to follow up, and transfer to other dialysis centers.
Statistical analysis
Statistical analysis was performed by SPSS for Windows software version 24 (SPSS Inc., Chicago). Descriptive data were presented as mean ± SD if normally distributed, or median (inter-quartile range) otherwise. Patients were grouped according to the degree of depression and frailty as defined above for analysis. Baseline clinical parameters between depression and frailty groups were compared by Kendall’s tau test and Spearman rank order correlation coefficient as appropriate, with post hoc subgroup analysis, when needed, by Student’s t-test or one way analysis of variance (ANOVA) for continuous variables, and Chi-square test for categorical variables.
The number of hospital admission and duration of hospitalization were compared between frailty groups by Spearman’s rank correlation. To adjust for clinical confounding factors, the log-linear regression model was then used to because the data were highly skewed. Potential confounders, including age, duration of dialysis, body weight, Charlson’s comorbidity score, serum albumin, total Kt/V, and residual GFR, were added to the model. Backward stepwise elimination was used to determine the independent predictor of hospitalization.
Kaplan-Meier method was used to present the data of patient and technique survival, and log-rank test was used to compare between survival curves. The Cox proportional hazards model was then used to adjust for potential confounders and identify independent predictors of patient survival. In addition to the degree of frailty and depression being added separately, the Cox models were constructed by similar clinical parameters used in the analysis of hospitalization. These parameters were selected because of their reported significance in determining the prognosis of PD patients. Backward stepwise elimination was applied to remove insignificant variables. P<0.05 was considered to be statistically significant. All probabilities were two-tailed.
Statistical analysis was performed by SPSS for Windows software version 24 (SPSS Inc., Chicago). Descriptive data were presented as mean ± SD if normally distributed, or median (inter-quartile range) otherwise. Patients were grouped according to the degree of depression and frailty as defined above for analysis. Baseline clinical parameters between depression and frailty groups were compared by Kendall’s tau test and Spearman rank order correlation coefficient as appropriate, with post hoc subgroup analysis, when needed, by Student’s t-test or one way analysis of variance (ANOVA) for continuous variables, and Chi-square test for categorical variables.
The number of hospital admission and duration of hospitalization were compared between frailty groups by Spearman’s rank correlation. To adjust for clinical confounding factors, the log-linear regression model was then used to because the data were highly skewed. Potential confounders, including age, duration of dialysis, body weight, Charlson’s comorbidity score, serum albumin, total Kt/V, and residual GFR, were added to the model. Backward stepwise elimination was used to determine the independent predictor of hospitalization.
Kaplan-Meier method was used to present the data of patient and technique survival, and log-rank test was used to compare between survival curves. The Cox proportional hazards model was then used to adjust for potential confounders and identify independent predictors of patient survival. In addition to the degree of frailty and depression being added separately, the Cox models were constructed by similar clinical parameters used in the analysis of hospitalization. These parameters were selected because of their reported significance in determining the prognosis of PD patients. Backward stepwise elimination was applied to remove insignificant variables. P<0.05 was considered to be statistically significant. All probabilities were two-tailed.