Abstract
Background and Aims
Chronic kidney disease (CKD) is known to have significant morbi-mortality worldwide. Patients with CKD and in particular those with ESRD normally carry a large burden of comorbidities and the beginning of hemodialysis leads to a higher risk of decompensation. In fact, annual mortality rates among hemodialysis patients is 10 to 30 times higher than those of the general population. Various studies have demonstrated that incident patients experience the higher mortality rate within the first 3 to 4 months of dialysis. Predicting early mortality is important to help the decision of initiating hemodialysis versus conservative care. Therefore we conducted a case control study to evaluate early mortality predictors in incident hemodialysis patients in our hemodialysis center.
Method
This is a retrospective case–control study, which to evaluate early mortality predictors in incident hemodialysis patients from January 2013 to December 2018. Descriptive statistics were calculated and expressed as mean (±standard deviation [SD]) or median (intraquartile range [IQR]) for parametric and non-parametric continuous variables and count (%) for categorical variables, respectively. We compared variables between survivors and non-survivors at 3 months after initiation of hemodialysis by using Student’s t-test, Mann-Whitney U test, or Fisher’s exact test where appropriate. Multivariate logistic regression was used to calculate the adjusted odds ratio (OR) with 95% confidence intervals (CI) for the variables associated with early mortality.
Results
From a total of 559 incident hemodialysis patients, 43 cases were identified (7.7%), and three controls were obtained for each case. From the 172 pts in the study mean (SD) age was 72.4 years (±14), 58.1% were male, and the most common etiologies of CKD were unknown etiology (22.1%, n=38) and diabetic nephropathy (16.9%, n=29). 34.4% (n=59) were dependent of assistance in daily living activities, median (IQR) Charlson Comorbidity Index was 8 (6.10). The non survivors compared to the survivors were older (78.8 ± 9.2 vs 70.3 ± 14.7, p < 0,001), had more AKI or acute-on-chronic CKD (18 (41.9%) vs 18 (14%), p <0,001), emergency start of hemodialysis (29 (67.4%) vs 48 (37.2%), p= 0.001), more catheter use as vascular access (38 (88.4%) vs 92 (71.3%), p=0.024), congestive heart failure (30 (69.8%) vs 32 (24.8%), p < 0.001), ischemic cardiomyopathy (20 (46.5%) vs 30 (23.3%), p=0.004), COPD (13 (30.2%) vs 11 (8.5%), p<0.001), peripheral vascular disease (14 (32.6%) vs 20 (15.5%), p=0.015), Charlson comorbidity index (10 (8-11) vs 7 (6-9), p<0.001), dependence of assistance in daily living activities (22 (51.2%) vs 37 (28.7%), presence of nephrology appointments for >3 months before ESRD (23 (53.5%) vs 102 (79.1%), p=0.01), eGFR (12.3 (6.1) vs 9.1 (4.2), p<0.001), serum albumin (3.1 (2.9-3.5) vs 3.5 (3-3.8), p=0.002). A multivariable analysis was performed and the most suitable model to predict early mortality was age (p=0.003, OR 1.07, 95% C.I. 1.023-1.121), emergency start of hemodialysis (p<0.001, OR 8.35, 95% CI 3.385-20.606), congestive heart failure (p=0.004, OR 3.65, 95% CI 1.519-8.776), peripheral vascular disease (p=0.035, OR 2.97, 95% CI 1.081-8.134). Hosmer-Lemeshow goodness-of-fit performed well (X2 6.67 DF 8; p =0.57), Nagelkerke R2 0.46; AUROC (95% CI) 0.86 (0.80-0.92).
Conclusion
The percentage of early mortality in our population (7.7%) was compatible with national and European rates. Our model identifies as independent mortality predictors age, emergency start of hemodialysis, congestive heart failure and peripheral vascular disease with an AUROC 0,86. This could help identify patients that could benefit from a more conservative care.
887-5 Diastolic heart failure in patients hospitalized with symptomatic cerebral and peripheral vascular disease
