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Chronic Kidney Disease (CKD) is a significant global health concern, and dialysis patients have a higher mortality rate from cardiovascular disease (CVD) compared to the general population. The use of Machine Learning (ML) and Artificial Intelligence in medicine has grown exponentially over the past few decades. However, few studies have focused on predictive models for determining factors associated with death in dialysis patients, with a predominant focus on the hemodialysis (HD) population. The primary objective of this study was to identify mortality-related factors in patients undergoing both hemodialysis (HD) and peritoneal dialysis (PD), encompassing both planned and urgent starts.
R software algorithms were employed to develop ML predictive models. The study included adult patients undergoing HD and PD, either in a planned or urgent manner, between January 2014 and January 2019, at Hospital das Clínicas da Faculdade de Medicina de Botucatu located in Botucatu, São Paulo, Brazil. Epidemiological, clinical, and laboratory data were collected.
The results showed that death occurred in 170 patients (29.3%). Cox regression analysis revealed that death was associated with older age, fewer Exit Site Infection(ESI)-free months, lower initial creatinine, dialysis-related infection (peritonitis for PD and bloodstream infection for HD), and hospitalizations (Table 1). Random forest (Figure 1) ranked the following main variables predictive of death in descending order of importance: ESI-free months; age and initial levels of creatinine.
Table 01 – COX Regression based on Machine Learning
Variables
HR1
95% CI1
p
Age
1.02
1.01- 1.04
<0.001
Male gender
1.07
0.75- 1.52
0.7
Number of comorbidities
1.08
0.95- 1.21
0.2
(ESI-free days)/(30)*
0.96
0.94- 0.98
Dialysis-related infection
0.55
0.37- 0.82
0.003
PTH
1.00
1.00- 1.00
Hemoglobin
0.93
0.84- 1.03
Albumin
0.83
0.60- 1.14
0.3
P
0.99
0.96- 1.02
0.6
Diabetes
0.81
0.55- 1.19
Creatinine
0.90
0.83- 0.98
0.015
Hospitalizations
1.72
1.08- 2.74
0.023
CVC for initial access (HD)
1.84
0.44- 7.71
0.4
APD as the initial modality (PD)
2.33
0.55- 9.90
AFV for initial dialysis access
0.21- 4.64
0.9
Dialysis modality switching
0.95
0.55- 1.65
1HR = Hazard Ratio, CI = Confidence Interval
*ESI-Free months
Figure 1: Variables importance ranked by Random Forest
In conclusion, this study revealed that ESI-free months, age and initial creatinine levels were associated with death on both multivariate and ML-based analyses. The content presented in this abstract was submitted for other meetings.