Analysis of risk factors and construction of prediction model of drop out from peritoneal dialysis

Abstract

This study is to investigate the risk factors for the drop out from peritoneal dialysis. We retrospectively analyzed patients who underwent catheterization between January 1, 2009 and September 30, 2019. The follow-up period ended on November 30, 2019. End point events were the cessation of peritoneal dialysis, including death, conversion to hemodialysis, and kidney transplantation. Kaplan–Meier method was used to analyze peritoneal dialysis curve. Significant factors were included in the multivariate Cox proportional hazards model. Calibration curve was plotted. A total of 377 patients were included in this study. The dropout rate of peritoneal dialysis was 41.38%. The main drop out reason was conversion to hemodialysis, accounting for 41.67% of the total number of drop out, followed by kidney transplantation (28.21%) and death (25%). According to multivariable Cox proportional hazards model analysis, the medium education level (hazard ratio (HR): 2.53, 95% confidence interval (CI): 1.08–5.91, P = .03), high education level (HR: 2.47, 95% CI: 1.03–5.93, P = .04), diabetes (HR: 1.87, 95% CI: 1.24–2.83, P < .03), hypertension (HR: 2.40, 95% CI: 1.64–3.51, P < .01), repeated peritonitis (HR: 5.18, 95% CI: 3.04–8.80, P < .01), and repeated chest complications (HR: 4.98, 95% CI: 2.79–8.89, P < .01) were independent risk factors for dropping out from peritoneal dialysis, while the number of hospitalizations after catheterization (HR: 0.94, 95% CI: 0.89–0.98, P = .01) was protective factor for maintenance of peritoneal dialysis. The C index of the prediction model was 0.74. Higher education level, diabetes, hypertension, repeated peritonitis, and repeated chest complications were the risk factors of dropping out from peritoneal dialysis, while higher number of hospitalizations after catheterization was a protective factor for the maintenance of peritoneal dialysis. The nomogram could predict the probability of dropping out from peritoneal dialysis.

Establishment of a Nomogram Model for Predicting Cardiovascular and Cerebrovascular Events in Diabetic Nephropathy Patients Receiving Maintenance Hemodialysis

“…A related study reported [ 7 ] that the nomogram prediction model could be used for risk monitoring and prevention of patients with diabetic foot ulcers. Li et al [ 8 ] showed that the nomogram model predicted the probability of withdrawal in peritoneal dialysis patients. In addition, You et al [ 9 ] developed a simple and practical nomogram prediction model that could assess the risk of cardiovascular events in long-term hemodialysis patients with chronic kidney disease.…”

Construction of Mental Health Education Model Based on Computer Multimedia Group Psychological Measurement

“…With the continuous development of social economy, people's material living standards have been greatly improved, but what needs to be paid attention to is that psychological problems are becoming one of the important issues, affecting people's life safety [1]. erefore, the understanding of the connotation of health is no longer limited to physical health but is related to the dual unity of mental health and physical health to achieve mutual complementarity and complete the true meaning of health [2,3].…”

Patients and nursing‐related factors associated with switching from peritoneal dialysis to hemodialysis: A cross‐sectional survey

“…Half of the study patients had CKD secondary to diabetes, and most had a cardiovascular comorbidity. This is an alarming situation given that the risk of mortality increases in hypertensive patients and rises to 50% in patients with diabetes who switch from PD to HD (Ferrer et al, 2015; Li et al, 2021). To prevent the switch from PD to HD in patients with diabetes and hypertension, it is recommended that PD nurses reinforce adherence to treatment as part of effective disease management, thus delaying the progression of kidney damage and improving patients' quality of life (Spigolon et al, 2016).…”