Linchuang shenzangbing zazhi (Jun 2024)

Construction and validation of risk nomogram model for sleep disorders in maintenance hemodialysis patients

  • Jie Wu,
  • Hou-liang Chen,
  • Yan-na Yang

DOI
https://doi.org/10.3969/j.issn.1671-2390.2024.06.005
Journal volume & issue
Vol. 24, no. 6
pp. 468 – 474

Abstract

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Objective To explore the risk factors for sleep disorders in maintenance hemodialysis （MHD） patients and construct a risk prediction model. Methods A retrospective study was conducted among 198 patients who underwent MHD between January 2018 and December 2022. Based upon whether or not there were complications of sleep disorders, they were assigned into two groups of sleep disorder （n = 134） and non-sleep disorder （n = 64）. Uni/multivariate Logistic regression analyses were performed for identifying the risk factors for sleep disorders in MHD patients. A nomogram model for sleep disorders in MHD patients was constructed with software R. The discrimination and calibration of this model were tested using the receiver operating characteristic （ROC） curve and calibration curve. Internal validation of this model was conducted byBootstrap. Results The total PSQI score was （10.32 ± 1.34）. There were 134 patients with PSQI score ≥7 and the incidence of sleep disorders was 67.68%（134/198）. Uni/multivariate Logistic regression analyses revealed that dialysis duration （OR = 1.573）, skin pruritus （OR = 3.044）, restless leg syndrome （RSL）（OR = 2.722） and serum parathyroid hormone （PTH）（OR = 1.030） were risk factors for sleep disorders in MHD patients （P<0.05）. And hemoglobin （Hb, OR = 0.893） and Kt/V（OR = 0.031） were protective factors （P<0.05）. The area under the ROC curve of risk prediction model was 0.901（P<0.001, 95%CI: 0.860-0.943）. Its cut-off value, sensitivity and specificity were 1.69, 85.8% and 76.6% respectively. C-index index of Bootstrap internal validation method was 0.92 and calibration curve fit well with an ideal curve. Conclusion The nomogram model constructed based upon dialysis duration, Hb, skin pruritus, RSL, serum PTH and Kt/V has an excellent performance in predicting sleep disorders in MHD patient s. This model may be employed for predicting and identifying the occurrence and risk factors of sleep disorders in MHD patients, thus providing scientific rationales for clin ical prevention.

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