Effects of age on the outcome of peritoneal dialysis–associated peritonitis

Wang, Ching-Chun; Tsai, Chang-Chih; Hsu, Chien‐Chin; Chen, Kuo-Tai

doi:10.1016/j.ajem.2016.10.013

Cited by 1 publication

(2 citation statements)

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“…Many studies have explored whether a single variable (such as a biomarker or a new clinical biochemical parameter) can predict or correlate with certain outcomes, whereas building clinical prediction models by incorporating multiple variables is relatively complex. Nevertheless, clinical predictive models have been developed in recent years for (6,(17)(18)(19)(20)(21). The results of some previous studies are consistent with those of the current paper.…”

Section: Discussionsupporting

confidence: 91%

“…Clinical prediction models can inform patients and their physicians or other healthcare providers about the likelihood of disease development or progression in a patient, which can facilitate treatment decisions. The main risk factors included in previous PDAP clinical prediction models are age, UA, serum C-reactive protein (CRP)-to-Albumin ratio (CAR), NLR, RDW, DM, and cardiovascular disease ( 6 , 17–21 ). The results of some previous studies are consistent with those of the current paper.…”

Section: Discussionmentioning

confidence: 99%

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Construction and validation of a predictive model for the risk of peritoneal dialysis-associated peritonitis after peritoneal dialysis catheterization

Dai,

Peng,

Sang

et al. 2023

Front. Med.

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AimTo construct and validate a risk prediction model for the development of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritoneal dialysis (PD).MethodsThis retrospective analysis included patients undergoing PD at the Department of Nephrology, the First Affiliated Hospital of Anhui University of Chinese Medicine, between January 2016 and January 2021. Baseline data were collected. The primary study endpoint was PDAP occurrence. Patients were divided into a training cohort (n = 264) and a validation cohort (n = 112) for model building and validation. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to optimize the screening variables. Predictive models were developed using multifactorial logistic regression analysis with column line plots. Receiver operating characteristic (ROC) curves, calibration curves, and Hosmer-Lemeshow goodness-of-fit tests were used to verify and evaluate the discrimination and calibration of the prediction models. Decision curve analysis (DCA) was used to assess the clinical validity of the prediction models.ResultsFive potential predictors of PDAP after PD catheterization were screened using LASSO regression analysis, including neutrophil-to-lymphocyte ratio (NLR), serum ALBumin (ALB), uric acid (UA), high sensitivity C-reactive protein (hsCRP), and diabetes mellitus (DM). Predictive models were developed by multi-factor logistic regression analysis and plotted in columns. The area under the ROC curve (AUC) values were 0.891 (95% confidence interval [CI]: 0.829–0.844) and 0.882 (95% CI: 0.722–0.957) for the training and validation cohorts, respectively. The Hosmer-Lemeshow test showed a good fit (p = 0.829 for the training cohort; p = 0.602 for the validation cohort). The DCA curves indicated that the threshold probabilities for the training and validation cohorts were 4–64% and 3–90%, respectively, predicting a good net gain for the clinical model.ConclusionNLR, ALB, UA, hsCRP, and DM are independent predictors of PDAP after PD catheterization. The column line graph model constructed based on the abovementioned factors has good discriminatory and calibrating ability and helps to predict the risk of PDAP after PD catheterization.

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