Nomogram for predicting 1-, 5-, and 10-year survival in hemodialysis (HD) patients: a single center retrospective study

Ouyang, Han; Shi, Qiuhong; Zhu, Jing; Shen, Huaying; Jiang, Shan; Song, Kai

doi:10.1080/0886022x.2021.1997762

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Ricardo Peralta, et al [51] established a machine learning model predicting arteriovenous stula failure, which had the ability to help vascular access doctors predict the rate of hemodialysis patients encountering with arteriovenous stula failure in early stage, as a result they could conduct an alternative plan in advance to solve the problem of vascular access of patients. However, there was no researches predicting the mortality risk of HIV/AIDS patients undergoing hemodialysis at present, and all the studies currently only focused on the mortality risk of HIV/AIDS patients [24] and hemodialysis patients [25] , the clinical application value of which were limited. As a consequence, we constructed a mortality prediction model of HIV/AIDS patients undergoing hemodialysis in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Fang FJ et al [24] construct a mortality prediction model of HIV/AIDS adult patients receiving ART, risk factors including CD4 counts, BMI and hemoglobin (HB), the AUC of which is 0.831. Han OY et al [25] constructed a mortality prediction model of hemodialysis patients, of which the AUC is 0.779, risk factors including age,use of smoking, drinking, predialysis and postdialysis systolic blood pressures between survival group and mortality group (p>0.05). The speci c demographic data were shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Construction and validation of a nomogram to predict mortality risk in HIV/AIDS patients undergoing maintenance hemodialysis

Xian,

Song,

Wang

et al. 2024

Preprint

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Objective: This study was aimed to explore the death risk factors in HIV/AIDS patients undergoing maintenance hemodialysis, and constructed a mortality prediction risk model. Methods: In this study, we retrospectively collected clinical data of HIV/AIDS patients who received hemodialysis in Chengdu Public Health Clinical Medical Center between June 6,2017 and June 6,2023, and were divided into survival group and mortality group on the basis of the follow-up result. Besides, we separated all patients into training set, which was used for model construction, and validation set for model verification according to 8:2 ratio. The t-test, non-parametric test, chi-square test, fisher’s precise test and ROC analysis were used for variable selection, and the logistic regression analysis was used for exploring the relationship between variables and death. And then, we used the stepwise logistic regression to construct a mortality risk prediction model in HIV/AIDS patients undergoing maintenance hemodialysis, and next, we used R software to visualize the prediction model which called a nomogram. And last, ROC analysis, calibration curve and decision curve were used for model evaluation, and meanwhile, we used a independent internal validation set for model verification. Result: In this study, we collected clinical data of 166 HIV/AIDS patients undergoing maintenance hemodialysis, including 123 patients in the training set(55 mortalities and 68 survivals)and 43 patients in the validation set(20mortalities and 23survivals). Stepwise Logistic regression showed that education level [OR(95%CI): 3.754 (1.247-11.300), p=0.019], dialysis age after diagnosis of HIV/AIDS [OR(95%CI):0.050 (0.013-0.187),p=0.000], creatine kinase isoenzyme (CK-MB)[OR(95%CI): 7.666 (2.237-26.271),p=0.001],neutrophil and lymphocyte counts ratio (NLR)[OR(95%CI):3.864 (1.266-11.795),p=0.018], magnesium (Mg2+)[OR(95%CI): 4.883 (1.479-16.118),p=0.009],HIV-RNA[OR(95%CI): 17.616 (3.797-81.721),p=0.000] were independent risk factors of HIV/AIDS patients undergoing hemodialysis, and afterwards, we constructed a nomogram based on the 6 independent risk factors. The AUC of the prediction model in ROC analysis was0.921 (95%CI 0.883~0.968), indicating that this nomogram had a good efficacy in predicting mortality. In addition, the calibration curve and decision curve both showed that the nomogram had good clinical application. Futhermore, there was a same result in the validation set. Conclusion: In present study, the nomogram model had a good performance in predicting the mortality of HIV/AIDS patients undergoing maintenance hemodialysis, which is worth promoting in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction and validation of a nomogram to predict mortality risk in HIV/AIDS patients undergoing maintenance hemodialysis

Xian,

Song,

Wang

et al. 2024

Preprint

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“…For instance, Cheng et al [31] designed a nomogram to predict the risk of initiating renal replacement therapy within 3 years in diabetic nephropathy patients, while Jing et al [32] developed a nomogram comprising multiple echocardiographic measures to assess 3-year all-cause mortality in hemodialysis patients, both of which showed favorable accuracy and reliability. Ouyang et al [33]developed and validated an easyto-use nomogram that can accurately predict 1-year, 5-year, and 10-year survival in hemodialysis patients. In addition, Mo, et al [34] reported the development of nomograms to predict sarcopenia in community older adults.…”

Section: Discussionmentioning

confidence: 99%

Development of a risk prediction nomogram for sarcopenia in hemodialysis patients

et al. 2022

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Background Sarcopenia is associated with various adverse outcomes in hemodialysis patients. However, current tools for assessing and diagnosing sarcopenia have limited applicability. In this study, we aimed to develop a simple and reliable nomogram to predict the risk of sarcopenia in hemodialysis patients that could assist physicians identify high-risk patients early. Methods A total of 615 patients undergoing hemodialysis at the First Affiliated Hospital College of Medicine Zhejiang University between March to June 2021 were included. They were randomly divided into either the development cohort (n = 369) or the validation cohort (n = 246). Multivariable logistic regression analysis was used to screen statistically significant variables for constructing the risk prediction nomogram for Sarcopenia. The line plots were drawn to evaluate the effectiveness of the nomogram in three aspects, namely differentiation, calibration, and clinical net benefit, and were further validated by the Bootstrap method. Results The study finally included five clinical factors to construct the nomogram, including age, C-reactive protein, serum phosphorus, body mass index, and mid-upper arm muscle circumference, and constructed a nomogram. The area under the ROC curve of the line chart model was 0.869, with a sensitivity and specificity of 77% sensitivity and 83%, the Youden index was 0.60, and the internal verification C-statistic was 0.783. Conclusions This study developed and validated a nomogram model to predict the risk of sarcopenia in hemodialysis patients, which can be used for early identification and timely intervention in high-risk groups.

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“…It was not surprising that death increases with advancing age. After HD treatment, elderly patients were prone to suffer the serious complications, cognitive dysfunction and the decreased quality of life, which caused an increased mortality [ 8 , 16 , 17 ]. Hence, we should give more attention for elderly patients with HD.…”

Section: Discussionmentioning

confidence: 99%

A Retrospective Study from a Single Center in China to Develop a Nomogram to Predict One-Year Mortality in Patients with End-Stage Renal Disease Who Are Receiving Hemodialysis

et al. 2022

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Background The prognosis of end-stage renal disease (ESRD) patients receiving hemodialysis (HD) remains Poor. This retrospective study from a single center in China aimed to develop a nomogram to predict one-year mortality in patients with ESRD on HD. Material/Methods We enrolled 299 ethnic Han Chinese ESRD patients undergoing HD at the Second Affiliated Hospital of Nantong University from April 29, 2011 to January 30, 2021. Univariate and multivariate Cox regression analyses were used to select the predictors incorporated in the prediction model to assess the one-year mortality for ESRD patients receiving HD. We used receiver operating characteristic curves, C-index, and calibration curves to evaluate the performance of the nomogram. The predictive performance of the nomogram was also verified in different subgroup populations. Results The median follow-up time was 23.30 months. The 299 ESRD patients receiving HD were divided into a death group (n=96) and a survival group (n=203), and the incidence of death was 32.11%. The main causes of death were cardiovascular disease, inflammation and cancer. A nomogram containing age, alkaline phosphatase, albumin, cystatin C, total bilirubin, and hypersensitive c-reactive protein was established. The performance of this nomogram was reflected by its moderate predictive ability, especially for patients who were male, had a primary disease of chronic glomerulonephritis, and had no history of comorbidities. Conclusions We developed and validated an easy-to-use nomogram for predicting the one-year mortality of ESRD patients undergoing HD.

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Nomogram for predicting 1-, 5-, and 10-year survival in hemodialysis (HD) patients: a single center retrospective study

Cited by 8 publications

References 23 publications

Construction and validation of a nomogram to predict mortality risk in HIV/AIDS patients undergoing maintenance hemodialysis

Construction and validation of a nomogram to predict mortality risk in HIV/AIDS patients undergoing maintenance hemodialysis

Development of a risk prediction nomogram for sarcopenia in hemodialysis patients

A Retrospective Study from a Single Center in China to Develop a Nomogram to Predict One-Year Mortality in Patients with End-Stage Renal Disease Who Are Receiving Hemodialysis

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