An easy-to-operate web-based calculator for predicting the progression of chronic kidney disease

Xu, Qian; Wang, Yunyun; Fang, Yiqun; Feng, Shanshan; Chen, Cuiyun; Jiang, Yanxia

doi:10.1186/s12967-021-02942-y

Cited by 5 publications

(8 citation statements)

References 40 publications

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“…Model performance was assessed using calibration plots, time-dependent ROC analysis, and DCA. For the convenience of clinical application, webbased calculators for predicting the OS and DFS of patients with sarcoma were built using the R packages 'DynNom' and 'survival' [40]. AGING Supplementary Table 2B…”

Section: Construction Of the Models And Web-based Calculatorsmentioning

confidence: 99%

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Quan

Cao

et al. 2022

Aging

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Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed. Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established. Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/). Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.

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Section: Construction Of the Models And Web-based Calculatorsmentioning

confidence: 99%

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Quan

Cao

et al. 2022

Aging

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“…Fig 1 shows that 31 studies implemented supervised models, and only 2 studies included unsupervised models with 1 of these 2 studies being a comparison study between supervised and unsupervised models. Of the studies that used supervised models, 21 studies implemented cox proportional hazards regression [ 41 – 61 ]. Seven studies used machine learning (ML) methods [ 9 , 67 – 71 ], and one compared the performance among a number of ML techniques [ 70 ].…”

Section: Resultsmentioning

confidence: 99%

“…Each predictive model was unique and incorporated different combinations of variables, and slightly different definitions of variables, such as high blood pressure. A recent paper by Xu et al [ 61 ] published in 2021 highlighted that there are currently no robust biomarkers to predict progressive CKD, but rather relied on multiple longitudinal kidney measurements, such as eGFR and proteinuria.…”

Section: Resultsmentioning

confidence: 99%

“…The smallest study [ 50 ] had 43 participants and the largest study included over 300,000 patient records [ 54 ]. Included study populations were from the United States [ 45 , 52 – 56 , 60 , 68 , 69 , 72 ], Canada [ 41 , 46 , 58 , 59 ], Taiwan [ 9 , 42 , 70 ], Germany [ 64 , 66 ], Japan [ 48 , 50 , 61 , 67 ], France [ 43 ], Croatia [ 44 ], Korea [ 49 , 51 ], United Kingdom [ 62 ], Iran [ 71 ], Romania [ 65 ], Spain [ 63 ], Netherlands [ 47 ] and China [ 57 ].…”

Section: Resultsmentioning

confidence: 99%

“…There were also studies that reported having unbalanced data and outlined the methods applied to re-balance the data before initiating model development [ 9 , 42 , 45 , 62 , 70 ]. Knowing these limitations, studies recognised that their prediction models would only be applicable to their own given study population and would require external validation to allow generalisation of their model to other populations [ 41 , 44 – 46 , 49 , 50 , 53 , 56 , 59 , 61 , 64 – 66 , 69 , 73 , 74 ].…”

Section: Resultsmentioning

confidence: 99%

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Prediction models used in the progression of chronic kidney disease: A scoping review

et al. 2022

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Objective To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD). Design Scoping review. Data sources Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022. Study selection All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression. Data extraction Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications. Results From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models. Conclusions Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.

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Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review

Walker,

Day,

Grant

et al. 2024

BMC Med

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Background Prognostic models that identify individuals with chronic kidney disease (CKD) at greatest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. It is recognised that people with CKD usually have multiple long-term health conditions (multimorbidity) and often experience frailty. We undertook a systematic review to evaluate the representation and consideration of multimorbidity and frailty within CKD cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure. Methods We identified studies that described derivation, validation or update of kidney failure prognostic models in MEDLINE, CINAHL Plus and the Cochrane Library—CENTRAL. The primary outcome was representation of multimorbidity or frailty. The secondary outcome was predictive accuracy of identified models in relation to presence of multimorbidity or frailty. Results Ninety-seven studies reporting 121 different kidney failure prognostic models were identified. Two studies reported prevalence of multimorbidity and a single study reported prevalence of frailty. The rates of specific comorbidities were reported in a greater proportion of studies: 67.0% reported baseline data on diabetes, 54.6% reported hypertension and 39.2% reported cardiovascular disease. No studies included frailty in model development, and only one study considered multimorbidity as a predictor variable. No studies assessed model performance in populations in relation to multimorbidity. A single study assessed associations between frailty and the risks of kidney failure and death. Conclusions There is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty, resulting in a lack of clear evidence-based practice for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for CKD patients who experience multimorbidity and/or frailty. Systematic review registration This review has been registered on PROSPERO (CRD42022347295).

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An easy-to-operate web-based calculator for predicting the progression of chronic kidney disease

Cited by 5 publications

References 40 publications

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Prediction models used in the progression of chronic kidney disease: A scoping review

Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review

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