Association of elevated blood serum high-sensitivity C-reactive protein levels and body composition with chronic kidney disease

Tsai, Yun-An; Chan, YC; Chen, Yi-Chuan; Cheng, Yiu-Hua; Chang, Shu‐Chiuan

doi:10.1097/md.0000000000011896

Cited by 5 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Tsai et al. [ 26 ] reported that a higher BFP and higher high-sensitivity (hs) CRP level were related to renal dysfunction in a general population. In this study, we also observed that CRP was an independent risk factor for CKD in the general population, and BFP was significantly associated with the prevalence of CKD in subjects with MAFLD without DM.…”

Section: Discussionmentioning

confidence: 99%

Association of metabolic dysfunction-associated fatty liver disease with chronic kidney disease: a Chinese population-based study

Chen

Bao

et al. 2022

Renal Failure

View full text Add to dashboard Cite

Background Metabolic dysfunction-associated fatty liver disease (MAFLD) is a multisystem disorder, but its relationship with kidney injury remains controversial. This study aimed to evaluate MAFLD effects on the chronic kidney disease (CKD) prevalence in a general population in China. Methods In total, 15,010 individuals from the Health Management Center of West China Hospital from July 2020 to June 2021 were screened. Hepatic steatosis was defined as a median FibroScan controlled attenuation parameter (CAP)≥240 dB/m using liver ultrasound transient elastography. CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m 2 and/or the presence of albuminuria. The association of MAFLD with CKD was examined using logistic regression. Risk factors for CKD in different MAFLD subgroups were also investigated. Results A total of 8226 individuals were finally included. Of them, 4406 (53.6%) had MAFLD, and 592 (7.2%) had CKD. After propensity score matching (PSM), 5530 eligible subjects were selected ( n = 2765 in each group). There was a higher CKD prevalence in subjects with MAFLD than in those without MAFLD (8.9% vs. 5.4%, p < 0.001). MAFLD was significantly associated with a higher CKD prevalence (OR 1.715, 95% CI 1.389–2.117, p < 0.001), although it was not an independent risk factor. The results indicated that age, diabetes mellitus (DM), overweight/obesity, hypertension, hyperuricemia, hypertriglyceridemia, remnant cholesterol (RC), and C-reactive protein (CRP) were independently associated with a higher CKD prevalence. In the subgroup analysis, hypertension, hyperuricemia, RC, and the nonalcoholic fatty liver disease fibrosis score (NFS) were independent risk factors for the prevalence of CKD in individuals with DM or prediabetes and MAFLD. Furthermore, hypertension, hyperuricemia, and body fat percentage (BFP) were independently associated with CKD in subjects with MAFLD without DM. Conclusion Individuals with MAFLD had a higher prevalence of CKD, whereas it was not an independent risk factor for CKD.

show abstract

Section: Discussionmentioning

confidence: 99%

Association of metabolic dysfunction-associated fatty liver disease with chronic kidney disease: a Chinese population-based study

Chen

Bao

et al. 2022

Renal Failure

View full text Add to dashboard Cite

show abstract

“…The most commonly used models are Child-Pugh score, the model for end-stage liver disease (MELD) and the model for end-stage liver disease-sodium (MELD-Na) in clinical. Although the above models were widely used in clinical, the weakness of ability was shown due to the poor detection and difficult evaluation [2,[4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach

Chen

Chang

et al. 2021

European Journal of Gastroenterology &Amp; Hepatology

Self Cite

View full text Add to dashboard Cite

Objective End-stage liver disease is a global public health problem with a high mortality rate. Early identification of people at risk of poor prognosis is fundamental for decision-making in clinical settings. This study created a machine learning prediction system that provides several related models with visualized graphs, including decision trees, ensemble learning and clustering, to predict mortality in patients with end-stage liver disease. Methods A retrospective cohort study was conducted: the training data were from patients enrolled from January 2009 to December 2010 and followed up to December 2014; validation data were from patients enrolled from January 2015 to December 2016 and followed up to January 2019. Hospitalized patients with noncancer-related chronic liver disease were identified from the hospital’s electrical medical records. Results In traditional multivariable logistic regression and Cox proportional hazard model, prothrombin time of international normalized ratio, which was significant with P value = 0.002, odds ratio = 2.790 and hazard ratio 1.363. Besides, blood urea nitrogen and C-reactive protein were also significant, with P value <0.001 and 0.026. The area under the curve was 0.771 in the receiver operating characteristic curve. In machine learning, blood urea nitrogen and age were regarded as the primary factors for predicting mortality. Creatinine, prothrombin time of international normalized ratio and bilirubin were also significant mortality predictors. The area under the curve of the random forest and AdaBoost was 0.838 and 0.792. Conclusion The machine learning techniques provided a comprehensive assessment of patient conditions; it could help physicians make an accurate diagnosis of chronic liver disease and improve healthcare management.

show abstract

“…Several ESLD risk prediction models have been developed using traditional statistical modeling, including the Child-Pugh score [ 9 ], model for end-stage liver disease (MELD) [ 9 , 10 ], adjusted MELD scores (eg, MELD-Na score and integrated MELD score) [ 11 - 13 ], albumin-bilirubin score [ 14 ], Chronic Liver Failure Consortium (CLIF) Acute Decompensation Score [ 15 ], CLIF Sequential Organ Failure Score [ 16 ], CLIF Consortium Acute-on-Chronic Liver Failure Score [ 17 ], and a novel score recently developed by our group [ 18 ]. Unfortunately, these prediction scores were all found to have poor discrimination between survival and death [ 19 - 22 ]. In addition, these traditional risk scores cannot differentiate patients that need acute care or palliative care.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

Lin¹,

Chen²,

Tang³

et al. 2020

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

Background Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care. Objective We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed. Methods A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set. Results The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group. Conclusions Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.

show abstract

Association of elevated blood serum high-sensitivity C-reactive protein levels and body composition with chronic kidney disease

Abstract: Supplemental Digital Content is available in the text

Cited by 5 publications

References 45 publications

Association of metabolic dysfunction-associated fatty liver disease with chronic kidney disease: a Chinese population-based study

Association of metabolic dysfunction-associated fatty liver disease with chronic kidney disease: a Chinese population-based study

Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

Contact Info

Product

Resources

About