Implementing GFR Estimation Guidelines Using Cystatin C: A Quality Improvement Project

Little, Dustin J.; Mascio, Heather M.; Altenburg, Ryan J.; Moon, Deepti S.; Deressa, Wondaye T.; Wong, Stephen C. P.; Yuan, Christina M.

doi:10.1053/j.ajkd.2015.10.014

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Low molecular-weight proteins have been used to estimate the value of eGFR. 30 Among them, cystatin-C has been identified as a superior GFR marker to creatinine in chronic renal insufficiency with small variability. 31–33 For instance, El-Gammacy et al 34 reported that serum cystatin-C on day 3 of life can predict AKI earlier than serum Cr and eGFR.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Assisted Ensemble Analysis for the Prediction of Acute Pancreatitis with Acute Kidney Injury

Yang¹,

Xiao²,

Liu³

et al. 2022

IJGM

View full text Add to dashboard Cite

Purpose Acute kidney injury (AKI) is a frequent complication of severe acute pancreatitis (AP) and carries a very poor prognosis. The present study aimed to construct a model capable of accurately identifying those patients at high risk of harboring occult acute kidney injury (AKI) characteristics. Patients and Methods We retrospectively recruited a total of 424 consecutive patients at the Gezhouba central hospital of Sinopharm and Xianning central hospital between January 1, 2016, and October 30, 2021. ML-assisted models were developed from candidate clinical features using two-step estimation methods. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model. Results Finally, a total of 30 candidate variables were included, and the AKI prediction model was established by an ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.725 (95% CI 0.223–1.227) to 0.902 (95% CI 0.400–1.403). Among them, RFC obtained the optimal prediction efficiency via adding inflammatory factors, which are serum creatinine (Scr), C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-albumin ratio (NAR), and CysC, respectively. Conclusion We successfully developed ML-based prediction models for AKI, particularly the RFC, which can improve the prediction of AKI in patients with AP. The practicality of prediction and early detection may be greatly beneficial to risk stratification and management decisions.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning-Assisted Ensemble Analysis for the Prediction of Acute Pancreatitis with Acute Kidney Injury

Yang¹,

Xiao²,

Liu³

et al. 2022

IJGM

View full text Add to dashboard Cite

show abstract

“…Previous projects improved processes at the service and hospital levels. Several have been presented at hospital-wide PI summits, two have been presented at national nephrology meetings, and one was recently published (36). All have met the criteria for successful trainee PI projects that show competency in PBLI and SBP as outlined by McClain et al (21) ( Table 3).…”

Section: The Multidisciplinary Qi Projectmentioning

confidence: 99%

Integrating Quality Improvement Education into the Nephrology Curricular Milestones Framework and the Clinical Learning Environment Review

Prince¹,

Little²,

Schexneider

et al. 2016

CJASN

Self Cite

View full text Add to dashboard Cite

The Accreditation Council for Graduate Medical Education requires that trainees show progressive milestone attainment in the practice-based learning and systems-based practice competencies. As part of the Clinical Learning Environment Review, sponsoring hospitals must educate trainees in health care quality improvement, provide them with specialty-specific quality data, and ensure trainee participation in quality improvement activities and committees. Subspecialty-specific quality improvement curricula in nephrology training programs have not been reported, although considerable curricular and assessment material exists for specialty residencies, including tools for assessing trainee and faculty competence. Nephrology-specific didactic material exists to assist nephrology fellows and faculty mentors in designing and implementing quality improvement projects. Nephrology is notable among internal medicine subspecialties for the emphasis placed on adherence to quality thresholds-specifically for chronic RRT shown by the Centers for Medicare and Medicaid Services Quality Incentive Program. We have developed a nephrology-specific curriculum that meets Accreditation Council for Graduate Medical Education and Clinical Learning Environment Review requirements, acknowledges regulatory quality improvement requirements, integrates with ongoing divisional quality improvement activities, and has improved clinical care and the training program. In addition to didactic training in quality improvement, we track trainee compliance with Kidney Disease Improving Global Outcomes CKD and ESRD quality indicators (emphasizing Quality Improvement Program indicators), and fellows collaborate on a yearly multidisciplinary quality improvement project. Over the past 6 years, each fellowship class has, on the basis of a successful quality improvement project, shown milestone achievement in Systems-Based Practice and Practice-Based Learning. Fellow quality improvement projects have improved nephrology clinical care within the institution and introduced new educational and assessment tools to the training program. All have been opportunities for quality improvement scholarship. The curriculum prepares fellows to apply quality improvement principals in independent clinical practice-while showing milestone advancement and divisional compliance with Clinical Learning Environment Review requirements.

show abstract

“…It is important to use a reliable serological indicator to predict HF in CKD patients with different eGFRs. Low molecular-weight proteins have been used to estimate the value of eGFR ( 18 ), and the most promising biomarker is cystatin C (CysC) ( 19 ). CysC is a non-glycosylated, 13-kDa basic protein that belongs to the cystatin superfamily of cysteine protease inhibitors.…”

Section: Introductionmentioning

confidence: 99%

The Ratio of NT-proBNP to CysC1.53 Predicts Heart Failure in Patients With Chronic Kidney Disease

Wang

et al. 2021

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

Background: The N-terminal pro B type natriuretic peptide (NT-proBNP) is important for prognosis of heart failure in patients with chronic kidney disease (CKD). However, the NT-proBNP level is easily affected by renal insufficiency, which limits its clinical use.Methods: This study included 396 patients with CKD. Plasma levels of NT-proBNP and cystatin C (CysC) were measured during hospitalization. The echocardiographic parameters were also detected. Patients were divided into the heart failure group and control group according to the European Society of Cardiology Guideline on Chronic Heart Failure 2021. Multiple modeling analysis of the values of NT-proBNP and CysC, including NT-proBNP/Cyscn and NT-proBNP/nCysC was performed. The receiver operating characteristic (ROC) curve, combined with the cardiac function, was used to determine the formula with the best diagnostic efficiency. Then, the sensitivity and specificity of new predictors for cardiac insufficiency in CKD patients were calculated. Pearson correlation analysis was used to analyze the relationship between new predictors and the NT-proBNP level. The clinical data of CKD patients from another local hospital were used to validate the new predictors and the cut-off values.Results: An elevated NT-proBNP/CysC1.53 ratio was an independent risk factor for cardiac dysfunction in CKD and the best predictor derived from multiple modeling analysis. There was no correlation between the NT-proBNP/CysC1.53 ratio and the NT-proBNP level (r = 0.376, p = 6.909). The area under the ROC curve for the NT-proBNP/CysC1.53 ratio was 0.815 (95% confidence interval: 0.772–0.858), and for a cut-off point of 847.964, this ratio had a sensitivity of 78.24%, and a specificity of 69.44%. When applied to the data of CKD patients from another local hospital, the NT-proBNP to CysC1.53 ratio had a sensitivity of 70.27% and a specificity of 67.74%.Conclusion: The NT-proBNP to CysC1.53 ratio was superior to NT-proBNP alone for predicting cardiac dysfunction in patients with CKD.

show abstract

Implementing GFR Estimation Guidelines Using Cystatin C: A Quality Improvement Project

Cited by 5 publications

References 10 publications

Machine Learning-Assisted Ensemble Analysis for the Prediction of Acute Pancreatitis with Acute Kidney Injury

Machine Learning-Assisted Ensemble Analysis for the Prediction of Acute Pancreatitis with Acute Kidney Injury

Integrating Quality Improvement Education into the Nephrology Curricular Milestones Framework and the Clinical Learning Environment Review

The Ratio of NT-proBNP to CysC1.53 Predicts Heart Failure in Patients With Chronic Kidney Disease

Contact Info

Product

Resources

About