Contemporary risk prediction models in chronic kidney disease: when less is more

Canney, Mark; Sood, Manish M.; Hundemer, Gregory L.

doi:10.1097/mnh.0000000000000788

Cited by 3 publications

(3 citation statements)

References 19 publications

(21 reference statements)

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“…Another trend in the field of kidney risk prediction modeling is a movement toward predicting earlier kidney outcomes. Traditional models have focused explicitly on the hard outcome of progression from CKD to kidney failure [1,2 ▪▪ ,3–11,12 ▪ ]. A growing number of models are being designed to function further upstream in the course of kidney disease, specifically predicting incident CKD which has most commonly been defined as an eGFR decline to below 60 mL/min/1.73 m 2 [24,25 ▪▪ ,26 ▪▪ ,28 ▪▪ ,29–32].…”

Section: Prediction Of Earlier Kidney Outcomesmentioning

confidence: 99%

“…In this context, accurate prognostication can assist in providing targeted education regarding kidney replacement therapies, empowering shared decision-making with patients, and allocating healthcare resources toward those individuals most in need. To this end, a number of risk prediction models are currently available [1,2 ▪▪ ,3–11,12 ▪ ]. These models have traditionally been developed among CKD populations using classical time-to-event modeling (e.g., Cox regression and models that account for competing risks such as that developed by Fine and Gray [13]) to predict the outcome of kidney failure, most commonly defined as the requirement for dialysis or kidney transplantation.…”

Section: Introductionmentioning

confidence: 99%

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Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes

Hundemer

Sood

Canney

2023

Current Opinion in Nephrology &Amp; Hypertension

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Purpose of reviewRecent years have witnessed the development of kidney risk prediction models which diverge from traditional model designs to incorporate novel approaches along with a focus on earlier outcomes. This review summarizes these recent advances, evaluates their pros and cons, and discusses their potential implications. Recent findingsSeveral kidney risk prediction models have recently been developed utilizing machine learning rather than traditional Cox regression. These models have demonstrated accurate prediction of kidney disease progression, often beyond that of traditional models, in both internal and external validation. On the opposite end of the spectrum, a simplified kidney risk prediction model was recently developed which minimized the need for laboratory data and instead relies primarily on self-reported data. While internal testing showed good overall predictive performance, the generalizability of this model remains uncertain. Finally, there is a growing trend toward prediction of earlier kidney outcomes (e.g., incident chronic kidney disease [CKD]) and away from a sole focus on kidney failure.

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Section: Prediction Of Earlier Kidney Outcomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes

Hundemer

Sood

Canney

2023

Current Opinion in Nephrology &Amp; Hypertension

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“…Identifying high-risk factors for VID in pediatric patients with epilepsy is beneficial for effective prevention strategies. Risk prediction models serve as valuable tools for this purpose ( Wallisch et al, 2021 ; Canney et al, 2022 ). However, it is worth noting that there have been no reports of risk prediction models for dyslipidemia in patients on long-term VPA use, especially in pediatric patients, where both risk factors and models have not been documented.…”

Section: Introductionmentioning

confidence: 99%

Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy

Liang,

Lin,

Ning

et al. 2024

Front. Pharmacol.

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Background: Valproic acid (VPA) stands as one of the most frequently prescribed medications in children with newly diagnosed epilepsy. Despite its infrequent adverse effects within therapeutic range, prolonged VPA usage may result in metabolic disturbances including insulin resistance and dyslipidemia. These metabolic dysregulations in childhood are notably linked to heightened cardiovascular risk in adulthood. Therefore, identification and effective management of dyslipidemia in children hold paramount significance.Methods: In this retrospective cohort study, we explored the potential associations between physiological factors, medication situation, biochemical parameters before the first dose of VPA (baseline) and VPA-induced dyslipidemia (VID) in pediatric patients. Binary logistic regression was utilized to construct a predictive model for blood lipid disorders, aiming to identify independent pre-treatment risk factors. Additionally, The Receiver Operating Characteristic (ROC) curve was used to evaluate the performance of the model.Results: Through binary logistic regression analysis, we identified for the first time that direct bilirubin (DBIL) (odds ratios (OR) = 0.511, p = 0.01), duration of medication (OR = 0.357, p = 0.009), serum albumin (ALB) (OR = 0.913, p = 0.043), BMI (OR = 1.140, p = 0.045), and aspartate aminotransferase (AST) (OR = 1.038, p = 0.026) at baseline were independent risk factors for VID in pediatric patients with epilepsy. Notably, the predictive ability of DBIL (AUC = 0.690, p < 0.0001) surpassed that of other individual factors. Furthermore, when combined into a predictive model, incorporating all five risk factors, the predictive capacity significantly increased (AUC = 0.777, p < 0.0001), enabling the forecast of 77.7% of dyslipidemia events.Conclusion: DBIL emerges as the most potent predictor, and in conjunction with the other four factors, can effectively forecast VID in pediatric patients with epilepsy. This insight can guide the formulation of individualized strategies for the clinical administration of VPA in children.

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Population characteristics and diagnosis rate of chronic kidney disease by eGFR and proteinuria in Japanese clinical practice: an observational database study

Tanaka,

Maruyama,

Chishima

et al. 2024

Sci Rep

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Chronic kidney disease (CKD) guidelines recommend early identification and intervention to delay the progression of CKD. The Kidney Disease: Improving Global Outcomes (KDIGO) heatmap is widely used for risk evaluation in CKD management; however, real-world evidence on clinical characteristics based on the KDIGO heatmap remains limited worldwide including Japan. In order to understand the management of CKD including its diagnostic rates in a Japanese clinical setting on the basis of KDIGO heatmap, we utilized a medical record database that contains estimated glomerular filtration rate (eGFR) and urine protein data. Adult individuals (≥ 18 years) with two eGFR results of < 90 mL/min/1.73 m2, 90–360 days apart, were included. Approximately half of patients (452,996/788,059) had proteinuria test results and 6.9% (54,073) had quantitative results. CKD diagnosis rate in patients without proteinuria data was 5.9%, with a lower rate (2.9%) in stage G2; the corresponding rates with quantitative test results were 43.5% and 31.3%, respectively. The most frequent comorbidities were hypertension, diabetes, and cardiovascular disease, and their prevalence increased as the eGFR and proteinuria stages progressed. This study revealed a low rate of proteinuria assessment, especially using quantitative methods, and diagnosis in individuals with suspected CKD. With emerging treatment options to prevent CKD progression and complication onset, there is a need for early evaluation and diagnosis of CKD.

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Contemporary risk prediction models in chronic kidney disease: when less is more

Cited by 3 publications

References 19 publications

Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes

Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes

Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy

Population characteristics and diagnosis rate of chronic kidney disease by eGFR and proteinuria in Japanese clinical practice: an observational database study

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