Nephrology consultation and mortality in people with stage 4 chronic kidney disease: a population-based study

Liu, Ping; Quinn, Robert R.; Karim, Mohammad Ehsanul; Bello, Aminu K.; Tam-Tham, Helen; Weaver, R. W.; Ronksley, Paul E.; Quan, Hude; Strippoli, Giovanni F.M.; Manns, Braden; Hemmelgarn, Brenda R.; Ravani, Pietro

doi:10.1503/cmaj.181372

Cited by 15 publications

(15 citation statements)

References 32 publications

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“…This integrated approach has near-term clinical implications, especially when linked to clinical decision support and embedded care pathways within the EHR. For example, patients with a high KidneyIntelX risk score, with a probability of .50% for the kidney end point, should be referred to a nephrologist, which has been associated with improved outcomes (37). In addition, referral of high-risk patients to a dietician and the delivery of educational materials regarding the importance and consequences of CKD should increase awareness and facilitate motivation for changes in lifestyles and behavior.…”

Section: Discussionmentioning

confidence: 99%

Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes

Chauhan

Nadkarni

Fleming³

et al. 2020

Kidney360

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BackgroundIndividuals with type 2 diabetes (T2D) or the apolipoprotein L1 high-risk (APOL1-HR) genotypes are at increased risk of rapid kidney function decline (RKFD) and kidney failure. We hypothesized that a prognostic test using machine learning integrating blood biomarkers and longitudinal electronic health record (EHR) data would improve risk stratification.MethodsWe selected two cohorts from the Mount Sinai BioMe Biobank: T2D (n=871) and African ancestry with APOL1-HR (n=498). We measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. We compared performance to a validated clinical model and applied thresholds to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories.ResultsOverall, 23% of those with T2D and 18% of those with APOL1-HR experienced the composite kidney end point over a median follow-up of 4.6 and 5.9 years, respectively. The area under the receiver operator characteristic curve (AUC) of KidneyIntelX was 0.77 (95% CI, 0.75 to 0.79) in T2D, and 0.80 (95% CI, 0.77 to 0.83) in APOL1-HR, outperforming the clinical models (AUC, 0.66 [95% CI, 0.65 to 0.67] and 0.72 [95% CI, 0.71 to 0.73], respectively; P<0.001). The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models (P<0.01) in high-risk (top 15%) stratum for T2D and APOL1-HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1-HR versus 85% and 93% for the clinical model, respectively (P=0.76 and 0.93, respectively), in low-risk stratum (bottom 50%).ConclusionsIn patients with T2D or APOL1-HR, a prognostic test (KidneyIntelX) integrating biomarker levels with longitudinal EHR data significantly improved prediction of a composite kidney end point of RKFD, 40% decline in eGFR, or kidney failure over validated clinical models.

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Section: Discussionmentioning

confidence: 99%

Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes

Chauhan

Nadkarni

Fleming³

et al. 2020

Kidney360

View full text Add to dashboard Cite

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“…Secondary conditions corresponded to common CKD comorbidities. Hypertensive chronic kidney disease and diabetes were the most common conditions, presumably registered as the cause of CKD, followed by conditions with a renal origin, such as anemia or secondary hyperparathyroidism 20,21 . In particular, the Spanish population with CKD had presented an important prevalence of cardiovascular risk factors as described in the EPIRCE study 22 .…”

Section: Discussionmentioning

confidence: 99%

Chronic kidney disease in Spain: analysis of patient characteristics, incidence and direct medical costs (2011–2017)

Darbà

Marsà²

2020

Journal of Medical Economics

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Objectives: To review the characteristics of patients with chronic kidney disease (CKD) who attended primary and specialized care centers in Spain, and to analyze patients' use of medical resources and direct medical costs of specialized care. Methods: Records of patients with CKD admitted to primary and specialized healthcare centers in Spain between 2011 and 2017 from two national discharge databases were analyzed in a retrospective multicenter observational study. Records were classified into one-to-five CKD stages plus a 5b stage, indicating end-stage renal disease. Results: Most of the patients registered in hospital settings were in stage 5. Registered secondary conditions included hypertensive chronic kidney, diabetes, anemia, hypercholesterolemia, and hypertension. The number of cases registered in primary care settings increased over time, whereas in specialized care centers incidence decreased; hospital incidence of CKD in 2017 was 10.72 per 10,000 persons. Mean in-hospital mortality was 5.90%, which remained stable during the study period. Mortality was associated with respiratory and heart failure. Mean length of hospital stay was 8.19 days, decreasing over the study period, whilst increasing with CKD progression. Mean annual direct medical cost of specialized care was e10,436 per patient. Complications of a transplant and bacterial infections were responsible for major increases in medical costs, that otherwise decreased over the study period. Conclusions: The costs of specialized care decreased with the length of hospital stay reduction. Cardiovascular risk factors were crucial in in-hospital mortality. This study provides population-based data to assist decision-makers at the national level and to contribute to worldwide evaluations and disease surveillance.

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“…For example, patients with a high RFKD score, who will have a predicted probability of > 50% of experiencing rapid kidney function decline should be referred to a nephrologist, which has been shown to be associated with improvement in outcomes. 31 In addition, referral to a dietician and delivery of educational materials regarding the importance and consequences of CKD can be provided to the high RFKD score patients to help increase awareness and facilitate motivation for changes in lifestyles and behavior. Finally, the optimization of medical therapy including renin-angiotensin aldosterone system inhibitors, statins for cardiovascular risk management, and intensification of antihypertensive medication to meet guideline recommended blood pressure targets can be pursued.…”

Section: Discussionmentioning

confidence: 99%

Prediction of rapid kidney function decline using machine learning combining blood biomarkers and electronic health record data

Nadkarni

Fleming²,

McCullough³

et al. 2019

Preprint

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Introduction: Individuals with type 2 diabetes (T2DM) or the APOL1 high-risk genotype (APOL1) are at increased risk of rapid kidney function decline (RKFD) as compared to the general population. Plasma biomarkers representing inflammatory and kidney injury pathways have been validated as predictive of kidney disease progression in several studies. In addition, routine clinical data in the electronic health record (EHR) may also be utilized for predictive purposes. The application of machine learning to integrate biomarkers with clinical data may lead to improved identification of RKFD. Methods:We selected two subpopulations of high-risk individuals: T2DM (n=871) and APOL1 high risk genotype of African Ancestry (n=498), with a baseline eGFR ≥ 45 ml/min/1.73 m 2 from the Mount Sinai BioMe Biobank. Plasma levels of tumor necrosis factor 1/2 (TNFR1/2), and kidney injury molecule-1 (KIM-1) were measured and a series of supervised machine learning approaches including random forest (RF) were employed to combine the biomarker data with longitudinal clinical variables. The primary objective was to accurately predict RKFD (eGFR decline of ≥ 5 ml/min/1.73 m 2 /year) based on an algorithm-produced score and probability cutoffs, with results compared to standard of care. Results:In 871 participants with T2DM, the mean age was 61 years, baseline estimated glomerular filtration rate (eGFR) was 74 ml/min/1.73 m 2 , and median UACR was 13 mg/g. The median follow-up was 4.7 years from the baseline specimen collection with additional retrospective data available for a median of 2.3 years prior to plasma collection. In the 498 African Ancestry patients with high-risk APOL1 genotype, the median age was 56 years, median baseline eGFR was 83 ml/min/1.73 m 2 ,and median UACR was 11 mg/g. The median follow-up was 4.7 years and there was additional retrospective data available for 3.1 years prior to plasma collection. Overall, 19% with T2DM, and 9% of the APOL1 high-risk genotype experienced RKFD. After evaluation of three supervised algorithms: random forest (RF), support vector machine (SVM), and Cox survival, the RF model was selected. In the training and test sets respectively, the RF model had an AUC of 0.82 (95% CI, 0.81-0.83) and 0.80 (95% CI, 0.78-0.82) in T2DM, and an AUC of 0.85 (95% CI, 0.84-0.87) and 0.80 (95% CI, 0.73-0.86) for the APOL1 high-risk group. The combined RF model outperformed standard clinical variables in both patient populations. Discrimination was comparable in two sensitivity analyses: 1) Using only data from ≤ 1 year prior to baseline biomarker measurement and 2) In individuals with eGFR ≤ 60 and/or albuminuria at baseline. The distribution of RFKD probability varied in the two populations. In patients with T2DM, the RKFD score stratified 18%, 49%, and 33% of patients to high-, intermediate-, and lowprobability strata, respectively, with a PPV of 53% in the high-probability group and an NPV of 97% in the lowprobability group. By comparison, in the APOL1 high-risk genotype, the RKFD score stratified 7%, 23%,...

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Nephrology consultation and mortality in people with stage 4 chronic kidney disease: a population-based study

Cited by 15 publications

References 32 publications

Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes

Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes

Chronic kidney disease in Spain: analysis of patient characteristics, incidence and direct medical costs (2011–2017)

Prediction of rapid kidney function decline using machine learning combining blood biomarkers and electronic health record data

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