Kidney Failure Risk Equation and Cost of Care in Patients with Chronic Kidney Disease

Background and objectivesPatients with CKD exhibit heterogeneity in their rates of progression to kidney failure. The kidney failure risk equation (KFRE) has been shown to accurately estimate progression to kidney failure in adults with CKD. Our objective was to determine health care utilization patterns of patients on the basis of their risk of progression.Design, setting, participants, & measurementsWe conducted a retrospective cohort study of adults with CKD and eGFR of 15–59 ml/min per 1.73 m2 enrolled in multidisciplinary CKD clinics in the province of Saskatchewan, Canada. Data were collected from January 1, 2004 to December 31, 2012 and followed for 5 years (December 31, 2017). We stratified patients by eGFR and risk of progression and compared the number and cost of hospital admissions, physician visits, and prescription drugs.ResultsIn total, 1003 adults were included in the study. Within the eGFR of 15–29 ml/min per 1.73 m2 group, the costs of hospital admissions, physician visits, and drug dispensations over the 5-year study period comparing high-risk patients with low-risk patients were (Canadian dollars) $89,265 versus $48,374 (P=0.008), $23,423 versus $11,231 (P<0.001), and $21,853 versus $16,757 (P=0.01), respectively. Within the eGFR of 30–59 ml/min per 1.73 m2 group, the costs of hospital admissions, physician visits, and prescription drugs were $55,944 versus $36,740 (P=0.10), $13,414 versus $10,370 (P=0.08), and $20,394 versus $14,902 (P=0.02) in high-risk patients in comparison with low-risk patients, respectively, for progression to kidney failure.ConclusionsIn patients with CKD and eGFR of 15–59 ml/min per 1.73 m2 followed in multidisciplinary clinics, the costs of hospital admissions, physician visits, and drugs were higher for patients at higher risk of progression to kidney failure by the KFRE compared with patients in the low-risk category. The high-risk group of patients with CKD and eGFR of 15–29 ml/min per 1.73 m2 had stronger association with hospitalizations costs, physician visits, and drug utilizations.

The Influence of Cardiovascular Risk Factors and Hypogonadism on Cardiac Outcomes in an Aging Population of Beta-Thalassemia Patients

Beta-thalassemia major (β-TM) is a hereditary genetic disease worsened by many comorbidities due to transfusion-related iron despite chelation therapy. Since there has recently been an increase in life expectancy of patients to up to 50 years old, which influences the prevalence of these diseases and the time span for traditional cardiovascular risk factors to play their role, this study aims to evaluate their distribution and prevalence in a population of thalassemia major patients and their relationship with observed cardiovascular events and potential modifying factors. One hundred and fifty-nine β-TM patients with at least 15 years of follow-up were included in this study. The mean age was 40.9 ± 8.4 years; 28% had diabetes mellitus and 62% had hypogonadism. The cardiovascular risk assessed using algorithms (CUORE and Pooled Cohort Risk Equation—PCRE) was low, but 3.8% of patients had at least one episode of heart failure, 35.9% showed early signs of heart failure, 22% received a diagnosis of diastolic dysfunction, and 21.4% showed supraventricular arrhythmias. Hypogonadism was shown to be related to the occurrence of cardiovascular events. The chronic accumulation of iron in the heart and the specific metabolic profile, mainly observed in patients with hypogonadism, allows us to define β-TM as a condition with a high level of cardiovascular risk from many points of view (iron-related myopathy, atherosclerosis and arrhythmias), which requires better stratification tools and a specific follow-up program.

Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD)

Current equation-based risk stratification algorithms for kidney failure (KF) may have limited applicability in real world settings, where missing information may impede their computation for a large share of patients, hampering one from taking full advantage of the wealth of information collected in electronic health records. To overcome such limitations, we trained and validated the Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD), a novel algorithm predicting end-stage kidney disease (ESKD). PROGRES-CKD is a naïve Bayes classifier predicting ESKD onset within 6 and 24 months in adult, stage 3-to-5 CKD patients. PROGRES-CKD trained on 17,775 CKD patients treated in the Fresenius Medical Care (FMC) NephroCare network. The algorithm was validated in a second independent FMC cohort (n = 6760) and in the German Chronic Kidney Disease (GCKD) study cohort (n = 4058). We contrasted PROGRES-CKD accuracy against the performance of the Kidney Failure Risk Equation (KFRE). Discrimination accuracy in the validation cohorts was excellent for both short-term (stage 4–5 CKD, FMC: AUC = 0.90, 95%CI 0.88–0.91; GCKD: AUC = 0.91, 95% CI 0.86–0.97) and long-term (stage 3–5 CKD, FMC: AUC = 0.85, 95%CI 0.83–0.88; GCKD: AUC = 0.85, 95%CI 0.83–0.88) forecasting horizons. The performance of PROGRES-CKD was non-inferior to KFRE for the 24-month horizon and proved more accurate for the 6-month horizon forecast in both validation cohorts. In the real world setting captured in the FMC validation cohort, PROGRES-CKD was computable for all patients, whereas KFRE could be computed for complete cases only (i.e., 30% and 16% of the cohort in 6- and 24-month horizons). PROGRES-CKD accurately predicts KF onset among CKD patients. Contrary to equation-based scores, PROGRES-CKD extends to patients with incomplete data and allows explicit assessment of prediction robustness in case of missing values. PROGRES-CKD may efficiently assist physicians’ prognostic reasoning in real-life applications.

Decision Rules: How one Calibration Software Vendor Approaches the Various Options of “taking uncertainty into account”

The latest release of ISO/IEC-17025 requires that calibrations laboratories must take their measurement uncertainty into account when making a statement of conformity to a specified requirement. The standard further requires the laboratory to take the risks (both consumer and producer risk) into consideration when employing these “decision rules”. While 17025 does not specify exactly what your laboratory decision rules must be, there are numerous documents that can offer guidance on the subject. Each method has its pros and cons relating to complexity, statistical rigor, and tradeoffs between the two sides of the risk equation. The modern calibration laboratory struggles to offer affordable services to customers who demand increased accuracy in their equipment. The old 4:1 TAR rule of thumb is long gone and with the customer shop floor equipment reaching the accuracies of the laboratory standards of just a few years ago, the challenge of maintaining an appropriate ratio of uncertainty is becoming progressively more difficult. This paper looks at the most common methods of taking that measurement uncertainty into account and how our software is configured and structured to allow the laboratory to apply several different methods depending on their individual customer requirements. These approaches are not fixed, one size fits all, but are customizable by the laboratory, to fit their customers exact requirements, even if their customers have widely different demands.

Risk Assessment of Accidental Fire Breakdown in a Residential Area of Khulna City, Bangladesh

The research aimed at determining the risk of fire hazard of the settlements in the area of Khalishpur, Khulna which is close to three oil depots and two power stations. The maintainence of land use zonal variance is completely absent in this area. The research has followed four stages. First, ptrliminary study was done for choosing the most vulnerable samples, then the factors for identifying the risk index are considered on the basis of hazard, vulnerability and exposure. After collecting the required primary and secondary data, the Analytical Hierarchy Process (AHP) method is used to identify the weightage of each of those sub-factors.Then the risk equation is used to identify the risk level of each buildings and a map is prepared by using their coordinates and risk level. The study states that, 55% of the total samples are in the medium risk profile. Journal of Engineering Science 12(2), 2021, 109-118

Prediction of non-responsiveness to pre-dialysis care program in patients with chronic kidney disease: a retrospective cohort analysis

The responsiveness of patients with chronic kidney disease (CKD) to nephrologists’ care is unpredictable. We defined the longitudinal stages (LSs) 1–5 of estimated glomerular filtration rate (eGFR) by group-based trajectory modeling for repeated eGFR measurements of 7135 patients with CKD aged 20–90 years from a 13-year pre-end-stage renal disease (ESRD) care registry. Patients were considered nonresponsive to the pre-dialysis care if they had a more advanced eGFR LS compared with the baseline. Conversely, those with improved or stable eGFR LS were considered responsive. The proportion of patients with CKD stage progression increased with the increase in the baseline CKD stage (stages 1–2: 29.2%; stage 4: 45.8%). The adjusted times to ESRD and all-cause mortality in patients with eGFR LS-5 were 92% (95% confidence interval [CI] 86–96%) and 57% (95% CI 48–65%) shorter, respectively, than in patients with eGFR LS-3A. Among patients with baseline CKD stages 3 and 4, the adjusted times to ESRD and all-cause death in the nonresponsive patients were 39% (95% CI 33–44%) and 20% (95% CI 14–26%) shorter, respectively, than in the responsive patients. Our proposed Renal Care Responsiveness Prediction (RCRP) model performed significantly better than the conventional Kidney Failure Risk Equation in discrimination, calibration, and net benefit according to decision curve analysis. Non-responsiveness to nephrologists’ care is associated with rapid progression to ESRD and all-cause mortality. The RCRP model improves early identification of responsiveness based on variables collected during enrollment in a pre-ESRD program. Urgent attention should be given to characterize the underlying heterogeneous responsiveness to pre-dialysis care.

A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility

Background The Kidney Failure Risk Equation (KFRE) predicts the 2- and 5-year risk of end-stage renal disease (ESRD) in patients with chronic kidney disease (CKD) stages 3a-5. Its predictive performance in advanced CKD and in specific disease aetiologies requires further exploration. This study validates the 4- and 8-variable KFREs in an advanced CKD population in the United Kingdom by evaluating discrimination, calibration and clinical utility. Methods Patients enrolled in the Salford Kidney Study who were referred to the Advanced Kidney Care Service (AKCS) clinic at Salford Royal NHS Foundation Trust between 2011 and 2018 were included. The 4- and 8-variable KFREs were calculated on the first AKCS visit and the observed events of ESRD (dialysis or pre-emptive transplantation) within 2- and 5-years were the primary outcome. The area under the receiver operator characteristic curve (AUC) and calibration plots were used to evaluate discrimination and calibration respectively in the whole cohort and in specific disease aetiologies: diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, autosomal dominant polycystic kidney disease (ADPKD) and other diseases. Clinical utility was assessed with decision curve analyses, comparing the net benefit of using the KFREs against estimated glomerular filtration rate (eGFR) cut-offs of < 20 ml/min/1.73m2 and < 15 ml/min/1.73m2 to guide further treatment. Results A total of 743 patients comprised the 2-year analysis and 613 patients were in the 5-year analysis. Discrimination was good in the whole cohort: the 4-variable KFRE had an AUC of 0.796 (95% confidence interval [CI] 0.762–0.831) for predicting ESRD at 2-years and 0.773 (95% CI 0.736–0.810) at 5-years, and there was good-to-excellent discrimination across disease aetiologies. Calibration plots revealed underestimation of risk at 2-years and overestimation of risk at 5-years, especially in high-risk patients. There was, however, underestimation of risk in patients with ADPKD for all KFRE calculations. The predictive accuracy was similar between the 4- and 8-variable KFREs. Finally, compared to eGFR-based thresholds, the KFRE was the optimal tool to guide further care based on decision curve analyses. Conclusions The 4- and 8-variable KFREs demonstrate adequate discrimination and calibration for predicting ESRD in an advanced CKD population and, importantly, can provide better clinical utility than using an eGFR-based strategy to inform decision-making.