Survival prognosis after the start of a renal replacement therapy in the Netherlands: a retrospective cohort study

A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients

Background

A prediction model for graft survival including donor and recipient characteristics could help clinical decision-making and optimize outcomes. The aim of this study was to develop a risk assessment tool for graft survival based on essential pre-transplantation parameters.

Methods

The data originated from the national Dutch registry (NOTR; Nederlandse OrgaanTransplantatie Registratie). A multivariable binary logistic model was used to predict graft survival, corrected for the transplantation era and time after transplantation. Subsequently, a prediction score was calculated from the β-coefficients. For internal validation, derivation (80%) and validation (20%) cohorts were defined. Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristics curve, Hosmer-Lemeshow test and calibration plots.

Results

In total, 1428 transplantations were performed. Ten-year graft survival was 42% for transplantations before 1990, which has improved to the current value of 92%. Over time, significantly more living and pre-emptive transplantations have been performed and overall donor age has increased (P < .05).The prediction model included 71 829 observations of 554 transplantations between 1990 and 2021. Other variables incorporated in the model were recipient age, re-transplantation, number of human leucocyte antigen (HLA) mismatches and cause of kidney failure. The predictive capacity of this model had AUCs of 0.89, 0.79, 0.76 and 0.74 after 1, 5, 10 and 20 years, respectively (P < .01). Calibration plots showed an excellent fit.

Conclusions

This pediatric pre-transplantation risk assessment tool exhibits good performance for predicting graft survival within the Dutch pediatric population. This model might support decision-making regarding donor selection to optimize graft outcomes.

Trial registration

ClinicalTrials.gov Identifier: NCT05388955.

Predicting mortality after start of long-term dialysis-International validation of one- and two-year prediction models

BACKGROUND

Mortality prediction is critical on long-term kidney replacement therapy (KRT), both for individual treatment decisions and resource planning. Many mortality prediction models already exist, but as a major shortcoming most of them have only been validated internally. This leaves reliability and usefulness of these models in other KRT populations, especially foreign, unknown. Previously two models were constructed for one- and two-year mortality prediction of Finnish patients starting long-term dialysis. These models are here internationally validated in KRT populations of the Dutch NECOSAD Study and the UK Renal Registry (UKRR).

METHODS

We validated the models externally on 2051 NECOSAD patients and on two UKRR patient cohorts (5328 and 45493 patients). We performed multiple imputation for missing data, used c-statistic (AUC) to assess discrimination, and evaluated calibration by plotting average estimated probability of death against observed risk of death.

RESULTS

Both prediction models performed well in the NECOSAD population (AUC 0.79 for the one-year model and 0.78 for the two-year model). In the UKRR populations, performance was slightly weaker (AUCs: 0.73 and 0.74). These are to be compared to the earlier external validation in a Finnish cohort (AUCs: 0.77 and 0.74). In all tested populations, our models performed better for PD than HD patients. Level of death risk (i.e., calibration) was well estimated by the one-year model in all cohorts but was somewhat overestimated by the two-year model.

CONCLUSIONS

Our prediction models showed good performance not only in the Finnish but in foreign KRT populations as well. Compared to the other existing models, the current models have equal or better performance and fewer variables, thus increasing models' usability. The models are easily accessible on the web. These results encourage implementing the models into clinical decision-making widely among European KRT populations.

Illness Perceptions and Medication Nonadherence to Immunosuppressants After Successful Kidney Transplantation: A Cross-Sectional Study

Background: Medication nonadherence to immunosuppressants is a well-known risk factor for suboptimal health outcomes in kidney transplant recipients (KTRs). This study examined the relationship between illness perceptions and medication nonadherence in prevalent Dutch KTRs and whether this relationship depended on post-transplant time.

Methods: Eligible KTRs transplanted in Leiden University Medical Center were invited for this cross-sectional study. The illness perceptions and medication nonadherence were measured via validated questionnaires. Associations between illness perceptions and medication nonadherence were investigated using multivariable logistic regression models.

Results: For the study, 627 participating KTRs were analyzed. 203 (32.4%) KTRs were considered nonadherent to their immunosuppressants with “taking medication more than 2 h from the prescribed dosing time” as the most prevalent nonadherent behaviour (n = 171; 27.3%). Three illness perceptions were significantly associated with medication nonadherence: illness identity (adjusted odds ratio [OR_adj] = 1.07; 95% confidence interval [CI], 1.00–1.14), concern (OR_adj = 1.07; 95%CI,1.00–1.14), and illness coherence (OR_adj = 1.11; 95%CI,1.01–1.22). The relationships between illness perceptions and medication nonadherence did not differ depending on post-transplant time (p-values ranged from 0.48 to 0.96).

Conclusion: Stronger negative illness perceptions are associated with medication nonadherence to immunosuppressants. Targeting negative illness perceptions by means of psychoeducational interventions could optimize medication adherence and consequently improve health outcomes in KTRs.

Donor type and 3-month hospital readmission following kidney transplantation: results from the Netherlands organ transplant registry

BACKGROUND

Hospital readmission after transplantation is common in kidney transplant recipients (KTRs). In this study, we aim to compare the risk of 3-month hospital readmission after kidney transplantation with different donor types in the overall population and in both young (< 65 years) and elderly (≥65 years) KTRs.

METHODS

We included all first-time adult KTRs from 2016 to 2018 in the Netherlands Organ Transplant Registry. Multivariable logistic regression models were used to estimate the effect while adjusting for baseline confounders.

RESULTS

Among 1917 KTRs, 615 (32.1%) had at least one hospital readmission. Living donor kidney transplantation (LDKT) recipients had an adjusted OR of 0.76 (95%CI, 0.61 to 0.96; p = 0.02) for hospital readmission compared to deceased donor kidney transplantation (DDKT) recipients. In the young and elderly, the adjusted ORs were 0.69 (95%CI, 0.52 to 0.90, p = 0.01) and 0.93 (95%CI, 0.62 to 1.39, p = 0.73) and did not differ significantly from each other (p-value for interaction = 0.38). In DDKT, the risk of hospital readmission is similar between recipients with donation after cardiac death (DCD) or brain death (DBD) and the risk was similar between the young and elderly.

CONCLUSION

A lower risk of post-transplant 3-month hospital readmission was found in recipients after LDKT compared to DDKT, and this benefit of LDKT might be less dominant in elderly patients. In DDKT, having either DCD or DBD donors is not associated with post-transplant 3-month hospital readmission, regardless of age. Tailored patient management is needed for recipients with DDKT and elderly KTRs.

Dynamic prediction models for graft failure in paediatric kidney transplantation

BACKGROUND

Several models have been proposed to predict kidney graft failure in adult recipients but none in younger recipients. Our objective was to propose a dynamic prediction model for graft failure in young kidney transplant recipients.

METHODS

We included 793 kidney transplant recipients waitlisted before the age of 18 years who received a first kidney transplantation before the age of 21 years in France in 2002-13 and survived >90 days with a functioning graft. We used a Cox model including baseline predictors only (sex, age at transplant, primary kidney disease, dialysis duration, donor type and age, human leucocyte antigen matching, cytomegalovirus serostatus, cold ischaemia time and delayed graft function) and two joint models also accounting for post-transplant estimated glomerular filtration rate (eGFR) trajectory. Predictive performances were evaluated using a cross-validated area under the curve (AUC) and R2 curves.

RESULTS

When predicting the risk of graft failure from any time within the first 7 years after paediatric kidney transplantation, the predictions for the following 3 or 5 years were accurate and much better with the joint models than with the Cox model (AUC ranged from 0.83 to 0.91 for the joint models versus 0.56 to 0.64 for the Cox model).

CONCLUSION

Accounting for post-transplant eGFR trajectory strongly increased the accuracy of graft failure prediction in young kidney transplant recipients.

Incidence of chronic kidney disease hospitalisations and mortality in Espírito Santo between 1996 to 2017

INTRODUCTION

Chronic kidney disease (CKD) has a set of clinical and laboratory abnormalities where renal function loss is noted. The high prevalence of comorbidity of people living with CKD, its economic impact and its prognosis have made it a public health problem, justifying the need to implement preventive measures.

OBJECTIVE

To analyse the mortality and incidence of hospital admissions for CKD.

METHODS

Ecological study with a time series design using secondary microdata of deaths and hospital admissions from patients with CKD from 1996 to 2017 in the State of Espírito Santo, Brazil.

RESULTS

The average mortality rate of CKD during the studied years was 2.92 per 100,000 inhabitants per year. During this period global mortality was a stationary phenomenon. In women, the trend of mortality from 2005 on increased 7,87% per year. Between 2008 and 2017, the average incidence hospital admissions due to CKD per year was 45.76 per 100,000 inhabitants. It was observed that the overall hospital admission increased by the equivalent of 6.23% per year. More than a half of mortality and hospitalisations correspond to male patients over 50 years of age. In terms of mortality, 32.99% corresponded to Caucasian patients, while 35.13% of hospitalisations were mixed race.

CONCLUSION

We found that age and gender are factors associated with deaths and hospitalisations for chronic kidney disease. While hospitalisation increases 6.23% per year, global mortality remains stationary. However, from 2005 onwards a trend towards increasing of 7.87%/annual in mortality was observed in women.

Living Donor Kidney Transplantation Should Be Promoted Among "Elderly" Patients

Age criteria for kidney transplantation have been liberalized over the years resulting in more waitlisted elderly patients. What are the prospects of elderly patients on the waiting list?

Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis

Background

Assessment of infection-related mortality remains inadequate in patients undergoing peritoneal dialysis. This study was performed to develop a risk model for predicting the 2-year infection-related mortality risk in patients undergoing peritoneal dialysis.

Methods

The study cohort comprised 606 patients who started and continued peritoneal dialysis for 90 at least days and was drawn from the Fukuoka Peritoneal Dialysis Database Registry Study in Japan. The patients were registered from 1 January 2006 to 31 December 2016 and followed up until 31 December 2017. To generate a prediction rule, the score for each variable was weighted by the regression coefficients calculated using a Cox proportional hazard model adjusted by risk factors for infection-related mortality, including patient characteristics, comorbidities, and laboratory data.

Results

During the follow-up period (median, 2.2 years), 138 patients died; 58 of them of infectious disease. The final model for infection-related mortality comprises six factors: age, sex, serum albumin, serum creatinine, total cholesterol, and weekly renal Kt/V. The incidence of infection-related mortality increased linearly with increasing total risk score (P for trend <0.001). Furthermore, the prediction model showed adequate discrimination (c-statistic = 0.79 [0.72–0.86]) and calibration (Hosmer–Lemeshow test, P = 0.47).

Conclusion

In this study, we developed a new model using clinical measures for predicting infection-related mortality in patients undergoing peritoneal dialysis.

Heart Valve Disease Predict Mortality in Hemodialysis Patients: A Single Center Experience

Our aim is to investigate the clinical and laboratory findings affecting the mortality of the patients in 3 years follow-up who underwent hemodialysis at our center. In this retrospective, observational cohort study, 432 patients who underwent hemodialysis at our center for at least 5 months were included. The first recorded data and subsequent clinical findings of patients who died and survived were compared. Two hundred and ninety patients survived, 142 patients died. The mean age of the patients who died was higher (63.4 ± 12.3 years, vs. 52 ± 16.1 years, P = 0.0001), 60.5% of them had coronary artery disease (P = 0.0001), 93.7% of them had a heart valve disease. Duration of hemodialysis (survived 57 [21-260] months; died 44 [5-183] months, P = 0.000) was lower in patients who died. Serum potassium level before dialysis (5.1 ± 0.6; 4.9 ± 0.7 mEq/L, P = 0.030), parathyroid hormone (435 [4-3054]; 304 [1-3145] pg/mL, P = 0.0001), albumin (3.9 ± 0.4; 3.8 ± 0.4 mg/dL, P = 0.0001) and Kt/V (1.48 ± 0.3; 1.40 ± 0.3, P = 0.019) levels were lower, C-reactive protein (5[1-208]; 8.7[2-256] mg/L, P = 0.000) levels were higher in patients who died. Logistic regression analysis showed age (OR = 1.1), coronary artery disease (OR = 1.7) and more than one heart valve disease (OR = 2.4) are independent risk factors for mortality. Potassium level before dialysis (OR = 0.60), parathyroid hormone (OR = 0.99), and higher Kt/V (OR = 0.28) were found to be an advantage for survival. Age, coronary artery disease and especially pathology in more than one heart valve are risk factors for mortality. Heart valve problems might develop because of malnutrition and inflammation caused by the chronic renal failure.

Predicting 6-month mortality risk of patients commencing dialysis treatment for end-stage kidney disease

Background

There is evidence that end-stage kidney disease patients who are older or with more comorbidity may have a poor trade-off between benefits of dialysis and potential harms. We aimed to develop a tool for predicting patient mortality in the early stages of receiving dialysis.

Methods

In 23 658 patients aged 15+ years commencing dialysis between 2000 and 2009 in Australia and New Zealand a point score tool was developed to predict 6-month mortality based on a logistic regression analysis of factors available at dialysis initiation. Temporal validation used 2009-11 data from Australia and New Zealand. External validation used the UK Renal Registry.

Results

Within 6 months of commencing dialysis 6.1% of patients had died. A small group (4.7%) of patients had a high predicted mortality risk (>20%), as predicted by the point score tool. Predictive variables were: older age, underweight, chronic lung disease, coronary artery disease, peripheral vascular disease, cerebrovascular disease (particularly for patients <60 years of age), late referral to nephrologist care and underlying cause of renal disease. The new point score tool outperformed existing models, and had an area under the receiver operating characteristic curve of 0.755 on temporal validation with acceptable calibration and 0.713 on external validation with poor calibration.

Conclusion

Our point score tool for predicting 6-month mortality in patients at dialysis commencement has sufficient prognostic accuracy to use in Australia and New Zealand for prognosis and identification of high risk patients who may be given appropriate supportive care. Use in other countries requires further study.

Prediction models for the mortality risk in chronic dialysis patients: a systematic review and independent external validation study

OBJECTIVE

In medicine, many more prediction models have been developed than are implemented or used in clinical practice. These models cannot be recommended for clinical use before external validity is established. Though various models to predict mortality in dialysis patients have been published, very few have been validated and none are used in routine clinical practice. The aim of the current study was to identify existing models for predicting mortality in dialysis patients through a review and subsequently to externally validate these models in the same large independent patient cohort, in order to assess and compare their predictive capacities.

METHODS

A systematic review was performed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To account for missing data, multiple imputation was performed. The original prediction formulae were extracted from selected studies. The probability of death per model was calculated for each individual within the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD). The predictive performance of the models was assessed based on their discrimination and calibration.

RESULTS

In total, 16 articles were included in the systematic review. External validation was performed in 1,943 dialysis patients from NECOSAD for a total of seven models. The models performed moderately to well in terms of discrimination, with C-statistics ranging from 0.710 (interquartile range 0.708-0.711) to 0.752 (interquartile range 0.750-0.753) for a time frame of 1 year. According to the calibration, most models overestimated the probability of death.

CONCLUSION

Overall, the performance of the models was poorer in the external validation than in the original population, affirming the importance of external validation. Floege et al's models showed the highest predictive performance. The present study is a step forward in the use of a prediction model as a useful tool for nephrologists, using evidence-based medicine that combines individual clinical expertise, patients' choices, and the best available external evidence.

One- and 2-Year Mortality Prediction for Patients Starting Chronic Dialysis

Introduction

Mortality risk of patients with end-stage renal disease (ESRD) is highly elevated. Methods to estimate individual mortality risk are needed to provide individualized care and manage expanding ESRD populations. Many mortality prediction models exist but have shown deficiencies in model development (data comprehensiveness, validation) and in practicality. Therefore, our aim was to design 2 easy-to-apply prediction models for 1- and 2-year all-cause mortality in patients starting long-term renal replacement therapy (RRT).

Methods

We used data from the Finnish Registry for Kidney Diseases with complete national coverage of RRT patients. Model training group included all incident adult patients who started long-term dialysis in Finland in 2000 to 2008 (n = 4335). The external validation cohort consisted of those who entered dialysis in 2009 to 2012 (n = 1768). Logistic regression with stepwise variable selection was used for model building.

Results

We developed 2 prognostic models, both of which only included 6 to 7 variables (age at RRT start, ESRD diagnosis, albumin, phosphorus, C-reactive protein, heart failure, and peripheral vascular disease) and showed sufficient discrimination (c-statistic 0.77 and 0.74 for 1- and 2-year mortality, respectively). Due to a significantly lower mortality in the newer cohort, the models, to a degree, overestimated mortality risk.

Discussion

Mortality prediction algorithms could be more widely implemented into management of ESRD patients. The presented models are practical with only a limited number of variables and fairly good performance.

Measuring senescence rates of patients with end-stage renal disease while accounting for population heterogeneity: an analysis of data from the ERA-EDTA Registry

PURPOSE

Although a population's senescence rate is classically measured as the increase in mortality rate with age on a logarithmic scale, it may be more accurately measured as the increase on a linear scale. Patients on dialysis, who suffer from accelerated senescence, exhibit a smaller increase in their mortality rate on a logarithmic scale, but a larger increase on a linear scale than patients with a functioning kidney transplant. However, this comparison may be biased by population heterogeneity.

METHODS

Follow-up data on 323,308 patients on dialysis and 91,679 patients with a functioning kidney transplant were derived from the ERA-EDTA Registry. We measured the increases in their mortality rates using Gompertz frailty models that allow individual variation in this increase.

RESULTS

According to these models, the senescence rate measured as the increase in mortality rate on a logarithmic scale was smaller in patients on dialysis, while the senescence rate measured as the increase on a linear scale was larger in patients on dialysis than patients with a functioning kidney transplant.

CONCLUSIONS

Also when accounting for population heterogeneity, a population's senescence rate is more accurately measured as the increase in mortality rate on a linear scale than a logarithmic scale.

A High Comorbidity Score Should Not be a Contraindication for Kidney Transplantation

BACKGROUND

Currently, potential kidney transplant patients more often suffer from comorbidities. The Charlson Comorbidity Index (CCI) was developed in 1987 and is the most used comorbidity score. We questioned to what extent number and severity of comorbidities interfere with graft and patient survival. Besides, we wondered whether the CCI was best to study the influence of comorbidity in kidney transplant patients.

METHODS

In our center, 1728 transplants were performed between 2000 and 2013. There were 0.8% cases with missing values. Nine pretransplant comorbidity covariates were defined: cardiovascular disease, cerebrovascular accident, peripheral vascular disease, diabetes mellitus, liver disease, lung disease, malignancy, other organ transplantation, and human immunodeficiency virus positivity. The CCI used was unadjusted for recipient age. The Rotterdam Comorbidity in Kidney Transplantation score was developed, and its influence was compared to the CCI. Kaplan-Meier analysis and multivariable Cox proportional hazards analysis, corrected for variables with a known significant influence, were performed.

RESULTS

We noted 325 graft failures and 215 deaths. The only comorbidity covariate that significantly influenced graft failure censored for death was peripheral vascular disease. Patient death was significantly influenced by cardiovascular disease, other organ transplantation, and the total comorbidity scores. Model fit was best with the Rotterdam Comorbidity in Kidney Transplantation score compared to separate comorbidity covariates and the CCI. In the population with the highest comorbidity score, 50% survived more than 10 years.

CONCLUSIONS

Despite the negative influence of comorbidity, patient survival after transplantation is remarkably good. This means that even patients with extensive comorbidity should be considered for transplantation.

Improved Mortality Prediction in Dialysis Patients Using Specific Clinical and Laboratory Data

BACKGROUND

Risk prediction models can be used to inform patients undergoing renal replacement therapy about their survival chances. Easily available predictors such as registry data are most convenient, but their predictive value may be limited. We aimed to improve a simple prediction model based on registry data by incrementally adding sets of clinical and laboratory variables.

METHODS

Our data set includes 1,835 Dutch patients from the Netherlands Cooperative Study on the Adequacy of Dialysis. The potential survival predictors were categorized on availability. The first category includes easily available clinical data. The second set includes laboratory values like albumin. The most laborious category contains glomerular filtration rate (GFR) and Kt/V. Missing values were substituted using multiple imputation. Within 1,225 patients, we recalibrated the registry model and subsequently added parameter sets using multivariate Cox regression analyses with backward selection. On the other 610 patients, calibration and discrimination (C-index, integrated discrimination improvement (IDI) index and net reclassification improvement (NRI) index) were assessed for all models.

RESULTS

The recalibrated registry model showed adequate calibration and discrimination (C-index=0.724). Adding easily available parameters resulted in a model with 10 predictors, with similar calibration and improved discrimination (C-index=0.784). The IDI and NRI indices confirmed this, especially for short-term survival. Adding laboratory values resulted in an alternative model with similar discrimination (C-index=0.788), and only the NRI index showed minor improvement. Adding GFR and Kt/V as candidate predictors did not result in a different model.

CONCLUSION

A simple model based on registry data was enhanced by adding easily available clinical parameters.

Association of Relapse with Renal Outcomes under the Current Therapy Regimen for IgA Nephropathy: A Multi-Center Study

Background and Objectives

Renal relapse is a very common manifestation of IgA nephropathy (IgAN). The clinical characteristics and long-term outcomes of this condition have not yet been carefully explored.

Design and Patients

Patients with biopsy-proven IgAN between January 2005 and December 2010 from three medical centers in China was a primary cohort of patients. From January 2010 to April 2012, data of an independent cohort of IgAN patients from Ren Ji Hospital, Shanghai, China was collected using the same inclusion and exclusion criteria. These patients formed the validation cohort of this study.

Results

Of the patients with biopsy-proven IgAN from three medical centers, 489 patients achieved remission within 6 months following the therapy. Additionally, 76 (15.5%) of these patients experienced a relapse after achieving remission. During the median follow-up period of 66 months, 6 patients (1.4%) in the non-relapse group experienced renal deterioration, compared with 22 patients (29.6%) in the relapse group. Our study indicated that each 1-mmHg increase in the baseline diastolic blood pressure (DBP) was associated with a 4.5% increase in the risk of renal relapse; additionally, the male patients had a 3.324-fold greater risk of relapse compared with the female patients according to the adjusted multivariate Cox analysis. The nomogram was based on 489 patients achieved remission. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling on the validation cohort.

Conclusions

This study demonstrated that renal relapse is a potential predictor of prognostic outcomes in patients under the current therapeutic regimens for IgAN. And male patients with higher diastolic blood pressure had a greater risk of experiencing relapse.