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

Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study

BACKGROUND

The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.

METHODS

Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA).

RESULTS

The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets.

CONCLUSIONS

The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients.

Evaluating and Updating the IMPACT Model to Predict Outcomes in Two Contemporary North American Traumatic Brain Injury Cohorts

The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) model is a widely recognized prognostic model applied after traumatic brain injury (TBI). However, it was developed with patient cohorts that may not reflect modern practice patterns in North America. We analyzed data from two sources: the placebo arm of the phase II double-blinded, multicenter, randomized controlled trial Prehospital Tranexamic Acid for TBI (TXA) cohort and an observational cohort with similar inclusion/exclusion criteria (Predictors of Low-risk Phenotypes after Traumatic Brain Injury Incorporating Proteomic Biomarker Signatures [PROTIPS] cohort). All three versions of the IMPACT model-core, extended, and laboratory-were evaluated for 6-month mortality (Glasgow Outcome Scale Extended [GOSE] = 1) and unfavorable outcomes (GOSE = 1-4). Calibration (intercept and slope) and discrimination (area under the receiver operating characteristic curve [ROC-AUC]) were used to assess model performance. We then compared three model updating methods-recalibration in the large, logistic recalibration, and coefficient update-with the best update method determined by likelihood ratio tests. In our calibration analysis, recalibration improved both intercepts and slopes, indicating more accurate predicted probabilities when recalibration was done. Discriminative performance of the IMPACT models, measured by AUC, showed mortality prediction ROCs between 0.61 and 0.82 for the TXA cohort, with the coefficient updated Lab model achieving the highest at 0.84. Unfavorable outcomes had lower AUCs, ranging from 0.60 to 0.79. Similarly, in the PROTIPS cohort, AUCs for mortality ranged from 0.75 to 0.82, with the coefficient updated Lab model also showing superior performance (AUC 0.84). Unfavorable outcomes in this cohort presented AUCs from 0.67 to 0.73, consistently lower than mortality predictions. The closed testing procedure using likelihood ratio tests consistently identified the coefficient update model as superior, outperforming the original and recalibrated models across all cohorts. In our comprehensive evaluation of the IMPACT model, the coefficient updated models were the best performing across all cohorts through a structured closed testing procedure. Thus, standardization of model updating procedures is needed to reproducibly determine the best performing versions of IMPACT that reflect the specific characteristics of a dataset.

Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis

RATIONALE & OBJECTIVE

The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD.

STUDY DESIGN

Predictive modeling study.

SETTING & PARTICIPANTS

42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers.

NEW PREDICTORS & ESTABLISHED PREDICTORS

Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR.

OUTCOME

For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival).

ANALYTICAL APPROACH

We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points.

RESULTS

All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%.

LIMITATIONS

The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids.

CONCLUSIONS

A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality.

PLAIN-LANGUAGE SUMMARY

Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.

Short-term and long-term survival in patients with prevalent haemodialysis-an integrated prognostic model: external validation

OBJECTIVES

Prognostic tools with evidence for external validity in routine clinical practice are needed to align care with patients' preferences and deliver timely supportive services. Current models have limited, if any, evidence for external validity and none have been implemented and evaluated in clinical practice on a large scale. This study sought to provide evidence for external validity in a real life setting of the Cohen prognostic model that integrates actuarial factors with the 'Surprise Question' to assess 6-month, 12-month and 18-month survival of prevalent haemodialysis patients.

METHODS

Cross-sectional study of 1372 patients in a Canadian university-based programme between 2010 and 2019. Survival probabilities were compared with observed survival. Discrimination and calibration were assessed through predicted risk-stratified observed survival, cumulative AUC, Somer's Dxy and a calibration slope estimate.

RESULTS

Discrimination performance was moderate with a C statistic of 0.71-0.72 for all three time points. The model overpredicted mortality risk with the best predictive accuracy for 6- month survival. The differences between observed and mean predicted survival at 6 months, 12 months and 18 months were 3.2%, 8.8% and 12.9%, respectively. Kaplan-Meier curves stratified by Cox-based risk group showed good discrimination between high-risk and low-risk patients with HR estimates (95% CI): C2 vs C1 3.07 (1.57-5.99), C3 vs C1 5.85 (3.06-11.17), C4 vs C1 13.24 (6.91-25.34)).

CONCLUSIONS

The Cohen prognostic model can be incorporated easily into routine dialysis care to identify patients at high risk for death over 6 months, 12 months and 18 months and help target vulnerable patients for timely supportive care interventions.

Prognostic Models in Nephrology: Where Do We Stand and Where Do We Go from Here? Mapping Out the Evidence in a Scoping Review

Prognostic models can strongly support individualized care provision and well-informed shared decision making. There has been an upsurge of prognostic research in the field of nephrology, but the uptake of prognostic models in clinical practice remains limited. Therefore, we map out the research field of prognostic models for kidney patients and provide directions on how to proceed from here. We performed a scoping review of studies developing, validating, or updating a prognostic model for patients with CKD. We searched all published models in PubMed and Embase and report predicted outcomes, methodological quality, and validation and/or updating efforts. We found 602 studies, of which 30.1% concerned CKD populations, 31.6% dialysis populations, and 38.4% kidney transplantation populations. The most frequently predicted outcomes were mortality ( n =129), kidney disease progression ( n =75), and kidney graft survival ( n =54). Most studies provided discrimination measures (80.4%), but much less showed calibration results (43.4%). Of the 415 development studies, 28.0% did not perform any validation and 57.6% performed only internal validation. Moreover, only 111 models (26.7%) were externally validated either in the development study itself or in an independent external validation study. Finally, in 45.8% of development studies no useable version of the model was reported. To conclude, many prognostic models have been developed for patients with CKD, mainly for outcomes related to kidney disease progression and patient/graft survival. To bridge the gap between prediction research and kidney patient care, patient-reported outcomes, methodological rigor, complete reporting of prognostic models, external validation, updating, and impact assessment urgently need more attention.

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

Patient-focused outcomes after initiation of dialysis for ESRD: mortality, hospitalization and functional impairment

BACKGROUND

Outcome data regarding clinically relevant endpoints after starting dialysis for end-stage renal disease (ESRD) are sparse, and early events after starting dialysis are particularly underestimated. The aim of this study was to describe patient-focused outcomes in ESRD patients starting from first dialysis.

METHODS

The data basis for this retrospective observational study were anonymized healthcare data from Germany's largest statutory health insurer. We identified ESRD patients who initiated dialysis in 2017. Deaths, hospitalizations and occurrence of functional impairment within 4 years after starting dialysis were recorded starting from first treatment. Hazard ratios in dialysis patients compared with an age- and sex-matched reference population without dialysis were generated, stratified by age.

RESULTS

The dialysis cohort included 10 328 ESRD patients who started dialysis in 2017. First dialysis was performed in-hospital for 7324 patients (70.9%), and 865 of these died during the same hospitalization. One-year mortality for ESRD patients initiating dialysis was 33.8%. Functional impairment occurred in 27.1% of patients, while 82.8% of patients required hospitalization within 1 year. Hazard ratios of dialysis patients compared with the reference population for mortality, functional impairment and hospitalization at 1-year were 8.6, 4.3 and 6.2. Dialysis patients <50 years were disproportionately affected, with >40-fold increased risk of adverse events compared with their peers.

CONCLUSIONS

The emergence of morbidity and mortality after starting dialysis for ESRD is significant, especially in younger patients. Patients have a right to be informed about the prognosis associated with their condition.

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.

Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative

INTRODUCTION

Several factors affect the survival of End Stage Kidney Disease (ESKD) patients on dialysis. Machine learning (ML) models may help tackle multivariable and complex, often non-linear predictors of adverse clinical events in ESKD patients. In this study, we used advanced ML method as well as a traditional statistical method to develop and compare the risk factors for mortality prediction model in hemodialysis (HD) patients.

MATERIALS AND METHODS

We included data HD patients who had data across a baseline period of at least 1 year and 1 day in the internationally representative Monitoring Dialysis Outcomes (MONDO) Initiative dataset. Twenty-three input parameters considered in the model were chosen in an a priori manner. The prediction model used 1 year baseline data to predict death in the following 3 years. The dataset was randomly split into 80% training data and 20% testing data for model development. Two different modeling techniques were used to build the mortality prediction model.

FINDINGS

A total of 95,142 patients were included in the analysis sample. The area under the receiver operating curve (AUROC) of the model on the test data with XGBoost ML model was 0.84 on the training data and 0.80 on the test data. AUROC of the logistic regression model was 0.73 on training data and 0.75 on test data. Four out of the top five predictors were common to both modeling strategies.

DISCUSSION

In the internationally representative MONDO data for HD patients, we describe the development of a ML model and a traditional statistical model that was suitable for classification of a prevalent HD patient's 3-year risk of death. While both models had a reasonably high AUROC, the ML model was able to identify levels of hematocrit (HCT) as an important risk factor in mortality. If implemented in clinical practice, such proof-of-concept models could be used to provide pre-emptive care for HD patients.

Machine Learning Case Study: Patterns of Kidney Function Decline and Their Association With Clinical Outcomes Within 90 Days After the Initiation of Renal Dialysis

A case study explores patterns of kidney function decline using unsupervised learning methods first and then associating patterns with clinical outcomes using supervised learning methods. Predicting short-term risk of hospitalization and death prior to renal dialysis initiation may help target high-risk patients for more aggressive management. This study combined clinical data from patients presenting for renal dialysis at Fresenius Medical Care with laboratory data from Quest Diagnostics to identify disease trajectory patterns associated with the 90-day risk of hospitalization and death after beginning renal dialysis. Patients were clustered into 4 groups with varying rates of estimated glomerular filtration rate (eGFR) decline during the 2-year period prior to dialysis. Overall rates of hospitalization and death were 24.9% (582/2341) and 4.6% (108/2341), respectively. Groups with the steepest declines had the highest rates of hospitalization and death within 90 days of dialysis initiation. The rate of eGFR decline is a valuable and readily available tool to stratify short-term (90 days) risk of hospitalization and death after the initiation of renal dialysis. More intense approaches are needed that apply models that identify high risks to potentially avert or reduce short-term hospitalization and death of patients with a severe and rapidly progressive chronic kidney disease.

A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation

Background

The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning–based tool for predicting mortality could inform patient-clinician shared decision making on whether to initiate dialysis or pursue medical management. We used the eXtreme Gradient Boosting (XGBoost) algorithm to predict mortality in the first 90 days after dialysis initiation in a nationally representative population from the United States Renal Data System.

Methods

A cohort of adults initiating dialysis between 2008–2017 were studied for outcome of death within 90 days of dialysis initiation. The study dataset included 188 candidate predictors prognostic of early mortality that were known on or before the first day of dialysis and was partitioned into training (70%) and testing (30%) subsets. XGBoost modeling used a complete-case set and a dataset obtained from multiple imputation. Model performance was evaluated by c-statistics overall and stratified by subgroups of age, sex, race, and dialysis modality.

Results

The analysis included 1,150,195 patients with ESKD, of whom 86,083 (8%) died in the first 90 days after dialysis initiation. The XGBoost models discriminated mortality risk in the nonimputed (c=0.826, 95% CI, 0.823 to 0.828) and imputed (c=0.827, 95% CI, 0.823 to 0.827) models and performed well across nearly every subgroup (race, age, sex, and dialysis modality) evaluated (c>0.75). Across predicted risk thresholds of 10%–50%, higher risk thresholds showed declining sensitivity (0.69–0.04) with improving specificity (0.79–0.99); similarly, positive likelihood ratio was highest at the 40% threshold, whereas the negative likelihood ratio was lowest at the 10% threshold. After calibration using isotonic regression, the model accurately estimated the probability of mortality across all ranges of predicted risk.

Conclusions

The XGBoost-based model developed in this study discriminated risk of early mortality after dialysis initiation with excellent calibration and performed well across key subgroups.

Artificial Intelligence for the Prediction of In-Hospital Clinical Deterioration: A Systematic Review

To analyze the available literature on the performance of artificial intelligence-generated clinical models for the prediction of serious life-threatening events in non-ICU adult patients and evaluate their potential clinical usage.

Personalizing treatment in end-stage kidney disease: deciding between hemodiafiltration and hemodialysis based on individualized treatment effect prediction

Background

Previous studies suggest that hemodiafiltration reduces mortality compared to hemodialysis in patients with end-stage kidney disease (ESKD), but controversy surrounding its benefits remain and it is unclear to what extent individual patients benefit from hemodiafiltration. This study aimed to develop and validate a treatment effect prediction model to determine which patients would benefit most from hemodiafiltration compared to hemodialysis in terms of all-cause mortality.

Methods

Individual participant data from four randomized controlled trials comparing hemodiafiltration with hemodialysis on mortality were used to derive a Royston-Parmar model for prediction of absolute treatment effect of hemodiafiltration based on pre-specified patient and disease characteristics. Validation of the model was performed using internal-external cross validation.

Results

The median predicted survival benefit was 44 (Q1-Q3: 44–46) days for every year of treatment with hemodiafiltration compared to hemodialysis. The median survival benefit with hemodiafiltration ranged from 2 to 48 months. Patients who benefited most from hemodiafiltration were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine and albumin levels. Internal-external cross validation showed adequate discrimination and calibration.

Conclusion

Although overall mortality is reduced by hemodiafiltration compared to hemodialysis in ESKD patients, the absolute survival benefit can vary greatly between individuals. Our results indicate that the effects of hemodiafiltration on survival can be predicted using a combination of readily available patient and disease characteristics, which could guide shared decision-making.

Physical Resilience Phenotype Trajectories in Incident Hemodialysis: Characterization and Mortality Risk Assessment

Introduction

Methods

Results

Conclusion

Optimising Access Surgery in Senior Haemodialysis Patients (OASIS): study protocol for a multicentre randomised controlled trial

INTRODUCTION

Current evidence on vascular access strategies for haemodialysis patients is based on observational studies that are at high risk of selection bias. For elderly patients, autologous arteriovenous fistulas that are typically created in usual care may not be the best option because a significant proportion of fistulas either fail to mature or remain unused. In addition, long-term complications associated with arteriovenous grafts and central venous catheters may be less relevant when considering the limited life expectancy of these patients. Therefore, we designed the Optimising Access Surgery in Senior Haemodialysis Patients (OASIS) trial to determine the best strategy for vascular access creation in elderly haemodialysis patients.

METHODS AND ANALYSIS

OASIS is a multicentre randomised controlled trial with an equal participant allocation in three treatment arms. Patients aged 70 years or older who are expected to initiate haemodialysis treatment in the next 6 months or who have started haemodialysis urgently with a catheter will be enrolled. To detect and exclude patients with an unusually long life expectancy, we will use a previously published mortality prediction model after external validation. Participants allocated to the usual care arm will be treated according to current guidelines on vascular access creation and will undergo fistula creation. Participants allocated to one of the two intervention arms will undergo graft placement or catheter insertion. The primary outcome is the number of access-related interventions required for each patient-year of haemodialysis treatment. We will enrol 195 patients to have sufficient statistical power to detect an absolute decrease of 0.80 interventions per year.

ETHICS AND DISSEMINATION

Because of clinical equipoise, we believe it is justified to randomly allocate elderly patients to the different vascular access strategies. The study was approved by an accredited medical ethics review committee. The results will be disseminated through peer-reviewed publications and will be implemented in clinical practice guidelines.

TRIAL REGISTRATION NUMBER

NL7933.

PROTOCOL VERSION AND DATE

V.5, 25 February 2021.

Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis

Introduction:

Mortality is a major primary endpoint for long-term hemodialysis (HD) patients. The clinical status of HD patients generally relies on longitudinal clinical observations such as monthly laboratory examinations and physical examinations.

Methods:

A total of 829 HD patients who met the inclusion criteria were analyzed. All patients were tracked from January 2009 to December 2013. Taken together, this study performed full-adjusted-Cox proportional hazards (CoxPH), stepwise-CoxPH, random survival forest (RSF)-CoxPH, and whale optimization algorithm (WOA)-CoxPH model for the all-cause mortality risk assessment in HD patients. The model performance between proposed selections of CoxPH models were evaluated using concordance index.

Results:

The WOA-CoxPH model obtained the highest concordance index compared with RSF-CoxPH and typical selection CoxPH model. The eight significant parameters obtained from the WOA-CoxPH model, including age, diabetes mellitus (DM), hemoglobin (Hb), albumin, creatinine (Cr), potassium (K), Kt/V, and cardiothoracic ratio, have also showed significant survival difference between low- and high-risk characteristics in single-factor analysis. By integrating the risk characteristics of each single factor, patients who obtained seven or more risk characteristics of eight selected parameters were dichotomized as high-risk subgroup, and remaining is considered as low-risk subgroup. The integrated low- and high-risk subgroup showed greater discrepancy compared with each single risk factor selected by WOA-CoxPH model.

Conclusion:

The study findings revealed WOA-CoxPH model could provide better risk assessment performance compared with RSF-CoxPH and typical selection CoxPH model in the HD patients. In summary, patients who had seven or more risk characteristics of eight selected parameters were at potentially increased risk of all-cause mortality in HD population.

Smoothing transition to dialysis to improve early outcomes after dialysis initiation among old and frail adults-a narrative review

The number of patients ≥65 years of age suffering from advanced chronic kidney disease and transitioning to end-stage kidney disease (ESKD) is increasing. However, elderly patients often have poor outcomes once haemodialysis is initiated, including high mortality within the first year as well as fast cognitive and functional decline and diminished quality of life. The question is how we can smooth this transition to ESKD in older patients who also exhibit much higher proportions of frailty when compared with community-dwelling non-dialysis older adults and who are generally more vulnerable to invasive treatment such as kidney replacement therapy. To avoid early death and poor quality of life, a carefully prepared smooth transition should precede the initiation of treatment. This involves pre-dialysis physical and educational care, as well as mental and psychosocial preparedness of the patient to enable an informed and shared decision about the individual choice of treatment modality. Communication between a healthcare professional and patient plays a pivotal role but can be challenging given the high rate of cognitive impairment in this particular population. In order to practise patient-centred care, adapting treatment tailored to the individual patient should include comprehensive conservative care. However, structured treatment pathways including multidisciplinary teams for such conservative care are still rare and may be difficult to establish outside of large cities. Generally, geriatric nephrology misses data on the comparative effectiveness of different treatment modalities in this population of old and very old age on which to base recommendations and decisions.

Low performance of prognostic tools for predicting death before dialysis in older patients with advanced CKD

INTRODUCTION

Chronic kidney disease (CKD) is a disease which is spreading worldwide, especially among older patients. Several prognostic scores have been developed to predict death in older CKD patients, but they have not been validated. We aimed to evaluate the existing risk scores for predicting death before dialysis start, identified via an in-depth review, in a cohort of elderly patients with advanced CKD.

METHODS

We performed a review to identify scores predicting death, developed in and applicable to CKD patients. Each score was evaluated with an absolute risk calculation from the patients' baseline characteristics. We used a French prospective multicentre cohort of elderly patients (> 75 years) with advanced CKD [estimated glomerular filtration rate (eGFR) < 20 mL/min/1.73 m²], recruited from nephrological centres, with a 5-year follow-up. The outcome considered was death before initiating dialysis. Discrimination [area under curve (AUC)], calibration and Brier score were calculated for each score at its time frame.

RESULTS

Our review found 6 equations predicting death before dialysis in CKD patients. Four of these (GOLDFARB, BANSAL, GRAMS 2 and 4 years) were evaluated. The validation cohort (Parcours de Soins des Personnes Âgées Parcours de Soins des Personnes Âgées, PSPA) included 573 patients, with a median age of 82 years and a median eGFR of 13 mL/min/1.73 m². At the end of follow-up, 287 (50%) patients had started dialysis and 238 (41%) patients had died before dialysis. The four equations evaluated showed average discrimination (AUC 0.61-0.70) and, concerning calibration, a global overestimation of the risk of death.

DISCUSSION

The available scores predicting death before dialysis showed low performance among older patients with advanced CKD in a French multicentre cohort, indicating the need to upgrade them or develop new scores for this population.

Person centred care provision and care planning in chronic kidney disease: which outcomes matter? A systematic review and thematic synthesis of qualitative studies : Care planning in CKD: which outcomes matter?

RATIONALE & OBJECTIVE

Explore priorities related to outcomes and barriers of adults with chronic kidney disease (CKD) regarding person centred care and care planning.

STUDY DESIGN

Systematic review of qualitative studies.

SEARCH STRATEGY & SOURCES

In July 2018 six bibliographic databases, and reference lists of included articles were searched for qualitative studies that included adults with CKD stages 1-5, not on dialysis or conservative management, without a previous kidney transplantation.

ANALYTICAL APPROACH

Three independent reviewers extracted and inductively coded data using thematic synthesis. Reporting quality was assessed using the COREQ and the review reported according to PRISMA and ENTREQ statements.

RESULTS

Forty-six studies involving 1493 participants were eligible. The period after diagnosis of CKD is characterized by feelings of uncertainty, social isolation, financial burden, resentment and fear of the unknown. Patients show interest in ways to return to normality and remain in control of their health in order to avoid further deterioration of kidney function. However, necessary information is often unavailable or incomprehensible. Although patients and healthcare professionals share the predominant interest of whether or not dialysis or transplantation is necessary, patients value many more outcomes that are often unrecognized by their healthcare professionals. We identified 4 themes with 6 subthemes that summarize these findings: 'pursuing normality and control' ('pursuing normality'; 'a search for knowledge'); 'prioritizing outcomes' ('reaching kidney failure'; 'experienced health'; 'social life'; 'work and economic productivity'); 'predicting the future'; and 'realising what matters'. Reporting quality was moderate for most included studies.

LIMITATIONS

Exclusion of non-English articles.

CONCLUSIONS

The realisation that patients' priorities do not match those of the healthcare professionals, in combination with the prognostic ambiguity, confirms fatalistic perceptions of not being in control when living with CKD. These insights may contribute to greater understanding of patients' perspectives and a more person-centred approach in healthcare prioritization and care planning within CKD care.

Nierenersatzverfahren bei Hochbetagten

Hochbetagte haben an der Hämodialyse eine 1‑Jahres-Mortalität, die im Zusammenhang mit Komorbiditäten und einem Katheter als Dialysezugang 30 % übersteigt. Metaanalysen zeigen aber, dass frühzeitige Vorbereitung und individuelle Verfahrensauswahl die Morbidität und Mortalität auch im hohen Lebensalter entscheidend bessern. Mit zunehmendem Alter und Gebrechlichkeit verschieben sich dabei die Behandlungsziele weg von der Verlängerung der Lebensdauer auf die Verbesserung der Lebensqualität. Damit kann die Präferenz von Heimdialyseverfahren, auch als assistierte Peritonealdialyse, ebenso Bedeutung erringen wie die fachnephrologische Behandlung ohne Nierenersatzverfahren mit palliativem Therapieziel. Im höheren Lebensalter bestimmen zunehmend Komorbiditäten, kognitive Einschränkungen, Gebrechlichkeit und die Gesamtprognose das sinnvolle Vorgehen. Bereits bei der Anlage von Gefäßzugängen ergeben sich hinsichtlich Anastomosenort und Anlagezeitpunkt bei Hochbetagten andere Entscheidungskriterien. Empfehlungen zu Dialysedauer und -frequenz folgen der Lebensqualität mit inkrementellen und am Ende des Lebens auch dekrementellen Therapieregimen. Die demographische Entwicklung stellt die Nephrologie mit einer Zunahme älterer Patienten vor besondere Herausforderungen. Frühzeitige Aufklärung über alle Nierenersatzverfahren und die Festlegung individueller Therapieziele können bei sorgfältiger Auswahl von Dialysemodalität und -intensität auch bei Hochbetagten entscheidend zur Verbesserung der Prognose und insbesondere der Lebensqualität beitragen.

Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD

BACKGROUND

Various prediction models have been developed to predict the risk of kidney failure in patients with CKD. However, guideline-recommended models have yet to be compared head to head, their validation in patients with advanced CKD is lacking, and most do not account for competing risks.

METHODS

To externally validate 11 existing models of kidney failure, taking the competing risk of death into account, we included patients with advanced CKD from two large cohorts: the European Quality Study (EQUAL), an ongoing European prospective, multicenter cohort study of older patients with advanced CKD, and the Swedish Renal Registry (SRR), an ongoing registry of nephrology-referred patients with CKD in Sweden. The outcome of the models was kidney failure (defined as RRT-treated ESKD). We assessed model performance with discrimination and calibration.

RESULTS

The study included 1580 patients from EQUAL and 13,489 patients from SRR. The average c statistic over the 11 validated models was 0.74 in EQUAL and 0.80 in SRR, compared with 0.89 in previous validations. Most models with longer prediction horizons overestimated the risk of kidney failure considerably. The 5-year Kidney Failure Risk Equation (KFRE) overpredicted risk by 10%-18%. The four- and eight-variable 2-year KFRE and the 4-year Grams model showed excellent calibration and good discrimination in both cohorts.

CONCLUSIONS

Some existing models can accurately predict kidney failure in patients with advanced CKD. KFRE performed well for a shorter time frame (2 years), despite not accounting for competing events. Models predicting over a longer time frame (5 years) overestimated risk because of the competing risk of death. The Grams model, which accounts for the latter, is suitable for longer-term predictions (4 years).

A Growth Differentiation Factor 15-Based Risk Score Model to Predict Mortality in Hemodialysis Patients

BACKGROUND

The risk of cardiovascular (CV) and fatal events remains extremely high in patients with maintenance hemodialysis (MHD), and the growth differentiation factor 15 (GDF15) has emerged as a valid risk stratification biomarker. We aimed to develop a GDF15-based risk score as a death prediction model for MHD patients.

METHODS

Age, biomarker levels, and clinical parameters were evaluated at study entry. One hundred and seventy patients with complete information were finally included for data analysis. We performed the Cox regression analysis of various prognostic factors for mortality. Then, age, GDF15, and robust clinical predictors were included as a risk score model to assess the predictive accuracy for all-cause and CV death in the receiver operating characteristic (ROC) curve analysis.

RESULTS

Age, GDF15, and albumin were significantly associated with higher all-cause and CV mortality risk that were combined as a risk score model. The highest tertile of GDF-15 (>1707.1 pg/mL) was associated with all-cause mortality (adjusted hazard ratios (aHRs): 3.06 (95% confidence interval (CI): 1.20-7.82), p < 0.05) and CV mortality (aHRs: 3.11 (95% CI: 1.02-9.50), p < 0.05). The ROC analysis of GDF-15 tertiles for all-cause and CV mortality showed 0.68 (95% CI = 0.59 to 0.77) and 0.68 (95% CI = 0.58 to 0.79), respectively. By contrast, the GDF15-based prediction model for all-cause and CV mortality showed 0.75 (95% CI: 0.67-0.82) and 0.72 (95% CI: 0.63-0.81), respectively.

CONCLUSION

Age, GDF15, and hypoalbuminemia predict all-cause and CV death in MHD patients, yet a combination scoring system provides more robust predictive powers. An elevated GDF15-based risk score warns clinicians to determine an appropriate intervention in advance. In light of this, the GDF15-based death prediction model could be developed in the artificial intelligence-based precision medicine.

Validation of prognostic indices for short term mortality in an incident dialysis population of older adults >75

Rational and objective

Prognosis provides critical knowledge for shared decision making between patients and clinicians. While several prognostic indices for mortality in dialysis patients have been developed, their performance among elderly patients initiating dialysis is unknown, despite great need for reliable prognostication in that context. To assess the performance of 6 previously validated prognostic indices to predict 3 and/or 6 months mortality in a cohort of elderly incident dialysis patients.

Study design

Validation study of prognostic indices using retrospective cohort data. Indices were compared using the concordance (“c”)-statistic, i.e. area under the receiver operating characteristic curve (ROC). Calibration, sensitivity, specificity, positive and negative predictive values were also calculated.

Setting & participants

Incident elderly (age ≥75 years; n = 349) dialysis patients at a tertiary referral center.

Established predictors

Variables for six validated prognostic indices for short term (3 and 6 month) mortality prediction (Foley, NCI, REIN, updated REIN, Thamer, and Wick) were extracted from the electronic medical record. The indices were individually applied as per each index specifications to predict 3- and/or 6-month mortality.

Results

In our cohort of 349 patients, mean age was 81.5±4.4 years, 66% were male, and median survival was 351 days. The c-statistic for the risk prediction indices ranged from 0.57 to 0.73. Wick ROC 0.73 (0.68, 0.78) and Foley 0.67 (0.61, 0.73) indices performed best. The Foley index was weakly calibrated with poor overall model fit (p <0.01) and overestimated mortality risk, while the Wick index was relatively well-calibrated but underestimated mortality risk.

Limitations

Small sample size, use of secondary data, need for imputation, homogeneous population.

Conclusion

Most predictive indices for mortality performed moderately in our incident dialysis population. The Wick and Foley indices were the best performing, but had issues with under and over calibration. More accurate indices for predicting survival in older patients with kidney failure are needed.

Artificial intelligence enabled applications in kidney disease

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

External validation of prognostic models: what, why, how, when and where?

Prognostic models that aim to improve the prediction of clinical events, individualized treatment and decision-making are increasingly being developed and published. However, relatively few models are externally validated and validation by independent researchers is rare. External validation is necessary to determine a prediction model’s reproducibility and generalizability to new and different patients. Various methodological considerations are important when assessing or designing an external validation study. In this article, an overview is provided of these considerations, starting with what external validation is, what types of external validation can be distinguished and why such studies are a crucial step towards the clinical implementation of accurate prediction models. Statistical analyses and interpretation of external validation results are reviewed in an intuitive manner and considerations for selecting an appropriate existing prediction model and external validation population are discussed. This study enables clinicians and researchers to gain a deeper understanding of how to interpret model validation results and how to translate these results to their own patient population.

Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study

BACKGROUND

The first-year survival rate among patients undergoing hemodialysis remains poor. Current mortality risk scores for patients undergoing hemodialysis employ regression techniques and have limited applicability and robustness.

OBJECTIVE

We aimed to develop a machine learning model utilizing clinical factors to predict first-year mortality in patients undergoing hemodialysis that could assist physicians in classifying high-risk patients.

METHODS

Training and testing cohorts consisted of 5351 patients from a single center and 5828 patients from 97 renal centers undergoing hemodialysis (incident only). The outcome was all-cause mortality during the first year of dialysis. Extreme gradient boosting was used for algorithm training and validation. Two models were established based on the data obtained at dialysis initiation (model 1) and data 0-3 months after dialysis initiation (model 2), and 10-fold cross-validation was applied to each model. The area under the curve (AUC), sensitivity (recall), specificity, precision, balanced accuracy, and F1 score were used to assess the predictive ability of the models.

RESULTS

In the training and testing cohorts, 585 (10.93%) and 764 (13.11%) patients, respectively, died during the first-year follow-up. Of 42 candidate features, the 15 most important features were selected. The performance of model 1 (AUC 0.83, 95% CI 0.78-0.84) was similar to that of model 2 (AUC 0.85, 95% CI 0.81-0.86).

CONCLUSIONS

We developed and validated 2 machine learning models to predict first-year mortality in patients undergoing hemodialysis. Both models could be used to stratify high-risk patients at the early stages of dialysis.

Prediction meets causal inference: the role of treatment in clinical prediction models

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a 'predictimand' framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference.

Predicting mortality risk on dialysis and conservative care: development and internal validation of a prediction tool for older patients with advanced chronic kidney disease

Background

Conservative care (CC) may be a valid alternative to dialysis for certain older patients with advanced chronic kidney disease (CKD). A model that predicts patient prognosis on both treatment pathways could be of value in shared decision-making. Therefore, the aim is to develop a prediction tool that predicts the mortality risk for the same patient for both dialysis and CC from the time of treatment decision.

Methods

CKD Stage 4/5 patients aged ≥70 years, treated at a single centre in the Netherlands, were included between 2004 and 2016. Predictors were collected at treatment decision and selected based on literature and an expert panel. Outcome was 2-year mortality. Basic and extended logistic regression models were developed for both the dialysis and CC groups. These models were internally validated with bootstrapping. Model performance was assessed with discrimination and calibration.

Results

In total, 366 patients were included, of which 126 chose CC. Pre-selected predictors for the basic model were age, estimated glomerular filtration rate, malignancy and cardiovascular disease. Discrimination was moderate, with optimism-corrected C-statistics ranging from 0.675 to 0.750. Calibration plots showed good calibration.

Conclusions

A prediction tool that predicts 2-year mortality was developed to provide older advanced CKD patients with individualized prognosis estimates for both dialysis and CC. Future studies are needed to test whether our findings hold in other CKD populations. Following external validation, this prediction tool could be used to compare a patient’s prognosis on both dialysis and CC, and help to inform treatment decision-making.

Shared decision-making in advanced kidney disease: a scoping review protocol

INTRODUCTION

Patients with advanced kidney disease (AKD) have to make difficult treatment modality decisions as their disease progresses towards end-stage kidney disease. International guidelines in nephrology suggest shared decision-making (SDM) to help patients make timely treatment modality decisions that align with their values and preferences. However, systematic reviews or scoping reviews on these SDM interventions and on their reported use or outcomes are lacking. This limits the adoption of SDM in clinical practice and hampers further research and development on the subject. Our aim is to provide a comprehensive and up-to-date overview of these SDM interventions by means of a scoping review of the literature. Scoping reviews can provide a broad overview of a topic, identify gaps in the research knowledge base and report on the types of evidence that address and inform practices. This paper presents our study protocol.

METHODS AND ANALYSIS

The proposed scoping review will be performed in accordance with the Joanna Briggs Institute's (JBI) methodology for scoping reviews. It will cover both qualitative and quantitative scientific literature, as well as the grey literature on SDM interventions for treatment modality decisions in AKD. Only literature written in English will be considered for inclusion. Two independent reviewers will participate in an iterative process of screening the literature, paper selection and data extraction. Disagreements between the reviewers will be resolved by discussion until consensus is reached or after consultation with the research team when needed. Results will be reported with descriptive statistics and diagrammatic or tabular displayed information, accompanied by narrative summaries as explained in the JBI guidelines.

ETHICS AND DISSEMINATION

Ethical approval for the conduct of this study is not required. We will analyse previously collected data for the proposed scoping review. Our results will be published in a peer-reviewed journal and disseminated through conferences and/or seminars.

Supporting quality care for ESRD patients: the social worker can help address barriers to advance care planning

BACKGROUND

Advance Care Planning (ACP) is essential for preparation for end-of-life. It is a means through which patients clarify their treatment wishes. ACP is a patient-centered, dynamic process involving patients, their families, and caregivers. It is designed to 1) clarify goals of care, 2) increase patient agency over their care and treatments, and 3) help prepare for death. ACP is an active process; the end-stage renal disease (ESRD) illness trajectory creates health circumstances that necessitate that caregivers assess and nurture patient readiness for ACP discussions. Effective ACP enhances patient engagement and quality of life resulting in better quality of care.

MAIN BODY

Despite these benefits, ACP is not consistently completed. Clinical, technical, and social barriers result in key challenges to quality care. First, ACP requires caregivers to have end-of-life conversations that they lack the training to perform and often find difficult. Second, electronic health record (EHR) tools do not enable the efficient exchange of requisite psychosocial information such as treatment burden, patient preferences, health beliefs, priorities, and understanding of prognosis. This results in a lack of information available to enable patients and their families to understand the impact of illness and treatment options. Third, culture plays a vital role in end-of-life conversations. Social barriers include circumstances when a patient's cultural beliefs or value system conflicts with the caregiver's beliefs. Caregivers describe this disconnect as a key barrier to ACP. Consistent ACP is integral to quality patient-centered care and social workers' training and clinical roles uniquely position them to support ACP.

CONCLUSION

In this debate, we detail the known barriers to completing ACP for ESRD patients, and we describe its benefits. We detail how social workers, in particular, can support health outcomes by promoting the health information exchange that occurs during these sensitive conversations with patients, their family, and care team members. We aim to inform clinical social workers of this opportunity to enhance quality care by engaging in ACP. We describe research to help further elucidate barriers, and how researchers and caregivers can design and deliver interventions that support ACP to address this persistent challenge to quality end-of-life care.

Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients

Background

A classification tree model (CT-PIRP) was developed in 2013 to predict the annual renal function decline of patients with chronic kidney disease (CKD) participating in the PIRP (Progetto Insufficienza Renale Progressiva) project, which involves thirteen Nephrology Hospital Units in Emilia-Romagna (Italy). This model identified seven subgroups with specific combinations of baseline characteristics that were associated with a differential estimated glomerular filtration rate (eGFR) annual decline, but the model’s ability to predict mortality and renal replacement therapy (RRT) has not been established yet.

Methods

Survival analysis was used to determine whether CT-PIRP subgroups identified in the derivation cohort (n = 2265) had different mortality and RRT risks. Temporal validation was performed in a matched cohort (n = 2051) of subsequently enrolled PIRP patients, in which discrimination and calibration were assessed using Kaplan-Meier survival curves, Cox regression and Fine & Gray competing risk modeling.

Results

In both cohorts mortality risk was higher for subgroups 3 (proteinuric, low eGFR, high serum phosphate) and lower for subgroups 1 (proteinuric, high eGFR), 4 (non-proteinuric, younger, non-diabetic) and 5 (non-proteinuric, younger, diabetic). Risk of RRT was higher for subgroups 3 and 2 (proteinuric, low eGFR, low serum phosphate), while subgroups 1, 6 (non-proteinuric, old females) and 7 (non-proteinuric, old males) showed lower risk. Calibration was excellent for mortality in all subgroups while for RRT it was overall good except in subgroups 4 and 5.

Conclusions

The CT-PIRP model is a temporally validated prediction tool for mortality and RRT, based on variables routinely collected, that could assist decision-making regarding the treatment of incident CKD patients. External validation in other CKD populations is needed to determine its generalizability.

Seeking new answers to old questions about public reporting of transplant program performance in the United States

The Scientific Registry of Transplant Recipients (SRTR) is mandated by the National Organ Transplant Act, the Final Rule, and the SRTR contract with the Health Resources and Services Administration to report program-specific information on the performance of transplant programs. Following a consensus conference in 2012, SRTR developed a new version of the public website to improve public reporting of often complex metrics, including changing from a 3-tier to a 5-tier summary metric for first-year posttransplant survival. After its release in December 2016, the new presentation was moved to a "beta" website to allow collection of additional feedback. SRTR made further improvements and released a new beta website in May 2018. In response to feedback, SRTR added 5-tier summaries for standardized waitlist mortality and deceased donor transplant rate ratios, along with an indicator of which metric most affects survival after listing. Presentation of results was made more understandable with input from patients and families from surveys and focus groups. Room for improvement remains, including continuing to make the data more useful to patients, deciding what additional data elements should be collected to improve risk adjustment, and developing new metrics that better reflect outcomes most relevant to patients.

Cycles, Arrows and Turbulence: Time Patterns in Renal Disease, a Path from Epidemiology to Personalized Medicine?

Patients with end-stage renal disease (ESRD) experience unique patterns in their lifetime, such as the start of dialysis and renal transplantation. In addition, there is also an intricate link between ESRD and biological time patterns. In terms of cyclic patterns, the circadian blood pressure (BP) rhythm can be flattened, contributing to allostatic load, whereas the circadian temperature rhythm is related to the decline in BP during hemodialysis (HD). Seasonal variations in BP and interdialytic-weight gain have been observed in ESRD patients in addition to a profound relative increase in mortality during the winter period. Moreover, nonphysiological treatment patters are imposed in HD patients, leading to an excess mortality at the end of the long interdialytic interval. Recently, new evidence has emerged on the prognostic impact of trajectories of common clinical and laboratory parameters such as BP, body temperature, and serum albumin, in addition to single point in time measurements. Backward analysis of changes in cardiovascular, nutritional, and inflammatory parameters before the occurrence as hospitalization or death has shown that changes may already occur within months to even 1-2 years before the event, possibly providing a window of opportunity for earlier interventions. Disturbances in physiological variability, such as in heart rate, characterized by a loss of fractal patterns, are associated with increased mortality. In addition, an increase in random variability in different parameters such as BP and sodium is also associated with adverse outcomes. Novel techniques, based on time-dependent analysis of variability and trends and interactions of multiple physiological and laboratory parameters, for which machine-learning -approaches may be necessary, are likely of help to the clinician in the future. However, upcoming research should also evaluate whether dynamic patterns observed in large epidemiological studies have relevance for the individual risk profile of the patient.