Clinical impact of the Kidney Failure Risk Equation for vascular access planning.

BACKGROUND: Risk-based thresholds for arteriovenous (AV) access creation has been proposed to aid vascular access planning. We aimed to assess the clinical impact of implementing the kidney failure risk equation (KFRE) for vascular access referral. METHODS: 16,102 nephrology-referred chronic kidney disease (CKD) patients from the Swedish Renal Registry 2008-2018 were included. The KFRE was calculated repeatedly, and the timing was identified for when the KFRE risk exceeded several pre-defined thresholds and/or the estimated glomerular filtration rate <15 ml/min/1.73m2 (eGFR15). To assess the utility of the KFRE/eGFR thresholds, cumulative incidence curves of kidney replacement therapy (KRT) or death, and decision-curve analyses were computed at 6, 12 months, and 2 years. The potential impact of using the different thresholds was illustrated by an example from the Swedish access registry. RESULTS: The 12-month specificity for KRT initiation was highest for KFRE>50% 94.5 (95% Confidence interval [CI] 94.3-94.7), followed by KFRE>40% 90.0 (95% CI 89.7-90.3), while sensitivity was highest for KFRE>30% 79.3 (95% CI 78.2-80.3) and eGFR<15 ml/min/1.73m2 81.2 (95% CI 80.2-82.2). The 2-year positive predictive value was 71.5 (95% CI 70.2-72.8), 61.7 (95% CI 60.4-63.0) and 47.2 (95% CI 46.1-48.3) for KFRE>50%, KFRE>40%, and eGFR<15 respectively. Decision curve analyses suggested the largest net benefit for KFRE>40% over two years and KFRE>50% over 12 months when it is important to avoid the harm of possibly unnecessary surgery. In Sweden, 54% of nephrology-referred patients started hemodialysis in a central venous catheter (CVC) of which only 5% had AV access surgery >6 months before initiation. 60% of the CVC patients exceeded KFRE>40% a median of 0.8 years (interquartile range 0.4-1.5) before KRT initiation. CONCLUSIONS: The utility of using KFRE>40% and KFRE>50% is higher compared to the more traditionally used eGFR threshold <15 ml/min/1.73m2 for vascular access planning.

Prediction or causality? A scoping review of their conflation within current observational research.

Etiological research aims to uncover causal effects, whilst prediction research aims to forecast an outcome with the best accuracy. Causal and prediction research usually require different methods, and yet their findings may get conflated when reported and interpreted. The aim of the current study is to quantify the frequency of conflation between etiological and prediction research, to discuss common underlying mistakes and provide recommendations on how to avoid these. Observational cohort studies published in January 2018 in the top-ranked journals of six distinct medical fields (Cardiology, Clinical Epidemiology, Clinical Neurology, General and Internal Medicine, Nephrology and Surgery) were included for the current scoping review. Data on conflation was extracted through signaling questions. In total, 180 studies were included. Overall, 26% (n = 46) contained conflation between etiology and prediction. The frequency of conflation varied across medical field and journal impact factor. From the causal studies 22% was conflated, mainly due to the selection of covariates based on their ability to predict without taking the causal structure into account. Within prediction studies 38% was conflated, the most frequent reason was a causal interpretation of covariates included in a prediction model. Conflation of etiology and prediction is a common methodological error in observational medical research and more frequent in prediction studies. As this may lead to biased estimations and erroneous conclusions, researchers must be careful when designing, interpreting and disseminating their research to ensure this conflation is avoided.

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.

Performance of bleeding risk scores in dialysis patients.

BACKGROUND: Bleeding risk scores have been created to identify patients with an increased bleeding risk, which could also be useful in dialysis patients. However, the predictive performances of these bleeding risk scores in dialysis patients are unknown. Therefore, the aim of this study was to validate existing bleeding risk scores in dialysis patients. METHODS: A cohort of 1745 incident dialysis patients was prospectively followed for 3 years during which bleeding events were registered. We evaluated the discriminative performance of the Hypertension, Abnormal kidney and liver function, Stroke, Bleeding, Labile INR, Elderly and Drugs or alcohol (HASBLED), the AnTicoagulation and Risk factors In Atrial fibrillation (ATRIA), the Hepatic or kidney disease, Ethanol abuse, Malignancy, Older age, Reduced platelet count or Reduced platelet function, Hypertension, Anaemia, Genetic factors, Excessive fall risk and Stroke (HEMORR2HAGES) and the Outcomes Registry for Better Informed Treatment (ORBIT) bleeding risk scores by calculating C-statistics with 95% confidence intervals (CI). In addition, calibration was evaluated by comparing predicted and observed risks. RESULTS: Of the 1745 dialysis patients, 183 patients had a bleeding event, corresponding to an incidence rate of 5.23/100 person-years. The HASBLED [C-statistic of 0.58 (95% CI 0.54-0.62)], ATRIA [C-statistic of 0.55 (95% CI 0.51-0.60)], HEMORR2HAGES [C-statistic of 0.56 (95% CI 0.52-0.61)] and ORBIT [C-statistic of 0.56 (95% CI 0.52-0.61)] risk scores had poor discriminative performances in dialysis patients. Furthermore, the calibration analyses showed that patients with a low risk of bleeding according to the HASBLED, ATRIA, HEMORR2HAGES and ORBIT bleeding risk scores had higher incidence rates for bleeding in our cohort than predicted. CONCLUSIONS: The HASBLED, ATRIA, HEMORR2HAGES and ORBIT bleeding risk scores had poor predictive abilities in dialysis patients. Therefore, these bleeding risk scores may not be useful in this population.

Longitudinal Associations Between Inflammation and Depressive Symptoms in Chronic Dialysis Patients.

OBJECTIVE: Patients undergoing chronic dialysis often display sustained elevations of inflammation markers and also have a high prevalence of depressive symptoms. Although multiple studies demonstrated cross-sectional associations between inflammation markers and depressive symptoms in this patient group, longitudinal associations have not been examined. We therefore investigated whether longitudinal associations exist between inflammation markers and depressive symptoms in chronic dialysis patients. METHODS: Data of three consecutive measurements of an observational, prospective cohort study among chronic dialysis patients were used. At baseline, 6-month, and 12-month follow-up, patients completed the Beck Depression Inventory, and inflammation markers (high-sensitivity C-reactive protein [HsCRP], interleukin (IL)-1ß, IL-6, IL-10, and tumor necrosis factor α) were measured. We examined cross-sectional associations between inflammation markers and depressive symptoms using linear regression models. The longitudinal association between inflammation and depressive symptoms was assessed using a linear mixed model analyses. RESULTS: A total of 513 patients were included. Cross-sectional associations were found between HsCRP and depressive symptoms at baseline (ß = 0.9, confidence interval [CI] = 0.4-1.4) and 6-month follow-up (ß = 1.1, CI = 0.3-2.0), and between IL-1ß and depressive symptoms at 6-month follow-up (ß = 1.3, CI = 0.8-1.8) and 12-month follow-up (ß = 1.2, CI = 0.4-1.9). Inflammation makers (HsCRP, IL-6, IL-1ß, IL-10, and tumor necrosis factor α) at baseline were not associated with depressive symptoms at follow-up and vice versa. CONCLUSIONS: We confirmed the presence of cross-sectional associations between inflammation markers and depressive symptoms in chronic dialysis patients, but with our longitudinal data, we found no longitudinal associations. This supports an associative instead of a causal relationship between inflammation and depressive symptoms.

Long-term Survival and Clinical Benefit from Adoptive T-cell Transfer in Stage IV Melanoma Patients Is Determined by a Four-Parameter Tumor Immune Signature.

The presence of tumor-infiltrating immune cells is associated with longer survival and a better response to immunotherapy in early-stage melanoma, but a comprehensive study of the in situ immune microenvironment in stage IV melanoma has not been performed. We investigated the combined influence of a series of immune factors on survival and response to adoptive cell transfer (ACT) in stage IV melanoma patients. Metastases of 73 stage IV melanoma patients, 17 of which were treated with ACT, were studied with respect to the number and functional phenotype of lymphocytes and myeloid cells as well as for expression of galectins-1, -3, and -9. Single factors associated with better survival were identified using Kaplan-Meier curves and multivariate Cox regression analyses, and those factors were used for interaction analyses. The results were validated using The Cancer Genome Atlas database. We identified four parameters that were associated with a better survival: CD8+ T cells, galectin-9+ dendritic cells (DC)/DC-like macrophages, a high M1/M2 macrophage ratio, and the expression of galectin-3 by tumor cells. The presence of at least three of these parameters formed an independent positive prognostic factor for long-term survival. Patients displaying this four-parameter signature were found exclusively among patients responding to ACT and were the ones with sustained clinical benefit. Cancer Immunol Res; 5(2); 170-9. ©2017 AACR.

The role of tryptophan degradation in the association between inflammatory markers and depressive symptoms in chronic dialysis patients.

BACKGROUND: Among chronic dialysis patients, associations have been found between inflammatory markers and depressive symptoms. In this population, no studies have examined the mechanism linking the association between inflammatory markers and depressive symptoms. We examined whether the association between inflammatory markers and depressive symptoms is mediated by tryptophan (TRP) degradation along the kynurenine (KYN) pathway. METHODS: The data are part of an observational, prospective cohort study in five urban dialysis centres in The Netherlands. Depressive symptoms were determined with the Beck Depression Inventory. Peripheral blood was collected before dialysis to measure inflammatory markers [high sensitivity C-reactive protein (HsCRP), interleukin (IL)-1ß, IL-6, IL-10 and tumour necrosis factor-α (TNF-α)], TRP, KYN and 3-hydroxykynurenine. The KYN/TRP ratio was used as a measure of TRP degradation. The association between inflammatory markers and depressive symptoms was determined using linear regression analysis and adjusted for the KYN/TRP ratio. RESULTS: In total, 490 chronic dialysis patients were included. HsCRP [ ß = 3.8; confidence interval (CI): 1.0-6.6], IL-6 ( ß = 9.1; CI: 4.0-14.1) and TNF-α ( ß = 1.3; CI: 0.9-1.7) were associated with the KYN/TRP ratio. We found significant associations between HsCRP ( ß = 0.8; CI: 0.3-1.3) and IL-6 ( ß = 1.2; CI: 0.3-2.2) levels and depressive symptoms. However, this association was not attenuated after adjustment for the KYN/TRP ratio. Also, no significant associations were found between the KYN/TRP ratio and depressive symptoms. CONCLUSION: The association between inflammatory markers and depressive symptoms in chronic dialysis patients was not mediated by TRP degradation along the KYN pathway.

Risk of postoperative acute kidney injury in patients undergoing orthopaedic surgery--development and validation of a risk score and effect of acute kidney injury on survival: observational cohort study.

STUDY QUESTION: What is the predicted risk of acute kidney injury after orthopaedic surgery and does it affect short term and long term survival? METHODS: The cohort comprised adults resident in the National Health Service Tayside region of Scotland who underwent orthopaedic surgery from 1 January 2005 to 31 December 2011. The model was developed in 6220 patients (two hospitals) and externally validated in 4395 patients from a third hospital. Several preoperative variables were selected for candidate predictors, based on literature, clinical expertise, and availability in the orthopaedic surgery setting. The main outcomes were the development of any severity of acute kidney injury (stages 1-3) within the first postoperative week, and 90 day, one year, and longer term survival. STUDY ANSWER AND LIMITATIONS: Using logistic regression analysis, independent predictors of acute kidney injury were older age, male sex, diabetes, number of prescribed drugs, lower estimated glomerular filtration rate, use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers, and American Society of Anesthesiologists grade. The model's predictive performance for discrimination was good (C statistic 0.74 in development cohort, 0.70 in validation cohort). Calibration was good in the development cohort and after recalibration in the validation cohort. Only the highest risks were over-predicted. Survival was worse in patients with acute kidney injury compared with those without (adjusted hazard ratio 1.53, 95% confidence interval 1.38 to 1.70). This was most noticeable in the short term (adjusted hazard ratio: 90 day 2.36, 1.94 to 2.87) and diminished over time (90 day-one year 1.40, 1.10 to 1.79; >1 year 1.28, 1.10 to 1.48). The model used routinely collected data in the orthopaedic surgery setting therefore some variables that could potentially improve predictive performance were not available. However, the readily available predictors make the model easily applicable. WHAT THIS STUDY ADDS: A preoperative risk prediction model consisting of seven predictors for acute kidney injury was developed, with good predictive performance in patients undergoing orthopaedic surgery. Survival was significantly poorer in patients even with mild (stage 1) postoperative acute kidney injury. FUNDING, COMPETING INTERESTS, DATA SHARING: SB received grants from Tenovus Tayside, Chief Scientist Office, and the Royal College of Physicians and Surgeons of Glasgow; PT receives grants from Novo Nordisk, GlaxoSmithKline, and the New Drugs Committee of the Scottish Medicines Consortium. No additional data are available.

The tumor area occupied by Tbet+ cells in deeply invading cervical cancer predicts clinical outcome.

BACKGROUND: Deep invasion of the normal surrounding tissue by primary cervical cancers is a prognostic parameter for postoperative radiotherapy and relatively worse survival. However, patients with tumor-specific immunity in the blood at the time of surgery displayed a much better disease free survival. Here we analyzed if this was due to a more tumor-rejecting immune population in the tumor. METHODS: Tumor sections from a group of 58 patients with deep normal tissue-invading cervical tumors were stained for the presence of immune cells (CD45), IFNγ-producing cells (Tbet) and regulatory T cells (Foxp3) by immunohistochemistry. The slides were scanned and both the tumor area and the infiltration of the differently stained immune cells were objectively quantified using computer software. RESULTS: We found that an increased percentage of tumor occupied by CD45+ cells was strongly associated with an enhanced tumor-infiltration by Tbet+ cells and Foxp3+ cells. Furthermore, the area occupied by CD45+ immune cells, Tbet+ cells but not Foxp3+ cells within the tumor were, in addition to the lymph node status of patients, associated with a longer disease free survival and disease specific survival. Moreover, interaction analyses between these immune parameters and lymph node status indicated an independent prognostic effect of tumor infiltrating Tbet+ cells. This was confirmed in a multivariate Cox analysis. CONCLUSIONS: The area occupied by a preferentially type I oriented CD45+ cell infiltrate forms an independent prognostic factor for recurrence-free and disease-specific survival on top of the patient's lymph node status.

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.

Predicting mortality in patients with diabetes starting dialysis.

BACKGROUND: While some prediction models have been developed for diabetic populations, prediction rules for mortality in diabetic dialysis patients are still lacking. Therefore, the objective of this study was to identify predictors for 1-year mortality in diabetic dialysis patients and use these results to develop a prediction model. METHODS: Data were used from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which incident patients with end stage renal disease (ESRD) were monitored until transplantation or death. For the present analysis, patients with DM at baseline were included. A prediction algorithm for 1-year all-cause mortality was developed through multivariate logistic regression. Candidate predictors were selected based on literature and clinical expertise. The final model was constructed through backward selection. The model's predictive performance, measured by calibration and discrimination, was assessed and internally validated through bootstrapping. RESULTS: A total of 394 patients were available for statistical analysis; 82 (21%) patients died within one year after baseline (3 months after starting dialysis therapy). The final prediction model contained seven predictors; age, smoking, history of macrovascular complications, duration of diabetes mellitus, Karnofsky scale, serum albumin and hemoglobin level. Predictive performance was good, as shown by the c-statistic of 0.810. Internal validation showed a slightly lower, but still adequate performance. Sensitivity analyses showed stability of results. CONCLUSIONS: A prediction model containing seven predictors has been identified in order to predict 1-year mortality for diabetic incident dialysis patients. Predictive performance of the model was good. Before implementing the model in clinical practice, for example for counseling patients regarding their prognosis, external validation is necessary.