Samuel A. Silver

Risk prediction models for contrast induced nephropathy: systematic review

Objectives To look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics. Design Systematic review. Data sources Medline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases. Review methods Databases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast). Results 16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes. Conclusions Most predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.

Framingham risk score and novel cardiovascular risk factors underpredict major adverse cardiac events in kidney transplant recipients.

Background. Framingham Risk Score (FRS) is an insufficient cardiovascular event predictor in unselected kidney transplant recipients. Its role in different risk subgroups and the value of adding novel risk factor candidates to FRS is unknown. Methods. We reviewed patients who underwent transplantation from 1998 to 2008 with minimum 3 months graft function, determining FRS-ascertained 10-year risk at 3 months along with relevant clinical and laboratory information. Major adverse cardiac events (MACE) (myocardial infarction, coronary artery revascularization, or cardiac death) 3 months posttransplant were captured. Time-to-MACE multivariate Cox modeling with FRS and novel risk factors (C-reactive protein, uric acid, urine albumin-to-creatinine ratio) as independent variables was performed. Results. Of 956 patients, 89 experienced MACE (2.17 events/100 patient-years). FRS-predicted 10-year risk was 14.7%±10.0% in males with and 9.2%±8.2% in those without subsequent MACE (P<0.0001), although FRS substantially underestimated MACE (actual-to-predicted event ratio 1.2–8.4 in different subgroups, all P<0.0001). Although patients with MACE had a higher C-reactive protein (5.4±6.0 vs. 3.8±2.5 mg/L, P=0.026) and uric acid (417±109 vs. 386±101 &mgr;mol/L, P=0.012) level as well as lower 3-month estimated glomerular filtration rate (50.1±20.1 vs. 54.8±18.3 mL/min/1.73 m2, P=0.022), only FRS more than or equal to 10% (hazard ratio 2.313, 95% confidence interval 1.49–3.58, P=0.0002) and estimated glomerular filtration rate less than 50 mL/min/1.73 m2 (hazard ratio 2.291, 95% confidence interval 1.06–4.94, P=0.034) predicted MACE in multivariate analysis. Adding novel risk factors to FRS did not improve FRS prediction. Conclusion. FRS substantially underpredicts MACE in kidney transplant recipients among all risk subgroups. Commonly available novel risk factors do not improve FRS predictive value.

Rehospitalizations and Emergency Department Visits after Hospital Discharge in Patients Receiving Maintenance Hemodialysis

Clinical outcomes after a hospital discharge are poorly defined for patients receiving maintenance in-center (outpatient) hemodialysis. To describe the proportion and characteristics of these patients who are rehospitalized, visit an emergency department, or die within 30 days after discharge from an acute hospitalization, we conducted a population-based study of all adult patients receiving maintenance in-center hemodialysis who were discharged between January 1, 2003, and December 31, 2011, from 157 acute care hospitals in Ontario, Canada. For patients with more than one hospitalization, we randomly selected a single hospitalization as the index hospitalization. Of the 11,177 patients included in the final cohort, 1926 (17%) were rehospitalized, 2971 (27%) were treated in the emergency department, and 840 (7.5%) died within 30 days of discharge. Complications of type 2 diabetes mellitus were the most common reason for rehospitalization, whereas heart failure was the most common reason for an emergency department visit. In multivariable analysis using a cause-specific Cox proportional hazards model, the following characteristics were associated with 30-day rehospitalization: older age, the number of hospital admissions in the preceding 6 months, the number of emergency department visits in the preceding 6 months, higher Charlson comorbidity index score, and the receipt of mechanical ventilation during the index hospitalization. Thus, a large proportion of patients receiving maintenance in-center hemodialysis will be readmitted or visit an emergency room within 30 days of an acute hospitalization. A focus on improving care transitions from the inpatient setting to the outpatient dialysis unit may improve outcomes and reduce healthcare costs.

Predictors of progression to chronic dialysis in survivors of severe acute kidney injury: a competing risk study

BackgroundSurvivors of acute kidney injury are at an increased risk of developing irreversible deterioration in kidney function and in some cases, the need for chronic dialysis. We aimed to determine predictors of chronic dialysis and death among survivors of dialysis-requiring acute kidney injury.MethodsWe used linked administrative databases in Ontario, Canada, to identify patients who were discharged from hospital after an episode of acute kidney injury requiring dialysis and remained free of further dialysis for at least 90 days after discharge between 1996 and 2009. Follow-up extended until March 31, 2011. The primary outcome was progression to chronic dialysis. Predictors for this outcome were evaluated using cause-specific Cox proportional hazards models, and a competing risk approach was used to calculate absolute risk.ResultsWe identified 4 383 patients with acute kidney injury requiring temporary in-hospital dialysis who survived to discharge. After a mean follow-up of 2.4 years, 356 (8%) patients initiated chronic dialysis and 1475 (34%) died. The cumulative risk of chronic dialysis was 13.5% by the Kaplan-Meier method, and 10.3% using a competing risk approach. After accounting for the competing risk of death, previous nephrology consultation (subdistribution hazard ratio (sHR) 2.03; 95% confidence interval (CI) 1.61-2.58), a history of chronic kidney disease (sHR3.86; 95% CI 2.99-4.98), a higher Charlson comorbidity index score (sHR 1.10; 95% CI 1.05-1.15/per unit) and pre-existing hypertension (sHR 1.82; 95% CI 1.28-2.58) were significantly associated with an increased risk of progression to chronic dialysis.ConclusionsAmong survivors of dialysis-requiring acute kidney injury who initially become dialysis independent, the subsequent need for chronic dialysis is predicted by pre-existing kidney disease, hypertension and global comorbidity. This information can identify patients at high risk of progressive kidney disease who may benefit from closer surveillance after cessation of the acute phase of illness.

Improving Care after Acute Kidney Injury: A Prospective Time Series Study.

Background: Acute kidney injury (AKI) complicates 15-20% of hospitalizations, and AKI survivors are at increased risk of chronic kidney disease and death. However, less than 20% of patients see a nephrologist within 3 months of discharge, even though a nephrologist visit within 90 days of discharge is associated with enhanced survival. To address this, we established an AKI Follow-Up Clinic and characterized the patterns of care delivered. Methods: We conducted a prospective time series study. All hospitalized patients who developed Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3 AKI were eligible. The pre-intervention period consisted of electronic reminders to the nephrology consults and cardiovascular surgery services to refer to the AKI Follow-Up Clinic. In the post-intervention period, eligible patients were automatically scheduled into the AKI Follow-Up Clinic at discharge. The primary outcome was the percentage of KDIGO stages 2-3 AKI survivors assessed by a nephrologist within 30 days of discharge. Results: In the pre-intervention period, 8 of 46 patients (17%) were seen by a nephrologist within 30 days after discharge, and no additional patients were seen for 90 days. In the post-intervention period, 17 of 69 patients (25%) were seen by a nephrologist within 30 days after discharge (p = 0.36), with an additional 30 patients seen in 90 days (47 of 69, 68%, p < 0.001). The mean serum creatinine was 99 (SD 35) µmol/l prior to hospitalization and 133 (58) µmol/l at 3 months. Fifty-five of 79 patients (70%) received at least 1 medical intervention at their first AKI Follow-Up Clinic visit. Conclusions: An AKI Follow-Up Clinic with an automatic referral process increased the proportion of patients seen at 90 days, but not 30 days post discharge. Being seen in the AKI Follow-Up Clinic was associated with interventions in most patients. Future research is needed to evaluate the effect of the AKI Follow-Up Clinic on patient-centered outcomes, but physicians should be aware that AKI survivors may benefit from close outpatient follow-up and a multipronged approach to care similarly for other high-risk populations.

How to Begin a Quality Improvement Project

Quality improvement involves a combined effort among health care staff and stakeholders to diagnose and treat problems in the health care system. However, health care professionals often lack training in quality improvement methods, which makes it challenging to participate in improvement efforts. This article familiarizes health care professionals with how to begin a quality improvement project. The initial steps involve forming an improvement team that possesses expertise in the quality of care problem, leadership, and change management. Stakeholder mapping and analysis are useful tools at this stage, and these are reviewed to help identify individuals who might have a vested interest in the project. Physician engagement is a particularly important component of project success, and the knowledge that patients/caregivers can offer as members of a quality improvement team should not be overlooked. After a team is formed, an improvement framework helps to organize the scientific process of system change. Common quality improvement frameworks include Six Sigma, Lean, and the Model for Improvement. These models are contrasted, with a focus on the Model for Improvement, because it is widely used and applicable to a variety of quality of care problems without advanced training. It involves three steps: setting aims to focus improvement, choosing a balanced set of measures to determine if improvement occurs, and testing new ideas to change the current process. These new ideas are evaluated using Plan-Do-Study-Act cycles, where knowledge is gained by testing changes and reflecting on their effect. To show the real world utility of the quality improvement methods discussed, they are applied to a hypothetical quality improvement initiative that aims to promote home dialysis (home hemodialysis and peritoneal dialysis). This provides an example that kidney health care professionals can use to begin their own quality improvement projects.

30-Day Readmissions After an Acute Kidney Injury Hospitalization

BACKGROUND The risk of hospital readmission in acute kidney injury survivors is not well understood. We estimated the proportion of acute kidney injury patients who were rehospitalized within 30 days and identified characteristics associated with hospital readmission. METHODS We conducted a population-based study of patients who survived a hospitalization complicated by acute kidney injury from 2003-2013 in Ontario, Canada. The primary outcome was 30-day hospital readmission. We used a propensity score model to match patients with and without acute kidney injury, and a Cox proportional hazards model with death as a competing risk to identify predictors of 30-day readmission. RESULTS We identified 156,690 patients who were discharged from 197 hospitals after an episode of acute kidney injury. In the subsequent 30 days, 27,457 (18%) patients were readmitted; 15,988 (10%) visited the emergency department and 7480 (5%) died. We successfully matched 111,778 patients with acute kidney injury 1:1 to patients without acute kidney injury. The likelihood of 30-day readmission was higher in acute kidney injury patients than those without acute kidney injury (hazard ratio [HR] 1.53; 95% confidence interval [CI], 1.50-1.57). Factors most strongly associated with 30-day rehospitalization were the number of hospitalizations in the preceding year (adjusted HR 1.45 for ≥2 hospitalizations; 95% CI, 1.40-1.51) and receipt of inpatient chemotherapy (adjusted HR 1.44; 95% CI, 1.32-1.58). CONCLUSIONS One in 5 patients who survive a hospitalization complicated by acute kidney injury is readmitted in the next 30 days. Better strategies are needed to identify and care for acute kidney injury survivors in the community.

South Asian Ethnicity as a Risk Factor for Major Adverse Cardiovascular Events after Renal Transplantation

BACKGROUND AND OBJECTIVES South Asians (SAs) comprise 25% of all Canadian visible minorities. SAs constitute a group at high risk for cardiovascular disease in the general population, but the risk in SA kidney transplant recipients has never been studied. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS In a cohort study of 864 kidney recipients transplanted from 1998 to 2007 and followed to June 2009, we identified risk factors including ethnicity associated with major cardiac events (MACEs, a composite of nonfatal myocardial infarction, coronary intervention, and cardiac death) within and beyond 3 months after transplant. Kaplan-Meier methodology and multivariate Cox regression analysis were used to determine risk factors for MACEs. RESULTS There was no difference among SAs (n = 139), whites (n = 550), blacks (n = 65), or East Asians (n = 110) in baseline risk, including pre-existing cardiac disease. Post-transplant MACE rate in SAs was 4.4/100 patient-years compared with 1.31, 1.16, and 1.61/100 patient-years in whites, blacks, and East Asians, respectively (P < 0.0001 versus each). SA ethnicity independently predicted MACEs along with age, male gender, diabetes, systolic BP, and prior cardiac disease. SAs also experienced more MACEs within 3 months after transplant compared with whites (P < 0.0001), blacks (P = 0.04), and East Asians (P = 0.006). However, graft and patient survival was similar to other groups. CONCLUSIONS SA ethnicity is an independent risk factor for post-transplant cardiac events. Further study of this high-risk group is warranted.

How to Sustain Change and Support Continuous Quality Improvement

To achieve sustainable change, quality improvement initiatives must become the new way of working rather than something added on to routine clinical care. However, most organizational change is not maintained. In this next article in this Moving Points in Nephrology feature on quality improvement, we provide health care professionals with strategies to sustain and support quality improvement. Threats to sustainability may be identified both at the beginning of a project and when it is ready for implementation. The National Health Service Sustainability Model is reviewed as one example to help identify issues that affect long-term success of quality improvement projects. Tools to help sustain improvement include process control boards, performance boards, standard work, and improvement huddles. Process control and performance boards are methods to communicate improvement results to staff and leadership. Standard work is a written or visual outline of current best practices for a task and provides a framework to ensure that changes that have improved patient care are consistently and reliably applied to every patient encounter. Improvement huddles are short, regular meetings among staff to anticipate problems, review performance, and support a culture of improvement. Many of these tools rely on principles of visual management, which are systems transparent and simple so that every staff member can rapidly distinguish normal from abnormal working conditions. Even when quality improvement methods are properly applied, the success of a project still depends on contextual factors. Context refers to aspects of the local setting in which the project operates. Context affects resources, leadership support, data infrastructure, team motivation, and team performance. For these reasons, the same project may thrive in a supportive context and fail in a different context. To demonstrate the practical applications of these quality improvement principles, these principles are applied to a hypothetical quality improvement initiative that aims to promote home dialysis (home hemodialysis and peritoneal dialysis).

Practical Considerations When Prescribing Icodextrin: A Narrative Review

Background: Icodextrin is a peritoneal dialysis solution that is commonly used to increase ultrafiltration during the long dwell. The other major clinical benefit of icodextrin is that it is glucose-sparing, which may help preserve peritoneal membrane function. Since it has a different chemical composition than dextrose, and with its increasing use, there are several clinical considerations healthcare providers must familiarize themselves with prior to prescribing icodextrin. Summary: Failure to recognize these special properties of icodextrin can lead to adverse events reaching patients. This narrative review explores the hemodynamic, metabolic, and idiopathic effects of icodextrin to facilitate the safe use of icodextrin in peritoneal dialysis. Key Messages: Hemodynamic effects include hypotension from enhanced ultrafiltration contributing to loss of residual kidney function. Metabolic effects include the chemical structure of icodextrin interfering with biochemical assays, resulting in misleading glucose readings on non-specific glucometers. Idiopathic adverse effects include a diffuse rash and sterile peritonitis. It is also important to remember that not all antibiotic combinations have undergone stability testing in icodextrin. This narrative review will help healthcare providers to confidently prescribe icodextrin to maximize its benefit in peritoneal dialysis patients.