Ana C. Alba

How to Use a Subgroup Analysis: Users’ Guide to the Medical Literature

Clinicians, when trying to apply trial results to patient care, need to individualize patient care and, potentially, manage patients based on results of subgroup analyses. Apparently compelling subgroup effects often prove spurious, and guidance is needed to differentiate credible from less credible subgroup claims. We therefore provide 5 criteria to use when assessing the validity of subgroup analyses: (1) Can chance explain the apparent subgroup effect; (2) Is the effect consistent across studies; (3) Was the subgroup hypothesis one of a small number of hypotheses developed a priori with direction specified; (4) Is there strong preexisting biological support; and (5) Is the evidence supporting the effect based on within- or between-study comparisons. The first 4 criteria are applicable to individual studies or systematic reviews, the last only to systematic reviews of multiple studies. These criteria will help clinicians deciding whether to use subgroup analyses to guide their patient care.

Risk prediction models for mortality in ambulatory patients with heart failure: a systematic review

Background—Optimal management of heart failure requires accurate assessment of prognosis. Many prognostic models are available. Our objective was to identify studies that evaluate the use of risk prediction models for mortality in ambulatory patients with heart failure and describe their performance and clinical applicability. Methods and Results—We searched for studies in Medline, Embase, and CINAHL in May 2012. Two reviewers selected citations including patients with heart failure and reporting on model performance in derivation or validation cohorts. We abstracted data related to population, outcomes, study quality, model discrimination, and calibration. Of the 9952 studies reviewed, we included 34 studies testing 20 models. Only 5 models were validated in independent cohorts: the Heart Failure Survival Score, the Seattle Heart Failure Model, the PACE (incorporating peripheral vascular disease, age, creatinine, and ejection fraction) risk score, a model by Frankenstein et al, and the SHOCKED predictors. The Heart Failure Survival Score was validated in 8 cohorts (2240 patients), showing poor-to-modest discrimination (c-statistic, 0.56–0.79), being lower in more recent cohorts. The Seattle Heart Failure Model was validated in 14 cohorts (16 057 patients), describing poor-to-acceptable discrimination (0.63–0.81), remaining relatively stable over time. Both models reported adequate calibration, although overestimating survival in specific populations. The other 3 models were validated in a cohort each, reporting poor-to-modest discrimination (0.66–0.74). Among the remaining 15 models, 6 were validated by bootstrapping (c-statistic, 0.74–0.85); the rest were not validated. Conclusions—Externally validated heart failure models showed inconsistent performance. The Heart Failure Survival Score and Seattle Heart Failure Model demonstrated modest discrimination and questionable calibration. A new model derived from contemporary patient cohorts may be required for improved prognostic performance.

Clinical Differences Between Continuous Flow Ventricular Assist Devices: A Comparison Between HeartMate II and HeartWare HVAD

The HeartWare ventricular assist device (HVAD) is a new generation centrifugal flow VAD recently introduced in Canada. The objective of this study was to compare the HVAD device to the HeartMate II (HMII) axial flow device. Very few studies have compared clinical outcomes between newer generation VADs.

Risk Prediction Models for Mortality in Ambulatory Patients With Heart Failure

Background—Optimal management of heart failure requires accurate assessment of prognosis. Many prognostic models are available. Our objective was to identify studies that evaluate the use of risk prediction models for mortality in ambulatory patients with heart failure and describe their performance and clinical applicability. Methods and Results—We searched for studies in Medline, Embase, and CINAHL in May 2012. Two reviewers selected citations including patients with heart failure and reporting on model performance in derivation or validation cohorts. We abstracted data related to population, outcomes, study quality, model discrimination, and calibration. Of the 9952 studies reviewed, we included 34 studies testing 20 models. Only 5 models were validated in independent cohorts: the Heart Failure Survival Score, the Seattle Heart Failure Model, the PACE (incorporating peripheral vascular disease, age, creatinine, and ejection fraction) risk score, a model by Frankenstein et al, and the SHOCKED predictors. The Heart Failure Survival Score was validated in 8 cohorts (2240 patients), showing poor-to-modest discrimination (c-statistic, 0.56–0.79), being lower in more recent cohorts. The Seattle Heart Failure Model was validated in 14 cohorts (16 057 patients), describing poor-to-acceptable discrimination (0.63–0.81), remaining relatively stable over time. Both models reported adequate calibration, although overestimating survival in specific populations. The other 3 models were validated in a cohort each, reporting poor-to-modest discrimination (0.66–0.74). Among the remaining 15 models, 6 were validated by bootstrapping (c-statistic, 0.74–0.85); the rest were not validated. Conclusions—Externally validated heart failure models showed inconsistent performance. The Heart Failure Survival Score and Seattle Heart Failure Model demonstrated modest discrimination and questionable calibration. A new model derived from contemporary patient cohorts may be required for improved prognostic performance.

Predictors of Acute Renal Dysfunction After Ventricular Assist Device Placement

BACKGROUND Acute renal dysfunction (ARD) is a frequent complication after ventricular assist device (VAD) implantation. The purpose was to identify predictors associated with ARD after VAD implantation. METHODS AND RESULTS ARD was defined using the risk of renal dysfunction, injury to the kidney, failure of kidney function, loss of kidney function, end-stage kidney disease (RIFLE) criteria. Patients who developed ARD during VAD support (ARD, n=24) were compared with patients who did not (NoARD, n=29). Patients with ARD before implant were excluded. Patient characteristics pre-VAD implant, incidence of complications during support and survival were analyzed. Baseline characteristics were similar. The ARD group had longer cardiopulmonary bypass (CPB) time, greater need for reoperation and higher risk of bleeding (>1 L) intraoperatively (P < .05). The ARD group had a higher incidence of infections (17% vs. 50%, OR 5, 95%CI 1.3-17), liver injury (58% vs. 17%, OR 7, 95%CI 2-24), ventricular arrhythmias (42% vs. 14%, OR 4, 95%CI 1.1-16), and right ventricular failure (73% vs. 30%, OR 7, 95%CI 2-24). Need for dialysis was associated with higher pre-VAD creatinine and worse outcomes. Survival was significantly lower in the ARD group (HR 8.5, 95%CI 2-29). CONCLUSIONS Acute renal dysfunction is a common and serious complication post-VAD implantation and is associated with reduced survival. Longer CPB time, higher intraoperative blood loss, and reoperation are associated factors with the development of this disease.

Cost-Effectiveness of Ventricular Assist Device Therapy as a Bridge to Transplantation Compared With Nonbridged Cardiac Recipients

Background— Current available treatment options for advanced heart failure include heart transplantation and ventricular assist device (VAD) therapy. This project aimed to evaluate the cost-effectiveness of a bridge-to-transplantation (BTT)–VAD approach relative to direct heart transplantation in transplant-eligible patients. Methods and Results— A Markov model was used to evaluate survival benefits and costs for BTT-VAD versus nonbridged heart transplant recipients. Three different scenarios were considered according to severity of patients’ baseline hemodynamic status (high, medium, and low risk). Results are presented in terms of survival, costs, and cost-effectiveness ratio. Sensitivity analyses were used to analyze uncertainty in model estimates. Over a 20-year time horizon, BTT-VAD therapy increased survival at an increased cost relative to nonbridged heart transplant recipients:

Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature

100 841more in costs and 1.19 increased life years (LYs) in high-risk patients (

Predictors of Mortality in Patients With an Implantable Cardiac Defibrillator: A Systematic Review and Meta-analysis

84 964/LY),

Factors associated with anti-human leukocyte antigen antibodies in patients supported with continuous-flow devices and effect on probability of transplant and post-transplant outcomes

112 779 more in costs and 1.14 more LYs (

Right Ventricular Function and Prognosis in Stable Heart Failure Patients

99 039/LY) in medium-risk patients, and an additional cost of