Quazi Ibrahim

Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study

Background: Frailty is a multidimensional syndrome characterized by loss of physiologic and cognitive reserves that confers vulnerability to adverse outcomes. We determined the prevalence, correlates and outcomes associated with frailty among adults admitted to intensive care. Methods: We prospectively enrolled 421 critically ill adults aged 50 or more at 6 hospitals across the province of Alberta. The primary exposure was frailty, defined by a score greater than 4 on the Clinical Frailty Scale. The primary outcome measure was in-hospital mortality. Secondary outcome measures included adverse events, 1-year mortality and quality of life. Results: The prevalence of frailty was 32.8% (95% confidence interval [CI] 28.3%–37.5%). Frail patients were older, were more likely to be female, and had more comorbidities and greater functional dependence than those who were not frail. In-hospital mortality was higher among frail patients than among non-frail patients (32% v. 16%; adjusted odds ratio [OR] 1.81, 95% CI 1.09–3.01) and remained higher at 1 year (48% v. 25%; adjusted hazard ratio 1.82, 95% CI 1.28–2.60). Major adverse events were more common among frail patients (39% v. 29%; OR 1.54, 95% CI 1.01–2.37). Compared with nonfrail survivors, frail survivors were more likely to become functionally dependent (71% v. 52%; OR 2.25, 95% CI 1.03–4.89), had significantly lower quality of life and were more often readmitted to hospital (56% v. 39%; OR 1.98, 95% CI 1.22–3.23) in the 12 months following enrolment. Interpretation: Frailty was common among critically ill adults aged 50 and older and identified a population at increased risk of adverse events, morbidity and mortality. Diagnosis of frailty could improve prognostication and identify a vulnerable population that might benefit from follow-up and intervention.

Long-term association between frailty and health-related quality of life among survivors of critical illness: a prospective multicenter cohort study.

Objective: Frailty is a multidimensional syndrome characterized by loss of physiologic reserve that gives rise to vulnerability to poor outcomes. We aimed to examine the association between frailty and long-term health-related quality of life among survivors of critical illness. Design: Prospective multicenter observational cohort study. Setting: ICUs in six hospitals from across Alberta, Canada. Patients: Four hundred twenty-one critically ill patients who were 50 years or older. Interventions: None. Measurements and Main Results: Frailty was operationalized by a score of more than 4 on the Clinical Frailty Scale. Health-related quality of life was measured by the EuroQol Health Questionnaire and Short-Form 12 Physical and Mental Component Scores at 6 and 12 months. Multiple logistic and linear regression with generalized estimating equations was used to explore the association between frailty and health-related quality of life. In total, frailty was diagnosed in 33% (95% CI, 28–38). Frail patients were older, had more comorbidities, and higher illness severity. EuroQol-visual analogue scale scores were lower for frail compared with not frail patients at 6 months (52.2 ± 22.5 vs 64.6 ± 19.4; p < 0.001) and 12 months (54.4 ± 23.1 vs 68.0 ± 17.8; p < 0.001). Frail patients reported greater problems with mobility (71% vs 45%; odds ratio, 3.1 [1.6–6.1]; p = 0.001), self-care (49% vs 15%; odds ratio, 5.8 [2.9–11.7]; p < 0.001), usual activities (80% vs 52%; odds ratio, 3.9 [1.8–8.2]; p < 0.001), pain/discomfort (68% vs 47%; odds ratio, 2.0 [1.1–3.8]; p = 0.03), and anxiety/depression (51% vs 27%; odds ratio, 2.8 [1.5–5.3]; p = 0.001) compared with not frail patients. Frail patients described lower health-related quality of life on both physical component score (34.7 ± 7.8 vs 37.8 ± 6.7; p = 0.012) and mental component score (33.8 ± 7.0 vs 38.6 ± 7.7; p < 0.001) at 12 months. Conclusions: Frail survivors of critical illness experienced greater impairment in health-related quality of life, functional dependence, and disability compared with those not frail. The systematic assessment of frailty may assist in better informing patients and families on the complexities of survivorship and recovery.

Comparative effectiveness of transitional care services in patients discharged from the hospital with heart failure: a systematic review and network meta‐analysis

To compare the effectiveness of transitional care services in decreasing all‐cause death and all‐cause readmissions following hospitalization for heart failure (HF).

Effectiveness of implementation strategies in improving physician adherence to guideline recommendations in heart failure: a systematic review protocol

Introduction The uptake of Clinical Practice Guideline (CPG) recommendations that improve outcomes in heart failure (HF) remains suboptimal. We will conduct a systematic review to identify implementation strategies that improve physician adherence to class I recommendations, those with clear evidence that benefits outweigh the risks. We will use American, Canadian and European HF guidelines as our reference. Methods and analysis We will conduct a literature search in the databases of MEDLINE, EMBASE, HEALTHSTAR, CINAHL, Cochrane Library, Campbell Collaboration, Joanna Briggs Institute Evidence Based Practice, Centre for Reviews and Dissemination and Evidence Based Practice Centres. We will include prospective studies evaluating implementation interventions aimed at improving uptake of class I CPG recommendations in HF. We will extract data in duplicate. We will classify interventions according to their level of application (ie, provider, organisation, systems level) and common underlying characteristics (eg, education, decision-support, financial incentives) using the Cochrane Effective Practice and Organisation of Care Taxonomy. We will assess the impact of the intervention on adherence to the CPGs. Outcomes will include proportion of eligible patients who were: prescribed a CPG-recommended pharmacological treatment; referred for device consideration; provided self-care education at discharge; and provided left ventricular function assessment. We will include clinical outcomes such as hospitalisations, readmissions and mortality, if data is available. We will identify the common elements of successful and failing interventions, and examine the context in which they were applied, using the Process Redesign contextual framework. We will synthesise the results narratively and, if appropriate, will pool results for meta-analysis. Discussion and dissemination In this review, we will assess the impact of implementation strategies and contextual factors on physician adherence to HF CPGs. We will explore why some interventions may succeed in one setting and fail in another. We will disseminate our findings through briefing reports, publications and presentations. Trial registration number CRD42015017155.

Prognostic validation of a non-laboratory and a laboratory based cardiovascular disease risk score in multiple regions of the world.

Objective To evaluate the performance of the non-laboratory INTERHEART risk score (NL-IHRS) to predict incident cardiovascular disease (CVD) across seven major geographic regions of the world. The secondary objective was to evaluate the performance of the fasting cholesterol-based IHRS (FC-IHRS). Methods Using measures of discrimination and calibration, we tested the performance of the NL-IHRS (n=100 475) and FC-IHRS (n=107 863) for predicting incident CVD in a community-based, prospective study across seven geographic regions: South Asia, China, Southeast Asia, Middle East, Europe/North America, South America and Africa. CVD was defined as the composite of cardiovascular death, myocardial infarction, stroke, heart failure or coronary revascularisation. Results Mean age of the study population was 50.53 (SD 9.79) years and mean follow-up was 4.89 (SD 2.24) years. The NL-IHRS had moderate to good discrimination for incident CVD across geographic regions (concordance statistic (C-statistic) ranging from 0.64 to 0.74), although recalibration was necessary in all regions, which improved its performance in the overall cohort (increase in C-statistic from 0.69 to 0.72, p<0.001). Regional recalibration was also necessary for the FC-IHRS, which also improved its overall discrimination (increase in C-statistic from 0.71 to 0.74, p<0.001). In 85 078 participants with complete data for both scores, discrimination was only modestly better with the FC-IHRS compared with the NL-IHRS (0.74 vs 0.73, p<0.001). Conclusions External validations of the NL-IHRS and FC-IHRS suggest that regionally recalibrated versions of both can be useful for estimating CVD risk across a diverse range of community-based populations. CVD prediction using a non-laboratory score can provide similar accuracy to laboratory-based methods.

Utility of the LACE index at the bedside in predicting 30-day readmission or death in patients hospitalized with heart failure

UNLABELLED The Length of stay, Acuity, Comorbidities, Emergency department visits in prior 6 months (LACE) index threshold of 10 predicts readmission or death in general medical patients in administrative databases. We assessed whether the unadjusted LACE index, computed at the bedside, can predict 30-day outcomes in patients hospitalized for heart failure. METHODS We used logistic regression with LACE as the continuous predictor and 30-day readmissions and 30-day readmission or death as outcomes. We determined a suitable LACE threshold using logistic regression and the closest-to-(0,1) criterion for dichotomized LACE scores. We assessed model discrimination with C statistics and 95% CI. RESULTS Of 378 patients, a majority (91%) had LACE scores ≥10. Incremental LACE scores increased the odds of 30-day readmissions (odds ratio [OR] 1.13, 95% CI 1.02-1.24) and 30-day readmissions or death (OR 1.11, 95% CI 1.01-1.22). C statistics for 30-day readmissions (0.59, 95% CI 0.52-0.65) and 30-day readmission or death (0.57, 95% CI 0.51-0.64) were nonsignificantly lower than the Centers for Medicare/Medicaid Services-endorsed readmission risk score (0.61, 95% CI 0.55-0.67 and 0.62, 95% CI 0.55-0.68, respectively). LACE ≥13 predicted 30-day readmissions (OR 1.91, 95% CI 1.17-3.09) and 30-day readmission or death (OR 1.59, 95% CI 1.00-2.54), and met the closest-to-(0,1) criterion for optimal threshold. CONCLUSIONS LACE calculated at the bedside predicts 30-day clinical outcomes in hospitalized heart failure patients. While there is a continuum of risk, a threshold of ≥13 is more suitable than ≥10 to identify high-risk patients. Given its modest discrimination, however, we do not recommend its preferential use over validated risk prediction tools such as readmission risk score.

Knowledge to action: Rationale and design of the patient-centered care transitions in heart failure (PACT-HF) stepped wedge cluster randomized trial

Introduction: Heart Failure (HF) is a common cause of hospitalization in older adults. The transition from hospital to home is high‐risk, and gaps in transitional care can increase the risk of re‐hospitalization and death. Combining health care services supported by meta‐analyses, we designed the PACT‐HF transitional care model. Methods: Adopting an integrated Knowledge Translation (iKT) approach in which decision‐makers and clinicians are partners in research, we implement and test the effectiveness of PACT‐HF among patients hospitalized for HF. We use a pragmatic stepped wedge cluster randomized trial design to introduce the complex health service intervention to 10 large hospitals in a randomized sequence until all hospitals initiate the intervention. The goal is for all patients hospitalized with HF to receive self‐care education, multidisciplinary care, and early follow‐up with their health care providers; and in addition, for high‐risk patients to receive post‐discharge nurse‐led home visits and outpatient care in Heart Function clinics. This requires integration of care across hospitals, home care agencies, and outpatient clinics in our publicly funded health care system. While hospitals are the unit of recruitment and analysis, patients (estimated sample size of 3200) are the unit of analysis. Primary outcomes are hierarchically ordered as time to composite all‐cause readmissions / emergency department (ED) visits / death at 3 months and time to composite all‐cause readmissions / ED visits at 30 days. In a nested study of 8 hospitals, we measure the patient‐centered outcomes of Discharge Preparedness, Care Transitions Quality, and Quality Adjusted Life Years (QALY); and the 6‐month health care resource use and costs. We obtain all clinical and cost outcomes via linkages to provincial administrative databases. Conclusions: This protocol describes the implementation and testing of a transitional care model comprising health care services informed by high‐level evidence. The study adopts an iKT and pragmatic approach, uses a robust study design, links clinical trial data with outcomes held in administrative databases, and includes patient‐reported outcomes. Findings will have implications on clinical practice, health care policy, and Knowledge Translation (KT) research methodology.

Erratum to: A prospective multicenter cohort study of frailty in younger critically ill patients

Author details Division of Critical Care Medicine (University of Alberta Hospital), Faculty of Medicine and Dentistry, University of Alberta, 2-124 Clinical Sciences Building, 8440-112 Street NW, Edmonton, AB T6G 2B7, Canada. Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, 5-112 Clinical Sciences Building, 8440-112 Street, Edmonton, AB T6G 2B7, Canada. Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, 13-103 Clinical Sciences Building, 8440-112 Street, Edmonton, AB T6G 2B7, Canada. Population Health Research Institute, McMaster University, 237 Barton Street East, Hamilton, ON L8L 2X2, Canada. Department of Medicine, Faculty of Medicine and Dentistry, University of British Columbia, 317-2194 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada. Department of Critical Care Medicine, Faculty of Medicine, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1 N4, Canada.