Dataset Information

Heart failure events with rosiglitazone in type 2 diabetes: data from the RECORD clinical trial.

ABSTRACT:

Aims

Thiazolidinediones are insulin sensitizers, and are associated with fluid retention and increased risk of heart failure (HF) in people with type 2 diabetes. We assessed fatal and non-fatal HF events and their outcome, and identified HF predictors in the RECORD (Rosiglitazone Evaluated for Cardiac Outcomes and Regulation of glycaemia in Diabetes) trial population.

Methods and results

In a multicentre, open-label study, we randomized 4447 people with type 2 diabetes on metformin or sulfonylurea monotherapy with a mean HbA(1c) of 7.9% to add-on rosiglitazone (n = 2220) or to a combination of metformin and sulfonylurea (n = 2227) and followed them over 5.5 years on average. Heart failure hospitalizations and deaths were adjudicated by a Clinical Endpoint Committee using pre-specified criteria. Independent predictors of HF events were identified out of a group of 30 pre-specified clinical, demographic, and biological variables. In the rosiglitazone group, the risk of HF death or hospitalization was doubled: HR = 2.10 (95% CI, 1.35-3.27): the excess HF event rate was 2.6 (1.1-4.1) per 1000 person-years. An excess in HF deaths was observed (10 vs. two), including four HF deaths as first HF events. By contrast, there was no increase in cardiovascular mortality or hospitalization (HR = 0.99, 95% CI, 0.85-1.16) or in cardiovascular deaths (60 vs. 71). Independent predictors of HF were rosiglitazone assignment, age, urinary albumin : creatinine ratio, body mass index, and systolic blood pressure at baseline. A history of previous cardiovascular disease was not predictive of HF. Duration of HF hospitalization and rate of HF re-hospitalization were similar in the two groups.

Conclusion

These findings confirm the increased risk of HF events in people treated with rosiglitazone and support the recommendation that this agent should not continue to be used in people developing symptomatic HF while using the medication. Close follow-up for the risk of HF should be offered to elderly people, people with markedly increased body mass index, people with microalbuminuria/proteinuria, and people with increased systolic blood pressure.

SUBMITTER: Komajda M

PROVIDER: S-EPMC2848325 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ImportanceAccurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation.ObjectiveTo develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital.Design, setting, and participantsWe performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data.Main outcomes and measuresHeart failure diagnosis based on discharge diagnosis and physician review of sampled medical records.ResultsA total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively.Conclusions and relevanceThe problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

Project description:Background: In patients with type 2 diabetes mellitus, sodium-glucose cotransporter 2 inhibitors reduce the risk of hospitalization for heart failure (HHF). We assessed the effect of ertugliflozin on HHF and related outcomes.Methods: VERTIS CV (Evaluation of Ertugliflozin Efficacy and Safety Cardiovascular Outcomes Trial), a double-blind, placebo-controlled trial, randomly assigned patients with type 2 diabetes mellitus and atherosclerotic cardiovascular (CV) disease to once-daily ertugliflozin 5 mg, 15 mg, or placebo. Prespecified secondary analyses compared ertugliflozin (pooled doses) versus placebo on time to first event of HHF and composite of HHF/CV death, overall and stratified by prespecified characteristics. Cox proportional hazards modeling was used with the Fine and Gray method to account for competing mortality risk, and Andersen-Gill modeling to analyze total (first+recurrent) HHF and total HHF/CV death events.Results: A total of 8246 patients were randomly assigned to ertugliflozin (n=5499) or placebo (n=2747); n=1958 (23.7%) had a history of heart failure (HF) and n=5006 (60.7%) had pretrial ejection fraction (EF) available, including n=959 with EF ?45%. Ertugliflozin did not significantly reduce first HHF/CV death (hazard ratio [HR], 0.88 [95% CI, 0.75-1.03]). Overall, ertugliflozin reduced risk for first HHF (HR, 0.70 [95% CI, 0.54-0.90]; P=0.006). Previous HF did not modify this effect (HF: HR, 0.63 [95% CI, 0.44-0.90]; no HF: HR, 0.79 [95% CI, 0.54-1.15]; P interaction=0.40). In patients with HF, the risk reduction for first HHF was similar for those with reduced EF ?45% versus preserved EF >45% or unknown. However, in the overall population, the risk reduction tended to be greater for those with EF ?45% (HR, 0.48 [95% CI, 0.30-0.76]) versus EF >45% (HR, 0.86 [95% CI, 0.58-1.29]). Effect on risk for first HHF was consistent across most subgroups, but greater benefit of ertugliflozin was observed in 3 populations: baseline estimated glomerular filtration rate <60 mL·min-1·1.73 m-2, albuminuria, and diuretic use (each P interaction <0.05). Ertugliflozin reduced total events of HHF (rate ratio, 0.70 [95% CI, 0.56-0.87]) and total HHF/CV death (rate ratio, 0.83 [95% CI, 0.72-0.96]).Conclusions: In patients with type 2 diabetes mellitus, ertugliflozin reduced the risk for first and total HHF and total HHF/CV death, adding further support for the use of sodium-glucose cotransporter 2 inhibitors in primary and secondary prevention of HHF. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01986881.