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2.
Heart Lung ; 61: 92-97, 2023.
Article in English | MEDLINE | ID: mdl-37196385

ABSTRACT

BACKGROUND: Multidisciplinary care models have been associated with improved clinical outcomes and an increase in adherence to guideline-directed medical therapy among patients hospitalized with heart failure (HF). OBJECTIVE: This study aims to evaluate the association between the creation of a multidisciplinary inpatient HF service and patient outcomes at a tertiary care center in the Middle East/ North Africa. METHODS: We used electronic health records to retrospectively identify patients hospitalized for acute HF between June-2015 and June-2018. Patients were categorized by whether they were hospitalized before (n = 71) or after (n = 86) the implementation of a multidisciplinary HF team (HF-MDT), starting on 1/1/2017. The groups were compared for optimal use of guideline-directed medical therapy, hospital length of stay, 30-day hospital readmission, and in-hospital mortality. RESULTS: The creation of the HF-MDT was associated with significantly better adherence to GDMT at discharge (27.5% vs. 55.4%, (OR 3.3, 95% CI [1.65-6.5]), P<0.001), shorter median hospital length of stay (8, IQR [4.8 - 19] vs. 5, IQR [3- 9], P = 0.02) and a reduction in 30-day hospital readmission (26.5% vs. 11%; OR 0.3, 95% CI [0.1-0.8], P = 0.02). However, there was no difference in-hospital mortality (10.5% vs. 3.5%; OR 0.3, 95% CI [0.1-1.2], P = 0.1). CONCLUSIONS: Implementing an HF-MDT in the care of patients hospitalized with HF is associated with better adherence to GDMT on discharge, reduced hospital length of stay, and lower 30-day readmission rates. The current data support the broader adoption of dedicated HF programs in the Middle East to improve outcomes of patients with HF.


Subject(s)
Heart Failure , Inpatients , Humans , Retrospective Studies , Patient Readmission , Heart Failure/therapy , Africa, Northern/epidemiology , Middle East/epidemiology , Hospitalization
3.
J Thromb Thrombolysis ; 56(1): 164-174, 2023 Jul.
Article in English | MEDLINE | ID: mdl-37097552

ABSTRACT

Anticoagulation during Impella® support is a challenge due to its complications and inconsistent practice across the globe. This observational, retrospective chart review included all patients with Impella® support at our advanced cardiac center at a quaternary care hospital in the Middle East gulf region. The study was conducted over six years (2016-2022), a time period during which manufacturer recommendations for purge solution, anticoagulation protocols as well as Impella® place in therapy and utilization were all evolving. We aimed to evaluate the efficacy of different anticoagulation practices and association with complications and outcomes. Forty-one patients underwent Impella® during the study period, including 25 patients with support for more than 12 h, and are the focus of our analysis. Cardiogenic shock (n = 25, 60.9%) was the primary indication for Impella®, followed by facilitating high-risk PCI (n = 15, 36.7%) and left ventricular afterload reduction in patients undergoing veno-arterial extracorporeal membrane oxygenation (n = 1, 2.4%). Our overall Impella® usage evolved over the years from a primary use to facilitate a high-risk PCI to the recent more common use of LV unloading in cardiogenic shock. No patients experienced device malfunction and the incidence of other complications including ischemic stroke and bleeding were comparable to those reported in the literature (12.2% and 24% respectively). The 30-day all-cause mortality of 41 patients was 53.6%. In line with the evolving recommendations and evidence, we observed an underutilization of non-heparin-based purge solutions and inconsistent management of anticoagulation in the setting of both Impella® and VA ECMO which necessitates more education and protocols.


Subject(s)
Heart-Assist Devices , Percutaneous Coronary Intervention , Humans , Anticoagulants/adverse effects , Blood Coagulation , Heart-Assist Devices/adverse effects , Retrospective Studies , Shock, Cardiogenic/therapy , Treatment Outcome
4.
Am Heart J ; 166(5): 895-903.e1, 2013 Nov.
Article in English | MEDLINE | ID: mdl-24176446

ABSTRACT

BACKGROUND: Most heart failure (HF) risk stratification models were developed for inpatient use, and available outpatient models use a complex set of variables. We hypothesized that routinely collected clinical data could predict the 6-month risk of death and all-cause medical hospitalization in HF clinic outpatients. METHODS AND RESULTS: Using a quality improvement database and multivariable Cox modeling, we derived the Heart Failure Patient Severity Index (HFPSI) in the University of Michigan HF clinic (UM cohort, n = 1,536; 314 reached primary outcome). We externally validated the HFPSI in the Ann Arbor Veterans' Affairs HF clinic (VA cohort, n = 445; 106 outcomes) and explored "real-time" HFPSI use (VA-RT cohort, n = 486; 141 outcomes) by tracking VA patients for 6 months from their most recently calculated HFPSI, rather than using an arbitrary start date for the cohort. The HFPSI model included blood urea nitrogen, B-type natriuretic peptide, New York Heart Association class, diabetes status, history of atrial fibrillation/flutter, and all-cause hospitalization within the prior 1 and 2 to 6 months. The concordance c statistics in the UM/VA/VA-RT cohorts were 0.71/0.68/0.74. Kaplan-Meier curves and log-rank testing demonstrated excellent risk stratification, particularly between a large, low-risk group (40% of patients, 6-month event rates in the UM/VA/VA-RT cohorts 8%/12%/12%) and a small, high-risk group (10% of patients, 6-month event rates in the UM/VA/VA-RT cohorts 57%/58%/79%). CONCLUSIONS: The HFPSI uses readily available data to predict the 6-month risk of death and/or all-cause medical hospitalization in HF clinic outpatients and could potentially help allocate specialized HF resources within health systems.


Subject(s)
Heart Failure/mortality , Hospitalization/statistics & numerical data , Aged , Female , Humans , Kaplan-Meier Estimate , Male , Middle Aged , Outpatients , Proportional Hazards Models , Risk Assessment , Severity of Illness Index , Survival Rate
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