Predictive Value of the Get With The Guidelines Heart Failure Risk Score in Unselected Cardiac Intensive Care Unit Patients

Abstract

Background The cardiac intensive care unit ( CICU ) population is no longer composed of only patients with acute coronary syndromes, and includes those with acute heart failure and multiple comorbidities. We hypothesized that the GWTG ‐ HF (Get With The Guidelines–Heart Failure) risk score that predicts inpatient mortality in hospitalized patients with heart failure would predict mortality in CICU patients. Methods and Results We retrospectively analyzed CICU patients at a tertiary care hospital from 2007 to 2015. The GWTG ‐ HF risk score was calculated at CICU admission. As a secondary analysis, the EFFECT (Enhanced Feedback for Effective Cardiac Treatment) , OPTIMIZE‐HF (Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure), and ADHERE (Acute Decompensated Heart Failure National Registry) risk scores were calculated. Kaplan–Meier survival analysis and the area under the receiver operating characteristic curve value were determined for inpatient and 1‐year mortality. The GWTG ‐ HF risk score was calculated in 9532 (95%) patients, with a median value of 40 (interquartile range, 35–47). Inpatient mortality occurred in 824 (8.6%) patients, and 2075 (21.8%) patients died by 1 year. Patients who died in hospital had a significantly higher mean GWTG ‐ HF score (47.7 versus 40.2; P <0.001). Inpatient and 1‐year mortality increased in each GWTG ‐ HF risk score quartile ( P <0.0001). Discrimination of the GWTG ‐ HF , EFFECT, OPTIMIZE‐HF, and ADHERE risk scores was assessed using area under the receiver operating characteristic curve values for hospital mortality, and were similar for all risk scores (0.72–0.74; P >0.05). The Hosmer–Lemeshow statistic suggested poor calibration for hospital mortality by the GWTG ‐ HF risk score ( P <0.001). Conclusions The GWTG ‐ HF risk score and other heart failure prediction tools demonstrate good discrimination for inpatient and 1‐year mortality in a heterogeneous cohort of CICU patients. Our study emphasizes that prognostic variables overlap in cardiac patients, regardless of the admission diagnosis.

Acute cardiovascular hospitalizations and illness severity before and during the COVID‐19 pandemic

“…The ADHERE risk scores were likewise similar between groups. Although these two risk models use different numbers of clinical variables, they have demonstrated relatively similar predictive performance, 13,22 consistent with our observations. There was a statistically significant difference in LVEF from before and after the NC shelter-in-place order in 2020 (37.2% vs. 39.0%; p = .034), yet There are many possible reasons why HF hospitalizations decreased during the study period.…”

Age and shock severity predict mortality in cardiac intensive care unit patients with and without heart failure

“…We retrospectively analysed a previously constructed database including data from the index CICU admission of consecutive unique adult patients aged ≥18 years admitted to the CICU at Mayo Clinic Hospital, St. Mary's Campus between 1 January 2007, and 31 December 2015. 1, [4][5][6]17,18,[20][21][22][23][24][25][26] To minimize bias associated with CICU readmission, only data from the first CICU admission during the study period were analysed.…”

“…Using the Multidisciplinary Epidemiology and Translational Research in Intensive Care Data Mart, we recorded demographic, vital signs, laboratory, clinical, and outcome data, as well as procedures and therapies performed during the CICU and hospital stay. 1, [4][5][6]17,18,[20][21][22][23][24][25][26][27] The admission vital signs, clinical measurements, and laboratory values were defined as either the first value recorded after or closest to the index CICU admission. Vital signs were recorded every 15 min during the first hour after CICU admission.…”

“…9,15,[31][32][33] Age is a critical variable in most mortality prediction risk scores, including those specifically designed for patients with CS as well as standard scoring systems such as APACHE. 1, 3,11,12,20,24 Illness severity scores have lower discrimination of mortality in older CICU patients, raising important questions about how best to risk-stratify older CICU patients given their higher risk of adverse outcomes than younger patients. 1 Even though age is an important risk factor for mortality in both ACS and HF, we unexpectedly observed that the relationship between age (and to a lesser extent SCAI shock stage) and mortality was weaker for patients with HF compared with ACS patients.…”

Predictive Value of the GWTG Score to Heart Failure in South Indian Patients Admitted in ICU

“…In the original article brief mention about the available risk stratification models such as Acute Decomp-ensated Heart Failure National Registry (ADHERE) study, 4 OPTIMIZE-HF, 5 Enhanced Feedback for Effective Cardiac Treatment (EFFECT), and Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic Heart Failure (OPTIME-CHF) models 6,7 have been elucidated and the complexity and limitations of each model have been briefly mentioned. 8 In conclusion the GWTG risk score is economical can be widely applicable, and it is readily available at the time of admission, with incorporate of limited variables and to triage, and to risk stratification which helps in implementing effective treatment strategies especially for the countries like India where resources are limited. We can divide the patients in to high-, moderate-, and low-risk groups and develop diagnostic and treatment strategies for better outcomes.…”