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Published on: April 25, 2014
BLEED-Myocardial Infarction Score: Predicting mid-term post-discharge bleeding events
Sérgio Barra1, Rui Providência, Francisca Caetano
1Sérgio Barra, Rui Providência, Francisca Caetano, Inês Almeida, Luís Paiva, Paulo Dinis, António Leitão Marques, Cardiology Department, Coimbra's Hospital and University Centre-General Hospital, 3041-801 S. Martinho do Bispo, Coimbra, Portugal.
Insights
A new BLEED-MI score predicts bleeding events after myocardial infarction (MI). This tool helps identify patients at high risk for bleeding, improving post-discharge care.
Area of Science:
- Cardiology
- Clinical Prediction Models
- Hemorrhagic Risk Assessment
Background:
- Post-myocardial infarction (MI) patients face significant risks of bleeding events after discharge.
- Accurate prediction of mid-term bleeding is crucial for optimizing patient management and preventing adverse outcomes.
Purpose of the Study:
- To derive and validate the BLEED-MI score for predicting mid-term bleeding events in patients post-MI.
- To assess the score's calibration, accuracy, and discrimination in independent cohorts.
Main Methods:
- A risk model (BLEED-MI) was developed using data from 1050 MI patients.
- The model incorporated clinical variables, laboratory values, and treatment data at admission and discharge.
- Validation was performed in an independent cohort of 852 MI patients.
Main Results:
- The BLEED-MI score demonstrated good calibration (P > 0.37) and accuracy (Brier score < 0.07) in both derivation and validation cohorts.
- Discriminative performance for bleeding events was high (c-statistic 0.718–0.753).
- A BLEED-MI score below 2 indicated a 98.7% negative predictive value for significant bleeding in the validation sample.
Conclusions:
- The BLEED-MI score is a novel, validated tool for predicting mid-term bleeding risk in post-MI patients.
- This score offers a bedside solution for identifying patients requiring closer monitoring or tailored therapeutic strategies.
- External validation in larger cohorts is recommended before widespread clinical implementation.
Aim:
To derive and validate a score for the prediction of mid-term bleeding events following discharge for myocardial infarction (MI).
Methods:
One thousand and fifty patients admitted for MI and followed for 19.9 ± 6.7 mo were assigned to a derivation cohort. A new risk model, called BLEED-MI, was developed for predicting clinically significant bleeding events during follow-up (primary endpoint) and a composite endpoint of significant hemorrhage plus all-cause mortality (secondary endpoint), incorporating the following variables: age, diabetes mellitus, arterial hypertension, smoking habits, blood urea nitrogen, glomerular filtration rate and hemoglobin at admission, history of stroke, bleeding during hospitalization or previous major bleeding, heart failure during hospitalization and anti-thrombotic therapies prescribed at discharge. The BLEED-MI model was tested for calibration, accuracy and discrimination in the derivation sample and in a new, independent, validation cohort comprising 852 patients admitted at a later date.
Results:
The BLEED-MI score showed good calibration in both derivation and validation samples (Hosmer-Lemeshow test P value 0.371 and 0.444, respectively) and high accuracy within each individual patient (Brier score 0.061 and 0.067, respectively). Its discriminative performance in predicting the primary outcome was relatively high (c-statistic of 0.753 ± 0.032 in the derivation cohort and 0.718 ± 0.033 in the validation sample). Incidence of primary/secondary endpoints increased progressively with increasing BLEED-MI scores. In the validation sample, a BLEED-MI score below 2 had a negative predictive value of 98.7% (152/154) for the occurrence of a clinically significant hemorrhagic episode during follow-up and for the composite endpoint of post-discharge hemorrhage plus all-cause mortality. An accurate prediction of bleeding events was shown independently of mortality, as BLEED-MI predicted bleeding with similar efficacy in patients who did not die during follow-up: Area Under the Curve 0.703, Hosmer-Lemeshow test P value 0.547, Brier score 0.060; low-risk (BLEED-MI score 0-3) event rate: 1.2%; intermediate risk (score 4-6) event rate: 5.6%; high risk (score ≥ 7) event rate: 12.5%.
Conclusion:
A new bedside prediction-scoring model for post-discharge mid-term bleeding has been derived and preliminarily validated. This is the first score designed to predict mid- term hemorrhagic risk in patients discharged following admission for acute MI. This model should be externally validated in larger cohorts of patients before its potential implementation.
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