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Predictors of Adverse Outcome in High-Risk Percutaneous Coronary Interventions Patients
Ido Peles1, Orit Barrett2, Carlos Cafri3
1Clinical Research Center, Soroka University Medical Center, Beer-Sheva, Israel; Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Joyce and Irving Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Insights
Identifying high-risk percutaneous coronary intervention (PCI) patients is crucial. Key predictors of adverse events include advanced heart failure symptoms, low blood pressure, anemia, and kidney dysfunction, aiding risk stratification.
Area of Science:
- Cardiology
- Interventional Cardiology
- Clinical Risk Stratification
Background:
- Identifying patients at high risk for percutaneous coronary intervention (PCI) is critical for optimizing outcomes.
- Adverse events following high-risk PCI can significantly impact patient morbidity and mortality.
Purpose of the Study:
- To evaluate predictors of adverse events in patients undergoing high-risk PCI.
- To develop a predictive score for 30-day major adverse cardiac events (MACE) and all-cause mortality.
Main Methods:
- Retrospective analysis of 1890 high-risk PCI patients from 2005-2018.
- Inclusion criteria: unprotected left main disease, last patent coronary vessel, or 3-vessel disease with low ejection fraction.
- Development of a predictive score for 30-day MACE (myocardial infarction, death, target-vessel revascularization).
Main Results:
- 30-day mortality was 8.8%, and 1-year mortality was 20.7%.
- 30-day MACE rate was 14.2%, and 1-year MACE rate was 33.5%.
- Key predictors of short-term MACE included NYHA class 4, low systolic blood pressure, elevated creatinine, anemia, pulmonary hypertension, atrial fibrillation, and specific coronary lesion types.
Conclusions:
- Specific clinical features like left main disease, pulmonary hypertension, atrial fibrillation, anemia, renal failure, low systolic blood pressure, and NYHA class 4 are vital for risk stratification.
- These factors aid in identifying high-risk PCI patients and guiding further treatment decisions.
- The developed predictive score demonstrated high accuracy in predicting MACE and mortality.
Background:
Identifying high-risk percutaneous coronary intervention (PCI) patients is challenging. We aimed to evaluate which high-risk patients are prone to adverse events.
Methods:
We performed a retrospective study including consecutive high-risk PCIs from 2005 to 2018 in a large tertiary medical centre. Patients with unprotected left main (LM) disease, last patent coronary vessel, or 3-vessel coronary artery disease with left ventricular ejection fraction < 35% were included. A predictive 30-day major adverse cardiac events (MACE) score consisting of any myocardial infarction, all-cause death, or target-vessel revascularisation was constructed.
Results:
From 2005 to 2018, a total of 1890 patients who underwent PCI met the predefined high-risk PCI criteria. Mortality rate was 8.8% at 30 days and 20.7% at 1 year, and 30-day MACE rate was 14.2% and 33.5% at 1 year. Predictors of short-term MACE were New York Heart Association functional class (NYHA) 4 (hazard ratio [HR] 6.65; P < 0.001), systolic blood pressure (SBP) < 90 mm Hg (HR 4.93; P < 0.001), creatinine > 1.3 mg/dL (HR 3.57; P < 0.001), hemoglobin < 11.0 g/dL (HR 3.07; P < 0.001), pulmonary artery systolic pressure > 50 mm Hg (HR 2.06; P < 0.001), atrial fibrillation (HR 1.74; P < 0.001), and LM disease (HR 2.04; P < 0.001) or last patent vessel (HR 1.70; P = 0.002). A score constructed from these parameters reached a sensitivity of 90% and a specificity of 81% with areas under the receiver operating characteristic curve of 0.92 for MACE and 0.94 with 89% sensitivity and 87% specificity for all-cause mortality.
Conclusions:
Specific features such as LM lesion or last patent conduit, pulmonary hypertension, atrial fibrillation, anemia, and renal failure, along with low SBP and NYHA 4, aid risk stratification and consideration of further treatment measures.
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