BACKGROUND: Early risk stratification is essential for in-hospital management of ST-segment-elevation myocardial infarction. Acute heart failure confers a worse prognosis, and although lung ultrasound (LUS) is recommended as a first-line test to assess pulmonary congestion, it has never been tested in this setting. Our aim was to evaluate the prognostic ability of admission LUS in patients with ST-segment-elevation myocardial infarction. METHODS: LUS protocol consisted of 8 scanning zones and was performed before primary percutaneous coronary intervention by an operator blinded to Killip classification. A LUS combined with Killip (LUCK) classification was developed. Receiver operating characteristic and net reclassification improvement analyses were performed to compare LUCK and Killip classifications. RESULTS: We prospectively investigated 215 patients admitted with ST-segment-elevation myocardial infarction between April 2018 and June 2019. Absence of pulmonary congestion detected by LUS implied a negative predictive value for in-hospital mortality of 98.1% (93.1-99.5%). The area under the receiver operating characteristic curve of the LUCK classification for in-hospital mortality was 0.89 (P=0.001), and of the Killip classification was 0.86 (P<0.001; P=0.05 for the difference between curves). LUCK classification improved Killip ability to predict in-hospital mortality with a net reclassification improvement of 0.18. CONCLUSIONS: In a cohort of patients with ST-segment-elevation myocardial infarction undergoing primary percutaneous coronary intervention, admission LUS added to Killip classification was more sensitive than physical examination to identify patients at risk for in-hospital mortality. LUCK classification had a greater area under the receiver operating characteristic curve and reclassified Killip classification in 18% of cases. Moreover, absence of pulmonary congestion on LUS provided an excellent negative predictive value for in-hospital mortality.
BACKGROUND: Early risk stratification is essential for in-hospital management of ST-segment-elevation myocardial infarction. Acute heart failure confers a worse prognosis, and although lung ultrasound (LUS) is recommended as a first-line test to assess pulmonary congestion, it has never been tested in this setting. Our aim was to evaluate the prognostic ability of admission LUS in patients with ST-segment-elevation myocardial infarction. METHODS: LUS protocol consisted of 8 scanning zones and was performed before primary percutaneous coronary intervention by an operator blinded to Killip classification. A LUS combined with Killip (LUCK) classification was developed. Receiver operating characteristic and net reclassification improvement analyses were performed to compare LUCK and Killip classifications. RESULTS: We prospectively investigated 215 patients admitted with ST-segment-elevation myocardial infarction between April 2018 and June 2019. Absence of pulmonary congestion detected by LUS implied a negative predictive value for in-hospital mortality of 98.1% (93.1-99.5%). The area under the receiver operating characteristic curve of the LUCK classification for in-hospital mortality was 0.89 (P=0.001), and of the Killip classification was 0.86 (P<0.001; P=0.05 for the difference between curves). LUCK classification improved Killip ability to predict in-hospital mortality with a net reclassification improvement of 0.18. CONCLUSIONS: In a cohort of patients with ST-segment-elevation myocardial infarction undergoing primary percutaneous coronary intervention, admission LUS added to Killip classification was more sensitive than physical examination to identify patients at risk for in-hospital mortality. LUCK classification had a greater area under the receiver operating characteristic curve and reclassified Killip classification in 18% of cases. Moreover, absence of pulmonary congestion on LUS provided an excellent negative predictive value for in-hospital mortality.
Authors: Brian J Douthit; Rachel L Walden; Kenrick Cato; Cynthia P Coviak; Christopher Cruz; Fabio D'Agostino; Thompson Forbes; Grace Gao; Theresa A Kapetanovic; Mikyoung A Lee; Lisiane Pruinelli; Mary A Schultz; Ann Wieben; Alvin D Jeffery Journal: Appl Clin Inform Date: 2022-02-09 Impact factor: 2.342
Authors: Diego Araiza-Garaygordobil; Luis A Baeza-Herrera; Rodrigo Gopar-Nieto; Fabio Solis-Jimenez; Alejandro Cabello-López; Pablo Martinez-Amezcua; Vianney Sarabia-Chao; Héctor González-Pacheco; Daniel Sierra-Lara Martinez; José Luis Briseño-De la Cruz; Alexandra Arias-Mendoza Journal: Front Physiol Date: 2022-05-10 Impact factor: 4.755
Authors: Jorge I Parras; Mariela Onocko; Liliana M Traviesa; Eva G Fernández; Pablo M Morel; Natalia G Cristaldo; María L Coronel; Stella M Macín; Eduardo R Perna Journal: Indian Heart J Date: 2020-11-24