• Silvia Solà-Muñoz, Morales Jorge, Xavier Jiménez-Fàbrega, Sonia Jiménez-Delgado, Youcef Azeli, J Ramon Marsal, Sara Jordán, Josepa Mauri, and Javier Jacob.
• Sistema d'Emergències Mèdiques de Catalunya, Catalonia, Spain. silviasola@gencat.cat.
• Intern Emerg Med. 2023 Aug 1; 18 (5): 131713271317-1327.

ObjectiveThe objective of this study was to develop and validate a risk scale (MARIACHI) for patients classified as non-ST-segment elevation acute coronary syndrome (NSTEACS) in a prehospital setting with the ability to identify patients at an increased risk of mortality at an early stage.MethodsA retrospective observational study conducted in Catalonia over two periods: 2015-2017 (development and internal validation cohort) and Aug 2018-Jan 2019 (external validation cohort). We included patients classified as prehospital NSTEACS, assisted by an advanced life support unit and requiring hospital admission. The primary outcome was in-hospital mortality. Cohorts were compared using logistic regression and a predictive model was created using bootstrapping techniques.ResultsThe development and internal validation cohort included 519 patients. The model is composed of five variables associated with hospital mortality: age, systolic blood pressure, heart rate > 95 bpm, Killip-Kimball III-IV and ST depression ≥ 0.5 mm. The model showed good overall performance (Brier = 0.043) and consistency in discrimination (AUC 0.88, 95% CI 0.83-0.92) and calibration (slope = 0.91; 95% CI 0.89-0.93). We included 1316 patients for the external validation sample. There was no difference in discrimination (AUC 0.83, 95% CI 0.78-0.87; DeLong Test p = 0.071), but there was in calibration (p < 0.001), so it was recalibrated. The finally model obtained was stratified and scored into three groups according to the predicted risk of patient in-hospital mortality: low risk: < 1% (-8 to 0 points), moderate risk: 1-5% (+ 1 to + 5 points) and high risk: > 5% (6-12 points).ConclusionThe MARIACHI scale showed correct discrimination and calibration to predict high-risk NSTEACS. Identification of high-risk patients may help with treatment and low referral decisions at the prehospital level.© 2023. The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).

14 keywords...

hide…