Translation, validation, predictive analysis of the Italian version of the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey and symptom cluster characterization in women

Background Nurses are the touch point for different aspects of patient care, particularly in identifying early prodromal symptoms of acute myocardial infarction in women. Although there is increasing awareness of women-specific symptoms like fatigue, anxiety, and sleep disturbances, acute myocardial infarction is frequently underdiagnosed or recognized too late in women. Instruments such as the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey have demonstrated effectiveness in identifying these prodromal symptoms. However, no validated Italian tool exists to assess prodromal symptoms and predict myocardial infarction; developing one would help nurses detect symptoms early and improve outcomes in women. Aim The study aims to validate an Italian version of the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey, and to evaluate the predictive ability of prodromal symptoms toward acute myocardial infarction in women, to secondarily test a potential cut-off score for easier implementation of the tool in screening contexts, and to identify prodromal symptom clusters in women with AMI. Materials A multicenter study was conducted from October 2023 to January 2025, including women with ischemic heart disease and healthy women. Both groups completed the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey , a structured questionnaire. Data analysis relied on test-re-test, regression models, and Receiver Operating Characteristic curve analysis to identify key prodromal symptoms of Acute Muocardial Infarction. Results The study involved 125 women with a mean age of 59.9 years. Women with acute myocardial infarction most commonly experienced chest pain, unusual fatigue, sleep disturbances, and anxiety. In contrast, healthy women reported fewer prodromal symptoms, with chest pain and unusual fatigue being less frequent. Reliability analysis showed excellent consistency in the tool (Cronbach’s α ≥0.90), with strong test-retest correlations (r = 0.973, p < 0.001). Logistic regression identified significant chest symptoms and general ones associated with acute myocardial infarction. Receiver Operating Characteristic curve analysis indicated excellent discriminative ability, defining a cut-off score with high sensitivity and specificity. Conclusions Results showed different identifying symptoms such as chest, back, neck/throat, and left arm pain, fatigue, shortness of breath, loss of appetite, arm weakness, and headache. The results suggest that it can be used as an effective screening tool to identify women at risk of acute myocardial infarction.