Infection during hospitalization is a serious complication among patients who suffered from acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI); however, there are no suitable and accurate means to assess risk. This study aimed to develop and validate a simple scoring system to predict post-AMI infection in such patients.
All patients with ST-segment elevation myocardial infarction (STEMI) undergoing PCI consecutively enrolled from January 2010 to May 2016 were served as derivation cohort, and those from June 2016 to May 2018 as validation cohort, respectively. The primary endpoint was post-AMI infection during hospitalization, and all-cause death and major adverse cardiovascular events (MACE) were considered as secondary endpoints. The simplified risk model was established using logistic regression. The area under the receiver operating curve and calibration of predicted and observed infection risk were calculated.
A 24-point risk score was developed, with infection risk ranging from 0.7 to 99.6% for patients with the lowest and highest score. Seven variables including age, Killip classification, insulin use, white blood cell count, serum albumin, diuretic use, and transfemoral approach were included. This model achieved the same high discrimination in the development and validation cohort (C-statistic:0.851) and revealed adequate calibration in both datasets. The incidences of post-AMI infection increased steadily across risk score groups in both development (1.3, 5.1, 26.3, and 69.1%;
This present risk score established a simple bedside tool to estimate the risk of developing infection and other in-hospital outcomes in patients with STEMI undergoing PCI. Clinicians can use this risk score to evaluate the infection risk and subsequently make evidence-based decisions.