Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Abstract

Background Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI). Methods This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981–0.994, P = 0.003) and 0.990 (95% CI: 0.987–0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit. Conclusions A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

Development of a nomogram for the prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary percutaneous coronary intervention: a multicentre, retrospective, observational study in Hebei province, China

“…Another nomogram based on other variables (left main CAD, grading of thrombus, TIMI classification, slow flow, use of IABP, use of β-blocker, use of ACEI/ARB, symptom-to-door time, symptom-to-balloon time, syntax score, LVEF, and CK-MB peak) also showed a high AUC for in-hospital mortality of patients with STEMI after PCI. 61 Three main reasons fame justify the different predictors we found in our study: different research methods, the hospitals and time nodes that included patients are different and different statistical methods. Nevertheless, we are planning to combine the two parts of patients to get a more accurate risk model of in-hospital mortality.…”

“…The results indicate that the nomogram had good discrimination, well prediction accuracy and could achieve satisfactory net benefit. Another nomogram based on other variables (left main CAD, grading of thrombus, TIMI classification, slow flow, use of IABP, use of β-blocker, use of ACEI/ARB, symptom-to-door time, symptom-to-balloon time, syntax score, LVEF, and CK-MB peak) also showed a high AUC for in-hospital mortality of patients with STEMI after PCI 61. Three main reasons fame justify the different predictors we found in our study: different research methods, the hospitals and time nodes that included patients are different and different statistical methods.…”

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…We identified 59 studies for the topic of in-hospital mortality. 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 While the majority of studies used a retrospective cohort design, 11 used a prospective approach, 120 122 129 140 143 166 169 170 172 174 175 and two were meta-analysis studies. 154 …”

Association between total ischemic time and in-hospital mortality after emergency PCI in patients with acute ST-segment elevation myocardial infarction: a retrospective study

“…Categorical variables are presented using n (%) and were compared using the chi-square test. According to a previous study by the authors’ group, in addition to SBT, risk factors for mortality after emergency PCI in acute STEMI patients are sex, Killip grade, LM lesion, TIMI classification, symptom onset to first-medical-contact, Syntax score, WBC, CK-MB peak, use of β-blockers and ACEI/ARB after surgery, BMI, EF, and LDL [ 13 ]. AKI was an independent prognostic factor for long-term mortality among patients with STEMI complicated by cardiogenic shock (CS) and treated with primary percutaneous coronary intervention [ 26 ].…”

“…Emergency primary percutaneous coronary intervention (PPCI) is considered as the first-line treatment for patients with acute ST-segment elevation myocardial infarction (STEMI) [ 6 – 8 ], and there is increasing evidence that emergency PCI can improve the outcomes of patients with AMI [ 9 – 12 ]. Still, many patients do not benefit from PCI, and the factors of poor prognosis include sex, thrombolysis in myocardial infarction (TIMI) classification, slow flow, infarct size, microvascular obstruction, intra-aortic balloon pump (IABP), use of β-blockers, use of angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor blockers (ARB), symptom-to-door time (SDT), symptom-to-balloon time (SBT), ejection fraction (EF) [ 13 – 15 ].…”