Nomograms based on pre-operative parametric for prediction of short-term mortality in acute myocardial infarction patients treated invasively

Wang, Qingjie; Qian, Wei; Sun, Zhi-Qin; Zhu, Wenwu; Liu, Yu; Chen, Xin; Ji, Yuan

doi:10.18632/aging.202230

Cited by 5 publications

(5 citation statements)

References 24 publications

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“… 35 In our previous studies, we have also found that a lower systolic blood pressure level at admission and a low level of free triiodothyronine are independently associated with the short-term outcomes in patients with AMI. 36–38 In the current study, hypotension and free triiodothyronine were not associated with the risk of VA. Therefore, more attention should be paid to blood glucose and renal function in patients with AMI.…”

Section: Discussionmentioning

confidence: 38%

A New Scoring System for Predicting Ventricular Arrhythmia Risk in Patients with Acute Myocardial Infarction

Han¹,

Wang²,

Zhu³

et al. 2023

CIA

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Objective In this study, a risk score for ventricular arrhythmias (VA) were evaluated for predicting the risk of ventricular arrhythmia (VA) of acute myocardial infarction (AMI) patients. Methods Patients with AMI were divided into two sets according to whether VA occurred during hospitalization. Another cohort was enrolled for external validation. The area under the curve (AUC) of receiver operating characteristic (ROC) was calculated to evaluate the accuracy of the model. Results A total of 1493 eligible patients with AMI were enrolled as the training set, of whom 70 (4.7%) developed VA during hospitalization. In-hospital mortality was significantly higher in the VA set than in the non-VA set (31.4% vs 2.7%, P=0.001). The independent predictors of VA in patients with AMI including Killip grade ≥3, STEMI patients, LVEF <50%, frequent premature ventricular beats, serum potassium <3.5 mmol/L, type 2 diabetes, and creatinine level. The AUC of the model for predicting VT/VF in the training set was 0.815 (95% CI: 0.763–0.866). A total of 1149 cases were enrolled from Xuzhou Center Hospital as the external validation set. The AUC of the model in the external validation set for predicting VT/VF was 0.755 (95% CI: 0.687–0.823). Calibration curves indicated a good consistency between the predicted and the observed probabilities of VA in both sets. Conclusion We have established a clinical prediction risk score for predicting the occurrence of VA in AMI patients. The prediction score is easy to use, performs well and can be used to guide clinical practice.

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Section: Discussionmentioning

confidence: 38%

A New Scoring System for Predicting Ventricular Arrhythmia Risk in Patients with Acute Myocardial Infarction

Han¹,

Wang²,

Zhu³

et al. 2023

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“…In our predictive task, the model that took all available patient characteristics represented by the proposed patient representation method as inputs showed a higher performance than other models on the same task in previous studies (AUROC, 0.973 vs 0.905 to 0.935 [19,[29][30][31]48]). This may be because the embedding representation contained a large number of diverse features extracted from a general EMR system, while many researchers selected AMI-related features with the assistance of clinical experts.…”

Section: Principal Findingsmentioning

confidence: 74%

“…Mortality risk prediction for AMI patients plays a crucial role in clinical work, helping doctors identify potential clinical factors, take early intervention measures based on timely alerts of patients’ adverse health statuses, and reduce the burdensome expenditure of related health care expenses. Therefore, researchers [ 19 , 29 - 31 ] have focused on building machine learning models for the outcome prediction of AMI patients, and most of them used specific clinical features, such as laboratory test results (eg, albumin), comorbidities (eg, diabetes), and demographic data (eg, gender).…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study

Huang¹,

Zheng²,

Ma³

et al. 2022

J Med Internet Res

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Background The widespread secondary use of electronic medical records (EMRs) promotes health care quality improvement. Representation learning that can automatically extract hidden information from EMR data has gained increasing attention. Objective We aimed to propose a patient representation with more feature associations and task-specific feature importance to improve the outcome prediction performance for inpatients with acute myocardial infarction (AMI). Methods Medical concepts, including patients’ age, gender, disease diagnoses, laboratory tests, structured radiological features, procedures, and medications, were first embedded into real-value vectors using the improved skip-gram algorithm, where concepts in the context windows were selected by feature association strengths measured by association rule confidence. Then, each patient was represented as the sum of the feature embeddings weighted by the task-specific feature importance, which was applied to facilitate predictive model prediction from global and local perspectives. We finally applied the proposed patient representation into mortality risk prediction for 3010 and 1671 AMI inpatients from a public data set and a private data set, respectively, and compared it with several reference representation methods in terms of the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score. Results Compared with the reference methods, the proposed embedding-based representation showed consistently superior predictive performance on the 2 data sets, achieving mean AUROCs of 0.878 and 0.973, AUPRCs of 0.220 and 0.505, and F1-scores of 0.376 and 0.674 for the public and private data sets, respectively, while the greatest AUROCs, AUPRCs, and F1-scores among the reference methods were 0.847 and 0.939, 0.196 and 0.283, and 0.344 and 0.361 for the public and private data sets, respectively. Feature importance integrated in patient representation reflected features that were also critical in prediction tasks and clinical practice. Conclusions The introduction of feature associations and feature importance facilitated an effective patient representation and contributed to prediction performance improvement and model interpretation.

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“…First, the NRI was calculated in the training cohort. Referring to reported studies [ 18 , 33 ], we used 10% and 30% as thresholds to define risk classes for low-risk (< 10%), intermediate risk (10%-30%), and high-risk (> 30%) patients, and compared with the LR1 model (< 10% for low risk, 10%-30% for intermediate risk, and 30% for highest risk), the LR2 model predicted an NRI of 8.92% for in-hospital all-cause mortality (Fig. 6 A).…”

Section: Resultsmentioning

confidence: 99%

Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients

Cai,

Chen,

et al. 2024

BMC Cardiovasc Disord

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Background The purpose of this study was to develop a Nomogram model to identify the risk of all-cause mortality during hospitalization in patients with heart failure (HF). Methods HF patients who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV databases were included. The primary outcome was the occurrence of all-cause mortality during hospitalization. Two Logistic Regression models (LR1 and LR2) were developed to predict in-hospital death for HF patients from the MIMIC-IV database. The MIMIC-III database were used for model validation. The area under the receiver operating characteristic curve (AUC) was used to compare the discrimination of each model. Calibration curve was used to assess the fit of each developed models. Decision curve analysis (DCA) was used to estimate the net benefit of the predictive model. Results A total of 16,908 HF patients were finally enrolled through screening, of whom 2,283 (13.5%) presented with in-hospital death. Totally, 48 variables were included and analyzed in the univariate and multifactorial regression analysis. The AUCs for the LR1 and LR2 models in the test cohort were 0.751 (95% CI: 0.735∼0.767) and 0.766 (95% CI: 0.751–0.781), respectively. Both LR models performed well in the calibration curve and DCA process. Nomogram and online risk assessment system were used as visualization of predictive models. Conclusion A new risk prediction tool and an online risk assessment system were developed to predict mortality in HF patients, which performed well and might be used to guide clinical practice.

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Nomograms based on pre-operative parametric for prediction of short-term mortality in acute myocardial infarction patients treated invasively

Cited by 5 publications

References 24 publications

A New Scoring System for Predicting Ventricular Arrhythmia Risk in Patients with Acute Myocardial Infarction

A New Scoring System for Predicting Ventricular Arrhythmia Risk in Patients with Acute Myocardial Infarction

Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study

Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients

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