Related Experiment Video
Updated: Sep 22, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Development and Validation of Predictive Model-HASBLAD Score-For Major Adverse Cardiovascular Events During
Menglin Zhao1, Zhi Shang1, Jiageng Cai1
1Department of Cardiology, Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Insights
New bedside tools, the nomogram and HASBLAD score, effectively predict major adverse cardiovascular events (MACEs) in Chinese patients undergoing non-cardiac surgery. These tools offer improved accuracy over existing methods for perioperative risk assessment.
Area of Science:
- Cardiology
- Anesthesiology
- Medical Informatics
Background:
- Major adverse cardiovascular events (MACEs) are a leading cause of morbidity and mortality in the perioperative period of non-cardiac surgery.
- Existing MACE prediction models lack validation in the Chinese population and are outdated for current clinical needs.
- There is a critical need for accurate, accessible tools to predict MACEs in Chinese patients undergoing non-cardiac surgery.
Purpose of the Study:
- To develop and validate simple bedside tools for predicting MACEs in Chinese patients during the perioperative period of non-cardiac surgery.
- To establish a nomogram and a risk score for MACE prediction.
- To compare the performance of the new tools against the Revised Cardiac Risk Index (RCRI).
Main Methods:
- A nested case-control study design was employed.
- Logistic regression analysis was used to develop a nomogram and a risk score (HASBLAD) based on seven predictors: Hemoglobin, CARDIAC diseases, AST, high Blood pressure, Leukocyte count, general Anesthesia, and Diabetes mellitus.
- Internal validation was performed using a separate cohort to assess discrimination and calibration, comparing against the RCRI.
Main Results:
- The nomogram and HASBLAD score demonstrated satisfactory prediction performance in both training and validation sets (C-statistics 0.781/0.768 and 0.865/0.843, respectively).
- Both models showed good calibration for predicting MACE probability.
- The nomogram and HASBLAD score exhibited superior discrimination compared to the RCRI in the validation set (P < 0.05).
Conclusions:
- The developed nomogram and HASBLAD score are effective bedside tools for predicting perioperative MACEs in Chinese patients undergoing non-cardiac surgery.
- These tools offer improved accuracy and clinical utility compared to the current RCRI.
- Implementation of these tools can aid in better risk stratification and patient management.
Background:
Major adverse cardiovascular events (MACEs) represent a significant reason of morbidity and mortality in non-cardiac surgery during perioperative period. The prevention of perioperative MACEs has always been one of the hotspots in the research field. Current existing models have not been validated in Chinese population, and have become increasingly unable to adapt to current clinical needs.
Objectives:
To establish and validate several simple bedside tools for predicting MACEs during perioperative period of non-cardiac surgery in Chinese hospitalized patients.
Design:
We used a nested case-control study to establish our prediction models. A nomogram along with a risk score were developed using logistic regression analysis. An internal cohort was used to evaluate the performance of discrimination and calibration of these predictive models including the revised cardiac risk index (RCRI) score recommended by current guidelines.
Setting:
Peking University Third Hospital between January 2010 and December 2020.
Patients:
Two hundred and fifty three patients with MACEs and 1,012 patients without were included in the training set from January 2010 to December 2019 while 38,897 patients were included in the validation set from January 2020 and December 2020, of whom 112 patients had MACEs.
Main Outcome Measures:
The MACEs included the composite outcomes of cardiac death, non-fatal myocardial infarction, non-fatal congestive cardiac failure or hemodynamically significant ventricular arrhythmia, and Takotsubo cardiomyopathy.
Results:
Seven predictors, including Hemoglobin, CARDIAC diseases, Aspartate aminotransferase (AST), high Blood pressure, Leukocyte count, general Anesthesia, and Diabetes mellitus (HASBLAD), were selected in the final model. The nomogram and HASBLAD score all achieved satisfactory prediction performance in the training set (C statistic, 0.781 vs. 0.768) and the validation set (C statistic, 0.865 vs. 0.843). Good calibration was observed for the probability of MACEs in the training set and the validation set. The two predictive models both had excellent discrimination that performed better than RCRI in the validation set (C statistic, 0.660, P < 0.05 vs. nomogram and HASBLAD score).
Conclusion:
The nomogram and HASBLAD score could be useful bedside tools for predicting perioperative MACEs of non-cardiac surgery in Chinese hospitalized patients.
More Related Videos
05:16Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020