NDLI: A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

Content Provider	Springer Nature : BioMed Central
Author	Wu, Ting Ting Zheng, Ruo Fei Lin, Zhi Zhong Gong, Hai Rong Li, Hong
Abstract	Background Currently, the risk stratification of critically ill patient with chest pain is a challenge. We aimed to use machine learning approach to predict the critical care outcomes in patients with chest pain, and simultaneously compare its performance with HEART, GRACE, and TIMI scores. Methods This was a retrospective, case-control study in patients with acute non-traumatic chest pain who presented to the emergency department (ED) between January 2017 and December 2019. The outcomes included cardiac arrest, transfer to ICU, and death during treatment in ED. In the randomly sampled training set (70%), a LASSO regression model was developed, and presented with nomogram. The performance was measured in both training set (70% participants) and testing set (30% participants), and findings were compared with the three widely used scores. Results We proposed a LASSO regression model incorporating mode of arrival, reperfusion therapy, Killip class, systolic BP, serum creatinine, creatine kinase-MB, and brain natriuretic peptide as independent predictors of critical care outcomes in patients with chest pain. Our model significantly outperformed the HEART, GRACE, TIMI score with AUC of 0.953 (95%CI: 0.922–0.984), 0.754 (95%CI: 0.675–0.832), 0.747 (95%CI: 0.664–0.829), 0.735 (95%CI: 0.655–0.815), respectively. Consistently, our model demonstrated better outcomes regarding the metrics of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Similarly, the decision curve analysis elucidated a greater net benefit of our model over the full ranges of clinical thresholds. Conclusion We present an accurate model for predicting the critical care outcomes in patients with chest pain, and provide substantial support to its application as a decision-making tool in ED.
Related Links	https://bmcemergmed.biomedcentral.com/counter/pdf/10.1186/s12873-021-00501-8.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s12873-021-00501-8
Journal	BMC Emergency Medicine
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2021-10-07
Access Restriction	Open
Subject Keyword	Emergency Medicine Medicine Public Health Machine learning LASSO regression Chest pain Critical care outcome Prediction model Emergency department Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Emergency Medicine
Journal Impact Factor	2.3/2023
5-Year Journal Impact Factor	2.5/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Factors associated with unfavorable outcomes in patients with acute abdominal pain visiting the emergency department

Acute fatal chest pain: optimized procedure in emergency department

Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain

Emergency department triage prediction of clinical outcomes using machine learning models

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.

Investigation of anxiety levels of patients with chest pain admitted to emergency department.

Factors associated with the door-to-electrocardiogram time in patients with acute myocardial infarction

Clinical Risk Stratification in the Emergency Department Predicts Long-Term Cardiovascular Outcomes in a Population-Based Cohort Presenting With Acute Chest Pain

A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain

A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

Similar Documents

Factors associated with unfavorable outcomes in patients with acute abdominal pain visiting the emergency department

Acute fatal chest pain: optimized procedure in emergency department

Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain

Emergency department triage prediction of clinical outcomes using machine learning models

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.

Investigation of anxiety levels of patients with chest pain admitted to emergency department.

Factors associated with the door-to-electrocardiogram time in patients with acute myocardial infarction

Clinical Risk Stratification in the Emergency Department Predicts Long-Term Cardiovascular Outcomes in a Population-Based Cohort Presenting With Acute Chest Pain

A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain

A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department