Ann Lab Med.  2025 Mar;45(2):209-217. 10.3343/alm.2024.0315.

A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis

• Park H
• Park CS

• KMID: 2565652
• DOI: http://doi.org/10.3343/alm.2024.0315

Abstract

Background
Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients’ risk profiles. We aimed to improve IHCA predictions by combining vital sign indicators with laboratory test results and, optionally, International Classification of Disease-10 block for diagnosis (ICD10BD).
Methods
We conducted a retrospective cohort study in the general ward (GW) and intensive care unit (ICU) of a 680-bed secondary healthcare institution. We included 62,061 adults admitted to the Department of Internal Medicine from January 2010 to August 2022. IHCAs were identified based on cardiopulmonary resuscitation prescriptions. Patient-days within three days preceding IHCAs were labeled as case days; all others were control days. The eXtreme Gradient Boosting (XGBoost) model was trained using daily vital signs, 14 laboratory test results, and ICD10BD.
Results
In the GW, among 1,299,448 patient-days from 62,038 patients, 1,367 days linked to 713 patients were cases. In the ICU, among 117,190 patient-days from 16,881 patients, 1,119 days from 444 patients were cases. The area under the ROC curve for IHCA prediction model was 0.934 and 0.896 in the GW and ICU, respectively, using the combination of vital signs, laboratory test results, and ICD10BD; 0.925 and 0.878, respectively, with vital signs and laboratory test results; and 0.839 and 0.828, respectively, with only vital signs.
Conclusions
Incorporating laboratory test results or combining laboratory test results and ICD10BD with vital signs as predictor variables in the XGBoost model potentially enhances clinical decision-making and improves patient outcomes in hospital settings.

Keyword

Cardiac arrest; Diagnosis; Hospital; International Classification of Disease; Machine learning; Prediction

Figure

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  Fig. 1 Assignment of patient-days to case and control groups. IHCA events were assigned using CPR codes in prescription records, categorizing 1–3 days before CPR as cases and ≥4 days before cardiopulmonary resuscitation or all days for patients without CPR as controls. Days of and after CPR were excluded from the modeling dataset. Abbreviations: IHCA, in-hospital cardiac arrest; CPR, cardiopulmonary resuscitation.

• 

  Fig. 2 Flowchart of data compilation. Patient-day was used as the unit of analysis.

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  Fig. 3 ROC curves for three feature sets derived from the three-stage expansion of variable groups in (A) the general ward and (B) the intensive care unit. Abbreviations: LR, logistic regression; XGBoost, eXtreme Gradient Boosting; FS1, feature set 1; FS2, feature set 2; FS3, feature set 3.

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  Fig. 4 Top 10 predictor variables identified by the XGBoost algorithm. The predictor variables are categorized by the three stages of variable group expansion in (A) the general ward and (B) the intensive care unit. (A) K90-K93, other diseases of the digestive system; R10-R19, symptoms and signs involving the digestive system and abdomen; I30-I52, other forms of heart disease; M86-M90, other osteopathies; and G40-G47, episodic and paroxysmal disorders. (B) I60-I69, cerebrovascular diseases; F99-F99, unspecified mental disorders; C15-C26, malignant neoplasms of the digestive organs; R00-R09, symptoms and signs involving the circulatory and respiratory systems; K65-K67, diseases of the peritoneum; K55-K64, other diseases of the intestines; and I30-I52, other forms of heart disease. Abbreviation: XGBoost, eXtreme Gradient Boosting; FS1, feature set 1; FS2, feature set 2; FS3, feature set 3; RR, respiration rate; PR, pulse rate; DBP, diastolic blood pressure; MBP, mean blood pressure; BMI, body mass index; BT, body temperature; WBC, white blood cell; SBP, systolic blood pressure.

Reference

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