26-Simplified Paediatric Index of Mortality 3 (PIM3) Score by Explainable Machine Learning Algorithm
Back to course
Video Transcription
Video Summary
Asset Subtitle
Pediatrics, Quality and Patient Safety, 2021
Asset Caption

The Society of Critical Care Medicine's Critical Care Congress features internationally renowned faculty and content sessions highlighting the most up-to-date, evidence-based developments in critical care medicine. This is a presentation from the 2021 Critical Care Congress held virtually from January 31-February 12, 2021.


Orkun Baloglu, MD

Introduction/Hypothesis: PIM3 is a validated tool for assessment of mortality risk in pediatric intensive care unit (PICU). Pupillary exam findings, type of admission, mechanical ventilation in the first 24 hours of admission, base excess, systolic blood pressure (SBP), fraction of inspired oxygen, partial pressure of arterial oxygen, surgical recovery (yes/no), weighted diagnostic category are the input variables of PIM3. SHapley Additive exPlanations (SHAP) is a new method for explaining predictions of machine learning (ML) models by calculating how much each input variable contributes to the final output of a ML model. We aimed to create a simpler version of PIM3 with input variables identified by SHAP analysis.

Methods: Data from the Virtual Pediatric Systems for patients admitted to The Cleveland Clinic Children`s PICU between 2008 and 2019 was obtained. LightGBM (a gradient boosting decision tree algorithm) was used for building the ML models. Stratified 10-fold cross-validation was performed. Input variables with SHAP values ≥0.1 were retained and used in the subsequent ML model. Mortality within 3, 7, 14 and 30 days of PICU admission and overall PICU mortality were the output of the predictive models. AUROC values were compared for discriminatory performance of the models.

Results: 5068 patients were analyzed, overall mortality was 1.04%. SHAP analysis of the ML model with the original PIM3 variables revealed 4 variables (type of admission, mechanical ventilation in the first 24 hours of PICU admission, SBP and weighted diagnostic category) as the most important input variables contributing to prediction of the ML model. The final ML model with those 4 variables achieved similar AUROC values compared to the ML model with the original PIM3 variables (0.90 95%CI [0.83-0.98] vs 0.84 [0.68-1.00] p:0.48 for mortality<3d, 0.95 [0.92-0.98] vs 0.93 [0.87-0.98] p:0.46 for <7d, 0.93 [0.90-0.96] vs 0.92 [0.87-0.97] p:0.71 for <14d, 0.91 [0.87-0.94] vs 0.92 [0.90-0.95] p:0.44 for <30 d of PICU admission,0.90 [0.87-0.94] vs 0.91 [0.88-0.94] p:0.87 for overall PICU mortality).

Conclusions: By utilizing SHAP analysis, the original PIM3 variables were reduced to 4 variables and discriminatory performance of the ML model utilizing these 4 variables was non-inferior to the original PIM3 variables for PICU mortality prediction.


Meta Tag
Content Type Presentation
Knowledge Area Pediatrics
Knowledge Area Quality and Patient Safety
Knowledge Level Advanced
Membership Level Select
Tag Scoring Systems
Tag Mortality
Year 2021
Simplified Pediatric Index of Mortality Score
machine learning models
SHAP analysis
pediatric intensive care (PIC) steps
mechanical ventilation


Society of Critical Care Medicine

500 Midway Drive
Mount Prospect, IL 60056 USA

Phone: +1 847 827-6888
Fax: +1 847 439-7226
Email: support@sccm.org

Contact Us

About SCCM


Advertising & Sponsorship




Patients & Families

Surviving Sepsis Campaign

Critical Care Societies Collaborative

kisspng-facebook-social-media-computer-icons-linkedin-soci-gray-5ac493cf1c2975.7867418415228323351154  - KW Symphony    Gray twitter 3 icon - Free gray social icons    Gray linkedin 3 icon - Free gray site logo icons    Gray instagram icon - Free gray social icons    YouTube Icon Gray Box - HONOR VETERANS NOW


© Society of Critical Care Medicine. All rights reserved.   |    Privacy Statement    |    Terms & Conditions
The Society of Critical Care Medicine, SCCM, and Critical Care Congress are registered trademarks of the Society of Critical Care Medicine.

Android App Download IOS App Download Powered By