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Using State Data to Predict a Single Institution Mortality for Patients That Fall.

Andrew Joseph Young1, Elinore Kaufman2, Allison Hare3

  • 1Division of Traumatology, Emergency Surgery, and Surgical Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Division of Trauma, Critical Care, and Burn, The Ohio State University, Columbus, Ohio.

The Journal of Surgical Research
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Summary

Machine learning accurately predicts mortality in fall patients over 45. An extreme gradient boost model using state trauma data can identify high-risk individuals for targeted care.

Keywords:
Artificial intelligenceMachine learningTrauma fall

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Area of Science:

  • Trauma surgery
  • Data science in healthcare
  • Predictive analytics

Background:

  • Falls are a leading cause of death in adults over 45.
  • Developing accurate predictive models for fall-related injuries is crucial for patient outcomes.
  • State-level registry data can potentially be leveraged for localized prediction models.

Purpose of the Study:

  • To hypothesize and test if a machine learning algorithm can accurately predict mortality in patients admitted for fall injuries.
  • To develop and validate a predictive model using state-wide trauma registry data for a level 1 trauma hospital.
  • To assess the performance of different machine learning algorithms in predicting fall-related mortality.

Main Methods:

  • Utilized Pennsylvania state trauma registry data from 2009-2019 for patients admitted with fall injuries.
  • Selected thirteen variables available upon patient arrival for prediction modeling.
  • Compared logistic regression (LR), random forest (RF), and extreme gradient boost (XGB) algorithms, withholding test institution data from model creation.

Main Results:

  • Included 180,284 patients with a mean age of 69 years and a 4.0% mortality rate.
  • Extreme gradient boost (XGB) demonstrated the highest predictive accuracy for mortality with an AUC of 0.880.
  • Key predictors for mortality included respiratory rate, pulse, systolic blood pressure, and Glasgow Coma Scale (GCS) components.

Conclusions:

  • An extreme gradient boost model trained on state-wide trauma data accurately predicts mortality in fall patients at a single center.
  • This machine learning model can aid local trauma systems in Pennsylvania to identify high-risk fall patients.
  • Implementation can lead to improved patient care through targeted attention, transfer decisions, and resource allocation.