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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection.

Andrea Ciarmiello1, Francesca Tutino1, Elisabetta Giovannini1

  • 1Nuclear Medicine Unit, Ospedale Civile Sant'Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy.

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|November 25, 2023
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Summary

A machine learning model using demographics, CT scans, and blood tests can predict severe outcomes in patients with SARS-CoV-2 (COVID-19). This tool helps identify high-risk individuals early for better critical care management.

Keywords:
CTSARS-CoV-2machine learningradiomicssurvival

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Area of Science:

  • Medical imaging and artificial intelligence
  • Infectious disease research
  • Computational pathology

Background:

  • The COVID-19 pandemic caused by SARS-CoV-2 has led to significant morbidity and mortality.
  • Predicting critical outcomes in SARS-CoV-2 patients is crucial for timely intervention and resource allocation.
  • Existing prediction models often lack comprehensive data integration.

Purpose of the Study:

  • To develop and evaluate a machine learning model for predicting critical outcomes in SARS-CoV-2 patients.
  • To assess the predictive performance of integrating demographic variables, blood tests, comorbidities, and CT-based radiomic features.
  • To identify key predictors of severe illness and mortality in SARS-CoV-2 infection.

Main Methods:

  • Retrospective analysis of 694 SARS-CoV-2 positive patients.
  • Data collection included demographics, blood tests (CRP, LDH, D-dimer), comorbidities, and CT-derived radiomic features.
  • A machine learning model was developed using LASSO Cox regression and evaluated on a separate test set.
  • Model performance was assessed using C-statistic and Brier scores.

Main Results:

  • The study identified D-dimer levels, age, and cardiovascular disease as significant risk factors for critical outcomes.
  • The developed machine learning model accurately classified 90% of non-survivors as high-risk in the test dataset.
  • High-risk patients identified by the model had a significantly shorter median survival (9 days) compared to low-risk patients (p < 0.001).

Conclusions:

  • A machine learning model integrating readily available clinical data (demographics, CT-radiomics, comorbidities, biomarkers) can effectively predict critical illness and mortality in SARS-CoV-2 patients.
  • This predictive model can aid clinicians in early risk stratification and management of severe COVID-19 cases.
  • The findings highlight the potential of multimodal data integration for improving prognostic accuracy in infectious diseases.