Deep Learning Redefines Mortality Forecasting in Pneumonia Patients

Sophisticated deep learning-based model helps in accurately predicting 30-day mortality for patients diagnosed with community-acquired pneumonia (CAP).

By utilizing initial chest radiographs, a deep learning-based model demonstrated enhanced prediction of 30-day mortality in patients with community-acquired pneumonia (CAP) compared to the established risk prediction tool (CURB-65 score) ().

“The deep learning (DL) model may guide clinical decision-making in the management of patients with CAP by identifying high-risk patients who warrant hospitalization and intensive treatment,” concluded first author Eui Jin Hwang, MD, Ph.D., from the Department of Radiology at Seoul National University College of Medicine in Korea.

Decoding the Chest: Deep Learning's Triumph

In this AJR -accepted manuscript, a DL model was developed in 7,105 patients via one institution from March 2013 to December 2019 (3:1:1 allocation to training, validation, and internal test sets) to predict the risk of all-cause mortality within 30 days after CAP diagnosis using patients’ initial chest radiograph.

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TOP INSIGHT

Leveraging initial chest radiographs, a deep learning-based model surpassed the performance of the CURB-65 score, a well-known risk prediction tool, in accurately forecasting 30-day mortality among patients with community-acquired pneumonia (CAP). # Pneumonia, #Deep Learning, #Chest Radiograph

Hwang et al. then evaluated their DL model in patients diagnosed with CAP during emergency department visits at the same institution as the development cohort from January 2020 to December 2020 [temporal test cohort (n = 947)] and from two additional different institutions [external test cohort A (n = 467), January 2020 to December 2020; external test cohort B (n = 381), March 2019 to October 2021].

AUCs were compared between the DL model and a risk score based on confusion, blood urea nitrogen level, respiratory rate, blood pressure, and age ≥ 65 years.

Ultimately, a DL model using initial chest radiographs predicted 30-day all-cause mortality in patients with CAP with AUC ranging from 0.77 to 0.80 in test cohorts from different institutions.

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Additionally, the model showed higher specificity (range, 61–69%) than the CURB-65 score (44–58%) at the same sensitivity (all p < .001).

Reference:

A Deep-Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation - (https://www.ajronline.org/doi/10.2214/AJR.23.29414)

Source-Eurekalert

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