Detection of patients with COVID-19 by the emergency medical services in Lombardy through an operator-based interview and machine learning models.

Abstract

BACKGROUND:

The regional emergency medical service (EMS) in Lombardy (Italy) developed clinical algorithms based on operator-based interviews to detect patients with COVID-19 and refer them to the most appropriate hospitals. Machine learning (ML)-based models using additional clinical and geospatial epidemiological data may improve the identification of infected patients and guide EMS in detecting COVID-19 cases before confirmation with SARS-CoV-2 reverse transcriptase PCR (rtPCR).

METHODS:

This was an observational, retrospective cohort study using data from October 2020 to July 2021 (training set) and October 2021 to December 2021 (validation set) from patients who underwent a SARS-CoV-2 rtPCR test within 7 days of an EMS call. The performance of an operator-based interview using close contact history and signs/symptoms of COVID-19 was assessed in the training set for its ability to determine which patients had an rtPCR in the 7 days before or after the call. The interview accuracy was compared with four supervised ML models to predict positivity for SARS-CoV-2 within 7 days using readily available prehospital data retrieved from both training and validation sets.

RESULTS:

The training set includes 264 976 patients, median age 74 (IQR 55-84). Test characteristics for the detection of COVID-19-positive patients of the operator-based interview were: sensitivity 85.5%, specificity 58.7%, positive predictive value (PPV) 37.5% and negative predictive value (NPV) 93.3%. Contact history, fever and cough showed the highest association with SARS-CoV-2 infection. In the validation set (103 336 patients, median age 73 (IQR 50-84)), the best-performing ML model had an AUC of 0.85 (95% CI 0.84 to 0.86), sensitivity 91.4% (95 CI% 0.91 to 0.92), specificity 44.2% (95% CI 0.44 to 0.45) and accuracy 85% (95% CI 0.84 to 0.85). PPV and NPV were 13.3% (95% CI 0.13 to 0.14) and 98.2% (95% CI 0.98 to 0.98), respectively. Contact history, fever, call geographical distribution and cough were the most important variables in determining the outcome.

CONCLUSION:

ML-based models might help EMS identify patients with SARS-CoV-2 infection, and in guiding EMS allocation of hospital resources based on prespecified criteria.

• Authors: Stefano Spina, Lorenzo Gianquintieri, Francesco Marrazzo, Maurizio Migliari, Giuseppe Maria Sechi, Maurizio Migliori, Andrea Pagliosa, Rodolfo Bonora, Thomas Langer, Enrico Gianluca Caiani, Roberto Fumagalli, AREU 118 EMS Network Collaborators
• Journal: Emergency medicine journal : EMJ (Emerg Med J) Vol. 40 Issue 12 Pg. 810-820 (Nov 28 2023) ISSN: 1472-0213 [Electronic] England
• PMID: 37775256 (Publication Type: Observational Study, Journal Article)
• Copyright: © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Topics

• Humans
• Aged
• COVID-19 (diagnosis, epidemiology)
• SARS-CoV-2
• Retrospective Studies
• Cough
• Sensitivity and Specificity
• Emergency Medical Services
• Machine Learning