Cardiac auscultation predicts mortality in elderly patients admitted for COVID-19

Roig-Marín, Noel; Roig-Rico, Pablo

doi:10.1080/21548331.2022.2069772

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…In our study, age, plasma creatinine, C-reactive protein, hemoglobin, and platelet values were used to develop the TensorFlow tabular model. They were chosen based on previous research findings 41 – 48 and their prompt availability in our emergency settings. However, other variables such as myoglobin, ferritin, and troponins 47 , 49 , 50 have also been found to affect COVID-19 prognosis.…”

Section: Discussionmentioning

confidence: 99%

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Dipaola

Gatti

Levra

et al. 2023

Sci Rep

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Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process.

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Section: Discussionmentioning

confidence: 99%

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Dipaola

Gatti

Levra

et al. 2023

Sci Rep

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show abstract

“…www.nature.com/scientificreports/ Numerous prognostic models have been developed for COVID-19, using both traditional statistics and ML. Blood parameters, like ferritin 22 , troponin, and myoglobin [23][24][25][26] , demographic parameters, including Charlson comorbidity index score 27 , age and gender 28,29 , chest CT images 30 , routine chest X-rays 31 , age-related dementia 32 , cardiac auscultation 33 and other factors are suggested to predict severity or death in patients with COVID-19. However, all these studies were aimed at a single-time assessment of patients' prognosis, close to admission.…”

Section: Discussionmentioning

confidence: 99%

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

Kogan

Robinson

Itelman

et al. 2022

Sci Rep

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Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.

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Ground-glass nodules in the lungs of COVID-19 patients

Roig-Marín

2024

Management, Body Systems, and Case Studies in COVID-19

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Cardiac auscultation predicts mortality in elderly patients admitted for COVID-19

Cited by 4 publications

References 47 publications

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

Ground-glass nodules in the lungs of COVID-19 patients

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