Artificial neural network model from a case series of COVID-19 patients: a prognostic analysis.: Clinical presentation prognostic factors in COVID-19.

Artificial neural network model from a case series of COVID-19 patients: a prognostic analysis.

Clinical presentation prognostic factors in COVID-19.

Authors

  • Sergio Venturini Department of Infectious Diseases, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Daniele Orso Department of Medicine, University of Udine, Udine, Italy; Department of Anesthesia and Intensive Care, ASUFC Santa Maria della Misericordia University Hospital of Udine, Udine, Italy
  • Francesco Cugini Department of Emergency Medicine, ASUFC Hospital of San Daniele, Udine, Italy
  • Massimo Crapis Department of Infectious Diseases, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Sara Fossati Department of Infectious Diseases, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Astrid Callegari Department of Infectious Diseases, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Tommaso Pellis Department of Anesthesia and Intensive Care, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Dario Carmelo Tomasello Department of Anesthesia and Intensive Care, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Maurizio Tonizzo Department of Internal Medicine, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Alessandro Grembiale Department of Internal Medicine, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy
  • Natascia D'Andrea Department of Medicine, University of Udine, Udine, Italy; Department of Anesthesia and Intensive Care, ASUFC Santa Maria della Misericordia University Hospital of Udine, Udine, Italy
  • Luigi Vetrugno Department of Medicine, University of Udine, Udine, Italy; Department of Anesthesia and Intensive Care, ASUFC Santa Maria della Misericordia University Hospital of Udine, Udine, Italy
  • Tiziana Bove Department of Medicine, University of Udine, Udine, Italy; Department of Anesthesia and Intensive Care, ASUFC Santa Maria della Misericordia University Hospital of Udine, Udine, Italy

Keywords:

COVID-19, Prognostic, Artificial Neural Network, Machine Learning, ICU, Mortality

Abstract

Background and aim: There is a need to determine which clinical variables predict the severity of COVID-19. We analyzed a series of critically ill COVID-19 patients to see if any of our dataset's clinical variables were associated with patient outcomes.

Methods: We retrospectively analyzed the data of COVID-19 patients admitted to the ICU of the Hospital in Pordenone from March 11, 2020, to April 17, 2020. Patients' characteristics of survivors and deceased groups were compared. The variables with a different distribution between the two groups were implemented in a generalized linear regression model (LM) and in an Artificial Neural Network (NN) model to verify the "robustness" of the association with mortality.

Results: In the considered period, we reviewed the data of 22 consecutive patients: 8 died. The causes of death were a severe respiratory failure (3), multi-organ failure (1), septic shock (1), pulmonary thromboembolism (2), severe hemorrhage (1). Lymphocyte and the platelet count were significantly lower in the group of deceased patients (p-value 0.043 and 0.020, respectively; cut-off values: 660/mm3; 280,000/mm3, respectively). Prothrombin time showed a statistically significant trend (p-value= 0.065; cut-off point: 16.8/sec). The LM model (AIC= 19.032), compared to the NN model (Mean Absolute Error, MAE = 0.02), was substantially alike (MSE 0.159 vs. 0.136).

Conclusions: In the context of critically ill COVID-19 patients admitted to ICU, lymphocytopenia, thrombocytopenia, and lengthening of prothrombin time were strictly correlated with higher mortality. Additional clinical data are needed to be able to validate this prognostic score.

References

Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study [published correction appears in Lancet. 2020 Mar 28;395(10229):1038]. Lancet. 2020;395(10229):1054‐1062. DOI:10.1016/S0140-6736(20)30566-3

Weiss P, Murdoch DR. Clinical course and mortality risk of severe COVID-19. Lancet. 2020;395(10229):1014‐1015. DOI:10.1016/S0140-6736(20)30633-4

Ye Q, Wang B, Mao J. The pathogenesis and treatment of the `Cytokine Storm' in COVID-19. J Infect. 2020;80(6):607‐613. DOI:10.1016/j.jinf.2020.03.037

McGonagle D, Sharif K, O'Regan A, Bridgewood C. The Role of Cytokines including Interleukin-6 in COVID-19 induced Pneumonia and Macrophage Activation Syndrome-Like Disease. Autoimmun Rev. 2020;19(6):102537. DOI:10.1016/j.autrev.2020.102537

Sarzi-Puttini P, Giorgi V, Sirotti S, et al. COVID-19, cytokines and immunosuppression: what can we learn from severe acute respiratory syndrome?. Clin Exp Rheumatol. 2020;38(2):337‐342.

Zhang C, Wu Z, Li JW, Zhao H, Wang GQ. Cytokine release syndrome in severe COVID-19: interleukin-6 receptor antagonist tocilizumab may be the key to reduce mortality. Int J Antimicrob Agents. 2020;55(5):105954.

Luo P, Liu Y, Qiu L, Liu X, Liu D, Li J. Tocilizumab treatment in COVID-19: A single center experience. J Med Virol. 2020;92(7):814‐818.

Orso D, Federici N, Copetti R, Vetrugno L, Bove T. Infodemic and the spread of fake news in the COVID-19-era. Eur J Emerg Med. 2020 Oct;27(5):327-328.

Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Crit Care Med. 2018;46(4):547‐553.

Meyer A, Zverinski D, Pfahringer B, et al. Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir Med. 2018;6(12):905‐914.

Yang AP, Li HM, Tao WQ, et al. Infection with SARS-CoV-2 causes abnormal laboratory results of multiple organs in patients [published online ahead of print, 2020 June 1]. Aging (Albany NY). 2020;12:10.18632/aging.103255. DOI:10.18632/aging.103255

Liu J, Li S, Liu J, et al. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients. EBioMedicine. 2020;55:102763.

Giannis D, Ziogas IA, Gianni P. Coagulation disorders in coronavirus infected patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past. J Clin Virol. 2020;127:104362.

Barrett CD, Moore HB, Yaffe MB, Moore EE. ISTH interim guidance on recognition and management of coagulopathy in COVID-19: A Comment [published online ahead of print, 2020 April 17]. J Thromb Haemost. 2020;10.1111/jth.14860. DOI:10.1111/jth.14860

Liu X, Zhang R, He G. Hematological findings in coronavirus disease 2019: indications of progression of disease [published online ahead of print, 2020 June 3]. Ann Hematol. 2020;1‐8. DOI:10.1007/s00277-020-04103-5

Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis. Clin Chim Acta. 2020;506:145‐148. DOI:10.1016/j.cca.2020.03.022

Yang X, Yang Q, Wang Y, et al. Thrombocytopenia and its association with mortality in patients with COVID-19. J Thromb Haemost. 2020;18(6):1469‐1472. DOI:10.1111/jth.14848

Xu P, Zhou Q, Xu J. Mechanism of thrombocytopenia in COVID-19 patients. Ann Hematol. 2020;99(6):1205‐1208. DOI:10.1007/s00277-020-04019-0

Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844‐847. DOI:10.1111/jth.14768

Al-Samkari H, Karp Leaf RS, Dzik WH, et al. COVID and Coagulation: Bleeding and Thrombotic Manifestations of SARS-CoV2 Infection [published online ahead of print, 2020 June 3]. Blood. 2020;blood.2020006520. DOI:10.1182/blood.2020006520

Cui S, Chen S, Li X, Liu S, Wang F. Prevalence of venous thromboembolism in patients with severe novel coronavirus pneumonia. J Thromb Haemost. 2020;18(6):1421‐1424. DOI:10.1111/jth.14830

Klok FA, Kruip MJHA, van der Meer NJM, et al. incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res. 2020;191:145‐147. DOI:10.1016/j.thromres.2020.04.013

Cummings MJ, Baldwin MR, Abrams D, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. Lancet. 2020;395(10239):1763‐1770. DOI:10.1016/S0140-6736(20)31189-2

Giacomelli A, Ridolfo AL, Milazzo L, et al. 30-day mortality in patients hospitalized with COVID-19 during the first wave of the Italian epidemic: A prospective cohort study [published online ahead of print, 2020 May 22]. Pharmacol Res. 2020;158:104931. DOI:10.1016/j.phrs.2020.104931

Zangrillo A, Beretta L, Scandroglio AM, et al. Characteristics, treatment, outcomes and cause of death of invasively ventilated patients with COVID-19 ARDS in Milan, Italy [published online ahead of print, 2020 April 23]. Crit Care Resusc. 2020

Grasselli G, Zangrillo A, Zanella A, et al. Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy [published online ahead of print, 2020 April 6]. JAMA. 2020;323(16):1574‐1581. DOI:10.1001/jama.2020.5394)

Liu F, Li L, Xu M, et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 2020;127:104370. doi:10.1016/j.jcv.2020.104370

Aziz M, Fatima R, Assaly R. Elevated interleukin-6 and severe COVID-19: A meta-analysis [published online ahead of print, 2020 April 28]. J Med Virol. 2020;10.1002/jmv.25948. DOI:10.1002/jmv.25948

Tian W, Jiang W, Yao J, et al. Predictors of mortality in hospitalized COVID-19 patients: A systematic review and meta-analysis [published online ahead of print, 2020 May 22]. J Med Virol. 2020;10.1002/jmv.26050. DOI:10.1002/jmv.26050

Cecconi M, Piovani D, Brunetta E, et al. Early Predictors of Clinical Deterioration in a Cohort of 239 Patients Hospitalized for Covid-19 Infection in Lombardy, Italy. J Clin Med. 2020;9(5): E1548. Published 2020 May 20. DOI:10.3390/jcm9051548

Downloads

Published

12-05-2021

Issue

Section

ORIGINAL INVESTIGATIONS/COMMENTARIES - SPECIAL COVID19

How to Cite

1.
Artificial neural network model from a case series of COVID-19 patients: a prognostic analysis.: Clinical presentation prognostic factors in COVID-19. Acta Biomed [Internet]. 2021 May 12 [cited 2024 Mar. 29];92(2):e2021202. Available from: https://www.mattioli1885journals.com/index.php/actabiomedica/article/view/11086