Temporal model of the behavior of critically ill patients with COVID-19 during their staying in intensive care. Lombardy, Italy
Keywords:
PO2/FiO2 index, COVID-19, predictive modelAbstract
Introduction: a time series is the product of the observation of a variable in time. It is a mathematical tool frequently applied in health. No temporal models have been developed to predict patients’ behavior during their staying in the Intensive Care Unit.
Objectives: to create a time series allowing to predict the behavior of seriously-ill patients due to COVID-19, during their staying in the Intensive Care Unit in the region of Lombardy, Italy.
Materials and methods: analytic, longitudinal prospective study with a group of critical patients who were admitted from April 1st to May 1st, with COVID-19 diagnosis, to Ospedale Maggiore di Crema, in the Lombardy region, Italy. The universe was formed by 28 patients and all of them were worked on.
Results: 48% of patients were male. Average age: 83 years; Time series: Model 1 holding PO2/FiO2 p = 0.251; Model 2 (ARIMA) SatO2/FiO2 p = 0.674 (in the two first models the result increased with the days, following a predictable behavior=; Model 3 (ARIMA) p = 0.406 (in this case the expected result decreased as time passed). The obtained functions allow to calculate the expected value according to the day from the admission.
Conclusions: predicting patient's evolution in the Intensive Care Unit allowed early detecting those with unexpected curves and targeting more aggressive therapies toward them.
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2. Jekarl DW, Lee S, Kim M, et al. Procalcitonin as a prognostic marker for sepsis based on SEPSIS-3. J Clin Lab Anal. 2019 Nov; 33(9): e22996. Citado en PubMed; PMID: 31420921.
3. Pallarés Carratalá V, Górriz-Zambrano C. Morillas Ariño C, et al. COVID-19 y enfermedad cardiovascular y renal: ¿Dónde estamos? ¿Hacia dónde vamos? Medicina de Familia. Semergen [Internet]. 2020 [citado 23/06/2020]; 46(1): 78-87. Disponible en: https://www.sciencedirect.com/science/article/abs/pii/S1138359320301441?via%3Dihub.
4. Medeiros Figueiredo A, Daponte-Codina A, Moreira Marculino D, et al. Factores asociados a la incidencia y la mortalidad por COVID-19 en las comunidades autónomas. Gac Sanit [Internet]. 2020 [citado 28/06/2020]. Disponible en: https://www.sciencedirect.com/science/article/pii/S0213911120301242?via%3Dihub.
5. Perrotta F, Corbi G, Mazzeo G, et al. COVID 19 and the elderly: insights into pathogenesis and clinical decision-making. Aging Clin Exp Res. 2020 Aug; 32(8): 1599-1608. Errata en: Aging Clin Exp Res. 2020 Sep; 32(9): 1909. Citado en PubMed; PMID: 32557332.
6. Wang L, He W, Yu X, et al. Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up. J Infect. 2020 Jun; 80(6): 639-45. Citado en PubMed; PMID: 32240670.
7. Kang S, Peng W, Zhu Y, et al. Recent progress in understanding 2019 novel coronavirus (SARS-CoV-2) associated with human respiratory disease: detection, mechanisms and treatment. Int J Antimicrob Agents. 2020 May; 55(5): 105950. Citado en PubMed; PMID: 32234465.
8. Du RH, Liang LR, Yang CQ, et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J. 2020 May 7; 55(5): 2000524. doi: 10.1183/13993003.00524-2020. Errata en: Eur Respir J. 2020 Sep 24;56(3). Citado en PubMed; PMID: 32269088.
9. Weiss P, Murdoch D. Clinical course and mortality risk of severe COVID-19. Lancet. 2020 Mar 28; 395(10229): 1014-5. Citado en PubMed; PMID: 32197108.
10. Ruan Q, Yang K, Wang W, et al. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020 May; 46(5): 846-8. doi: 10.1007/s00134-020-05991-x. Errata en: Intensive Care Med. 2020 Apr 6. Citado en PubMed; PMID: 32125452.
11. Villar J, Zhang H, Slutsky A. Lung Repair and Regeneration in ARDS: Role of PECAM1 and Wnt Signaling. Chest. 2019 Mar; 155(3): 587-94. Citado en PubMed; PMID: 30392791.
12. Hendrickson C, Matthay M. Endothelial biomarkers in human sepsis: pathogenesis and prognosis for ARDS. Pulm Circ. 2018 Apr-Jun; 8(2): 2045894018769876. Citado en PubMed; PMID: 29575977.
13. Venegas Sosa AM, Cortés Munguía JA, Flores López EN, et al. Correlación entre PaO2/FiO2 versus SO2/FiO2 para monitoreo de oxigenación en pacientes con trauma de tórax. Col Mex Med Crít [Internet]. 2018 [citado 04/07/2020]; 32(4). Disponible en: http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S2448-89092018000400201.
14. Ameghino Bautista J, Morales Corbacho J, Apolaya-Segura M. Correlación entre SO2/FiO2 y PaO2/FiO2 en pacientes con insuficiencia respiratoria en ventilación mecánica. Rev Cubana de Invest Bioméd [Internet]. 2018 [citado 04/07/2020]; 37(3): 1-9. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-03002018000300002.
15. Mehta Y, Chaudhry D, Abraham O, et al. Critical Care for COVID-19 Affected Patients: Position Statement of the Indian Society of Critical Care Medicine. Indian J Crit Care Med. 2020 Apr; 24(4): 222-41. Citado en PubMed; PMID: 32565632.
16. Carter C, Osborn M, Agagah G, et al. COVID-19 disease: invasive ventilation. Clinics in Integrated Care. 2020 Jul; 1: 100004. Citado en PubMed; PMCID: PMC7261657.
17. Tan C, Huang Y, Shi F, et al. C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early. J Med Virol. 2020 Jul; 92(7): 856-62. Citado en PubMed; PMID: 32281668.
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