García ÁPJ, Morejón RL, Grasso LF

Language: Spanish
References: 17
Page: 601-615
PDF size: 12236.30 Kb.

ABSTRACT

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.

REFERENCES

1. Li X, Xu S, Yu M, et al. Risk factors for severity and mortality in adult COVID-19 in patients in Wuhan. J Allergy Clin Immunol. 2020 Jul;146(1):110-8. Citado en PubMed; PMID: 32294485.

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: 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: 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. 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. 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. 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: 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: 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.