medigraphic.com
Instituto Nacional de Salud Pública

2021, Número 1

Siguiente >>

salud publica mex 2021; 63 (1)


Puntaje de riesgo para predecir el ingreso a una unidad de cuidados intensivos en pacientes con Covid-19: puntaje ABC-GOALS

Mejía-Vilet JM, Córdova-Sánchez BM, Fernández-Camargo DA, Méndez-Pérez RA, Morales-Buenrostro LE, Hernández-Gilsoul T

Idioma: Ingles.
Referencias bibliográficas: 38
Paginas: 1-12
Archivo PDF: 343.90 Kb.


RESUMEN

Objetivo. Desarrollar un puntaje predictivo de la necesidad de ingreso a una unidad de cuidados intensivos (UCI) en Covid-19. Material y métodos. Se evaluaron pacientes ingresados por Covid-19 en México. Se dividieron en un grupo que requirió ingreso a UCI y un grupo que nunca lo requirió. Se derivaron modelos predictivos incluyendo variables clínicas, de laboratorio e imagen y se integraron en el puntaje ABC-GOALS. Resultados. Se incluyeron 329 y 240 pacientes en cohortes de desarrollo y validación, respectivamente. Ciento quince pacientes de cada cohorte requirieron ingreso a UCI. Las áreas bajo la curva de los modelos clínico (ABC-GOALSc), clínico+laboratorio (ABCGOALS cl), clínico+laboratorio+imagen (ABC-GOALSclx) fueron 0.79 (IC95%=0.74-0.83) y 0.77 (IC95%=0.71-0.83); 0.86 (IC95%=0.82-0.90) y 0.87 (IC95%=0.83-0.92); 0.88 (IC95%=0.84-0.92) y 0.86 (IC95%=0.81-0.90) en las cohortes de derivación y validación, respectivamente. El desempeño del ABC-GOALS fue superior a otros puntajes de riesgo. Conclusión. ABC-GOALS es una herramienta para predecir oportunamente la necesidad de ingreso a UCI en Covid-19.


REFERENCIAS (EN ESTE ARTÍCULO)

  1. Emanuel EJ, Persad G, Upshur R, Thome B, Parker M, Glickman A, et al. Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. 2020;382(21):2049-55. https://doi.org/10.1056/NEJMsb2005114

  2. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA. 2020;323(13):1239-42. https://doi.org/10.1001/jama.2020.2648

  3. Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al. Clinical characteristics of Covid-19 in New York City. N Engl J Med. 2020;382(24):2372-4. https://doi.org/10.1056/NEJMc2010419

  4. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson DW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA.2020;323(20):2052-9. https://doi.org/10.1001/jama.2020.6775

  5. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934-43. https://doi.org/10.1001/jamainternmed.2020.0994

  6. Hu L, Chen S, Fu Y, Gao Z, Long H, Wang JM, et al. Risk factors associated with clinical outcomes in 323 COVID-19 hospitalized patients in Wuhan, China. Clin Infect Dis. 2020;ciaa539. https://doi.org/10.1093/cid/ciaa539

  7. Guan W, Ni Z, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-20. https://doi.org/10.1056/NEJMoa2002032

  8. Chen R, Liang W, Jiang M, Guan W, Zhan C, Wang T, et al. Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China. Chest. 2020;158(1):97-105. https://doi. org/10.1016/j.chest.2020.04.010

  9. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507-13. https://doi.org/10.1016/S0140-6736(20)30211-7

  10. Deng G, Yin M, Chen X, Zeng F. Clinical determinants for fatality of 44,672 patients with COVID-19. Crit Care. 2020;24(1):179. https://doi. org/10.1186/s13054-020-02902-w

  11. Zhang H, Shi T, Wu X, Zhang X, Wang K, Bean D, et al. Risk prediction for poor outcome and death in hospital in patients with COVID-19: derivation in Wuhan, China and external validation in London, UK. medRxiv [preprint]. 2020. https://doi.org/10.1101/2020.04.28.20082222

  12. Wang K, Zuo P, Liu Y, Zhang M, Zhao X, Xie S, et al. Clinical and laboratory predictors of in-hospital mortality in patients with COVID-19: a cohort study in Wuhan, China. Clin Infect Dis. 2020;ciaa538. https://doi. org/10.1093/cid/ciaa538

  13. Yang R, Gui X, Zhang Y, Xiong Y. The role of essential organ-based comorbidities in the prognosis of COVID-19 infection patients. Expert Rev Respir Med. 2020. https://doi.org/10.1080/17476348.2020.1761791

  14. Zhang J, Yu M, Tong S, Liu LY, Tang LV. Predictive factors for disease progression in hospitalized patients with coronavirus disease 2019 in Wuhan, China. J Clin Virol. 2020;127:104392. https://doi.org/10.1016/j. jcv.2020.104392

  15. Yang W, Cao Q, Qin L, Wang X, Cheng Z, Pan A, et al. Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): A multi-center study in Wenzhou city, Zhejiang, China. J Infect. 2020;80(4):388-93. https://doi.org/10.1016/j.jinf.2020.02.016

  16. Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID- 19 outbreak in Lombardy, Italy. JAMA. 2020;323(16):1545-6. https:// doi.org/10.1001/jama.2020.4031

  17. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis. 1987;40(5):373-83. https://doi.org/10.1016/0021- 9681(87)90171-8

  18. Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardising the assessment of acute-illness severity in the NHS. London: RCP Press, 2017.

  19. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonca A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med. 1996;22(7):707-10. https://doi.org/10.1007/BF01709751

  20. Lim WS, Van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58(5):377-82. https://doi.org/10.1136/thorax.58.5.377

  21. Guo L, Wei D, Zhang X, Wu Y, Li Q, Zhou M, Qu J. Clinical features predicting mortality risk in patients with viral pneumonia: the MuLBSTA score. Front Microbiol. 2019;10:2752. https://doi.org/10.3389/ fmicb.2019.02752

  22. Roca O, Messika J, Caralt B, García-de-Acilu M, Sztrymf B, Ricard JD, Masclans JR. Predicting success of high-flow nasal cannula in pneumonia patients with hypoxemic respiratory failure: The utility of the ROX index. J Crit Care. 2016;35:200-5. https://doi.org/10.1016/j. jcrc.2016.05.022

  23. Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, et al. Prediction for progression risk in patients with COVID-19 pneumonia: the CALL score. Clin Infect Dis. 2020; ciaa414. https://doi.org/10.1093/cid/ciaa414

  24. Liang W, Liang H, Ou L, Chen B, Chen A, Li C, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Int Med. 2020. https:// doi.org/10.1001/jamainternmed.2020.2033

  25. Mejia-Vilet JM, Cordova-Sanchez BM, Fernandez-Camargo DA, Mendez- Perez RA, Morales-Buenrostro LE, Hernández-Gilsoul T. Supplementary material to the manuscript “A risk score to predict admission to the intensive care unit in patients with COVID-19: the ABC-GOALS score. Journal Contribution. 2020. https://doi.org/10.6084/m9.figshare.12611174

  26. Johns Hopkins University Coronavirus Resource Center. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Baltimore: JHU, 2020 [cited Jul 3, 2020]. Available from: https://coronavirus.jhu.edu/map.html

  27. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med. 2020;382:1199-207. https://doi.org/10.1056/NEJMoa2001316

  28. Bello-Chavolla OY, Bahena-López JP, Antonio-Villa NE, Vargas-Vázquez A, González-Díaz A, Márquez-Salinas A, et al. Predicting mortality due to SARS-CoV-2: a mechanistic score relating obesity and diabetes to COVID-19 outcomes in Mexico. J Clin Endocrinol Metab. 2020;105(8). https://doi.org/10.1210/clinem/dgaa346

  29. Bhatraju PK, Ghassemieh BJ, Nichols M, Kim R, Jerome KR, Nalla AK, et al. Covid-19 in Critically Ill Patients in the Seattle Region: Case Series. N Engl J Med 2020;382(21):2012-22. https://doi.org/10.1056/ NEJMoa2004500

  30. Luzi L, Radaelli MG. Influenza and obesity: its odd relationship and the lessons for COVID-19 pandemic. Acta Diabetol. 2020;57(6):759-64. https://doi.org/10.1007/s00592-020-01522-8

  31. Dietz W, Santos-Burgoa C. Obesity and its Implications for COVID-19 Mortality. Obesity 2020;28(6):1005. https://doi.org/10.1002/oby.22818

  32. Levy TJ, Richardson S, Coppa K, Barnaby DP, McGinn T, Becker LB, et al. Estimating survival of hospitalized COVID-19 patients from admission information. medRxiv [preprint]. 2020. https://doi. org/10.1101/2020.04.22.20075416

  33. Mo P, Xing Y, Xiao Y, Deng L, Zhao Q, Wang H, et al. Clinical characteristics of refractory COVID-19 pneumonia in Wuhan, China. Clin Infect Dis. 2020;ciaa270. https://doi.org/10.1093/cid/ciaa270

  34. Guo T, Fan Y, Chen M, Wu X, Zhang L, He T, et al. Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19). JAMA Cardiol. 2020. https://doi.org/10.1001/ jamacardio.2020.1017

  35. Wang D, Yin Y, Hu C, Liu X, Zhang X, Zhou S, et al. Clinical course and outcome of 107 patients infected with the novel coronavirus, SARS-CoV-2, discharged from two hospitals in Wuhan, China. Crit Care 2020;24(1):188. https://doi.org/10.1186/s13054-020-02895-6

  36. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. https:// doi.org/10.1136/bmj.g7594

  37. Pérez-Padilla JR, Vázquez-García JC. Estimation of gasometric values at different altitudes above sea level in Mexico. Rev Invest Clin. 2000;52(2):148-55

  38. Ramirez-Sandoval JC, Castilla-Peón MF, Gotés-Palazuelos J, Vázquez- García JC, Wagner MP, Merelo-Arias CA, et al. Bicarbonate Values for Healthy Residents Living in Cities Above 1500 Meters of Altitude: A Theoretical Model and Systematic Review. High Alt Med Biol. 2016;17(2):85-92. https://doi.org/10.1089/ham.2015.0097




2020     |     www.medigraphic.com