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Análisis
Medwave 2020;20(9):e8039 doi: 10.5867/medwave.2020.09.8039
Modelamiento predictivo para el cálculo de demanda de camas hospitalarias de cuidados intensivos a nivel nacional en el marco de la pandemia por COVID-19
Predictive modeling to estimate the demand for intensive care hospital beds nationwide in the context of the COVID-19 pandemic
Víctor Hugo Peña, Alejandra Espinosa
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Palabras clave: 2019 novel coronavirus disease, epidemiology, public health, viruses, emergency medicine, hospitalization

Abstract

Introduction
SARS CoV-2 pandemic is pressing hard on the responsiveness of health systems worldwide, notably concerning the massive surge in demand for intensive care hospital beds.

Aim
This study proposes a methodology to estimate the saturation moment of hospital intensive care beds (critical care beds) and determine the number of units required to compensate for this saturation.

Methods
A total of 22,016 patients with diagnostic confirmation for COVID-19 caused by SARS-CoV-2 were analyzed between March 4 and May 5, 2020, nationwide. Based on information from the Chilean Ministry of Health and ministerial announcements in the media, the overall availability of critical care beds was estimated at 1,900 to 2,000. The Gompertz function was used to estimate the expected number of COVID-19 patients and to assess their exposure to the available supply of intensive care beds in various possible scenarios, taking into account the supply of total critical care beds, the average occupational index, and the demand for COVID-19 patients who would require an intensive care bed.

Results
A 100% occupancy of critical care beds could be reached between May 11 and May 27. This condition could be extended for around 48 days, depending on how the expected over-demand is managed.

Conclusion
A simple, easily interpretable, and applicable to all levels (nationwide, regionwide, municipalities, and hospitals) model is offered as a contribution to managing the expected demand for the coming weeks and helping reduce the adverse effects of the COVID-19 pandemic.


 

No English version is available for this article.

Licencia Creative Commons Esta obra de Medwave está bajo una licencia Creative Commons Atribución-NoComercial 3.0 Unported. Esta licencia permite el uso, distribución y reproducción del artículo en cualquier medio, siempre y cuando se otorgue el crédito correspondiente al autor del artículo y al medio en que se publica, en este caso, Medwave.

 

Introduction
SARS CoV-2 pandemic is pressing hard on the responsiveness of health systems worldwide, notably concerning the massive surge in demand for intensive care hospital beds.

Aim
This study proposes a methodology to estimate the saturation moment of hospital intensive care beds (critical care beds) and determine the number of units required to compensate for this saturation.

Methods
A total of 22,016 patients with diagnostic confirmation for COVID-19 caused by SARS-CoV-2 were analyzed between March 4 and May 5, 2020, nationwide. Based on information from the Chilean Ministry of Health and ministerial announcements in the media, the overall availability of critical care beds was estimated at 1,900 to 2,000. The Gompertz function was used to estimate the expected number of COVID-19 patients and to assess their exposure to the available supply of intensive care beds in various possible scenarios, taking into account the supply of total critical care beds, the average occupational index, and the demand for COVID-19 patients who would require an intensive care bed.

Results
A 100% occupancy of critical care beds could be reached between May 11 and May 27. This condition could be extended for around 48 days, depending on how the expected over-demand is managed.

Conclusion
A simple, easily interpretable, and applicable to all levels (nationwide, regionwide, municipalities, and hospitals) model is offered as a contribution to managing the expected demand for the coming weeks and helping reduce the adverse effects of the COVID-19 pandemic.

Autores: Víctor Hugo Peña[1], Alejandra Espinosa[1]

Filiación:
[1] Departamento de Tecnología Médica, Facultad de Medicina, Universidad de Chile, Chile

E-mail: vicarcl@gmail.com

Correspondencia a:
[1] 6 ADB Ave., Mandaluyong
Metro-Manila
Philippines

Citación: Peña VH, Espinosa A. Predictive modeling to estimate the demand for intensive care hospital beds nationwide in the context of the COVID-19 pandemic. Medwave 2020;20(9):e8039 doi: 10.5867/medwave.2020.09.8039

Fecha de envío: 24/4/2020

Fecha de aceptación: 2/9/2020

Fecha de publicación: 5/10/2020

Origen: No solicitado

Tipo de revisión: Con revisión por pares externa, por tres árbitros a doble ciego

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Siordia JA Jr. Epidemiology and clinical features of COVID-19: A review of current literature. J Clin Virol. 2020 Jun;127:104357. | CrossRef | PubMed |

Cuestas E. La pandemia por el nuevo coronavirus covid-19 [The novel coronavirus covid-19 pandemic]. Rev Fac Cien Med Univ Nac Cordoba. 2020 Mar 18;77(1):1-3. | CrossRef | PubMed |

Sen-Crowe B, McKenney M, Elkbuli A. Social distancing during the COVID-19 pandemic: Staying home save lives. Am J Emerg Med. 2020 Jul;38(7):1519-1520. | CrossRef | PubMed |

Güner R, Hasanoğlu I, Aktaş F. COVID-19: Prevention and control measures in community. Turk J Med Sci. 2020 Apr 21;50(SI-1):571-577. | CrossRef | PubMed |

Worldometer. Coronavirus Cases. 2020. [On line]. | Link |

Córdova-Lepe F, Gutiérrez-Aguilar R, Gutiérrez-Jara JP. Número de casos COVID-19 en Chile a 120 días con datos al 21/03/2020 y umbral del esfuerzo diario para aplanar la epi-curva [Number of COVID-19 cases in Chile at 120 days with data at 21/03/2020 and threshold of daily effort to flatten the epi-curve]. Medwave. 2020 Mar 27;20(2):e7861. | CrossRef | PubMed |

Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. JAMA. 2020 Apr 28;323(16):1545-1546. | CrossRef | PubMed |

Ministerio de Salud de Chile. Unidad de Gestión Centralizada de Camas, UGCC. Santiago, Chile: MINSAL; 2018. [On line]. | Link |

Litton E, Bucci T, Chavan S, Ho YY, Holley A, Howard G, et al. Surge capacity of intensive care units in case of acute increase in demand caused by COVID-19 in Australia. Med J Aust. 2020 Jun;212(10):463-467. | CrossRef | PubMed |

Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, Mastri M, et al. Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors. PLoS Comput Biol. 2020 Feb 25;16(2):e1007178. | CrossRef | PubMed |

Bürger R, Chowell G, Lara-Díıaz LY. Comparative analysis of phenomenological growth models applied to epidemic outbreaks. Math Biosci Eng. 2019 May 16;16(5):4250-4273. | CrossRef | PubMed |

Departamento de Estadística e Información en Salud de Chile. 2020. [On line]. | Link |

Latorre R, Sandoval G. El mapa actualizado de las camas de hospitales en Chile. La Tercera. 2020. [On line]. | Link |

Departamento de Políticas de Salud y Estudios. 2do Informe COVID 19. Colegio Médico de Chile; 2020. [On line]. | Link |

Gonzalez RI, Munoz F, Moya PS, Kiwi M. Is a COVID19 Quarantine Justified in Chile or USA Right Now? 2020 Mar. [On line]. | Link |

Canals M. Proyección de la demanda de camas UCI (datos hasta el 30 de marzo de 2020). Escuela de Salud Pública, Universidad de Chile. 2020. [On line]. | Link |