Portal de investigación
Mortality and advanced support requirement for patients with cancer with COVID-19: A mathematical dynamic model for latin America
Fecha de publicación:
Autores organización
Autores
- Ruiz-Patiño A
- Arrieta O
- Pino LE
- Rolfo C
- Ricaurte L
- Recondo G
- Zatarain-Barron ZL
- Corrales L
- Martín C
- Barrón F
- Vargas C
- Carranza H
- Otero J
- Rodriguez J
- Sotelo C
- Viola L
- Russo A
- Rosell R
Grupos de investigación
Resumen
PURPOSE In the midst of a global pandemic, evidence suggests that similar to other severe respiratory viral infections, patients with cancer are at higher risk of becoming infected by COVID-19 and have a poorer prognosis. METHODS We have modeled the mortality and the intensive care unit (ICU) requirement for the care of patients with cancer infected with COVID-19 in Latin America. A dynamic multistate Markov model was constructed. Transition probabilities were estimated on the basis of published reports for cumulative probability of complications. Basic reproductive number (R0) values were modeled with R using the EpiEstim package. Estimations of days of ICU requirement and absolute mortality were calculated by imputing number of cumulative cases in the Markov model. RESULTS Estimated median time of ICU requirement was 12.7 days, median time to mortality was 16.3 days after infection, and median time to severe event was 8.1 days. Peak ICU occupancy for patients with cancer was calculated at 16 days after infection. Deterministic sensitivity analysis revealed an interval for mortality between 18.5% and 30.4%. With the actual incidence tendency, Latin America would be expected to lose approximately 111,725 patients with cancer to SARS-CoV-2 (range, 87,116-143,154 patients) by the 60th day since the start of the outbreak. Losses calculated vary between, 1% to 17.6% of all patients with cancer in the region. CONCLUSION Cancer-related cases and deaths attributable to SARS-CoV-2 will put a great strain on health care systems in Latin America. Early implementation of interventions on the basis of data given by disease modeling could mitigate both infections and deaths among patients with cancer. © 2020 2020 by American Society of Clinical Oncology.
Datos de la publicación
- ISSN/ISSNe:
- 2687-8941, 2687-8941
- Tipo:
- Article
- Páginas:
- 752-759
- DOI:
- 10.1200/GO.20.00156
- PubMed:
- 32469610
- Enlace a otro recurso:
- www.scopus.com
Jco Global Oncology NLM (Medline)
Citas Recibidas en Web of Science: 6
Citas Recibidas en Scopus: 8
Documentos
- No hay documentos
Filiaciones
Keywords
- Betacoronavirus; Coronavirus Infections; Delivery of Health Care; Health Plan Implementation; Humans; Incidence; Intensive Care Units; Latin America; Markov Chains; Models, Statistical; Neoplasms; Pandemics; Pneumonia, Viral; Prognosis; Resuscitation; Time Factors; Article; basic reproduction number; cancer prognosis; cohort analysis; coronavirus disease 2019; disease severity; epidemic; human; incidence; infection complication; intensive care unit; major clinical study; malignant neoplasm; Markov chain; mathematical analysis; mortality rate; patient care; priority journal; sensitivity analysis; Severe acute respiratory syndrome coronavirus 2; South and Central America; time to treatment; treatment duration; Betacoronavirus; complication; coronavirus disease 2019; Coronavirus infection; epidemiology; health care delivery; health care planning; Markov chain; mortality; neoplasm; organization and management; pandemic; pathogenicity; prognosis; resuscitation; statistical model; time facto