Prognosis of Old Intensive Care COVID-19 Patients at a Glance: The Senior COVID Study
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Original Article
P: 57-61
February 2022

Prognosis of Old Intensive Care COVID-19 Patients at a Glance: The Senior COVID Study

Turk J Anaesthesiol Reanim 2022;50(1):57-61
1. Department of Anaesthesiology and Reanimation, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France
2. Biometrics and Evolutionary Biology Laboratory, Biostatistics-Health Team, Hospices Civils de Lyon, Villeurbanne, France
3. Division of Public Health, Department of Biostatistics and Bioinformatics, Lyon, France
4. Department of Intensive Medicine and Resuscitation, Croix Rousse Hospital, Hospices Civils de Lyon, Lyon, France
5. Department of Anaesthesia and Resuscitation, Édouard Herriot Hospital, Hospices Civils de Lyon, Lyon, France
6. Claude Bernard University Faculty of Medicine, Lyon, France
7. Medipole Lyon Villeurbanne Hospital, Villeurbanne, France
8. Department of Anaesthesia and Resuscitation, Pierre Wertheimer Hospital, Hospices Civils de Lyon, Lyon, France
9. North West Hospital of Villefranche, France
10. Inter-University Laboratory of Human Movement Biology, Villeurbanne, France
11. Department of Anaesthesia and Surgical Resuscitation, Croix Rousse Hospital, Hospices Civils de Lyon, Lyon, France
12. Department of Intensive Care Medicine, Emile Roux Hospital Center, le Puy en Velay, France
13. Plateforme IC-HCL, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France
14. Department of Biological Hematology, Lyon Sud Hospital South Biology and Pathology Center, Pierre-Bénite, France
15. Department of Intensive Care, Henry Gabrielle Hospital, Hospices Civils de Lyon, Lyon, France
16. Department of Critical Care, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
17. Division of Geriatrics, Center Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France
No information available.
No information available
Received Date: 09.09.2021
Accepted Date: 01.11.2021
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ABSTRACT

Objective:

Admission in the intensive care unit of the old patient with coronavirus disease 19 raises an ethical question concerning the scarce resources and their short-term mortality.

Methods:

Patients aged over 60 from 7 different intensive care units admitted between March 1, 2020 and May 6, 2020, with a diagnosis of coronavirus disease 19 were included in the cohort. Twenty variables were collected during the admission, such as age, severity (Simplified Acute Physiology Score [SAPS] II), several data on physiological status before intensive care unit comorbidities, evaluation of autonomy, frailty, and biological variables. The objective was to model the 30-day mortality with relevant variables, compute their odds ratio associated with their 95% CI, and produce a nomogram to easily estimate and communicate the 30-day mortality. The performance of the model was estimated with the area under the receiving operating curve.

Results:

We included 231 patients, among them 60 (26.0%) patients have died on the 30th day. The relevant variables selected to explain the 30-day mortality were Instrumental Activities of Daily Living (IADL) score (0.82 [0.71-0.94]), age 1.12 (1.07-1.18), SAPS II 1.05 (1.02-1.08), and dementia 6.22 (1.00-38.58). A nomogram was computed to visually represent the final model. Area under the receiving operating curve was at 0.833 (0.776-0.889).

Conclusions:

Age, autonomy, dementia, and severity at admission were important predictive variables for the 30-day mortality status, and the nomogram could help the physician in the decision-making process and the communication with the family.

Keywords: Coronavirus disease 2019, ethical, mortality, nomogram, prognostic factors, statistical modeling

References

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