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Enregistrement W4292604004 · doi:10.1111/irv.13033

Functional outcomes in elderly patients with hospitalized COVID‐19 pneumonia: A 1 year follow‐up study

2022· letter· en· W4292604004 sur OpenAlexaboutno aff
Naoyuki Miyashita, Yasushi Nakamori, Makoto Ogata, Naoki Fukuda, Akihisa Yamura

Notice bibliographique

RevueInfluenza and Other Respiratory Viruses · 2022
Typeletter
Langueen
DomaineMedicine
ThématiqueLong-Term Effects of COVID-19
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePneumoniaActivities of daily livingBathingCoronavirus disease 2019 (COVID-19)GeriatricsPhysical therapyPediatricsEmergency medicineInternal medicineDiseaseInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Dear Editor, Because super aging is progressing in Japan, society with extended healthy life expectancy is required. Basic activities of daily living (ADL) in the elderly are affected by pneumonia in the elderly [1]. Thus, the Japan Respiratory Society (JRS) pneumonia guidelines emphasize the importance of pneumonia prevention rather than antibiotic therapy to avoid deterioration of physical function [2]. The objective of this study was to clarify the functional outcomes at 1 year after hospital discharge in elderly patients (≥65 years old) hospitalized for COVID-19 pneumonia. The present study was conducted at five institutions between February 2020 and June 2021. The ADL assessment for calculating the Barthel index consisted of the following 10 indices: feeding, bathing, grooming, dressing, bowels, bladder, toilet use, transfers, morbidity, and stairs [3]. In the present study, we calculated the difference in ADL scores between baseline (1 week before admission), at hospital discharge, and 1 year after discharge from our hospitals. The difference was categorized into two groups: declined (≥1) and not declined (0). Of the pneumonia cases, we excluded bedridden cases because these patients were not able to change their ADL score between before and after admission to hospital. During the study period, 438 elderly patients with COVID-19 pneumonia were recognized. Table 1 shows the outcomes of patients with COVID-19 pneumonia stratified by the three age groups. Functional decline rates at the time of hospital discharge and at 1 year after hospital discharge were highest in the ≥80 years old group (51.7% and 42.5%, respectively), followed by the 70–79 years old group (39.7% and 11.0%, respectively) and the 65–69 years old group (18.3% and 0%, respectively). Of 62 patients in the ≥80 years old group who had a decline in physical function at the time of hospital discharge, 51 patients (82.3%) still showed functional decline at 1 year later. In contrast, no patients with functional decline were observed at 1 year after hospital discharge in the 65–69 years old group. In multivariate analysis, age (per year) (odds ratio [OR] 1.05, 95% confidence interval [CI] 1.02–1.07, p < 0.0001), chronic heart disease (OR 1.91, 95% CI 1.04–3.23, p = 0.0376), cerebrovascular disease (OR 1.66, 95% CI 1.05–2.79, p = 0.0125), and diabetes mellitus (OR 2.19, 95% CI 1.06–3.93, p = 0.0335) were risk factors for functional decline at 1 year after hospital discharge in elderly patients with COVID-19 pneumonia. It is well known that hospital admission in elderly patients is associated with a decline in physical function, and functional decline after hospitalization is associated with adverse health outcomes. Recently, a Canadian longitudinal study found that community-living middle-aged and older adults with confirmed, probable, or suspected COVID-19 had nearly twofold higher odds of worsening mobility and physical function compared with adults without COVID-19, although most participants with COVID-19 had mild to moderate disease and were not hospitalized [4]. Our present study focused on hospitalized elderly patients with COVID-19 pneumonia and demonstrated that 37.7% and 16.9% of patients showed a decline in function at the time of hospital discharge and at 1 year after hospital discharge, respectively, compared with their baseline ADL function. Functional decline rates were significantly higher in the older age group. Especially in the ≥80 years old group, 62 patients showed a decline in function at the time of hospital discharge and 51 patients (82.3%) still showed the functional decline at 1 year later. Advanced age and medical comorbidities have been associated with severe illness associated with infection resulting in hospitalization, admission to an intensive care unit, intubation or mechanical ventilation, or death [5-7]. Our results demonstrated that more advanced ages, chronic heart disease, cerebrovascular disease, and diabetes mellitus were risk factors for functional decline at 1 year later in hospitalized elderly patients with COVID-19 pneumonia. Physicians should recommend the SARS-CoV-2 vaccination and the positively use anti-SARS-CoV-2 drugs when COVID-19 is found in patients who are ≥80 years old or who have comorbidities in elderly. The authors declare that they have no competing interests. Naoyuki Miyashita: Conceptualization; data curation; formal analysis; investigation; methodology. Yasushi Nakamori: Conceptualization; data curation; investigation; methodology. Makoto Ogata: Conceptualization; data curation. Naoki Fukuda: Conceptualization; data curation. Akihisa Yamura: Conceptualization; data curation. The peer review history for this article is available at https://publons.com/publon/10.1111/irv.13033. The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,979
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,061
Tête enseignante GPT0,340
Écart entre enseignants0,279 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2022
Routes d'admission1
Résumé présentoui

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