The changing nature of the population of intensive-care patients
Notice bibliographique
Résumé
Background: The increase in the number of Australia’s frail, very elderly ( 80 years of age) population will have an impact on admissions to intensive care. As the number of very elderly patients increase, it will be important to have information about what the impact of increasing age will have on aspects such as: the impact of age and chronic health conditions on intensive care treatment, and the impact on prognosis in the short and longer-term as well as how we should be involving the very elderly in determining their own goals of care.Objective: To evaluate the long-term trend in the rates of the very elderly ( 80 years of age) admitted to intensive care, as well as describe their chronic health conditions, length of stay, and mortality rates.Methods: This study was a retrospective review that used a database from a 40-bed, multidisciplinary, adult intensive care unit (ICU), located in South-Western Sydney, Australia. The setting is an 877-bed tertiary hospital that has medical and surgical specialties; including a referral trauma unit, with approximately 80,000 admissions a year. Data were acquired over 15-years, from January 1st, 2000 to December 31st, 2015.Results: Data were available for 32,796 patients, and of these, 4,137 (12.5%) were aged ≥ 80 years. The percentage of the very elderly admitted to ICU progressively increased from 8.6% in 2000 to (14.5% in 2015, p < .001). Overall, the median length of stay (LOS) in the ICU was 2-days (interquartile range: 1.2-4.1), and increased from 2.0 to 2.3 (p < .001). Similarly, the median hospital LOS increased over time from 9 to 11 days (p < .001). Intensive care and hospital death rates decreased over time from 19.9% to 9.8% (p < .001), and 31.8% to 19.9% (p < .001), respectively. The majority of the very elderly were admitted from the emergency department (ED) (38.1%), other sources of admission being from the operating theatres (OT) (33.5%), and the general ward (18.1%).Conclusions: The number and percentage of very elderly patients being managed in ICU is increasing, representing a different population from the one that much of our practice has been previously based. For example, we may need to review the way we estimate severity of illness on admission to the ICU with more weight given to the chronic health component of the very elderly. The acute indications for admission to ICU such as falls and infections are relatively straightforward to manage and usually have a good outcome. However, because age and the chronic health status of the very elderly are largely progressive and irreversible, we as health care professionals working in intensive care may have to consider longer-term post hospital outcomes as a basis for evaluating the effectiveness of the interventions in ICU.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».