The value of electronic patient records for determining the prevalence and prognosis of cognitive and physical frailty in a large hospital-based cohort
Notice bibliographique
Résumé
Older people with frailty account for an increasing proportion of hospital admissions but access to specialised geriatric care varies. Data on the burden of cognitive and physical frailty hospital-wide and by specialty are currently limited but such data are necessary for informing clinical guidance, service-planning and policy. Routine clinical data captured in hospital electronic patient records (EPRs) offers an alternative to existing prospective and administrative coding data study designs but the value of these data for measuring frailty has not yet been studied. Therefore, my thesis aimed to evaluate ‘the value of hospital EPRs for determining the prevalence and prognosis of cognitive and physical frailty’ primarily using data from a large hospital-based cohort, i.e., the Oxford Cognitive Comorbidity, Ageing and Frailty Research Database-Electronic Patient Records (ORCHARD-EPR) study. First, in a systematic review/meta-analysis of 45 hospital-wide and general medicine cohorts, I showed that global frailty, measured using validated tools, was prevalent in older people with unplanned hospital admissions although heterogeneity was high and not explained by frailty tool, setting, or risk of bias. Despite variation in prevalence, frailty was consistently associated with mortality, length of stay (LoS) and discharge destination including after adjustment for confounders, although findings on readmissions were mixed. Notably, cognitive impairment, and in particular delirium, was poorly ascertained by all the frailty measures used in included studies and therefore prevalence of cognitive frailty and the degree of overlap with physical frailty was uncertain. In addition, all studies were either small prospective studies or large administrative datasets based on ICD-10 diagnostic coding except for three which used EPR data but were not validated. Second, I assembled, cleaned and validated data from the Oxford and Reading Cognitive Comorbidity, Frailty and Ageing Research Database-Electronic Patient Record (ORCHARD-EPR) 2017-2019 dataset which contains pseudo-anonymised Oxfordshire University Hospital NHS Foundation Trust EPR data for >=100,000 unplanned hospital admissions. Importantly, ORCHARD-EPR includes the results of mandatory cognitive screening in those >=70 years (existing dementia diagnosis, delirium diagnosis informed by the Confusion Assessment Methods-CAM and recorded as “certain” or “uncertain”, Abbreviated Mental Test-AMT). I validated cognitive frailty data from the cognitive screen for general medicine admissions in ORCHARD-EPR against a reference cohort. The prevalence of cognitive frailty (certain delirium, dementia, AMT<8) in general medicine admissions was 35% in ORCHARD-EPR (increasing to 41% when uncertain delirium diagnosis was included) compared to 50% in the reference data with the difference in prevalence largely explained by lower rates of delirium diagnosis in ORCHARD-EPR. Third, I determined that cognitive frailty (certain+uncertain delirium, dementia, AMT<8) was present in 35% of all ORCHARD-EPR admissions (n=51,202) across 29 specialties, with delirium the most common diagnosis. I also showed that any cognitive frailty predicted survival over up to four years of follow-up as well as LoS, delayed discharge, and discharge destination over and above age, sex, comorbidity and illness severity, with associations strongest for delirium. Additionally, only delirium in non-care home residents predicted readmission. Fourth, using a modified version of the Hospital Frailty Risk Score to measure physical frailty from ICD-10 codes, I found that moderate/severe physical frailty was present in 73% of individuals with cognitive frailty, but only 46% with physical frailty were cognitively frail. Despite being independently associated with survival, physical frailty added little to mortality risk in those with cognitive frailty but added markedly to LoS and risks of delayed discharge and discharge to a destination other than home. In conclusion, routinely acquired clinical frailty data is a valuable tool for research, capable of combining large, inclusive, hospital-wide sampling with accurate ascertainment as shown using ORCHARD-EPR. Similar approaches could be adopted at other centres but will likely only be useful if routine screening is embedded into the EPR and shown to reliably identify frail patients. Moreover, findings of high frailty prevalence across multiple specialties and impact on outcomes supports more widespread implementation of routine cognitive and physical frailty screening in older people with unplanned hospital admission in line with current guidance. Additionally, findings support increased emphasis on delirium in policy and research.
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 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 | 0,039 | 0,119 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 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 ».