Time from injury to hip-fracture surgery in low-income and middle-income regions: a secondary analysis of data from the International Orthopaedic Multicentre Study in Fracture Care (INORMUS)
Notice bibliographique
Résumé
BACKGROUND: Globally, fall-related injuries are a substantial problem, and 80% of fatal falls occur in low-income and middle-income countries. We aimed to measure time from injury to hip-fracture surgery in people aged 50 years or older living in low-income and middle-income regions, as well as to measure the proportion of patients with surgical stabilisation of their hip fracture within 72 h of admission to hospital and to identify risk factors associated with surgical delay. METHODS: For this secondary analysis, we analysed data collected from Africa, Latin America, China, India, and Asia (excluding China and India) for the International Orthopaedic Multicentre Study in Fracture Care (INORMUS) between March 29, 2014, and June 15, 2022. Patients from INORMUS were included in this analysis if they were aged 50 years or older and had an isolated, primary hip fracture sustained from a ground-level fall. Staff at participating hospitals identified patients with musculoskeletal injury and referred them for assessment of eligibility. We report time from injury to surgery as three distinct time periods: time from injury to hospital admission, time from admission to surgery, and a total time from injury to surgery. Date and time of injury were self-reported by patients at the time of study recruitment. If time to hospital admission after injury exceeded 24 h, patients reported the primary reason for delayed admission. Reasons for surgery, no surgery, and surgical delay were reported by the treating team. For patients undergoing surgery, multivariable regression analyses were used to identify risk factors for surgical delay. FINDINGS: 4486 adults aged 50 years or older with an isolated, primary hip fracture were enrolled in INORMUS from 55 hospitals in 24 countries. Countries were grouped into five regions: Africa (418 [9·3%] of 4486), Latin America (558 [12·4%]), China (1680 [37·4%]), India (1059 [23·6%]) and Asia (excluding China and India; 771 [17·2%]). Of 4486 patients, 3805 (84·8%) received surgery. The rate of surgery was similar in all regions except in Africa, where only 193 (46·3%) of 418 patients had surgery. Overall, 2791 (62·2%) of 4486 patients were admitted to hospital within 24 h of injury. However, 1019 (22·7%) of 4486 patients had delayed hospital admission of 72 h or more from injury. The two most common reasons for delayed admission of more than 24 h were transfer from another hospital (522 [36·2%] of 1441) and delayed care-seeking because patients thought the injury would heal on its own (480 [33·3%]). Once admitted to hospital, 1451 (38·1%) of 3805 patients who received surgery did so within 72 h (median 4·0 days [IQR 1·7-6·0]). Regional variation was seen in the proportion of patients receiving surgery within 72 h of hospital admission (92 [17·9%] of 514 in Latin America, 53 [27·5%] of 193 in Africa, 454 [30·9%] of 1471 in China, 318 [44·4%] of 716 in Asia [excluding China and India], and 534 [58·6%] of 911 in India). Of all 3805 patients who received operative treatment, 2353 (61·8%) waited 72 h or more from hospital admission. From time of injury, the proportion of patients who were surgically stabilised within 72 h was 889 (23·4%) of 3805 (50 [9·7%] of 517 in Latin America, 31 [16·1%] of 193 in Africa, 277 [18·8%] of 1471 in China, 189 [26·4%] of 716 in Asia [excluding China and India], and 342 [37·5%] of 911 in India). INTERPRETATION: Access to surgery within 72 h of hospital admission was poor, with factors that affected time to surgery varying by region. Data are necessary to understand existing pathways of hip-fracture care to inform the local development of quality-improvement initiatives. FUNDING: The National Health and Medical Research Council of Australia, the Canadian Institutes of Health Research, McMaster Surgical Associates, Hamilton Health Sciences, and the US National Institutes of Health.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».