MétaCan
Menu
Retour à la cohorte
Enregistrement W2552229967 · doi:10.1111/j.1742-6723.2011.01415.x

From Other Journals February 2011

2011· article· en· W2552229967 sur OpenAlexaboutno aff
Michael Yeoh

Notice bibliographique

RevueEmergency Medicine Australasia · 2011
Typearticle
Langueen
DomaineMedicine
ThématiqueCardiac Arrest and Resuscitation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineOvercrowdingTriageMedical emergencyDashboardEmergency medicine

Résumé

récupéré en direct d'OpenAlex

In Emergency Medicine Australasia vol 23 iss 1,1 the article ‘From Other Journals’ was misleading in layout due to a typesetting error. The publisher apologizes for this error. The correct format should be as follows. Please note there has been a reference correction in the section ‘Factors associated with procedural sedation complications’: From Other Journals Most EDs suffer from periods of overcrowding, and this can lead to lengthy delays in ambulance offload times, which in turn affects the timeliness of response for prehospital emergencies. The present paper reports on the effects that a regional dashboard can have on individual ED capacity. The dashboard took real-time information from all three tertiary EDs in a single Canadian city. ED capacity thresholds were developed individually for the three EDs, taking into account numbers of resuscitation patients, boarded patients, patients in waiting room and expected ambulance arrivals. Each ED then had a capacity represented by a colour code – green/yellow (favourable) or orange/red (unfavourable). Central dispatch had the status of all three EDs and ambulances were advised to avoid the most overcrowded ED. Bypass (again with pre-defined criteria) remained an option. The authors presented before and after results in three 6-month blocks. Comparing April to September for the year before, with the same period the year after showed that overall attendances increased, as did ambulance arrivals. Triage acuity was similar, as was the proportion of patients aged over 65 years. However, the proportion of time that the EDs were green/yellow increased from 58% to 79%. The number of hours of bypass fell from 198 to 27. Clearly there might have been other factors affecting ED capacity over this period that were not described or accounted for. Although the cost of implementing this relatively simple programme was not detailed, given the significant clinical and political issues around delays in ambulance offloads, stretched prehospital dispatch centres should investigate this programme. (McLeod B. Acad. Emerg. Med. 2010; 17: 1383–9) Despite some ill-informed and misleading comments about low-acuity patients, ED overcrowding principally results from the inability of admitted patients to be transferred to ward beds in a timely manner. Most experts agree that a greater inpatient capacity is required in order to relieve access block and decrease ED overcrowding. The present paper examines the potential effect of changing inpatient culture and work processes (i.e. more timely discharges) on ED overcrowding. Using real data from a single month at a single hospital, a computer model was developed to examine the relationship between admissions, discharges and ED overcrowding (number of hours admitted patients waited in ED before transfer to an inpatient bed). Ward admissions were those via ED and elective surgery (i.e. from recovery), and those patients discharged to a ward from ICU. For simplicity, the model assumed that all wards could take any patient. Discharge times were recorded for all patients discharged home from the wards. For the reference month there was a daily average of 39 admissions from the ED, 23 surgical admissions and 20 discharges from ICU. Each ED patient waited an average 2.6 h for an inpatient bed – a total of 81 h of ED boarding time each day. Using the model, moving discharges forward by 1 h decreased total ED boarding time by 40%, and moving it forward 3 h decreased ED boarding time to a total of 1 h. A ‘Discharge by Noon’ policy (75% of all discharges by noon) decreased ED boarding to just 3 h. A ‘Dayshift Uniform Discharge’ policy (all discharges evenly throughout period 0800–1600) also reduced total ED boarding time to 3 h. Most large hospitals have a significant number of staff employed to count things. It would seem sensible that readers now ask their own hospitals to record the time of discharge from inpatient beds. Significant improvements in bed access are likely if the time of discharge of inpatients can be brought forward – by even just 1 h. (Powell ES. J. Emerg. Med. 2010. Epub. [Cited 1 December 2010.] Available from URL: http://dx.doi.org/10.1016/j.jemermed.2010.06.028) ED overcrowding is a significant problem for ED patients, ED staff and the community. The two papers above demonstrate that there are simple things that can be implemented which might have significant impacts on patient flow. Whereas each hospital could claim to be different from those mentioned in the studies, there are likely to be more similarities than differences. This prospective randomized blinded trial examined which clinical method best identified endobronchial intubation in elective surgical patients. Patients undergoing elective surgery underwent either tracheal intubation (2.5–4 cm above carina) or endobronchial intubation (right mainstem bronchus). Both positions were confirmed by fibreoptic bronchoscopy. Anaesthetists and first year residents were then asked to separately identify tube position by one of four methods: auscultation, observation and palpations of symmetrical chest movements, cm scale on the tube, and a combination of all three methods. One hundred and sixty patients were randomized with 74% being female. Each patient was assessed by one anaesthetist and one resident which produced 320 measurements. The sensitivity for detection of endobronchial position was poor in the auscultation observation methods, but high for both depth and the combined methods. Experience was important in the auscultation and observation methods, but had little influence on the accuracy of the depth and combined methods. The present study was limited by small numbers and uneven sex distribution, but there are important lessons for readers: noting tube depth is a critical adjunct to other clinical methods for identifying potential endobronchial intubation, particularly in a noisy ED environment. (Sitzwohl C. BMJ 2010. Epub. doi: 10.1136/bmj.c5943) This single centre study used a procedural sedation registry data to identify factors associated with complications in procedural sedation. A 2 year period was analysed with the complications pre-specified: hypotension (systolic BP < 90 mmHg), hypoxia (SpO2 < 90%), vomiting, aspiration, apnoea or cardiac arrest. The data collection form also contained patient demographics, clinician seniority, medications used and procedure performed (including success). Sedation guidelines existed in the study ED, but formal competency was not assessed. There were 1420 patients entered into the registry over 2 years with most receiving an opiate combined with midazolam or propofol. Joint reductions were the most common indication. There was a reported complication rate of 3.5% (49/1420). Most of these were respiratory – desaturation (20), apnoea (8) and laryngospasm/bronchospasm (5). Of the 49 complications, 22 patients had their sedation reversed by naloxone or flumazenil. No patient suffered morbidity because of sedation; however, two patients suffered humeral fractures during shoulder relocation! Multivariate analysis showed two factors were significantly associated with complications: the depth of sedation (sedation level of 4 or 5) and the time of the procedure (20.00 hours to 08.00 hours). There might be some debate about the intended level of sedation and what constitutes a complication, i.e. apnoea would be expected at level 5 and is not necessarily a complication. However, the message about the timing of the procedure is important, and is supported by other work in this field. Readers should evaluate what procedures really need to be carried out at night in their ED, particularly after midnight, and who should be doing them. (Jacques KG. Emerg. Med. J. 2010. Epub. doi:10.1136/emj.2010.102475) There is a paucity of evidence behind many guidelines, and prehospital protocols are no exception. This prospective randomized controlled trial was designed to address the issue of optimal oxygen therapy for COAD patients in the prehospital setting. Eligible patients were those aged over 35 years with breathlessness plus either a history of COAD or greater than 10 pack year smoking history. Cluster randomization was carried out with paramedics administering either titrated oxygen via nasal prongs to achieve SpO2 of 88–92%, or standard high flow oxygen of 6–8 L/min via facemask. All other treatments were according to ambulance protocols. Patients were to have arterial blood gas performed on arrival to hospital. All other treatment in ED and hospital was at treating clinician discretion and not standardized. The primary outcome was hospital mortality. Sixty-two paramedics consented to the study and they transported 405 eligible patients during the 13 month study period. Just over half these patients were later confirmed to have COAD. Many patients had protocol violations – mostly application of high flow oxygen in the titrated oxygen group. Blood gas measurements were only undertaken in 57% of patients and only 19% had this performed within 30 min of ED arrival. In hospital mortality was significantly higher for the high-flow group (9% vs 4%) and also in the subgroup of confirmed COAD patients (9% vs 2%). For the COAD patients the mean CO2 on arrival was 78 mmHg in high-flow oxygen group, compared with 55 mmHg in the titrated group. Mean oxygen levels were 98 mmHg and 79 mmHg, respectively. Although there are methodological issues in the present paper, it would seem reasonable that prehospital protocols now specify titrated oxygen therapy for patients with known, or at risk of, COAD. (Austin MA. BMJ 2010; 341: c5462)

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,464
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,1130,001

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 Opus0,077
Tête enseignante GPT0,345
Écart entre enseignants0,268 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

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

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueEmergency Medicine AustralasiaMême sujetCardiac Arrest and ResuscitationTravaux en français237 207