Continuous low-dose antibiotic prophylaxis to prevent urinary tract infection in adults who perform clean intermittent self-catheterisation: the AnTIC RCT
Notice bibliographique
Résumé
Background People carrying out clean intermittent self-catheterisation (CISC) to empty their bladder often suffer repeated urinary tract infections (UTIs). Continuous once-daily, low-dose antibiotic treatment (antibiotic prophylaxis) is commonly advised but knowledge of its effectiveness is lacking. Objective To assess the benefit, harms and cost-effectiveness of antibiotic prophylaxis to prevent UTIs in people who perform CISC. Design Parallel-group, open-label, patient-randomised 12-month trial of allocated intervention with 3-monthly follow-up. Outcome assessors were blind to allocation. Setting UK NHS, with recruitment of patients from 51 sites. Participants Four hundred and four adults performing CISC and predicted to continue for ≥ 12 months who had suffered at least two UTIs in the previous year or had been hospitalised for a UTI in the previous year. Interventions A central randomisation system using random block allocation set by an independent statistician allocated participants to the experimental group [once-daily oral antibiotic prophylaxis using either 50 mg of nitrofurantoin, 100 mg of trimethoprim (Kent Pharmaceuticals, Ashford, UK) or 250 mg of cefalexin (Sandoz Ltd, Holzkirchen, Germany);n = 203] or the control group of no prophylaxis (n = 201), both for 12 months. Main outcome measures The primary clinical outcome was relative frequency of symptomatic, antibiotic-treated UTI. Cost-effectiveness was assessed by cost per UTI avoided. The secondary measures were microbiologically proven UTI, antimicrobial resistance, health status and participants’ attitudes to antibiotic use. Results The frequency of symptomatic antibiotic-treated UTI was reduced by 48% using prophylaxis [incidence rate ratio (IRR) 0.52, 95% confidence interval (CI) 0.44 to 0.61;n = 361]. Reduction in microbiologically proven UTI was similar (IRR 0.49, 95% CI 0.39 to 0.60;n = 361). Absolute reduction in UTI episodes over 12 months was from a median (interquartile range) of 2 (1–4) in the no-prophylaxis group (n = 180) to 1 (0–2) in the prophylaxis group (n = 181). The results were unchanged by adjustment for days at risk of UTI and the presence of factors giving higher risk of UTI. Development of antimicrobial resistance was seen more frequently in pathogens isolated from urine andEscherichia colifrom perianal swabs in participants allocated to antibiotic prophylaxis. The use of prophylaxis incurred an extra cost of £99 to prevent one UTI (not including costs related to increased antimicrobial resistance). The emotional and practical burden of CISC and UTI influenced well-being, but health status measured over 12 months was similar between groups and did not deteriorate significantly during UTI. Participants were generally unconcerned about using antibiotics, including the possible development of antimicrobial resistance. Limitations Lack of blinding may have led participants in each group to use different thresholds to trigger reporting and treatment-seeking for UTI. Conclusions The results of this large randomised trial, conducted in accordance with best practice, demonstrate clear benefit for antibiotic prophylaxis in terms of reducing the frequency of UTI for people carrying out CISC. Antibiotic prophylaxis use appears safe for individuals over 12 months, but the emergence of resistant urinary pathogens may prejudice longer-term management of recurrent UTI and is a public health concern. Future work includes longer-term studies of antimicrobial resistance and studies of non-antibiotic preventative strategies. Trial registration Current Controlled Trials ISRCTN67145101 and EudraCT 2013-002556-32. Funding This project was funded by the National Institute for Health Research Health Technology Assessment programme and will be published in full inHealth Technology AssessmentVol. 22, No. 24. See the NIHR Journals Library website for further project information.
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 | 0,008 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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.
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 ».