Perioperative Complications During Living Donor Nephrectomy: Results From a Multicenter Cohort Study
Notice bibliographique
Résumé
BACKGROUND: While living kidney donation is considered safe in healthy individuals, perioperative complications can occur due to several factors. OBJECTIVE: We explored associations between the incidence of perioperative complications and donor characteristics, surgical technique, and surgeon's experience in a large contemporary cohort of living kidney donors. DESIGN: Living kidney donors enrolled prospectively in a multicenter cohort study with some data collected retrospectively after enrollment was complete (eg, surgeon characteristics). SETTING: Living kidney donor centers in Canada (n = 12) and Australia (n = 5). PATIENTS: Living kidney donors who donated between 2004 and 2014 and the surgeons who performed the living kidney donor nephrectomies. MEASUREMENTS: Operative and hospital discharge medical notes were collected prospectively, with data on perioperative (intraoperative and postoperative) information abstracted from notes after enrollment was complete. Complications were graded using the Clavien-Dindo system and further classified into minor and major. In 2016, surgeons who performed the nephrectomies were invited to fill an online survey on their training and experience. METHODS: Multivariable logistic regression models with generalized estimating equations were used to compare perioperative complication rates between different groups of donors. The effect of surgeon characteristics on the complication rate was explored using a similar approach. Poisson regression was used to test rates of overall perioperative complications between high- and low-volume centers. RESULTS: Of the 1421 living kidney donor candidates, 1042 individuals proceeded with donation, where 134 (13% [95% confidence interval (CI): 11%-15%]) experienced 142 perioperative complications (55 intraoperative; 87 postoperative). The most common intraoperative complication was organ injury and the most common postoperative complication was ileus. No donors died in the perioperative period. Most complications were minor (90% of 142 complications [95% CI: 86%-96%]); however, 12 donors (1% of 1042 [95% CI: 1%-2%]) experienced a major complication. No statistically significant differences were observed between donor groups and the rate of complications. A total of 43 of 48 eligible surgeons (90%) completed the online survey. Perioperative complication rates did not vary significantly by surgeon characteristics or by high- versus low-volume centers. LIMITATIONS: Operative and discharge reporting is not standardized and varies among surgeons. It is possible that some complications were missed. The online survey for surgeons was completed retrospectively, was based on self-report, and has not been validated. We had adequate statistical power only to detect large effects for factors associated with a higher risk of perioperative complications. CONCLUSIONS: This study confirms the safety of living kidney donation as evidenced by the low rate of major perioperative complications. We did not identify any donor or surgeon characteristics associated with a higher risk of perioperative complications. TRIAL REGISTRATIONS: Living Kidney Donor Study (https://clinicaltrials.gov/ct2/show/NCT00936078).
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 | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».