PD08-02 IMMERSIVE VIRTUAL-REALITY FOR PERCUTANOUS NEPHROLITHOTOMY: IMPACT ON PATIENT EDUCATION, SURGICAL PLANNING AND TREATMENT OUTCOME
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Résumé
You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology II (PD08)1 Apr 2019PD08-02 IMMERSIVE VIRTUAL-REALITY FOR PERCUTANOUS NEPHROLITHOTOMY: IMPACT ON PATIENT EDUCATION, SURGICAL PLANNING AND TREATMENT OUTCOME Egor Parkhomenko*, Mitchell O'Leary, Shoaib Safiullah, Francis Jefferson, Sartaaj Walia, Ryan James, Cyrus Lin, Roshan Patel, Kamaljot Kaler, Jaime Landman, and Ralph Clayman Egor Parkhomenko*Egor Parkhomenko* More articles by this author , Mitchell O'LearyMitchell O'Leary More articles by this author , Shoaib SafiullahShoaib Safiullah More articles by this author , Francis JeffersonFrancis Jefferson More articles by this author , Sartaaj WaliaSartaaj Walia More articles by this author , Ryan JamesRyan James More articles by this author , Cyrus LinCyrus Lin More articles by this author , Roshan PatelRoshan Patel More articles by this author , Kamaljot KalerKamaljot Kaler More articles by this author , Jaime LandmanJaime Landman More articles by this author , and Ralph ClaymanRalph Clayman More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555247.96110.71AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Percutaneous nephrolithotomy (PCNL) requires urologists to have detailed knowledge of the stone and its relationship to the renal anatomy. Immersive virtual reality (iVR) provides patient-specific 3D models that might be beneficial in this regard. Our objective is to evaluate the impact of iVR on surgeon's preoperative planning, clinical outcomes, and patient education. METHODS: Four endourologists used iVR models (Figure 1) to acquaint themselves with the renal anatomy prior to PCNL in 25 patients. iVR renderings were also viewed by patients using the same head-mounted Oculus Rift display (Facebook Inc.). Using a Likert-type scale (1=strongly disagree to 5=strongly agree), surgeons rated their understanding of the anatomy after viewing CT images only and then after the iVR experience; using a similar Likert-type scale, patients recorded their experience with iVR. Next, iVR patients were matched with 25 retrospective non-iVR patients with similar age, ASA, BMI, stone burden, gender, and nephrostomy tract location. Student's t-test (Excel) was used for data analysis. RESULTS: iVR improved surgeons' understanding of the optimal calyx of entry and the stone's location, size/orientation (p<0.01) (Table 1). iVR altered the surgical approach in 10 (40%) cases. Patients strongly agreed that iVR reduced their preoperative anxiety (p<0.05). In the retrospective matched-paired analysis, the iVR group had a significant decrease in fluoroscopy time (139 vs. 269 sec, p=0.027) and blood loss (66 vs. 206 mL, p=0.019) as well as a trend toward fewer nephrostomy needle passes (1.13 vs. 1.46 passes; p=0.10) and a higher 100% stone-free rate (9/25 vs 5/25, p=0.15). CONCLUSIONS: iVR prior to PCNL improved urologists' understanding of the renal anatomy, altered the operative approach, and mitigated patients' preoperative anxiety. Clinically, iVR decreased both fluoroscopy time and blood loss and trended toward fewer access tracts and higher stone free rates. Source of Funding: none Boston, MA; Orange, CA; Colombia, MO; Orange, CA; Seattle, WA; Orange, CA; Calgary, Canada; Orange, CA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e148-e148 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Egor Parkhomenko* More articles by this author Mitchell O'Leary More articles by this author Shoaib Safiullah More articles by this author Francis Jefferson More articles by this author Sartaaj Walia More articles by this author Ryan James More articles by this author Cyrus Lin More articles by this author Roshan Patel More articles by this author Kamaljot Kaler More articles by this author Jaime Landman More articles by this author Ralph Clayman More articles by this author Expand All Advertisement PDF downloadLoading ...
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».