213 A Population-based Analysis of Predictors to Penile Surgical Intervention among Inpatients with Acute Priapism
Notice bibliographique
Résumé
ABSTRACT Introduction In cases of priapism not amenable to conservative treatment, penile surgical interventions (PSI) such as surgical shunts and inflatable penile prosthesis are often indicated. While risk factors predisposing patients to priapism have been well-established, predictors specific to penile surgical intervention are less well-known and restricted and largely limited to retrospective, single institution studies. Objective To identify predictors associated with penile surgical intervention for patients admitted with acute priapism. The secondary objective was to assess the association of PSI with inpatient outcomes such as length of hospital stay and total hospital charges. Methods Using the National Inpatient Sample (2010-2015), a cross-sectional descriptive analysis of inpatients with acute priapism was performed and stratified by the presence of any PSI. Previously identified risk factors for priapism were also captured based on biological plausibility and evidence from the literature. Given the paucity of known risk factors to PSI, backwards elimination Akaike Information Criterion was used to construct a survey-weighted multivariable logistic model. Additional survey-weighted negative binomial regression and generalized linear models with logarithmic transformation were utilized to compare association of PSI to length of hospital stay (LOS) and total hospital charges, respectively. Results Among a weighted total of 14,529 hospitalizations with a diagnosis of acute priapism, 4,953 (34.1%) underwent PSI. Compared with patients with Medicare, those with Medicaid (OR: 1.47; p=0.003), private insurance (OR: 1.87; p<0.001), and other insurance (OR: 2.70; p<0.001) were at increased odds of undergoing surgical intervention (Figure 1). Similarly, those with a history of substance abuse (OR: 2.04; p<0.001) and ≥3 Elixhauser comorbidities (OR: 1.65; p=0.020) were at increased odds of PSI. Conversely, Black patients (OR: 0.75; p=0.039), sickle cell disease (OR: 0.28; p<0.001), alcohol abuse (OR: 0.48; p<0.001), neurologic diseases (OR: 0.46; p<0.001), solid (OR: 0.16; p<0.001) and hematologic (OR: 0.55; p = 0.012) malignancies, and patients at teaching hospitals (OR: 0.79; p=0.019) were less likely to undergo PSI. Surgical interventions coincided with shorter median hospital length of stay (adjusted Incidence Rate Ratio (IRR):0.62; p<0.001) and lower ratio of the mean hospital charges (adjusted Ratio: 0.49; p <0.001). Conclusions Approximately one-third of patients admitted with priapism undergo surgical intervention. Numerous patient and facility-level risk factors have been associated with undergoing PSI, particularly in those with a history of substance abuse. Moreover, patients undergoing PSI were associated with shorter hospital stays and lower hospital charges. Future research exploring which patients may benefit most from surgical intervention would not only curb delays in management, but also potentially reduce healthcare charges. Disclosure No
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».