Can the FEAR Index Be Used to Predict Microinstability in Patients Undergoing Hip Arthroscopic Surgery?
Notice bibliographique
Résumé
Background: Atraumatic hip instability, or microinstability, is a challenging diagnosis for clinicians to make. Several radiographic parameters have been proposed to help identify patients with instability as a means to direct treatment. The Femoro-epiphyseal Acetabular Roof (FEAR) index was recently offered as a parameter to predict instability in a borderline dysplastic population. Purpose: To evaluate the FEAR index in a series of predominantly nondysplastic patients undergoing hip arthroscopic surgery to determine if it can accurately predict patients with diagnosed microinstability at the time of surgery. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A consecutive series of 200 patients undergoing hip arthroscopic surgery were evaluated for microinstability intraoperatively. Microinstability was diagnosed based on previously published criteria. Retrospectively, radiographic parameters were measured including the lateral center edge angle of Wiberg (LCEA), Tönnis angle, physeal scar angle, and FEAR index. Patients were excluded if they previously had any type of bony procedures performed, underwent prior open hip surgery or total hip arthroplasty of the ipsilateral hip, had osteoarthritis (Tönnis grade >1), or had any radiographic features of moderate-to-severe acetabular dysplasia including an LCEA <18°. Results: After applying exclusion criteria, 167 hips in 150 patients were analyzed. Based on an intraoperative assessment, 96 hips (57.5%) were considered stable, and 71 hips (42.5%) had signs of microinstability (unstable group). Patients in the unstable group had fewer radiographic findings of femoroacetabular impingement and higher rates of borderline dysplasia. All 4 measured angles were found to have excellent interobserver agreement. The FEAR index was significantly more positive in the unstable group compared with the stable group (−7.8° vs −11.3°, respectively; P = .004). A more positive FEAR index was also found in patients meeting intraoperative criteria for instability, with the exception of chondral wear pattern. Unstable nondysplastic patients (LCEA ≥25°, Tönnis angle ≤10°) also were found to have higher FEAR index values (−9.0° vs −12.0°, respectively; P = .012). A FEAR index cut-off of −5.0° was associated with a specificity of 92.4% and accuracy of 69.4% for predicting instability in a nondysplastic population. Conclusion: The FEAR index was validated to improve the recognition of unstable patients preoperatively across a population with both borderline dysplastic and nondysplastic features.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».