Validity of Medical Insurance Guidelines for Orthognathic Surgery

Investigation of orthognathic surgery indicators-combination with index of orthognathic functional treatment needs (IOFTN) and maxillofacial morphometric analysis

PURPOSE

The aim of this retrospective study was to determine orthognathic surgery indicators for Japanese patients with jaw deformities using both Index of Orthognathic Functional Treatment Needs (IOFTN) and maxillofacial morphometric analysis.

SUBJECTS AND METHODS

The subjects were 89 patients treated with orthognathic surgery and 92 patients treated with orthodontic treatment alone, and were classified as class I, II, or III according to the ANB angle. Based on the results for IOFTN and the results of cephalometric analysis, the indication criteria for orthognathic surgery were examined.

RESULTS

In IOFTN analysis, none of patients in the orthognathic surgery group were classified as category 1 or 2, while 48% of the patients in the orthodontic treatment group were classified as category 4 or 5. The results of the cephalometric analysis of patients in classified categories 4 and 5 showed that the orthognathic surgery group had significantly greater lateral mandibular deviation in Class I cases, significantly more severe degree of mandibular retrusion in Class II cases, and significantly more severe degree of mandibular prognathism in Class III cases. The results of the logistic regression analysis showed that IOFTN was a common variable as an indication criterion for orthognathic surgery, and several different variables were also selected from the cephalometric measurements in each group.

CONCLUSION

IOFTN is a highly sensitive and useful indicator as a criterion for orthognathic surgery. However, in the choice of treatment strategy, maxillofacial morphometric analyses and the patient's desired goal are important.

Risk Factors for Hardware Removal Following Bimaxillary Surgery: A National Database Analysis

Orthognathic surgery typically relies on the rigid fixation of fracture fragments using metal hardware. Though hardware is usually intended to be implanted permanently, the removal of hardware (ROH) is sometimes indicated for a variety of reasons. The authors sought to identify risk factors for ROH following orthognathic surgery. The authors conducted a retrospective analysis of the Merative MarketScan Research Databases, 2007-2021 using Current Procedural Terminology (CPT) and International Classification of Disease (ICD-9 and ICD-10) codes to identify patients who underwent an index Le Fort 1 osteotomy and bilateral sagittal split osteotomy operation on the same day. Statistical analysis involved χ 2 , Shapiro-Wilk, Wilcoxon-Mann-Whitney, Poisson regression, and multivariable logistic regression tests. 4698 patients met the inclusion criteria. The mean age at surgery was 25 years, and 57% were female. ROH occurred in 5.9% of patients. The mean time to hardware removal was 190.5±172.4 days. In a multivariate logistic regression, increased odds of ROH were associated with older patient age [OR: 1.02 (1.01-1.03), P =0.046], sleep apnea [OR: 1.62 (1.13-2.32), P =0.018], and craniofacial syndrome and/or cleft diagnoses [OR: 1.88 (1.14-2.55), P <0.001]. In the same model, postoperative oral antibiotic prophylaxis was not associated with ROH ( P =0.494). The incidence of all-cause complications [IRR: 1.03 (1.01-1.05), P <0.001] rose over the study period, while the incidence of ROH did not change significantly ( P =0.281). Patients at elevated risk should be counseled on the increased possibility of a second operation for ROH before having orthognathic surgery to ensure expectations and health care utilization decisions align with the evidence.

Machine Learning Effectively Diagnoses Mandibular Deformity Using Three-Dimensional Landmarks

BACKGROUND

Jaw deformity diagnosis requires objective tests. Current methods, like cephalometry, have limitations. However, recent studies have shown that machine learning can diagnose jaw deformities in two dimensions. Therefore, we hypothesized that a multilayer perceptron (MLP) could accurately diagnose jaw deformities in three dimensions (3D).

PURPOSE

Examine the hypothesis by focusing on anomalous mandibular position. We aimed to: (1) create a machine learning model to diagnose mandibular retrognathism and prognathism; and (2) compare its performance with traditional cephalometric methods.

STUDY DESIGN, SETTING, SAMPLE

An in-silico experiment on deidentified retrospective data. The study was conducted at the Houston Methodist Research Institute and Rensselaer Polytechnic Institute. Included were patient records with jaw deformities and preoperative 3D facial models. Patients with significant jaw asymmetry were excluded.

PREDICTOR VARIABLES

The tests used to diagnose mandibular anteroposterior position are: (1) SNB angle; (2) facial angle; (3) mandibular unit length (MdUL); and (4) MLP model.

MAIN OUTCOME VARIABLE

The resultant diagnoses: normal, prognathic, or retrognathic.

COVARIATES

None.

ANALYSES

A senior surgeon labeled the patients' mandibles as prognathic, normal, or retrognathic, creating a gold standard. Scientists at Rensselaer Polytechnic Institute developed an MLP model to diagnose mandibular prognathism and retrognathism using the 3D coordinates of 50 landmarks. The performance of the MLP model was compared with three traditional cephalometric measurements: (1) SNB, (2) facial angle, and (3) MdUL. The primary metric used to assess the performance was diagnostic accuracy. McNemar's exact test tested the difference between traditional cephalometric measurement and MLP. Cohen's Kappa measured inter-rater agreement between each method and the gold standard.

RESULTS

The sample included 101 patients. The diagnostic accuracy of SNB, facial angle, MdUL, and MLP were 74.3, 74.3, 75.3, and 85.2%, respectively. McNemar's test shows that our MLP performs significantly better than the SNB (P = .027), facial angle (P = .019), and MdUL (P = .031). The agreement between the traditional cephalometric measurements and the surgeon's diagnosis was fair. In contrast, the agreement between the MLP and the surgeon was moderate.

CONCLUSION AND RELEVANCE

The performance of the MLP is significantly better than that of the traditional cephalometric measurements.