Type II odontoid fractures in the elderly presenting to the emergency department: an assessment of factors affecting in-hospital mortality and discharge to skilled nursing facilities.

BACKGROUND CONTEXT: Type II odontoid fractures (OF) are among the most common cervical spine injuries in the geriatric population. However, there is a paucity of literature regarding their epidemiology. Additionally, the optimal management of these injuries remains controversial, and no study has evaluated the short-term outcomes of geriatric patients presenting to emergency departments (ED). PURPOSE: This study aims to document the epidemiology of geriatric patients presenting to EDs with type II OFs and determine whether surgical management was associated with early adverse outcomes such as in-hospital mortality and discharge to skilled nursing facilities (SNF). STUDY DESIGN: This is a retrospective cohort study. PATIENT SAMPLE: Data was used from the 2016-2020 Nationwide Emergency Department Sample. Patient encounters corresponding to type II OFs were identified. Patients younger than 65 at the time of presentation to the ED and those with concomitant spinal pathology were excluded. OUTCOME MEASURES: The association between the surgical management of geriatric type II OFs and outcomes such as in-hospital mortality and discharge to SNFs. METHODS: Patient, fracture, and surgical management characteristics were recorded. A propensity score matched cohort was constructed to reduce differences in age, comorbidities, and injury severity between patients undergoing operative and nonoperative management. Additionally, to develop a positive control for the analysis of geriatric patients with type II OFs and no other concomitant spinal pathology, a cohort of patients that had been excluded due to the presence of a concomitant spinal cord injury (SCI) was also constructed. Multivariate regressions were then performed on both the matched and unmatched cohorts to ascertain the associations between surgical treatment and in-hospital mortality, inpatient length of stay, encounter charges, and discharge to SNFs. RESULTS: A total of 11,325 encounters were included. The mean total charge per encounter was $60,221. 634 (5.6%) patients passed away during their encounters. In total, 1,005 (8.9%) patients were managed surgically. Surgical management of type II OFs was associated with a 316% increase in visit charge (95% CI: 291%-341%, p<.001), increased inpatient length of stay (IRR: 2.87, 95% CI: 2.62-3.12, p<.001), and increased likelihood of discharge to SNFs (OR=2.62, 95% CI: 2.26-3.05, p<.001), but decreased in-hospital mortality (OR=0.32, CI: 0.21-0.45, p<.001). The propensity score matched cohort consisted of 2,010 patients, matching each of the 1,005 that underwent surgery to 1,005 that did not. These cohorts were well balanced across age (78.24 vs 77.91 years), Elixhauser Comorbidity Index (3.68 vs 3.71), and Injury Severity Score (30.15 vs 28.93). This matching did not meaningfully alter the associations determined between surgical management and in-hospital mortality (OR=0.34, CI=0.21-0.55, p<.001) or SNF discharge (OR=2.59, CI=2.13-3.16, p<.001). Lastly, the positive control cohort of patients with concurrent SCI had higher rates of SNF discharge (50.0% vs 42.6%, p<.001), surgical management (32.3% vs 9.7%, p<.001), and in-hospital mortality (28.9% vs 5.6%, p<.001). CONCLUSIONS: This study lends insight into the epidemiology of geriatric type II OFs and quantifies risk factors influencing adverse outcomes. Patient informed consent should include a discussion of the protective association between definitive surgical management and in-hospital mortality against potential operative morbidity, increased lengths of hospital stay, and increased likelihood of discharge to SNFs. This information may impact patient treatment selection and decision making.

Clinical Outcomes and Cost Profiles for Cage and Allograft Anterior Cervical Discectomy and Fusion Procedures in the Adult Population: A Propensity Score-Matched Study.

STUDY DESIGN: Retrospective cohort study. PURPOSE: To characterize the postoperative outcomes and economic costs of anterior cervical discectomy and fusion (ACDF) procedures using synthetic biomechanical intervertebral cage (BC) and structural allograft (SA) implants. OVERVIEW OF LITERATURE: ACDF is a common spine procedure that typically uses an SA or BC for the cervical fusion. Previous studies that compared the outcomes between the two implants were limited by small sample sizes, short-term postoperative outcomes, and procedures with single-level fusion. METHODS: Adult patients who underwent an ACDF procedure in 2007-2016 were included. Patient records were extracted from MarketScan, a national registry that captures person-specific clinical utilization, expenditures, and enrollments across millions of inpatient, outpatient, and prescription drug services. Propensity-score matching (PSM) was employed to match the patient cohorts across demographic characteristics, comorbidities, and treatments. RESULTS: Of 110,911 patients, 65,151 (58.7%) received BC implants while 45,760 (41.3%) received SA implants. Patients who underwent BC surgeries had slightly higher reoperation rates within 1 year after the index ACDF procedure (3.3% vs. 3.0%, p=0.004), higher postoperative complication rates (4.9% vs. 4.6%, p=0.022), and higher 90-day readmission rates (4.9% vs. 4.4%, p =0.001). After PSM, the postoperative complication rates did not vary between the two cohorts (4.8% vs. 4.6%, p=0.369), although dysphagia (2.2% vs. 1.8%, p<0.001) and infection (0.3% vs. 0.2%, p=0.007) rates remained higher for the BC group. Other outcome differences, including readmission and reoperation, decreased. Physician's fees remained high for BC implantation procedures. CONCLUSIONS: We found marginal differences in clinical outcomes between BC and SA ACDF interventions in the largest published database cohort of adult ACDF surgeries. After adjusting for group-level differences in comorbidity burden and demographic characteristics, BC and SA ACDF surgeries showed similar clinical outcomes. Physician's fees, however, were higher for BC implantation procedures.

Comparison of Deep Learning and Classical Machine Learning Algorithms to Predict Postoperative Outcomes for Anterior Cervical Discectomy and Fusion Procedures With State-of-the-art Performance.

STUDY DESIGN: Retrospective cohort. OBJECTIVE: Due to anterior cervical discectomy and fusion (ACDF) popularity, it is important to predict postoperative complications, unfavorable 90-day readmissions, and two-year reoperations to improve surgical decision-making, prognostication, and planning. SUMMARY OF BACKGROUND DATA: Machine learning has been applied to predict postoperative complications for ACDF; however, studies were limited by sample size and model type. These studies achieved ≤0.70 area under the curve (AUC). Further approaches, not limited to ACDF, focused on specific complication types and resulted in AUC between 0.70 and 0.76. MATERIALS AND METHODS: The IBM MarketScan Commercial Claims and Encounters Database and Medicare Supplement were queried from 2007 to 2016 to identify adult patients who underwent an ACDF procedure (N=176,816). Traditional machine learning algorithms, logistic regression, and support vector machines, were compared with deep neural networks to predict: 90-day postoperative complications, 90-day readmission, and two-year reoperation. We further generated random deep learning model architectures and trained them on the 90-day complication task to approximate an upper bound. Last, using deep learning, we investigated the importance of each input variable for the prediction of 90-day postoperative complications in ACDF. RESULTS: For the prediction of 90-day complication, 90-day readmission, and two-year reoperation, the deep neural network-based models achieved AUC of 0.832, 0.713, and 0.671. Logistic regression achieved AUCs of 0.820, 0.712, and 0.671. Support vector machine approaches were significantly lower. The upper bound of deep learning performance was approximated as 0.832. Myelopathy, age, human immunodeficiency virus, previous myocardial infarctions, obesity, and documentary weakness were found to be the strongest variable to predict 90-day postoperative complications. CONCLUSIONS: The deep neural network may be used to predict complications for clinical applications after multicenter validation. The results suggest limited added knowledge exists in interactions between the input variables used for this task. Future work should identify novel variables to increase predictive power.