The Impact of Non-Elective Admission on Cost of Care and Length of Stay in Anterior Cervical Discectomy and Fusion

Abstract

Study Design. Retrospective cohort study. Objective. To evaluate the impact of admission status on patient outcomes and healthcare costs in anterior cervical discectomy and fusion (ACDF). Summary of Background Data. Undergoing ACDF nonelectively has been associated with higher patient comorbidity burdens. However, the impact of non-elective status on the total cost of hospital stay has yet to be quantified. Methods. Patients undergoing ACDF at a single institution were placed into elective or non-elective cohorts. Propensity scorematching analysis in a 5:1 ratio controlling for insurance type and comorbidities was used to minimize selection bias. Demographics were compared by univariate analysis. Cost of care, length of stay (LOS), and clinical outcomes were compared between groups using multivariable linear and logistic regression with elective patients as reference cohort. All analyses controlled for sex, preoperative diagnosis, elixhauser comorbidity index (ECI), age, length of surgery, number of segments fused, and insurance type. Results. Of 708 patients in the final ACDF cohort, 590 underwent an elective procedure and 118 underwent a nonelective procedure. The non-elective group was significantly younger (53.7 vs. 49.5 yr; P ¼ 0.0007). Cohorts had similar proportions of private versus public health insurance, although elective had higher rates of commercial insurance (39.22% vs. 15.25%; P < 0.0001) and non-elective had higher rates of managed care (32.77% vs. 56.78%; P < 0.0001). Operation duration was significantly longer in non-elective patients (158 vs. 177 minutes; P ¼ 0.01). Adjusted analysis also demonstrated that admission status independently affected cost

Machine Learning Models for Predicting Postoperative Outcomes following Skull Base Meningioma Surgery

“…Our results demonstrating that Medicare or Medicaid insurance, non-elective admission, greater tumor volume, higher ASA score, and less surgeon years of experience were all associated with increased odds of prolonged hospital LOS corroborates prior findings within the neurosurgical and spine surgery literature. 11 29 30 31 Further, the fact that older patient age, Medicare or Medicaid insurance, greater tumor volume, higher ASA score, and higher mFI-5 score are all associated with higher odds of nonroutine discharge also validates prior research findings. 32 33 34 35 Finally, the fact that Medicare or Medicaid insurance, non-elective admission, greater tumor volume, and less surgeon years of experience were all associated with higher odds in high hospital charges according to our predictive model is also in line with previous research.…”

“…[32][33][34][35] Finally, the fact that Medicare or Medicaid insurance, non-elective admission, greater tumor volume, and less surgeon years of experience were all associated with higher odds in high hospital charges according to our predictive model is also in line with previous research. 16,30,[36][37][38][39][40] Interestingly, several associations that were significant in bivariate analysis were excluded from the final predictive models during the training process (such as the significant association between older patient age and higher odds of prolonged LOS), and some associations that were not significant in bivariate analysis were included as inputs in the final elastic net models (such as the association between patient sex and discharge disposition). This also highlights the importance of differentiating between inferential statistics and predictive analytics.…”

“…32 33 34 35 Finally, the fact that Medicare or Medicaid insurance, non-elective admission, greater tumor volume, and less surgeon years of experience were all associated with higher odds in high hospital charges according to our predictive model is also in line with previous research. 16 30 36 37 38 39 40…”

Prediction of Admission Costs Following Anterior Cervical Discectomy and Fusion Utilizing Machine Learning

“…ML models can generate patient-specific reimbursement that incorporates variables that current bundled payment models do not consider. Following lumbar fusion, implementation of bundled payment models did not result in cost reduction; however, a higher frequency of performed procedures was observed 32. Additional procedures may be performed more frequently to offset losses incurred from managing complicated patients under bundled payment models 31.…”

“…In addition, these models may assist with patient selection for outpatient procedures, which allows for improved financial efficiency as outpatient ACDF has lower direct and total healthcare costs. 12,34,35 By utilizing ML, physicians and healthcare systems can ensure adequate resources are available to provide effective and safe care for all patients without causing a significant burden to the hospital system. ML has multiple utilities and has the capability to improve costeffectiveness, patient selection, access to healthcare, and patient outcomes through individualized and effective patient care.…”