Are the Umbilicus and Iliac Crests Truly at the Level of L4 to L5? A Computed Tomography-Based Study of Surface Anatomy of the Anterior Lumbar Spine.

BACKGROUND: This study aimed to determine whether the iliac crests are truly at the level of L4 to L5, accounting for patient demographic and anthropometric characteristics. METHODS: We measured the umbilicus and iliac crests relative to the lumbar spine using computed tomography of patients without spinal pathology, accounting for the influences of patient height, weight, body mass index (BMI), sex, race, and ethnicity. RESULTS: A total of 834 patients (391 men and 443 women) were reviewed. The location of the umbilicus relative to the lumbar spine demonstrated a unimodal distribution pattern clustered at L4, while the iliac crests were most frequently located from L4 to L5. Iliac crests were located above the L4 to L5 disc space 26.5% of the time. Iliac crests were located at the L4 to L5 disc space 29.8% of the time. No correlations were observed between the umbilicus and iliac crests with patient height, weight, or BMI. There was no difference in the location of the umbilicus with respect to patient sex, race, and ethnicity. The locations of the iliac crests were cephalad in women compared with men and in Hispanics compared with African American, Caucasian, and Asian patients. CONCLUSIONS: The iliac crests were located above the level of the L4 to L5 disc space approximately 26% of the time. The umbilicus is most frequently at the level of the L4 vertebral body. Patient height, weight, and BMI do not influence the location of the umbilicus or the iliac crests relative to the lumbar spine. Patient sex and ethnicity influence the location of the iliac crests but not the umbilicus relative to the lumbar spine. CLINICAL RELEVANCE: Modern neurosurgical techniques require clearance of the iliac crests during anterior and anterolateral approaches. Understanding the level of the iliac crests is crucial in planning for transpsoas fusion approaches.

Correlations among MRI-based cervical and thoracic vertebral bone quality score, CT-based Hounsfield Unit score, and DEXA t-score in assessment of bone mineral density.

BACKGROUND: Further optimization of the validated vertebral bone quality (VBQ) score using magnetic resonance imaging (MRI) may expand its clinical utility for bone mineral density (BMD) assessment. This study evaluated the correlations among cervical and thoracic VBQ scores, the validated Hounsfield Unit (HU) measured on computed tomography (CT), and dual-energy x-ray absorptiometry (DEXA) values. METHODS: We retrieved the medical and radiographic records of 165 patients who underwent synchronous MRI of the cervical and thoracic spine, as well as DEXA and CT imaging of the spine obtained within 1 year of each other between 2015 and 2022. Radiographic data consisted of the MRI-based cervical and thoracic VBQ scores, CT-based HU, and DEXA T-scores of the spine and hip. Patient age, sex, body mass index (BMI), and ethnicity were also obtained. RESULTS: Mean cervical and thoracic VBQ scores were 3.99 ± 1.68 and 3.82 ± 2.11, respectively. Mean HU and DEXA T-scores of the spine and hip were 135.75 ± 60.36, -1.01 ± 1.15, and -0.47 ± 2.27. All correlations among VBQ, HU, and DEXA were insignificant except for weak correlations between cervical and thoracic VBQ, and cervical VBQ and HU. No correlations were observed between radiographic scores and patient age or BMI. No differences based on ethnicity or sex were observed with respect to cervical or thoracic VBQ, HU, or DEXA. CONCLUSION: Cervical and thoracic VBQ scores are distinct from Hounsfield Unit and DEXA values. VBQ scoring in the cervical and thoracic spine is not influenced by patient age, ethnicity, sex, or BMI.

Predicting Prolonged Length of Hospital Stay and Identifying Risk Factors Following Total Ankle Arthroplasty: A Supervised Machine Learning Methodology.

Ankle osteoarthritis (OA) is a debilitating condition that arises as a result of trauma or injury to the ankle and often progresses to chronic pain and loss of function that may require surgical intervention. Total ankle arthroplasty (TAA) has emerged as a means of operative treatment for end-stage ankle OA. Increased hospital length of stay (LOS) is a common adverse postoperative outcome that increases both the complications and cost of care associated with arthroplasty procedures. The purpose of this study was to employ four machine learning (ML) algorithms to predict LOS in patients undergoing TAA using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. The ACS-NSQIP database was queried to identify adult patients undergoing elective TAA from 2008 to 2018. Four supervised ML classification algorithms were utilized and tasked with predicting increased hospital length of stay (LOS). Among these variables, female sex, ASA Class III, preoperative sodium, preoperative hematocrit, diabetes, preoperative creatinine, other arthritis, BMI, preoperative WBC, and Hispanic ethnicity carried the highest importance across predictions generated by 4 independent ML algorithms. Predictions generated by these algorithms were made with an average AUC of 0.7257, as well as an average accuracy of 73.98% and an average sensitivity and specificity of 48.47% and 79.38%, respectively. These findings may be useful for guiding decision-making within the perioperative period and may serve to identify patients at increased risk for a prolonged LOS.

Application of machine learning algorithms to predict 30-day hospital readmission following cement augmentation for osteoporotic vertebral compression fractures.

Objective: Osteoporosis is a common skeletal disease that greatly increases the risk of pathologic fractures and accounts for approximately 700,000 vertebral compression fractures (VCFs) annually in the United States. Cement augmentation procedures such as balloon kyphoplasty (KP) and percutaneous vertebroplasty (VP) have demonstrated efficacy in the treatment of VCFs, however, some studies report rates of readmission as high as 10.8% following such procedures. The purpose of this study was to employ Machine Learning (ML) algorithms to predict 30-day hospital readmission following cement augmentation procedures for the treatment of VCFs using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Methods: ACS-NSQIP was queried to identify patients undergoing either KP or VP from 2011 to 2014. Three ML algorithms were constructed and tasked with predicting post-operative readmissions within this cohort of patients. Results: Postoperative pneumonia, ASA Class 2 designation, age, partially-dependent functional status, and a history of smoking were independently identified as highly predictive of readmission by all ML algorithms. Among these variables postoperative pneumonia (p < 0.01), ASA Class 2 designation (p < 0.01), age (p = 0.002), and partially-dependent functional status (p < 0.01) were found to be statistically significant. Predictions were generated with an average AUC value of 0.757 and an average accuracy of 80.5%. Conclusions: Postoperative pneumonia, ASA Class 2 designation, partially-dependent functional status, and age are perioperative variables associated with 30-day readmission following cement augmentation procedures. The use of ML allows for quantification of the relative contributions of these variables toward producing readmission.

Prediction of In-Hospital Mortality Following Vertebral Fracture Fixation in Patients With Ankylosing Spondylitis or Diffuse Idiopathic Skeletal Hyperostosis: Machine Learning Analysis.

BACKGROUND: Ankylosing spondylitis (AS) and diffuse idiopathic skeletal hyperostosis (DISH) are distinct pathological entities that similarly increase the risk of vertebral fractures. Such fractures can be clinically devastating and frequently portend significant neurological injury, thus making their prevention a critical focus. Of particular significance, spinal fractures in patients with AS or DISH carry a considerable risk of mortality, with reports on 1-year injury-related deaths ranging from 24% to 33%. As such, the purpose of this study was to conduct machine learning (ML) analysis to predict postoperative mortality in patients with AS or DISH using the Nationwide Inpatient Sample Healthcare Cost and Utilization Project (HCUP-NIS) database. METHODS: HCUP-NIS was queried to identify adult patients carrying a diagnosis of AS or DISH who were admitted for spinal fractures and underwent subsequent fusion or corpectomy between 2016 and 2018. Predictions of in-hospital mortality in this cohort were then generated by three independent ML algorithms. RESULTS: An in-hospital mortality rate of 5.40% was observed in our selected population, including a rate of 6.35% in patients with AS, 2.81% in patients with DISH, and 8.33% in patients with both diagnoses. Increasing age, hypertension with end-organ complications, spinal cord injury, and cervical spinal fractures each carried considerable predictive importance across the algorithms utilized in our analysis. Predictions were generated with an average area under the curve of 0.758. CONCLUSIONS: This study's application of ML algorithms to predict in-hospital mortality among patients with AS or DISH identified a number of clinical risk factors relevant to this outcome. CLINICAL RELEVANCE: These findings may serve to provide physicians with an awareness of risk factors for in-hospital mortality and, subsequently, guide management and shared decision-making among patients with AS or DISH.

Accounting for age in prediction of discharge destination following elective lumbar fusion: a supervised machine learning approach.

BACKGROUND CONTEXT: The number of elective spinal fusion procedures performed each year continues to grow, making risk factors for post-operative complications following this procedure increasingly clinically relevant. Nonhome discharge (NHD) is of particular interest due to its associations with increased costs of care and rates of complications. Notably, increased age has been found to influence rates of NHD. PURPOSE: To identify aged-adjusted risk factors for nonhome discharge following elective lumbar fusion through the utilization of Machine Learning-generated predictions within stratified age groupings. STUDY DESIGN: Retrospective Database Study. PATIENT SAMPLE: The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database years 2008 to 2018. OUTCOME MEASURES: Postoperative discharge destination. METHODS: ACS-NSQIP was queried to identify adult patients undergoing elective lumbar spinal fusion from 2008 to 2018. Patients were then stratified into the following age ranges: 30 to 44 years, 45 to 64 years, and ≥65 years. These groups were then analyzed by eight ML algorithms, each tasked with predicting post-operative discharge destination. RESULTS: Prediction of NHD was performed with average AUCs of 0.591, 0.681, and 0.693 for those aged 30 to 44, 45 to 64, and ≥65 years respectively. In patients aged 30 to 44, operative time (p<.001), African American/Black race (p=.003), female sex (p=.002), ASA class three designation (p=.002), and preoperative hematocrit (p=.002) were predictive of NHD. In ages 45 to 64, predictive variables included operative time, age, preoperative hematocrit, ASA class two or class three designation, insulin-dependent diabetes, female sex, BMI, and African American/Black race all with p<.001. In patients ≥65 years, operative time, adult spinal deformity, BMI, insulin-dependent diabetes, female sex, ASA class four designation, inpatient status, age, African American/Black race, and preoperative hematocrit were predictive of NHD with p<.001. Several variables were distinguished as predictive for only one age group including ASA Class two designation in ages 45 to 64 and adult spinal deformity, ASA class four designation, and inpatient status for patients ≥65 years. CONCLUSIONS: Application of ML algorithms to the ACS-NSQIP dataset identified a number of highly predictive and age-adjusted variables for NHD. As age is a risk factor for NHD following spinal fusion, our findings may be useful in both guiding perioperative decision-making and recognizing unique predictors of NHD among specific age groups.

Analysis of reasons for medical malpractice litigation due to laminectomy.

OBJECTIVE: The aim of this study was to identify the incidence and characteristics of malpractice lawsuits pertaining to laminectomy performed either as a stand-alone operation or concurrent with another procedure by querying the Westlaw Edge and VerdictSearch databases. Malpractice claims analysis is performed by several medical specialties to provide insight into patient values, methods to improve quality of care, and risk factors for litigation pertaining to specific procedures or treatments. METHODS: Westlaw and VerdictSearch were queried using the keywords "laminectomy" and "spine." Claims were reviewed, with the inclusion criteria defined as a case filed between 2000 and 2022 that involved the plaintiff's basis of litigation resting on a claim of medical malpractice due to laminectomy. Additional collected data included the case date, verdict ruling, state or federal location of the filed claim, sustained injuries, and payment or settlement amount. RESULTS: After review of 4732 cases, 201 were identified as malpractice claims due to laminectomy. The most common reasons for litigation were delayed or denied treatment (n = 106), procedural errors (n = 38), inadequate management of postlaminectomy syndrome (n = 26), and incorrect procedural selection (n = 14). Regarding the verdict ruling, 47.3% (n = 95) of cases ruled in favor of the defendant, 9.0% (n = 18) resulted in a mixed ruling, 15.9% (n = 32) ruled in favor of the plaintiff, and 9.5% (n = 19) were resolved with an out-of-court settlement. An average payment of $4,530,277 resulted from the cases that ruled in favor of the plaintiff, while out-of-court settlements yielded an average payment of $1,193,146. CONCLUSIONS: This study suggests that there are several well-documented risk factors for malpractice claims attributed to laminectomy. The study findings suggest that prompt and accurate diagnosis, coordination of care, timely referral for surgical intervention, and understanding of the indications versus limitations of conservative therapy may help to mitigate the risk of litigation associated with laminectomy.

Use of random forest machine learning algorithm to predict short term outcomes following posterior cervical decompression with instrumented fusion.

Random Forest (RF) is a widely used machine learning algorithm that can be utilized for identification of patient characteristics important for outcome prediction. Posterior cervical decompression with instrumented fusion (PCDF) is a procedure for the management of cervical spondylosis, cervical spinal stenosis, and degenerative disorders that can cause cervical myelopathy or radiculopathy. An RF algorithm was employed to predict and describe length of stay (LOS), readmission, reoperation, transfusion, and infection rates following elective PCDF using The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database 2008 through 2018. The RF algorithm was tasked with determining the importance of independent clinical variables in predicting our outcomes of interest and importance of each variable based on the reduction in the Gini index. Application of an RF algorithm to the ACS-NSQIP database yielded a highly predictive set of patient characteristics and perioperative events for five outcomes of interest related to elective PCDF. These variables included postoperative infection, increased age, BMI, operative time, and LOS, and decreased preoperative hematocrit and white blood cell count. Risk factors that were predictive for rate of reoperation, readmission, hospital length of stay, transfusion requirement, and post-operative infection were identified with AUC values of 0.781, 0.791, 0.781, 0.902, and 0.724 respectively. Utilization of these findings may assist in risk analysis during the perioperative period and may influence clinical or surgical decision-making.