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.

Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients.

Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various applications can optimize the care of peripheral artery disease (PAD) patients in diagnosis, predicting patient outcomes, and imaging interpretation. In this review, we introduce various AI applications such as natural language processing, supervised machine learning, and deep learning, and we analyze the current literature in which these algorithms have been applied to PAD.

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.

Association Between Vertebral Bone Quality Score and Dual-Energy X-ray Absorptiometry for the Assessment of Bone Mineral Density in Adolescent Patients.

BACKGROUND: The MRI-based vertebral bone quality (VBQ) score is an assessment tool for bone mineral density (BMD) that has been validated in adults against the clinical standard of dual-energy X-ray absorptiometry (DEXA). However, VBQ has yet to be validated against DEXA for use in adolescents. This study evaluated the associations between adolescent VBQ scores, DEXA Z-scores, and BMD values. METHODS: The radiographic records of 63 consecutive patients between the ages of 11 and 21 who underwent MRI of the abdomen and pelvis and DEXA of the spine and hip were retrieved. The collected radiographic data consisted of the MRI-based VBQ score, DEXA Z-score, and BMD values of the femoral neck, L1-4 vertebrae, and total body. The VBQ score was calculated by taking the median signal intensity (MSI) from L1-L4 and the SI of the L3 cerebrospinal fluid (CSF). The VBQ score was derived as the quotient of MSIL1-L4 divided by SICSF. RESULTS: A mean VBQ score of 2.41 ± 0.29 was observed. Strong correlations of -0.749 (p<0.0001) and -0.780 (p<0.0001) were detected between the VBQ score and DEXA femoral neck and spine Z-scores, respectively. Correlations between VBQ score and DEXA femoral neck, spine, and total body BMD scores were -0.559 (p<0.0001), -0.611 (p<0.0001), and -0.516 (p<.0001), respectively. No significant correlations were found between the VBQ score and age, BMI, weight, or height. A mean difference in VBQ score of -0.155 (p=0.035) was observed between sexes. VBQ demonstrated moderate predictive ability for DEXA-derived Z-scores and BMD scores. CONCLUSIONS: VBQ scores were strongly correlated with DEXA Z-scores and moderately correlated with BMD values. The VBQ score can also be used by adolescent patients as an accessory tool to assess bone health.

Correlations among Cervical, Thoracic, and lumbar Hounsfield Unit measurements for assessment of bone mineral density.

OBJECTIVE: Bone mineral density assessment using Hounsfield Unit (HU) currently depends upon the availability of computed tomography (CT) of the lumbar spine. The primary aim of this study was to evaluate the associations among HU measurements of the cervical (CHU), thoracic (THU), and lumbar (LHU) spine. The secondary aim of this study was to analyze the influence of patient demographic and anthropometric characteristics on HU measurements. METHODS: Radiographic records of 165 patients who underwent CT of the cervical, thoracic, and lumbar spine were retrieved. The CHU, THU, and LHU were calculated by obtaining the mean signal intensity from the medullary portions of C3-C7, T8-T12, and L1-L4 vertebral bodies. RESULTS: Mean CHU, THU, and LHU values were 266.26 ± 88.69, 165.57 ± 55.06, and 166.45 ± 51.38. Significant differences of 100.69, 99.81, and 0.88 were observed between CHU and THU (p <.001), CHU and LHU (p <.001), and THU and LHU (p =.023). Correlations of 0.574, 0.488, and 0.686 were observed between CHU and THU (p <.001), CHU and LHU (p <.001), and THU and LHU (p <.001). No differences in HU based on sex, age, height, weight, or ethnicity were observed. Multivariate regression models demonstrated R2 values of 0.770 - 0.790 (p <.001) in prediction of LHU. CONCLUSIONS: Hounsfield Unit measurements derived from the cervical and thoracic spine correlate with the validated lumbar Hounsfield Unit. Hounsfield Unit measurements do not vary based on sex, ethnicity, age, height, or weight.

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.

Applications of Interpedicular Distance and Anteroposterior Diameter in the Approximation of the Spinal Canal Area.

INTRODUCTION:  Advancements within the field of medicine revolve around increasing the efficiency of diagnosing and subsequently treating patients. One such advancement is measurements of the central canal using artificial intelligence (AI). The authors propose the possibility of AI measuring two linear distances followed by a subsequent approximation via an area equation. The lumbar spinal canal was approximated by an area calculation using the interpedicular distance (IPD) and anteroposterior diameter (AP diameter). The three shapes evaluated were an ellipse, triangle, and rectangle. METHODS:  IPD, AP diameter, and spinal canal area from L1-L5 were measured in 555 patients using the IMPAX6 (Mortsel, Belgium: Agfa-Gevaert) picture archiving and communication system. Subsequently, an approximated area of the lumbar spinal canal, assuming an ellipse shape, was calculated using ellipse equation/approximation. Triangular and rectangular approximations were done using triangle equation/approximation and rectangle equation/approximation, respectively. The equations used are the geometric equations for the area of each shape described. For example, the triangular approximation used the IPD as the base of the triangle and the AP diameter as the height. Thus, the area approximation was calculated by half of the IPD times the AP diameter. RESULTS:  The percent error of the ellipse approximation was the lowest with a range of error from 8.44% at L1 to 15.51% at L5. The triangle approximation again was the second most accurate with a range of error starting at -26.46% at L5 to -30.96% at L1. Lastly, the percentage errors of the rectangle approximation began at 38.07% at L1 to 47.07% at L5. The ellipse and rectangle approximation consistently overestimated the area of the spinal canal, while the opposite was true for the triangle approximation. A combination of these approximations could be used to construct a second-order approximation. The approximations were all highly correlated with the authors' manual measurements. Approximations at the L2 vertebrae were highest with a correlation of 0.934 closely followed by all approximations at L5 with a value of 0.931. Approximations were least correlated with the L4 vertebrae with a value of 0.905. CONCLUSION: The correlation between the approximation equations and the measured values is significantly related. The ellipse equation best predicted the area of the spinal canal followed by the triangle and then the rectangle approximation. The percent error difference of the ellipse approximation at L1 was similar in error compared to other causes of measurement error. Continued investigation into a second-order approximation may yield a more accurate approximation.

Anatomic Assessment of L1-S1 Neuroforaminal Dimensions Using Computed Tomography.

BACKGROUND: Although the radiographic parameters for diagnosing central lumbar canal stenosis are well described, parameters for the diagnosis of neuroforaminal stenosis (NFS) are less well defined. Previous studies have used magnetic resonance imaging (MRI) and radiography to describe neuroforaminal dimensions (NFDs). Those methods, however, have limitations that may substantially distort measurements. Existing literature on the use of computed tomography (CT) to investigate normal NFDs is limited. METHODS: This anatomic assessment evaluated CT imaging of 300 female and 300 male subjects between 18 and 35 years of age to determine normal NFDs, specifically the sagittal anteroposterior width, axial anteroposterior width, craniocaudal height, and area. Statistical analyses were performed to assess differences in NFDs according to variables including sex, age, height, weight, body mass index, and ethnicity. RESULTS: Overall, mean NFDs were 9.08 mm for sagittal anteroposterior width, 8.93 mm for axial anteroposterior width, 17.46 mm for craniocaudal height, and 134.78 mm 2 for area (n = 6,000 measurements each). Male subjects had larger NFDs than females at multiple levels. Both Caucasian and Asian subjects had larger NFDs than African-American subjects at multiple levels. There were no associations between foraminal dimensions and anthropometric factors. CONCLUSIONS: This study describes CT-based L1-S1 NFDs in young, healthy patients who presented with reasons other than back pain or pathology affecting the neuroforamen. Dimensions were influenced by sex and ethnicity but were not influenced by anthropometric factors. LEVEL OF EVIDENCE: Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Gaps in care following first time seizure in an underserved region: A retrospective analysis.

PURPOSE: This study investigated the characteristics of patients presenting with the first-time seizure (FTS) and whether neurology follow-up occurred in a medically underserved area. METHODS: A retrospective study of adults with a FTS discharged from the Emergency Department (ED) at Loma Linda University between January 1, 2017 and December 31, 2018 was performed. The primary outcome was days from the ED visit to the first neurology visit. Secondary outcomes included repeat ED visits, percentage of patients who had specialty assessment in one year, type of neurologist seen, and percentage lost to follow-up. RESULTS: Of the 1327 patients screened, 753 encounters met criteria for manual review, and after exclusion criteria were applied, 66 unique encounters were eligible. Only 30% of FTS patients followed up with a neurologist. The median duration for neurology follow-up was 92 days (range=5-1180). After initial ED visit, 20% of follow-up patients were diagnosed with epilepsy within 189 days, and 20% of patients re-presented to the ED with recurrent seizures while awaiting their initial neurology appointment. Reasons for lack of follow-up included: referral issues, missed appointments, and shortage of available neurologists. CONCLUSION: This study highlights the significant treatment gap that a first-time seizure clinic (FTSC) could fill in underserved communities. FTSC may reduce the morbidity and mortality associated with untreated recurrent seizures.

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.

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.