Supervised machine learning to predict non-home discharge following surgical treatment of pelvic fractures

Background: Decision-tree-based machine learning (ML) algorithms such as random forest (RF) are useful for their ability to predict outcomes and rank variables according to their utility in the decision-making process. This study utilizes RF to identify important predictors of discharge to facility following surgical stabilization of pelvis fractures, a traumatic injury that often precludes mortality and diminished quality of life. Methods: The American College of Surgeons national surgical quality improvement program (ACS-NSQIP) database was queried for patients aged 16 to 70 undergoing surgical fixation of pelvis fractures between 2008 and 2018. Outcome of interest was discharge home versus to facility. RF was trained with surgical variables, comorbidities, and other patient factors and tasked with predicting discharge location. Permutation feature importance (PFI) was then generated to identify important variables. Results: Out of 492 patients, 184 patients were discharged to facility, and 308 patients were discharged home. RF identified age, American Society of Anesthesiologists (ASA) classification, and preoperative hematocrit as top predictors for discharge to facility. Patients being discharged home were younger, had lower ASA scores, and had higher preoperative hematocrit. Conclusions: RF identified age, ASA classification, and preoperative hematocrit as top predictors for discharge destination following pelvic surgery. Knowledge of the impact of these variables can inform preoperative planning for both patients and their care team, while highlighting the opportunity to address preoperative hematocrit to both reduce cost and improve quality of care

Supervised machine learning algorithms used to predict post-surgical outcomes following anterior surgical fixation of odontoid fractures

Background: Odontoid fractures have a high mortality rate, and numerous classification systems have previously predicted surgical outcomes with mixed consensus. We generated a machine learning (ML) construct to predict post-operative adverse events following anterior (ORIF) of odontoid fractures. Methods: 266 patients from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) with anterior ORIF (CPT 22318) of odontoid fractures from 2008-2018 were analyzed using ML algorithms random forest classifier (RF), gradient boosting classifier (GB), support vector machine classifier (SVM), Gaussian Naive Bayes classifier (GNB), and multi-layer perceptron classifier (MLP), and were compared to logistic regression classifier (LR). Algorithms predicted increased length of stay (LOS), need for transfusion (Transf), non-home discharge (NHD), and any adverse event (AAE). Permutation feature importance (PFI) identified risk factors. Results: ML algorithms outperformed LR. The average AUC for predicting Transf was 0.635 (accuracy=77.4%), extended LOS=0.652 (accuracy 59.6%), NHD 0.788 (accuracy=71.9%) and AAE 0.649 (accuracy 68.1%). GB performed highest for Transf (AUC=0.861), identifying operative time (PFI 0.253, p=0.016). GB and RF performed equally for NHD (AUC=0.819), highlighting preoperative hematocrit (PFI=0.157, p<0.001). GB predicted AAE (AUC=0.720) also identifying preoperative hematocrit (PFI=0.112, p<0.001). RF predicted extended LOS (AUC=0.669) highlighting preoperative hematocrit (PFI=0.049, p<0.001). Conclusions: ML outperformed LR, successfully predicting Transf, extended LOS, NHD, and AAE for anterior ORIF of odontoid fractures. Our construct may complement conventional risk stratification to reduce adverse outcomes and excess cost

Complications Associated with Pedicle Screw Placement Using Cortical Bone Trajectory

International audienc

Synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome presenting with a cervical vertebral fracture: A case report

Background context: Synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome is a rare condition that can be difficult to diagnose. There are no guidelines for the treatment of SAPHO syndrome, but newer modalities of medications show promising results. We present the case of a patient who presented with a pathologic fracture of her cervical spine who ended up being diagnosed with SAPHO syndrome. Case: A 51-year-old female presented with severe neck pain and a rash on her hands and feet. Imaging showed a C5 vertebral compression fracture and multiple sites of bony involvement concerning for malignancy or widespread infection. The patient underwent corpectomy and fusion to address the instability and cervical stenosis and was started on immunomodulating therapy. Based on the biopsy findings showing left shifted bone marrow versus mild acute inflammation, and in conjunction with the cutaneous findings, the patent was diagnosed with SAPHO syndrome. Outcome: At two year follow up, although posterior stabilization was required, her overall condition was improved. Nonetheless, she continued to have fatigue, malaise, and total body pain involving: the cervical spine, the mid thoracic spine, the left costal margin, bilateral sternoclavicular joints, and bilateral hips and knees. Conclusion: SAPHO syndrome can mimic infection and neoplasia. It should be suspected in patients presenting with multifocal osteitis and associated rash. Accurate and timely diagnosis is paramount as the treatment of this condition may require immunomodulating agents

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

Reoperation and Perioperative Complications after Surgical Treatment of Cervical Radiculopathy: A Comparison between Three Procedures

STUDY DESIGN: A retrospective database study. OBJECTIVE: The purpose of our study was to compare the perioperative complications and reoperation rates after ACDF, CDA, and PCF in patients treated for cervical radiculopathy. SUMMARY OF BACKGROUND DATA: Cervical radiculopathy results from compression or irritation of nerve roots in the cervical spine. While most cervical radiculopathy is treated nonoperatively, anterior cervical discectomy and fusion (ACDF), cervical disc arthroplasty (CDA), and posterior cervical foraminotomy (PCF) are the techniques most commonly used if operative intervention is indicated. There is limited research evaluating the perioperative complications of these surgical techniques. METHODS: A retrospective review was performed using the PearlDiver Patient Record Database to identify cases of cervical radiculopathy that underwent ACDF, CDA, or PCF at 1 or 2 levels from 2007 to 2016. Perioperative complications and reoperations following each of the procedures were assessed. RESULTS: During the study period, 25,051 patients underwent ACDF, 522 underwent CDA, and 3,986 underwent PCF. After propensity score matching, each of the three groups consisted of 507 patients. Surgical site infection rates were highest after PCF (2.17%) compared with ACDF (0.20%) and CDA (0.59%) at 30-days and 3-months, P=0.003, P\u3c0.001 respectively. New onset cervicalgia was highest following ACDF (34.32%) and lowest after PCF (22.88%) at 3- and 6-months, P\u3c0.001 and P=0.003, respectively. Revision surgeries were highest among those who underwent CDA (6.90%) versus ACDF (3.16%) and PCF (3.55%) at 6-months, P=0.007. Limb paralysis was significantly higher after PCF compared to CDA and ACDF at 6-months, P\u3c0.017. CONCLUSION: The rate of surgical site infection was higher in PCF compared to ACDF and CDA. New-onset cervicalgia was higher after ACDF compared to PCF and CDA at short term follow up. Revision surgeries were highest among those undergoing CDA and lowest in those undergoing ACDF. LEVEL OF EVIDENCE: 3