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

Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR XL Randomised Intervention Trial in Congestive Heart Failure (MERIT-HF)

BACKGROUND: Metoprolol can improve haemodynamics in chronic heart failure, but survival benefit has not been proven. We investigated whether metoprolol controlled release/extended release (CR/XL) once daily, in addition to standard therapy, would lower mortality in patients with decreased ejection fraction and symptoms of heart failure. METHODS: We enrolled 3991 patients with chronic heart failure in New York Heart Association (NYHA) functional class II-IV and with ejection fraction of 0.40 or less, stabilised with optimum standard therapy, in a double-blind randomised controlled study. Randomisation was preceded by a 2-week single-blind placebo run-in period. 1990 patients were randomly assigned metoprolol CR/XL 12.5 mg (NYHA III-IV) or 25.0 mg once daily (NYHA II) and 2001 were assigned placebo. The target dose was 200 mg once daily and doses were up-titrated over 8 weeks. Our primary endpoint was all-cause mortality, analysed by intention to treat. FINDINGS: The study was stopped early on the recommendation of the independent safety committee. Mean follow-up time was 1 year. All-cause mortality was lower in the metoprolol CR/XL group than in the placebo group (145 [7.2%, per patient-year of follow-up]) vs 217 deaths [11.0%], relative risk 0.66 [95% CI 0.53-0.81]; p=0.00009 or adjusted for interim analyses p=0.0062). There were fewer sudden deaths in the metoprolol CR/XL group than in the placebo group (79 vs 132, 0.59 [0.45-0.78]; p=0.0002) and deaths from worsening heart failure (30 vs 58, 0.51 [0.33-0.79]; p=0.0023). INTERPRETATION: Metoprolol CR/XL once daily in addition to optimum standard therapy improved survival. The drug was well tolerated