Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases.

STUDY DESIGN: A retrospective study. PURPOSE: This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis. OVERVIEW OF LITERATURE: This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care. METHODS: A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery. CONCLUSIONS: These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.

Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery.

STUDY DESIGN: Retrospective cohort study. PURPOSE: This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis. OVERVIEW OF LITERATURE: Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals. METHODS: This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thailand between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classifier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score. RESULTS: In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory outcome (AUC, 0.94; F1-score, 0.88). CONCLUSIONS: Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.

Deep vein thrombosis following arthroscopic meniscal root repair: A case report.

INTRODUCTION: Deep vein thrombosis (DVT) following arthroscopic surgery is a rare condition, especially arthroscopic meniscal surgery. There have been three reported cases of DVT after arthroscopic meniscal procedure, all related to arthroscopic meniscectomy. In this study, we reported the first case of symptomatic DVT at the level of the femoral vein to the popliteal vein following arthroscopic meniscal root repair. CASE PRESENTATION: The case was a 55-year-old Thai female who presented with left knee pain for 2 months after a fall. She was diagnosed as left medial meniscal root injury and had had an arthroscopic meniscal root repair. At 6 weeks post-operatively, she developed left leg swelling without pain. She was diagnosed as DVT and was initially treated with enoxaparin for three days then warfarin for three months. CONCLUSION: We report a case of symptomatic DVT that extended from the femoral vein to the popliteal vein after arthroscopic meniscal root repair. The risks of DVT following arthroscopic surgery are aged more than 40 years old and tourniquet time more than 60 min.