Dataset Information

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

ABSTRACT:

Background

Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model.

Methods

We identified 38,788 adult patients who underwent lumbar fusion at any California hospital between 2015 and 2017. The primary outcome was major perioperative complication or readmission within 30 days. We build logistic regression and advanced machine learning models: XGBoost, AdaBoost, Gradient Boosting, and Random Forest. Discrimination and calibration were assessed using area under the receiver operating characteristic curve and Brier score, respectively.

Results

There were 4470 major complications (11.5%). The XGBoost algorithm demonstrates the highest discrimination of the machine learning models, outperforming regression. The variables most important to XGBoost performance include angina pectoris, metastatic cancer, teaching hospital status, history of concussion, comorbidity burden, and workers' compensation insurance. Teaching hospital status and concussion history were not found to be important for regression.

Conclusions

We report a machine learning algorithm for prediction of major complications and readmission after lumbar fusion that outperforms logistic regression. Notably, the predictors most important for XGBoost differed from those for regression. The superior performance of XGBoost may be due to the ability of advanced machine learning methods to capture relationships between variables that regression is unable to detect. This tool may identify and address potentially modifiable risk factors, helping risk-stratify patients and decrease complication rates.

SUBMITTER: Shah AA

PROVIDER: S-EPMC8338911 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

Shah Akash A AA Devana Sai K SK Lee Changhee C Bugarin Amador A Lord Elizabeth L EL Shamie Arya N AN Park Don Y DY van der Schaar Mihaela M SooHoo Nelson F NF

World neurosurgery 20210528

<h4>Background</h4>Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model.<h4>Methods</h4>We identified 38,788 adult patients who underwent lumbar fusion at any California hospital between 2015 and 2017 ...[more]

PMID: 34058366

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Project description:Study designA retrospective, case-control study.ObjectiveWe aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF).Summary of background dataThe average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility.MethodsThis is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020.ResultsA total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator.ConclusionWe report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.

Project description:ObjectiveTo comprehensively compare and assess the effects of different lumbar fusion techniques in patients with lumbar spinal stenosis (LSS).MethodsPubMed, Embase, Cochrane Library, and Web of Science databases were systematically searched up to December 24, 2022 in this network meta-analysis. Outcomes were pain (pain, low back pain, and leg pain), Japanese Orthopaedic Association (JOA), Oswestry Disability Index (ODI), complications, reoperation, and fusion. Network plots illustrated the direct and indirect comparisons of different fusion techniques for the outcomes. League tables showed the comparisons of any two fusion techniques, based on both direct and indirect evidence. The efficacy of each fusion technique for LSS was ranked by rank probabilities.ResultsTotally 29 studies involving 2,379 patients were eligible. For pain, percutaneous endoscopic transforaminal lumbar interbody fusion (Endo-TLIF) was most likely to be the best technique, followed by minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF), extreme lateral interbody fusion (XLIF), and transforaminal lumbar interbody fusion (TLIF). Percutaneous endoscopic posterior lumbar interbody fusion (Endo-PLIF) had the greatest likelihood to be the optimal technique for low back pain, followed sequentially by MIS-TLIF, minimally invasive posterior lumbar interbody fusion (MIS-PLIF), XLIF, Endo-TLIF, TLIF, oblique lumbar interbody fusion (OLIF), posterior lumbar interbody fusion (PLIF), and posterolateral lumbar fusion (PLF). MIS-PLIF was ranked the most effective technique concerning leg pain, followed by Endo-TLIF, MIS-TLIF, TLIF, Endo-PLIF, PLIF, OLIF, PLF, and XLIF. As regards JOA scores, Endo-TLIF had the maximum probability to be the best technique, followed by MIS-TLIF and TLIF. Endo-PLIF had the greatest likelihood to be the optimum technique for complications, followed by TLIF, MIS-TLIF, Endo-TLIF, OLIF, and XLIF.ConclusionMinimally invasive fusion techniques may be effective in the treatment of LSS, compared with traditional techniques. Minimally invasive techniques were likely non-inferior with regards to postoperative complications.

Project description:BackgroundAdvanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit.MethodsOur goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach.ResultsThe two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature.ConclusionsThe proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.

Dataset Information

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

Background

Methods

Results

Conclusions

Publications

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

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