Dataset Information

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

ABSTRACT:

Objectives

Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery.

Materials & methods

In this multicenter, retrospective, longitudinal cohort study, ML algorithms were trained on n-grams extracted from free-text neurology notes, EEG and MRI reports, visit codes, medications, procedures, laboratories, and demographic information. Site-specific algorithms were developed at two epilepsy centers: one pediatric and one adult. Cases were defined as patients who underwent resective epilepsy surgery, and controls were patients with epilepsy with no history of surgery. The output of the ML algorithms was the estimated likelihood of candidacy for resective epilepsy surgery. Model performance was assessed using 10-fold cross-validation.

Results

There were 5880 children (n = 137 had surgery [2.3%]) and 7604 adults with epilepsy (n = 56 had surgery [0.7%]) included in the study. Pediatric surgical patients could be identified 2.0 years (range: 0-8.6 years) before beginning their presurgical evaluation with AUC =0.76 (95% CI: 0.70-0.82) and PR-AUC =0.13 (95% CI: 0.07-0.18). Adult surgical patients could be identified 1.0 year (range: 0-5.4 years) before beginning their presurgical evaluation with AUC =0.85 (95% CI: 0.78-0.93) and PR-AUC =0.31 (95% CI: 0.14-0.48). By the time patients began their presurgical evaluation, the ML algorithms identified pediatric and adult surgical patients with AUC =0.93 and 0.95, respectively. The mean squared error of the predicted probability of surgical candidacy (Brier scores) was 0.018 in pediatrics and 0.006 in adults.

Conclusions

Site-specific machine learning algorithms can identify candidates for epilepsy surgery early in the disease course in diverse practice settings.

SUBMITTER: Wissel BD

PROVIDER: S-EPMC8178229 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Wissel Benjamin D BD Greiner Hansel M HM Glauser Tracy A TA Pestian John P JP Kemme Andrew J AJ Santel Daniel D Ficker David M DM Mangano Francesco T FT Szczesniak Rhonda D RD Dexheimer Judith W JW

Acta neurologica Scandinavica 20210326 1

<h4>Objectives</h4>Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery.<h4>Materials & methods</h4>In this multicenter, retrospective, longitudinal cohort study, ML algorithms were trained on n-grams extracted from free-text neurology notes, EEG and MRI reports, visit codes, medications, p ...[more]

PMID: 33769560

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Project description:BackgroundEarly complications increase in-hospital stay and mortality after intestinal obstruction surgery. It is important to identify the risk of postoperative early complications for patients with intestinal obstruction at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of patients with intestinal obstruction. A risk predictive model based on machine learning is helpful for early diagnosis and timely intervention.ObjectiveThis study aimed to construct an online risk calculator for early postoperative complications in patients after intestinal obstruction surgery based on machine learning algorithms.MethodsA total of 396 patients undergoing intestinal obstruction surgery from April 2013 to April 2021 at an independent medical center were enrolled as the training cohort. Overall, 7 machine learning methods were used to establish prediction models, with their performance appraised via the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1-score. The best model was validated through 2 independent medical centers, a publicly available perioperative dataset the Informative Surgical Patient dataset for Innovative Research Environment (INSPIRE), and a mixed cohort consisting of the above 3 datasets, involving 50, 66, 48, and 164 cases, respectively. Shapley Additive Explanations were measured to identify risk factors.ResultsThe incidence of postoperative complications in the training cohort was 47.44% (176/371), while the incidences in 4 external validation cohorts were 34% (17/50), 56.06% (37/66), 52.08% (25/48), and 48.17% (79/164), respectively. Postoperative complications were associated with 8-item features: Physiological Severity Score for the Enumeration of Mortality and Morbidity (POSSUM physiological score), the amount of colloid infusion, shock index before anesthesia induction, ASA (American Society of Anesthesiologists) classification, the percentage of neutrophils, shock index at the end of surgery, age, and total protein. The random forest model showed the best overall performance, with an AUROC of 0.788 (95% CI 0.709-0.869), accuracy of 0.756, sensitivity of 0.695, specificity of 0.810, and F1-score of 0.727 in the training cohort. The random forest model also achieved a comparable AUROC of 0.755 (95% CI 0.652-0.839) in validation cohort 1, a greater AUROC of 0.817 (95% CI 0.695-0.913) in validation cohort 2, a similar AUROC of 0.786 (95% CI 0.628-0.902) in validation cohort 3, and the comparable AUROC of 0.720 (95% CI 0.671-0.768) in validation cohort 4. We visualized the random forest model and created a web-based online risk calculator.ConclusionsWe have developed and validated a generalizable random forest model to predict postoperative early complications in patients undergoing intestinal obstruction surgery, enabling clinicians to screen high-risk patients and implement early individualized interventions. An online risk calculator for early postoperative complications was developed to make the random forest model accessible to clinicians around the world.

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Dataset Information

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Objectives

Materials & methods

Results

Conclusions

Publications

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

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