Investigating Predictors of Increased Length of Stay After Resection of Vestibular Schwannoma Using Machine Learning

Dang, Sabina; Manzoor, Nauman F.; Chowdhury, Naweed I.; Tittman, Sarah M.; Yancey, Kristen L; Monsour, Meredith A; O’Malley, Matthew; Rivas, Alejandro; Haynes, David S.; Bennett, Marc

doi:10.1097/mao.0000000000003042

Cited by 11 publications

(11 citation statements)

References 34 publications

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“…Nine studies evaluated the relationship between obesity and postoperative CSF leaks [16–18, 22–26, 29]. Six studies described the relationship between obesity and postoperative LOS [19, 20, 22, 24, 26, 28]. Five papers studied the influence of obesity on readmission and reoperation rates [20, 21, 24, 25, 27].…”

Section: Resultsmentioning

confidence: 99%

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Impact of Obesity on Postoperative Complications after Lateral Skull Base Surgery: A Systematic Review

Bridgham,

Shikara,

Ludeman

et al. 2023

ORL

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Introduction: The relationship between obesity and complications after lateral skull base tumor resection is not clear. There is conflicting evidence regarding the incidence of postoperative complications in this patient population. The purpose of this study is to examine the relationship between obesity and outcomes following lateral skull base tumor resection. Data Sources: Data were extracted from PubMed, Embase, CINAHL, and Cochrane CENTRAL. Methods: Included studies assessed the relationship between obesity and outcomes following lateral skull base tumor removal. Studies with ≤5 patients, pediatric patients, duplicate patient populations, or insufficient data were excluded. Two independent investigators reviewed each study for inclusion. A third reviewer served as a tie-breaker for any conflicts. Extracted data includes patient demographics, tumor pathology, surgical approach, and postoperative outcomes including incidence of cerebrospinal fluid (CSF) leak and other postoperative complications, length of stay (LOS), and readmission and reoperation rates. Descriptive statistics were used to compare postoperative outcomes for obese and nonobese controls. Results: 14 studies met final inclusion criteria. Nine studies evaluated the relationship between obesity and CSF leaks. Four studies found a significant increase in postoperative CSF leak in obese patients compared to nonobese controls. The remaining studies trended toward an increased incidence of CSF leak in the obese population but did not reach statistical significance. One out of seven studies found that obesity increased postoperative LOS, and one out of five studies found that obesity increased reoperation rates following tumor resection. Conclusions: Based on the results, obesity does not appear to increase LOS, readmission, or reoperation rates after lateral skull base tumor resection. The relationship between obesity and postoperative CSF leak, however, warrants further analysis.

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Section: Resultsmentioning

confidence: 99%

“…Six studies included in this review evaluated the impact of obesity on postoperative LOS [19,20,22,24,26,28]. Only one study demonstrated that obesity was associated with a longer LOS using the ACS-NSQIP database [26].…”

Section: Length Of Hospital Staymentioning

confidence: 99%

Impact of Obesity on Postoperative Complications after Lateral Skull Base Surgery: A Systematic Review

Bridgham,

Shikara,

Ludeman

et al. 2023

ORL

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“…Thirty three articles were excluded due to predicting only pathological features, e.g., grade (n = 16), or differentiating between tumor entities (n = 8) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Thirty four articles were excluded due to predicting only clinical parameters, e.g., tumor consistency (n = 7), response/treatment outcome (n = 12) or brain/bone invasion (n = 4) [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , …”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusion: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

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“…The overall prediction of the model on new data is then obtained by averaging across all decision trees. RFs have become increasingly popular machine learning models owing to their high degree of accuracy, ability to manage nonlinear data, and diminished likelihood of overfitting (37)(38)(39).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Patro

Perkins

Ortega³

et al. 2023

Otol Neurotol

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Objective To develop a machine learning–based referral guideline for patients undergoing cochlear implant candidacy evaluation (CICE) and to compare with the widely used 60/60 guideline. Study Design Retrospective cohort. Setting Tertiary referral center. Patients 772 adults undergoing CICE from 2015 to 2020. Interventions Variables included demographics, unaided thresholds, and word recognition score. A random forest classification model was trained on patients undergoing CICE, and bootstrap cross-validation was used to assess the modeling approach's performance. Main Outcome Measures The machine learning–based referral tool was evaluated against the 60/60 guideline based on ability to identify CI candidates under traditional and expanded criteria. Results Of 587 patients with complete data, 563 (96%) met candidacy at our center, and the 60/60 guideline identified 512 (87%) patients. In the random forest model, word recognition score; thresholds at 3000, 2000, and 125; and age at CICE had the largest impact on candidacy (mean decrease in Gini coefficient, 2.83, 1.60, 1.20, 1.17, and 1.16, respectively). The 60/60 guideline had a sensitivity of 0.91, a specificity of 0.42, and an accuracy of 0.89 (95% confidence interval, 0.86–0.91). The random forest model obtained higher sensitivity (0.96), specificity (1.00), and accuracy (0.96; 95% confidence interval, 0.95–0.98). Across 1,000 bootstrapped iterations, the model yielded a median sensitivity of 0.92 (interquartile range [IQR], 0.85–0.98), specificity of 1.00 (IQR, 0.88–1.00), accuracy of 0.93 (IQR, 0.85–0.97), and area under the curve of 0.96 (IQR, 0.93–0.98). Conclusions A novel machine learning–based screening model is highly sensitive, specific, and accurate in predicting CI candidacy. Bootstrapping confirmed that this approach is potentially generalizable with consistent results.

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Investigating Predictors of Increased Length of Stay After Resection of Vestibular Schwannoma Using Machine Learning

Cited by 11 publications

References 34 publications

Impact of Obesity on Postoperative Complications after Lateral Skull Base Surgery: A Systematic Review

Impact of Obesity on Postoperative Complications after Lateral Skull Base Surgery: A Systematic Review

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

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