The use of machine learning for predicting candidates for outpatient spine surgery: a review
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ABSTRACT:
SUBMITTER: Wellington I
PROVIDER: S-EPMC10570640 | biostudies-literature | 2023 Jul
REPOSITORIES: biostudies-literature
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Project description:BackgroundDays before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression.MethodsData were collected from patients at an ambulatory surgery center. The primary outcome measurement was determined to have a value of 1 (versus 0) if they met both criteria: (1) surgery ends by 5 pm and (2) patient is discharged from the recovery room by 7 pm. We developed models to determine if a procedure would meet both criteria if it were scheduled at 1 pm, 2 pm, 3 pm, or 4 pm. We implemented regression, random forest, balanced random forest, balanced bagging, neural network, and support vector classifier, and included the following features: surgery, surgeon, service line, American Society of Anesthesiologists score, age, sex, weight, and scheduled case duration. We evaluated model performance with Synthetic Minority Oversampling Technique (SMOTE). We compared the following performance metrics: F1 score, area under the receiver operating characteristic curve (AUC), specificity, sensitivity, precision, recall, and Matthews correlation coefficient.ResultsAmong 13,447 surgical procedures, the median total perioperative time (actual case duration and PACU length stay) was 165 minutes. When SMOTE was not used, when predicting whether surgery will end by 5 pm and patient will be discharged by 7 pm, the average F1 scores were best with random forest, balanced bagging, and balanced random forest classifiers. When SMOTE was used, these models had improved F1 scores compared to no SMOTE. The balanced bagging classifier performed best with F1 score of 0.78, 0.80, 0.82, and 0.82 when predicting our outcome if cases were to start at 1 pm, 2 pm, 3 pm, or 4 pm, respectively.ConclusionsWe demonstrated improvement in predicting the outcome at a range of start times when using ensemble learning versus regression techniques. Machine learning may be adapted by operating room management to allow for a better determination whether an add-on case at an outpatient surgery center could be appropriately booked.
| S-EPMC9172889 | biostudies-literature
Project description:BackgroundInflammatory bowel disease (IBD) is a chronic disease characterized by unpredictable episodes of flares and periods of remission. Tools that accurately predict disease course would substantially aid therapeutic decision-making. This study aims to construct a model that accurately predicts the combined end point of outpatient corticosteroid use and hospitalizations as a surrogate for IBD flare.MethodsPredictors evaluated included age, sex, race, use of corticosteroid-sparing immunosuppressive medications (immunomodulators and/or anti-TNF), longitudinal laboratory data, and number of previous IBD-related hospitalizations and outpatient corticosteroid prescriptions. We constructed models using logistic regression and machine learning methods (random forest [RF]) to predict the combined end point of hospitalization and/or corticosteroid use for IBD within 6 months.ResultsWe identified 20,368 Veterans Health Administration patients with the first (index) IBD diagnosis between 2002 and 2009. Area under the receiver operating characteristic curve (AuROC) for the baseline logistic regression model was 0.68 (95% confidence interval [CI], 0.67-0.68). AuROC for the RF longitudinal model was 0.85 (95% CI, 0.84-0.85). AuROC for the RF longitudinal model using previous hospitalization or steroid use was 0.87 (95% CI, 0.87-0.88). The 5 leading independent risk factors for future hospitalization or steroid use were age, mean serum albumin, immunosuppressive medication use, and mean and highest platelet counts. Previous hospitalization and corticosteroid use were highly predictive when included in specified models.ConclusionsA novel machine learning model substantially improved our ability to predict IBD-related hospitalization and outpatient steroid use. This model could be used at point of care to distinguish patients at high and low risk for disease flare, allowing individualized therapeutic management.
| S-EPMC5931801 | biostudies-literature
Project description:Study designRetrospective cohort study.ObjectiveTo assess the association between undergoing spine surgery in an ambulatory surgical center (ASC) vs a hospital outpatient department (HOPD) and (a) perioperative opioid prescription patterns and (b) prolonged opioid use.MethodsData from the Merative MarketScan Database included patients aged 18-64 who underwent single-level or multilevel anterior cervical discectomy and fusion (ACDF) or lumbar decompression between January 2017 and June 2021. Primary outcomes included receipt of a perioperative opioid prescription, perioperative oral morphine milligram equivalents (MMEs), and prolonged opioid use (defined as opioid prescription 91-180 days post-surgery). Secondary outcomes included the number of perioperative opioid prescriptions filled (single/multiple) and type of initial perioperative opioid filled (potent/weak). Analysis of prolonged opioid use was limited to opioid-naive patients. Propensity score matching (1 ASC to 3 HOPD cases) and logistic regression models were used for analysis.ResultsThe study included 11,654 ACDF and 26,486 lumbar decompression patients. For ACDF, ASCs had higher odds of an initial potent opioid prescription (OR = 1.18, 95% CI 1.08-1.30, P < .001) and higher total adjusted mean MMEs (+21.14, 95% CI 3.08-39.20, P = .02). For lumbar decompression, ASCs had increased odds of an initial potent opioid (OR = 1.23, 95% CI 1.16-1.30, P < .001) but lower odds of multiple opioid prescriptions (OR = 0.90, 95% CI 0.85-0.96, P < .001). There was no significant association between the surgery setting and prolonged opioid use.ConclusionDifferences in perioperative opioid prescribing were observed between ASCs and HOPDs, but there was no increase in prolonged opioid use in ASCs. Further research is needed to optimize postoperative pain management in different outpatient settings.
| S-EPMC11565511 | biostudies-literature
Project description:BackgroundMachine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided.MethodsDatabases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models.ResultsA total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration.ConclusionsMachine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.
| S-EPMC9636121 | biostudies-literature
Project description:ObjectivesEpilepsy 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 & methodsIn 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.ResultsThere 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.ConclusionsSite-specific machine learning algorithms can identify candidates for epilepsy surgery early in the disease course in diverse practice settings.
| S-EPMC8178229 | biostudies-literature
Project description:Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.
| S-EPMC10741524 | biostudies-literature
Project description:Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. In this study, we profile 80 FDA-approved and clinically tested drugs in neural cell cultures, with the goal of producing a ranked list of possible repurposing candidates.
2021-01-14 | GSE164788 | GEO
Project description:The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.
| S-EPMC7472375 | biostudies-literature
Project description:BackgroundHypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration.ObjectiveThis systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population.MethodsA systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.ResultsAfter screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67%) using a long short-term memory algorithm.ConclusionsMachine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data. The heterogeneity of the studies and limited generalizability of their results highlight the need for further validation studies to assess their predictive performance.
| S-EPMC10879670 | biostudies-literature
Project description:Study Design Systematic review. Study Rationale As the length of stay after cervical spine surgery has decreased substantially, the feasibility and safety of outpatient cervical spine surgery come into question. Although minimal length of stay is a targeted metric for quality and costs for medical centers, the safety of outpatient cervical spine surgery has not been clearly defined. Objective The objective of this article is to evaluate the safety of inpatient versus outpatient surgery in the cervical spine for adult patients with symptomatic or asymptomatic degenerative disc disease. Methods A systematic review of the literature was undertaken for articles published through February 19, 2014. Electronic databases and the bibliographies of key articles were searched to identify comparative studies evaluating the safety of inpatient versus outpatient surgery in the cervical spine. Spinal cord stimulation, spinal injections, and diagnostic procedures were excluded. Two independent reviewers assessed the strength of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system, and disagreements were resolved by consensus. Results Five studies that met the inclusion criteria were identified. One study reported low risk of hematoma (0% of outpatients and 1.6% of inpatients). Two studies reported on mortality and both reported no deaths in either group following surgery. Dysphagia risks ranged from 0 to 10% of outpatients and 1.6 to 5% of inpatients, and infection risks ranged from 0 to 1% of outpatients and 2 to 2.8% of inpatients. One study reported that no (0) outpatients were readmitted to the hospital due to a complication, compared with four inpatients (7%). The overall strength of evidence was insufficient for all safety outcomes examined. Conclusion Though the studies in our systematic review did not suggest an increased risk of complication with outpatient cervical spine surgery, the strength of evidence to make a recommendation was insufficient. Further study is needed to more clearly define the role of outpatient cervical spine surgery.
| S-EPMC4174186 | biostudies-literature