Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR

Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models

OBJECTIVES

To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models.

METHODS

A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI). Feature selection was performed using Spearman's rank correlation coefficient and least absolute shrinkage and selection operator (LASSO). The classic machine learning (CML), deep learning (DL), and deep learning radiomics (DLR) models were constructed using logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and test sets. In addition, combined with clinical characteristics, the best CML and the best DL models (SVM classifier), the DL radiomics nomogram (DLRN) was constructed to aid clinical decision-making.

RESULTS

Seven CML features, 96 DL features, and 107 DLR features were selected to construct CML, DL and DLR models. Compared to CML and DL model, the DLR model had the best performance. The AUC, sensitivity, specificity, accuracy, NPV and PPV were 0.827, 0.792, 0.800, 0.796, 0.800 and 0.792 in the test set, respectively.

CONCLUSIONS

Compared with CML and DL models, the DLR model shows the best performance in predicting the Ki67 and PIT-1 expression in PAs simultaneously.

Postoperative Nausea and Vomiting After Orthopaedic Surgery: Prevalence and Associated Factors

Postoperative nausea and vomiting (PONV) is a common complication after surgery and can lead to additional complications and delayed discharge. This descriptive, cross-sectional study assessed the prevalence of PONV and its associated factors in patients undergoing orthopaedic surgery. The study was conducted between November 2020 and July 2021 with 149 patients in a public hospital in the Central Anatolia region of Turkey. In the first 48 hours after surgery, 40.9% of the patients had nausea and 17.4% had vomiting. Gender, age, medical diagnosis, surgical procedure, operative time, postoperative opioid use, and anxiety were identified as significant risk factors for PONV after orthopaedic surgery (p < .05). These factors should be considered during postoperative follow-up, and patients who are older, female, and have prolonged operative time or anxiety should be monitored more closely for PONV.

Fairness in the prediction of acute postoperative pain using machine learning models

Introduction

Overall performance of machine learning-based prediction models is promising; however, their generalizability and fairness must be vigorously investigated to ensure they perform sufficiently well for all patients.

Objective

This study aimed to evaluate prediction bias in machine learning models used for predicting acute postoperative pain.

Method

We conducted a retrospective review of electronic health records for patients undergoing orthopedic surgery from June 1, 2011, to June 30, 2019, at the University of Florida Health system/Shands Hospital. CatBoost machine learning models were trained for predicting the binary outcome of low (≤4) and high pain (>4). Model biases were assessed against seven protected attributes of age, sex, race, area deprivation index (ADI), speaking language, health literacy, and insurance type. Reweighing of protected attributes was investigated for reducing model bias compared with base models. Fairness metrics of equal opportunity, predictive parity, predictive equality, statistical parity, and overall accuracy equality were examined.

Results

The final dataset included 14,263 patients [age: 60.72 (16.03) years, 53.87% female, 39.13% low acute postoperative pain]. The machine learning model (area under the curve, 0.71) was biased in terms of age, race, ADI, and insurance type, but not in terms of sex, language, and health literacy. Despite promising overall performance in predicting acute postoperative pain, machine learning-based prediction models may be biased with respect to protected attributes.

Conclusion

These findings show the need to evaluate fairness in machine learning models involved in perioperative pain before they are implemented as clinical decision support tools.

Artificial intelligence in perioperative medicine - a narrative review

Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

BACKGROUND

Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI- and ML-based application for cognitive support and decision-making in TJA.

METHODS

A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models.

RESULTS

After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories.

CONCLUSIONS

AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patient-reported outcomes and were less accurate in predicting hospital readmissions and reoperations.

A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging

AIM

To develop and validate a triple-classification radiomics model for the preoperative differentiation of pleomorphic adenoma (PA), Warthin tumour (WT), and malignant salivary gland tumour (MSGT) based on diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Data from 217 patients with histopathologically confirmed salivary gland tumours (100 PAs, 68 WTs, and 49 MSGTs) from January 2015 to March 2019 were analysed retrospectively and divided into a training set (n=173), and a validation set (n=44). A total of 396 radiomic features were extracted from the DWI of all patients. Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression were used to select radiomic features, which were then constructed using three classification models, namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN). The diagnostic performance of the radiomics model was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of the training and validation data sets.

RESULTS

The 20 most valuable features were investigated based on the LASSO regression. LR and SVM methods exhibited better diagnostic ability than KNN for multiclass classification. LR and SVM had the best performance and yielded the AUC values of 0.857 and 0.824, respectively, in the training data set and the AUC values of 0.932 and 0.912, respectively, in the validation data set of MSGT diagnosis.

CONCLUSION

DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.

The potential of artificial intelligence to improve patient safety: a scoping review

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.

Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors

OBJECTIVE

To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI).

METHODS

A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets.

RESULTS

Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively.

CONCLUSIONS

Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.

Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis

RATIONALE AND OBJECTIVES

To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer.

MATERIALS AND METHODS

This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method. Five-fold cross-validation and two machine learning algorithms (support vector machine [SVM]; logistic regression [LR]) were utilized for predictive model constructing. The diagnostic performance of the models was evaluated by receiver operating characteristic curves with indicators of accuracy, sensitivity, specificity and area under the curve, and compared by DeLong test.

RESULTS

Five, 8, and 22 optimal features were selected from 1029 T2WI, 1029 VP, and 2058 combining features, respectively. Four-group models were constructed using the five T2WI features (Model_T2), the 8 VP features (Model_VP), the combined 13 optimal features (Model_combined), and the 22 optimal features selected from 2058 features (Model_optimal). In Model_VP, the LR was superior to the SVM algorithm (P = 0.0303). The Model_optimal using LR algorithm showed the best prediction performance (P = 0.0019-0.0081) with accuracy, sensitivity, specificity, and area under the curve of 0.80, 0.83, 0.76, and 0.87, respectively.

CONCLUSION

Radiomics models based on baseline rectal magnetic resonance imaging has high potential for MLM prediction, especially the Model_optimal using LR algorithm. Moreover, except for Model_VP, the LR was not superior to the SVM algorithm for model construction.

An autoregressive logistic model to predict the reciprocal effects of oviductal fluid components on in vitro spermophagy by neutrophils in cattle

After intercourse/insemination, large numbers of sperm are deposited in the female reproductive tract (FRT), triggering a massive recruitment of neutrophils (PMNs) into the FRT, possibly to eliminate excessive sperm via phagocytosis. Some bovine oviductal fluid components (BOFCs) have been shown to regulate in vitro sperm phagocytosis (spermophagy) by PMNs. The modeling approach-based logistic regression (LR) and autoregressive logistic regression (ALR) can be used to predict the behavior of complex biological systems. We, first, compared the LR and ALR models using in vitro data to find which of them provides a better prediction of in vitro spermophagy in bovine. Then, the best model was used to identify and classify the reciprocal effects of BOFCs in regulating spermophagy. The ALR model was calibrated using an iterative procedure with a dynamical search direction. The superoxide production data were used to illustrate the accuracy in validating logit model-based ALR and LR. The ALR model was more accurate than the LR model. Based on in vitro data, the ALR predicted that the regulation of spermophagy by PMNs in bovine oviduct is more sensitive to alpha-1 acid glycoprotein (AGP), PGE2, bovine serum albumin (BSA), and to the combination of AGP or BSA with other BOFCs.

Postoperative pain and patient-controlled epidural analgesia-related adverse effects in young and elderly patients: a retrospective analysis of 2,435 patients

In this retrospective study, data of 2,435 patients who received fentanyl and ropivacaine-based patient-controlled epidural analgesia (PCEA) for pain relief after elective surgery under general or spinal anesthesia were reviewed. Differences in postoperative pain, incidence of patient-controlled analgesia (PCA)-related adverse effects, and risk factors for the need for rescue analgesics for 48 hours postsurgery in young (age 20–39 years) and elderly (age ≥70 years) patients were evaluated. Although there were no significant differences in postoperative pain intensity between the two groups until 6 hours postsurgery, younger patients experienced greater postoperative pain intensity compared with older patients 6–48 hours postsurgery. While younger patients exhibited greater incidence of numbness, motor weakness, and discontinuation of PCA postsurgery, elderly patients exhibited greater incidence of hypotension, nausea/vomiting, rescue analgesia, and antiemetic administration. Upon multivariate analysis, low fentanyl dosage and history of smoking were found to be associated with an increased need for rescue analgesia among younger patients, while physical status classification III/IV and thoracic surgery were associated with a decreased need for rescue analgesia among the elderly. Discontinuation of PCA was more frequent among younger patients than the elderly (18.5% vs 13.5%, P=0.001). Reasons for discontinuation of PCA among young and elderly patients, respectively, were nausea and vomiting (6.8% vs 26.6%), numbness or motor weakness (67.8% vs 11.5%), urinary retention (7.4% vs 8.7%), dizziness (2.2% vs 5.2%), and hypotension (3.1% vs 20.3%). In conclusion, PCEA was more frequently associated with numbness, motor weakness, and discontinuation of PCA in younger patients and with hypotension, nausea/vomiting, and a greater need for rescue analgesics/antiemetics among elderly patients. Therefore, in order to minimize the adverse effects of PCEA and enhance pain relief, different PCEA regimens and administration/prevention strategies should be considered for young and elderly patients.