Application of Machine Learning in Developing Decision-Making Support Models for Decompressed Vertebroplasty

Exploring the application of machine learning to identify the correlations between phthalate esters and disease: enhancing nursing assessments

Background

Health risks associated with phthalate esters depend on exposure level, individual sensitivities, and other contributing factors.

Purpose

This study employed artificial intelligence algorithms while applying data mining techniques to identify correlations between phthalate esters [di(2-ethylhexyl) phthalate, DEHP], lifestyle factors, and disease outcomes.

Methods

We conducted exploratory analysis using demographic and laboratory data collected from the Taiwan Biobank. The study developed a prediction model to examine the relationship between phthalate esters and the risk of developing certain diseases based on various artificial intelligence algorithms, including logistic regression, artificial neural networks, and Bayesian networks.

Results

The results indicate that phthalate esters exhibited a greater impact on bone and joint issues than heart problems. We observed that DEHP metabolites, such as mono(2-carboxymethylhexyl) phthalate, mono-n-butyl phthalate, and monoethylphthalate, leave higher residue in females than in males, with statistically significant differences. Monoethylphthalate levels were lower in individuals who exercised regularly than those who did not, indicating statistically significant differences.

Conclusions

This study's findings can serve as a valuable reference for clinical nursing assessments regarding diseases related to osteoporosis, arthritis, and musculoskeletal pain. Medical professionals can enhance care quality by considering factors beyond patients' essential physical assessment items.Trial Registration: This study was registered under NCT05892029 on May 5, 2023, retrospectively.

Application of machine learning algorithms to predict 30-day hospital readmission following cement augmentation for osteoporotic vertebral compression fractures

Objective

Osteoporosis is a common skeletal disease that greatly increases the risk of pathologic fractures and accounts for approximately 700,000 vertebral compression fractures (VCFs) annually in the United States. Cement augmentation procedures such as balloon kyphoplasty (KP) and percutaneous vertebroplasty (VP) have demonstrated efficacy in the treatment of VCFs, however, some studies report rates of readmission as high as 10.8% following such procedures. The purpose of this study was to employ Machine Learning (ML) algorithms to predict 30-day hospital readmission following cement augmentation procedures for the treatment of VCFs using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database.

Methods

ACS-NSQIP was queried to identify patients undergoing either KP or VP from 2011 to 2014. Three ML algorithms were constructed and tasked with predicting post-operative readmissions within this cohort of patients. Results: Postoperative pneumonia, ASA Class 2 designation, age, partially-dependent functional status, and a history of smoking were independently identified as highly predictive of readmission by all ML algorithms. Among these variables postoperative pneumonia (p < 0.01), ASA Class 2 designation (p < 0.01), age (p = 0.002), and partially-dependent functional status (p < 0.01) were found to be statistically significant. Predictions were generated with an average AUC value of 0.757 and an average accuracy of 80.5%.

Conclusions

Postoperative pneumonia, ASA Class 2 designation, partially-dependent functional status, and age are perioperative variables associated with 30-day readmission following cement augmentation procedures. The use of ML allows for quantification of the relative contributions of these variables toward producing readmission.

Impact of Aspiration Percutaneous Vertebroplasty in Reducing Bone Cement Leakage and Enhancing Distribution-An Ex Vivo Study in Goat Vertebrae

Osteoporosis-induced vertebral compression fracture (OVCF) occurs commonly in people over the age of 50, especially among menopausal women. Besides conservative therapy, minimally invasive percutaneous vertebroplasty (PVP) and kyphoplasty (PKP) have been widely used in clinical treatment and achieved good efficacy. However, the leakage of bone cement (CL) during vertebroplasty (PV) is a major risk that can cause (serious) complications such as compression of the spinal cord, pulmonary embolism, or even paraplegia. In this study, we introduced a new aspiration technique with standard PV procedures (APV) to ameliorate the risk of leakage with quantitative verifications of its effectiveness. APV intends to create a differential pressure to guide the direction of cement flow within the vertebrae. To test this technique, Nubian goats' ex vivo vertebral bodies (VBs) were used to simulate the PV surgical process in humans. Results show that the proposed APV has a lower leakage rate of 13% compared to the 53% of conventional PV. Additionally, the APV approach achieves more uniform cement distribution via the 9-score method with a value of 7 ± 1.30 in contrast to 4 ± 1.78 by conventional PV.

Vertebral Related Diseases in Healthcare: The Role of Pain Management and Rehabilitation

The increase in the life expectancy of the general population implied for vertebral diseases an impacting role for the medical condition of the person, not only in the clinical context, but also from a social and economic point of view, due to the underestimation of primary prevention and complex secondary prevention rehabilitation frameworks [...]