BMC Oral Health· 2026Q1
Machine learning-based analysis of factors influencing maximum tongue pressure in patients after cardiac surgery: a cross-sectional study
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- Q1SCImago
- 2026year
Short summary
Machine learning identified prolonged endotracheal intubation (β = -0.254) as the strongest predictor of reduced maximum tongue pressure (MTP) after cardiac surgery, followed by older age and higher NT-proBNP levels, while handgrip strength and BMI were positive predictors.
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Key points
- Prolonged endotracheal intubation duration was the strongest predictor of reduced postoperative maximum tongue pressure (MTP) in cardiac surgery patients (β = -0.254).
- Older age (β = -0.167) and higher NT-proBNP levels (β = -0.111) also independently predicted lower MTP.
- Greater handgrip strength (β = 0.221) and higher body mass index (BMI) (β = 0.133) were independent positive predictors of MTP.
- A hybrid machine learning approach combining Random Forest and LASSO regression was used to identify these predictors from 18 candidate variables.
AI-generated from the title and abstract; the full text is not read.
Abstract
A low postoperative maximum tongue pressure (MTP) reflects diminished tongue muscle strength, which can compromise oral-phase swallowing function and lead to adverse clinical outcomes. However, the specific perioperative determinants of MTP in cardiac surgery patients remain poorly understood. This study aimed to identify the independent clinical predictors of postoperative MTP using a hybrid machine learning approach. In this cross-sectional study conducted at a teaching hospital in Shanghai from April 2025 to January 2026, a total of 470 adult patients undergoing cardiac surgery were included. MTP was assessed 8–24 h post-extubation, and comprehensive perioperative data were systematically extracted. To overcome multicollinearity and prevent model overfitting, a dual-algorithm feature selection was employed. Variables were ranked by a Random Forest (RF) algorithm, and optimal feature subsets were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation. The overlapping core variables were subsequently incorporated into a multivariable stepwise linear regression model. A total of 470 patients were included. Based on the RF and LASSO models, 12 core features were extracted from 18 candidate variables. Multivariable linear regression confirmed five independent predictors. According to the variable importance ranking and standardized coefficients, prolonged endotracheal intubation duration ( β = -0.254, p < 0.001) emerged as the strongest independent predictor of postoperative MTP, followed by older age ( β = -0.167, p < 0.001) and higher log-transformed NT-proBNP levels ( β = -0.111, p = 0.011). In contrast, greater handgrip strength ( β = 0.221, p < 0.001) emerged as the strongest independent positive predictor of postoperative MTP, followed by higher body mass index ( β = 0.133, p = 0.001). Prolonged endotracheal intubation, advanced age, and diminished physiological reserves (low handgrip strength, elevated NT-proBNP, and lower BMI) are independent predictors of lower postoperative tongue pressure. Although extubation timing is dictated by overall clinical status, close monitoring of tongue strength and early rehabilitative support are essential for patients experiencing prolonged intubation. Furthermore, preoperative physical and nutritional optimization are key strategies to preserve postoperative tongue pressure and facilitate oral-motor recovery in vulnerable cardiac surgery patients.
The authors' abstract, as published at the source. BMC Oral Health, 2026 · DOI ↗
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Field: Speech and Hearing
Speech and HearingHealth Professions