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BMC Oral Health· 2026Q1

Makine Öğrenmesi, Kardiyak Cerrahi Sonrası Dil Basıncını Etkileyen Faktörleri Belirliyor

Machine learning-based analysis of factors influencing maximum tongue pressure in patients after cardiac surgery: a cross-sectional study

Aimin Shao, Run Huang, Tingting Zhang, Jia Xin Wan ve diğerleri

Kısa özet

Makine öğrenmesi, kardiyak cerrahi sonrası maksimum dil basıncında (MTP) azalmanın en güçlü öngörücüsü olarak uzamış endotrakeal entübasyonu (β = -0.254) belirledi; bunu ileri yaş ve yüksek NT-proBNP düzeyleri izledi, el kavrama gücü ve VKI ise pozitif öngörücülerdi.

Yapay zekâ ile başlık ve abstract'tan üretildi; tam metin okunmaz.

Ana noktalar

  • Kardiyak cerrahi hastalarında uzamış endotrakeal entübasyon süresi, postoperatif maksimum dil basıncında (MTP) azalmanın en güçlü öngörücüsüydü (β = -0.254).
  • İleri yaş (β = -0.167) ve yüksek NT-proBNP düzeyleri (β = -0.111) de bağımsız olarak daha düşük MTP'yi öngördü.
  • Daha güçlü el kavrama gücü (β = 0.221) ve daha yüksek vücut kitle indeksi (VKİ) (β = 0.133) MTP'nin bağımsız pozitif öngörücüleriydi.
  • Bu öngörücüleri belirlemek için 18 aday değişken arasından Rastgele Orman ve LASSO regresyonunu birleştiren hibrit bir makine öğrenmesi yaklaşımı kullanıldı.

Yapay zekâ ile başlık ve abstract'tan üretildi; tam metin okunmaz.

Özet (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.

Yazarların özeti; kaynağından alınmıştır. BMC Oral Health, 2026 · DOI ↗

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