In this study, surface electromyography (sEMG) of the lower limbs of cerebral-palsy (CP) subjects in gait cycle was recorded and its parameters of gait cycle characters were analyzed to assess their clinical severity. Three algorithms, including integrated profile (IP), sample-entropy (SampEN) and smooth nonlinear energy operator (SNEO) algorithm, were applied to calculate the duration of walking sEMG segments in simulated SEMG signals. After that, the efficiency and accuracy were compared among these three algorithms. SNEO was then selected as the optimal algorithm among the three algorithms and employed for real sEMG signal processing of CP subjects. The results indicated that there was no significant difference in the accuracy of sEMG segement detection for the three algorithms. However, the computation speed of SNEO algorithm was much faster than those of the others and thus it was a suitable algorithm for detecting walking sEMG segments of CP subjects. In addition, the positive correlation was found between the clinical severity and the mean duration of walking sEMG segments in CP subjects. The results indicated that there was a significant difference in the three groups of CP subjects with different levels of severity. Our findings showed that the mean duration of walking sEMG segments could be considered as an assistant index to evaluate the clinical severity of CP subjects.
Postoperative pulmonary infection (PPI) after esophageal cancer surgery occurs frequently and severely impairs patients’ prognosis. Most existing prediction models cannot realize staged classification of risk factors, which limits targeted risk identification and intervention. Based on machine learning algorithms, this study integrates preoperative baseline characteristics and perioperative indicators to construct a preoperative-perioperative two-stage risk prediction model for postoperative pulmonary infection. Clinical data of 2 200 patients undergoing esophageal cancer surgery admitted to the Cancer Hospital, Chinese Academy of Medical Sciences between October 2022 and August 2024 were retrospectively enrolled. The least absolute shrinkage and selection operator was combined with multivariate logistic regression to screen independent predictive variables for the two stages, and five machine learning models were established accordingly. Six independent predictive variables were identified. The preoperative predictors included gender, American Society of Anesthesiologists (ASA) physical status classification, and colonization of multidrug-resistant bacteria. All models yielded area under the curve values ranging from 0.71 to 0.72 with a specificity higher than 98%, which can be used for preoperative risk stratification of high-risk individuals. On the basis of preoperative variables, the perioperative stage additionally incorporated operation duration, postoperative intensive care unit (ICU) admission, and peak C-reactive protein level within 0-3 days after surgery, leading to a remarkable improvement in predictive performance with all area under the curve values greater than 0.82. The gradient boosting machine (GBM) model achieved a favorable balance between a sensitivity of 69.07% and a specificity of 82.59%, providing support for risk stratification and clinical management decision-making. Further multicenter studies are required to validate the generalization ability of the model.