This study aims to investigate the effects of Tai Chi Chuan (TCC) and brisk walking (BW) on balance and training duration for the two exercises to significantly improve balance. A total of 48 elderly women were randomly divided into three groups. The TCC and BW groups completed a 60-min intervention training program with five sessions weekly for 16 weeks. Single-leg standing balance was tested every 4 weeks. Results showed that all the variables with eyes open improved on the eighth week (p < .05) in the TCC group and on the 12th week (p < .01) in the BW group. All variables with eyes closed improved on the 12th week (p < .01) in the TCC group and on the 16th week (p < .05) in the BW group. The results showed that 12 and 16 weeks of TCC and BW, respectively were essential to improve balance with eyes closed among the women aged 60–70 years.

Background: This study aimed to develop machine learning models to predict Low Cardiac Output Syndrome (LCOS) in patients following cardiac surgery using machine learning algorithms.Methods: The clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and November 2020 were retrospectively extracted from the electronic medical records. Six conventional machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting and light gradient boosting machine, were employed to construct the LCOS predictive models with all predictive features (full models) and selected predictive features (reduced models). The discrimination of these models was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration of the models was assessed by the calibration curve. Shapley Additive explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the predictive models.Results: Data from 1,585 patients [982 (62.0%) were male, aged 18 to 88, 212 (13.4%) with LCOS] were employed to train and validate the LCOS models. Among the full models, the RF model (AUC: 0.909, 95% CI: 0.875–0.943; Sensitivity: 0.849, 95% CI: 0.724–0.933; Specificity: 0.835, 95% CI: 0.796–0.869) and the XGB model (AUC: 0.897, 95% CI: 0.859–0.935; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.809, 95% CI: 0.768–0.845) exhibited well predictive power for LCOS. Eleven predictive features including left ventricular ejection fraction (LVEF), first post-operative blood lactate (Lac), left ventricular diastolic diameter (LVDd), cumulative time of mean artery blood pressure (MABP) lower than 65 mmHg (MABP < 65 time), hypertension history, platelets level (PLT), age, blood creatinine (Cr), total area under curve above threshold central venous pressure (CVP) 12 mmHg and 16 mmHg, and blood loss during operation were used to build the reduced models. Among the reduced models, RF model (AUC: 0.895, 95% CI: 0.857–0.933; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.806, 95% CI: 0.765–0.843) revealed the best performance. SHAP and LIME plot showed that LVEF, Lac, LVDd and MABP < 65 time significantly contributed to the prediction model.Conclusion: In this study, we successfully developed several machine learning models to predict LCOS after surgery, which may avail to risk stratification, early detection and management of LCOS after cardiac surgery.

Background: The aim of this study was to investigate the relationship between baseline lymphocyte-monocyte ratio (LMR) and postoperative acute kidney injury (AKI) in patients with acute type A aortic dissection (ATAAD). Methods: ATAAD patients undergoing surgery in Nanjing First Hospital were enrolled from January 2019 to April 2021. Lymphocyte and monocyte were measured on admission. Multivariable logistic regression analyses were performed to explore the relationship between LMR and postoperative AKI. We also used receiver operating characteristic (ROC), net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses to assess the predictive ability of LMR. Results: Among the 159 recruited patients, 47 (29.6%) were diagnosed with AKI. Univariate logistic regression analysis indicated that ATAAD patients with higher levels of LMR were prone to have lower risk to develop AKI (odds ratio [OR], 0.493; 95% confidence interval [CI] 0.284–0.650, P = 0.001). After adjustment for the potential confounders, LMR remained an independent related factor with postoperative AKI (OR 0.527; 95% CI 0.327–0.815, P = 0.006). The cutoff value for LMR to predict AKI was determined to be 2.67 in the ROC curve analysis (area under curve: 0.719). NRI and IDI further confirmed the predictive capability of LMR in postoperative AKI. Conclusion: Elevated baseline LMR levels were independently associated with lower risk of postoperative AKI in ATAAD patients.

Background: The association between hypernatremia and delirium after cardiac surgery has rarely been investigated. This study aimed to determine whether hypernatremia increases the risk of delirium after exposure.Materials and Methods: From April 2016 to June 2021, 7,831 consecutive patients receiving cardiac surgery were screened for potential enrollment. The primary outcome was postoperative delirium (POD). For the respective case of delirium, 10 controls were matched according to the index date within the nested case-control design. Hypernatremia exposure was defined as serum sodium > 145 mmol/L within 7 days before the index date. A generalized estimation equation was performed to assess excess risks for POD associated with hypernatremia, adjusted for demographics and clinical variables.Results: About 7,277 patients were included in the final analyses. About 669 (9.2%) patients with POD were assigned to the case group, and 6,690 controls were identified from the whole population. About 66.5% of the cases and 36.3% of the controls had hypernatremia exposure. After being adjusted to certain well-recognized confounding factors, hypernatremia showed a significant correlation with increased risk of delirium after cardiac surgery (adjusted OR, 1.73; 95% CI, 1.41~2.12). An e-value analysis suggested the robustness to unmeasured confounding.Conclusions: Hypernatremia was associated with an increased risk of delirium after cardiac surgery. This finding could have implications for risk stratification, early detection, and management of delirium in patients receiving cardiac surgery.