Therapeutics and Clinical Risk Management最新文献

Objective: Our study evaluated the risk factors for new postoperative atrial fibrillation (POAF) by analyzing the data collected from patients who underwent first coronary artery bypass grafting (CABG).

Methods: Our study retrospectively collected data from January 2021 to December 2023 at Changzhi People's Hospital. The perioperative period data were collected, and logistic regression was used to analyze the independent predictors of the occurrence of POAF after CABG and the related predictive values of risk factors were analyzed by using the subjects' work characteristic curve (ROC).

Results: A total of 169 patients were included, and there are 45 patients in the POAF group, with an incidence of 26.6%, and 124 in the non-POAF group. The POAF group was significantly higher than the non-POAF group in terms of age (69.2±8.8 years vs 62.3±9.3 years) and preoperative LAD (42.7±7.2mm vs 36.8±5.5mm), and the difference was significant (P<0.05). Preoperative HDL-C in the POAF group were lower than non-POAF group (1.0±0.5 mmol/l vs 1.4±0.7 mmol/l, P<0.05). The logistic regression analysis revealed a significant correlation between age, LAD, HDL-C and the occurrence of POAF (P<0.05). According to the ROC curve analysis, age >64.5 years, LAD >41mm, and HDL-C <0.9 mmol/l were the cut-off values for predicting the occurrence of POAF (AUC1=0.733; AUC2=0.741; AUC3=0.647, P < 0.05). The combined age + LAD + HDL-C (AUC = 0.755; P < 0.05) had a higher diagnostic value and high sensitivity.

Conclusion: The age, LAD, and HDL-C are independent risk factors for the POAF after CABG, and clinicians should assess these risk factors as much as possible when managing patients in the perioperative period and make corresponding measures to prevent the development of POAF.

Objective: This study aims to explore the influencing factors of cough after pulmonary resection (CAP) after thoracoscopic lung resection in lung cancer patients and to develop a predictive model.

Methods: A total of 374 lung cancer patients who underwent lung resection in our hospital from March 2020 to October 2023 were randomly divided into a modeling group (n=262) and a validation group (n=112). Based on the occurrence of CAP in the modeling group, the patients were divided into a CAP group (n=85) and a non-CAP group (n=177). Multivariate Logistic regression analysis was used to identify the influencing factors of CAP in lung cancer patients. A nomogram model for predicting the risk of CAP was constructed using R4.3.1. The consistency of the model's predictions was evaluated, and a clinical decision curve (DCA) was drawn to assess the clinical utility of the nomogram. The predictive performance of the model was evaluated using ROC curves and the Hosmer-Lemeshow test.

Results: Multivariate Logistic regression analysis showed that smoking history (OR=6.285, 95% CI: 3.031-13.036), preoperative respiratory function training (OR=20.293, 95% CI: 7.518-54.779), surgical scope (OR=20.667, 95% CI: 7.734-55.228), and peribronchial lymph node dissection (OR=5.883, 95% CI: 2.829-12.235) were significant influencing factors of CAP in lung cancer patients (P<0.05). ROC curves indicated good discriminatory power of the model, and the Hosmer-Lemeshow test showed a high degree of agreement between predicted and actual probabilities. The DCA curve revealed that the nomogram model had high clinical value when the high-risk threshold was between 0.08 and 0.98.

Conclusion: The nomogram model based on smoking history, preoperative respiratory function training, surgical scope, and peribronchial lymph node dissection has high predictive performance for CAP in lung cancer patients. It is useful for clinical prediction, guiding preoperative preparation, and postoperative care.

Objective: To investigate the impact of various sedative medications on hemodynamics and plasma levels of epinephrine (E) and norepinephrine (NE) in mechanically ventilated patients postoperatively in the intensive care unit (ICU).

Methods: Ninety-seven patients admitted to the ICU undergoing postoperative mechanical ventilation with tracheal intubation and continuous analgesic sedation following general anesthesia were randomly assigned to either the observation group (dexmedetomidine) (n = 49) or the control group (propofol) (n = 48) in this randomized controlled trial. Upon transfer to the ICU, vital signs (heart rate [HR], respiratory rate [RR], mean arterial pressure [MAP]) were recorded prior to the initiation of the sedation treatment (T0), at one-hour post sedation (T1) and two hours following tracheal extubation (T2), plasma levels of epinephrine (E) and norepinephrine (NE) were measured at these time points. The incidence of delirium was recorded in both groups.

Results: MAP between the two groups at both T0 and T1 At T2 plasma NE and HR were found to be lower in the observation group compared to the control group (P < 0.001). Among the patients receiving antihypertensive medication in the ICU, NE levels were significantly lower in the observation group compared to the control group (P = 0.019) Among the patients not receiving antihypertensive medication, both NE (P < 0.001) and MAP (P = 0.001) levels were lower in the observation group compared to the control group. The incidence of delirium in the observation group (dexmedetomidine) was not significantly different from that in the control group (propofol).

Conclusion: With dexmedetomidine sedation, blood pressure fluctuated less, plasma catecholamine levels were lower, and sympathetic inhibition was stronger in patients before and after extubation. However, it did not significantly reduce the incidence of postoperative delirium.

Objective: To observe whether maintaining the appropriate depth of anesthesia with Bispectral Index (BIS) can improve the prognosis of Spinal Cord stimulation (SCS) implantation in patients with chronic Disorders of consciousness (DoC).

Methods: 103 patients with DoC undergoing SCS implantation were reviewed, and 83 patients with DoC were included according to the standard of inclusion and exclusion Criteria. Patients were divided into a BIS group (n =45) and a non-BIS group (n =38) according to whether BIS monitoring was used during the operation. The depth of anesthesia in the BIS group was maintained between 40-60. The anesthesiologist adjusted the depth of anesthesia in the non-BIS group according to clinical experience. Relevant information such as disease course, cause, anesthesia time, and operation time were collected. Preoperative CRS-R_{(preoperative)} score, postoperative CRS-R_(24h), and postoperative CRS-R_(3m) changes were collected.

Results: The CRS-R(3m) score in the BIS group was higher than that in the non-BIS group _{(preoperative)}, and the difference was statistically significant (P < 0.05). In CRS-R _(24h), the BIS group was higher than the non-BIS group, and the difference was statistically significant (X²=8.787, P =0.004). The improvement of consciousness was included in the multivariate Logistic regression analysis model, and it was found that the thalamus was an independent factor affecting the improvement of consciousness (P < 0.05). During follow-up, 1 patient in the BIS group had a decrease in consciousness from MCS^- to VS/ UWS and 2 patients in the non-BIS group died during follow-up.

Conclusion: Patients can be benefit in hearing in CRS-R _(24h). We recommend the use of BIS to monitor the depth of anesthesia in patients with DoC to improve patient outcomes.

Background and aim: Traumatic brain injury (TBI) is a severe public health problem in elderly patients, and frailty is associated with higher mortality rates in older patients. This study aims to assess the prognostic value of frailty in patients with TBI.

Methods: Clinical data from 348 TBI patients treated at Affiliated Kunshan Hospital of Jiangsu University and Kunshan Hospital of Traditional Chinese Medicine between December 2018 and December 2020 were retrospectively collected. Univariate and multivariate logistic regression analyses were used to determine risk factors affecting in-hospital mortality, and receiver operating characteristic (ROC) curves were plotted to assess the discriminatory power of the frailty index. Frailty was assessed using the FRAIL scale, where FRAIL stands for Fatigue, Resistance, Ambulation, Illness, and Loss of weight, with each item scored as 0 or 1.

Results: Using the FRIAL questionnaire, 122 patients had low frailty and 226 had high frailty. Multivariate logistic regression analysis showed that high frailty was a risk factor for in-hospital mortality in TBI patients (P<0.001, OR=2.012 [1.788-2.412]). The proportion of infections occurring in the two groups was statistically different (P=0.015), with severely infected TBI patients being more likely to develop complications. The ROC curve showed an area under the curve for the FRAIL score of 0.845 [0.752-0.938].

Conclusion: Frailty is an important risk factor for in-hospital mortality in elderly TBI patients, and more attention should be paid to patients with high levels of frailty. Clinicians should consider the degree of frailty when assessing TBI and making treatment decisions.

Purpose: Mechanical ventilation (MV) is one of the most common treatments for patients with blunt chest trauma (BCT) admitted to the intensive care unit (ICU). Our study aimed to investigate the performance of machine learning algorithms in predicting the prolonged duration of mechanical ventilation (PDMV) in patients with BCT.

Methods: In this single-center observational study, patients with BCT who were treated with MV through nasal or oral intubation were selected. PDMV was defined as the duration of mechanical ventilation ≥7 days after endotracheal intubation (normal vs prolonged MV; dichotomous outcomes). K-means was used to cluster data from the original cohort by an unsupervised learning method. Multiple machine learning algorithms were used to predict DMV categories. The most significant predictors were identified by feature importance analysis. Finally, a decision tree based on the chi-square automatic interaction detection (CHAID) algorithm was developed to study the cutoff points of predictors in clinical decision-making.

Results: A total of 426 patients and 35 characteristics were included. K-means clustering divided the cohort into two clusters (high risk and low risk). The area under the curve (AUC) of the DMV classification algorithms ranged from 0.753 to 0.923. The importance analysis showed that the volume of pulmonary contusion (VPC) was the most important feature to predict DMV. The prediction accuracy of the decision tree based on CHAID reached 86.4%.

Conclusion: Machine learning algorithms can predict PDMV in patients with BCT. Therefore, limited medical resources can be more appropriately allocated to BCT patients at risk for PDMV.