Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
ObjectiveApproximately 10%–70% of patients may develop diaphragmatic dysfunction after cardiac surgery, which may lead to delayed weaning from mechanical ventilation, increased ICU stays, postoperative hospitalization stays, and respiratory complications. However, its impact on prognosis and risk factors remain controversy. Therefore, we conducted a retrospective cohort study in which we evaluated diaphragmatic dysfunction in patients who underwent cardiac surgery via bedside diaphragm ultrasound to investigate its prognosis and possible risk factors.MethodsData from the electronic medical records system included case records and ultrasound images of the diaphragm for 177 consecutive patients admitted to the ICU following cardiac thoracotomy surgeries performed between June and September 2020. Diaphragmatic dysfunction was defined as a diaphragmatic excursion of less than 9 mm in women and less than 10 mm in men at rest, with an average thickening fraction of less than 20%. SPSS 25.0 software was used to analyse the relationships between patients' general information, intraoperative and postoperative factors and diaphragmatic dysfunction, as well as the impact on patients' hospitalization days, mechanical ventilation time and respiratory system complications.ResultsThe incidence of early postoperative diaphragmatic dysfunction after cardiac surgery was 40.7%. Patients with diaphragmatic insufficiency were more likely to sequentially use noninvasive ventilation within 24 h after weaning off mechanical ventilation (3.8% vs. 12.5%, P = 0.029) and to require more oxygen support (23.8% vs. 40.3%, P = 0.019). Although there was no significant difference, the diaphragmatic dysfunction group tended to have longer ICU stays and postoperative hospital stays than did the normal diaphragmatic function group (P = 0.119, P = 0.073). Univariate and multivariate logistic regression analyses both revealed that chest tube drainage placed during surgery accompanied by bloody drainage fluid was an independent risk factor for diaphragmatic dysfunction (univariate analysis: 95% CI: 1.126–4.137, P = 0.021; multivariate analysis: 95% CI: 1.036–3.897, P = 0.039).ConclusionEearly diaphragmatic dysfunction after cardiac surgery increased the proportion of patients who underwent sequential noninvasive ventilation after weaning from mechanical ventilation and who required more oxygen. Chest tube drainage placed during surgery accompanied by bloody drainage fluid was an independent risk factor for diaphragmatic dysfunction, providing evidence-based guidance for respiratory rehabilitation after cardiac surgery.
ObjectiveApproximately 10%–70% of patients may develop diaphragmatic dysfunction after cardiac surgery, which may lead to delayed weaning from mechanical ventilation, increased ICU stays, postoperative hospitalization stays, and respiratory complications. However, its impact on prognosis and risk factors remain controversy. Therefore, we conducted a retrospective cohort study in which we evaluated diaphragmatic dysfunction in patients who underwent cardiac surgery via bedside diaphragm ultrasound to investigate its prognosis and possible risk factors.MethodsData from the electronic medical records system included case records and ultrasound images of the diaphragm for 177 consecutive patients admitted to the ICU following cardiac thoracotomy surgeries performed between June and September 2020. Diaphragmatic dysfunction was defined as a diaphragmatic excursion of less than 9 mm in women and less than 10 mm in men at rest, with an average thickening fraction of less than 20%. SPSS 25.0 software was used to analyse the relationships between patients' general information, intraoperative and postoperative factors and diaphragmatic dysfunction, as well as the impact on patients' hospitalization days, mechanical ventilation time and respiratory system complications.ResultsThe incidence of early postoperative diaphragmatic dysfunction after cardiac surgery was 40.7%. Patients with diaphragmatic insufficiency were more likely to sequentially use noninvasive ventilation within 24 h after weaning off mechanical ventilation (3.8% vs. 12.5%, P = 0.029) and to require more oxygen support (23.8% vs. 40.3%, P = 0.019). Although there was no significant difference, the diaphragmatic dysfunction group tended to have longer ICU stays and postoperative hospital stays than did the normal diaphragmatic function group (P = 0.119, P = 0.073). Univariate and multivariate logistic regression analyses both revealed that chest tube drainage placed during surgery accompanied by bloody drainage fluid was an independent risk factor for diaphragmatic dysfunction (univariate analysis: 95% CI: 1.126–4.137, P = 0.021; multivariate analysis: 95% CI: 1.036–3.897, P = 0.039).ConclusionEearly diaphragmatic dysfunction after cardiac surgery increased the proportion of patients who underwent sequential noninvasive ventilation after weaning from mechanical ventilation and who required more oxygen. Chest tube drainage placed during surgery accompanied by bloody drainage fluid was an independent risk factor for diaphragmatic dysfunction, providing evidence-based guidance for respiratory rehabilitation after cardiac surgery.
ObjectiveTo develop predictive models for neonatal respiratory distress syndrome (NRDS) using machine learning algorithms to improve the accuracy of severity predictions.MethodsThis double-blind cohort study included 230 neonates admitted to the neonatal intensive care unit (NICU) of Yantaishan Hospital between December 2020 and June 2023. Of these, 119 neonates were diagnosed with NRDS and placed in the NRDS group, while 111 neonates with other conditions formed the non-NRDS (N-NRDS) group. All neonates underwent lung ultrasound and various clinical assessments, with data collected on the oxygenation index (OI), sequential organ failure assessment (SOFA), respiratory index (RI), and lung ultrasound score (LUS). An independent sample test was used to compare the groups’ LUS, OI, RI, SOFA scores, and clinical data. Use Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify predictor variables, and construct a model for predicting NRDS severity using logistic regression (LR), random forest (RF), artificial neural network (NN), and support vector machine (SVM) algorithms. The importance of predictive variables and performance metrics was evaluated for each model.ResultsThe NRDS group showed significantly higher LUS, SOFA, and RI scores and lower OI values than the N-NRDS group (p < 0.01). LUS, SOFA, and RI scores were significantly higher in the severe NRDS group compared to the mild and moderate groups, while OI was markedly lower (p < 0.01). LUS, OI, RI, and SOFA scores were the most impactful variables for the predictive efficacy of the models. The RF model performed best of the four models, with an AUC of 0.894, accuracy of 0.808, and sensitivity of 0.706. In contrast, the LR, NN, and SVM models have lower AUC values than the RF model with 0.841, 0.828, and 0.726, respectively.ConclusionFour predictive models based on machine learning can accurately assess the severity of NRDS. Among them, the RF model exhibits the best predictive performance, offering more effective support for the treatment and care of neonates.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.