Knowledge, attitude, and practice towards bacterial multidrug-resistance and structural equation modeling analysis among intensive care unit nurses and physicians.

BACKGROUND: The intensive care unit (ICU) is a department with a high risk of MDR bacteria, and ICU nurses and physicians play critical roles in bacterial multidrug resistance (MDR) prevention. OBJECTIVES: To explore the knowledge, attitudes, and practice (KAP) towards bacterial MDR among ICU nurses and physicians. METHODS: A self-designed questionnaire was administered to collect data. Structural equation modeling (SEM) was applied to assess the associations among study variables. RESULTS: A total of 369 questionnaires were collected; 43 questionnaires were excluded due to self-contradictory on the trap question or the obviously repeated pattern. Finally, 326 (88.35%) valid questionnaires were included in the analysis. The knowledge, attitudes, and practice were 13.57 ± 1.69 (90.47%, possible range: 0-15), 38.75 ± 2.23 (96.88%, possible range: 8-40), and 47.40 ± 3.59 (94.80%, possible range: 10-50). The SEM showed that knowledge had a direct effect on attitude with a direct effect value of 0.61 (P < 0.001) and a direct negative effect on practice with a direct effect value of -0.30 (P = 0.009). The direct effect of attitude on practice was 0.89 (P < 0.001); the indirect effect of knowledge through attitude on practice was 0.52 (P < 0.001). Job satisfaction had a direct effect on attitude and practice, with an effect value of 0.52 (P = 0.030) and 0.75 (P = 0.040). Being a physician (OR = 0.354, 95%CI: 0.159-0.790, P = 0.011), 5-9.9 years of practice (OR = 4.534, 95%CI: 1.878-8.721, P < 0.001), and ≥ 10 years of practice (OR = 3.369, 95%CI: 1.301-8.721, P = 0.012) were independently associated with good knowledge. The attitude scores (OR = 1.499, 95%CI: 1.227-1.830, P < 0.001), male gender (OR = 0.390, 95%CI: 0.175-0.870, P = 0.022), and 5-9.9 years of experience (OR = 0.373, 95%CI: 0.177-0.787, P = 0.010) were independently associated with proactive practice. CONCLUSION: Nurses and physicians in the ICU showed good knowledge, positive attitudes, and proactive practice toward bacterial MDR. Nurses and physicians' knowledge had a direct effect on their attitude, while attitude might directly influence the practice and also play a mediating role between knowledge and practice. Job satisfaction might directly support the positive attitude and practice toward bacterial MDR.

Hypoperfusion Intensity Ratio Correlates with Angiographic Collaterals and Infarct Growth in Acute Stroke with Thrombectomy.

BACKGROUND: The assessment of collaterals before endovascular thrombectomy (EVT) therapy play a pivotal role in clinical decision-making for acute stroke patients. OBJECTIVE: To investigate the correlation between hypoperfusion intensity ratio (HIR), collaterals on digital subtraction angiography (DSA), and infarct growth in acute stroke patients who underwent EVT therapy. METHODS: Patients with acute ischemic stroke (AIS) who underwent EVT therapy were enrolled retrospectively. HIR was assessed through magnetic resonance imaging (MRI) and was defined as the Tmax > 10 s lesion volume divided by the Tmax > 6 s lesion volume. Collaterals were assessed on DSA using the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) scale. Good collaterals were defined as ASITN/SIR score 3-4 and poor collaterals were defined as ASITN/SIR score 0-2. Spearman's rank correlation analysis was used to evaluate the correlation between HIR, collaterals, infarct growth, and functional outcome. RESULTS: A total of 115 patients were included. Patients with good collateral (n = 59) had smaller HIR (0.29 ± 0.07 vs. 0.52 ± 0.14; t = 10.769, P < 0.001) and infarct growth (8.47 ± 2.40 vs. 14.37 ± 5.28; t = 7.652, P < 0.001) than those with poor collateral (n = 56). DISCUSSION: The ROC analyses showed that the optimal cut-off value of HIR was 0.40, and the sensitivity and specificity for predicting good collateral were 85.70% and 96.61%, respectively. With the optimal cut-off value, patients with HIR < 0.4 (n = 67) had smaller infarct growth (8.86 ± 2.59 vs. 14.81 ± 5.52; t = 6.944, P < 0.001) than those with HIR ≥ 0.4 (n = 48). Spearman's rank correlation analysis showed that HIR had a correlation with ASITN/SIR score (r = -0.761, P < 0.001), infarct growth (r = 0.567, P < 0.001), and mRS at 3 months (r = -0.627, P < 0.001). CONCLUSION: HIR < 0.4 is significantly correlated with good collateral status and small infarct growth. Evaluating HIR before treatment may be useful for guiding EVT and predicting the functional outcome of AIS patients.

Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading.

Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades.

Aberrant static and dynamic functional network connectivity in heart failure with preserved ejection fraction.

AIMS: Heart failure may lead to brain functional alterations related to cognitive impairment. This study aimed to detect alterations of static functional network connectivity (FNC) and dynamic FNC in heart failure with preserved ejection fraction (HFpEF) and to estimate the association between the altered FNC and clinical features related to HFpEF. METHODS AND RESULTS: The clinical and resting-state functional magnetic resonance imaging (fMRI) data of HFpEF patients (n = 35) and healthy controls (HCs) (n = 35) were acquired at baseline. Resting-state networks (RSNs) were established based on independent component analysis (ICA) and FNC analyses were performed. The associations between the FNC abnormalities and clinical features related to HFpEF were analysed. Compared with HCs, HFpEF patients showed decreased functional connectivity within the default mode network, left frontoparietal network, and right frontoparietal network and increased functional connectivity within the right frontoparietal network and visual network. Negative correlations were observed between decreased dynamic FNC and the left ventricular end-diastolic diameter (LVDd) (r = -0.435, P = 0.015) as well as the left ventricular end-systolic diameter (LVDs) (r = -0.443, P = 0.013). CONCLUSIONS: The FNC disruption and altered temporal properties of functional dynamics in HFpEF patients may reflect the neural mechanisms of brain injury after HFpEF, which may deepen our understanding of the disease.

MRI Radiomics Features From Infarction and Cerebrospinal Fluid for Prediction of Cerebral Edema After Acute Ischemic Stroke.

Neuroimaging biomarkers that predict the edema after acute stroke may help clinicians provide targeted therapies and minimize the risk of secondary injury. In this study, we applied pretherapy MRI radiomics features from infarction and cerebrospinal fluid (CSF) to predict edema after acute ischemic stroke. MRI data were obtained from a prospective, endovascular thrombectomy (EVT) cohort that included 389 patients with acute stroke from two centers (dataset 1, n = 292; dataset 2, n = 97), respectively. Patients were divided into edema group (brain swelling and midline shift) and non-edema group according to CT within 36 h after therapy. We extracted the imaging features of infarct area on diffusion weighted imaging (DWI) (abbreviated as DWI), CSF on fluid-attenuated inversion recovery (FLAIR) (CSFFLAIR) and CSF on DWI (CSFDWI), and selected the optimum features associated with edema for developing models in two forms of feature sets (DWI + CSFFLAIR and DWI + CSFDWI) respectively. We developed seven ML models based on dataset 1 and identified the most stable model. External validations (dataset 2) of the developed stable model were performed. Prediction model performance was assessed using the area under the receiver operating characteristic curve (AUC). The Bayes model based on DWI + CSFFLAIR and the RF model based on DWI + CSFDWI had the best performances (DWI + CSFFLAIR: AUC, 0.86; accuracy, 0.85; recall, 0.88; DWI + CSFDWI: AUC, 0.86; accuracy, 0.84; recall, 0.84) and the most stability (RSD% in DWI + CSFFLAIR AUC: 0.07, RSD% in DWI + CSFDWI AUC: 0.09), respectively. External validation showed that the AUC of the Bayes model based on DWI + CSFFLAIR was 0.84 with accuracy of 0.77 and area under precision-recall curve (auPRC) of 0.75, and the AUC of the RF model based on DWI + CSFDWI was 0.83 with accuracy of 0.81 and the auPRC of 0.76. The MRI radiomics features from infarction and CSF may offer an effective imaging biomarker for predicting edema.

Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI.

OBJECTIVES: To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI. METHODS: DWI and FLAIR images of stroke patients within 24 h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5 h or > 4.5 h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis. RESULTS: svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy. CONCLUSIONS: The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset. KEY POINTS: • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.

Predictive value of perfusion weighted imaging for early new lesions after stroke patients receive endovascular treatment.

BACKGROUND: Previous studies have focused on early new lesion-associated factors, but the differences in the perfusion status between the at-risk hypoperfusion areas with new lesions and the other hypoperfusion areas in stroke patients before thrombectomy is not clear. We investigated the value of perfusion-weighted imaging (PWI) in predicting early new lesions in patients after stroke. METHODS: Fifty-five acute stroke patients who underwent diffusion-weighted imaging (DWI) and PWI before and after thrombectomy within 24 h were eligible. The PWI parameters of the core infarct areas (high signal tissue on the DWI), the at-risk hypoperfusion areas (hypoperfusion area with new lesions at follow-up PWI) and the other hypoperfusion areas of patients with new lesions were collected. Statistical analysis was performed to predict new lesions after stroke. The differences in the PWI parameters of the core infarct areas, the at-risk hypoperfusion areas and the other hypoperfusion areas were compared. Receiver operating characteristic (ROC) curve analysis was used to assess the predictive value of the PWI parameters (P<0.05) for the occurrence of new lesions in patients with acute stroke after thrombectomy. RESULTS: Fifty-five stroke patients were analyzed, including forty patients (72.73%) with new lesions and fifteen patients (27.27%) without new lesions. Acute stroke patients with new lesions had a longer mean transit time (MTT) and time to peak (TTP) in the at-risk hypoperfusion areas (11.95±3.29; 38.30±11.39) than in the other hypoperfusion areas (8.68±2.08; 29.76±6.86), both of which were significantly different (P<0.0001; P<0.0001, respectively). The ROC analysis showed that the sensitivity and specificity of MTT for predicting the occurrence of new lesions after stroke were 70.00% and 87.50%, respectively; the sensitivity and specificity of TTP were 70.00% and 80.00%, respectively. CONCLUSIONS: MTT and TTP may be useful in predicting early new lesions in acute stroke patients after thrombectomy.

Variability and Influencing Factors of QOL in Breast Cancer Patients Having Chemotherapy.

Variability and factors influencing quality of life (QOL) in breast cancer patients having chemotherapy were examined in a longitudinal prospective cohort study in two teaching hospitals in China. Physical, mental, social/family, and functional QOL changed significantly over time with varying patterns. In addition, various factors influenced the QOL of breast cancer patients at each chemotherapy cycle. Health professionals should focus on critical time periods during chemotherapy, particularly at baseline and during the fourth and fifth cycles when the QOL in our sample was at the lowest point, and they should provide additional support to patients to ensure that chemotherapy is delivered in an optimal fashion.

The Effectiveness of Parenting Interventions on Psychosocial Adjustment in Parents of Children and Adolescents with Type 1 Diabetes: A Meta-Analysis.

AIMS: Parenting interventions in this review refer to supportive parenting training provided for parents or primary caregivers of children and adolescents with type 1 diabetes mellitus (T1DM). The review aimed to synthesize evidence about parenting interventions in parents or caregivers of children and adolescents with T1DM, and to evaluate the effect of interventions in reducing parents' or caregivers' psychological distress, helping them share diabetes management responsibility, seek social support, and improve their quality of life. METHODS: We searched PubMed, MEDLINE, EMBASE, CINAHL, Cochrane, and Web of Science from January 1978 to October 2018. Randomized controlled trials (RCTs) comparing an intervention group of parenting programs with a control group of usual care were included. The primary outcomes were stress, family responsibility and conflict, and social support. Secondary outcomes included other psychological index and quality of life. Pooled effect sizes of weighted mean difference (WMD) were calculated. RESULTS: A total of 17 RCTs with 962 participants met the inclusion criteria. Findings of the meta-analysis showed parenting interventions could significantly reduce parents' depression (WMD = -5.78, 95% CI: -6.23 to -5.33, I2  = 0%) and distress (WMD = -5.28, 95% CI: -10.31 to -.25, I2  = 0%), and help them ask for positive social support (WMD = .83, 95% CI: .03 to 1.64, I2  = 0%). No beneficial changes of other outcomes were found. LINKING EVIDENCE TO ACTION: Parents of children and adolescents with T1DM need support from the multidisciplinary team in health care, especially in mental health, family management of childhood diabetes, and social support. Parenting interventions may help parents reduce psychological distress and depression and assist them to ask for social support. Future research should include well-designed RCTs with large samples, appropriate measures with clear definitions, objective assessment, and separation of effects on mothers and fathers.

Factors associated with unplanned extubation in the Intensive Care Unit for adult patients: A systematic review and meta-analysis.

OBJECTIVES: To explore factors associated with unplanned extubation in Intensive Care Unit for adult patients. RESEARCH METHODOLOGY: A systematic review and meta-analysis were performed of studies identified through Pubmed, CINAHL, Cochrane Library, PsycINFO and Web of Science published from initiation to September 2017. Only articles in English were included. The Newcastle-Ottawa Scale was used to evaluate the quality of the included articles. RESULTS: Ten eligible studies were identified, encompassing a total of 2092 patients (457 in the unplanned extubation group; 1635 in the control group). The subsequent meta-analysis identified significant risk factors for unplanned extubation are male [odds ratio (OR) 1.54, 95% CI 1.12-2.12; P = 0.008], confusion [OR 0.10, 95% CI 0.05-0.17; P < 0.00001], physical restraint [OR 3.10, 95% CI 2.21-4.34; P < 0.00001], higher GCS scores [mean difference (MD) 1.06, 95% CI 0.59-1.52; P < 0.00001] and lower APACHE II scores [MD -2.26, 95% CI -3.35- -1.16; P < 0.0001]. Renal disease is a protective factor for unplanned extubation [OR 0.32, 95% CI 0.15-0.70; P = 0.004]. CONCLUSION: Patients were male, confused, having physical restraint, with higher GCS and lower APACHE II scores are significant risk factors for unplanned extubation in Intensive Care Unit adult patients.