Integrated virtual simulation and face-to-face simulation for clinical judgment training among undergraduate nursing students: a mixed-methods study.

BACKGROUND: Virtual simulation and face-to-face simulation are effective for clinical judgment training. Rare studies have tried to improve clinical judgment ability by applying virtual simulation and face-to-face simulation together. This study aimed to evaluate the effect of an integrated non-immersive virtual simulation and high-fidelity face-to-face simulation program on enhancing nursing students' clinical judgment ability and understanding of nursing students' experiences of the combined simulation. METHODS: A sequential exploratory mixed-methods study was conducted in a nursing simulation center of a university in Central China. Third-year nursing students (n = 122) taking clinical training in ICUs were subsequentially assigned to the integrated non-immersive virtual simulation and high-fidelity face-to-face simulation program arm (n = 61) or the face-to-face simulation-only arm (n = 61) according to the order in which they entered in ICU training. Clinical judgment ability was measured by the Lasater Clinical Judgment Rubric (LCJR). Focus group interviews were conducted to gather qualitative data. RESULTS: Students in both arms demonstrated significant improvement in clinical judgment ability scores after simulation, and students in the integrated arm reported more improvement than students in the face-to-face simulation-only arm. The qualitative quotes provided a context for the quantitative improvement measured by the LJCR in the integrated arm. Most of the quantitative findings were confirmed by qualitative findings, including the domains and items in the LJCR. The findings verified and favored the effect of the combination of non-immersive virtual simulation and high-fidelity face-to-face simulation integrated program on enhancing nursing students' clinical judgment ability. CONCLUSIONS: The integrated virtual simulation and face-to-face simulation program was feasible and enhanced nursing students' self-reported clinical judgment ability. This integrated non-immersive virtual simulation and high-fidelity face-to-face simulation program may benefit nursing students and newly graduated nurses in the ICU more than face-to-face simulation only.

Preoperative computed tomography-guided localization for pulmonary nodules: comparison between hook-wire and anchored needle localization.

Introduction: Both hook-wire (HW) and anchored needle (AN) techniques can be used for preoperative computed tomography (CT)-guided localization for pulmonary nodules (PNs). But the outcomes associated with these two materials remain unclear. Aim: To assess the relative safety and efficacy of preoperative CT-guided HW and AN localization for PNs. Material and methods: This was a retrospective analysis of data collected from two institutions. Consecutive patients with PNs between January 2020 and December 2021 who underwent preoperative CT-guided HW or AN localization followed by video-assisted thoracoscopic surgery (VATS) procedures were included in these analyses, which compared the safety and clinical efficiency of these two localization strategies. Results: In total, 98 patients (105 PNs) and 93 patients (107 PNs) underwent CT-guided HW and AN localization procedures, respectively. The HW and AN groups exhibited similar rates of successful PN localization (95.2% vs. 99.1%, p = 0.117), but the dislodgement rate in the HW group was significantly higher than that for the AN group (4.8% vs. 0.0%, p = 0.029). The mean pain score of patients in the HW group was significantly higher than that for the AN group (p = 0.001). HW and AN localization strategies were associated with comparable pneumothorax (21.4% vs. 16.1%, p = 0.349) and pulmonary hemorrhage (29.6% vs. 23.7%, p = 0.354) rates. All patients other than 1 individual in the HW group successfully underwent VATS-guided limited resection. Conclusions: These data suggest that AN represents a safe, well-tolerated, feasible preoperative localization strategy for PNs that may offer value as a replacement for HW localization.

Establishment and validation of a predictive model for peripherally inserted central catheter-related thrombosis in patients with liver cancer.

BACKGROUND: Peripherally inserted central catheters (PICCs) are commonly used in hospitalized patients with liver cancer for the administration of chemotherapy, nutrition, and other medications. However, PICC-related thrombosis is a serious complication that can lead to morbidity and mortality in this patient population. Several risk factors have been identified for the development of PICC-related thrombosis, including cancer type, stage, comorbidities, and catheter characteristics. Understanding these risk factors and developing a predictive model can help healthcare providers identify high-risk patients and implement preventive measures to reduce the incidence of thrombosis. AIM: To analyze the influencing factors of PICC-related thrombosis in hospitalized patients with liver cancer, construct a predictive model, and validate it. METHODS: Clinical data of hospitalized patients with liver cancer admitted from January 2020 to December 2023 were collected. Thirty-five cases of PICC-related thrombosis in hospitalized patients with liver cancer were collected, and 220 patients who underwent PICC placement during the same period but did not develop PICC-related thrombosis were randomly selected as controls. A total of 255 samples were collected and used as the training set, and 77 cases were collected as the validation set in a 7:3 ratio. General patient information, case data, catheterization data, coagulation indicators, and Autar Thrombosis Risk Assessment Scale scores were analyzed. Univariate and multivariate unconditional logistic regression analyses were performed on relevant factors, and the value of combined indicators in predicting PICC-related thrombosis in hospitalized patients with liver cancer was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Univariate analysis showed statistically significant differences (P < 0.05) in age, sex, Karnofsky performance status score (KPS), bedridden time, activities of daily living impairment, parenteral nutrition, catheter duration, distant metastasis, and bone marrow suppression between the thrombosis group and the non-thrombosis group. Other aspects had no statistically significant differences (P > 0.05). Multivariate regression analysis showed that age ≥ 60 years, KPS score ≤ 50 points, parenteral nutrition, stage III to IV, distant metastasis, bone marrow suppression, and activities of daily living impairment were independent risk factors for PICC-related thrombosis in hospitalized patients with liver cancer (P < 0.05). Catheter duration of 1-6 months and catheter duration > 6 months were protective factors for PICC-related thrombosis (P < 0.05). The predictive model for PICC-related thrombosis was obtained as follows: P predictive probability = [exp (Logit P)]/[1 + exp (Logit P)], where Logit P = age × 1.907 + KPS score × 2.045 + parenteral nutrition × 9.467 + catheter duration × 0.506 + tumor-node-metastasis (TNM) staging × 2.844 + distant metastasis × 2.065 + bone marrow suppression × 2.082 + activities of daily living impairment × 13.926. ROC curve analysis showed an area under the curve (AUC) of 0.827 (95%CI: 0.724-0.929, P < 0.001), with a corresponding optimal cut-off value of 0.612, sensitivity of 0.755, and specificity of 0.857. Calibration curve analysis showed good consistency between the predicted occurrence of PICC-related thrombosis and actual occurrence (P > 0.05). ROC analysis showed AUCs of 0.888 and 0.729 for the training and validation sets, respectively. CONCLUSION: Age, KPS score, parenteral nutrition, TNM staging, distant metastasis, bone marrow suppression, and activities of daily living impairment are independent risk factors for PICC-related thrombosis in hospitalized patients with liver cancer, while catheter duration is a protective factor for the disease. The predictive model has an AUC of 0.827, indicating high predictive accuracy and clinical value.