Objective To systematically analyze influencing factors of migration stress of patients'families transferred out of intensive care unit(ICU)in China,and provide evidence-based guidance for alleviating migration stress level of families of transferred patients.Methods Computer searches were conducted on China National Knowledge Infrastructure,Wanfang Data Knowledge Service Platform,VIP,China Biomedical Literature Database,Web of Science,PubMed,the Cochrane Library and Embase for observational studies such as cohort studies,case-control studies and cross-sectional studies on factors influencing migration stress among family members of ICU patients.Search deadline was from database establishment to October 2023.Literature screening,quality assessment and data extraction were conducted independently by two researchers in accordance with the inclusion and exclusion criteria.RevMan5.4 software was used to conduct a Meta-analysis of the data results.Results Fifteen studies were finally included,with a total sample size of 2558 and 12 influencing factors were extracted.Fifteen articles were divided into positive scoring group(higher scores,higher levels of migration stress,nine articles)and negative scoring group(higher scores,lower levels of migration stress,six articles)based on instruments used to measure outcome indicators.When analyzing each group,only factors mentioned in the group more than twice were included in Meta analysis.Meta analysis showed that within positive scoring group,family member education level,family member gender,patient awareness,ICU treatment time and family member uncertainty about the disease were factors affecting migration stress of ICU transfer patients'families(P<0.05);In reverse calculation group,educational level of family member,gender of family member,age of patient and ICU treatment time were influencing factors of migration stress of family members of ICU transfer patients(P<0.05).Sensitivity analysis result showed that result of each factor was relatively stable.Conclusion Educational level of family member,gender of family member,age of patient,ICU treatment time,uncertainty of family member's illness are influencing factors of migration stress of family members of patients transferred out of ICU in China.Influence of family member's religious beliefs and patient's consciousness needs further demonstration.

Objective To investigate the value of abdominal aortic combined with routine one-stop triple rule-out computed tomography angiography(TRO-CTA)in the examination of patients with acute chest pain.Methods A total of 1 482 patients with nontraumatic chest pain were included in this retrospective study.Of them 414 patients underwent the conventional TRO-CTA scanning while 1 068 patients underwent TRO-CTA that included the abdominal aorta(TRO-CTAwAA)under the request of clinicians.All scanning parameters were the same,except the scanning range for the third phase in TRO-CTA:conventional TRO-CTA covered only the thoracic aorta,while TRO-CTAwAA extended to the entire aorta.Patient etiology was investigated and the detection rates of major vessel abnormalities(aortic dissection,aneurysm,penetrating ulcer,intramural hematoma,vascular occlusion,and thrombosis)between the two groups was compared using chi square tests.The radiation dose(CTDIvol and DLP)and scanning time between the two groups were compared using analysis of variance(ANOVA).Results The TRO-CTAwAA had significantly higher detection rate of major artery abnormalities than the TRO-CTA group(35.1％vs.4.8％,P＜0.001).In the TRO-CTAwAA group,26.5％of the vascular anomalies were detected in both the thoracic and abdominal aortas,and another 8.6％were seen only in the abdominal aorta.With regard to the radiation dose between the two groups,the total DLP was significantly higher in the TRO-CTAwAA group than in the conventional TRO-CTA group(P＜0.001).The two groups did not significantly differ in scanning time(P=0.410).Conclusion TRO-CTA with scan range including the abdominal aorta significantly improves the detection rate for major vessel abnormalities in patients with chest pain without increasing the examination process.

Objective:To investigate the practicality of TrueFidelity deep learning reconstruction algorithm in low-dose abdominal and pelvic CT angiography (CTA).Methods:The patients who required abdominal and pelvic CTA were prospectively included at the First Affiliated Hospital of Xi′an Jiaotong University from June 2020 to March 2021. All patients underwent low-dose CTA with a tube voltage of 80 kV and smart tube current modulation (100-720 mA). Images were reconstructed using the traditional FBP, adaptive statistical iterative reconstruction with a strength of 50% (ASIR-V 50%), TrueFidelity with medium (TF-M) and high (TF-H) strength. The CT value and standard deviation (SD value) of the abdominal aorta, psoas major muscle and subcutaneous fat in the same layer were measured, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated. We also introduced the measurement of skewness of CT value in psoas major muscle with uniform density. The above indexes of the four groups of reconstructed images were compared. A 5-point scoring method was used to evaluate the granularity, fuzziness and beam-hardening artifacts of all images. Objective measurement indicators, such as CT values, were tested by repeated measure ANOVA with the Bonferroni post hoc test.Results:There were forty-six patients in the study. The volume CT dose index of the scan was low at (1.09±0.31)mGy. There was no significant difference in CT values of vessels and muscles between the four groups ( P>0.05), but there was a significant difference in SD value( P<0.001). The SD value of the FBP group was the largest and that of the TF-H group was the smallest. The difference between SNR and CNR was statistically significant ( P<0.001), and the overall trend was opposite to that of the SD value. There was no significant difference in the skewness between the four groups. The granularity score of the FBP group was the largest, that of the TF-H group was the smallest, and there was a significant difference among the four groups. The score of fuzziness in the TF-H group was slightly higher than that in the other three groups, but there was no significant difference. The beam-hardening artifact score of FBP and ASIR-V 50% group was the worst, and the TF-H group was the best ( P<0.001). Conclusions:Compared with FBP and ASIR-V, TrueFidelity reconstruction algorithm provides better image quality (comprehensively considering image noise, fuzziness, uniformity, and hardening artifacts) in low-dose CT scanning of abdominal and pelvic vessels, and TF-H has the best image quality.

【Objective】 To investigate the value of deep learning image reconstruction (DLIR) in improving image quality and reducing beam-hardening artifacts of low-dose abdominal CT. 【Methods】 For this study we prospectively enrolled 26 patients (14 males and 12 females, mean age of 60.35±10.89 years old) who underwent CT urography between October 2019 and June 2020. All the patients underwent conventional-dose unenhanced CT and contrast-enhanced CT in the portal venous phase (noise index of 10; volume computed tomographic dose index: 9.61 mGy) and low-dose CT in the excretory phase(noise index of 23; volume computed tomographic dose index: 2.95 mGy). CT images in the excretory phase were reconstructed using four algorithms: ASiR-V 50%, DLIR-L, DLIR-M, and DLIR-H. Repeated measures ANOVA and Kruskal-Wallis H test were used to compare the quantitative (skewness, noise, SNR, CNR) and qualitative (image quality, noise, beam-hardening artifacts) values among the four image groups. Post hoc comparisons were performed using Bonferroni test. 【Results】 In either quantitative or qualitative evaluation, the SNR, CNR, overall image quality score, and noise of DLIR images were similar or better than ASiR-V 50%. In addition, the SNR, CNR, and overall image quality scores increased as the DLIR weight increased, while the noise decreased. There was no statistically significant difference in the distortion artifacts (P=0.776) and contrast-induced beam-hardening artifacts (P=0.881) scores among these groups. 【Conclusion】 Compared with the ASiR-V 50% algorithm, DLIR algorithm, especially DLIR-M and DLIR-H, can significantly improve the image quality of low-dose abdominal CT, but has limitations in reducing contrast-induced beam-hardening artifacts.