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Morimoto Y, Lian CPL, Lai C, Kyogoku S, Daida H. Health literacy in medical imaging: a scoping review of current evidence and future directions. Public Health 2024; 234:84-90. [PMID: 38968928 DOI: 10.1016/j.puhe.2024.05.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/07/2024] [Accepted: 05/28/2024] [Indexed: 07/07/2024]
Abstract
OBJECTIVE The importance of health literacy in medical imaging is well recognized, yet the current landscape remains inadequately understood. This study aims to explore the extent of health literacy studies contextualized to medical imaging. STUDY DESIGN Scoping review. METHODS A scoping review was conducted using three online bibliographic databases namely, PubMed, ScienceDirect, and CINAHL. We have adopted the concept of health literacy, as a clinical risk and personal asset, to guide this review. RESULTS Of 311 unique articles, 39 met our selection criteria. Five themes (categories) were identified by the authors: appropriate communication with patients who receive medical imaging test results, appropriate usage of medical imaging, classes and characteristics of eHealth literacy, disease/deterioration prevention, and patient education. Additionally, 17 health literacy assessment tools were identified, including 11 original creations. Finally, 11 recommendations have emerged from this scoping review, offering valuable insights into methods, considerations, and strategies for promoting health literacy. CONCLUSIONS Health literacy studies in medical imaging cover both clinical and public health perspectives, benefiting diverse populations, regardless of underlying medical conditions. Notably, the majority of assessment tools used in these studies were author-generated, hindering cross-study comparisons. Given the innate capacity of medical images to convey intuitive information, those images do not solely benefit the patients who are given medical imaging examinations, but they also hold significant potential to enhance public health literacy. Health literacy and medical imaging are closely associated and mutually reinforce each other.
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Affiliation(s)
- Yuh Morimoto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
| | - Cheryl Pei Ling Lian
- Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore.
| | - Christopher Lai
- Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore
| | - Shinsuke Kyogoku
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
| | - Hiroyuki Daida
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
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Elmohr MM, Javed Z, Dubey P, Jordan JE, Shah L, Nasir K, Rohren EM, Lincoln CM. Social Determinants of Health Framework to Identify and Reduce Barriers to Imaging in Marginalized Communities. Radiology 2024; 310:e223097. [PMID: 38376404 PMCID: PMC10902599 DOI: 10.1148/radiol.223097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 02/21/2024]
Abstract
Social determinants of health (SDOH) are conditions influencing individuals' health based on their environment of birth, living, working, and aging. Addressing SDOH is crucial for promoting health equity and reducing health outcome disparities. For conditions such as stroke and cancer screening where imaging is central to diagnosis and management, access to high-quality medical imaging is necessary. This article applies a previously described structural framework characterizing the impact of SDOH on patients who require imaging for their clinical indications. SDOH factors can be broadly categorized into five sectors: economic stability, education access and quality, neighborhood and built environment, social and community context, and health care access and quality. As patients navigate the health care system, they experience barriers at each step, which are significantly influenced by SDOH factors. Marginalized communities are prone to disparities due to the inability to complete the required diagnostic or screening imaging work-up. This article highlights SDOH that disproportionately affect marginalized communities, using stroke and cancer as examples of disease processes where imaging is needed for care. Potential strategies to mitigate these disparities include dedicating resources for clinical care coordinators, transportation, language assistance, and financial hardship subsidies. Last, various national and international health initiatives are tackling SDOH and fostering health equity.
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Affiliation(s)
- Mohab M. Elmohr
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
| | - Zulqarnain Javed
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
| | - Prachi Dubey
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
| | - John E. Jordan
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
| | - Lubdha Shah
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
| | - Khurram Nasir
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
| | - Eric M. Rohren
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
| | - Christie M. Lincoln
- From the Department of Radiology, Baylor College of Medicine, Houston, 1 Baylor Plaza, BCM 360, Houston, TX 77030 (M.M.E., E.M.R.); Division of Health Equity and Disparities Research, Center for Outcomes Research, Houston Methodist Hospital, Houston, Tex (Z.J., K.N.); Houston Radiology Associates, Houston Methodist Hospital, Houston, Tex (P.D.); ACR Commission on Neuroradiology, American College of Radiology, Reston, Va (J.E.J.); Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University School of Medicine, Stanford, Calif (J.E.J.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (L.S.); Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex (K.N.); Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist Hospital, Houston, Tex (K.N.); and Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, Tex (C.M.L.)
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Alali AA. Level of Education Matters in Regard to Participants' Compliance With Screening in the National Lung Screening Trial. J Thorac Imaging 2024; 39:W1-W4. [PMID: 37732698 DOI: 10.1097/rti.0000000000000741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
PURPOSE The success of cancer screening depends on patient adherence to the screening program. The purpose of this study is to assess how the level of education might affect participants' compliance with screening in the National Lung Screening Trial (NLST). MATERIALS AND METHODS Secondary data analyses of the participants in the NLST were performed. A total of 50,104 participants were included in this study. Participants who enrolled in the trial but refused the initial screening were compared with those who completed the screening. A multivariate logistic regression model was used to assess the association between participant noncompliance and education level. RESULTS A total of 3712 (7.41%) participants refused lung cancer screening in the NLST. Compared with the reference group, participants with an education level of eighth grade or less (odds ratio [OR]: 2.1, CI: 1.68-2.76), ninth-11th grade (OR: 1.9, CI: 1.7-2.34), high school graduates (OR: 1.3, CI: 1.22-1.54), after high school training (OR: 1.1, CI: 1-1.31), or an associate's degree (OR: 1.2, CI: 1.07-1.36) had significantly higher odds of refusing lung cancer screening. Participants with a bachelor's degree showed no significant association with compliance with screening (OR: 0.9, P = 0.86). Multivariate regression analysis also showed that younger, single, male participants with a longer duration of smoking history had significantly higher odds of refusing the screening. CONCLUSION A lower level of education was significantly associated with refusing lung cancer screening. A strategic targeted approach for this group might be necessary to promote their compliance rate.
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Affiliation(s)
- Akeel A Alali
- College of Medicine, Clinical Affairs, King Saud Bin Abdulaziz University for Health Sciences
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
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4
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McCrory M, Smith L, Heidel E, Daley B. Enhanced Notification of Radiographic Incidental Findings in Trauma Does Not Guarantee Follow-Up Compliance. South Med J 2023; 116:938-941. [PMID: 38051166 DOI: 10.14423/smj.0000000000001630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
OBJECTIVES Follow-up care for incidental findings (IFs) on trauma computed tomography scans is a component of comprehensive healthcare. Our objective was to assess the effectiveness of our IF predischarge disclosure practice guideline and identify factors contributing to follow-up failure. METHODS This was a secondary analysis of a prospective observational database: 615 patients with IFs from November 2019 to February 2020. Follow-up compliance was determined by electronic medical record review and/or a telephone call after a mail-out request for voluntary participation. Volunteers answered a predetermined questionnaire regarding follow-up care. RESULTS A total of 115 patients (19%) had computed tomography-based IFs recommending additional imaging or other follow-ups. Seventy-four (64%) patients were lost to inclusion as a result of death (12.1%), inability to contact (51.3%), or noninterest (5.2%). Of the remaining 36 patients, 19 received follow-up care (52.7%) and 17 did not (47.2%). No statistical differences existed among groups in age, sex, mechanism of injury, Glasgow Coma Scale score, whether informed by physicians or midlevel providers, or type of IF. A total of 15 (88%) nonfollow-up patients did not recall the disclosure or discharge paperwork instructions. Of 19 compliant patients: 9 had additional imaging only, 5 had biopsies and/or surgical intervention (n = 3 cancer, n = 2 benign), 3 had primary care advice against additional studies and 2 were referred to specialists. CONCLUSIONS Predischarge disclosure of IFs can contribute significantly to overall patient health. Nonetheless, fewer than half of patients do not pursue follow-up recommendations, most often citing failure to recall verbal/written instructions. More effective communication with attention to health literacy, follow-up telephone calls, and postdischarge appointments are potential catalysts for improved patient compliance.
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Affiliation(s)
| | - Lou Smith
- From the Department of Surgery, University of Tennessee Medical Center, Knoxville, Knoxville, TN
| | - Eric Heidel
- From the Department of Surgery, University of Tennessee Medical Center, Knoxville, Knoxville, TN
| | - Brian Daley
- From the Department of Surgery, University of Tennessee Medical Center, Knoxville, Knoxville, TN
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5
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Zhao K, Zhang L, Wang L, Zeng J, Zhang Y, Xie X. Benign incidental cardiac findings in chest and cardiac CT imaging. Br J Radiol 2023; 96:20211302. [PMID: 35969186 PMCID: PMC9975525 DOI: 10.1259/bjr.20211302] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 07/25/2022] [Accepted: 08/06/2022] [Indexed: 02/01/2023] Open
Abstract
With the continuous expansion of the disease scope of chest CT and cardiac CT, the number of these CT examinations has increased rapidly. In addition to their common indications, many incidental cardiac findings can be observed when carefully evaluating the coronary arteries, valves, pericardium, ventricles, and large vessels. These findings may have clinical significance or risk of complications, but they are sometimes overlooked or may not be described in the final reports. Although most of the incidental findings are benign, timely detection and treatment can improve the management of chronic diseases or reduce the possibility of severe complications. In this review, we summarized the imaging findings, incidence rate, and clinical relevance of some benign cardiac findings such as coronary artery calcification, aortic and mitral valve calcification, aortic calcification, cardiac thrombus, myocardial bridge, aortic dilation, cardiac myxoma, pericardial cyst, and coronary artery fistula. Reporting incidental cardiac findings will help reduce the risk of severe complications or disease deterioration and contribute to the recovery of patients.
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Affiliation(s)
- Keke Zhao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, China
| | - Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, China
| | - Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, China
| | - Jinghui Zeng
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, China
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, China
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6
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Wu H, Yang J, Wang H, Li L. Mendelian randomization to explore the direct or mediating associations between socioeconomic status and lung cancer. Front Oncol 2023; 13:1143059. [PMID: 37207156 PMCID: PMC10189779 DOI: 10.3389/fonc.2023.1143059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
Objective The purpose of this study was to verify whether there are direct or mediated causal associations between socioeconomic status and lung cancer. Methods Pooled statistics were obtained from corresponding genome-wide association studies. The inverse-variance weighted, weighted median, MR-Egger, MR-PRESSO and contamination-mixture methods were used as supplements to Mendelian randomization (MR) statistical analysis. Cochrane's Q value and the MR-Egger intercept were used for sensitivity analysis. Results In the univariate MR analysis, household income and education had protective effects on overall lung cancer (income: P = 5.46×10-4; education: P = 4.79×10-7) and squamous cell lung cancer (income: P = 2.67×10-3; education: P = 1.42×10-10). Smoking and BMI had adverse effects on overall lung cancer (smoking: P = 2.10×10-7; BMI: P = 5.67×10-4) and squamous cell lung cancer (smoking: P = 5.02×10-6; BMI: P = 2.03×10-7). Multivariate MR analysis found that smoking and education were independent risk factors for overall lung cancer (smoking: P = 1.96×10-7; education: P = 3.11×10-3), while smoking was an independent risk factor for squamous cell lung cancer (P = 2.35×10-6). Smoking, education, and household income mediate the effect of BMI on overall lung cancer (smoking 50.0%, education 49.2%, income 25.3%) and squamous cell lung cancer (smoking 34.8%, education 30.8%, income 21.2%). Smoking, education, and BMI mediate the effect of income on overall lung cancer (smoking 13.9%, education 54.8%, BMI 9.4%) and squamous cell lung cancer (smoking 12.6%, education 63.3%, BMI 11.6%). Smoking, BMI, and income mediate the effect of education on squamous cell lung cancer (smoking 24.0%, BMI 6.2%, income 19.4%). Conclusion Income, education, BMI, and smoking are causally associated with both overall lung cancer and squamous cell lung cancer. Smoking and education are independent association factors for overall lung cancer, while smoking is an independent association factor for squamous cell lung cancer. Smoking and education also play important mediating roles in overall lung cancer and squamous cell lung cancer. No causal relationship was found between multiple risk factors associated with socioeconomic status and lung adenocarcinoma.
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Affiliation(s)
- Hong Wu
- Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Jing Yang
- Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Hui Wang
- Department of Orthopaedic Trauma and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Lei Li
- Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China
- *Correspondence: Lei Li,
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Patel PB, Marcaccio CL, de Guerre LEVM, Patel VI, Wang G, Giles K, Schermerhorn ML. Complications after thoracic endovascular aortic repair for ruptured thoracic aortic aneurysms remain high compared with elective repair. J Vasc Surg 2022; 75:842-850. [PMID: 34655686 PMCID: PMC8863631 DOI: 10.1016/j.jvs.2021.09.047] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 09/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Thoracic endovascular aortic repair (TEVAR) for ruptured thoracic aortic aneurysms is associated with increased perioperative mortality and morbidity compared with intact repair. The purpose of our study was to evaluate the factors associated with the presentation of ruptured aneurysms and adverse outcomes after repair. METHODS The Vascular Quality Initiative (VQI) registry was queried (2010-2020) to identify patients who had undergone TEVAR for ruptured and intact thoracic aortic aneurysms. The primary outcome was to identify the factors associated with ruptured thoracic aortic aneurysms. The secondary outcomes included perioperative mortality and morbidity, 5-year survival, and the identification of factors associated with adverse outcomes after TEVAR. RESULTS Of the 3039 patients identified with a thoracic aortic aneurysm, 2806 (92%) had undergone repair for an intact aneurysm and 233 (8%) had undergone repair for a ruptured aneurysm. Chronic kidney disease was associated with a greater odds of a presentation with a ruptured aneurysm (odds ratio [OR], 3.1; 95% confidence interval [CI], 2.0-4.9; P < .001). The factors associated with a lower odds of rupture included prior aortic aneurysm repair (OR, 0.71; 95% CI, 0.49-0.97; P = .05), prior smoker (OR, 0.36; 95% CI, 0.24-0.53; P < .001), preoperative beta-blocker therapy (OR, 0.57; 95% CI, 0.41-0.80; P = .001), and preoperative statin therapy (OR, 0.68; 95% CI, 0.49-0.94; P = .020). TEVAR for ruptured thoracic aortic aneurysms was associated with higher perioperative mortality (rupture vs intact, 27% vs 4.6%; OR, 6.6; 95% CI 4.3-10; P < .001) and the composite outcome of mortality, new dialysis, paralysis, and stroke (38% vs 9.5%; OR, 5.1; 95% CI, 3.5-7.4; P < .001). The 5-year survival was significantly lower after TEVAR for ruptured thoracic aortic aneurysms (50% vs 76%; P < .001; hazard ratio, 0.39; 95% CI, 0.29-0.52; P < .001). Preoperative statin therapy was associated with higher 5-year survival (hazard ratio, 1.3; 95% CI, 1.0-1.6; P = .021). CONCLUSIONS TEVAR for ruptured thoracic aortic aneurysms results in increased perioperative mortality and morbidity and lower 5-year survival compared with TEVAR for intact aneurysms. Patients with prior aortic aneurysm repair, prior smoking, and preoperative beta-blocker or statin therapy were less likely to present with ruptured thoracic aneurysms. This correlation might be attributed to increased exposure to cardiovascular healthcare providers and, thus, subsequently increased screening and surveillance, allowing for elective repair of thoracic aortic aneurysms.
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Affiliation(s)
- Priya B Patel
- Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Boston, Mass
| | - Christina L Marcaccio
- Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Boston, Mass
| | - Livia E V M de Guerre
- Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Boston, Mass
| | - Virendra I Patel
- Division of Vascular Surgery and Endovascular Interventions, Columbia University Medical Center, New York, NY
| | - Grace Wang
- Division of Vascular and Endovascular Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pa
| | - Kristina Giles
- Division of Vascular and Endovascular Surgery, Maine Medical Center, Portland, Me
| | - Marc L Schermerhorn
- Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Boston, Mass.
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8
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Chen N, Li N, Jiang J, Yang X, Wu D. Urinary Phytoestrogen Metabolites Positively Correlate with Serum 25(OH)D Level Based on National Health and Nutrition Examination Survey 2009-2010. J Nutr Sci Vitaminol (Tokyo) 2022; 67:375-383. [PMID: 34980715 DOI: 10.3177/jnsv.67.375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Studies showed that vitamin D (25-hydroxyvitamin D) level in the human blood circulation could be affected by exogenous estrogen exposure. This study aims to explore the relationships between urinary phytoestrogens metabolites and serum total 25(OH)D in general population, urinary phytoestrogens metabolites (daidzein, enterodiol, enterolactone, equol, genistein and o-desmethylangolensin). Totally 2,609 adults ≥6 y old from the 2009-2010 National Health and Nutrition Examination Surveys (NHANES) were recruited into the cross-sectional analyses and information including demographic, socioeconomic, examinations and laboratory test were collected. All analyses were performed using Stata13.0, one-way analysis of variance and multivariable regression were utilised according to data characteristics, respectively. It showed that age, race, education level, body mass index (BMI), and sampling season had significant effects on serum 25(OH)D level (all p<0.001). In the whole population, urinary enterodiol and equol were significantly positively associated with serum total 25(OH)D level (β=0.86, 95%CI=0.08-1.65, p<0.05; β=1.68, 95%CI=0.91-2.45, p<0.001). Equol was also found significantly positively correlated with total 25(OH)D in both female and male separately (β=1.69, 95%CI=0.51-2.87, p<0.05; β=1.66, 95%CI=0.63-2.69, p<0.05). Phytoestrogen concentrations in the urinary and 25(OH)D levels in the serum had proved a positive correlation in our study, which provide theoretical basis and reference for the dietary nutrient intake in the population.
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Affiliation(s)
- Na Chen
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University.,Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University
| | - Ningning Li
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University.,Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University
| | - Jin Jiang
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University.,Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University
| | - Xiaona Yang
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University.,Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University
| | - Di Wu
- State Key Laboratory of Reproductive Medicine, Center for Global Health, School of Public Health, Nanjing Medical University.,Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University
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Mazzone PJ, Silvestri GA, Souter LH, Caverly TJ, Kanne JP, Katki HA, Wiener RS, Detterbeck FC. Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. Chest 2021; 160:e427-e494. [PMID: 34270968 PMCID: PMC8727886 DOI: 10.1016/j.chest.2021.06.063] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/11/2021] [Accepted: 06/16/2021] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND Low-dose chest CT screening for lung cancer has become a standard of care in the United States, in large part because of the results of the National Lung Screening Trial (NLST). Additional evidence supporting the net benefit of low-dose chest CT screening for lung cancer, and increased experience in minimizing the potential harms, has accumulated since the prior iteration of these guidelines. Here, we update the evidence base for the benefit, harms, and implementation of low-dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not. METHODS Approved panelists reviewed previously developed key questions using the Population, Intervention, Comparator, Outcome format to address the benefit and harms of low-dose CT screening, and key areas of program implementation. A systematic literature review was conducted using MEDLINE via PubMed, Embase, and the Cochrane Library on a quarterly basis since the time of the previous guideline publication. Reference lists from relevant retrievals were searched, and additional papers were added. Retrieved references were reviewed for relevance by two panel members. The quality of the evidence was assessed for each critical or important outcome of interest using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Meta-analyses were performed when enough evidence was available. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and ungraded statements were drafted, voted on, and revised until consensus was reached. RESULTS The systematic literature review identified 75 additional studies that informed the response to the 12 key questions that were developed. Additional clinical questions were addressed resulting in seven graded recommendations and nine ungraded consensus statements. CONCLUSIONS Evidence suggests that low-dose CT screening for lung cancer can result in a favorable balance of benefit and harms. The selection of screen-eligible individuals, the quality of imaging and image interpretation, the management of screen-detected findings, and the effectiveness of smoking cessation interventions can impact this balance.
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Affiliation(s)
| | | | | | - Tanner J Caverly
- Ann Arbor VA Center for Clinical Management Research, Ann Arbor, MI; University of Michigan Medical School, Ann Arbor, MI
| | - Jeffrey P Kanne
- University of Wisconsin School of Medicine and Public Health, Madison, WI
| | | | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA; Boston University School of Medicine, Boston, MA
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Predict multicategory causes of death in lung cancer patients using clinicopathologic factors. Comput Biol Med 2020; 129:104161. [PMID: 33307409 DOI: 10.1016/j.compbiomed.2020.104161] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/25/2020] [Accepted: 11/29/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND Random forests (RF) is a widely used machine-learning algorithm, and outperforms many other machine learning algorithms in prediction-accuracy. But it is rarely used for predicting causes of death (COD) in cancer patients. On the other hand, multicategory COD are difficult to classify in lung cancer patients, largely because they have multiple labels (versus binary labels). METHODS We tuned RF algorithms to classify 5-category COD among the lung cancer patients in the surveillance, epidemiology and end results-18, whose lung cancers were diagnosed in 2004, for the completeness in their follow-up. The patients were randomly divided into training and validation sets (1:1 and 4:1 sample-splits). We compared the prediction accuracy of the tuned RF and multinomial logistic regression (MLR) models. RESULTS We included 42,257 qualified lung cancers in the database. The COD were lung cancer (72.41%), other causes or alive (14.43%), non-lung cancer (6.85%), cardiovascular disease (5.35%), and infection (0.96%). The tuned RF model with 300 iterations and 10 variables outperformed the MLR model (accuracy = 69.8% vs 64.6%, 1:1 sample-split), while 4:1 sample-split produced lower prediction-accuracy than 1:1 sample-split. The top-10 important factors in the RF model were sex, chemotherapy status, age (65+ vs < 65 years), radiotherapy status, nodal status, T category, histology type and laterality, all of which except T category and laterality were also important in MLR model. CONCLUSION We tuned RF models to predict 5-category CODs in lung cancer patients, and show RF outperforms MLR in prediction accuracy. We also identified the factors associated with these COD.
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Stowell JT, Narayan AK, Wang GX, Fintelmann FJ, Flores EJ, Sharma A, Petranovic M, Shepard JAO, Little BP. Factors affecting patient adherence to lung cancer screening: A multisite analysis. J Med Screen 2020; 28:357-364. [PMID: 32847462 DOI: 10.1177/0969141320950783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To identify factors associated with delayed adherence to follow-up in lung cancer screening. METHODS Utilizing a data warehouse and lung cancer screening registry, variables were collected from a referred sample of 3110 unique participants with follow-up CT during the study period (1 January 2016 to 17 October 2018). Adherence was defined as undergoing chest CT within 90 days and 30 days of the recommended time for follow-up and was determined using proportions and multiple variable logistic regression models across the American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS®) categories. RESULTS Of 1954 lung cancer screening participants (51.9% (1014/1954) males, 48.1% (940/1954) female; mean age 65.7 (range 45-87), smoking history median 40 pack-years, 60.2% and 44.5% did not follow-up within 30 and 90 days, respectively. Participants receiving Lung-RADS® category 1 or 2 presented later than those with Lung-RADS® category 3 at 90 days (coefficient -27.24, 95% CI -51.31, -3.16, p = 0.027). Participants with Lung-RADS® category 1 presented later than those with Lung-RADS® category 2 at both 90- and 30-days past due (OR 0.76 95% CI [0.59-0.97], p = 0.029 and OR 0.63 95% CI [0.48-0.83], p = 0.001, respectively). CONCLUSIONS Adherence to follow-up was higher among participants receiving more suspicious Lung-RADS® results at index screening CT and among those who had undergone more non-lung cancer screening imaging examinations prior to index lung cancer screening CT. These observations may inform strategies aimed at prospectively identifying participants at risk for delayed or nonadherence to prevent potential morbidity and mortality from incident lung cancers.
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Affiliation(s)
| | - Anand K Narayan
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Gary X Wang
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Efren J Flores
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Amita Sharma
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Milena Petranovic
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jo-Anne O Shepard
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Brent P Little
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
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