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1.
Vasc Endovascular Surg ; 58(6): 640-644, 2024 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-38279905

RESUMO

Inferior vena cava (IVC) filters are used to prevent fatal and nonfatal pulmonary embolism in patients who otherwise cannot receive anticoagulation for venous thrombosis. While generally safe and effective, complications can arise, especially after prolonged implantation. Timely retrieval is essential once the indication for insertion has resolved. However, encountering patients with long-standing embedded filters is not uncommon. This case report discusses the successful retrieval of a permanent Greenfield IVC filter after 29 years.


Assuntos
Remoção de Dispositivo , Desenho de Prótese , Embolia Pulmonar , Filtros de Veia Cava , Humanos , Resultado do Tratamento , Fatores de Tempo , Embolia Pulmonar/prevenção & controle , Embolia Pulmonar/etiologia , Embolia Pulmonar/diagnóstico por imagem , Flebografia , Implantação de Prótese/instrumentação , Implantação de Prótese/efeitos adversos , Trombose Venosa/diagnóstico por imagem , Trombose Venosa/terapia , Trombose Venosa/etiologia , Feminino , Masculino , Pessoa de Meia-Idade , Veia Cava Inferior/diagnóstico por imagem
2.
JAMA Netw Open ; 5(8): e2229289, 2022 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-36044215

RESUMO

Importance: The efficient and accurate interpretation of radiologic images is paramount. Objective: To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities. Design, Setting, and Participants: This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH). Main Outcomes and Measures: The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding. Results: A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001). Conclusions and Relevance: These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.


Assuntos
Aprendizado Profundo , Derrame Pleural , Pneumonia , Pneumotórax , Adulto , Inteligência Artificial , Estudos de Coortes , Humanos , Masculino , Pessoa de Meia-Idade , Pneumonia/diagnóstico por imagem
3.
AORN J ; 108(6): 634-642, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30480793

RESUMO

There are many sources of contamination in the perioperative environment. Patient experience can be negatively affected by the presence of environmental contamination, especially if it is the cause of a surgical site infection. Perioperative and environmental services staff members and leaders are tasked with ensuring a clean and safe environment for their patients while maintaining an awareness of time and budgetary constraints. In addition, leaders are responsible for the competency of their staff members and must address performance issues when needed. New technological advances designed to streamline monitoring and reporting processes related to OR cleanliness are available for use. This article describes the quality improvement project that one multifacility organization completed related to the use of remote video auditing and the positive effect it had on the organization's environmental contamination.


Assuntos
Desinfecção/normas , Zeladoria Hospitalar , Salas Cirúrgicas , Gravação em Vídeo , Infecção Hospitalar/prevenção & controle , Contaminação de Equipamentos , Retroalimentação , Humanos , New England , Melhoria de Qualidade
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