Notes

A survey about deviation within diagnosis and treatment regarding chorioamnionitis throughout tertiary centers throughout Canada.
We report a unique case of expanded polytetrafluoroethylene (ePTFE) tube graft rupture that occurred 14 years after abdominal aortic aneurysm (AAA) repair. Endovascular repair with a thoracic endograft was performed. Postoperatively, an increase in the size of the existing hematoma with active extravasation occurred and was managed with iliolumbar artery embolization. Τo the best of our knowledge, rupture of ePTFE graft used for AAA repair has not been reported in the literature.A 69-year-old man presented with a chronic Stanford Type A aortic dissection (CTAAD). The patient had undergone bilateral sequential lung transplantation 15 years prior for α-1-antitrypsin deficiency. We describe the management of CTAAD in the context of lung transplantation from the surgical and anesthetic perspectives.
 The aim of this study was to investigate the reproducibility of anterior-posterior diameter (APd
) and three-dimensional lumen volume (3DLV) measurements of abdominal aortic aneurysms (AAA) in a classical murine AAA model. We also compared the magnitude of change in the aortic size detected with each method of assessment.

 Periadventitial application of porcine pancreatic elastase (PPE AAA) or sham surgery was performed in two cohorts of mice. Cohort 1 was used to assess for observer variability with the APd
and 3DLV measurements. Cohort 2 highlighted the relationship between APd
and 3DLV and changes in AAA detected.

 There was no significant observer variability detected with APd
measurement. Similarly, no significant intraobserver variability was evident with 3DLV; however, a small but significant interobserver difference was present. APd
and 3DLV measurements of PPE AAA significantly correlated. However, changes in the AAA morphology were detected earlier with 3DLV.

 APd
and 3DLV are both reliable methods for measuring an AAA. Both these methods correlate with each other. However, changes in AAA morphology were detected earlier with 3DLV, which is important to detect subtle but important changes to aortic geometry in a laboratory setting. 3DLV measurement of AAA is a simple, reproducible, and comprehensive method for assessing changes in disease morphology.
 APdmax and 3DLV are both reliable methods for measuring an AAA. Both these methods correlate with each other. However, changes in AAA morphology were detected earlier with 3DLV, which is important to detect subtle but important changes to aortic geometry in a laboratory setting. 3DLV measurement of AAA is a simple, reproducible, and comprehensive method for assessing changes in disease morphology.Over the years, the cardiovascular department of Johannes Gutenberg University in Mainz-West-German Heart Centre in Essen (Germany) designed and implemented the hybrid operating room (2003) along with advanced endovascular and surgical procedures, including the frozen elephant trunk technique. For the study purpose, the Mainz-Essen experience on acute aortic syndromes was summarized by considering original articles from single-center or multicenter studies performed at West German Heart Centre, Essen, Germany, or at the cardiovascular department of Johannes Gutenberg University, Mainz, Germany. We present the 35-year-long Mainz-Essen research, education, and patient management journey in creating an integrated multidisciplinary "Aortic Center" in the heart of Europe.More than 5 million persons in Germany suffer from swallowing disorders (dysphagia), predominantly oropharyngeal dysphagia. Due to the demographic changes in our population, the prevalence rates are expected to increase. Multiple conditions may be the cause of dysphagia and dysphagia may lead to various symptoms. Esophageal dysphagia is predominantly accompanied by regurgitation, vomiting, pain and foreign body sensation in the esophageal region. Oropharyngeal dysphagia is characterized by cough, hawking and airway disease. In some patients with oropharyngeal dysphagia, symptoms may be completely absent or are not attributed to dysphagia. Both forms of dysphagia are regularly accompanied by difficulties with nutritional intake and consecutive malnutrition. Therefore, the diagnostic and therapy of dysphagia should always be accompanied by nutritional support.
The effectiveness of endoscopic screening on gastric cancer (GC) is less investigated and screening interval of repeated screening is yet to be optimized in China.

In a population-based prospective study, we included 375,800 subjects based on the Upper Gastrointestinal Cancer Early Detection Program in Linqu, a GC high-risk area in China, 14,670 of which underwent endoscopic screening(2012-2018). We assessed the associations of the risk of incident GC and GC-specific deaths with endoscopic screening and examined the changes in overall survival (OS) and disease-specific survival (DSS) of GCs by endoscopic screening. The optimal screening interval of repeated endoscopy for early detection of GC was explored.

Ever receiving endoscopic screening significantly decreased the risk of invasive GC(age and sex-adjusted RR=0.69, 95%CI0.52-0.92) and GC-specific deaths(RR=0.33, 95%CI 0.20-0.56), particularly for non-cardia GC. Repeated screening strengthened the beneficial effect on invasive GC-specific deaths by on of gastric lesions.Developing a diverse informatics workforce broadens the research agenda and ensures the growth of innovative solutions that enable equity-centered care. The American Medical Informatics Association (AMIA) established the AMIA First Look Program in 2017 to address workforce disparities among women, including those from marginalized communities. GDC-0077 PI3K inhibitor The program exposes women to informatics, furnishes mentors, and provides career resources. In 4 years, the program has introduced 87 undergraduate women, 41% members of marginalized communities, to informatics. Participants from the 2019 and 2020 cohorts reported interest in pursuing a career in informatics increased from 57% to 86% after participation, and 86% of both years' attendees responded that they would recommend the program to others. A June 2021 LinkedIn profile review found 50% of participants working in computer science or informatics, 4% pursuing informatics graduate degrees, and 32% having completed informatics internships, suggesting AMIA First Look has the potential to increase informatics diversity.
Guidance is needed on studying engagement and treatment effects in digital health interventions, including levels required for benefit. We evaluated multiple analytic approaches for understanding the association between engagement and clinical outcomes.

We defined engagement as intervention participants' response rate to interactive text messages, and considered moderation, standard regression, mediation, and a modified instrumental variable (IV) analysis to investigate the relationship between engagement and clinical outcomes. We applied each approach to two randomized controlled trials featuring text message content in the intervention REACH (Rapid Encouragement/Education and Communications for Health), which targeted diabetes, and VERB (Vanderbilt Emergency Room Bundle), which targeted hypertension.

In REACH, the treatment effect on hemoglobin A1c was estimated to be -0.73% (95% CI [-1.29, -0.21]; P = 0.008), and in VERB, the treatment effect on systolic blood pressure was estimated to be -10.1 mmHg (95% CI [-17.7, -2.8]; P = 0.007). Only the IV analyses suggested an effect of engagement on outcomes; the difference in treatment effects between engagers and non-engagers was -0.29% to -0.51% in the REACH study and -1.08 to -3.25 mmHg in the VERB study.

Standard regression and mediation have less power than a modified IV analysis, but the IV approach requires specification of assumptions. This is the first review of the strengths and limitations of various approaches to evaluating the impact of engagement on outcomes.

Understanding the role of engagement in digital health interventions can help reveal when and how these interventions achieve desired outcomes.
Understanding the role of engagement in digital health interventions can help reveal when and how these interventions achieve desired outcomes.
To characterize variation in clinical documentation production patterns, how this variation relates to individual resident behavior preferences, and how these choices relate to work hours.

We used unsupervised machine learning with clinical note metadata for 1265 progress notes written for 279 patient encounters by 50 first-year residents on the Hospital Medicine service in 2018 to uncover distinct note-level and user-level production patterns. We examined average and 95% confidence intervals of median user daily work hours measured from audit log data for each user-level production pattern.

Our analysis revealed 10 distinct note-level and 5 distinct user-level production patterns (user styles). Note production patterns varied in when writing occurred and in how dispersed writing was through the day. User styles varied in which note production pattern(s) dominated. We observed suggestive trends in work hours for different user styles residents who preferred producing notes in dispersed sessions had higher median daily hours worked while residents who preferred producing notes in the morning or in a single uninterrupted session had lower median daily hours worked.

These relationships suggest that note writing behaviors should be further investigated to understand what practices could be targeted to reduce documentation burden and derivative outcomes such as resident work hour violations.

Clinical note documentation is a time-consuming activity for physicians; we identify substantial variation in how first-year residents choose to do this work and suggestive trends between user preferences and work hours.
Clinical note documentation is a time-consuming activity for physicians; we identify substantial variation in how first-year residents choose to do this work and suggestive trends between user preferences and work hours.
Hospital-acquired infections (HAIs) are associated with significant morbidity, mortality, and prolonged hospital length of stay. Risk prediction models based on pre- and intraoperative data have been proposed to assess the risk of HAIs at the end of the surgery, but the performance of these models lag behind HAI detection models based on postoperative data. Postoperative data are more predictive than pre- or interoperative data since it is closer to the outcomes in time, but it is unavailable when the risk models are applied (end of surgery). The objective is to study whether such data, which is temporally unavailable at prediction time (TUP) (and thus cannot directly enter the model), can be used to improve the performance of the risk model.

An extensive array of 12 methods based on logistic/linear regression and deep learning were used to incorporate the TUP data using a variety of intermediate representations of the data. Due to the hierarchical structure of different HAI outcomes, a comparison of single and multi-task learning frameworks is also presented.

The use of TUP data was always advantageous as baseline methods, which cannot utilize TUP data, never achieved the top performance. The relative performances of the different models vary across the different outcomes. Regarding the intermediate representation, we found that its complexity was key and that incorporating label information was helpful.

Using TUP data significantly helped predictive performance irrespective of the model complexity.
Using TUP data significantly helped predictive performance irrespective of the model complexity.
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