Editorial Commentary: Big Data Suggest That Because of a Significant Increased Risk of Postoperative Infection, Steroid Injection Is Not Recommended After Ankle Arthroscopy

2016 ◽  
Vol 32 (2) ◽  
pp. 355 ◽  
Author(s):  
Jefferson C. Brand
Keyword(s):  
Big Data ◽  
Postoperative Infection ◽  
Steroid Injection ◽  
Ankle Arthroscopy ◽  
Editorial Commentary ◽  
Increased Risk

Abstract WP200: Post-operative Infection Does not Increase Risk of Post-operative Stroke: Analysis From a Nationwide Quality Initiative Program

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Benjamin R Kummer ◽  
Rebecca Hazan ◽  
Hooman Kamel ◽  
Alexander E Merkler ◽  
Joshua Z Willey ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Odds Ratio ◽  
Postoperative Infection ◽  
Adjusted Odds Ratio ◽  
Risk Of Infection ◽  
Postoperative Stroke ◽  
Increased Risk ◽  
Increase Risk ◽  
Initiative Program ◽  
The Relationship

Introduction: Infection has been described as a trigger for acute ischemic stroke, but the relationship between postoperative infection and the risk of postoperative stroke is unclear. We investigated the association between postoperative infection and stroke using the American College of Surgeons National Surgical Quality Initiative Program (NSQIP) database. Hypothesis: Postoperative infection is associated with an increased risk of postoperative stroke. Methods: We used the NSQIP database to identify all patients who underwent surgery between the years of 2000 and 2010 and developed a postoperative stroke within 30 days of surgery. The group was further stratified according to the presence of infection preceding stroke. Using a logistic regression model adjusted for age, race, sex, medical comorbidities, surgical type, and dichotomized functional status, we compared the risk of stroke in patients with and without preceding infections, and investigated the risk of infection following stroke. Results: 729,886 surgical patients were identified, of whom 2,703 (0.3%) developed postoperative stroke. 848 (0.12%) patients developed both postoperative stroke and infection. Among patients who had postoperative stroke, 100 (3.7%) had developed an infection prior to developing a stroke. Patients with infection prior to stroke had a lower risk of stroke than patients who did not develop infection prior to stroke (adjusted odds ratio [OR] 0.25, 95%CI 0.20-0.32). 748 patients (0.1%) developed an infection after having a postoperative stroke. These patients had a higher risk of infection (incidence rate ratio 2.76, 95%CI 2.57-2.97) and a higher odds of infection (adjusted odds ratio [OR] 3.47, 95%CI 3.18-3.78) than patients who did not have a stroke. Conclusions: We found that the presence of a preceding infection was associated with a low risk of postoperative stroke in a large surgical inpatient sample. Although the total number of strokes may have been under-reported, these results conflict with other studies that report that infection is a trigger for ischemic stroke. Further analyses using more granular data are needed to investigate the relationship between postoperative infection and the risk of postoperative stroke.

scholarly journals Factors Affecting the Outcomes of Tibiotalocalcaneal Fusion

2020 ◽  
Vol 5 (4) ◽  
pp. 2473011420S0038
Author(s):  
Charles C. Pitts ◽  
Bradley Alexander ◽  
Joshua L. Washington ◽  
Hannah M. Barranco ◽  
Romil K. Patel ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Postoperative Complications ◽  
Kidney Disease ◽  
Bone Graft ◽  
Postoperative Infection ◽  
High Rate ◽  
Diabetic Patients ◽  
Charcot Arthropathy ◽  
Infection Rates ◽  
Increased Risk

Category: Hindfoot; Ankle; Ankle Arthritis Introduction/Purpose: Tibiotalocalcaneal (TTC) fusion is used to treat a variety of conditions affecting the ankle and subtalar joint, including osteoarthritis (OA), Charcot arthropathy, avascular necrosis (AVN) of the talus, failed total ankle arthroplasty, and severe deformity. The prevalence of postoperative complications remains high due to the complexity of hindfoot disease seen in these patients. The aim of this study was to analyze the relationship between preoperative conditions and postoperative complications in order to predict the outcome following primary TTC fusion. Methods: We retrospectively reviewed the medical records of 101 patients who underwent TTC fusion at the same institution between 2011 and 2019. Risk ratios (RRs) associated with age, sex, diabetes, cardiovascular disease, smoking, preoperative ankle deformity, and the use of bone graft during surgery were related to the postoperative complications. We determined from these data which pre- and perioperative factors significantly affected the outcome. Results: Patients with a preoperative diagnosis of Charcot arthropathy and non-traumatic OA had significantly higher nonunion rates of 44.4% (12 patients) and 39.1% (18 patients) (p = 0.016) and infection rates of 29.6% (eight patients) and 37% (17 patients) compared to patients with traumatic arthritis, respectively (p = 0.002). There was a significantly increased rate of nonunion in diabetic patients (RR 2.22; p = 0.010). Patients with chronic kidney disease were 2.37-times more likely to have a nonunion (p = 0.006). Patients aged over 60 years had more than a three-fold increase in the rate of postoperative infection (RR 3.60; p = 0.006). The use of bone graft appeared to be significantly protective against postoperative infection (p = 0.019). Conclusion: We were able to confirm, in the largest series of TTC ankle fusions currently in the literature, that there remains a high rate of complications following this procedure. Those with diabetes, chronic kidney disease, or aged over 60 years had an increased risk of nonunion. These findings help to confirm those of previous studies. Additionally, our study adds to the literature by showing that autologous bone graft may help in decreasing infection rates. This helps surgeons further understand which patients are at a higher risk for postoperative complications when undergoing TTC fusion. [Table: see text]

scholarly journals “Data makes the story come to life.” Understanding the ethical and legal implications of Big Data research involving ethnic minority healthcare workers: a qualitative study

2021 ◽  
Author(s):  
Edward S. Dove ◽  
Ruby Reed-Berendt ◽  
Manish Pareek
Keyword(s):  
Public Health ◽  
Big Data ◽  
Ethnic Minority ◽  
Health Research ◽  
Healthcare Workers ◽  
Minority Groups ◽  
Significant Implication ◽  
Structured Interviews ◽  
Increased Risk ◽  
The Uk

The aim of UK-REACH (“The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers”) is to understand if, how, and why healthcare workers (HCWs) in the UK from ethnic minority groups are at increased risk of poor outcomes from COVID-19. In this article, we present findings from Work Package 3, the ethico-legal stream, which undertook qualitative research seeking to understand and address legal, ethical, and social acceptability issues around data protection, privacy, and information governance associated with the linkage of HCWs’ registration data and healthcare data. We interviewed 22 key opinion leaders in healthcare and health research from across the UK in two-to-one semi-structured interviews. Transcripts were manually coded using qualitative thematic analysis. Participants told us that a significant implication across all stages of Big Data research in public health are drivers of mistrust – of the research itself, research staff and funders, and broader concerns of mistrust within participant communities, particularly in the context of COVID-19 and those situated in more marginalised community settings. However, despite the challenges, participants also identified ways in which legally compliant and ethically informed approaches to research can be crafted to mitigate or overcome mistrust and establish confidence in Big Data public health research. Overall, our research indicates that a “Big Data Ethics by Design” approach can help assure 1) that meaningful engagement is taking place and that extant challenges are addressed, and 2) that any new challenges or hitherto unknown unknowns can be rapidly and properly considered to ensure potential (but material) harms are identified and minimised where necessary. Our findings indicate such an approach, in turn, will help drive better scientific breakthroughs that translate into medical innovations and effective public health interventions, which benefit the publics studied.

scholarly journals Automating Open Science for Big Data

2015 ◽  
Vol 659 (1) ◽  
pp. 260-273 ◽  
Author(s):  
Mercè Crosas ◽  
Gary King ◽  
James Honaker ◽  
Latanya Sweeney
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Science Research ◽  
Science Research ◽  
Open Science ◽  
Streaming Data ◽  
Sensitive Information ◽  
Increased Risk ◽  
The Social ◽  
New Challenges

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.

Big Data Analytics for Childhood Pneumonia Monitoring

Web Services ◽  
2019 ◽  
pp. 1129-1145 ◽  
Author(s):  
Suresh Kumar Peddoju ◽  
Kavitha K. ◽  
Sharma S. C.
Keyword(s):  
Big Data ◽  
Early Identification ◽  
Big Data Analytics ◽  
Previous Treatment ◽  
Medical Attention ◽  
Appropriate Treatment ◽  
Pediatric Pneumonia ◽  
Increased Risk ◽  
Cause Of Deaths ◽  
History Of

In developing countries pediatric pneumonia is the second leading cause of deaths and 98% of pneumonia-induced deaths are identified across the world. It is mandatory to identify the symptoms of pneumonia in children to avoid mortality causing complications. Early identification of children at risk for treatment failure or at increased risk for death will help to improve overall health outcomes. If pneumonia is suspected, it is important to seek medical attention promptly so that an accurate diagnosis can be made and appropriate treatment is given in time. The proposed approach quickly provides history of previous patient's details, expert doctor's opinions who are in globe and their previous treatment for the same symptoms, all diagnostic reports such as blood tests, x-ray etc., from the cloud and gives analytics from big data to take fast and precise decisions by the doctors.

Risk Factors for Infection After Shoulder Arthroscopy in a Large Medicare Population

2018 ◽  
Vol 46 (4) ◽  
pp. 809-814 ◽  
Author(s):  
Jourdan M. Cancienne ◽  
Stephen F. Brockmeier ◽  
Eric W. Carson ◽  
Brian C. Werner
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Postoperative Infection ◽  
Independent Risk Factor ◽  
Steroid Injection ◽  
Shoulder Arthroscopy ◽  
Safe Procedure ◽  
Chronic Anemia ◽  
Related Risk ◽  
Male Sex

Background: Shoulder arthroscopy is well established as a highly effective and safe procedure for the treatment for several shoulder disorders and is associated with an exceedingly low risk of infectious complications. Few data exist regarding risk factors for infection after shoulder arthroscopy, as previous studies were not adequately powered to evaluate for infection. Purpose: To determine patient-related risk factors for infection after shoulder arthroscopy by using a large insurance database. Study Design: Case-control study; Level of evidence, 3. Methods: The PearlDiver patient records database was used to query the 100% Medicare Standard Analytic Files from 2005 to 2014 for patients undergoing shoulder arthroscopy. Patients undergoing shoulder arthroscopy for a diagnosis of infection or with a history of prior infection were excluded. Postoperative infection within 90 days postoperatively was then assessed with International Classification of Diseases, Ninth Revision codes for a diagnosis of postoperative infection or septic shoulder arthritis or a procedure for these indications. A multivariate binomial logistic regression analysis was then utilized to evaluate the use of an intraoperative steroid injection, as well as numerous patient-related risk factors for postoperative infection. Adjusted odds ratios (ORs) and 95% CIs were calculated for each risk factor, with P < .05 considered statistically significant. Results: A total of 530,754 patients met all inclusion and exclusion criteria. There were 1409 infections within 90 days postoperatively (0.26%). Revision shoulder arthroscopy was the most significant risk factor for infection (OR, 3.25; 95% CI, 2.7-4.0; P < .0001). Intraoperative steroid injection was also an independent risk factor for postoperative infection (OR, 1.46; 95% CI, 1.2-1.9; P = .002). There were also numerous independent patient-related risk factors for infection, the most significant of which were chronic anemia (OR, 1.58; 95% CI, 1.4-1.8; P < .0001), malnutrition (OR, 1.42; 95% CI, 1.2-1.7; P = .001), male sex (OR, 2.71; 95% CI, 2.4-3.1; P < .0001), morbid obesity (OR, 1.41; 95% CI, 1.2-1.6; P < .0001), and depression (OR, 1.36; 95% CI, 1.2-1.5; P < .0001). Conclusion: Intraoperative steroid injection was a significant independent risk factor for postoperative infection after shoulder arthroscopy. There were also numerous significant patient-related risk factors for postoperative infection, including revision surgery, obesity, male sex, chronic anemia, malnutrition, depression, and alcohol use, among others.

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