| 1. |
- Biancari, F, et al.
(författare)
-
Preoperative risk stratification of deep sternal wound infection after coronary surgery
- 2020
-
Ingår i: Infection control and hospital epidemiology. - : Cambridge University Press (CUP). - 1559-6834 .- 0899-823X. ; 41:4, s. 444-451
-
Tidskriftsartikel (refereegranskat)abstract
- Objective:To develop a risk score for deep sternal wound infection (DSWI) after isolated coronary artery bypass grafting (CABG).Design:Multicenter, prospective study.Setting:Tertiary-care referral hospitals.Participants:The study included 7,352 patients from the European multicenter coronary artery bypass grafting (E-CABG) registry.Intervention:Isolated CABG.Methods:An additive risk score (the E-CABG DSWI score) was estimated from the derivation data set (66.7% of patients), and its performance was assessed in the validation data set (33.3% of patients).Results:DSWI occurred in 181 (2.5%) patients and increased 1-year mortality (adjusted hazard ratio, 4.275; 95% confidence interval [CI], 2.804–6.517). Female gender (odds ratio [OR], 1.804; 95% CI, 1.161–2.802), body mass index ≥30 kg/m2(OR, 1.729; 95% CI, 1.166–2.562), glomerular filtration rate <45 mL/min/1.73 m2(OR, 2.410; 95% CI, 1.413–4.111), diabetes (OR, 1.741; 95% CI, 1.178–2.573), pulmonary disease (OR, 1.935; 95% CI, 1.178–3.180), atrial fibrillation (OR, 1.854; 95% CI, 1.096–3.138), critical preoperative state (OR, 2.196; 95% CI, 1.209–3.891), and bilateral internal mammary artery grafting (OR, 2.088; 95% CI, 1.422–3.066) were predictors of DSWI (derivation data set). An additive risk score was calculated by assigning 1 point to each of these independent risk factors for DSWI. In the validation data set, the rate of DSWI increased along with the E-CABG DSWI scores (score of 0, 1.0%; score of 1, 1.8%; score of 2, 2.2%; score of 3, 6.9%; score ≥4: 12.1%;P< .0001). Net reclassification improvement, integrated discrimination improvement, and decision curve analysis showed that the E-CABG DSWI score performed better than other risk scores.Conclusions:DSWI is associated with poor outcome after CABG, and its risk can be stratified using the E-CABG DSWI score.Trial registration:clinicaltrials.gov identifier: NCT02319083
|
|
| 2. |
- Chan, Derwin, et al.
(författare)
-
Why people failed to adhere to COVID-19 preventive behaviors? Perspectives from an integrated behavior change model
- 2021
-
Ingår i: Infection control and hospital epidemiology. - New York : Cambridge University Press. - 0899-823X .- 1559-6834. ; 42:3, s. 375-376
-
Tidskriftsartikel (refereegranskat)abstract
- Many preventive behaviors such as the practice of hand, personal, and respiratory hygiene; maintaining social distance (eg, staying home); and cleaning and disinfection are recommended for the prevention of the new coronavirus (COVID-19). However, a growing number of reports have revealed individuals’ violations to these COVID-19 preventive behaviors.1 These violations might endanger the community by increasing the risk of an outbreak of COVID-19. The uptake of and adherence to health behaviors, including behaviors related to the prevention of infectious diseases (eg, COVID-19), are likely highly dependent on individuals’ motivation, intention, and other decision-making factors.2 We aim to apply an integrated behavior change model of health psychology to explain why individuals fail to comply and adhere to these behaviors. © 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.
|
|
| 3. |
- Naucler, Pontus, et al.
(författare)
-
HAI-Proactive : Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden
- 2020
-
Ingår i: Infection control and hospital epidemiology. - : Cambridge University Press. - 0899-823X .- 1559-6834. ; 41, s. S39-S39
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None
|
|
| 4. |
|
|
