| 1. |
- Biancari, F, et al.
(författare)
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Preoperative risk stratification of deep sternal wound infection after coronary surgery
- 2020
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Ingår i: Infection control and hospital epidemiology. - : Cambridge University Press (CUP). - 1559-6834 .- 0899-823X. ; 41:4, s. 444-451
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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
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| 2. |
- Chan, Derwin, et al.
(författare)
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Why people failed to adhere to COVID-19 preventive behaviors? Perspectives from an integrated behavior change model
- 2021
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Ingår i: Infection control and hospital epidemiology. - New York : Cambridge University Press. - 0899-823X .- 1559-6834. ; 42:3, s. 375-376
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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.
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| 3. |
- Edman-Wallér, Jon, et al.
(författare)
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Clostridioides difficile outbreak detection: Evaluation by ribotyping and whole-genome sequencing of a surveillance algorithm based on ward-specific cutoffs
- 2023
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Ingår i: Infection control and hospital epidemiology. - 0899-823X. ; 44:12, s. 1948-1952
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Tidskriftsartikel (refereegranskat)abstract
- Objective:We evaluated the performance of an early-warning algorithm, based on ward-specific incidence cutoffs for detecting Clostridioides difficile transmission in hospitals. We also sought to determine the frequency of intrahospital Clostridioides difficile transmission in our setting. Design:Diagnostic performance of the algorithm was tested with confirmed transmission events as the comparison criterion. Transmission events were identified by a combination of high-molecular-weight typing, ward history, ribotyping, and whole-genome sequencing (WGS). Setting:The study was conducted in 2 major and 2 minor secondary-care hospitals with adjacent catchment areas in western Sweden, comprising a total population of & SIM;480,000 and & SIM;1,000 hospital beds. Patients:All patients with a positive PCR test for Clostridioides difficile toxin B during 2020 and 2021. Methods:We conducted culturing and high-molecular-weight typing of all positive clinical samples. Ward history was determined for each patient to find possible epidemiological links between patients with the same type. Transmission events were determined by PCR ribotyping followed by WGS. Results:We identified 4 clusters comprising a total of 10 patients (1.5%) among 673 positive samples that were able to be cultured and then typed by high-molecular-weight typing. The early-warning algorithm performed no better than chance; patient diagnoses were made at wards other than those where the transmission events likely occurred. Conclusions:In surveillance of potential transmission, it is insufficient to consider only the ward where diagnosis is made, especially in settings with high strain diversity. Transmission within wards occurs sporadically in our setting.
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| 4. |
- Myhrman, Sofia, et al.
(författare)
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Unexpected details regarding nosocomial transmission revealed by whole-genome sequencing of SARS-CoV-2
- 2022
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Ingår i: Infection Control and Hospital Epidemiology. - : Cambridge University Press (CUP). - 0899-823X .- 1559-6834. ; 43:10, s. 1403-1407
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Effective infection prevention and control (IPC) measures are key for protecting patients from nosocomial infections and require knowledge of transmission mechanisms in different settings. We performed a detailed outbreak analysis of the transmission and outcome of coronavirus disease 2019 (COVID-19) in a geriatric ward by combining whole-genome sequencing (WGS) with epidemiological data. Design: Retrospective cohort study. Setting: Tertiary care hospital. Participants: Patients and healthcare workers (HCWs) from the ward with a nasopharyngeal sample (NPS) positive for SARS-CoV-2 RNA during the outbreak period. Methods: Patient data regarding clinical characteristics, exposure and outcome were collected retrospectively from medical records. Stored NPS from 32 patients and 15 HCWs were selected for WGS and phylogenetic analysis. Results: Median patient age was 84 years and 17/32 (53%) were male. Fourteen patients (44%) died within 30 days after sampling. Viral load was significantly higher among the deceased. WGS was successful in 28/32 (88%) patient samples and 14/15 (93%) HCW samples. Three separate viral clades were identified, whereof one clade and two subclades among both patient and HCW samples. Integrated epidemiological and genetic analysis revealed six probable transmission events between patients and supported hospital-acquired COVID-19 in 25/32 patients. Conclusion: WGS provided a deep insight into the outbreak dynamics and true extent of nosocomial COVID-19. The extensive transmission between patients and HCWs indicated that current IPC measures were insufficient. We suggest increased use of WGS in outbreak investigations for identification of otherwise unknown transmission links and evaluation of IPC measures.
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| 5. |
- Naucler, Pontus, et al.
(författare)
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HAI-Proactive : Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden
- 2020
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Ingår i: Infection control and hospital epidemiology. - : Cambridge University Press. - 0899-823X .- 1559-6834. ; 41, s. S39-S39
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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
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