Scoring systems for prediction of malaria and dengue fever in non-endemic areas among travellers arriving from tropical and subtropical areas.

BACKGROUND: Fever is a common symptom among travellers returning from tropical/subtropical areas to Europe, and promptly distinguishing severe illnesses from self-limiting febrile syndromes is important but can be challenging due to non-specific clinical presentation. METHODS: A cross-sectional study enrolled adults and children who sought care during 2015-2020 at Karolinska University Hospital, Stockholm, Sweden with fever within 2 months after returning from travel to a tropical/subtropical area. Data on symptoms and laboratory parameters were prospectively and retrospectively collected. Two separate scoring systems for malaria and dengue were developed based on backward elimination regressions. RESULTS: In total, 2113 adults (18-94 years) and 202 children (1-17 years) were included, with 112 (4.8%) confirmed malaria by blood thick smear and 90 (3.9%) PCR/serology dengue-positive cases. Malaria was more likely in a patient who had visited sub-Saharan Africa and presented with combination of thrombocytopenia, anaemia and fever ≥39.5°C. Leucopenia, muscle pain and rash after travelling to Asia or South/Latin America indicated high probability of dengue. Two scoring systems with points between 0 and 7 for prediction of malaria or dengue were created based on the above predictors. Scores ≥3 indicated >80% sensitivity and specificity for malaria and >90% specificity for dengue in children and adults (area under the curve (AUC) for dengue: 0.92 in adults (95% CI 0.90 to 0.95) and 0.95 in children (95% CI 0.88 to 1.0); AUC for malaria: 0.93 in adults (95% CI 0.91 to 0.96) and 0.88 in children (95% CI 0.78 to 0.99)). Internal validation of optimism and overfitting was managed with bootstrap. CONCLUSION: The presented scoring systems provide novel tools for structured assessment of patients with tropical fever in a non-endemic area and highlight clinical signs associated with a potential severe aetiology to direct the need for microbial investigation.

The accuracy of fully-automated algorithms for the surveillance of central venous catheter-related bloodstream infection in hospitalised patients.

BACKGROUND: Continuous surveillance for healthcare-associated infections such as central venous catheter-related bloodstream infections (CVC-BSI) is crucial for prevention. However, traditional surveillance methods are resource-intensive and prone to bias. This study aimed to develop and validate fully-automated surveillance algorithms for CVC-BSI. METHODS: Two algorithms were developed using electronic health record data from 1000 admissions with a positive blood culture (BCx) at Karolinska University Hospital from 2017: (1) Combining microbiological findings in BCx and CVC cultures with BSI symptoms; (2) Only using microbiological findings. These algorithms were validated in 5170 potential CVC-BSI-episodes from all admissions in 2018-2019, and results extrapolated to all potential CVC-BSI-episodes within this period (n = 181,354). The reference standard was manual record review according to ECDC's definition of microbiologically confirmed CVC-BSI (CRI3-CVC). RESULTS: In the potential CVC-BSI-episodes, 51 fulfilled ECDC's definition and the algorithms identified 47 and 49 episodes as CVC-BSI, respectively. Both algorithms performed well in assessing CVC-BSI. Overall, algorithm 2 performed slightly better with in the total period a sensitivity of 0.880 (95%-CI 0.783-0.959), specificity of 1.000 (95%-CI 0.999-1.000), PPV of 0.918 (95%-CI 0.833-0.981) and NPV of 1.000 (95%-CI 0.999-1.000). Incidence according to the reference and algorithm 2 was 0.33 and 0.31 per 1000 in-patient hospital-days, respectively. CONCLUSIONS: Both fully-automated surveillance algorithms for CVC-BSI performed well and could effectively replace manual surveillance. The simpler algorithm, using only microbiology data, is suitable when BCx testing adheres to recommendations, otherwise the algorithm using symptom data might be required. Further validation in other settings is necessary to assess the algorithms' generalisability.

The accuracy of fully automated algorithms for surveillance of healthcare-onset Clostridioides difficile infections in hospitalized patients.

We developed and validated a set of fully automated surveillance algorithms for healthcare-onset CDI using electronic health records. In a validation data set of 750 manually annotated admissions, the algorithm based on International Classification of Disease, Tenth Revision (ICD-10) code A04.7 had insufficient sensitivity. Algorithms based on microbiological test results with or without addition of symptoms performed well.

Impact of the COVID-19 pandemic on the incidence and mortality of hospital-onset bloodstream infection: a cohort study.

The COVID-19 pandemic burdens hospitals, but consequences for quality of care outcomes such as healthcare-associated infections are largely unknown. This cohort included all adult hospital episodes (n=186 945) at an academic centre between January 2018 and January 2021. Data were collected from the hospitals' electronic health record data repository. Hospital-onset bloodstream infection (HOB) was defined as any positive blood culture obtained ≥48 hours after admission classified based on microbiological and hospital administrative data. Subgroup analyses were performed with exclusion of potential contaminant bacteria. The cohort was divided into three groups: controls (prepandemic period), non-COVID-19 (pandemic period) and COVID-19 (pandemic period) based on either PCR-confirmed SARS-CoV-2 infections from respiratory samples or International Classification of Diseases 10th Revision diagnoses U071 and U72 at discharge. Adjusted incidence rate ratios (aIRR) and risk of death in patients with HOB were compared between the prepandemic and pandemic periods using Poisson and logistic regression. The incidence of HOB was increased for the COVID-19 group compared with the prepandemic period (aIRR 3.34, 95% CI 2.97 to 3.75). In the non-COVID-19 group, the incidence was slightly increased compared with prepandemic levels (aIRR 1.20, 95% CI 1.08 to 1.32), but the difference decreased when excluding potential contaminant bacteria (aIRR 1.15, 95% CI 1.00 to 1.31, p=0.04). The risk of dying increased for both the COVID-19 group (adjusted odds ratio (aOR) 2.44, 95% CI 1.75 to 3.38) and the non-COVID-19 group (aOR 1.63, 95% CI 1.22 to 2.16) compared with the prepandemic controls. These findings were consistent also when excluding potential contaminants. In summary, we observed a higher incidence of HOB during the COVID-19 pandemic, and the mortality risk associated with HOB was greater, compared with the prepandemic period. Results call for specific attention to quality of care during the pandemic.