OBJECTIVES: The Sepsis-3 definition states the clinical criteria for sepsis but lacks clear definitions of the underlying infection. To address the lack of applicable definitions of infection for sepsis research, we propose new criteria, termed the Linder-Mellhammar criteria of infection (LMCI). The aim of this study was to validate these new infection criteria. DESIGN: A multicenter cohort study of patients with suspected infection who were admitted to emergency departments or ICUs. Data were collected from medical records and from study investigators. SETTING: Four academic hospitals in Sweden, Switzerland, the Netherlands, and Germany. PATIENTS: A total of 934 adult patients with suspected infection or suspected sepsis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Agreement of infection site classification was measured using the LMCI with Cohen κ coefficient, compared with the Calandra and Cohen definitions of infection and diagnosis on hospital discharge as references. In one of the cohorts, comparisons were also made to adjudications by an expert panel. A subset of patients was assessed for interobserver agreement. MEASUREMENTS AND MAIN RESULTS: The precision of the LMCI varied according to the applied reference. LMCI performed better than the Calandra and Cohen definitions (κ = 0.62 [95% CI, 0.59–0.65] vs κ = 0.43 [95% CI, 0.39–0.47], respectively) and the diagnosis on hospital discharge (κ = 0.57 [95% CI, 0.53–0.61] vs κ = 0.43 [95% CI, 0.39–0.47], respectively). The interobserver agreement for the LMCI was evaluated in 91 patients, with agreement in 77%, κ = 0.72 (95% CI, 0.60–0.85). When tested with adjudication as the gold standard, the LMCI still outperformed the Calandra and Cohen definitions (κ = 0.65 [95% CI, 0.60–0.70] vs κ = 0.29 [95% CI, 0.24–0.33], respectively). CONCLUSIONS: The LMCI is useful criterion of infection that is intended for sepsis research, in and outside of the ICU. Useful criteria for infection have the potential to facilitate more comparable sepsis research and exclude sepsis mimics from clinical studies, thus improving and simplifying sepsis research.

Background The number of patients receiving direct oral anticoagulants (DOACs) is increasing, however, this treatment is associated with the risk of bleeding. More than 10 percent of patients on DOACs have to interrupt their anticoagulation for an invasive procedure every year. For this reason, the correct management of DOACs in the perioperative setting is mandatory. Case Presentation An 81-year-old male patient, with known impaired renal function, presented to our emergency department with a severe enoral bleeding after tooth extraction. The DOAC therapy—indicated by known atrial fibrillation—was interrupted perioperatively and bridged with Low Molecular Weight Heparin (LMWH). The acute bleeding was stopped by local surgery. The factors contributing to the bleeding complication were bridging of DOAC treatment, together with prolonged drug action in chronic kidney disease. Conclusion In order to decide whether it is necessary to stop DOAC medication for tooth extraction, it is important to carefully weigh up the individual risks of bleeding and thrombosis. If DOAC therapy is interrupted, bridging should be reserved for thromboembolic high-risk situations. Particular caution is required in patients with impaired kidney function, due to the risk of accumulation and prolonged anticoagulant effect of both DOACs and LMWH.

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Background: Urine flow cytometry (UFC) analyses urine samples and determines parameter counts. We aimed to predict different types of urine culture growth, including mixed growth indicating urine culture contamination. Methods: A retrospective cohort study (07/2017–09/2020) was performed on pairs of urine samples and urine cultures obtained from adult emergency department patients. The dataset was split into a training (75%) and validation set (25%). Statistical analysis was performed using a machine learning approach with extreme gradient boosting to predict urine culture growth types (i.e., negative, positive, and mixed) using UFC parameters obtained by UF-4000, sex, and age. Results: In total, 3835 urine samples were included. Detection of squamous epithelial cells, bacteria, and leukocytes by UFC were associated with the different types of culture growth. We achieved a prediction accuracy of 80% in the three-class approach. Of the n = 126 mixed cultures in the validation set, 11.1% were correctly predicted; positive and negative cultures were correctly predicted in 74.0% and 96.3%. Conclusions: Significant bacterial growth can be safely ruled out using UFC parameters. However, positive urine culture growth (rule in) or even mixed culture growth (suggesting contamination) cannot be adequately predicted using UFC parameters alone. Squamous epithelial cells are associated with mixed culture growth.