Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis

Background: Acute cholangitis is a life-threatening bacterial infection of the biliary tract. Main focus of this study was to create a useful risk prediction model that helps physicians to assign patients with acute cholangitis into different management groups. Methods: 981 cholangitis episodes from 810 patients were analysed retrospectively at a German tertiary center. Results: Out of eleven investigated statistical models fit to 22 predictors, the Random Forest model achieved the best (cross-) validated performance to predict mortality. The receiver operating characteristics (ROC) curve revealed a mean area under the curve (AUC) of 91.5 %. Dependent on the calculated mortality risk, we propose to stratify patients with acute cholangitis into a high and low risk group. The mean sensitivity, specificity, positive and negative predictive value of the corresponding optimal cutpoint were 82.9 %, 85.1 %, 19.0 % and 99.3 %, respectively. All of these results emerge from nested (cross-) validation and are supposed to reflect the model's performance expected for external data. An implementation of our risk prediction model including the specific treatment recommendations adopted from the Tokyo guidelines is available on http://www2.imse.med.tum.de:3838/. Conclusion: Our risk prediction model for mortality appears promising to stratify patients with acute cholangitis into different management groups. Additional validation of its performance should be provided by further prospective trails

Strategies for increasing diagnostic yield of community-onset bacteraemia within the emergency department: A retrospective study

Bloodstream infections (BSI) are associated with high mortality. Therefore, reliable methods of detection are of paramount importance. Efficient strategies to improve diagnostic yield of bacteraemia within the emergency department (ED) are needed. We conducted a retrospective analysis of all ED encounters in a high-volume, city-centre university hospital within Germany during a five-year study period from October 2013 to September 2018. A time-series analysis was conducted for all ED encounters in which blood cultures (BCs) were collected. BC detection rates and diagnostic yield of community-onset bacteraemia were compared during the study period (which included 45 months prior to the start of a new diagnostic Antibiotic Stewardship (ABS) bundle and 15 months following its implementation). BCs were obtained from 5,191 out of 66,879 ED admissions (7.8%). Bacteraemia was detected in 1,013 encounters (19.5% of encounters where BCs were obtained). The overall yield of true bacteraemia (defined as yielding clinically relevant pathogens) was 14.4%. The new ABS-related diagnostic protocol resulted in an increased number of hospitalised patients with BCs collected in the ED (18% compared to 12.3%) and a significant increase in patients with two or more BC sets taken (59% compared to 25.4%), which resulted in an improved detection rate of true bacteraemia (2.5% versus 1.8% of hospital admissions) without any decrease in diagnostic yield. This simultaneous increase in BC rates without degradation of yield was a valuable finding that indicated success of this strategy. Thus, implementation of the new diagnostic ABS bundle within the ED, which included the presence of a skilled infectious disease (ID) team focused on obtaining BCs, appeared to be a valuable tool for the accurate and timely detection of community-onset bacteraemia

A diagnostic algorithm for detection of urinary tract infections in hospitalized patients with bacteriuria: The "Triple F" approach supported by Procalcitonin and paired blood and urine cultures

For acute medicine physicians, distinguishing between asymptomatic bacteriuria (ABU) and clinically relevant urinary tract infections (UTI) is challenging, resulting in overtreatment of ABU and under-recognition of urinary-source bacteraemia without genitourinary symptoms (USB). We conducted a retrospective analysis of ED encounters in a university hospital between October 2013 and September 2018 who met the following inclusion criteria: Suspected UTI with simultaneous collection of paired urinary cultures and blood cultures (PUB) and determination of Procalcitonin (PCT). We sought to develop a simple algorithm based on clinical signs and PCT for the management of suspected UTI. Individual patient presentations were retrospectively evaluated by a clinical "triple F" algorithm (F1 ="fever", F2 ="failure", F3 ="focus") supported by PCT and PUB. We identified 183 ED patients meeting the inclusion criteria. We introduced the term UTI with systemic involvement (SUTI) with three degrees of diagnostic certainty: bacteremic UTI (24.0%;44/183), probable SUTI (14.2%;26/183) and possible SUTI (27.9%;51/183). In bacteremic UTI, half of patients (54.5%;24/44) presented without genitourinary symptoms. Discordant bacteraemia was diagnosed in 16 patients (24.6% of all bacteremic patients). An alternative focus was identified in 67 patients, five patients presented withS.aureusbacteremia. 62 patients were diagnosed with possible UTI (n = 20) or ABU (n = 42). Using the proposed "triple F" algorithm, dichotomised PCT of < 0.25 pg/ml had a negative predictive value of 88.7% and 96.2% for bacteraemia und accordant bacteraemia respectively. The application of the algorithm to our cohort could have resulted in 33.3% reduction of BCs. Using the diagnostic categories "possible" or "probable" SUTI as a trigger for initiation of antimicrobial treatment would have reduced or streamlined antimicrobial use in 30.6% and 58.5% of cases, respectively. In conclusion, the "3F" algorithm supported by PCT and PUB is a promising diagnostic and antimicrobial stewardship tool

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors

Clinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences. From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods. Correlation and regression analyses showed significant associations between IA rupture status and patient’s sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors. This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction

Disease severity in hospitalized COVID-19 patients: comparing routine surveillance with cohort data from the LEOSS study in 2020 in Germany

Introduction Studies investigating risk factors for severe COVID-19 often lack information on the representativeness of the study population. Here, we investigate factors associated with severe COVID-19 and compare the representativeness of the dataset to the general population. Methods We used data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) of hospitalized COVID-19 patients diagnosed in 2020 in Germany to identify associated factors for severe COVID-19, defined as progressing to a critical disease stage or death. To assess the representativeness, we compared the LEOSS cohort to cases of hospitalized patients in the German statutory notification data of the same time period. Descriptive methods and Poisson regression models were used. Results Overall, 6672 hospitalized patients from LEOSS and 132,943 hospitalized cases from the German statutory notification data were included. In LEOSS, patients above 76 years were less likely represented (34.3% vs. 44.1%). Moreover, mortality was lower (14.3% vs. 21.5%) especially among age groups above 66 years. Factors associated with a severe COVID-19 disease course in LEOSS included increasing age, male sex (adjusted risk ratio (aRR) 1.69, 95% confidence interval (CI) 1.53–1.86), prior stem cell transplantation (aRR 2.27, 95% CI 1.53–3.38), and an elevated C-reactive protein at day of diagnosis (aRR 2.30, 95% CI 2.03–2.62). Conclusion We identified a broad range of factors associated with severe COVID-19 progression. However, the results may be less applicable for persons above 66 years since they experienced lower mortality in the LEOSS dataset compared to the statutory notification data.Peer Reviewe

HIV pre-exposure prophylaxis was associated with no impact on sexually transmitted infection prevalence in a high-prevalence population of predominantly men who have sex with men, Germany, 2018 to 2019

Introduction: Despite increased use of pre-exposure prophylaxis (PrEP) in Germany, HIV infection rates are not declining and little is known about how this prevention method affects the prevalence of sexually transmitted infections (STI) among men who have sex with men (MSM). Aim: We studied, in a large multicentre cohort, STI point prevalence, co-infection rates, anatomical location and influence of PrEP. Methods: The BRAHMS study was a prospective cohort study conducted at 10 sites in seven major German cities that enrolled MSM reporting increased sexual risk behaviour. At screening visits, MSM were tested for Mycoplasma genitalium (MG), Neisseria gonorrhoeae (NG), Chlamydia trachomatis (CT) and Treponema pallidum (TP), and given a behavioural questionnaire. With binomial regression, we estimated prevalence ratios (PR) and 95% confidence intervals (CI) for the association of PrEP and STI. Results: We screened 1,043 MSM in 2018 and 2019, with 53.0% currently using PrEP. At screening, 370 participants (35.5%) had an STI. The most common pathogen was MG in 198 (19.0%) participants, followed by CT (n = 133; 12.8%), NG (n = 105; 10.1%) and TP (n = 37; 3.5%). Among the 370 participants with at least one STI, 14.6% (n = 54) reported STI-related symptoms. Infection prevalence was highest at anorectal site (13.4% MG, 6.5% NG, 10.2% CT). PrEP use was not statistically significant in adjusted models for STI (PR: 1.10; 95% CI: 0.91–1.32), NG/CT, only NG or only CT. Conclusions: Prevalence of asymptomatic STI was high, and PrEP use did not influence STI prevalence in MSM eligible for PrEP according to national guidelines.Peer Reviewe

The Democratic Biopolitics of PrEP

PrEP (Pre-Exposure Prophylaxis) is a relatively new drug-based HIV prevention technique and an important means to lower the HIV risk of gay men who are especially vulnerable to HIV. From the perspective of biopolitics, PrEP inscribes itself in a larger trend of medicalization and the rise of pharmapower. This article reconstructs and evaluates contemporary literature on biopolitical theory as it applies to PrEP, by bringing it in a dialogue with a mapping of the political debate on PrEP. As PrEP changes sexual norms and subjectification, for example condom use and its meaning for gay subjectivity, it is highly contested. The article shows that the debate on PrEP can be best described with the concepts ‘sexual-somatic ethics’ and ‘democratic biopolitics’, which I develop based on the biopolitical approach of Nikolas Rose and Paul Rabinow. In contrast, interpretations of PrEP which are following governmentality studies or Italian Theory amount to either farfetched or trivial positions on PrEP, when seen in light of the political debate. Furthermore, the article is a contribution to the scholarship on gay subjectivity, highlighting how homophobia and homonormativity haunts gay sex even in liberal environments, and how PrEP can serve as an entry point for the destigmatization of gay sexuality and transformation of gay subjectivity. ‘Biopolitical democratization’ entails making explicit how medical technology and health care relates to sexual subjectification and ethics, to strengthen the voice of (potential) PrEP users in health politics, and to renegotiate the profit and power of Big Pharma

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results