In Chinese patients with cirrhosis, lack of early clinical response to empiric antibiotics independently predicts short-term mortality and is associated with a higher risk of clinically distinct secondary infection.
Abstract
Background & Aims Bacterial and fungal infections are major drivers of acute decompensation and acute-on-chronic liver failure (ACLF) in cirrhosis, but real-world data on the effectiveness of empiric antibiotic therapy in China are scarce. We evaluated clinical response to empiric antibiotics, identified predictors of non-response, and assessed the impact of response and secondary infection on short-term outcomes in Chinese patients with cirrhosis. Approach & Results In this multicenter retrospective cohort from 24 tertiary centers, 1,401 adults with cirrhosis and bacterial or fungal infections were included. Clinical response to first-line empiric therapy occurred in 913 patients (65%). Non-responders had higher MELD/MELD-Na scores, more organ failures and ACLF, and more intense systemic inflammation. In multivariable models, greater ACLF grade 2–3, systemic inflammatory response syndrome, higher neutrophil-to-lymphocyte ratio, C-reactive protein, bilirubin, culture positivity, and lower albumin and mean arterial pressure independently predicted non-response. Non-response was independently associated with 28-day and 90-day mortality (HR 4.20 and 3.16, respectively), with consistent findings in time-dependent and center-clustered sensitivity analyses. Secondary infection developed in 7.0% of patients, was four-fold more frequent in non-responders (13.9% vs 3.3%), and nearly doubled 90-day mortality, whereas microbiological features at secondary infection did not clearly distinguish second-course responders from non-responders. Comparative analyses revealed substantial center-to-center and China-global differences in etiology, infection profile, and multidrug-resistant (MDR) burden. Conclusions In Chinese patients with cirrhosis, lack of early clinical response to empiric antibiotics independently predicts short-term mortality and is associated with a higher risk of clinically distinct secondary infection. Our findings support risk-adapted, locally informed empiric strategies.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.