Observational studyBMC infectious diseases2025
Incidence and risk factor of sepsis in patients with severe community-acquired pneumonia: a Chinese, single-center, retrospective study.
Observational study in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Analysis of factors associated with severe disease and construction of a monogram for elderly patients with community-acquired pneumonia complicated by heart failure.Pakistan journal of medical sciences · 2026Article
- The effectiveness of intravenous (IV) to oral (PO) antibiotic switch (IVOS) interventions in managing community- and hospital-acquired pneumonia-a systematic review.JAC-antimicrobial resistance · 2026Review
- Clinical characteristics and risk factors associated with mortality in patients with severe community-acquired pneumonia in the intensive care unit.Journal of thoracic disease · 2026Article
- Early diagnostic value of Serum GDF-15, RBP4 and SOCS3 in severe pneumonia with sepsis.Journal of medical biochemistry · 2026Article
- Development and validation of a 28-day mortality risk prediction model for patients with AIDS suffering from sepsis secondary to pulmonary infections in the ICU.BMC infectious diseases · 2026Article
- Evaluating the Predictive Potential of an AI-Driven Deep Learning Model for Pneumonia-Associated Sepsis.Journal of clinical medicine · 2026Article
- Blood-based inflammatory parameters and machine learning models for mortality prediction in critically ill pediatric pneumonia: a retrospective cohort study.Translational pediatrics · 2026Article
- Low BMI and postoperative outcomes in elderly hip fracture patients: a Japanese nationwide database study.Journal of bone and mineral metabolism · 2026Article
- The Impact of Human Immunodeficiency Virus Co‑Infection on the Pathogen Spectrum and Outcomes of Severe Community‑Acquired Pneumonia: Insights from Metagenomic Next‑Generation Sequencing.Infection and drug resistance · 2026Article
- ICU-acquired infections and mortality in community-acquired pneumonia-induced sepsis: Insights from a transcriptomic analysis.PloS one · 2026Article
- Hospital acquired drug resistant pathogens infections in patients with viral respiratory tract infections: a retrospective study.BMC infectious diseases · 2025Article
- Risk factors for mortality and development of a predictive model in pediatric sepsis.Frontiers in pediatrics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundSepsis represents a high-risk mortality cohort among patients with severe community-acquired pneumonia (SCAP). Rapid and precise identification along with prompt decision-making, serves as a practical approach to improve patient prognosis.
methodsThis retrospective observational study enrolled adult patients with severe community-acquired pneumonia (SCAP) who were continuously hospitalized in the intensive care unit (ICU) of West China Hospital, Sichuan University, from September 2011 to September 2019. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for co-sepsis, followed by the utilization of LASSO regression to filter features to establish a nomogram. Model robustness was evaluated via the C index, receiver operating characteristic (ROC) analysis, and calculation of the area under the curve (AUC). Furthermore, its predictive accuracy was assessed via decision curve analysis (DCA).
resultsIn total, 5855 SCAP patients were included in the present study, of whom 654 developed sepsis. Patients with sepsis exhibited a prolonged length of stay in the ICU and higher mortality rates, indicating a worse prognosis than those without sepsis. We identified 15 independent risk factors associated with the development of sepsis in SCAP patients. Further analysis incorporating 9 of these features to construct a nomogram demonstrated a C index of 0.722 (95%CI 0.702-0.742), including lactate, D-dimer, respiratory rate, heart rate, albumin, hemoglobin, activated partial thromboplastin time (APTT), glucose, and C-reactive protein (CRP) levels. The AUC values and DCA curves demonstrated that the model exhibited superior accuracy and overall net benefit in predicting co-sepsis development compared with the qSOFA, CURB-65, SOFA, and APACHE II scores. Additionally, the calibration curve confirmed good concordance between the predicted probabilities of the model.
conclusionsThis study investigated the risk factors for co-sepsis in SCAP patients and constructed an expedited, cost-effective and personalized model for predicting the probability of co-sepsis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.