ArticleFrontiers in cellular and infection microbiology2023
Microbiology and prognostic prediction model of bloodstream infection in patients with hematological malignancies.
Article in Frontiers in cellular and infection microbiology, 2023. 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, 18 citations in OpenAlex.
- Multiplex droplet digital polymerase chain reaction for rapid diagnosing suspected bloodstream infections in patients with hematologic malignancies.Translational cancer research · 2026Article
- A multidimensional nomogram for clinical risk stratification of bloodstream infection and mortality in hematologic malignancies.Frontiers in cellular and infection microbiology · 2026Article
- Who is at risk? Clinical features and a predictive model for 30-day mortality in hematologic patients with enterococcal bloodstream infection.Frontiers in cellular and infection microbiology · 2026Article
- Construction and assessment of a predictive framework for Enterobacterales bloodstream infection in real-world adult onco-hematology patients using Lasso-Cox regression.Antimicrobial agents and chemotherapy · 2025Article
- Development and validation of a nomogram for predicting PICC catheter-related bloodstream infection among patients with hematologic malignancies.BMC infectious diseases · 2025Article
- Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.BMC infectious diseases · 2025Article
- Personalized machine learning-based prognostic model for ICU-acquired bloodstream infections.Frontiers in cellular and infection microbiology · 2025Article
- A Mortality Prediction Nomogram forInfection and drug resistance · 2025Article
- Clinical Characteristics and Optimization of Empirical Antimicrobial Therapy for Febrile Neutropenia in Patients With Hematologic Malignancies.Infection and drug resistance · 2025Review
- Adult acute leukemia patients with gram-negative bacteria bloodstream infection: Risk factors and outcomes of antibiotic-resistant bacteria.Annals of hematology · 2024Article
- Pathogenic spectrum and drug resistance of bloodstream infection in patients with acute myeloid leukaemia: a single centre retrospective study.Frontiers in cellular and infection microbiology · 2024Article
- Risk Factors for Development and Mortality of Bloodstream Infections Caused by Carbapenem-ResistantInfection and drug resistance · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In recent years, with the continuous development of treatments for hematological malignancies (HMs), the remission and survival rates of patients with HMs have been significantly improved. However, because of severe immunosuppression and long-term recurrent neutropenia during treatment, the incidence and mortality of bloodstream infection (BSI) were all high in patients with HMs. Therefore, we analyzed pathogens' distribution and drug-resistance patterns and developed a nomogram for predicting 30-day mortality in patients with BSIs among HMs. Methods: In this retrospective study, 362 patients with positive blood cultures in HMs were included from June 2015 to June 2020 at West China Hospital of Sichuan University. They were randomly divided into the training cohort (n = 253) and the validation cohort (n = 109) by 7:3. A nomogram for predicting 30-day mortality after BSIs in patients with HMs was established based on the results of univariate and multivariate logistic regression. C-index, calibration plots, and decision curve analysis were used to evaluate the nomogram. Results: Among 362 patients with BSIs in HMs, the most common HM was acute myeloid leukemia (48.1%), and the most common pathogen of BSI was gram-negative bacteria (70.4%). The final nomogram included the septic shock, relapsed/refractory HM, albumin <30g/l, platelets <30×10 Conclusion: In our study, gram-negative bacterial BSIs were predominant in patients with HMs. We developed and validated a nomogram with good predictive ability to help clinicians evaluate the prognosis of patients.
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.