ArticleWorld journal of surgery2019
A Novel Prediction Model for Bloodstream Infections in Hepatobiliary-Pancreatic Surgery Patients.
Article in World journal of surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 11 citations in OpenAlex.
- Antibiotic prophylaxis after 48 h postoperatively are not associated with decreased surgical site infections and other healthcare associated infections in pancreatic surgery patients: a retrospective cohort study.Antimicrobial resistance and infection control · 2023Article
- Clinical characteristics of bloodstream infections in adult patients with solid tumours and a nomogram for mortality prediction: a 5-year case-controlled retrospective study in a tertiary-level hospital.Frontiers in cellular and infection microbiology · 2023Article
- Infection with a human-derived enteroinvasive Escherichia coli strain altered intestinal barrier function in guinea pigs.International microbiology : the official journal of the Spanish Society for Microbiology · 2022Article
- A novel prognostic model for malignant patients with Gram-negative bacteremia based on real-world research.Scientific reports · 2022Observational
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBloodstream infections (BSI) are an important source of postoperative mortality in hepatobiliary-pancreatic surgery (HBPS) patients, and no prediction model has been analyzed before.
methodsUsing big data from the electronic medical records of the administrative and culture databases of MacKay Memorial Hospital, we identified the potential risk factors for community-acquired and healthcare-associated BSI and mortality of patients who received HBPS. Subsequently, we analyzed the microorganisms' profiles and antimicrobial susceptibility patterns for these BSI.
resultsBSI were found in 6.3% patients (349 of 5513 HBPS patients), and hospital mortality was 1.48% (82 of 5513). Dividing patients into low-, intermediate-, and high-risk groups on the basis of sex, age, status of comorbidity (renal failure, peptic ulcer disease, fluid and electrolyte disorders, and acute cholecystitis), a predictive BSI risk score model was developed. According to this model, BSI risk ranged from 1.43% to 11.95%; AUROC to predict BSI risk was 0.72 (95% CI 0.69-0.75). From this retrospective study, Enterobacteriaceae were the most common microorganisms that were isolated from BSI. For both community-acquired and healthcare-associated BSI, imipenem and colistin are the most successful.
conclusionThis novel model can be useful to predict who is at risk of BSI after HBPS, and new prophylactic protocols for these patients are needed.
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.