Evidence map›Paper›PMID 38652226›Full record

ArticleInfection2024

Assessment of a novel BLOOMY score for predicting mortality in hospitalised adults with bloodstream infection.

Johanna Tietäväinen, Tapio Seiskari, Janne Aittoniemi, Heini Huhtala, Jukka Mustonen, Reetta Huttunen, Jaana Syrjänen, Juha Rannikko

Open access · hybridAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Infection, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 92% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Johanna TietäväinenDepartment of Internal Medicine, Tampere University Hospital, P.O. Box 2000, 33521, Tampere, Finland.
Tapio SeiskariDepartment of Clinical Microbiology, Fimlab Laboratories Ltd, P.O. Box 66, 33013, Tampere, Finland.
Janne AittoniemiDepartment of Clinical Microbiology, Fimlab Laboratories Ltd, P.O. Box 66, 33013, Tampere, Finland.
Heini HuhtalaFaculty of Social Sciences, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland.
Jukka MustonenFaculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland.
Reetta HuttunenDepartment of Internal Medicine, Tampere University Hospital, P.O. Box 2000, 33521, Tampere, Finland.
Jaana SyrjänenDepartment of Internal Medicine, Tampere University Hospital, P.O. Box 2000, 33521, Tampere, Finland.
Juha RannikkoDepartment of Internal Medicine, Tampere University Hospital, P.O. Box 2000, 33521, Tampere, Finland. juha.rannikko@gmail.com.ORCID http://orcid.org/0000-0002-7199-6306
Tampere University · FIFimlab (Finland) · FITampere University Hospital · FITampere University of Applied Sciences · FI

Funding

Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital 9N075Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital X50060
6 · The paper itself

Abstract

purposeA German multicentre study BLOOMY was the first to use machine learning approach to develop mortality prediction scores for bloodstream infection (BSI) patients, but the scores have not been assessed in other cohorts. Our aim was to assess how the BLOOMY 14-day and 6-month scores estimate mortality in our cohort of 497 cases with BSI.

methodsClinical data, laboratory data, and patient outcome were gathered retrospectively from patient records. The scores were calculated as presented in the BLOOMY study with the exception in the day of the evaluation.

resultsIn our cohort, BLOOMY 14-day score estimated death by day 14 with an area under curve (AUC) of 0.87 (95% Confidence Interval 0.80-0.94). Using ≥ 6 points as a cutoff, sensitivity was 68.8%, specificity 88.1%, positive predictive value (PPV) 39.3%, and negative predictive value (NPV) 96.2%. These results were similar in the original BLOOMY cohort and outweighed both quick Sepsis-Related Organ Failure Assessment (AUC 0.76) and Pitt Bacteraemia Score (AUC 0.79) in our cohort. BLOOMY 6-month score to estimate 6-month mortality had an AUC of 0.79 (0.73-0.85). Using ≥ 6 points as a cutoff, sensitivity was 98.3%, specificity 10.7%, PPV 25.7%, and NPV 95.2%. AUCs of 6-month score to estimate 1-year and 5-year mortality were 0.80 (0.74-0.85) and 0.77 (0.73-0.82), respectively.

conclusionThe BLOOMY 14-day and 6-month scores performed well in the estimations of mortality in our cohort and exceeded some established scores, but their adoption in clinical work remains to be seen.

Indexed as

BacteremiaAdultAgedAged, 80 and overFemaleGermanyHospitalizationHumansMaleMiddle AgedPrognosisRetrospective StudiesSensitivity and SpecificitySepsisBacteraemiaMachine learningScoreSepsis

Identifiers

PMID38652226
PMCPMC11289069
OpenAlexW4395050201

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.