Evidence map›Paper›PMID 41961688›Full record

ArticleMedicine2026

Biomarkers in predicting mortality among ICU patients with sepsis.

Sümeyye Uğur, Mustafa Kaçmaz

Abstract read
In one paragraph

Article in Medicine, 2026. 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
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.

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

2 authors.

Sümeyye UğurDepartment of Anesthesiology and Intensive Care Medicine, Ömer Halisdemir University, Niğde Training and Research Hospital, Niğde, Turkey.ORCID 0000-0003-2950-8660

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is one of the most challenging conditions in intensive care units (ICUs), with an increased incidence and mortality, making it critical to predict both mortality and timing of resuscitation. This retrospective study aimed to analyze the association of a number of routinely used biomarkers with mortality in patients admitted to the ICU with sepsis. Patients >18 years of age, who had been admitted to the ICU with sepsis were included. At admission, baseline levels of C-reactive protein (CRP), procalcitonin (PCT), albumin and lactate, as well as white blood cell (WBC), neutrophil, and lymphocyte counts were recorded. The primary outcome was 28-day mortality. Of 235 patients (92 males; mean age 73.57 ± 13.63 years), 28-day mortality occurred in 113 patients (48.1%). The non-survivors differed significantly from survivors with higher WBC, neutrophil and lymphocyte counts, higher levels of PCT, CRP and lactate, and a lower albumin level (for all, P < .05). Mortality was associated with lactate, and inversely associated with albumin levels. In logistic regression analysis, albumin and lactate were risk factors to predict 28-day mortality, with odds ratios of 1.51 and 0.89, respectively. In receiver operating characteristic analysis, PCT, lactate and albumin had the greatest values for area under the curve (0.709, 0.654, and 0.696, respectively). Besides established but time-consuming and complicated scoring systems, lactate and albumin levels may be used to predict prognosis in ICU settings, with the advantage of ease in their availability.

Indexed as

BiomarkersIntensive Care UnitsSepsisAgedAged, 80 and overC-Reactive ProteinFemaleHospital MortalityHumansLactic AcidLeukocyte CountMaleMiddle AgedPredictive Value of TestsProcalcitoninPrognosisBiomarkersC-Reactive ProteinLactic AcidProcalcitoninSerum Albuminbiomarkersintensive care unitmortalitysepsis

Identifiers

PMID41961688
PMCPMC13593156

What OpenQuestion holds

Textmetadata
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.