Evidence map›Paper›PMID 41063291›Full record

ArticleTropical medicine and health2025

Prognostic value of low-cost white blood cell indices and procalcitonin for mortality in Rwandan sepsis patients: a prospective intensive care unit study.

Emmanuel Kundukundwe, Theodette Nizeyimana, Ayingeneye Grace Mutoni, Aline Muhimpundu, Enatha Mukantwari, Cedrick Izere, Solomon Ali, Araya Gebreyesus Wasihun, Tiruzer Bekele, Thaddee Nshimiyimana and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Tropical medicine and health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

17 authors.

Emmanuel Kundukundwe *Department of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Theodette Nizeyimana *Department of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Ayingeneye Grace Mutoni *Department of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Aline Muhimpundu *Department of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Enatha MukantwariDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Cedrick IzereDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Solomon AliDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Araya Gebreyesus WasihunDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Tiruzer BekeleDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Thaddee NshimiyimanaDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Augustin NzitakeraDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Ella Larissa NdoricyimpayeDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Schifra UwamunguDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Eliah ShemaDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Elizabeth GoriDepartment of Medical Biochemistry, Molecular Biology and Genetics, School of Medicine and Pharmacy, College of Medicine and Health Sciences, University of Rwanda, Huye, Rwanda. gori.elizabeth@gmail.com.
Wossenseged LemmaDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.
Cuthbert MusarurwaDepartment of Biomedical Laboratory Sciences, School of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, P.O. Box 3286, Kigali, Rwanda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn resource-limited settings, early identification of sepsis and low-cost mortality predictors is critical for intensive care unit (ICU) triage. This study evaluated the prognostic value of baseline sociodemographic factors, routine hematological indices, and serum procalcitonin (PCT) levels for 40-day mortality among adult ICU patients meeting Sepsis-2 criteria in Rwanda.

methodsA prospective cohort of 125 ICU patients was followed for 40 days. Baseline variables included sex, age, PCT, total white blood cell (WBC) count, differential counts (neutrophils, basophils, eosinophils, monocytes, lymphocytes), and neutrophil-to-lymphocyte ratio (NLR). Survival probabilities were estimated using Kaplan-Meier curves and log-rank tests. Cox proportional hazards models identified independent mortality predictors, with assumptions tested via Schoenfeld residuals and multicollinearity assessed using variance inflation factors. Time-dependent receiver operator curve (ROC) analysis evaluated model performance at days 6, 10, and 15 using the area under the curve (AUC) values.

resultsOf 125 patients, 56 (44.8%) were female. Median age was 41 years for survivors and 50 years for non-survivors (p = 0.097). In multivariable Cox regression, elevated neutrophil counts were independently associated with increased mortality [adjusted hazard ratio (aHR)] 1.99; 95% CI (confidence intervals) 1.37-2.88; p < 0.001), corresponding to a twofold higher hazard of death for approximately a threefold increase in neutrophil count. No significant associations were found for sex, age, or PCT. ROC analysis showed that models integrating neutrophils and total WBC (TotalWBC) achieved the highest predictive accuracy, with AUCs ranging from ~68% to 71% across all time points, outperforming simpler models.

conclusionsElevated neutrophil counts at ICU admission are independently associated with increased mortality. Integrating absolute neutrophil and WBC data into predictive models enhances early mortality risk stratification. These findings underscore the value of routine biomarkers and robust modeling to guide timely interventions in resource-constrained ICU settings.

Indexed as

AUCBiomarkersIntensive care unitNeutrophilsProcalcitoninProspective cohortROC curvesRwandaSepsisSurvival analysisWhite blood cells

Identifiers

PMID41063291
PMCPMC12509366

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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.