Evidence map›Paper›PMID 36936903›Full record

ArticleFrontiers in immunology2023

Blood count derangements after sepsis and association with post-hospital outcomes.

Scott J Denstaedt, Jennifer Cano, Xiao Qing Wang, John P Donnelly, Sarah Seelye, Hallie C Prescott

Open access · goldFull text read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.2field-weighted citation impact, top 7% 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

12 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
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  7. Observational
  8. Why should lymphocytes immune profile matter in sepsis?World journal of critical care medicine · 2025
    Review
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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

6 authors at 2 institutions in 1 country.

Scott J DenstaedtDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.
Jennifer CanoVA Center for Clinical Management Research, Ann Arbor, MI, United States.
Xiao Qing WangDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.
John P DonnellyDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States.
Sarah SeelyeVA Center for Clinical Management Research, Ann Arbor, MI, United States.
Hallie C PrescottDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.
University of Michigan–Ann Arbor · USVA Center for Clinical Management Research · US

Funding

TRAINING IN THE DEMOGRAPHY OF AGINGT32AG000221 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sarah A. Burgard · 1992 to 2026
$15.3M
Understanding and Informing Early Hospital Antibiotic Prescribing for Potential InfectionR01HS026725 · AHRQ · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LIU, VINCENT, PRESCOTT, HALLIE CHRISTINE · 2019 to 2023
$2.2M
Predisposition for Lung Injury in Sepsis SurvivalK08HL153799 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DENSTAEDT, SCOTT JOHN · 2021 to 2025
$848k
AHRQ HHS R01 HS026725NHLBI NIH HHS K08 HL153799NIA NIH HHS T32 AG000221
6 · The paper itself

Abstract

Rationale: Predicting long-term outcomes in sepsis survivors remains a difficult task. Persistent inflammation post-sepsis is associated with increased risk for rehospitalization and death. As surrogate markers of inflammation, complete blood count parameters measured at hospital discharge may have prognostic value for sepsis survivors. Objective: To determine the incremental value of complete blood count parameters over clinical characteristics for predicting 90-day outcomes in sepsis survivors. Methods: Electronic health record data was used to identify sepsis hospitalizations at United States Veterans Affairs hospitals with live discharge and relevant laboratory data (2013 to 2018). We measured the association of eight complete blood count parameters with 90-day outcomes (mortality, rehospitalization, cause-specific rehospitalizations) using multivariable logistic regression models. Measurements and main results: We identified 155,988 eligible hospitalizations for sepsis. Anemia (93.6%, N=142,162) and lymphopenia (28.1%, N=29,365) were the most common blood count abnormalities at discharge. In multivariable models, all parameters were associated with the primary outcome of 90-day mortality or rehospitalization and improved model discrimination above clinical characteristics alone (likelihood ratio test, p<0.02 for all). A model including all eight parameters significantly improved discrimination (AUROC, 0.6929 v. 0.6756) and reduced calibration error for the primary outcome. Hemoglobin had the greatest prognostic separation with a 1.5 fold increased incidence of the primary outcome in the lowest quintile (7.2-8.9 g/dL) versus highest quintile (12.70-15.80 g/dL). Hemoglobin and neutrophil lymphocyte ratio provided the most added value in predicting the primary outcome and 90-day mortality alone, respectively. Absolute lymphocyte count added little value in predicting 90-day outcomes. Conclusions: The incorporation of discharge complete blood count parameters into prognostic scoring systems could improve prediction of 90-day outcomes. Hemoglobin had the greatest prognostic value for the primary composite outcome of 90-day rehospitalization or mortality. Absolute lymphocyte count provided little added value in multivariable model comparisons, including for infection- or sepsis-related rehospitalization.

Indexed as

LymphocytesSepsisHospitalsHumansInflammationRetrospective StudiesUnited Statesanemiacomplete blood cell (CBC) countneutrophil lymphocyte ratio (NLR)sepsissurvivor

Identifiers

PMID36936903
PMCPMC10018394
OpenAlexW4322774710

What OpenQuestion holds

Textfull text, public
LicenceCC BY
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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.