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Prognostic utility of serial procalcitonin measurements in ICU sepsis: a laboratory-led modelling approach in a resource-limited setting procalcitonin modelling for sepsis mortality in a low-resource ICU

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Published/Copyright: November 12, 2025
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 64 Issue 3

Abstract

Objectives

Procalcitonin (PCT) is increasingly used to support sepsis diagnosis, but its role in predicting outcomes remains uncertain, particularly in low-resource settings. We evaluated whether single and serial PCT measurements were associated with 28-day mortality in critically ill adults with suspected sepsis.

Methods

We conducted a retrospective study of adult intensive care unit (ICU) patients with suspected sepsis and at least one PCT measurement at a tertiary hospital in South Africa (August 2022-July 2023). Baseline PCT was analysed using multivariable logistic regression. Among patients with ≥2 PCT values, we examined the association between PCT slope (from patient-level linear regression) and mortality. Latent class mixed models (LCMM) were used to identify PCT trajectory subgroups.

Results

Of 371 patients, 119 (32 %) died within 28 days. Higher baseline log-PCT was independently associated with increased mortality (adjusted odds ratio [aOR] 1.58; 95 % CI 1.01–2.50). A rising PCT slope trended toward higher mortality (aOR 3.56 per unit/day; p=0.06). LCMM identified three trajectory classes with distinct mortality risks (class 2: aOR 4.53; class 3: aOR 4.35, vs. reference). These models were based entirely on laboratory data and did not assess clinical scoring systems.

Conclusions

Both baseline and serial PCT measurements predicted mortality in ICU patients with suspected sepsis. Modelling approaches based on routine laboratory data may offer scalable tools for early risk stratification in resource-limited settings.

Keywords: biomarkers; critical care; mortality prediction; trajectory analysis
Corresponding author: Dr. Aaqilah Fataar, Division of Chemical Pathology, Department of Pathology, Stellenbosch University, Francie van Zijl Drive, Cape Town, 7505, South Africa; and National Health Laboratory Service, Division of Chemical Pathology, Tygerberg Hospital, Cape Town, South Africa, E-mail:

  1. Research ethics: This study was approved by the Stellenbosch University Health Research Ethics Committee (Ref: S23/11/290) on 16 February 2024. The need for individual informed consent was waived due to the retrospective design and use of de-identified data.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0778).

Received: 2025-07-03
Accepted: 2025-08-20
Published Online: 2025-11-12
Published in Print: 2026-02-24

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2025-0778
Keywords for this article
biomarkers; critical care; mortality prediction; trajectory analysis

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  1. Frontmatter
  2. Editorial
  3. The European Health Data Space: challenges and opportunities for laboratory medicine
  4. Review
  5. Saliva sampling matters for salivary diagnostics of viral infections
  6. Opinion Papers
  7. Mass spectrometry based precision diagnostics: on the cusp of selective testing
  8. Supporting trend detection in the cumulative display of electronic laboratory reports from multiple laboratories while preserving measurement provenance
  9. Secondary use of external quality assessment data – estimating inter-assay variation in LOINC-coded datasets
  10. Methodological evaluation and clinical interpretation of hs-cTnI and hs-cTnT variations: a reappraisal
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  22. Creatinine measurement from finger stick dried blood spots with a routine chemistry analyzer for estimation of GFR
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  25. Reference Values and Biological Variations
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  28. VeRUS: verification of reference intervals based on the uncertainty of sampling
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