The procalcitonin trajectory as an effective tool for identifying sepsis patients at high risk of mortality

Sepsis is a critical condition that significantly burdens healthcare systems globally. Given the heterogeneity among sepsis patients, identifying high-risk mortality groups is crucial [1]. Procalcitonin (PCT) is a well-established biomarker for evaluating sepsis severity and guiding antibiotic therapy [2]. In practice, PCT is usually measured repeatedly during the hospital stay. While single PCT values are helpful, dynamic trends through repeated measurements offer deeper insights into patient prognosis. Traditional analysis methods often fail to fully capture the complexity of these data [3]. By employing a hierarchical linear mixed-effects (HLME) model [4], this study aims to explore distinct PCT trajectories in sepsis patients and their association with mortality, providing a refined approach to risk stratification.

We here report our main findings in this study. The medical ethics committee of Zhongshan Hospital Fudan University reviewed and approved this study (B2021-501R). Informed consent was waived because of the retrospective nature of the study and the analysis used anonymous clinical data. Between Jan 2019 and March 2024, 537 patients (167 females, 370 males; median age 69 years old [IQR 59–77]) were included. The proportion of patients with septic shock is 47.5%. Abdomen (274/51.0%) and respiratory (202/37.6%) were the two main sites of infection. The median length of stay (LOS) was 10 days [IQR 4–20] in ICU and 15 days [IQR 10–25] in hospital. One hundred sixty-five in-hospital deaths were observed.

A total of 2492 PCT measurements were available for trajectory modeling analyses. Three classes were identified using the HLME model (Fig. 1A). Class 1, also known as the “high-value-slow-decrease” class, included 43 patients (8%) and was characterized by initially high PCT values that remained stable for the first three days before gradually declining. Class 2, the “consistent-low” class, included 354 patients (66%) and displayed low initial PCT values that remained consistently low over the first 7 days in the ICU. Class 3, the “high-value-fast-decrease” class, included 140 patients (26%) and was marked by high initial PCT values that declined rapidly over time. Baseline characteristics differed significantly between the three PCT classes (Table 1). Patients in Class 1 and Class 3 had higher baseline SOFA scores and required more norepinephrine to maintain blood pressure compared to Class 2. In-hospital mortality was highest in Class 1 (42%) compared to Class 2 (32%) and Class 3 (24%) (P = 0.044). Baseline variables (age, sex, baseline SOFA, baseline lactate, presence of septic shock, surgical intervention, infection sites) and PCT classes were included in the Cox proportional hazards model for in-hospital mortality. With Class 1 as the reference level, Class 2 (HR: 0.507 [95% CI 0.287–0.895], P = 0.020) and Class 3 (HR 0.449 [95% CI 0.244–0.827], P = 0.011) were independent protective factors for in-hospital mortality. Kaplan–Meier survival curves were used to illustrate the in-hospital mortality of the 3 classes (Fig. 1B).

Fig. 1

A Shows the 3 distinct procalcitonin classes. B Contains Kaplan–Meier curves for patients in the 3 classes. Class 1: “high-value-slow-decrease” class; Class 2: “consistent-low” class; Class 3: “high-value-fast-decrease” class

Full size image

Table 1 Comparison of baseline characteristics among the three PCT classes

Full size table

Three distinct PCT trajectories were identified in this study. Despite notable baseline differences across the classes, the “high-value-slow-decrease” PCT trajectory is an independent risk factor for higher in-hospital mortality. Given the strong link between PCT trajectories and mortality, continuous monitoring of PCT levels is essential for clinicians to detect potential high-risk sepsis patients. The insights from this study provide clinicians with information to optimize clinical decision-making and may support the development of more personalized and effective sepsis management strategies, ultimately benefiting patient outcomes.

The datasets generated and/or analysed during the current study are not publicly available due to containing information that could compromise the privacy of research participants, but are available from the corresponding authors, JS and MZ, on reasonable request.

1. Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, Machado FR, McIntyre L, Ostermann M, Prescott HC, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181–247.

   Article PubMed PubMed Central Google Scholar

2. Papp M, Kiss N, Baka M, Trásy D, Zubek L, Fehérvári P, Harnos A, Turan C, Hegyi P, Molnár Z. Procalcitonin-guided antibiotic therapy may shorten length of treatment and may improve survival-a systematic review and meta-analysis. Crit Care. 2023;27(1):394.

   Article PubMed PubMed Central Google Scholar

3. Wang X, Andrinopoulou ER, Veen KM, Bogers A, Takkenberg JJM. Statistical primer: an introduction to the application of linear mixed-effects models in cardiothoracic surgery outcomes research-a case study using homograft pulmonary valve replacement data. Eur J Cardiothorac Surg. 2022;62(4):ezac429.

   Article PubMed PubMed Central Google Scholar

4. Leyland AH, Goldstein H. Multilevel modelling of health statistics. Hoboken: Wiley; 2001.

   Google Scholar

Download references

This research received funding from Shanghai municipal hospital diagnosis and treatment technology promotion and optimization management project (SHDC22022203).

Authors and Affiliations

1. Department of Critical Care Medicine, Zhongshan Hospital of Fudan University, No. 180, Fenglin Road, Xuhui District, Shanghai, China

   Xu Wang, Shilong Lin, Ming Zhong & Jieqiong Song

Authors

1. Xu WangView author publications

   You can also search for this author in PubMed Google Scholar

2. Shilong LinView author publications

   You can also search for this author in PubMed Google Scholar

3. Ming ZhongView author publications

   You can also search for this author in PubMed Google Scholar

4. Jieqiong SongView author publications

   You can also search for this author in PubMed Google Scholar

Contributions

XW was responsible for the methodological design, coordination, data preparation, and statistical analysis. SL contributed to data collection and statistical analysis. XW drafted and revised the manuscript. JS and MZ conceived and designed the study and assisted in drafting the paper. XW, SL, MZ, and JS contributed to the preparation and critical review of the manuscript. All authors approved the final manuscript.

Corresponding authors

Correspondence to Ming Zhong or Jieqiong Song.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

Cite this article

Wang, X., Lin, S., Zhong, M. et al. The procalcitonin trajectory as an effective tool for identifying sepsis patients at high risk of mortality. Crit Care 28, 312 (2024). https://doi.org/10.1186/s13054-024-05100-0

Download citation

• Received: 11 September 2024

• Accepted: 14 September 2024

• Published: 19 September 2024

• DOI: https://doi.org/10.1186/s13054-024-05100-0

Share this article

Anyone you share the following link with will be able to read this content:

Get shareable link

Sorry, a shareable link is not currently available for this article.

Copy to clipboard

Provided by the Springer Nature SharedIt content-sharing initiative