
==== Front
Crit Care
Critical Care
1364-8535
1466-609X
BioMed Central London

5100
10.1186/s13054-024-05100-0
Correspondence
The procalcitonin trajectory as an effective tool for identifying sepsis patients at high risk of mortality
Wang Xu
Lin Shilong
Zhong Ming zhong.ming@zs.hospital.sh.cn

Song Jieqiong song.jieqiong@zs-hospital.sh.cn

https://ror.org/032x22645 grid.413087.9 0000 0004 1755 3939 Department of Critical Care Medicine, Zhongshan Hospital of Fudan University, No. 180, Fenglin Road, Xuhui District, Shanghai, China
19 9 2024
19 9 2024
2024
28 31211 9 2024
14 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ 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/.
http://dx.doi.org/10.13039/501100008750 Shanghai Municipal Hospital Development Center SHDC22022203 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcSepsis 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

Table 1 Comparison of baseline characteristics among the three PCT classes

	Class 1	Class 2	Class 3	P-value	
N = 43	N = 354	N = 140	
Age, years	68 (55, 78)	70 (59, 77)	67 (59.75, 75)	0.205	
Sex: female, n(%)	7 (16)	110 (31)	50 (36)	0.055	
Surgery, n (%)	35 (81)	151 (43)	97 (69)	 < 0.001	
Infection sites, n (%)	
 - Abdomen	33 (77)	143 (40)	95 (68)	 < 0.001	
 - Respiratory	5 (12)	178 (50)	19 (14)	 < 0.001	
 - Others	6 (14)	51 (14)	37 (26)	0.005	
Septic shock, n (%)	29 (67)	128 (36)	98 (70)	 < 0.001	
SOFA, baseline	7 (5, 10)	6 (4, 8)	8 (6, 10)	 < 0.001	
Lactate, baseline, mmol/L	3.20 (2.30, 4.60)	1.7 0(1.26, 2.30)	2.50 (1.57, 4.70)	 < 0.001	
NE, baseline, n (%)	34 (79)	169 (48)	102 (73)	 < 0.001	
LOS ICU, days	5 (3, 13)	11 (5, 21)	7 (3, 15.25)	0.002	
LOS hospital, days	17 (9.5, 25)	15 (10, 25)	13 (7, 20)	0.014	
In-hospital death, n (%)	18 (42)	114 (32)	33 (24)	0.044	
SOFA, Sequential organ failure assessment; NE, Norepinephrine; LOS, Length of stay

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.

Author 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.

Funding

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

Availability of data and materials

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.

Declarations

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.
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References

1. Evans L Rhodes A Alhazzani W Antonelli M Coopersmith CM French C Machado FR McIntyre L Ostermann M Prescott HC Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021 Intensive Care Med 2021 47 11 1181 1247 10.1007/s00134-021-06506-y 34599691
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):1181–247.34599691
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 10.1186/s13054-023-04677-2 37833778
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):394.37833778
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 10.1093/ejcts/ezac429 36005884
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):ezac429.36005884
4. Leyland AH Goldstein H Multilevel modelling of health statistics 2001 Hoboken Wiley
Leyland AH, Goldstein H. Multilevel modelling of health statistics. Hoboken: Wiley; 2001.
