Sage Journals: Discover world-class research

Abstract

Background:

Thrombocytopenia is the most frequent hemostatic abnormality in sepsis and is closely linked to excess mortality.

Methods:

We undertook a retrospective cohort study of 82 septic patients admitted to Changzhou Second People's Hospital between 1 January 2023 and 30 June 2024. Participants were stratified by 28-day survival status. The clinical differences, platelet trends, prognostic value, and risk-factor analysis were analyzed between the two groups.

Results:

Survivors and non-survivors differed significantly in age, oxygenation index, white-blood-cell count, serum albumin, cholinesterase and in the prevalence of diabetes and hypertension (all comparisons P < .05). Platelet counts measured on intensive care unit (ICU) days 1, 3, 7 and 14 were assessed for their prognostic value. By day 14, platelet counts were markedly higher in survivors than in non-survivors (P < .05). The day-14 platelet count yielded an area under the receiver-operating-characteristic (ROC) curve (AUC) of 0.640, outperforming the day-1 platelet count (AUC 0.514), APACHE II score (AUC 0.488) and Sequential Organ Failure Assessment (SOFA) score (AUC 0.394; all comparisons P < .05). Kaplan–Meier analysis showed that patients whose day-14 platelet count was ≥ 224 × 10⁹ L⁻¹ had significantly better 28-day survival than those below this threshold (P < .05). Platelet count < 224 × 10⁹ L⁻¹ in day 14 was an independent predictor of 28-day mortality (P < .05).

Conclusion:

The survival group shares high platelet count and maintaining platelet count at a higher level is associated with improving the 28-day prognosis of sepsis patients.

Keywords

sepsis thrombocytopenia platelet trend 28-day mortality ICU

Introduction

Sepsis is life-threatening organ dysfunction resulting from a dysregulated host response to infection and carries a persistently high mortality rate.¹ Early lactate dynamics could predict 48-h mortality in adults with sepsis.² Clinicians commonly gauge prognosis with the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores. Yet both scores incorporate subjective clinical judgments, which can compromise accuracy. Identifying a more objective prognostic marker is therefore essential.

Beyond their classical functions in haemostasis and thrombosis, platelets play pivotal roles in sepsis.³ Peng et al reported a 36.2% mortality in septic patients with thrombocytopenia—significantly higher than in those without (P < .001).⁴ Emerging evidence indicates that platelets are among the first cells to react to the hyper-inflammatory and pro-coagulant milieu of sepsis.⁵ Triggered by the coagulation cascade, inflammatory mediators and endothelial injury, platelets become activated,⁶ Critical to this defense is the platelet–neutrophil interaction, which augments neutrophil antibacterial activity.^7,8 Excessive platelet activation, however, can tip the balance toward coagulopathy and disseminated intravascular coagulation (DIC). Septic DIC fosters widespread microthrombi and bleeding, culminating in multiorgan failure.⁹ Given this dual nature, dynamic platelet changes are closely linked to sepsis outcomes, making platelet monitoring a key consideration in clinical management.

To clarify this relationship, we performed a retrospective case–control study of 82 intensive care unit (ICU) patients with sepsis, analyzing how temporal fluctuations in platelet counts correlate with 28-day mortality and assessing the prognostic utility of platelet trends.

Method

Patients

We carried out a single-center, retrospective observational cohort study of adults with sepsis admitted to the ICU of Changzhou Second People's Hospital between 1 January 2023 and 30 June 2024. Sepsis was defined according to the Sepsis-3 criteria.¹ Eligible patients were ≥18 years old and remained in the ICU for at least 14 days were collected.

Exclusion criteria were: (i) receipt of chemotherapy or immunosuppressive therapy within the previous six months; (ii) drug-induced thrombocytopenia (eg, secondary to antibiotics or heparin); (iii) thrombocytopenia associated with disseminated intravascular coagulation-related bleeding; (iv) hypersplenism; (v) haemophagocytic syndrome; (vi) the index platelet measurement within 24 h after platelet transfusion; or (vii) discharged or transferred out within 14 days.

After applying these criteria, 82 patients were included in the final analysis. Consent and informed signature were obtained from all patients or their immediate family members. The study protocol was approved by the Ethics Committee of Nanjing Medical University (approval No. [2024]KY030-01) and conducted in accordance with the Declaration of Helsinki.

Data collection

For each patient, we documented the following information:

Demographic and baseline data—age, sex and underlying comorbidities.

Illness severity—Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores at ICU admission.

Laboratory results at admission—complete blood count, serum biochemistry, coagulation profile and arterial blood-gas analysis.

Serial platelet counts—on ICU days 1, 3, 7 and 14, and on the day of ICU discharge.

Microbiological and infectious work-up—viral screens, serum galactomannan and (1→3)-β-D-glucan assays, and bacterial cultures from sputum, blood, urine, wound secretions and drainage fluid.

Organ-support interventions—invasive mechanical ventilation, high-flow nasal cannula (HFNC) oxygen therapy and continuous renal replacement therapy (CRRT).

Statistical Analysis

All analyses were performed with SPSS version 16.0 (SPSS Inc., Chicago, IL, USA). Continuous variables were first assessed for normality with the Shapiro–Wilk test. Data are presented as mean ± standard deviation (SD) for normally distributed variables and as median (interquartile range, IQR) for non-normal variables. Between-group differences were examined with the independent-samples (two-tailed) t-test or the Mann–Whitney U-test, as appropriate. Within-patient changes in platelet counts over time were evaluated with the paired-samples t-test. Categorical variables were compared using the χ² test (or Fisher's exact test when indicated).

Independent predictors of 28-day mortality were identified by binary logistic regression. Prognostic performance of clinical variables was assessed with receiver-operating-characteristic (ROC) analysis, and discriminatory power was quantified by the area under the ROC curve (AUC). The optimal platelet cutoff on ICU-day 14 was determined by maximizing the Youden index. Survival according to this cutoff was visualized with Kaplan–Meier curves and compared with the log-rank test. All P-values were two-sided, and values <.05 were considered statistically significant (*P < 0.05).

Result

Comparison of Baseline Characteristics Between 28-day Survivors and non-Survivors

Table 1 summarizes the clinical differences between patients who survived to day 28 and those who did not. There were no significant between-group differences in sex distribution, procalcitonin (PCT), C-reactive protein (CRP), platelet count on admission, APACHE II score, SOFA score, or in the distribution of infecting pathogens and infection sites (all P > .05).

Table 1.

Comparison Between the Sepsis Patients Who Died or Survived at 28 Days.

Variables	Survivor (n = 48)	Non-Survivor (n = 34)	P-Value
Age(year)	72 (61.25, 79.50)	78 (69.75, 84.50)	.011*
Male, n (%)	31 (64.6%)	21 (61.8%)	.794
APACHE II	22 ± 7	24 ± 6	.361
SOFA	7 ± 3	9 ± 3	.066
Septic Shock, n (%)	21 (43.8%)	21 (61.8%)	.108
Mechanical Ventilation, n (%)	32 (66.7%)	28 (82.4%)	.114
pH	7.44 (7.34, 7.48)	7.44 (7.35, 7.48)	.865
LAC	1.60 (1.30, 2.48)	1.95 (1.40, 4.10)	.399
PCT (ng/mL)	1.05 (0.39, 3.13)	0.99 (0.33, 6.99)	.829
CRP	59.35 (11.04, 146.80)	86.14 (32.75, 132.85)	.197
Oxygenation Index	202.43 (150.63, 307.32)	165.00 (111.62, 241.99)	.031*
MAP (mm Hg)	92.60 ± 18.86	91.15 ± 18.44	.73
White blood cell (×10⁹/L)	13.35 (9.26, 18.23)	10.37 (7.93, 13.03)	.025*
Platelet count (×10⁹/L)	169 (141, 264)	184 (124, 236)	.832
BUN (mmol/L)	10.60 (6.80, 16.98)	12.35 (7.73, 17.45)	.457
Creatinine (μmol/L)	100.35 (64.90, 172.50)	97.40 (65.25, 181.83)	.959
Total bilirubin (μmol/L)	11.90 (8.85, 21.30)	16.60 (11.30, 22.93)	.092
Serum albumin (g/L)	32.39 ± 5.84	28.38 ± 4.79	.002*
Cholinesterase (U/L)	4640.00 (3348.25, 6427.25)	3580.50 (2811.50, 4362.50)	.004*
Infection pathogens (%)
Klebsiella pneumoniae	20 (41.7%)	12 (35.3%)	.56
Pseudomonas aeruginosa	11 (22.9%)	6 (17.6%)	.562
Escherichia coli	7 (14.6%)	7 (20.6%)	.476
Acinetobacter baumanii	13 (27.1%)	10 (29.4%)	.817
Staphylococcus aureus	7 (14.6%)	4 (11.8%)	.968
Streptococcus pneumonae	1 (2.1%)	1 (2.9%)	1
Enterococcus faecium	11 (22.9%)	7 (20.6%)	.802
Maltophilic bacteria	7 (14.6%)	1 (2.9%)	.17
Other Staphylococcus species	13 (27.1%)	5 (14.7%)	.182
Candida albicans	1 (2.1%)	1 (2.9%)	1
Aspergillus	1 (2.1%)	0 (0%)	1
COVID-19	1 (2.1%)	2 (5.9%)	.76
Infection sites (%)
Lung	43 (89.6%)	32 (94.1%)	.747
Abdominal cavity	4 (8.3%)	3 (8.8%)	1
Bloodstream	10 (20.8%)	9 (26.5%)	.551
Urinary system	13 (27.1%)	8 (23.5%)	.716
Skin and soft tissue	2 (4.2%)	1 (2.9%)	1
Basic diseases
Hypertension	26 (54.2%)	27 (79.4%)	.018*
Diabetes	15 (31.3%)	18 (52.9%)	.048*
Hyperlipidemia	12 (25.0%)	6 (17.6%)	.428
Coronary heart disease	9 (18.8%)	5 (14.7%)	.632
Heart failure	6 (12.5%)	5 (14.7%)	.773
Atrial fibrillation	5 (10.4%)	5 (14.7%)	.559
CKD	7 (14.6%)	9 (26.5%)	.181
COPD	2 (4.2%)	5 (14.7%)	.092
Tumour	7 (14.6%)	3 (8.8%)	.432

APACHE II, Acute Physiology and Chronic Health Evaluation II; SOFA, Sequential Organ Failure Assessment scores; LAC, Lactate; PCT, procalcitonin; CRP, C-reactive protein; MAP, mean arterial pressure; BUN, blood urea nitrogen; CKD, chronic kidney disease; COPD, Chronic Obstructive Pulmonary Disease. *P < 0.05.

Non-survivors were significantly older than survivors (median age 78 y vs 72 y; P = .011) and exhibited poorer oxygenation (median PaO₂/FiO₂ 165.00 mm Hg vs 202.43 mm Hg; P = .031). Survivors had higher admission white-blood-cell counts (median 10.37 × 10⁹ L⁻¹ vs 13.35 × 10⁹ L⁻¹; P = .025), serum albumin (mean 28.38 ± 4.79 g L⁻¹ vs 32.39 ± 5.84 g L⁻¹; P = .002) and cholinesterase activity (median 3580.50 U L⁻¹ vs 4640.00U L⁻¹; P = .004). The presence of hypertension (79.4% vs 54.2%; P = .018) or diabetes (52.9% vs 31.3%; P = .048) was associated with higher mortality.

Platelet Trends and 28-day Outcome

Serial platelet counts were obtained on ICU days 1, 3, 7 and 14, and on the day of hospital discharge. Day-14 and discharge platelet values were significantly higher in survivors than in non-survivors (both P < .05; Figure 1A). Within the survivor cohort, platelet counts rose steadily from day 1 to day 14 and remained elevated at discharge (both vs day 1, P < .05; Figure 1B). By contrast, platelet counts in the non-survivor cohort declined over the same interval (both vs day 1, P < .05; Figure 1C).

Figure 1.

Analysis of the changes of platelet counts at different time in the survivor group and the non-survivor group (*P < 0.05). (A) Compare platelet counts at different time between the survivor group and the non-survivor group. (B) Compare the changes in platelets at different time points in the survivor group. (C) Compare the changes in platelets at different time points in the non-survivor group. PLT1, platelet count at day1; PLT14, platelet count at day14.

Prognostic Value of the day-14 Platelet Count

Admission PCT levels correlated inversely with platelet counts (Spearman r = −0.364, P < .05; Figure 2A). Receiver-operating-characteristic analysis confirmed that the day-14 platelet counts best discriminated 28-day survivors from non-survivors (AUC = 0.640), outperforming the day-1 platelet count (AUC = 0.514), APACHE II score (AUC = 0.448) and SOFA score (AUC = 0.394; Figure 2B). The sensitivity and specificity of the day-14 platelet counts at the optimal cutoff value of 224 × 10⁹ L⁻¹ were 58.3% and 70.6%, respectively, with a Youden index of 0.289. Patients were therefore stratified into a high-platelet group (≥224 × 10⁹ L⁻¹) and a low-platelet group (<224 × 10⁹ L⁻¹). Kaplan–Meier analysis showed that patients in the high-platelet group had a significantly greater probability of 28-day survival than those in the low-platelet group (log-rank P < .05; Figure 2C).

Figure 2.

Analysis platelet count at the 14^th day on 28-day prognosis of sepsis patients. (A) Collection analysis was conducted to analyze the correlation between PCT AND platelet count in day14. (B) Receiver operating characteristic (ROC) curves were conducted to analyze the 28-day prognostic value. (C) Kaplan-Meier survival curve was conducted to analyze the overall survival curve. PLT1, platelet count at day1; PLT14, platelet count at day14; PCT14, PCT at day14.

Risk-Factor Analysis

To explore independent predictors of 28-day mortality, we entered APACHE II score, SOFA score, low-platelet group in day14 (<224 × 10⁹ L⁻¹), the presence of septic shock and mechanical ventilation into a binary logistic-regression model. Among these variables, low-platelet group in day14 showed the strongest association with outcome (P = .011), whereas APACHE II, SOFA, the presence of septic shock and mechanical ventilation all had higher P-values (Table 2).

Table 2.

Risk Factor Analysis of the Sepsis Patients.

Index	β	P	OR	95%CI
APACHE II	0.022	.603	1.022	(0.941～1.111)
SOFA	0.030	.774	1.031	(0.840, 1.265)
Low-platelet group in day14 (<224 × 10⁹ L⁻¹)	1.326	.011*	3.764	(1.356～10.453)
Septic Shock	0.565	.277	1.759	(0.635～4.873)
Mechanical Ventilation	0.832	.176	2.298	(0.689～7.670)

*P < 0.05.

Discussion

Sepsis triggers a complex cascade of systemic inflammation, immune dysregulation, coagulation abnormalities and tissue injury, resulting in prolonged critical illness and persistently high mortality. Although composite scores such as APACHE II and SOFA are routinely employed, an objective, readily available biomarker that reliably refines prognosis is still lacking. Daily complete blood counts are standard practice in the ICU, making platelet trends an attractive candidate for real-time risk stratification.

Our study demonstrates that the platelet count on ICU-day 14 was associated with improved 28-day outcomes. Patients whose counts rebounded to ≥224 × 10⁹ L⁻¹ by day 14 had a markedly higher survival probability, whereas failure of platelet recovery portended death. This observation supports the notion that persistent thrombocytopenia reflects ongoing bone-marrow suppression, sustained coagulation activation and unresolved inflammation—all hallmarks of a malignant septic trajectory.

Platelets can be activated in septic patients through various mechanisms including the excessive formation of thrombin,¹⁰ extensive exposure of collagen with upregulation of expression of von Willebrand factor and tissue factor on activated endothelial cells,¹¹ as well as C1q binding to its receptor on platelets.¹² The activation of platelets results in the release of many bio-active molecules. These have broad interactions with both the coagulation and immune systems, and are involved in sepsis-induced coagulation disorders and inflammatory dysfunction during sepsis.¹³ In addition, platelet can be activated by engagement of TLR4 in a non-canonical manner, and promotes pro-thrombotic, procoagulant responses, and immune responses.¹⁴ Beyond haemostasis, platelets orchestrate host defense by recruiting leukocytes to infected tissues and enhancing their microbicidal activity, ultimately facilitating pathogen clearance.^15–18 This exuberant activation, however, accelerates platelet consumption and correlates with greater disease severity.¹⁹ Bacteria release endotoxin to causes spurt of proinflammatory markers, including high levels of tumor necrosis factor-α, interleukin (IL)-1β, IL-6, and IL-8, which aggregate platelets and lead to a decrease in platelet count.^20,21 Antiplatelet autoantibodies can also be detected in some sepsis patients which will deplete platelets.²² Besides these, multiple articles have described several other causes of thrombocytopenia in sepsis, different from sepsis mechanism like hypersplenism, marrow failure, heparin-induced thrombocytopenia, drug-induced thrombocytopenia, and hemodilution.^23–25 Platelet indices such as mean platelet volume (MPV), platelet distribution width (PDW), and platelet to lymphocyte ratio (PLR) could provide prognostic or diagnostic advantages in thrombo-inflammatory disease.²⁶

We enrolled sepsis patients who remained in the ICU for >14 days and stratified them according to 28-day survival. Although disease severity is commonly gauged with the APACHE II and SOFA scores, neither score differed significantly between survivors and non-survivors (Table 1), nor did the admission platelet count. Thus, among patients who required at least two weeks of critical care, initial illness severity was broadly comparable. Between the two groups, we found that differences existed in age, Oxygenation Index, white blood cell count, albumin level, cholinesterase level and basic diseases like hypertension and diabetes. However, these was no difference in types of bacteria and infection sites. We found that patients with a decrease in platelet count on the 14th day usually share a poor prognosis. One study from Zou et al found that decreased platelet-count percentage ≤10% on day 4 after ICU admission was associated with lower probability of 30-day non-survival.²⁷ Nijsten et al calculated platelet proportions 10 days after ICU admission and found that the value was more than five times higher in survivors compared with non-survivors.²⁸ Obviously, continuous monitoring of platelet count is necessary. However, the optimal time for prognosis remains controversial. From our study, we found that for patients who cannot maintain a high level of platelet count, especially on the 14^th day after entering the ICU, the prognosis of these patients is poor. Gurung et al found that at least one platelet count greater than 450 × 10⁹/L was associated with lower ICU mortality, lower hospital mortality (P = .006), but longer duration of ICU stay.²⁹ Collectively, these findings suggest that preserving—or restoring—platelet counts to at least 224 × 10⁹/L by day 14 was associated with improved outcomes in sepsis. At present, we increase platelet count via platelet transfusion, intravenous immunoglobulin (IVIG), recombinant human thrombopoietin (rh-TPO), and administration of platelet-elevating drugs.

This study had several limitations. The number of patients in this study is relatively small, and it is a single-center study which only reflect the treatment situation of our hospital. In order to identify patients with ineffective platelet transfusion and avoid the occurrence of false positives, we excluded the index platelet measurement within 24 h after platelet transfusion. Further analysis is needed regarding the impact of platelet transfusion on prognosis. Patients with early mortality or rapid improvement were systematically excluded because data collection was difficult. Some patients was transferred to other department, the therapeutic regimens were changed. The AUC of the day-14 platelet counts was 0.640 which sensitivity and specificity were moderate. We wondered underlying comorbidities, infection source, and treatment strategies could affect the results.

In conclusion, the survivor group shares higher platelet count than the non-survivor group in sepsis patients. Dynamically monitoring platelet count and higher platelet recovery are associated with improved outcomes of sepsis patients.

Footnotes

Acknowledgments

So many thanks to Jiaqian Qi, PhD., a doctor and a statistician, for statistical aid.

ORCID iD

Rui Zhang

Ethics Approval and Consent to Participate

Informed consent was obtained from all patients or their immediate family members. All protocols are conformed to the guidelines with the ethic committee of Nanjing Medical University and in accordance with the Declaration of Helsinki (Ethics Number: [2024]KY030-01).

Authors’ Contributions

Rui Zhang designed and performed research studies, analyzed the data, and wrote the manuscript. Wenmin Han analyzed the data and wrote the manuscript. Fanfeng Kong and Dongwei Shi add to the collection and analysis of clinical data. Yi Zhou and Gang Zhao contributed to the data analysis and manuscript writing. Tongrong Xu contributed to the research design, data analysis, writing the manuscript, and supervision of the study.

Consent for Publication

Written informed consent for publication was obtained from all participants.

Funding

This work was supported by Technology Project of Changzhou Municipal Health Commission (QN202222), Technology Project of Changzhou Municipal Health Commission (QN202228), Science and Technology Projects of Changzhou City (CJ20220249), and Changzhou Sci & Tech Program Grant No. CJ20245022.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of Data and Materials

All the data and materials are available if necessary.

References

Singer

Deutschman

Seymour

, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–810. E-pub ahead of print 2016/02/24. doi:10.1001/jama.2016.0287

Senguldur

Demir

. Early lactate dynamics for short-term mortality in emergency department sepsis. BMC Emerg Med. 2025;25(1):213. E-pub ahead of print 2025/10/24. doi:10.1186/s12873-025-01391-w

Garcia

Compagnon

Poette

, et al.

Platelet versus megakaryocyte: who is the real bandleader of thromboinflammation in sepsis?

Cells. 2022;11(9):1507. E-pub ahead of print 2022/05/15. doi:10.3390/cells11091507.

Fan

, et al. Platelet desialylation is a novel mechanism and a therapeutic target in thrombocytopenia during sepsis: an open-label, multicenter, randomized controlled trial. J Hematol Oncol. 2017;10(1):104. E-pub ahead of print 2017/05/13. doi:10.1186/s13045-017-0476-1

Wang

Tao

Dang

Yang

. The role of platelets in sepsis: a review. Biomol Biomed. 2024;24(4):741–752. E-pub ahead of print 2024/01/18. doi:10.17305/bb.2023.10135

Vardon-Bounes

Ruiz

Gratacap

Garcia

Payrastre

Minville

. Platelets are critical key players in sepsis. Int J Mol Sci. 2019;20(14):3494. E-pub ahead of print 2019/07/19. doi:10.3390/ijms20143494

Assinger

Laky

Schabbauer

, et al. Efficient phagocytosis of periodontopathogens by neutrophils requires plasma factors, platelets and TLR2. J Thromb Haemost. 2011;9(4):799–809. E-pub ahead of print 2011/01/22. doi:10.1111/j.1538-7836.2011.04193.x

Miyabe

Sakamoto

Mori

Sakamoto

. Effects of platelet release products on neutrophilic phagocytosis and complement receptors. Thromb Res. 2004;114(1):29–36. E-pub ahead of print 2004/07/21. doi:10.1016/j.thromres.2004.04.003

Levi

Schultz

van der Poll

. Sepsis and thrombosis. Semin Thromb Hemost. 2013;39(05):559–566. E-pub ahead of print 2013/04/30. doi:10.1055/s-0033-1343894

10.

Schouten

Wiersinga

Levi

van der Poll

. Inflammation, endothelium, and coagulation in sepsis. J Leukoc Biol. 2008;83(3):536–545. E-pub ahead of print 2007/11/23. doi:10.1189/jlb.0607373

11.

Linden

. Platelet physiology. Methods Mol Biol. 2013;992:13–30. E-pub ahead of print 2013/04/03. doi:10.1007/978-1-62703-339-8_2

12.

Peerschke

Reid

Ghebrehiwet

. Platelet activation by C1q results in the induction of alpha IIb/beta 3 integrins (GPIIb-IIIa) and the expression of P-selectin and procoagulant activity. J Exp Med. 1993;178(2):579–587. E-pub ahead of print 1993/08/01. doi:10.1084/jem.178.2.579

13.

Deng

Neal

Billiar

Scott

. Platelet-monocyte aggregates: understanding mechanisms and functions in sepsis. Shock. 2021;55(2):156–166. E-pub ahead of print 2020/07/23. doi:10.1097/SHK.0000000000001619

14.

Sanderson

Fox

Robbins

Losche

Spangenberg

Heptinstall

. Role of GPIIb-IIIa in platelet-monocyte and platelet-neutrophil conjugate formation in whole blood. Platelets. 1998;9(3-4):245–250. E-pub ahead of print 2006/06/24. doi:10.1080/09537109876780

15.

Yang

Dunn

Gross

Smyth

. Platelets as immune mediators: their role in host defense responses and sepsis. Thromb Res. 2011;127(3):184–188. E-pub ahead of print 2010/11/16. doi:10.1016/j.thromres.2010.10.010

16.

Lipinska-Gediga

. Platelets in sepsis - are there any new aspects? Anaesthesiol Intensive Ther. 2017;49(2):167–172. E-pub ahead of print 2017/05/18. doi:10.5603/AIT.a2017.0025

17.

Nurden

. The biology of the platelet with special reference to inflammation, wound healing and immunity. Front Biosci (Landmark Ed). 2018;23(4):726–751. E-pub ahead of print 2017/09/21. doi:10.2741/4613

18.

Rayes

Bourne

Brill

Watson

. The dual role of platelet-innate immune cell interactions in thrombo-inflammation. Res Pract Thromb Haemost. 2020;4(1):23–35. E-pub ahead of print 2020/01/29. doi:10.1002/rth2.12266

19.

Ghimire

Ravi

Budhathoki

, et al. Current understanding and future implications of sepsis-induced thrombocytopenia. Eur J Haematol. 2021;106(3):301–305. E-pub ahead of print 2020/11/17. doi:10.1111/ejh.13549

20.

Johansson

Rasmussen

Inghammar

. Thrombocytopenia in bacteraemia and association with bacterial species. Epidemiol Infect. 2018;146(10):1312–1317. E-pub ahead of print 2018/05/16. doi:10.1017/S0950268818001206

21.

Gardlund

Sjolin

Nilsson

Roll

Wickerts

Wretlind

. Plasma levels of cytokines in primary septic shock in humans: correlation with disease severity. J Infect Dis. 1995;172(1):296–301. E-pub ahead of print 1995/07/01. doi:10.1093/infdis/172.1.296

22.

Stephan

Cheffi

Kaplan

, et al. Autoantibodies against platelet glycoproteins in critically ill patients with thrombocytopenia. Am J Med. 2000;108(7):554–560. E-pub ahead of print 2000/05/12. doi:10.1016/s0002-9343(00)00332-6

23.

Wang

Aguilera

Knopf

Chen

Maslove

Kuschner

. Thrombocytopenia in the intensive care unit. J Intensive Care Med. 2013;28(5):268–280. E-pub ahead of print 2012/01/11. doi:10.1177/0885066611431551

24.

Hunt

. Bleeding and coagulopathies in critical care. N Engl J Med. 2014;370(9):847–859. E-pub ahead of print 2014/02/28. doi:10.1056/NEJMra1208626

25.

Stephan

Hollande

Richard

Cheffi

Maier-Redelsperger

Flahault

. Thrombocytopenia in a surgical ICU. Chest. 1999;115(5):1363–1370. E-pub ahead of print 1999/05/20. doi:10.1378/chest.115.5.1363

26.

Goze

Senguldur

. Diagnostic value of platelet indices in pulmonary thromboembolism: a retrospective analysis. Clin Appl Thromb Hemost. 2025;31:10760296251388987. E-pub ahead of print 2025/10/16. doi:10.1177/10760296251388987

27.

Liu

Yin

Qin

Zou

. Association between minimal decrease in platelet counts and outcomes in septic patients: a retrospective observational study. BMJ Open. 2023;13(4):e069027. E-pub ahead of print 2023/05/15. doi:10.1136/bmjopen-2022-069027

28.

Nijsten

ten Duis

Zijlstra

, et al. Blunted rise in platelet count in critically ill patients is associated with worse outcome. Crit Care Med. 2000;28(12):3843–3846. E-pub ahead of print 2001/01/12. doi:10.1097/00003246-200012000-00017

29.

Gurung

Carr

Smith

. Thrombocytosis in intensive care. Br J Anaesth. 2001;87(6):926–928. E-pub ahead of print 2002/03/07. doi:10.1093/bja/87.6.926.

Dynamic 14-day Platelet Count Predicts 28-day Mortality in Sepsis: A Single-Center Retrospective Cohort Study

Abstract

Background:

Methods:

Results:

Conclusion:

Keywords

Introduction

Method

Patients

Statistical Analysis

Result

Comparison of Baseline Characteristics Between 28-day Survivors and non-Survivors

Platelet Trends and 28-day Outcome

Prognostic Value of the day-14 Platelet Count

Risk-Factor Analysis

Discussion

Footnotes

Acknowledgments

ORCID iD

Ethics Approval and Consent to Participate

Authors’ Contributions

Consent for Publication

Funding

Declaration of Conflicting Interests

Availability of Data and Materials

References