You are here

  • Home
  • Archive
  • Volume 13, Issue 4
  • Association between minimal decrease in platelet counts and outcomes in septic patients: a retrospective observational study

Article Text

Article menu
Intensive care
Original research
Association between minimal decrease in platelet counts and outcomes in septic patients: a retrospective observational study
  1. Xing Liu1,
  2. Wanhong Yin1,
  3. Yi Li1,
  4. Yiwei Qin2,
  5. Tongjuan Zou1
  1. 1Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, China
  2. 2Department of Intensive Medicine, Chengdu Medical College, The First Affiliated Hospital, Chengdu, Sichuan, China
  1. Correspondence to Dr Wanhong Yin; yinwanhong{at}wchscu.cn

Abstract

Objectives Although platelets have been linked to inflammatory development in sepsis, knowledge on their role as an indicator in sepsis treatment is scarce. Here, we investigated the association between time-dependent changes in platelet counts with mortality rates to reveal the role of platelets in sepsis therapy.

Design A retrospective cohort study.

Setting We screened the Medical Information Mart for Intensive Care (MIMIC-IV), a public database comprising data from critical care subjects at the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA.

Participants A total of 7981 patients, who were admitted to the BIDMC between 2008 and 2019, were analysed based on Sepsis-3 criteria from MIMIC-IV.

Primary and secondary outcome measures Primary and secondary outcomes included 30-day mortality after admission and length of intensive care unit (ICU) stay and hospitalisation, respectively.

Results Patients with ≤10% reduction in proportion of platelet counts were associated with significantly lower 30-day mortality (14.1% vs 23.5%, p<0.001, Kaplan-Meier analysis, p<0.0001). Multivariable analysis revealed that decreased platelet-count percentage ≤10% on day 4 after ICU admission was associated with lower probability of 30-day non-survival (OR=0.73, 95% CI 0.64 to 0.82, p＜0.001). Patients in the ≤10% group had significantly shorter ICU stays than those in the >10% group (6.8 vs 7.5, p＜0.001). Restricted cubic spline curves revealed that mortality rates decreased with increase in proportion of platelet counts.

Conclusions A ≤10% decrease in platelet-count percentage among sepsis patients after treatments is independently associated with decreased 30-day mortality, suggesting that changes in proportion of platelet counts after treatments could be an indicator for assessing the therapeutic effects of sepsis.

  • intensive & critical care
  • adult intensive & critical care
  • infectious diseases

Data availability statement

Data are available upon reasonable request. The data used in this study can be obtained by the corresponding author upon request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2022-069027

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    STRENGTHS AND LIMITATIONS OF THIS STUDY

    • This retrospective study employed a large sample size, from the high-quality Medical Information Mart for Intensive Care database, which increases the credibility of the findings.

    • We employed the restricted cubic spline curve model to reveal the association between rates of platelet count change and 30-day mortality in septic patients.

    • There was a small amount of missing data, which was handled by multiple imputation using classification and regression trees.

    • We did not evaluate differences in long-term outcomes between groups due to a lack of long-term follow-up data in the targeted database.

    Introduction

    Sepsis is a life-threatening condition that requires acute treatment. The associated high incidence and mortality as well as poor patient prognosis have made it a primary global health concern. In America, sepsis does not only comprise one of the highest expenses but also causes numerous deaths among hospitalised patients.1 2 Estimates from some high-income nations indicate that about 50.9 million patients develop sepsis every year, of which 5.3 million die annually due to the associated complications.3

    Studies have shown that sepsis is characterised by multiple organ failure, which subsequently endanger life due to dysregulated host response to infections.4 Sepsis is a complicated pathophysiological process in which a pathogen triggers a person’s inflammatory-immune response, thereby leading to activation or repression of various facets, including endothelium, coagulopathy, immunological and hormonal functions. Endothelial damage, inflammatory pathways and coagulation synergise to activate platelets in sepsis, which are crucial for pathogenic defence. Platelets not only possess unambiguous structures but also play crucial functions in host defence, including regulating expression of toll-like receptors that detect hallmark signals of bacterial infection, an array of microbicidal peptides, as well as other host defence molecules and functions.5–7 Some studies have shown that platelets can exert a bactericidal effect by releasing platelet antimicrobial peptides,8 while others have demonstrated that platelet hyperreactivity could contribute to sepsis complications, such as acute respiratory distress syndrome, disseminated intravascular coagulation, acute kidney injury and septic cardiomyopathy.9 Moreover, a recent study revealed that sepsis induces platelet transcription and translation, while circulating platelets exhibited higher levels of integrin subunit αIIb (ITGA2B), which is linked to higher mortality.10

    Platelet counts have been used as critical markers for sequential organ failure assessment (ie, Sofa Score) in patients with sepsis, and effectively reflect patient prognosis.11 Numerous studies have demonstrated that thrombocytopenia is correlated with poor prognosis.12–14 Moreover, Mavrommatis et al 15 found that a lower platelet count was associated with more severe sepsis incidence. Consequently, researchers have hypothesised that thrombocytopenia patients could benefit from platelet-elevating medications to improve sepsis prognosis.16 One study targeted recombinant human thrombopoietin and found that sepsis with thrombocytopenia could effectively promote platelet counts in patients, thereby resulting in shorter stays in the intensive care unit (ICU).17 Other trials investigating the efficacy of antiplatelet drugs in sepsis subjects showed that they reduce undesirable thrombosis, inflammatory host responses and organ damage.18 19 To date, however, the potential for platelets as an indicator in evaluation of the effects of sepsis treatments remains unknown. Therefore, this study aimed to retrospectively analyse the relationship between changes in proportion of platelet counts after treatments with clinical outcomes of patients with sepsis.

    Materials and methods

    Data sources

    This study was conducted in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology guidelines.20 Data were retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV V.1.0),21 which comprises clinical data from a custom hospital-wide electronic health record and an ICU-specific clinical information system for more than 380 000 patients who were admitted to the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA, between 2008 and 2019. The database includes detailed information on patient demographics, laboratory tests, medication use, vital signs and disease diagnosis, among others. It also contains records for patients admitted to the BIDMC emergency department or the ICUs, with clearly defined data standards. Patient records were fully anonymised, and data collection was following approval by the BIDMC and Massachusetts Institute of Technology Institutional Review Board. We first underwent training on the programme and passed the Collaborative Institutional Training Initiative test, before we were eligible to receive free access to the database. Thereafter, we conducted the related research in accordance with the rules. The author (XL) passed certification for the Collaborative Institutional Training Initiative (Certification Number 48605954).

    Patient and public involvement

    Neither patients nor members of the public were involved in any part of this study.

    Selection criteria

    Data were included in the study if the patients met the following criteria: (1) were diagnosed with sepsis, according to Sepsis-3 standard;4 (2) were adults, aged 18 years and above and (3) their ICU stay was >72 hours. For patients with records showing multiple ICU stays and admissions, only data involving the first ICU stay and admission were included. We excluded data for patients diagnosed with cirrhosis, lymphoma and taking clopidogrel, aspirin, rivaroxaban and warfarin. In addition, we did not consider patients with prednisone while they were transferred to the ICU and also excluded data sets with missing data for day 1 and day 4 platelet counts.

    Data extraction

    Data extraction from the MIMIC-IV database was achieved using PostgreSQL. Platelet counts, recorded on the first and fourth day after admission to the ICU, were extracted from MIMIC-IV. Differences in platelet counts were calculated using the formula: (platelet countsday4−platelet countsday1)/platelet countsday1×100%. The variables at day 1 of ICU admission included age, gender, weight, ethnicity, chronic diseases, sofa score, acute physiology score (aps iii), simplified acute physiology score (saps ii), prothrombin time (pt), activated partial thromboplastin time (aptt) and white cell counts. These characteristics served as possible confounders in this study.

    Outcomes

    The primary endpoint was 30-day mortality after admission, whereas length of hospitalisation and ICU stay were considered secondary outcomes.

    Missing values

    All variables in this study had less than 11% missing values (online supplemental table S1). We employed classification and regression trees22 23 for multiple imputation of the missing values for variables, including weight, pt, aptt and white cell counts.

    Statistical analysis

    The percentage change in platelet counts was recorded on day 4 following ICU admission. We generated receiver operating characteristic curve (ROC) to calculate the cut-off of the platelet-count percentage, which was subsequently employed to categorise patients in the baseline characteristics table. For computational simplicity, the threshold for the ROC was −9.5% (almost equivalent to −10%). Statistical significance was defined as two-sided p values <0.05. Furthermore, we used a multivariate logistic regression analysis to assess the relationship between the proportion of platelet counts and 30-day mortality.

    Next, we applied the variables listed in online supplemental table S2 to identify potential confounding variables for logistic regression. These factors were incorporated into the multivariate regression model, as adjusting variables, at a p value of less than 0.05. We generated Kaplan-Meier (KM) curves to visualise the survival curves and compare changes in proportion of platelet counts over 30 days, while the log-rank test was used for survival analysis. Restricted cubic spline curves were generated using logistic models and used to evaluate the relationship between proportion of platelet counts and the principal endpoint.

    Data with normal distribution were presented as means and respective SD, whereas non-normally distributed variables were presented as medians and IQRs. Normally and non-normally distributed continuous variables were compared between groups using analysis of variance and Kruskal-Wallis test, respectively. For subgroup analysis, variables were subclassified into age, gender, sofa score, pt, aptt and white cell count categories, then forest plots generated to depict the relationship between proportion of platelet counts and mortality rates. All statistical analyses were performed using packages implemented in R software, V.4.2.1.

    Results

    The MIMIC-IV database contains data for 35 010 adult sepsis patients. A total of 7981 adult patients diagnosed with sepsis using Sepsis-3 criteria met our inclusion criteria and were included in the study (figure 1). All participants were classified into two groups, and basic characteristics are outlined in online supplemental table S2. Analysis of platelet-count percentages on day 4 showed that 3802 and 4179 sepsis patients had reduced platelet-count percentage of >10%, and ≤10%, respectively. Patients in the >10% group were significantly older (age 66.2 vs 65.3, p=0.012) and displayed markedly severe coagulation dysfunction (pt 14.6 vs 14.1, p<0.001 and aptt 33.0 vs 30.8, p<0.001) than their counterparts in the ≤10% group. Similarly, patients with >10% reduction in platelet-count percentage displayed significantly higher white cell counts (13.0 vs 11.3, p<0.001) and a markedly higher sofa score (8 vs 6, p<0.001) than those with ≤10%. The higher score in patients with >10% reduction in proportion of platelet counts might also be indicated in other organ dysfunction rating systems, such as aps iii and saps ii.

    Figure 1
    Figure 1

    Flowchart showing step-by-step selection on patients included in the study. ICU, intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care.

    Patients with ≤10% reduced proportion of platelet counts had significantly lower mortality rates within 30 days (14.1% (591) vs 23.5% (892), p<0.001) and significantly shorter ICU stays (6.8 vs 7.5, p<0.001) than their counterparts in the >10% group. However, we found no statistically significant differences between the groups with regards to the length of hospital stays (13.7 vs 14.1, p=0.122). KM curves showed that patients in the ≤10% group had significantly longer survival times within 30 days than those in the >10% group (mean survival time 26.4 vs 24.4 days; p<0.0001) (figure 2). Multivariable logistic regression model showed that reduced platelet-count percentage ≤10% was an independent predictor of reduction in mortality rates within 30 days (OR 0.73; 95% CI 0.64 to 0.82; p<0.001). In the modified analysis, we adjusted for confounders such as, age, saps ii score, sofa score, aps iii score and white cell counts (online supplemental table S3). Restricted cubic spline model showed that lower reduction in proportion of platelet counts on day 4 were associated with lower mortality rates within 30 days. Additionally, the model indicated that a greater proportion of platelet counts could predict a reduction in 30-day mortality for septic patients with one subset excluding reduction in the proportion of platelet counts >10% on day 4 after ICU admission, while the other having elevated proportion of platelet counts on day 4 after ICU admission (figure 3). Our findings were supported by different subanalysis in which one subset excluded no decline or even an increase in platelet counts on day 4 compared with day 1 after ICU admission, while the other subset excluded platelet count <100 k/μl on day 1 after ICU admission (online supplemental tables S4, S5 and figure S1, S2). Subgroup analysis revealed that age, gender, organ dysfunction status, infection level and coagulation functional condition were all stratified, a trend that was mirrored by a reduction in mortality within 30 days among patients in the ≤10% group (figure 4).

    Figure 2
    Figure 2

    Kaplan-Meier survival curves for the mortality within 30 days.

    Figure 3
    Figure 3

    The association between proportion of platelet counts (PPC) and mortality within 30 days was shown in restricted cubic spline curves (RCS) based on logistic models in the whole population and different subsets. Solid red lines are OR, with dashed black lines showing 95% CIs derived from restricted cubic spline regressions with four knots. Reference lines for no association are indicated by the dashed grey lines at an OR of 1.0. Violet density curves show the fraction of the population with different levels of the proportion of platelet counts. Refvalue indicates PPC improves mortality within 30 days. (A, B) The proportion of platelet counts was modelled as a continuous variable and fitted in an unadjusted and adjusted model using restricted cubic spline analysis in the whole population. (C, D) The proportion of platelet counts was modelled as a continuous variable and fitted in the adjusted models in the patients, with a decline of the proportion of platelet counts >10% excluded and an increasing proportion of platelet counts ≥8.99% included. Analysis was adjusted for age, cardiovascular disease, liver disease, renal disease, vascular disease, cancer, sofa, saps ii, aps iii, pt, aptt and white cell counts at baseline.

    Figure 4
    Figure 4

    Forest plot of subgroups. In different subgroups, the continuity variables including age, sofa score, pt, aptt and white cell counts (WBC) were stratified by the median.

    Discussion

    In the present study, we analysed data for 7981 patients with sepsis and found that a ≤10% decrease in proportion of platelet count on day 4 after ICU admission was associated with low mortality rates within 30 days. Subgroup analysis results corroborated these findings. Moreover, restricted cubic spline curves revealed that increased proportion of platelet counts was associated with reduced mortality rates within 30 days. These findings showed that change in proportion of platelet counts would be used as a reference to evaluate the effect of sepsis treatments.

    Platelets, anucleate cells, originate from mature megakaryocytes in the bone marrow. Previous studies have shown that platelets not only play a role in haemostasis but also in various other tasks, such as host defence against infection, including phagocytosis of bacteria and viruses, superoxide production and platelet-derived microbactericidal proteins.24 Notably, platelets have a series of surface receptors and adhesion molecules that allow them to interact with leucocytes and pathogens in the bloodstream, which is critical for the proinflammatory and chemotactic processes.6 When sepsis occurs, due to conditions such as infections, platelets are activated and directly interact with leucocytes in the blood.25–27 Through the interaction, circulating leucocytes can effectively exert anti-infection effects. Neutrophils may locate infection sites because of their interaction with platelets,28 while activated neutrophils produce and release neutrophil extracellular traps that subsequently capture and destroy infections.29

    Recent research has revealed that thrombocytopenia is associated with a poor patient prognosis. For example, Moreau et al 12 showed that a 30% fall in platelet counts was an independent predictor of mortality in both medical and surgical ICUs, while Nijsten et al30 demonstrated that a slow or lack of increase in platelet counts among surgical ICU patients was associated with higher mortality rates. The authors 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 (30±46×103/mm3/day vs 6±28×103/mm3/day, p<0.001). However, results from a retrospective analysis of sepsis patients with leucocytosis revealed a 6.9% increase in hospital mortality rates among patients in the thrombocytosis group, which was classified as having >500 000 platelets/L.31 To date, a handful of studies have evaluated the correlation between platelet counts and sepsis outcomes while the role of platelets as an indicator of therapeutic efficacy remains unknown.

    Platelets are not only often applied as clinical monitoring indices but also play a role in anti-infective responses.26 Nevertheless, few studies have employed platelets as a new inflammatory cell for clinical evaluation of inflammatory response in sepsis. Host response is the core pathomechanism of sepsis, and different pathogens causing sepsis may require various monitoring of the inflammatory response, such as procalcitonin and 1,3-β-d-glucan testing. Currently, platelets, as an inflammatory mediator, essentially respond to the sepsis host response process.8 Results of the present study showed that septic patients with minimal decrease of platelet counts on day 4 compared with day 1 had improved 30-day mortality. Day 4 is considered the ideal timeframe to evaluate the efficacy of sepsis treatment, and thus, changes in proportion of platelet counts on this day can serve as a reference for future evaluation of clinical efficacy and optimisation of treatment protocols. Additionally, thrombocytosis has potential as a standalone indicator of a favourable prognosis in ICU patients. A previous study found that patients with general and trauma ICU platelet counts of more than 450×109/L on at least one occasion were associated with lower ICU mortality (p=0.003).32 This outcome was consistent with our findings of research.

    This study had several limitations. First, considering that this was an observational study, we did not elucidate the underlying mechanism by which changes in platelet counts might affect sepsis outcomes, thus further studies are needed to clarify this. Second, our findings may not generalise to all critical care patients in the ICU because the eligible population was limited to septic patients. Third, our results lack a causal association because we only examined data from an extensive public retrospective database. In future, larger clinical trials are needed to compare changes in proportion of platelet counts and their effect in evaluating the therapeutic effect of sepsis. Finally, we did not explore the association between treatments and platelet levels owing to the observational nature of the study. However, by taking advantage of the large sample size of the public database, this study provides a reference for further prospective studies in sepsis.

    Conclusions

    In summary, reduction in proportion of platelet counts of ≤10% in sepsis patients after treatments is an independent predictor of improved mortality rates within 30 days. Meanwhile, this study found a downward trend in mortality within 30 days in sepsis patients as the platelet counts increased. Collectively, these findings provide new insights regarding the role of platelets in evaluating efficacy of sepsis treatments.

    Data availability statement

    Data are available upon reasonable request. The data used in this study can be obtained by the corresponding author upon request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study was in accordance with the ethical standards of the Declaration of Helsinki and was approved by the ethics review boards of Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center (researcher certification number 48605954). MIMIC-IV was retrospective with lack of patient intervention, and all patients’ data were de-identified; thus individual patient informed consent was not required.

    References

    1. ↵
      1. Liu V,
      2. Escobar GJ,
      3. Greene JD, et al
      . Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 2014;312:90–2. doi:10.1001/jama.2014.5804
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      1. Rudd KE,
      2. Johnson SC,
      3. Agesa KM, et al
      . Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the global burden of disease study. Lancet 2020;395:200–11. doi:10.1016/S0140-6736(19)32989-7
      OpenUrlCrossRefPubMed
    3. ↵
      1. Fleischmann C,
      2. Scherag A,
      3. Adhikari NKJ, et al
      . Assessment of global incidence and mortality of hospital-treated sepsis. current estimates and limitations. Am J Respir Crit Care Med 2016;193:259–72. doi:10.1164/rccm.201504-0781OC
      OpenUrlCrossRefPubMed
    4. ↵
      1. Singer M,
      2. Deutschman CS,
      3. Seymour CW, et al
      . The third International consensus definitions for sepsis and septic shock (sepsis-3). JAMA 2016;315:801–10. doi:10.1001/jama.2016.0287
      OpenUrlCrossRefPubMed
    5. ↵
      1. Yeaman MR
      . Platelets in defense against bacterial pathogens. Cell Mol Life Sci 2010;67:525–44. doi:10.1007/s00018-009-0210-4
      OpenUrlCrossRefPubMed
    6. ↵
      1. Semple JW,
      2. Italiano Jr JE,
      3. Freedman J
      . Platelets and the immune continuum. Nat Rev Immunol 2011;11:264–74. doi:10.1038/nri2956
      OpenUrlCrossRefPubMed
    7. ↵
      1. Schouten M,
      2. Wiersinga WJ,
      3. Levi M, et al
      . Inflammation, endothelium, and coagulation in sepsis. J Leukoc Biol 2008;83:536–45. doi:10.1189/jlb.0607373
      OpenUrlCrossRefPubMedWeb of Science
    8. ↵
      1. de Stoppelaar SF,
      2. van ’t Veer C,
      3. van der Poll T
      . The role of platelets in sepsis. Thromb Haemost 2014;112:666–77. doi:10.1160/TH14-02-0126
      OpenUrlCrossRefPubMed
    9. ↵
      1. Greco E,
      2. Lupia E,
      3. Bosco O, et al
      . Platelets and multi-organ failure in sepsis. Int J Mol Sci 2017;18:2200. doi:10.3390/ijms18102200
    10. ↵
      1. Middleton EA,
      2. Rowley JW,
      3. Campbell RA, et al
      . Sepsis alters the transcriptional and translational landscape of human and murine platelets. Blood 2019;134:911–23. doi:10.1182/blood.2019000067
      OpenUrlAbstract/FREE Full Text
    11. ↵
      1. Vincent JL,
      2. Moreno R,
      3. Takala J, et al
      . The sofa (sepsis-related organ failure assessment) score to describe organ dysfunction/failure. on behalf of the Working group on sepsis-related problems of the European Society of intensive care medicine. Intensive Care Med 1996;22:707–10. doi:10.1007/BF01709751
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      1. Moreau D,
      2. Timsit JF,
      3. Vesin A, et al
      . Platelet count decline: an early prognostic marker in critically ill patients with prolonged ICU stays. Chest 2007;131:1735–41. doi:10.1378/chest.06-2233
      OpenUrlCrossRefPubMedWeb of Science
    13. ↵
      1. Akca S,
      2. Haji-Michael P,
      3. de Mendonça A, et al
      . Time course of platelet counts in critically ill patients. Crit Care Med 2002;30:753–6. doi:10.1097/00003246-200204000-00005
      OpenUrlCrossRefPubMedWeb of Science
    14. ↵
      1. Jacoby RC,
      2. Owings JT,
      3. Holmes J, et al
      . Platelet activation and function after trauma. J Trauma 2001;51:639–47. doi:10.1097/00005373-200110000-00003
      OpenUrlCrossRefPubMedWeb of Science
    15. ↵
      1. Mavrommatis AC,
      2. Theodoridis T,
      3. Orfanidou A, et al
      . Coagulation system and platelets are fully activated in uncomplicated sepsis. Crit Care Med 2000;28:451–7. doi:10.1097/00003246-200002000-00027
      OpenUrlCrossRefPubMedWeb of Science
    16. ↵
      1. Zhou Z,
      2. Feng T,
      3. Xie Y, et al
      . The effect of recombinant human thrombopoietin (rhTPO) on sepsis patients with acute severe thrombocytopenia: a study protocol for a multicentre randomised controlled trial (RESCUE trial). BMC Infect Dis 2019;19:780. doi:10.1186/s12879-019-4388-2
    17. ↵
      1. Liu Y,
      2. Jin G,
      3. Sun J, et al
      . Recombinant human thrombopoietin in critically ill patients with sepsis-associated thrombocytopenia: a clinical study. Int J Infect Dis 2020;98:144–9. doi:10.1016/j.ijid.2020.06.045
      OpenUrlPubMed
    18. ↵
      1. Pu Q,
      2. Wiel E,
      3. Corseaux D, et al
      . Beneficial effect of glycoprotein IIb/IIIa inhibitor (AZ-1) on endothelium in Escherichia coli endotoxin-induced shock. Crit Care Med 2001;29:1181–8. doi:10.1097/00003246-200106000-00019
      OpenUrlCrossRefPubMed
    19. ↵
      1. Valerio-Rojas JC,
      2. Jaffer IJ,
      3. Kor DJ, et al
      . Outcomes of severe sepsis and septic shock patients on chronic antiplatelet treatment: a historical cohort study. Crit Care Res Pract 2013;2013:1-9. doi:10.1155/2013/782573
    20. ↵
      1. von Elm E,
      2. Altman DG,
      3. Egger M, et al
      . The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg 2014;12:1495–9. doi:10.1016/j.ijsu.2014.07.013
      OpenUrlCrossRefPubMed
    21. ↵
      1. Johnson A,
      2. Bulgarelli L,
      3. Pollard T, et al
      . MIMIC-IV (version 1.0). 2021.
    22. ↵
      1. Zhang Z
      . Missing data imputation: focusing on single imputation. Ann Transl Med 2016;4:9. doi:10.3978/j.issn.2305-5839.2015.12.38
    23. ↵
      1. Speybroeck N
      . Classification and regression trees. Int J Public Health 2012;57:243–6. doi:10.1007/s00038-011-0315-z
      OpenUrlCrossRefPubMedWeb of Science
    24. ↵
      1. van der Poll T,
      2. Parker RI
      . Platelet activation and endothelial cell dysfunction. Crit Care Clin 2020;36:233–53. doi:10.1016/j.ccc.2019.11.002
      OpenUrl
    25. ↵
      1. McDonald B,
      2. Urrutia R,
      3. Yipp BG, et al
      . Intravascular neutrophil extracellular traps capture bacteria from the bloodstream during sepsis. Cell Host Microbe 2012;12:324–33. doi:10.1016/j.chom.2012.06.011
      OpenUrlCrossRefPubMedWeb of Science
    26. ↵
      1. Clark SR,
      2. Ma AC,
      3. Tavener SA, et al
      . Platelet TLR4 activates neutrophil extracellular traps to ensnare bacteria in septic blood. Nat Med 2007;13:463–9. doi:10.1038/nm1565
      OpenUrlCrossRefPubMedWeb of Science
    27. ↵
      1. Zarbock A,
      2. Singbartl K,
      3. Ley K
      . Complete reversal of acid-induced acute lung injury by blocking of platelet-neutrophil aggregation. J Clin Invest 2006;116:3211–9. doi:10.1172/JCI29499
      OpenUrlCrossRefPubMedWeb of Science
    28. ↵
      1. Mine S,
      2. Fujisaki T,
      3. Suematsu M, et al
      . Activated platelets and endothelial cell interaction with neutrophils under flow conditions. Intern Med 2001;40:1085–92. doi:10.2169/internalmedicine.40.1085
      OpenUrlCrossRefPubMedWeb of Science
    29. ↵
      1. Brinkmann V,
      2. Reichard U,
      3. Goosmann C, et al
      . Neutrophil extracellular traps kill bacteria. Science 2004;303:1532–5. doi:10.1126/science.1092385
      OpenUrlAbstract/FREE Full Text
    30. ↵
      1. Nijsten MW,
      2. ten Duis HJ,
      3. Zijlstra JG, et al
      . Blunted rise in platelet count in critically ill patients is associated with worse outcome. Crit Care Med 2000;28:3843–6. doi:10.1097/00003246-200012000-00017
      OpenUrlCrossRefPubMedWeb of Science
    31. ↵
      1. Bakey S,
      2. Karamanos E,
      3. Louwers L, et al
      . 1047: thrombocytosis versus thrombocytopenia as risk factor for increased mortality in sepsis. Crit Care Med 2013;41:A263. doi:10.1097/01.ccm.0000440283.47246.33
    32. ↵
      1. Gurung AM,
      2. Carr B,
      3. Smith I
      . Thrombocytosis in intensive care. Br J Anaesth 2001;87:926–8. doi:10.1093/bja/87.6.926
      OpenUrlCrossRefPubMedWeb of Science

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • Contributors WHY and XL designed this study. YWQ and XL conducted data collection and data analysis. XL wrote the manuscript. WHY, YL and TJZ analysed and interpreted the results. WHY designed and supervised this study, and is responsible for the overall content as guarantor. All authors have reviewed and approved this manuscript.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.