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Platelet count as a prognostic marker for acute respiratory distress syndrome

BMC Pulmonary Medicine volume 24, Article number: 396 (2024) Cite this article

Abstract

Background

This study aimed to evaluate the role of platelet count (PLT) in the prognosis of patients with acute respiratory distress syndrome (ARDS).

Methods

The data were extracted from the Medical Information Mart for Intensive Care database (version 2.2). Patients diagnosed with ARDS according to criteria from Berlin Definition and had the platelet count (PLT) measured within the first day after intensive care unit admission were analyzed. Based on PLT, ARDS patients were divided into four groups: PLT ≤ 100 × 109/L, PLT 101–200 × 109/L, PLT 201–300 × 109/L, and PLT > 300 × 109/L. The primary outcome was 28-day mortality. Survival probabilities were analyzed using Kaplan–Meier. Furthermore, the association between PLT and mortality in ARDS patients was assessed using a univariate and multivariable Cox proportional hazards model.

Results

Overall, the final analysis included 3,207 eligible participants with ARDS. According to the Kaplan–Meier curves for 28-day mortality of PLT, PLT ≤ 100 × 109/L was associated with a higher incidence of mortality (P = 0.001), the same trends were observed in the 60-day (P = 0.001) and 90‐day mortality (P = 0.001). In the multivariate model adjusted for the potential factors, the adjusted hazard ratio at PLT 101–200 × 109/L group, PLT 201–300 × 109/L, and PLT > 300 × 109/L was 0.681 [95% confidence interval (CI): 0.576–0.805, P < 0.001], 0.733 (95% CI: 0.604–0.889, P = 0.002), and 0.787 (95% CI: 0.624–0.994, P = 0.044) compared to the reference group (PLT ≤ 100 × 109/L), respectively. Similar relationships between the PLT ≤ 100 × 109/L group and 28-day mortality were obtained in most subgroups.

Conclusion

PLT appeared to be an independent predictor of mortality in critically ill patients with ARDS.

Peer Review reports

Background

Acute respiratory distress syndrome (ARDS) is a relatively common clinical syndrome caused by pulmonary or extra-pulmonary disease among critically ill patients. It is characterized by refractory hypoxemia, progressive respiratory distress, and non-cardiac pulmonary edema, resulting from diffuse lung inflammation, vascular endothelial injury, and severe capillary leakage [1]. ARDS accounts for 10% of intensive care unit (ICU) patients and 23% of mechanically ventilated patients. Depending on the severity of lung injury at onset, the in-hospital mortality of patients with ARDS ranges from 30–45% [2, 3]. A variety of factors, including respiratory infections and sepsis, can result in ARDS [4, 5]. The Coronavirus disease-19 pandemic has increased ARDS morbidity and awareness of the challenges it presents [4]. Since its initial publication in 2012, the Berlin definition has been widely used as a standard diagnostic criterion for ARDS. In order to address the limitations of the Berlin Definition, a new global definition of ARDS built on the accepted Berlin definition was developed most recently. Despite this, ARDS as a syndrome with complex pathophysiological processes, is still only diagnosed by purely clinical criteria currently, without consistent histological findings or biomarkers for early identification [6, 7]. Given the high mortality of ARDS, it is crucial to plumb some valuable and readily available biomarkers for the prognosis.

The initial manifestation of the inflammatory reaction that causes the ARDS is composed of thromboembolic in pulmonary arterioles [8, 9]. Platelets significantly form immunothrombosis via platelet-leukocyte aggregates and platelet-endothelial adhesion, gradually resulting in microthrombosis in small vessels [10]. Besides, platelets have attracted increasing attention for their potential to orchestrate complicated inflammatory or immune processes in the pathophysiology of inflammatory, immune, and infectious diseases. Given the multiple biological functions in the host response to injury, platelets have been widely used to assess the severity of the illness and the effectiveness of treatment in inflammatory diseases [11,12,13], and they appear to be a promising mediator and potential target for treating ARDS. Most studies use a 150 × 109/L threshold to define thrombocytopenia (TP), but its definition varies, while some researchers use other cut-offs, such as 100 × 109/L, 80 × 109/L, 50 × 109/L, and 30 × 109/L [14]. Previous studies have suggested that platelet counts (PLT) lower than 100 × 109/L in patients with ARDS or sepsis have a greater predictive value. However, current research on the correlation between platelets and ARDS is limited, with a relatively small sample size [15,16,17]. Our study aimed to investigate whether PLT could be used as a prognostic biomarker in patients with ARDS and the predictive value of PLT.

Methods

Data sources

All data were obtained from the Medical Information Mart for Intensive Care database (MIMIC-IV, version 2.2) [18]. This publicly available database includes critically ill patients at the Beth Israel Deaconess Medical Center (Boston, Massachusetts) as a repository for structural data from 2008 to 2019. MIMIC-IV version 2.2 is the latest, containing 431,231 hospital admissions of critical patients, including 73,181 ICU admissions, and can be freely obtained on PhysioNet. The exam in the Collaborative Training Program on the website of the National Institutes of Health was completed by Qianwen Wang (certification number 51704866), who allowed access to the database. Data were extracted using structured query language with pgAdmin4 v6 and PostgreSQL 15.1.

Study population

In this retrospective cohort study, the Berlin definition was adopted as the benchmark for ARDS diagnosis. All diseases were identified according to the International Classification of Diseases, Clinical Modification, 9th revision (ICD-9-CM). The inclusion criteria were as follows: (a) adults (≥ 18 years), (b) patients diagnosed with ARDS, and (c) PLT data collected within 24 h of ICU admission. Only the first ICU hospitalization records of the patients readmitted to the hospital were reserved for the final analysis. Furthermore, patients with hematologic diseases, including leukemia and myelodysplastic syndrome, were excluded. The Berlin definition of ARDS is as follows: Acute onset is bilateral infiltration on chest radiographs that is not fully explained by effusion, atelectasis, or masses and an inability to explain respiratory failure by cardiac failure or fluid overload. Arterial hypoxemia was defined as arterial oxygen (PaO2) to a fraction of inspired oxygen (FiO2) [PaO2/FiO2] ≤ 300 mmHg, with positive end-expiratory pressure (PEEP) or continuous positive airway pressure ≥ 5 cmH2O. ARDS was classified as mild, moderate, and severe, according to the PaO2/FiO2.

  1. (1)

    Mild: 200 < PaO2/FiO2 ≤ 300 mmHg with PEEP ≥ 5 cmH2O.

  2. (2)

    Moderate: 100 < PaO2/FiO2 ≤ 200 mmHg with PEEP ≥ 5 cmH2O.

  3. (3)

    Severe: PaO2/FiO2 ≤ 100 mmHg with PEEP ≥ 5 cmH2O.

Ultimately, 3,207 eligible patients were included in the final analysis (Fig. 1).

Fig. 1
figure 1

Study flowchart of the study participants

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Data extraction

Transact-SQL language was used to extract data from the database. Demographic information was obtained from the detailed structured table, including age, gender, and vital data, including systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (MAP), respiratory rate, heart rate, pulse oxygen saturation (SPO2), and PEEP. The following comorbidities were identified based on ICD-9-CM codes: chronic obstructive pulmonary disease (COPD), hypertension, diabetes, coronary heart disease (CHD), carcinoma, chronic kidney disease (CKD), and ventilation status (whether mechanical ventilation was required during ICU stay). The following laboratory parameters were collected: red blood cell (RBC) count, white blood cell (WBC) count, PLT, base excess, hemoglobin, lactate, serum creatinine, glucose, red blood cell distribution width (RDW), serum urea nitrogen, prothrombin time (PT), partial thromboplastin time (PTT), PaO2/FiO2, and arterial partial pressure of carbon dioxide (PaCO2). The worst Acute Physiology Score (APS III) and Sequential Organ Failure Assessment (SOFA) scores were recorded. In cases where multiple measurements were obtained within the first 24 h of ICU admission, the worst vital data and laboratory testing values were used, and the lowest PLT was recorded. When the PLT was less than 100 × 109/L, it strongly predicted the potential risk of sepsis-related death [19]. Given that sepsis-induced ARDS accounted for most of the etiologies of ARDS in our study, TP was defined as a PLT below 100 × 109/L. In this study, patients were categorized based on their baseline PLT: PLT ≤ 100 × 109/L, PLT 101–200 × 109/L, PLT 201–300 × 109/L, and PLT > 300 × 109/L. Clinical outcomes were compared among the four groups. Missing data in the indicators were replaced using mean or expectation-maximization interpolation.

The primary outcome of our study was 28-day mortality. The secondary outcomes were 60-day and 90-day mortality rates. Moreover, the predictive value of PLT was assessed for 28-day mortality in this patient population.

Statistical analysis

All data were analyzed using SPSS (version 25.0) for Windows and GraphPad Prism (version 7.00). According to whether the variables are normally distributed, the continuous and categorical variables are presented as mean ± standard deviation (SD) or median with an interquartile range (IQR). Continuous and categorical variables were compared using an independent-sample t-test or nonparametric Mann–Whitney U test. Numbers with percentages were used to describe categorical variables and tested using chi-square. Clinical outcomes were summarized according to PLT and compared among the four groups using one-way ANOVA or the Kruskal–Wallis test. Kaplan–Meier estimates were used to compare the 28-day, 60-day, and 90-day cumulative survival rates between various PLT-level groups. Additionally, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox regression models. The risk variables for 28-day mortality were identified using univariate analyses of admission parameters. After excluding collinearity among independent variables, confounders with P < 0.05 were chosen in univariate analysis for involvement in the multivariate Cox regression to ascertain the correlation between 28-day mortality and PLT level. Three models were constructed according to the univariate or multivariate analyses. Model 1 was unadjusted. Model 2 was adjusted for age, diabetes, hypertension, carcinoma, COPD, CKD, ARDS classification, and sepsis. Model 3 was adjusted for PEEP, SBP, DBP, MAP, respiratory rate, heart rate, SPO2, temperature, WBC, PT, PTT, RDW, and ventilator status. Additionally, subgroup analysis was performed to determine the interaction across different subgroups with distinct PLT levels according to age, gender, CHD, hypertension, carcinoma, sepsis, and diabetes at admission. Besides, the receiver operating characteristic (ROC) curve analysis was conducted to evaluate the prognostic value of the SOFA score and the APSIII score. To further investigate the predictive abilities of PLT, we incorporated it into the APSIII score. The area under the curve (AUC) was calculated as an indicator. A two-sided P < 0.05 was considered statistically significant.

Results

Finally, 3,207 patients with ARDS were enrolled in this study. Table 1 illustrates the baseline characteristics of the participants according to their survival situation on day 28. Among them, 891 (27.8%) died within 28 days. No statistically significant differences were found between the two groups in gender, heart rate, the presence of CHD, sepsis and PaCO2 (all P > 0.05). Compared to the survivors, patients who died within 28 days were older and more likely to have moderate-to-severe ARDS (P < 0.001), were mechanically ventilated (82.5% versus 51.3%, P < 0.001), and had a longer duration of mechanical ventilation [45.0 (18.0, 106.0) versus 30.3 (12.07, 85.90), P < 0.001]. Regarding the baseline vital data, deaths had lower blood pressure, PaO2/FiO2 (161.51 ± 2.34 versus 190.74 ± 1.37, P < 0.001), and higher PEEP levels [8 (5, 10) versus 5 (5, 10), P < 0.001]. Besides, PLT was significantly lower in non-survivors [153 (86, 226) versus 165 (118, 223), P < 0.001], who had higher PT, PTT, RDW, lactate, SOFA, and APS III scores (P < 0.001) than in survivors.

Table 1 Baseline characteristics of patients with ARDS involved in the study*
Full size table

Table 2 subdivides 3,207 patients into four groups based on baseline PLT. The ARDS classification exhibited no statistically significant differences between these groups. This study included 1,391 patients with mild ARDS and 1,816 with moderate-to-severe ARDS. The proportion of patients with moderate ARDS was higher in the PLT ≤ 100 × 109/L group than in the other three groups (P > 0.05). Overall, patients with lower PLT (≤ 100 × 109/L) had the highest 28-day mortality (P = 0.001); similar trends were detected in the 60-day and 90-day mortality (all P < 0.001). Furthermore, ICU-LOS and MV duration were longer in patients with PLT > 300 × 109/L than in the other three groups (P = 0.031 and P = 0.006, respectively).

Table 2 Outcomes of patients with ARDS across different levels of PLT*
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Kaplan–Meier curves constructed for 28-day mortality of these four PLT strata among patients with ARDS also showed significantly worse survival among patients in the PLT ≤ 100 × 109/L group (Fig. 2, log-rank P = 0.001).

Fig. 2
figure 2

Association between PLT level and mortality on the ARDS.

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Basic demographic and laboratory parameters for predicting 28-day mortality were investigated using a univariate Cox analysis regression model. After excluding collinearity among independent variables, confounders (P < 0.05) in univariate analysis, including age, diabetes, hypertension, carcinoma, COPD, CKD, ARDS classification, sepsis, PEEP, SBP, DBP, MAP, respiratory rate, heart rate, SPO2, temperature, WBC, PT, PTT, RDW, and ventilator status, were included as adjustment variables in multivariable analyses. In unadjusted Cox proportional hazards regression model 1, PLT ≤ 100 × 109/L was associated with an increased risk of 28-day mortality [HR for PLT 101–200 × 109/L, PLT 201–300 × 109/L, and PLT > 300 × 109/L were 0.520 (95% CI: 0.442-0.611, P < 0.001), 0.579 (95% CI: 0.479–0.700, P < 0.001), and 0.717 (95% CI: 0.570–0.902, P = 0.005), respectively (Table 3]. After further adjustment for age, diabetes, hypertension, carcinoma, COPD, CKD, ARDS classification, and sepsis, model 2 presents that the adjusted hazard ratios (aHR) for PLT 101–200 × 109/L, PLT 201–300 × 109/L, and PLT > 300 × 109/L were 0.527 (95% CI: 0.448–0.620, P < 0.001), 0.575 (95% CI: 0.475–0.696, P < 0.001), and 0.670 (95% CI: 0.532–0.843, P = 0.001), respectively. In the fully adjusted models, PLT ≤ 100 × 109/L remained consistently correlated with higher 28-day mortality in patients with ARDS [aHR for PLT 101–200 × 109/L, PLT 201–300 × 109/L, and PLT > 300 × 109/L were 0.681 (95% CI: 0.576–0.805, P < 0.001), 0.733 (95% CI: 0.604–0.889, P = 0.002), and 0.787 (95% CI: 0.624–0.994, P = 0.044), respectively].

Table 3 Cox regression analysis for 28-day mortality in different models
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In our cohort, subgroup analysis was performed to observe if the association between PLT level and ARDS 28-day mortality was stable in different subgroups. We used multivariable COX regression to identify the demographic variables associated with ARDS based on demographic characteristics, including CHD, hypertension, carcinoma, sepsis, and age. Known ARDS-related factors, including diabetes and gender, were also tested by forcing as subgroups. Consistent correlations were retained in various subgroups stratified by age, gender, carcinoma, diabetes, and hypertension (all P < 0.05). We found that PLT ≤ 100 × 109/L was also associated with a higher risk of 28-day mortality in ARDS in patients without sepsis but not in the sepsis subgroup (P < 0.001 and P = 0.119, respectively). The data revealed an interaction between PLT level and the presence of age, gender, carcinoma, hypertension, and diabetes (all P < 0.05). No significant associations were found between those with CHD and those without CHD or sepsis. Participants with CHD had an increased risk of mortality in both PLT 201–300 × 109/L group and PLT > 300 × 109/L group compared to PLT ≤ 100 × 109/L [HR for PLT 201–300 × 109/L and PLT > 300 × 109/L, respectively: 1.490 (95% CI: 0.760–2.918, P = 0.245), 1.964 (95% CI: 0.774, 4.983, P = 0.155)], but the differences in the subgroup were statistically non-significant (P > 0.05, Fig. 3, Fig. S3).

Fig. 3
figure 3

Forest plot of the association between PLT level and 28-day mortality in subgroups. Ref, reference; HR, hazard ratios

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As shown in Fig. 4, ROC curves for different measures are indicated in various colors. The single SOFA score and single APSIII score demonstrated similar performance with AUC values of 0.73 and 0.74 relatively (P < 0.05). To further evaluate the prognostic ability of PLT, we incorporated platelet count into the APSIII score. The combined parameters of PLT and APSIII score showed significantly better predictability for mortality (AUC = 0.79, P < 0.05) in patients with ARDS compared with the AUC of APSIII score and SOFA score alone.

Fig. 4
figure 4

ROC curves of single-parameter SOFA score, single-parameter APSIII score, and the combined assessment of APSIII score and PLT for predicting 28-day mortality in ARDS. AUC, the area under the curve;

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Discussion

This is a retrospective study of patients with ARDS. The main findings of this study suggested that low PLT ≤ 100 × 109/L indicates a poor prognosis in critically ill patients with ARDS. The consistency of relevance remained even after adjusting for multiple potential covariates. The result of our study provides more evidence about the predictive value of PLT in terms of mortality risk and prognosis in patients with ARDS. Accordingly, physicians should raise vigilance in patients with decreased PLT levels after ICU admission. Early intervention and strengthening management are required for those vulnerable patients to improve the outcomes.

Activation and dysregulation of diversified interacting pathways of inflammation, immune, injury, and coagulation can explain the major physiological features of ARDS [1, 20]. The discovery of ARDS biomarkers has contained substantial development over the decades and has identified multiple valuable laboratory parameters to assess disease severity and clinical prognosis. However, no single biomarker is closely related to prognosis or diagnosis currently [21,22,23,24]. Consistent with previous studies [16, 17, 25,26,27], our study indicated that platelets were correlated with mortality in patients with ARDS.

PLT are routinely measured daily during hospitalization, and they are most widely known as essential cellular effectors of hemostasis, but recent studies have demonstrated an increased focus on biological response modifier functions, for example, inflammatory and immune activities [28,29,30,31,32]. Although platelets contribute to the biological response modifier functions in the lungs, platelet activation and aggregation simultaneously result in ARDS. Dysregulated intrapulmonary platelet production mediates acute lung inflammation, severe thrombocytopenia, and increased endothelial permeability, resulting in leakage of infiltration of protein-rich pulmonary edema fluid and inflammatory cells in the pulmonary interstitium and alveoli, which is a characteristic feature of the pathological changes in ARDS [33,34,35,36]. Thrombocytopenia is generally considered a poor prognostic symptom in critical patients and a common occurrence in ARDS [17, 37, 38]. PLT markedly decreased during the first four days in critically ill patients [39, 40]. Our analysis offers compelling evidence that PLT ≤ 100 × 109/L is a valid threshold value to identify patients with ARDS with a higher mortality risk, supporting the findings of earlier studies [17].

Remarkably, the results of the subgroup analyses remained consistent with the main findings even after stratification. One intriguing finding was the disappearance of PLT’s predictive value in the CHD subgroups, although this was not statistically significant (P > 0.05). CHD is characterized by vascular endothelial dysfunction and deposition of lipoprotein, immune cells, and inflammatory mediators within the arterial intima [41, 42]. PLT contributes to the early initiation of atherosclerotic atherothrombosis by recruiting leukocytes [43, 44]. Besides, platelets are a key player as a biomarker of CHD to predict the prothrombotic state and plaque vulnerability [45]. A greater PLT may indicate inflammatory activation during atherosclerosis [46]. Iijima R et al. reported that in patients with CHD who received the percutaneous coronary intervention, an upper tertile (PLT count > 244 × 109/L) had increased 30-day mortality compared to a lower tertile (PLT count < 198 × 109/L) [47]. The result of another study implied that the patients with the highest PLT (mean PLT, 305 × 109/L) had 2.5 times significantly higher CHD mortality than the contrast group during 13.5 years [48]. Given the high likelihood of mortality with high PLT among patients with CHD, the strength of the associations may have been attenuated in the subgroup analyses.

Considering that the decline in platelets is a component of the SOFA score, we applied ROC analysis to evaluate the predictive performances of the SOFA. The result demonstrated that the SOFA score had a moderate predictive accuracy for the 28-day mortality of ARDS patients. In addition to the SOFA score, there is APSIII score, which does not include platelets, is also commonly used for mortality prediction in critically ill patients and showed a similar AUC (AUC = 0.74) value with that of the SOFA score (AUC = 0.73) in our study. After incorporating platelet count into the APSIII score, the combination of the APSIII score and PLT achieved an AUC of 0.79, which provided evidence supporting the predictive value of platelet counts in the ARDS.

Mechanistically, the progression and overall survival in ARDS are primarily determined by the overlap of excessive systemic inflammatory cascades, alveolar capillary damage, increased permeability and microthrombosis, alveolar epithelial damage, dysregulated coagulation, and reduced or absent pulmonary surfactant to various degrees [23, 49]. Disturbances of multifarious biological synthesis and secretion of inflammatory factors and signal transduction pathways allow the exploration of potential biomarkers that correlate strongly with the risk of death or prognosis of ARDS [50]. We further explore the ROC curves of PT and PTT to predict the 28-day mortality of patients with ARDS (Supplemental Fig S2). This result revealed that PT has a similar AUC compared to PLT, while a single PT does not fully reflect the inflammatory status, a combination of PLT and PT may provide better diagnostic and prognostic capacity of ARDS. An accumulating body of studies indicated that RDW, as a biomarker of oxidative stress and inflammation, was associated with the prognosis of ARDS [51,52,53,54]. Our data also obtained similar results (Supplemental Fig S2).

Limitations

Although conducted with a relatively large sample size, this study has several limitations. First, our study was a retrospective, single-center analysis. Second, baseline PLT was not reliably recorded before the first time of ICU arrival, and only the minimum values of PLT were obtained within the first 24 h after ICU admission; dynamic monitoring PLT is considered warranted. Third, our study used data from the 2008 to 2019 MIMIC database over 11 years; nevertheless, the diagnosis and treatment of patients with ARDS have improved considerably over the past decade. Fourth, the small size of the PLT > 300 × 109/L group was another obvious limitation; a larger population of this group is proposed to draw a proper conclusion.

Conclusion

In summary, our findings suggest that patients with ARDS having PLT ≤ 100 × 109/L after ICU admission are more likely to have higher mortality. PLT could be a useful and practical parameter to identify patients with ARDS who have poor outcomes in the ICU. In the future, the relationship between PLT and adverse clinical outcomes must be prospectively validated by larger prospective studies.

Data availability

All datasets analyzed during the current study are publicly available in the MIMIC-IV v2.2 database and can be accessed from: https://doi.org/10.13026/6mm1-ek67.

Abbreviations

PLT:

Platelet count

ARDS:

Acute respiratory distress syndrome

MIMIC-IV:

Medical Information Mart for Intensive Care-IV

ICU:

Intensive care unit

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

MAP:

Mean blood pressure

SPO2 :

Pulse oxygen saturation

PEEP:

Positive end-expiratory pressure

ICD-9-CM:

International classification of diseases, clinical modification, 9th revision

COPD:

Chronic obstructive pulmonary disease

CHD:

Coronary heart disease

CKD:

Chronic kidney disease

RBC:

Red blood cell

WBC:

White blood cells

BE:

Base excess

RDW:

Red blood cell distribution width

PT:

Prothrombin time

PTT:

Partial thromboplastin time

PaO2/FiO2 :

Arterial partial pressure of oxygen to fraction of inspired oxygen

PaCO2 :

Arterial partial pressure of carbon dioxide

APS III:

Acute Physiology Score III

SOFA:

Sequential Organ Failure Assessment

MV:

Mechanical ventilation

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  1. Department of Intensive care unit, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hang Zhou, Zhe Jiang, 310000, China, No 3 East Road Qingchun

    Qianwen Wang & Ge Zhang

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Wang, Q., Zhang, G. Platelet count as a prognostic marker for acute respiratory distress syndrome. BMC Pulm Med 24, 396 (2024). https://doi.org/10.1186/s12890-024-03204-5

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BMC Pulmonary Medicine

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