Thrombocytopenia in intensive care unit patients: A scoping review

Platelet transfusions in adult thrombocytopenic ICU patients: Protocol for a sub-study of the PLOT-ICU cohort

INTRODUCTION

Platelet transfusions are frequently used in intensive care unit (ICU) patients, but contemporary epidemiological data are sparse. We aim to present contemporary international data on the use of platelet transfusions in adult ICU patients with thrombocytopenia.

METHODS

This is a protocol and statistical analysis plan for a post hoc sub-study of 504 thrombocytopenic patients from the 'Thrombocytopenia and platelet transfusions in ICU patients: an international inception cohort study (PLOT-ICU)'. The primary outcome will be the number of patients receiving platelet transfusion in the ICU reported according to the type of product received (apheresis-derived versus pooled whole-blood-derived transfusions). Secondary platelet transfusion outcomes will include platelet transfusion volumes; timing of platelet transfusion; approach to platelet transfusion dosing (fixed dosing versus weight-based dosing) and platelet count increments for prophylactic transfusions. Secondary clinical outcomes will include the number of patients receiving red blood cell- and plasma transfusions during ICU stay; the number of patients who bled in the ICU, the number of patients who had a new thrombosis in the ICU, and the number of patients who died. The duration of follow-up was 90 days. Baseline characteristics and secondary clinical outcomes will be stratified according to platelet transfusion status in the ICU and severity of thrombocytopenia. Data will be presented descriptively.

CONCLUSIONS

The outlined study will provide detailed epidemiological data on the use of platelet transfusions in adult ICU patients with thrombocytopenia using data from the large international PLOT-ICU cohort study. The findings will inform the design of future randomised trials evaluating platelet transfusions in ICU patients.

Prospective evaluation of bleeding risk among thrombocytopenic patients admitted in intensive care unit

PURPOSE

Bleeding risk evaluation of thrombocytopenic patients admitted in ICU has been poorly investigated.

METHODS

A prospective observational study conducted in an 18-bed medical ICU. Consecutive patients with thrombocytopenia (<150 Giga/L) and no bleeding at admission were included.

RESULTS

Over one year, 91 patients were included, mainly men (63%), with an age of 61 [46-68] years and a SOFA score of 6 [3-8]. Twenty-three patients (25%) had an hemorrhagic event during ICU stay, mainly digestive (n = 9; 39%) and urological (n = 6; 26%). The time between ICU admission and bleeding was 8 [2-19] days. Almost half of bleeding events required vasopressor infusion and a hemostatic procedure. At admission, two variables were significantly different between the Bleeding and No-Bleeding groups: plasma urea level was significantly higher in the Bleeding group (9 [5.1; 13] vs. 13 [8.9; 31] mmol/L; p < 0.001) and the presence of skin purpura was associated with a 3-fold higher risk for bleeding during ICU stay (HR: 3.4 [1.3-8.3]; p < 0.05). In contrast, admission platelet count was not significantly different between the 2 groups (90 [32; 128] vs 62 [36; 103] G/L; p = 0.26).

CONCLUSION

Plasma urea levels and the presence of skin purpura are helpful in identifying thrombocytopenic patients at high-risk of bleeding during ICU stay.

Controversies Surrounding Albumin Use in Sepsis: Lessons from Cirrhosis

This narrative review critically examines the role of albumin in sepsis management and compares it to its well-established application in liver cirrhosis. Albumin, a key plasma protein, is effective in the management of fluid imbalance, circulatory dysfunction, and inflammation-related complications. However, its role in sepsis is more intricate and characterized by ongoing debate and varied results from clinical studies. In sepsis, the potential benefits of albumin include maintaining vascular integrity and modulating inflammation, yet its consistent clinical efficacy is not as definitive as that in cirrhosis. This review evaluated various clinical trials and evidence, highlighting their limitations and providing practical insights for clinicians. It emphasizes identifying sepsis patient subgroups that are most likely to benefit from albumin therapy, particularly exploring the correction of hypoalbuminemia. This condition, which is significantly corrected in patients with cirrhosis, may have similar therapeutic advantages in sepsis. The potential effectiveness of albumin in the low-volume resuscitation and deresuscitation phases of sepsis management was noted. Given the safety concerns observed in cirrhosis, such as pulmonary edema and hypervolemia associated with albumin therapy, cautious integration of albumin into sepsis treatment is mandatory. Personalized albumin therapy is advocated for tailoring strategies to the specific needs of each patient, based on their clinical presentation and underlying conditions. The need for further research to delineate the role of albumin in sepsis pathophysiology is underscored. The review emphasizes the importance of conducting trials to assess the effectiveness of albumin in correcting hypoalbuminemia in sepsis, its impact on patient outcomes, and the establishment of appropriate dosing and administration methods. This approach to albumin use in sepsis management is posited as a way to potentially improve patient outcomes in this complex clinical scenario while being mindful of the lessons learned from its use in cirrhosis.

Thrombocytopenia and platelet transfusions in ICU patients: an international inception cohort study (PLOT-ICU)

PURPOSE

Thrombocytopenia (platelet count < 150 × 109/L) is common in intensive care unit (ICU) patients and is likely associated with worse outcomes. In this study we present international contemporary data on thrombocytopenia in ICU patients.

METHODS

We conducted a prospective cohort study in adult ICU patients in 52 ICUs across 10 countries. We assessed frequencies of thrombocytopenia, use of platelet transfusions and clinical outcomes including mortality. We evaluated pre-selected potential risk factors for the development of thrombocytopenia during ICU stay and associations between thrombocytopenia at ICU admission and 90-day mortality using pre-specified logistic regression analyses.

RESULTS

We analysed 1166 ICU patients; the median age was 63 years and 39.5% were female. Overall, 43.2% (95% confidence interval (CI) 40.4-46.1) had thrombocytopenia; 23.4% (20-26) had thrombocytopenia at ICU admission, and 19.8% (17.6-22.2) developed thrombocytopenia during their ICU stay. Absence of acquired immune deficiency syndrome (AIDS), non-cancer-related immune deficiency, liver failure, male sex, septic shock, and bleeding at ICU admission were associated with the development of thrombocytopenia during ICU stay. Among patients with thrombocytopenia, 22.6% received platelet transfusion(s), and 64.3% of in-ICU transfusions were prophylactic. Patients with thrombocytopenia had higher occurrences of bleeding and death, fewer days alive without the use of life-support, and fewer days alive and out of hospital. Thrombocytopenia at ICU admission was associated with 90-day mortality (adjusted odds ratio 1.7; 95% CI 1.19-2.42).

CONCLUSION

Thrombocytopenia occurred in 43% of critically ill patients and was associated with worse outcomes including increased mortality. Platelet transfusions were given to 23% of patients with thrombocytopenia and most were prophylactic.

Platelet count predicts mortality in patients with sepsis: A retrospective observational study

Platelet count is a key component of sepsis severity score. However, the predictive value of the platelet count at admission for mortality in sepsis remains unclear. We designed a retrospective observational study of patients with sepsis admitted to our hospital from January 2017 to September 2021 to explore the predictive value of platelet count at admission for mortality. A total of 290 patients with sepsis were included in this study. Multivariate logistic regression analysis was used to evaluate the risk factors for mortality and construct a predictive model with statistically significant factors. Compared with survivors, nonsurvivors tended to be much older and had significantly higher acute physiology and chronic health evaluation II and sequential organ failure assessment scores (P < .001). The platelet count was significantly lower in the nonsurvivor group than in the survivor group (P < .001). Multivariate logistic regression analysis indicated that age (P = .003), platelet count (P < .001) and lactate level (P = .018) were independent risk factors for mortality in patients with sepsis. Finally, the area under the receiver operating characteristic curve of platelet count predicting mortality in sepsis was 0.763 (95% confidence interval, 0.709-0.817, P < .001), with a sensitivity of 55.6% and a specificity of 91.8%. In our study, platelet count at admission as a single biomarker showed good predictability for mortality in patients with sepsis.

Clinical significance of thrombocytopenia in patients with septic shock: An observational retrospective study

PURPOSE

Whether thrombocytopenia in critically ill patients accounts for a bystander of severity or drives specific complications is unclear. We addressed the effect of thrombocytopenia on septic shock, with emphasis on intensive care unit (ICU)-acquired bleeding, infections and thrombotic complications.

MATERIALS AND METHODS

A retrospective (2008-2019) single-center study of patients with septic shock. Thrombocytopenia was assessed over the first seven days and was defined as severe (nadir <50 G/L), mild (nadir 50-150 G/L) and relative (30% decrease with nadir >150 G/L). Outcomes were ICU mortality and ICU-acquired complications defined by severe bleeding, infections and thrombotic events during the ICU stay.

RESULTS

The study comprised 1024 patients. Severe, mild and relative thrombocytopenia occurred in 33%, 40% and 9% of patients. The in-ICU mortality rate was 27%, independently associated with severe thrombocytopenia. ICU-acquired infections, hemorrhagic and thrombotic complications occurred in 27.5%, 13.3% and 11.6% of patients, respectively. Patients with severe, mild or relative thrombocytopenia exhibited higher incidences of bleeding events (20.3%, 15.3% and 14.4% vs. 3.6% in non-thrombocytopenic, p < 0.001), infections (35.2%, 21.9% and 33.3% vs. 23.1% in non-thrombocytopenic, p < 0.001) and thrombotic events (14.6%, 10.8% and 17.8% vs. 7.8% in non-thrombocytopenic, p = 0.03). Only severe thrombocytopenia remained independently associated with increased risk of bleeding.

CONCLUSIONS

Severe thrombocytopenia was independently associated with ICU mortality and increased risk of bleeding, but not with infectious and thrombotic events.

Development and Validation Nomogram for Predicting the Survival of Patients with Thrombocytopenia in Intensive Care Units

Background

The number of patients with thrombocytopenia (TCP) is relatively high in intensive care units (ICUs). It is therefore necessary to evaluate the prognostic risk of such patients.

Aim

This study investigated the risk factors affecting the survival of patients with TCP in the ICU. Using the findings of this investigation, we developed and validated a risk prediction model.

Methods

We evaluated patients admitted to the ICU who presented with TCP. We used LASSO regression to identify important clinical indicators. Based on these indicators, we developed a prediction model complete with a nomogram for the development cohort set. We then evaluated the mode's accuracy using a receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA) in a validation cohort.

Results

A total of 141 cases of ICU TCP were included in the sample, of which 47 involved death of the patient. Clinical results were as follows: N (HR 0.91, 95% CI 0.86-0.97, P=0.003); TBIL (HR 1.98, 95% CI 1.02-1.99, P=0.048); APACHE II (HR 1.94, 95% CI 1.39, 2.48, P=0.045); WPRN (HR 6.22, 95% CI 2.86-13.53, P<0.001); WTOST (HR 0.56, 95% CI 0.21-1.46, P<0.001); and DMV [HR1.87, 95% CI 1.12-2.33]. The prediction model yielded an area under the curve (AUC) of 0.918 (95% CI 0.863-0.974) in the development cohort and 0.926 (95% CI 0.849-0.994) in the validation cohort. Application of the nomogram in the validation cohort gave good discrimination (C-index 0.853, 95% CI 0.810-0.922) and good calibration. DCA indicated that the nomogram was clinically useful.

Conclusion

The individualized nomogram developed through our analysis demonstrated effective prognostic prediction for patients with TCP in ICUs. Use of this prediction metric may reduce TCP-related morbidity and mortality in ICUs.

Identification of subphenotypes in critically ill thrombocytopenic patients with different responses to therapeutic interventions: a retrospective study

Introduction

The causes of thrombocytopenia (TP) in critically ill patients are numerous and heterogeneous. Currently, subphenotype identification is a popular approach to address this problem. Therefore, this study aimed to identify subphenotypes that respond differently to therapeutic interventions in patients with TP using routine clinical data and to improve individualized management of TP.

Methods

This retrospective study included patients with TP admitted to the intensive care unit (ICU) of Dongyang People's Hospital during 2010-2020. Subphenotypes were identified using latent profile analysis of 15 clinical variables. The Kaplan-Meier method was used to assess the risk of 30-day mortality for different subphenotypes. Multifactorial Cox regression analysis was used to analyze the relationship between therapeutic interventions and in-hospital mortality for different subphenotypes.

Results

This study included a total of 1,666 participants. Four subphenotypes were identified by latent profile analysis, with subphenotype 1 being the most abundant and having a low mortality rate. Subphenotype 2 was characterized by respiratory dysfunction, subphenotype 3 by renal insufficiency, and subphenotype 4 by shock-like features. Kaplan-Meier analysis revealed that the four subphenotypes had different in-30-day mortality rates. The multivariate Cox regression analysis indicated a significant interaction between platelet transfusion and subphenotype, with more platelet transfusion associated with a decreased risk of in-hospital mortality in subphenotype 3 [hazard ratio (HR): 0.66, 95% confidence interval (CI): 0.46-0.94]. In addition, there was a significant interaction between fluid intake and subphenotype, with a higher fluid intake being associated with a decreased risk of in-hospital mortality for subphenotype 3 (HR: 0.94, 95% CI: 0.89-0.99 per 1 l increase in fluid intake) and an increased risk of in-hospital mortality for high fluid intake in subphenotypes 1 (HR: 1.10, 95% CI: 1.03-1.18 per 1 l increase in fluid intake) and 2 (HR: 1.19, 95% CI: 1.08-1.32 per 1 l increase in fluid intake).

Conclusion

Four subphenotypes of TP in critically ill patients with different clinical characteristics and outcomes and differential responses to therapeutic interventions were identified using routine clinical data. These findings can help improve the identification of different subphenotypes in patients with TP for better individualized treatment of patients in the ICU.

RISK OF HOSPITAL MORTALITY IN CRITICALLY ILL PATIENTS WITH TRANSIENT AND PERSISTENT THROMBOCYTOPENIA: A RETROSPECTIVE STUDY

ABSTRACT

Background: Thrombocytopenia (TP), a common occurrence among patients admitted to the intensive care unit (ICU), is significantly associated with prolonged ventilator use, prolonged ICU stay, and increased mortality. The duration of TP serves as an indicator of patient outcome, although the exact duration of TP associated with poor patient outcome remains unclear. In this study, the data of 3,291 patients on their first admission to the ICU between January 2010 and December 2020 were retrospectively analyzed. Participants were divided into the no TP, TP 1-2 days, TP 3-6 days, and TP ≥7 days groups based on the duration of TP. External validation was performed using the Medical Information Mart for Intensive Care III data set. Results: A longer duration of TP was significantly associated with high volume of transfusion and high hospital mortality ( P < 0.01), and 37.3% of the participants developed TP during their ICU stay. The results of Kaplan-Meier survival analysis and Cox regression analysis after excluding the effects of patients who died shortly after ICU admission revealed the absence of significant differences between the no TP and TP 1-2 days groups ( P > 0.05). However, when the duration of TP exceeded 2 days, patient mortality increased with an increase in the duration of TP ( P < 0.01). Similar findings were obtained with the Medical Information Mart for Intensive Care III data set. Conclusions: The duration of TP in critically ill patients is positively correlated with poor patient outcome. We classified TP as either transient TP or persistent TP based on a cutoff duration of 2 days. Monitoring the duration of TP may aid in the prediction of patients' outcome in the ICU.

A nomogram for predicting the risk of mortality in patients with acute pancreatitis and Gram-negative bacilli infection

OBJECTIVE

Gram-negative bacilli (GNB) are common pathogens of infection in severe acute pancreatitis (SAP), and their occurrence increases the mortality of SAP. Early identification of SAP severity and prognosis is of great significance to SAP treatment. This study explored risk factors for mortality in patients with SAP and GNB infection and established a model for early prediction of the risk of death in GNB-infected SAP patients.

METHODS

Patients diagnosed with SAP from January 1, 2016, to March 31, 2022, were included, and their baseline clinical characteristics were collected. Univariate logistic regression analysis was performed to screen for death related variables, and concurrently, a Boruta analysis was performed to identify potentially important clinical features associated with mortality. The intersection of the two results was taken for further multivariate logistic regression analysis. A logistic regression model was constructed according to the independent risk factor of death and then visualized with a nomogram. The performance of the model was further validated in the training and validation cohort.

RESULTS

A total of 151 patients with SAP developed GNB infections. Univariate logistic regression analysis identified 11 variables associated with mortality. The Boruta analysis identified 11 clinical features, and 4 out of 9 clinical variables: platelet counts (odds ratio [OR] 0.99, 95% confidence interval [CI] 0.99-1.00; p = 0.007), hemoglobin (OR 0.96, 95% CI 0.92-1; p = 0.037), septic shock (OR 6.33, 95% CI 1.12-43.47; p = 0.044), and carbapenem resistance (OR 7.99, 95% CI 1.66-52.37; p = 0.016), shared by both analyses were further selected as independent risk factors by multivariate logistic regression analysis. A nomogram was used to visualize the model. The model demonstrated good performance in both training and validation cohorts with recognition sensitivity and specificity of 96% and 80% in the training cohort and 92.8% and 75% in the validation cohort, respectively.

CONCLUSION

The nomogram can accurately predict the mortality risk of patients with SAP and GNB infection. The clinical application of this model allows early identification of the severity and prognosis for patients with SAP and GNB infection and identification of patients requiring urgent management thus allowing rationalization of treatment options and improvements in clinical outcomes.

How I use platelet transfusions

Platelet transfusions are commonly administered for the prevention or treatment of bleeding in patients with acquired thrombocytopenia across a range of clinical contexts. Recent data, including randomized trials, have highlighted uncertainties in the risk-benefit balance of this therapy, which is the subject of this review. Hemovigilance systems report that platelets are the most frequently implicated component in transfusion reactions. There is considerable variation in platelet count increment after platelet transfusion, and limited evidence of efficacy for clinical outcomes, including prevention of bleeding. Bleeding events commonly occur despite the different policies for platelet transfusion prophylaxis. The underlying mechanisms of harm reported in randomized trials may be related to the role of platelets beyond hemostasis, including mediating inflammation. Research supports the implementation of a restrictive platelet transfusion policy. Research is needed to better understand the impact of platelet donation characteristics on outcomes, and to determine the optimal thresholds for platelet transfusion before invasive procedures or major surgery (eg, laparotomy). Platelet transfusion policies should move toward a risk-adapted approach that does not focus solely on platelet count.

Platelet Function in Acute Kidney Injury: A Systematic Review and a Cohort Study

Acute kidney injury (AKI) patients have increased bleeding risk, which could be partially due to acquired platelet dysfunction. We conducted a systematic review and a cohort study to investigate platelet function and count in AKI and their association with AKI-related bleeding and mortality. Through a systematic literature search in PubMed and Embase, we identified 9 studies reporting platelet function and 56 studies reporting platelet count or platelet indices in AKI patients. Overall, platelet aggregation was reduced in AKI patients in nonintensive care unit (ICU) settings but not in ICU settings, except that reduced aggregation was associated with renal replacement therapy. Thrombocytopenia in AKI was frequent and often predictive of mortality. In our cohort study, we prospectively included 54 adult ICU patients who developed AKI within 24 hours of ICU admission and 33 non-AKI ICU controls. Platelet function was measured with light transmission aggregometry and flow cytometry. AKI patients bled more frequently than non-AKI patients (p = 0.04), and bleeding was associated with increased 30-day mortality in AKI (p = 0.02). However, platelet function was not different between AKI and non-AKI patients (aggregation: all p > 0.52; flow cytometry: all p > 0.07) and platelet function was not associated with bleeding in AKI. In conclusion, a reduced platelet count is frequent in AKI, but the literature on platelet function in AKI is sparse. In a cohort study, we demonstrated that patients with AKI within 24 hours of ICU admission exhibited increased bleeding tendency but this was not associated with reduced platelet function.

Association of longitudinal platelet count trajectory with ICU mortality: A multi-cohort study

Objective

Platelet (PLT) engages in immune and inflammatory responses, all of which are related to the prognosis of critically ill patients. Although thrombocytopenia at ICU admission contributes to in-hospital mortality, PLT is repeatedly measured during ICU hospitalization and the role of longitudinal PLT trajectory remains unclear. We aimed to identify dynamic PLT trajectory patterns and evaluate their relationships with mortality risk and thrombocytopenia.

Methods

We adopted a three-phase, multi-cohort study strategy. Firstly, longitudinal PLT trajectory patterns within the first four ICU days and their associations with 28-day survival were tested in the eICU Collaborative Research Database (eICU-CRD) and independently validated in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Secondly, the relationships among PLT trajectory patterns, thrombocytopenia, and 28-day mortality were explored and validated. Finally, a Mortality GRade system for ICU dynamically monitoring patients (Mortality-GRID) was developed to quantify the mortality risk based on longitudinal PLT, which was further validated in the Molecular Epidemiology of Acute Respiratory Distress Syndrome (MEARDS) cohort.

Results

A total of 35,332 ICU patients were included from three cohorts. Trajectory analysis clustered patients into ascending (AS), stable (ST), or descending (DS) PLT patterns. DS patients with high baseline PLT decline quickly, resulting in poor prognosis. AS patients have low baseline PLT but recover quickly, favoring a better prognosis. ST patients maintain low PLT, having a moderate prognosis in between (HRSTvsAS = 1.26, 95% CI: 1.14–1.38, P = 6.15 × 10−6; HRDSvsAS = 1.58, 95% CI: 1.40–1.79, P = 1.41 × 10−13). The associations remained significant in patients without thrombocytopenia during the entire ICU hospitalization and were robust in sensitivity analyses and stratification analyses. Further, the trajectory pattern was a warning sign of thrombocytopenia, which mediated 27.2% of the effects of the PLT trajectory on 28-day mortality (HRindirect = 1.11, 95% CI: 1.06–1.17, P = 9.80 × 10−6). Mortality-GRID well predicts mortality risk, which is in high consistency with that directly estimated in MEARDS (r = 0.98, P = 1.30 × 10−23).

Conclusion

Longitudinal PLT trajectory is a complementary predictor to baseline PLT for patient survival, even in patients without risk of thrombocytopenia. Mortality-GRID could identify patients at high mortality risk.

Platelet transfusions and thrombocytopenia in intensive care units: protocol for an international inception cohort study (PLOT‐ICU)

Introduction

Thrombocytopenia is frequent in intensive care unit (ICU) patients and has been associated with worse outcome. Platelet transfusions are often used in the management of ICU patients with severe thrombocytopenia. However, the reported frequencies of thrombocytopenia and platelet transfusion practices in the ICU vary considerably. Therefore, we aim to provide contemporary epidemiological data on thrombocytopenia and platelet transfusion practices in the ICU.

Methods

We will conduct an international inception cohort, including at least 1000 acutely admitted adult ICU patients. Routinely available data will be collected at baseline (ICU admission), and daily during ICU stay up to a maximum of 90 days. The primary outcome will be the number of patients with thrombocytopenia (a recorded platelet count < 150 × 10⁹/L) at baseline and/or during ICU stay. Secondary outcomes include mortality, days alive and out of hospital, days alive without life‐support, the number of patients with at least one bleeding episode, at least one thromboembolic event and at least one platelet transfusion in the ICU, the number of platelet transfusions and the indications for transfusion. The primary and secondary outcomes will be presented descriptively. In addition, we will assess risk factors for developing thrombocytopenia during ICU stay and the association between thrombocytopenia at baseline and 90‐day mortality using logistic regression analyses.

Conclusion

The outlined international PLOT‐ICU cohort study will provide contemporary epidemiological data on the burden and clinical significance of thrombocytopenia in adult ICU patients and describe the current platelet transfusion practice.

Predictive ability of the sequential organ failure assessment score for in-hospital mortality in patients with cardiac critical illnesses: a nationwide observational study

AIMS

Several studies have reported a high predictive ability of the Sequential Organ Failure Assessment (SOFA) score for in-hospital mortality specifically for patients with cardiac critical illnesses, however, differences according to the admission classification (surgical or non-surgical) are unknown. The present study aimed to evaluate the predictive ability of the SOFA score in surgical and non-surgical patients with cardiac critical illnesses.

METHODS AND RESULTS

Using the Japanese nationwide Diagnosis Procedure Combination database, we identified patients with cardiac critical illnesses, defined as patients admitted to the intensive care unit (ICU) and treated by cardiologists or cardiovascular surgeons as their physicians in charge from April 2018 to March 2020. The discriminatory ability of the SOFA score for in-hospital mortality was assessed by calculating the area under the receiver operating characteristic curve (AUROC). Among 52 819 eligible patients with available data on their SOFA scores, 33 526 (64%) were postoperative cardiac surgeries. The median SOFA score on ICU admission was 5.0 (interquartile range, 2.0-8.0) and overall in-hospital mortality 6.8%. The AUROC of the SOFA score was 0.75 [95% confidence interval (CI), 0.75-0.76]. In the subgroup analyses, the AUROCs were 0.76 (95% CI, 0.74-0.77) in the surgical patients, 0.83 (95% CI, 0.83-0.84) in the non-surgical patients, and 0.88 (95% CI, 0.87-0.89) in the non-surgical acute coronary syndrome patients.

CONCLUSIONS

The predictive ability of the SOFA score on the day of ICU admission for in-hospital mortality was confirmed to be acceptable in the patients with cardiac critical illnesses and varied according to the admission classification and primary diagnoses.

Platelet Transfusion in Patients With Sepsis and Thrombocytopenia: A Propensity Score-Matched Analysis Using a Large ICU Database

Purpose

Sepsis with thrombocytopenia is highly prevalent in critically ill intensive care unit (ICU) patients and is associated with adverse outcomes. Platelet transfusion is the primary treatment of choice. However, evidence for the beneficial effects of platelet transfusion in patients with sepsis and thrombocytopenia is scarce and low in quality. This study aimed to evaluate the association between platelet transfusion and mortality among ICU patients with sepsis and thrombocytopenia.

Patients and Methods

Using the Medical Information Mart for Intensive Care III database (v. 1.4), the outcomes of sepsis patients with platelet counts of ≤ 150,000/μL were compared between those who did and did not receive platelet transfusion. The primary outcomes were 28- and 90-day all-cause mortalities. The secondary outcomes were red blood cell (RBC) transfusion, ICU-free days, and hospital-free days. Propensity score matching was employed to assemble a cohort of patients with similar baseline characteristics.

Results

Among 7,765 eligible patients, 677 received platelet transfusion and were matched with 677 patients who did not receive platelet transfusion according to propensity scores. Platelet transfusion, as compared with no platelet transfusion, was associated with an increased risk of 28-day all-cause mortality [36.9 vs. 30.4%, odds ratio (OR), 1.21; 95% confidence interval (CI), 1.01–1.46; p = 0.039], increased risk of 90-day all-cause mortality (50.8 vs. 44.6%, OR, 1.13; 95% CI, 1.00–1.31; p = 0.048), fewer mean (standard deviation) 28-day ICU-free days (15.88 ± 8.97 vs. 18.64 ± 8.33 days, p < 0.001), and fewer hospital-free days (10.29 ± 8.49 vs. 11.43 ± 8.85 days, p = 0.017). The rate of RBC transfusion was not significantly different between the platelet transfusion and non-transfusion groups (p = 0.149). The results were maintained across several subgroup and sensitivity analyses.

Conclusion

In this study, platelet transfusion was associated with higher 28- and 90-day all-cause mortalities. These results suggest the potential hazards of platelet transfusion in ICU patients with sepsis and thrombocytopenia.

Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning Algorithm

Sepsis-associated thrombocytopenia (SAT) is a common complication in the intensive care unit (ICU), which significantly increases the mortality rate and leads to poor prognosis of diseases. Machine learning (ML) is widely used in disease prediction in critically ill patients. Here, we aimed to establish prediction models for platelet decrease and severe platelet decrease in ICU patients with sepsis based on four common ML algorithms and identify the best prediction model. The research subjects were 1,455 ICU sepsis patients admitted to Dongyang People's Hospital affiliated with Wenzhou Medical University from January 1, 2015, to October 31, 2019. Basic clinical demographic information, biochemical indicators, and clinical outcomes were recorded. The prediction models were based on four ML algorithms: random forest, neural network, gradient boosting machine, and Bayesian algorithms. Thrombocytopenia was found to occur in 732 patients (49.7%). The mechanical ventilation time and length of ICU stay were longer, and the mortality rate was higher for the thrombocytopenia group than for the non-thrombocytopenia group. The models were validated on an online international database (Medical Information Mart for Intensive Care III). The areas under the receiver operating characteristic curves (AUCs) of the four models for the prediction of thrombocytopenia were between 0.54 and 0.72. The AUCs of the models for the prediction of severe thrombocytopenia were between 0.70 and 0.77. The neural network and gradient boosting machine models effectively predicted the occurrence of SAT, and the Bayesian models had the best performance in predicting severe thrombocytopenia. Therefore, these models can be used to identify such high-risk patients at an early stage and guide individualized clinical treatment, to improve the prognosis of diseases.

Prophylactic platelet transfusions in hospitalised patients with thrombocytopenia-Protocol for a systematic review with meta-analysis

BACKGROUND

Prophylactic platelet transfusions are frequently used to prevent bleeding in hospitalised patients with thrombocytopenia. Recommendations regarding the use of prophylactic platelet transfusions in non-haematological patients are based on extrapolations, observational studies, and expert opinions, and transfusion with platelets has been associated with adverse effects. We aim to assess the overall benefits and harms of prophylactic platelet transfusions in hospitalised patients with thrombocytopenia.

METHODS/DESIGN

We will conduct a systematic review with meta-analyses and trial sequential analyses in compliance with the recommendations by the Cochrane Collaboration and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We will prepare the manuscript in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We will include randomised clinical trials assessing prophylactic platelet transfusion versus no prophylaxis or placebo in hospitalised patients with thrombocytopenia. The primary outcome is mortality at longest follow-up. Secondary outcomes include episodes of clinically important bleeding, nosocomial infections, transfusion-related adverse events, thromboembolic events, length of hospital stay, quality of life, and days alive without the use of life support. We will conduct prespecified subgroup analyses and sensitivity analyses and assess the risk of random errors by trial sequential analyses.

DISCUSSION

The proposed systematic review will provide an overview of the certainty of evidence for the benefits and harms of prophylactic platelet transfusion in hospitalised patients with thrombocytopenia.