Diagnostic and prognostic role of platelets in patients with sepsis and septic shock

Risk and mediation analyses of hemoglobin glycation index and survival prognosis in patients with sepsis

An increasing number of studies have reported the close relation of the hemoglobin glycation index (HGI) with metabolism, inflammation, and disease prognosis. However, the prognostic relationship between the HGI and patients with sepsis remains unclear. Thus, this study aimed to analyze the association between the HGI and all-cause mortality in patients with sepsis using data from the MIMIC-IV database. In this study, 2605 patients with sepsis were retrospectively analyzed. The linear regression equation was established by incorporating glycated hemoglobin (HbA1c) and fasting plasma glucose levels. Subsequently, the HGI was calculated based on the difference between the predicted and observed HbA1c levels. Furthermore, the HGI was divided into the following three groups using X-tile software: Q1 (HGI ≤  - 0.50%), Q2 (- 0.49% ≤ HGI ≤ 1.18%), and Q3 (HGI ≥ 1.19%). Kaplan-Meier survival curves were further plotted to analyze the differences in 28-day and 365-day mortality among patients with sepsis patients in these HGI groups. Multivariate corrected Cox proportional risk model and restricted cubic spline (RCS) were used. Lastly, mediation analysis was performed to assess the factors through which HGI affects sepsis prognosis. This study included 2605 patients with sepsis, and the 28-day and 365-day mortality rates were 19.7% and 38.9%, respectively. The Q3 group had the highest mortality risk at 28 days (HR = 2.55, 95% CI: 1.89-3.44, p < 0.001) and 365 days (HR = 1.59, 95% CI: 1.29-1.97, p < 0.001). In the fully adjusted multivariate Cox proportional hazards model, patients in the Q3 group still displayed the highest mortality rates at 28 days (HR = 2.02, 95% CI: 1.45-2.80, p < 0.001) and 365 days (HR = 1.28, 95% CI: 1.08-1.56, p < 0.001). The RCS analysis revealed that HGI was positively associated with adverse clinical outcomes. Finally, the mediation effect analysis demonstrated that the HGI might influence patient survival prognosis via multiple indicators related to the SOFA and SAPS II scores. There was a significant association between HGI and all-cause mortality in patients with sepsis, and patients with higher HGI values had a higher risk of death. Therefore, HGI can be used as a potential indicator to assess the prognostic risk of death in patients with sepsis.

Genetic prediction of asthma increases multiple sepsis risks: A Mendelian randomization study

Background

Observational epidemiological studies have indicated a potential association between asthma and sepsis, although the causal relationship between these 2 conditions remains uncertain. To further investigate this relationship, the present study utilized Mendelian randomization (MR) analysis approach to explore the potential links between asthma and various types of sepsis.

Methods

In a large-scale genome-wide association study, single nucleotide polymorphisms (SNPs) associated with asthma were selected as instrumental variables. Three methods, including inverse-variance weighted (IVW), MR-Egger regression, and weighted median were used to assess the causal relationship between asthma and sepsis. The odds ratio (OR) and 95% confidence interval (CI) were used as the evaluation metrics for causal relationships, and sensitivity analysis was conducted to assess pleiotropy and instrument validity. Finally, a reverse MR analysis was conducted to investigate whether there is a causal relationship between sepsis and asthma.

Results

We found a positive association between asthma and an increased risk of sepsis (OR=1.18, P＜0.05), streptococcal sepsis (OR=1.23, P=0.04), pneumonia-related sepsis (OR=1.57, P＜0.05), pneumococcal sepsis (OR=1.58, P=0.01), other sepsis (OR=1.15, P＜0.05), and sepsis in intensive care unit (ICU) settings (OR=1.23, P=0.02). Sensitivity analysis showed consistent results without heterogeneity or pleiotropy. The reverse MR analysis reveals no causal relationship between various types of sepsis and asthma.

Conclusion

Our study demonstrates a causal relationship between asthma and different types of sepsis. These findings suggest the importance of healthcare providers paying attention to the potential risk of sepsis in asthma patients and implementing appropriate preventive and intervention measures in a timely manner.

Associations between static and dynamic changes of platelet counts and in-hospital mortality in critical patients with acute heart failure

To investigate the predictive value of baseline platelet count and its short-term dynamic changes in the prognosis of patients with acute heart failure (AHF) in the intensive care unit. Patients diagnosed with AHF in the medical information mart for intensive care III and their clinical data were retrospectively filtered. Patients were divided into survivor and non-survivor groups based on their prognosis during hospitalization, and differences in baseline data between groups were compared. Logistic regression models and restricted cubic spline (RCS) plots were performed to evaluate the relationship between baseline platelet counts and in-hospital mortality. Changes and trends in platelet counts were compared between the survivor and non-survivor groups after adjusting for confounders with the generalized additive mixing model (GAMM). A total of 2930 critical patients with acute heart failure were included, of which 2720 were survivors and 210 were non-survivors. Multiple logistic regression models revealed that baseline platelet count was an independent factor in hospital mortality (OR 0.997, 95% CI 0.994-0.999, P-value = 0.018). The RCS plot demonstrated a U-shaped dose-response relationship between baseline platelet count and in-hospital mortality. GAMM analysis suggested that the platelet counts decreased and then increased in the survivor group and gradually decreased in the non-survivor group, with a gradual increase of difference between two groups. After adjusting for confounders, the mean daily increase was -6.014 (95% CI -7.076-4.953, P-value < 0.001). Baseline platelet demonstrated a U-shaped dose-response relationship with adverse outcomes in critical patients with AHF. Early elevation of platelet was correlated with higher in-hospital mortality, indicating that tracking early changes in platelet might help determine the short-term prognosis of critical patients with AHF.

Desialylation by neuraminidases in platelets, kiss of death or bittersweet?

PURPOSE OF REVIEW

Loss of surface sialic acid by neuraminidases is known as 'desialylation'. Platelets are desialylated in bacterial or viral infections, during storage, senescence, various mutations, platelet auto antibodies, hemostasis and shear stress. In this review the recent literature on the different sialic acid capped glycan structures will be covered as well as platelet desialylation in inherited glycan disorders and induced by external neuraminidases.

RECENT FINDINGS

Neuraminidases are released from platelet intracellular stores and translocated to the platelet surface. Apart from clearance, loss of surface sialic acid by neuraminidases ('desialylation') affects platelet signaling including ligand binding and their procoagulant function. Platelets are also desialylated in infections, various mutations, presence of platelet auto antibodies.

SUMMARY

Since platelet desialylation occurs in various healthy and pathological conditions, measuring desialylation might be a new diagnostic tool.

Platelet Count During Course of Cardiogenic Shock

The study investigates the prognostic value of the platelet count in patients with cardiogenic shock (CS). Limited data regarding the prognostic value of platelets in patients suffering from CS is available. Consecutive patients with CS from 2019 to 2021 were included at one institution. Firstly, the prognostic value of the baseline platelet count was tested for 30-day all-cause mortality. Thereafter, the prognostic impact of platelet decline during course of intensive care unit (ICU) hospitalization was assessed. A total of 249 CS patients were included with a median platelet count of 224 × 10 6 /ml. No association of the baseline platelet count with the risk of 30-day all-cause mortality was found (log-rank p = 0.563; hazard ratio [HR] = 0.879; 95% confidence interval [CI] 0.557-1.387; p = 0.579). In contrast, a decrease of platelet count by ≥ 25% from day 1 to day 3 was associated with an increased risk of 30-day all-cause mortality (55% vs. 39%; log-rank p = 0.045; HR = 1.585; 95% CI 0.996-2.521; p = 0.052), which was still evident after multivariable adjustment (HR = 1.951; 95% CI 1.116-3.412; p = 0.019). Platelet decrease during the course of ICU hospitalization but not the baseline platelet count was associated with an increased risk of 30-day all-cause mortality in CS patients.

A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study

Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil-lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785-0.824) and model 2 was 0.791 (95% CI 0.772-0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. This simple score is useful for predicting bacteremia-assisted clinical decisions.

The prognostic value of C-reactive protein to albumin ratio in patients with sepsis: a systematic review and meta-analysis

OBJECTIVE

This study aimed to determine whether the C-reactive protein-to-albumin ratio (CAR) can serve as a prognostic marker in patients with sepsis.

METHODS

Chinese and English databases were searched to retrieve the included literature. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) with their 95% confidence interval (CI) were calculated using the bivariate model. Moreover, the hazard ratio (HR) and 95% CI were calculated using the random effect model.

RESULTS

Nine articles comprising 3224 patients with sepsis were included in the meta-analysis. The pooled SEN was 0.73 (95% CI 0.65-0.80), the pooled SPE was 0.78 (95% CI 0.69-0.84), the pooled PLR was 3.29 (95% CI 2.15-5.03), the pooled NLR was 0.35 (95% CI 0.24-0.49), and the pooled DOR was 9.50 (95% CI 4.38-20.59). The AUC under the SROC was 0.82 (95% CI 0.78-0.85) for the prognostic meta-analysis. The pooled HR was 1.10 (95% CI 1.02-1.18).

CONCLUSIONS

This meta-analysis suggests that a high CAR level is associated with increased mortality and a poor prognosis.

A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis

Background

Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings.

Methods

A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score.

Results

A PLT count <100×109/L independently increased the risk of death in sepsis patients (OR = 2.523; 95% CI: 1.084-5.872). The ML model, based on five platelet-related genes, demonstrated impressive area under the curve (AUC) values ranging from 0.5 to 0.795 across various validation platforms. On the GPL6947 platform, our ML model outperformed the APACHE II score with an AUC of 0.795 compared to 0.761. Additionally, by incorporating age, the model's performance was further improved to an AUC of 0.812. On the GPL4133 platform, the initial AUC of the machine learning model based on five platelet-related genes was 0.5. However, after including age, the AUC increased to 0.583. In comparison, the AUC of the APACHE II score was 0.604, and the AUC of the SOFA score was 0.542.

Conclusion

Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.

Clinical Value of Laboratory Biomarkers for the Diagnosis and Early Identification of Culture-Positive Sepsis in Neonates

Background

Neonatal sepsis (NS) is an important cause of mortality and morbidity in newborn infants. However, early diagnosis of proven sepsis (culture-positive sepsis) is difficult. We aimed to define the best combination of biomarkers to diagnose the onset of neonatal sepsis, distinguish culture-positive neonatal sepsis and predict the time of confirmation of neonatal sepsis.

Methods

This retrospective cohort study was conducted from January 2016 to December 2020. Clinical characteristics and laboratory results were collected from the electronic medical records. Hematology profiles and biochemical indices were obtained upon hospital admission. Multivariate logistic regression analysis was used to evaluate the risk factors and construct a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Multivariable linear regression was used to identify the association between admission-to-diagnosis interval (ADI) and correlated variables.

Results

Overall, 148 infants with neonatal sepsis (67 culture positive sepsis and 81 culture negative sepsis) and 150 controls were included. C-reactive protein (CRP) (p<0.001), platelets (PLT) (p=0.011), urea nitrogen (BUN) (p=0.001) and conjugated bilirubin (BC) (p=0.007) were independent risk factors for neonatal sepsis. The diagnostic nomogram based on CRP, PLT, BUN and BC showed excellent diagnostic accuracy for neonatal sepsis (AUC=0.928). The nomogram based on red blood cell distribution width (RDW) and mean platelet volume (MPV) was efficient in distinguishing proven neonatal sepsis from clinical sepsis, with an AUC of 0.700 in the training group and 0.689 in the validation group. Decision curve analysis (DCA) showed that the nomogram had good clinical utility. Multivariable analysis revealed gestational age, CRP, and MPV were significantly associated with admission-to-diagnosis interval in culture-positive sepsis (p < 0.001).

Conclusion

Different combinations biomarkers were performant to diagnose the onset of neonatal sepsis, distinguish culture-positive neonatal sepsis, predict the time of confirmation, and aid in individual therapy.

Does Atrial Fibrillation Deteriorate the Prognosis in Patients With Septic or Cardiogenic Shock?

Atrial fibrillation (AF) is associated with increased risk of mortality in various clinical conditions. However, the prognostic role of preexisting and new-onset AF in critically ill patients, such as patients with septic or cardiogenic shock remains unclear. This study investigates the prognostic impact of preexisting and new-onset AF on 30-day all-cause mortality in patients with septic or cardiogenic shock. Consecutive patients with sepsis, or septic or cardiogenic shock were enrolled in 2 prospective, monocentric registries from 2019 to 2021. Statistical analyses included Kaplan-Meier, multivariable logistic, and Cox proportional regression analyses. In total, 644 patients were included (cardiogenic shock: n = 273; sepsis/septic shock: n = 361). The prevalence of AF was 41% (29% with preexisting AF, 12% with new-onset AF). Within the entire study cohort, neither preexisting AF (log-rank p = 0.542; hazard ratio [HR] 1.075, 95% confidence interval [CI] 0.848 to 1.363, p = 0.551) nor new-onset AF (log-rank p = 0.782, HR = 0.957, 95% CI 0.683 to 1.340, p = 0.797) were associated with 30-day all-cause mortality compared with non-AF. In patients with AF, ventricular rates >120 beats/min compared with ≤120 beats/min were shown to increase the risk of reaching the primary end point in AF patients with cardiogenic shock (log-rank p = 0.006, HR 1.886, 95% CI 1.164 to 3.057, p = 0.010). Furthermore, logistic regression analyses suggested increased age was the only predictor of new-onset AF (odds ratio 1.042, 95% CI 1.018 to 1.066, p = 0.001). In conclusion, neither the presence of preexisting AF nor the occurrence of new-onset AF was associated with the risk of 30-day all-cause mortality in consecutive patients admitted with cardiogenic shock.

Current knowledge of thrombocytopenia in sepsis and COVID-19

Thrombocytopenia, characterized by a decrease in platelet count, is commonly observed in sepsis and COVID-19. In sepsis, thrombocytopenia can result from various mechanisms, including impaired platelet production in the bone marrow, accelerated platelet destruction due to increased inflammation, sequestration of platelets in the spleen, immune-mediated platelet destruction, or dysregulated host responses. Similarly, thrombocytopenia has been reported in COVID-19 patients, but the immune-related mechanisms underlying this association remain unclear. Notably, interventions targeting thrombocytopenia have shown potential for improving outcomes in both sepsis and COVID-19 patients. Understanding these mechanisms is crucial for developing effective treatments.

Blood purification for treatment of non-liquefied multiple liver abscesses and improvement of T-cell function: A case report

BACKGROUND

Non-liquefied multiple liver abscesses (NMLA) can induce sepsis, septic shock, sepsis-associated kidney injury (SA-AKI), and multiple organ failure. The inability to perform ultrasound-guided puncture and drainage to eradicate the primary disease may allow for the persistence of bacterial endotoxins and endogenous cytokines, exacerbating organ damage, and potentially causing immunosuppression and T-cell exhaustion. Therefore, the search for additional effective treatments that complement antibiotic therapy is of great importance.

CASE SUMMARY

A 45-year-old critically ill female patient presented to our hospital's intensive care unit with intermittent vomiting, diarrhea, and decreased urine output. The patient exhibited a temperature of 37.8 °C. Based on the results of liver ultrasonography, laboratory tests, fever, and oliguria, the patient was diagnosed with NMLA, sepsis, SA-AKI, and immunosuppression. We administered antibiotic therapy, entire care, continuous renal replacement therapy (CRRT) with an M100 hemofilter, and hemoperfusion (HP) with an HA380 hemofilter. The aforementioned treatment resulted in a substantial reduction in disease severity scores and a decrease in the extent of infection and inflammatory factors. In addition, the treatment stimulated the expansion of the cluster of differentiation 8+ (CD8+) T-cells and led to the complete recovery of renal function. The patient was discharged from the hospital. During the follow-up period of 28 d, she recovered successfully.

CONCLUSION

Based on the entire therapeutic regimen, the early combination of CRRT and HP therapy may control sepsis caused by NMLA and help control infections, reduce inflammatory responses, and improve CD8+ T-cell immune function.

Prediction of respiratory failure risk in patients with pneumonia in the ICU using ensemble learning models

The aim of this study was to develop early prediction models for respiratory failure risk in patients with severe pneumonia using four ensemble learning algorithms: LightGBM, XGBoost, CatBoost, and random forest, and to compare the predictive performance of each model. In this study, we used the eICU Collaborative Research Database (eICU-CRD) for sample extraction, built a respiratory failure risk prediction model for patients with severe pneumonia based on four ensemble learning algorithms, and developed compact models corresponding to the four complete models to improve clinical practicality. The average area under receiver operating curve (AUROC) of the models on the test sets after ten random divisions of the dataset and the average accuracy at the best threshold were used as the evaluation metrics of the model performance. Finally, feature importance and Shapley additive explanation values were introduced to improve the interpretability of the model. A total of 1676 patients with pneumonia were analyzed in this study, of whom 297 developed respiratory failure one hour after admission to the intensive care unit (ICU). Both complete and compact CatBoost models had the highest average AUROC (0.858 and 0.857, respectively). The average accuracies at the best threshold were 75.19% and 77.33%, respectively. According to the feature importance bars and summary plot of the predictor variables, activetx (indicates whether the patient received active treatment), standard deviation of prothrombin time-international normalized ratio, Glasgow Coma Scale verbal score, age, and minimum oxygen saturation and respiratory rate were important. Compared with other ensemble learning models, the complete and compact CatBoost models have significantly higher average area under the curve values on the 10 randomly divided test sets. Additionally, the standard deviation (SD) of the compact CatBoost model is relatively small (SD:0.050), indicating that the performance of the compact CatBoost model is stable among these four ensemble learning models. The machine learning predictive models built in this study will help in early prediction and intervention of respiratory failure risk in patients with pneumonia in the ICU.

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.