Time to maximum amplitude of thromboelastography can predict mortality in patients with severe COVID-19: a retrospective observational study.

Objective: To predict mortality in severe patients with COVID-19 at admission to the intensive care unit (ICU) using thromboelastography (TEG). Methods: This retrospective, two-center, observational study involved 87 patients with PCR-and chest CT-confirmed severe COVID-19 who were admitted to at Wuhan Huoshenshan Hospital and the 908th Hospital of Chinese PLA Logistic Support Force between February 2020 and February 2023. Clinic demographics, laboratory results, and outcomes were compared between those who survived and those who died during hospitalization. Results: Thromboelastography showed that of the 87 patients, 14 were in a hypercoagulable state, 25 were in a hypocoagulable state, and 48 were normal, based on the time to maximum amplitude (TMA). Patients who died showed significantly lower α angle, but significantly longer R-time, K-time and TMA than patients who survived. Random forest selection showed that K-time, TMA, prothrombin time (PT), international normalized ratio (INR), D-dimer, C-reactive protein (CRP), aspartate aminotransferase (AST), and total bilirubin (Tbil) were significant predictors. Multivariate logistic regression identified that TMA and CRP were independently associated with mortality. TMA had a greater predictive power than CRP levels based on time-dependent AUCs. Patients with TMA ≥ 26.4 min were at significantly higher risk of mortality (hazard ratio 3.99, 95% Confidence Interval, 1.92-8.27, p < 0.01). Conclusion: TMA ≥26.4 min at admission to ICU may be an independent predictor of in-hospital mortality for patients with severe COVID-19.

A newly proposed heatstroke-induced coagulopathy score in patients with heat illness: A multicenter retrospective study in China.

PURPOSE: In patients with heatstroke, disseminated intravascular coagulation (DIC) is associated with greater risk of in-hospital mortality. However, time-consuming assays or a complex diagnostic system may delay immediate treatment. Therefore, the present study proposes a new heatstroke-induced coagulopathy (HIC) score in patients with heat illness as an early warning indicator for DIC. METHODS: This retrospective study enrolled patients with heat illness in 24 Chinese hospitals from March 2021 to May 2022. Patients under 18 years old, with a congenital clotting disorder or liver disease, or using anticoagulants were excluded. Data were collected on demographic characteristics, routine blood tests, conventional coagulation assays and biochemical indexes. The risk factors related to coagulation function in heatstroke were identified by regression analysis, and used to construct a scoring system for HIC. The data of patients who met the diagnostic criteria for HIC and International Society on Thrombosis and Haemostasis defined-DIC were analyzed. All statistical analyses were performed using SPSS 26.0. RESULTS: The final analysis included 302 patients with heat illness, of whom 131 (43.4%) suffered from heatstroke, including 7 death (5.3%). Core temperature (OR = 1.681, 95% CI 1.291 - 2.189, p < 0.001), prothrombin time (OR = 1.427, 95% CI 1.175 - 1.733, p < 0.001) and D-dimer (OR = 1.242, 95% CI 1.049 - 1.471, p = 0.012) were independent risk factors for heatstroke, and therefore used to construct an HIC scoring system because of their close relation with abnormal coagulation. A total score ≥ 3 indicated HIC, and HIC scores correlated with the score for International Society of Thrombosis and Hemostasis -DIC (r = 0.8848, p < 0.001). The incidence of HIC (27.5%) was higher than that of DIC (11.2%) in all of 131 heatstroke patients. Meanwhile, the mortality rate of HIC (19.4%) was lower than that of DIC (46.7%). When HIC developed into DIC, parameters of coagulation dysfunction changed significantly: platelet count decreased, D-dimer level rose, and prothrombin time and activated partial thromboplastin time prolonged (p < 0.05). CONCLUSIONS: The newly proposed HIC score may provide a valuable tool for early detection of HIC and prompt initiation of treatment.

Nomogram for predicting disseminated intravascular coagulation in heatstroke patients: A 10 years retrospective study.

Background: Disseminated intravascular coagulation (DIC) can lead to multiple organ failure and death in patients with heatstroke. This study aimed to identify independent risk factors of DIC and construct a predictive model for clinical application. Methods: This retrospective study included 87 patients with heatstroke who were treated in the intensive care unit of our hospital from May 2012 to October 2022. Patients were divided into those with DIC (n = 23) or without DIC (n = 64). Clinical and hematological factors associated with DIC were identified using a random forest model, least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE). Overlapping factors were used to develop a nomogram model, which was diagnostically validated. Survival at 30 days after admission was compared between patients with or without DIC using Kaplan-Meier analysis. Results: Random forest, LASSO, and SVM-RFE identified a low maximum amplitude, decreased albumin level, high creatinine level, increased total bilirubin, and aspartate transaminase (AST) level as risk factors for DIC. Principal component analysis confirmed that these independent variables differentiated between patients who experienced DIC or not, so they were used to construct a nomogram. The nomogram showed good predictive power, with an area under the receiver operating characteristic curve of 0.976 (95% CI 0.948-1.000) and 0.971 (95% CI, 0.914-0.989) in the internal validation. Decision curve analysis indicated clinical utility for the nomogram. DIC was associated with significantly lower 30 days survival for heatstroke patients. Conclusion: A nomogram incorporating coagulation-related risk factors can predict DIC in patients with heatstroke and may be useful in clinical decision-making.

Thromboelastography maximum amplitude as an early predictor of disseminated intravascular coagulation in patients with heatstroke.

OBJECTIVES: The aim of this study was to investigate the ability of TEG to predict DIC associated with heatstroke. METHODS: We carried out a retrospective, single-center study of 67 patients with heatstroke admitted to an intensive care unit (ICU) at a comprehensive hospital between July 2016 and August 2021. Conventional coagulation tests (CCTs) and TEG were performed within 2 h of admission in ICU. Patients were diagnosed with DIC based on International Society of Thrombosis and Hemostasis criteria, and those with or without DIC were compared in terms of CCTs and TEG findings. The ability of individual parameters to predict DIC was assessed based on logistic regression and the area under receiver operating characteristic curves (AUC). RESULTS: Of the 67 patients, 19 (28.4%) were diagnosed with DIC. Compared to patients without DIC, those with DIC had significantly longer reaction time [14.5(10.6-26.0) vs. 6.2(5.1-10.1)min](p < 0.001) and kinetic time [10.9(5.9-25.0) vs. 2.7(2.2-4.7) min](p < 0.001). Conversely, those with DIC had significantly lower alpha angle [22(9.1-43.3) vs. 55.0(44.8-61.7)°](p < 0.001), maximum amplitude (MA) [(26.9(17.7-41.4) vs. 52.2(45.8-58.1) mm)] (p < 0.001) and coagulation index [-17.3(-39 to -7.9）vs. -2.4(-6.2to-0.6)](p < 0.001). MA at a cutoff value of 45.4 mm gave an AUC of 0.9 for predicting DIC, with sensitivity of 77.1%, specificity of 89.5%, positive predictive value of 10.5% and negative predictive value of 22.9%. Multifactorial logistic regression identified MA < 45.4 mm as an independent predictor of DIC (odds ratio 9, 95% confidence interval 1.2-69.2, p = 0.035). MA decreased significantly as DIC score increased and was significantly lower in the non-survivors on admission. CONCLUSIONS: MA < 45.4 mm in patients with heatstroke may predict elevated risk of DIC. HighlightsPatients with heatstroke-induced disseminated intravascular coagulation (DIC) has high mortality.A retrospective, single-center study was performed to investigate the ability of thromboelastography (TEG) to predict DIC associated with heatstroke.The maximum amplitude (MA) value of TEG decreased significantly with the increase of DIC score.MA < 45.4 mm was firstly demonstrated to an independent predictor of heatstroke-induced DIC.

Tissue-Type Plasminogen Activator-Inhibitor Complex as an Early Predictor of Septic Shock: A Retrospective, Single-Center Study.

Background: Sepsis can progress to septic shock and death, and identifying biomarkers of this progression may permit timely intervention to prevent it. This study explored whether levels of tissue-type plasminogen activator-inhibitor complex (t-PAIC) in serum can predict septic shock early. Methods: We retrospectively analyzed 311 sepsis patients who had been admitted to the intensive care unit (ICU) at our tertiary care hospital between May 2018 and April 2021, and we divided them into those who progressed to septic shock (n = 203) or not (n = 108) based on sepsis-3 definition. After matching patients in the two groups based on propensity scoring, we screened for risk factors of septic shock using logistic regression. We assessed potential predictors of such shock based on the area under the receiver-operating characteristic curve (AUC), Kaplan-Meier survival curves, and correlation analysis. Results: After propensity score matching to generate two equal groups of 108 patients, we found that serum t-PAIC was significantly higher in septic shock patients. Uni- and multivariate logistic regression identified t-PAIC as an independent risk factor for septic shock (OR 1.14, 95% CI 1.09-1.19, P < 0.001) and a biomarker that predicted it with an AUC up to 0.875 (95% CI, 0.829-0.920). Based on the optimal cut-off of t-PAIC = 17.9 ng/mL, we found that patients at or above this threshold had significantly higher lactate levels and scores on the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA). Such patients also had significantly worse survival (HR 2.4, 95% CI 1.38-4.34, P = 0.004). Spearman's correlation coefficients were 0.66 between t-PAIC and lactate, and 0.52 between t-PAIC and SOFA. Conclusions: Serum levels of t-PAIC may be an independent risk factor for septic shock, and they may correlate with the severity of such shock.

Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices.

BACKGROUND: Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality. METHODS: We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis. RESULTS: Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. CONCLUSION: This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.