Objective: To evaluate the clinical value of new coagulation biomarkers including soluble thrombomodulin (sTM) and tissue plasminogen activator inhibitor complex (t-PAI·C) for the diagnosis and prognosis of sepsis in children. Methods: The prospective observational study enrolled 59 children who were diagnosed with sepsis including severe sepsis and septic shock in the Department of Pediatric Critical Care Medicine of Shanghai Children's Medical Center affiliated to the Medical College of Shanghai Jiao Tong University from June 2019 to June 2021. The sTM, t-PAI·C and conventional coagulation tests were detected on illness day one of sepsis. Twenty healthy children were selected as the control group, and the above parameters were detected on the day of inclusion. Children with sepsis were divided into survival group and non-survival group according to prognosis at discharge. Baseline comparisons between groups were performed using Mann-Whitney U test. Multivariate Logistic regression analysis was used to evaluate the risk factors for the diagnosis and prognosis of sepsis in children. Receiver operating characteristic (ROC) curve was conducted to evaluate the predictive values of above variables for the diagnosis and prognosis of sepsis in children. Results: The sepsis group included 59 patients (39 boys and 20 girls), aged 61(22, 136)months. There were 44 patients in the survival group and 15 patients in the non-survival group, respectively. The control group consisted of 20 boys, aged 107 (94,122) months. Patients in the sepsis group had higher sTM and t-PAI·C ((12 (9, 17)×10³ vs. 9(8, 10)×10³ TU/L, 10(6, 22) vs. 2 (1, 3) μg/L, Z=-2.15, -6.05, both P<0.05) compared with children in the control group. The t-PAI·C was superior to sTM for the diagnosis of sepsis. The areas under the curve (AUC) of t-PAI·C and sTM for the diagnosis of sepsis were 0.95 and 0.66, respectively, and the optimal cut-off value were 3 μg/L and 12×10³ TU/L, respectively. Patients in the survival group had lower sTM (10 (8, 14)×10³ vs. 17 (11, 36)×10³ TU/L, Z=-2.73, P=0.006) than those in the non-survival group. Logistic regression analysis showed that sTM was a risk factor for death at discharge (OR=1.14, 95%CI 1.04-1.27, P=0.006). The AUC of sTM and t-PAI·C for predicting death at discharge were 0.74 and 0.62, respectively, and the optimal cut-off values were 13×10³ TU/L and 6 μg/L, respectively. The AUC of sTM combined with platelet counts for predicting death at discharge was 0.89, which was superior to sTM and t-PAI·C. Conclusion: The sTM and t-PAI·C had clinical application values in diagnosing and predicting prognosis in pediatric sepsis.
Child ; Female ; Humans ; Male ; Biomarkers ; China ; Sepsis/diagnosis* ; Shock, Septic ; Tissue Plasminogen Activator ; Infant ; Child, Preschool

OBJECTIVE: To investigate the correlation of hemoglobin (Hb) level with prognosis of elderly patients diagnosed as sepsis. METHODS: A retrospective cohort study was conducted. Information on the cases of elderly patients with sepsis in the Medical Information Mart for Intensive Care-IV (MIMIC-IV), including basic information, blood pressure, routine blood test results [the Hb level of a patient was defined as his/her maximum Hb level from 6 hours before admission to intensive care unit (ICU) and 24 hours after admission to ICU], blood biochemical indexes, coagulation function, vital signs, severity score and outcome indicators were extracted. The curves of Hb level vs. 28-day mortality risk were developed by using the restricted cubic spline model based on the Cox regression analysis. The patients were divided into four groups (Hb < 100 g/L, 100 g/L ≤ Hb < 130 g/L, 130 g/L ≤ Hb < 150 g/L, Hb ≥ 150 g/L groups) based on these curves. The outcome indicators of patients in each group were analyzed, and the 28-day Kaplan-Meier survival curve was drawn. Logistic regression model and Cox regression model were used to analyze the relationship between Hb level and 28-day mortality risk in different groups. RESULTS: A total of 7 473 elderly patients with sepsis were included. There was a "U" curve relationship between Hb levels within 24 hours after ICU admission and the risk of 28-day mortality in patients with sepsis. The patients with 100 g/L ≤ Hb < 130 g/L had a lower risk of 28-day mortality. When Hb level was less than 100 g/L, the risk of death decreased gradually with the increase of Hb level. When Hb level was ≥ 130 g/L, the risk of death gradually increased with the increase of Hb level. Multivariate Logistic regression analysis revealed that the mortality risks of patients with Hb < 100 g/L [odds ratio (OR) = 1.44, 95% confidence interval (95%CI) was 1.23-1.70, P < 0.001] and Hb ≥ 150 g/L (OR = 1.77, 95%CI was 1.26-2.49, P = 0.001) increased significantly in the model involving all confounding factors; the mortality risks of patients with 130 g/L ≤ Hb < 150 g/L increased, while the difference was not statistically significant (OR = 1.21, 95%CI was 0.99-1.48, P = 0.057). The multivariate Cox regression analysis suggested that the mortality risks of patients with Hb < 100 g/L [hazard ratio (HR) = 1.27, 95%CI was 1.12-1.44, P < 0.001] and Hb ≥ 150 g/L (HR = 1.49, 95%CI was 1.16-1.93, P = 0.002) increased significantly in the model involving all confounding factors; the mortality risks of patients with 130 g/L ≤ Hb < 150 g/L increased, while the difference was not statistically significant (HR = 1.17, 95%CI was 0.99-1.37, P = 0.053). Kaplan-Meier survival curve showed that the 28-day survival rate of elderly septic patients in 100 g/L ≤ Hb < 130 g/L group was significantly higher than that in Hb < 100 g/L, 130 g/L ≤ Hb < 150 g/L and Hb ≥ 150 g/L groups (85.26% vs. 77.33%, 79.81%, 74.33%; Log-Rank test: χ² = 71.850, P < 0.001). CONCLUSIONS Elderly patients with sepsis exhibited low mortality risk if their 100 g/L ≤ Hb < 130 g/L within 24 hours after admission to ICU, and both higher and lower Hb levels led to increased mortality risks.
Humans ; Male ; Female ; Aged ; Retrospective Studies ; Sepsis/diagnosis* ; Critical Care ; Intensive Care Units ; Prognosis ; Hemoglobins ; ROC Curve

OBJECTIVE: To investigate the correlation between early-stage blood pressure indexes and prognosis in sepsis patients. METHODS: A retrospective cohort study was conducted on the medical records of patients diagnosed with sepsis from 2001 to 2012 in the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Patients were divided into survival group and death group according to the 28-day prognosis. General data of patients and heart rate (HR) and blood pressure at admission to ICU and within 24 hours after admission were collected. The blood pressure indexes including the maximum, median and mean value of systolic index, diastolic index and mean arterial pressure (MAP) index were calculated. The data were randomly divided into training set and validation set (4 : 1). Univariate Logistic regression analysis was used to screen covariates, and multivariate Logistic stepwise regression models were further developed. Model 1 (including HR, blood pressure, and blood pressure index related variables with P < 0.1 and other variables with P < 0.05) and Model 2 (including HR, blood pressure, and blood pressure index related variables with P < 0.1) were developed respectively. The receiver operator characteristic curve (ROC curve), precision recall curve (PRC) and decision curve analysis (DCA) curve were used to evaluate the quality of the two models, and the influencing factors of the prognosis of sepsis patients were analyzed. Finally, nomogram model was developed according to the better model and effectiveness of it was evaluated. RESULTS: A total of 11 559 sepsis patients were included in the study, with 10 012 patients in the survival group and 1 547 patients in the death group. There were significant differences in age, survival time, Elixhauser comorbidity score and other 46 variables between the two groups (all P < 0.05). Thirty-seven variables were preliminarily screened by univariate Logistic regression analysis. After multivariate Logistic stepwise regression model screening, among the indicators related to HR, blood pressure and blood pressure index, the HR at admission to ICU [odds ratio (OR) = 0.992, 95% confidence interval (95%CI) was 0.988-0.997] and the maximum HR (OR = 1.006, 95%CI was 1.001-1.011), maximum MAP index (OR = 1.620, 95%CI was 1.244-2.126), mean diastolic index (OR = 0.283, 95%CI was 0.091-0.856), median systolic index (OR = 2.149, 95%CI was 0.805-4.461), median diastolic index (OR = 3.986, 95%CI was 1.376-11.758) were selected (all P < 0.1). There were 14 other variables with P < 0.05, including age, Elixhauser comorbidity score, continuous renal replacement therapy (CRRT), use of ventilator, sedation and analgesia, norepinephrine, norepinephrine, highest serum creatinine (SCr), maximum blood urea nitrogen (BUN), highest prothrombin time (PT), highest activated partial thromboplastin time (APTT), lowest platelet count (PLT), highest white blood cell count (WBC), minimum hemoglobin (Hb). The ROC curve showed that the area under the curve (AUC) of Model 1 and Model 2 were 0.769 and 0.637, respectively, indicating that model 1 had higher prediction accuracy. The PRC curve showed that the AUC of Model 1 and Model 2 were 0.381 and 0.240, respectively, indicating that Model 1 had a better effect. The DCA curve showed that when the threshold was 0-0.8 (the probability of death was 0-80%), the net benefit rate of Model 1 was higher than that of Model 2. The calibration curve showed that the prediction effect of the nomogram model developed according to Model 1 was in good agreement with the actual outcome. The Bootstrap verification results showed that the nomogram model was consistent with the above results and had good prediction effects. CONCLUSIONS The nomogram model constructed has good prediction effects on the 28-day prognosis in sepsis patients, and the blood pressure indexes are important predictors in the model.
Humans ; Cohort Studies ; Retrospective Studies ; Blood Pressure ; Intensive Care Units ; ROC Curve ; Sepsis/diagnosis* ; Prognosis ; Critical Care ; Norepinephrine

OBJECTIVE: To explore the relevant clinical test indicators that affect the prognosis of patients with acute fatty liver of pregnancy (AFLP), and to provide a basis for early diagnosis and correct selection of treatment methods. METHODS: A retrospective analysis was conducted. Clinical data of AFLP patients in the intensive care unit (ICU) of the First Affiliated Hospital of Zhengzhou University from January 2010 to May 2021 were collected. According to the 28-day prognosis, the patients were divided into death group and survival group. The clinical data, laboratory examination indicators, and prognosis of the two groups were compared, and further binary Logistic regression analysis was used to analyze the risk factors affecting the prognosis of patients. At the same time, the values of related indicators at each time point (24, 48, 72 hours) after the start of treatment were recorded. The receiver operator characteristic curve (ROC curve) of prothrombin time (PT) and international normalized ratio (INR) for evaluating the prognosis of patients at each time point was drawn, and the area under the ROC curve (AUC) was calculated to evaluate the predictive value of relevant indicators at each time point for the prognosis of AFLP patients. RESULTS: A total of 64 AFLP patients were selected. The patients developed the AFLP during pregnancy (34.5±6.8) weeks, with 14 deaths (mortality of 21.9%) and 50 survivors (survival rate of 78.1%). There was no statistically significant difference in general clinical data between the two groups of patients, including age, time from onset to visit, time from visit to cessation of pregnancy, acute physiology and chronic health evaluations II (APACHE II), hospitalization time in ICU, and total hospitalization cost. However, the proportion of male fetuses and stillbirths in the death group was higher than that in the survival group. The laboratory examination indicators including the white blood cell count (WBC), alanine transaminase (ALT), serum creatinine (SCr), PT extension, INR elevation, and hyperammonia in the death group were significantly higher than those in the survival group (all P < 0.05). Through Logistic regression analysis of the above indicators showed that PT > 14 s and INR > 1.5 were risk factors affecting the prognosis of AFLP patients [PT > 14 s: odds ratio (OR) = 1.215, 95% confidence interval (95%CI) was 1.076-1.371, INR > 1.5: OR = 0.719, 95%CI was 0.624-0.829, both P < 0.01]. ROC curve analysis showed that both PT and INR at ICU admission and 24, 48, and 72 hours of treatment can evaluate the prognosis of AFLP patients [AUC and 95%CI of PT were 0.772 (0.599-0.945), 0.763 (0.608-0.918), 0.879 (0.795-0.963), and 0.957 (0.904-1.000), respectively; AUC and 95%CI of INR were 0.808 (0.650-0.966), 0.730 (0.564-0.896), 0.854 (0.761-0.947), and 0.952 (0.896-1.000), respectively; all P < 0.05], the AUC of PT and INR after 72 hours of treatment was the highest, with higher sensitivity (93.5%, 91.8%) and specificity (90.9%, 90.9%). CONCLUSIONS AFLP often occurs in the middle and late stages of pregnancy, and the initial symptoms are mainly gastrointestinal symptoms. Once discovered, pregnancy should be terminated immediately. PT and INR are good indicators for evaluating AFLP patient efficacy and prognosis, and PT and INR are the best prognostic indicators after 72 hours of treatment.
Humans ; Male ; Prognosis ; ROC Curve ; Retrospective Studies ; Intensive Care Units ; Sepsis/diagnosis*

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

OBJECTIVE: To explore the characteristics of changes in peripheral blood lymphocyte subsets in patients with sepsis in intensive care unit (ICU) and analyze their predictive value for prognosis. METHODS: The clinical data of sepsis patients admitted to the surgical intensive care unit (SICU) of the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2021 were analyzed retrospectively. The patients met the diagnostic criteria of Sepsis-3 and were ≥ 18 years old. Peripheral venous blood samples were collected from all patients on the next morning after admission to SICU for routine blood test and peripheral blood lymphocyte subsets. According to the 28-day survival, the patients were divided into two groups, and the differences in immune indexes between the two groups were compared. Logistic regression analysis was used to analyze the risk factors of immune indexes that affect prognosis. RESULTS: (1) A total of 279 patients with sepsis were enrolled in the experiment, of which 198 patients survived at 28 days (28-day survival rate 71.0%), and 81 patients died (28-day mortality 29.0%). There were no significant differences in age (years old: 57.81±1.71 vs. 54.99±1.05) and gender (male: 60.5% vs. 63.6%) between the death group and the survival group (both P > 0.05), and the baseline data was comparable.(2) Acute physiology and chronic health evalution II (APACHE II: 22.06±0.08 vs. 14.08±0.52, P < 0.001), neutrophil percentage [NEU%: (88.90±1.09)% vs. (84.12±0.77)%, P = 0.001], procalcitonin [PCT (μg/L): 11.97±2.73 vs. 5.76±1.08, P = 0.011], platelet distribution width (fL: 16.81±0.10 vs. 16.57±0.06, P = 0.029) were higher than those in the survival group, while lymphocyte percentage [LYM%: (6.98±0.78)% vs. (10.59±0.86)%, P = 0.012], lymphocyte count [LYM (×10⁹/L): 0.70±0.06 vs. 0.98±0.49, P = 0.002], and platelet count [PLT (×10⁹/L): 151.38±13.96 vs. 205.80±9.38, P = 0.002], and thrombocytocrit [(0.15±0.01)% vs. (0.19±0.07)%, P = 0.012] were lower than those in the survival group. (3) There was no statistically significant difference in the percentage of lymphocyte subsets between the death group and the survival group, but the absolute value of LYM (pieces/μL: 650.24±84.67 vs. 876.64±38.02, P = 0.005), CD3⁺ absolute value (pieces/μL: 445.30±57.33 vs. 606.84±29.25, P = 0.006), CD3⁺CD4⁺ absolute value (pieces/μL: 239.97±26.96 vs. 353.49±18.59, P = 0.001), CD19⁺ absolute value (pieces/μL: 111.10±18.66 vs. 150.30±10.15, P = 0.049) in the death group was lower than those in the survival group. Other lymphocyte subsets in the death group, such as CD3⁺CD8⁺ absolute value (pieces/μL: 172.40±24.34 vs. 211.22±11.95, P = 0.112), absolute value of natural killer cell [NK (pieces/μL): 101.26±18.15 vs. 114.72±7.64, P = 0.420], absolute value of natural killer T cell [NKT (pieces/μL): 33.22±5.13 vs. 39.43±2.85, P = 0.262], CD4^-CD8^- absolute value (pieces/μL: 41.07±11.07 vs. 48.84±3.31, P = 0.510), CD4⁺CD8⁺ absolute value (pieces/μL: 3.39±1.45 vs. 3.47±0.36, P = 0.943) were not significantly different from those in the survival group. (4)Logistic regression analysis showed that lymphocyte subsets were not selected as immune markers with statistical significance for the prognosis of sepsis. CONCLUSIONS The changes of immune indexes in sepsis patients are closely related to their prognosis. Early monitoring of the above indexes can accurately evaluate the condition and prognosis of sepsis patients.
Humans ; Male ; Adolescent ; Retrospective Studies ; ROC Curve ; Sepsis/diagnosis* ; Lymphocyte Count ; Lymphocyte Subsets ; Prognosis ; Killer Cells, Natural

OBJECTIVE: To analyze the clinical characteristics and risk factors of early acute liver injury in patients with heat stroke (HS), and to provide basis for early identification of HS-related liver injury and its pathogenesis in clinical practice. METHODS: The clinical data of patients with HS admitted to the department of critical care medicine of Haian People's Hospital from June 2015 to August 2022 were retrospectively analyzed. The patients with HS were divided into early liver injury group and early non-liver injury group according to the occurrence of acute liver injury within 24 hours of admission. The differences of basic data, clinical data, laboratory indexes and clinical outcomes of the two groups were analyzed. Logistic regression was used to analyze the risk factors for early HS-related acute liver injury, and receiver operator characteristic (ROC) curves were drawn to evaluate their value in predicting the occurrence of early HS-related acute liver injury. RESULTS: A total of 76 patients with HS were enrolled, and 46 patients with acute liver injury, accounting for 60.53%. In the early liver injury group, 14 patients (30.43%) had elevated aminotransferase alone, 9 patients (19.57%) had elevated total bilirubin (TBil) alone, and 23 patients (50.00%) had elevated both aminotransferase and TBil. Among the patients with elevated aminotransferases, 24 patients (64.87%) had mild elevation, 5 patients (13.51%) had moderate elevation, 8 patients (21.62%) had severe elevation. Compared with the early non-liver injury group, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), arterial blood lactate (Lac), interleukin-6 (IL-6), procalcitonin (PCT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), TBil, γ-gamma glutamyl transferase (γ-GGT), lactate dehydrogenase (LDH), creatine kinase (CK), MB isoenzyme of creatine kinase (CK-MB), cardiac troponin I (cTnI), myoglobin (MYO), N-terminal B-type pro-brain natriuretic peptide (NT-proBNP), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer in the early liver injury group were significantly increased, while platelet count (PLT) were significantly decreased within 24 hours after admission, the 28-day mortality was significantly increased [28.26% (13/46) vs. 6.67% (2/30)], and the differences were statistically significant (all P < 0.05). Univariate Logistic regression analysis showed that APACHE II score, SOFA score, PLT, Lac, IL-6, PCT, γ-GGT, LDH, CK, CK-MB, cTnI, MYO, PT, APTT, D-dimer were risk factors of early HS-related acute liver injury (all P < 0.05). Multivariate Logistic regression analysis showed that PLT, IL-6, and LDH were independent risk factors of early HS-related acute liver injury [odds ratio (OR) and 95% confidence interval (95%CI) were 0.986 (0.974-0.998), 1.027 (1.012-1.041), and 1.002 (1.000-1.004), all P < 0.05]. The ROC curve analysis showed that the area under the ROC curve (AUC) of PLT, IL-6 and LDH for predicting the occurrence of early HS-related acute liver injury was 0.672 (95%CI was 0.548-0.797), 0.897 (95%CI was 0.824-0.971) and 0.833 (95%CI was 0.739-0.927), respectively. IL-6 had the highest predictive value for early HS-related liver injury. When the optimal diagnostic threshold of IL-6 was 48.25 ng/L, the sensitivity was 95.7%, the specificity was 73.3%, and the predictive value of PLT was the lowest. CONCLUSIONS The early HS-related liver injury is mainly manifested as the simultaneous elevation of aminotransferase and TBil, and most of cases are mild liver injury. PLT, IL-6 and LDH are independent risk factors of early HS-related acute liver injury.
Humans ; Prognosis ; Retrospective Studies ; Interleukin-6 ; ROC Curve ; Sepsis/diagnosis* ; Heat Stroke/complications* ; Risk Factors ; Alanine Transaminase ; Creatine Kinase, MB Form ; Lactic Acid ; Creatine Kinase

OBJECTIVE: To investigate the clinical significance of early troponin I (TnI) level in the prognosis of severe heat stroke. METHODS: Clinical data of 131 patients with severe heat stroke in the intensive care unit (ICU) of the Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University (study dataset) and ICU 67 patients with severe heat stroke in Jintan First People's Hospital of Changzhou (validation dataset) were retrospectively analyzed from June 2013 to September 2022. The patients were divided into survival group and death group according to 30-day outcomes. TnI was collected within 24 hours after admission to the emergency department. Cox regression analysis was performed to analyze the risk factors of severe heat stroke death. Spearman correlation test was used to analyze the correlation between TnI and heart rate, and peripheral systolic blood pressure. The receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of TnI for death in patients with severe heat stroke. Decision curve analysis (DCA) was conducted to assess the clinical net benefit rate of TnI prediction. Grouping by TnI cut-off value, Kaplan-Meier survival curve was used to analyze 30-day cumulative survival. Sensitivity analysis included modified Possion regression, E-value, and subgroup forest map was used to evaluate the mortality risk of TnI in different populations. External dataset was used to verify the predictive value of TnI. RESULTS: The death group had significantly higher TnI compared to the survival group [μg/L: 0.623 (0.196, 1.510) vs. 0.084 (0.019, 0.285), P < 0.01]. Multivariate Cox regression analysis after adjusting for confounding factors showed that TnI was an independent risk factor for death [hazard ratio (HR) = 1.885, 95% confidence interval (95%CI) was 1.528-2.325,P < 0.001]. Spearman correlation test showed that TnI was positively correlated with heart rate (r = 0.537, P < 0.001) and negatively correlated with peripheral systolic blood pressure (r = -0.611, P < 0.001). ROC curve showed that the area under the curve (AUC) of the TnI (0.817) was better than that of the acute physiology and chronic health evaluation II (APACHE II, 0.756). The DCA curve showed that the range of clinical net benefit rate of TnI (6.21%-20.00%) was higher than that of APACHE II score (5.14%-20.00%). Kaplan-Meier survival curve showed that patients in the low-risk group (TnI ≤ 0.106) had a significantly higher 30-day survival rate than that in the high-risk group (TnI > 0.106) group (Log-Rank test: χ² = 17.350, P < 0.001). Modified Possion regression with adjustment for confounding factors showed that TnI was still an independent risk factor for death in patients with severe heat stroke [relative risk (RR) = 1.425, 95%CI was 1.284-1.583, P < 0.001]. The E-value was 2.215. The subgroup forest plot showed that the risk factors of TnI were obvious in male patients and patients ≤ 60 years old (male: HR = 1.731, 95%CI was 1.402-2.138, P < 0.001; ≤ 60 years old: HR = 1.651, 95%CI was 1.362-2.012, P < 0.001). In the validation dataset, ROC curve analysis showed that the AUC (0.836) of TnI predicting the prognosis of severe heat stroke was still higher than the APACHE II score (0.763). CONCLUSIONS Early elevation of TnI is a high-risk factor for death in patients with severe heat stroke, and it has a good predictive value for death.
Humans ; Male ; Middle Aged ; Troponin I ; Retrospective Studies ; Clinical Relevance ; ROC Curve ; Prognosis ; Intensive Care Units ; Heat Stroke/diagnosis* ; Sepsis

Objective: To determine the ability of the ratio of platelet to lymphocyte (PLR) for predicting extubation failure in septic patients receiving invasive mechanical ventilation (IMV). Methods: The retrospective cohort study was conducted in ICU at Beijing Chao-Yang Hospital Shijingshan District, Capital Medical University in China from January, 2010 to December, 2019, including patients with sepsis who received IMV. 180 patients were enrolled in the study, including 111 male and 69 female, with the age range of 23-93 years and the median age of 76 years, and with an average age of 71.22 years. The medical records were reviewed, such as age, sex, acute physiology and chronic health evaluation II (APACHEII), sequential organ failure assessment (SOFA), spontaneous breathing trial (SBT) outcome, weaning outcome, complete blood count before SBT. According to weaning outcome, patients were divided into weaning failure and weaning success group. The difference of PLR, white blood cell(WBC), C-reaction protein (CRP) and procalcitonin (PCT) were compared between weaning failure and success group. Receiver-operating characteristics (ROC) curves and multivariate logistical regression analysis were employed to analyze the performance of these inflammatory markers for predicting weaning failure in patients with sepsis. Results: 180 patients with sepsis were included in the study and 37 patients (20.5%) experienced weaning failure (31 SBT failure and 6 extubation failure after successful SBT). PLR was higher in weaning failure group than that in weaning success group (Z=-5.793,P<0.001). Other inflammation biomarkers such as WBC, CRP and PCT were also higher in weaning failure group than that in weaning success group(Z=-4.356, -3.118 and -2.743, P<0.001, 0.002 and 0.006, respectively). According to ROC curves, PLR has a better predictive ability for weaning failure (AUC=0.809,95%CI 0.733-0.885) when compared to WBC (AUC=0.773,95%CI 0.648-0.817), CRP (AUC=0.666,95%CI 0.577-0.755) and PCT (AUC=0.603,95%CI 0.508-0.698). The cutoff value of PLR for predicting weaning failure was 257.69 with sensitivity 78.38%, specificity 76.22%, and diagnostic accuracy 71.66%. According to multivariate logistic regression analyses, PLR>257.69 was an independent risk factor for predicting weaning failure in patients with sepsis. Conclusions: PLR may be a valuable biomarker for predicting weaning failure in septic patients receiving IMV, and the patients with higher PLR should be handled with caution since they are at higher risk of weaning failure, and some more effective treatment should be in consideration after extubation.
Humans ; Male ; Female ; Aged ; Young Adult ; Adult ; Middle Aged ; Aged, 80 and over ; Respiration, Artificial ; Retrospective Studies ; Sepsis/diagnosis* ; Procalcitonin ; C-Reactive Protein ; Biomarkers ; ROC Curve ; Lymphocytes

OBJECTIVE: To investigate the prognostic value of hemoglobin-to-red cell distribution width ratio (HRR) in patients with cardiopulmonary resuscitation (CPR) after out-of-hospital cardiac arrest (OHCA). METHODS: A retrospective study was conducted. Patients aged ≥ 18 years with OHCA who were transferred to intensive care unit (ICU) after successful CPR from the emergency room of the First Affiliated Hospital of Zhengzhou University from August 2016 to February 2022 were enrolled. General clinical data, initial vital signs, acute physiology and chronic health evaluation II (APACHE II), Glasgow coma scale (GCS), first laboratory indicators after admission to ICU [including white blood cell count (WBC), red blood cell count (RBC), hemoglobin (Hb), pH value, lactic acid (Lac), 6-hour lactic acid clearance (LCR), red cell distribution width (RDW), HRR], length of ICU stay were collected. According to whether the patients died in hospital, the patients were divided into survival group and death group. Binary Logistic regression was used to analyze the independent factors influencing the prognosis of patients after CPR. Receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of independent influencing factors for the prognosis of patients after CPR. RESULTS: A total of 122 patients were enrolled after OHCA CPR, of which 88 died in hospital, the in-hospital mortality was 72.13%. There were no significant differences in age, past medical history, initial vital signs and WBC in ICU between the two groups. Compared with the death group, the survival group had higher GCS score, RBC, Hb, pH value, 6-hour LCR, HRR, lower APACHE II score, Lac, RDW level, and longer length of ICU stay. Multivariate Logistic regression analysis showed that APACHE II score, GCS score, 6-hour LCR, HRR, length of ICU stay were independent factors influencing the prognosis of patients after CPR [APACHE II score: odds ratio (OR) = 0.784, 95% confidence interval (95%CI) was 0.683-0.901, P = 0.001; GCS score: OR = 1.390, 95%CI was 1.059-1.823, P = 0.018; 6-hour LCR: OR = 1.039, 95%CI was 1.015-1.064, P = 0.001; HRR: OR = 2.047, 95%CI was 1.383-3.029, P < 0.001; length of ICU stay: OR = 1.128, 95%CI was 1.046-1.216, P = 0.002]. ROC curve analysis showed that HRR, 6-hour LCR and APACHE II score could predict the prognosis of patients after CPR. The sensitivity was 85.3% and the specificity was 54.5% when the area under the ROC curve (AUC) of HRR was 0.731, and the cut-off value was 8.555. The sensitivity was 88.2% and the specificity was 46.6%, when the AUC of 6-hour LCR was 0.701, and the cut-off value was 28.947%. The sensitivity was 73.9% and the specificity was 79.4% when the AUC of APACHE II score was 0.848, the cut-off value was 22.000. The predictive value of the combination of HRR and 6-hour LCR was higher than that of a single index. The sensitivity was 79.3% and the specificity was 76.1%, when the AUC was 0.796, the cut-off value was 0.296. CONCLUSIONS HRR, 6-hour LCR and APACHE II score have high prognostic value in patients with OHCA after CPR. HRR < 8.555, 6-hour LCR < 28.947% and APACHE II score > 22.000 indicated poor prognosis.
Humans ; Erythrocyte Indices ; Prognosis ; Retrospective Studies ; Out-of-Hospital Cardiac Arrest/therapy* ; ROC Curve ; Intensive Care Units ; Hemoglobins ; Lactic Acid ; Cardiopulmonary Resuscitation ; Sepsis/diagnosis*