Significado biológico y utilidad clínica del lactato en la sepsis / Biological significance and clinical utility of lactate in sepsis

La sepsis es un problema global de salud y la progresión hacia el shock séptico se asocia con un incremento marcado de la morbimortalidad. En este escenario, el aumento del lactato plasmático demostró ser un indicador de gravedad y un predictor de mortalidad, y suele interpretarse casi exclusivamente como marcador de baja perfusión tisular. Sin embargo, últimamente se produjo un cambio de paradigma en la exégesis del metabolismo y propiedades biológicas del lactato. En efecto, la adaptación metabólica al estrés, aun con adecuado aporte de oxígeno, puede justificar la elevación del lactato circulante. Asimismo, otras consecuencias fisiopatológicas de la sepsis, como la disfunción mitocondrial, se asocian con el desarrollo de hiperlactatemia sin que necesariamente se acompañen de baja perfusión tisular. Interpretar el origen y la función del lactato puede resultar de suma utilidad clínica en la sepsis, especialmente cuando sus niveles circulantes fundamentan las medidas de reanimación.

Sepsis is a global health problem; progression to septic shock is associated with a marked increase in morbidity and mortality. In this setting, increased plasma lactate levels demonstrated to be an indicator of severity and a predictor of mortality, and are usually interpreted almost exclusively as a marker of low tissue perfusion. However, a recent paradigm shift has occurred in the exegesis of lactate metabolism and its biological properties. Indeed, metabolic adaptation to stress, even with an adequate oxygen supply, may account for high circulating lactate levels. Likewise, other pathophysiological consequences of sepsis, such as mitochondrial dysfunction, are associated with the development of hyperlactatemia, which is not necessarily accompanied by low tissue perfusion. Interpreting the origin and function of lactate may be of great clinical utility in sepsis, especially when circulating lactate levels are the basis for resuscitative measures.

Herramientas para el diagnóstico de la sepsis / Tools for Diagnosis in Sepsis

La sepsis representa en la actualidad un problema emergente en salud. Los consensos alcanzados sobre las definiciones de sepsis y sus complicaciones asociadas han permitido establecer con mayor precisión la magnitud del problema. A pesar de la instauración de protocolos uniformes de actuación, la sepsis continúa siendo la primera causa de muerte en la Unidad de Cuidados Intensivos Pediátricos (UCIP) y la cuarta causa de defunción en los hospitalizados no coronarios.1 La sepsis severa fue definida en The Third International Consensus Definitions for Sepsis an Septic Shock como el síndrome de respuesta inflamatoria sistémica asociada a infección que cursa con disfunción aguda de un órgano, hipoperfusión o hipotensión, considerada como un proceso continuo.2 Se han realizado diversos estudios donde la mortalidad anual atribuible a sepsis grave es de 135 000 casos en Europa, y es superior en Estados Unidos con 200 000 casos, superada discretamente por las muertes por infarto agudo de miocardio. Estados Unidos ocupa el lugar número 11 como causa aislada de fallecimiento, se estima que más de 500 pacientes mueren diariamente a consecuencia de esta enfermedad, hecho que se transforma en un significativo desafío para la salud pública.3 Por lo que significa lo anteriormente expuesto, en reuniones de consensos, los laboratorios se han visto retados y han facilitado el diagnóstico con herramientas útiles. Marcadores biológicos de infección El retraso en la instauración de un tratamiento adecuado de las infecciones y de la sepsis se asocia a una mayor mortalidad, por lo que es crucial establecer un diagnóstico precoz en este contexto. Los análisis microbiológicos que confirman o no la presencia de infección suelen tardar, en ese sentido se han buscado marcadores biológicos que puedan servir como indicadores fiables de la infección grave y la sepsis. Dada la complejidad creciente de la fisiopatología de la sepsis a medida que avanza en su conocimiento, es probable que el éxito llegue, no a través de un único marcador, sino a través de la combinación de varios de ellos que tengan en cuenta distintos aspectos de la respuesta del huésped. La combinación de varios marcadores puede ayudar a vencer las limitaciones en sensibilidad y especificidad de un solo biomarcador. Características de un marcador ideal Precisión Sensibilidad alta: pacientes que presentan respuesta inflamatoria mínima o ausente. Especificidad: lograr discriminar la infección de otros padecimientos que causan el síndrome de respuesta inflamatoria sistémica (SRIS). Valor predictivo positivo (VPP) y valor predictivo negativo (VPN). Diagnóstico certero con reactantes de fase aguda La proteína C reactiva (PCR) de alta sensibilidad es un marcador precoz de infección e inflamación con una vida media plasmática de 19 h. Su concentración plasmática en adulto sano es de 0,08 mg/dL. Su biocinética se hace útil como marcador de respuesta terapéutica y diagnóstica de infecciones intercurrentes. Las determinaciones seriadas de estas es un buen indicador de la actividad inflamatoria. Sus grandes elevaciones se deben a infecciones bacterianas. Las determinaciones seriadas diarias de PCR pueden ser útiles para el diagnóstico precoz en las infecciones nosocomiales es de ≥ 5 mg/dL o ≥ 25 % del valor previo. La procalcitonina es una hormona producida por células parenquimatosas, proteína de la familia CAPA precursora de la calcitonina. En condiciones normales las concentraciones en sangre son muy bajas, menor de 0,09 ng/mL. Tienen una vida media de 22-35 h. No se conocen valores umbrales, diagnósticos y pronósticos, pero los valores mayores de 2 ng/mL suelen indicar la presencia de sepsis. Los valores mayores de 10 ng/mL suelen asociarse a sepsis grave y shock séptico. Los niveles de procalcitonina (PCT) son superiores a la PCR en el diagnóstico de sepsis por lo que debería ser incluida en las guías diagnósticas de sepsis. Podemos decir además, que la interleucina 6 (IL-6), fue descrita inicialmente como interferón beta-2, como factor de crecimiento de plasmocitoma o factor estimulante de hepatocitos.4 Es generada por un único gen que codifica un producto de 212 aminoácidos y es la citoquina que más consistentemente se ha asociado con la mortalidad por sepsis, por su acción proinflamatoria es uno de los principales inductores de la síntesis de la PCR en el hígado, por lo que muestra picos séricos más precoces que esta.5 Las determinaciones secuenciales de estas en el suero plasma en pacientes internados en la unidad de cuidados intensivos han demostrado ser útiles para evaluar la severidad del síndrome de respuesta inflamatoria(AU)

Application and Prospect of Nanopore Sequencing Technology in Etiological Diagnosis of Blood Stream Infection / 中国医学科学院学报

Blood stream infection (BSI),a blood-borne disease caused by microorganisms such as bacteria,fungi,and viruses,can lead to bacteremia,sepsis,and infectious shock,posing a serious threat to human life and health.Identifying the pathogen is central to the precise treatment of BSI.Traditional blood culture is the gold standard for pathogen identification,while it has limitations in clinical practice due to the long time consumption,production of false negative results,etc.Nanopore sequencing,as a new generation of sequencing technology,can rapidly detect pathogens,drug resistance genes,and virulence genes for the optimization of clinical treatment.This paper reviews the current status of nanopore sequencing technology in the diagnosis of BSI.

The predictive value of platelet-to-lymphocyte ratio for weaning failure in septic patients receiving mechanical ventilation / 中华预防医学杂志

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.

Analysis of clinical characteristics and risk factors of early heat stroke-related acute liver injury / 中华危重病急救医学

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.

Analysis of lymphocyte subsets in patients with sepsis and its impact on prognosis / 中华危重病急救医学

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.

Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning / 中华危重病急救医学

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.

Prognostic evaluation of coagulation indicators for patients with acute fatty liver of pregnancy / 中华危重病急救医学

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.

Correlation between blood pressure indexes and prognosis in sepsis patients: a cohort study based on MIMIC-III database / 中华危重病急救医学

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.

A nonlinear relationship between the hemoglobin level and prognosis of elderly patients with sepsis: an analysis based on MIMIC-IV / 中华危重病急救医学

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.

Association between blood glucose-to-lymphocyte ratio and prognosis of patients with sepsis-associated acute kidney injury / 中华危重病急救医学

OBJECTIVE@#To investigate the association between the glucose-to-lymphocyte ratio (GLR) and prognosis of patients with sepsis-associated acute kidney injury (SA-AKI).@*METHODS@#Based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV), SA-AKI patients aged ≥ 18 years were selected. According to the tertiles of GLR, the patients were divided into GLR1 group (GLR ≤ 4.97×10-9 mmol), GLR2 group (4.97×10-9 mmol < GLR < 9.75×10-9 mmol) and GLR3 group (GLR ≥ 9.75×10-9 mmol). Patients with SA-AKI were divided into survival group and death group according to whether they survived 28 days after admission. The patient's gender, age, vital signs, laboratory test results, comorbidities, sequential organ failure assessment (SOFA), acute physiology score III (APS III) score and treatment measures were extracted from the database. Kaplan-Meier survival analysis was used to make the survival curves of patients with SA-AKI at 28 days, 90 days, 180 days and 1 year. Multivariate Logistic regression analysis model was used to explore the independent risk factors of 28-day mortality in patients with SA-AKI. Receiver operator characteristic curve (ROC curve) was used to analyze the predictive efficacy of GLR for the prognosis of patients with SA-AKI.@*RESULTS@#A total of 1 524 patients with SA-AKI were included, with a median age of 68.28 (58.96, 77.24) years old, including 612 females (40.16%) and 912 males (59.84%). There were 507 patients in the GLR1 group, 509 patients in the GLR2 group and 508 patients in the GLR3 group. There were 1 181 patients in the 28-day survival group and 343 patients in the death group. Grouping according to GLR tertiles showed that with the increase of GLR, the 28-day, 90-day, 180-day and 1-year mortality of SA-AKI patients gradually increased (28-day mortality were 11.64%, 22.00%, 33.86%, respectively; 90-day mortality were 15.98%, 26.72%, 40.55%, respectively; 180-day mortality were 17.16%, 28.29% and 41.73%, and the 1-year mortality were 17.95%, 29.27% and 42.72%, respectively, all P < 0.01). According to 28-day survival status, the GLR of the death group was significantly higher than that of the survival group [×10-9 mmol: 9.81 (5.75, 20.01) vs. 6.44 (3.64, 10.78), P < 0.01]. Multivariate Logistic regression analysis showed that GLR was an independent risk factor for 28-day mortality in patients with SA-AKI [when GLR was used as a continuous variable: odds ratio (OR) = 1.065, 95% confidence interval (95%CI) was 1.045-1.085, P < 0.001; when GLR was used as a categorical variable, compared with GLR1 group: GLR2 group OR = 1.782, 95%CI was 1.200-2.647, P = 0.004; GLR3 group OR = 2.727, 95%CI was 1.857-4.005, P < 0.001]. ROC curve analysis showed that the area under the ROC curve (AUC) of GLR for predicting 28-day mortality in patients with SA-AKI was 0.674, when the optimal cut-off value was 8.769×10-9 mmol, the sensitivity was 57.1% and the specificity was 67.1%. The predictive performance was improved when GLR was combined with APS III score and SOFA score, and the AUC was 0.806, the sensitivity was 74.6% and the specificity was 71.4%.@*CONCLUSIONS@#GLR is an independent risk factor of 28-day mortality in patients with SA-AKI, and high GLR is associated with poor prognosis in patients with SA-AKI.

Construction of anomogram for predicting the prognosis of patients with sepsis-associated acute kidney injury / 中华危重病急救医学

OBJECTIVE@#To explore the risk factors for poor prognosis in sepsis-associated acute kidney injury (SA-AKI) and establish a nomogram predictive model.@*METHODS@#The clinical data of patients with SA-AKI admitted to the department of critical care medicine of Shandong Provincial Hospital Affiliated to Shandong First Medical University from January 2019 to September 2022 were retrospectively analyzed, including demographic information, worst values of blood cell counts and biochemical indicators within 24 hours of SA-AKI diagnosis, whether the patient received renal replacement therapy (RRT), mechanical ventilation, vasopressor therapy during hospitalization, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), fibrinogen-to-albumin ratio (FAR) within 24 hours of diagnosis, acute kidney injury (AKI) staging, total length of hospital stay, length of intensive care unit (ICU) stay, and others. According to the 28-day outcome, the patients were divided into survival group and death group, and the indicators between the two groups were compared. Univariate and multivariate Logistic regression analyses were used to screen for risk factors associated with mortality in SA-AKI patients. A nomogram predictive model for SA-AKI prognosis was constructed based on the identified risk factors. Receiver operator characteristic curve (ROC curve) and calibration plots were generated to evaluate the predictive value of the nomogram model for SA-AKI prognosis.@*RESULTS@#A total of 113 SA-AKI patients were included, with 67 in the survival group and 46 in the death group. The 28-day mortality among SA-AKI patients was 40.7%. The comparison between the two groups showed that there were statistically significant differences in age ≥ 65 years, AKI stage, mechanical ventilation, vasopressors, RRT, length of ICU stay, and laboratory indicators cystatin C (Cys C), fibrinogen (Fib), and FAR. Multivariate Logistic regression analysis showed that age ≥ 65 years [odds ratio (OR) = 7.967, 95% confidence interval (95%CI) was 1.803-35.203, P = 0.006], cystatin C (OR = 7.202, 95%CI was 1.756-29.534, P = 0.006), FAR (OR = 2.444, 95%CI was 1.506-3.968, P < 0.001), and RRT (OR = 7.639, 95%CI was 1.391-41.951, P = 0.019) were independent risk factors for mortality in SA-AKI patients. ROC curve analysis showed that the area under the ROC curve (AUC) for age ≥ 65 years, cystatin C, FAR, and RRT in predicting SA-AKI patient mortality were 0.713, 0.856, 0.911, and 0.701, respectively. A nomogram predictive model for SA-AKI patient prognosis was constructed based on age ≥ 65 years, cystatin C, FAR, and RRT, with an AUC of 0.967 (95%CI was 0.932-1.000) according to ROC curve analysis. The calibration plot indicated good consistency between predicted and actual probabilities.@*CONCLUSIONS@#Age ≥ 65 years, cystatin C, FAR, and RRT are independent risk factors for mortality in SA-AKI patients. The nomogram predictive model based on these four factors can accurately predict SA-AKI patient prognosis, helping physicians adjust treatment strategies in a timely manner and improve patient outcomes.

Expression level of cytokines in patients with sepsis and its effect on prognosis / 中华危重病急救医学

OBJECTIVE@#To observe the expression level of cytokines in patients with sepsis and its effect on prognosis.@*METHODS@#The clinical data of sepsis patients admitted to the intensive care unit (ICU) of the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2022 were analyzed retrospectively, including gender, age, and acute physiology and chronic health evaluation II (APACHE II), blood routine, procalcitonin (PCT), C-reactive protein (CRP), and cytokines levels [interleukins (IL-2, IL-4, IL-6, IL-10, IL-17), tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ)] within 24 hours of admission to ICU. The 28-day prognosis of the patients was followed up. The patients were divided into survival group and death group according to the prognosis. The clinical data between the two groups of sepsis patients with different prognosis were compared. Binary Logistic regression analysis was used to analyze the independent risk factors affecting the prognosis of patients with sepsis, and the receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of each risk factor for the prognosis of patients with sepsis.@*RESULTS@#(1) A total of 227 patients with sepsis were enrolled, including 168 patients in the survival group (survival rate 74.0%) and 59 patients in the death group (mortality 26.0%). There were no significant differences in age (years old: 55.97±2.13 vs. 54.67±1.11) and gender (male: 71.2% vs. 57.1%) between the death group and the survival group (both P > 0.05), indicating that the baseline data of the two groups were comparable. (2) The APACHE II (19.37±0.99 vs. 14.88±0.61, P < 0.001) and PCT (μg/L: 12.39±2.94 vs. 4.14±0.90, P < 0.001) in the death group were significantly higher than those in the survival group, while the platelet count [PLT (×109/L): 144.75±12.50 vs. 215.99±11.26, P = 0.001] and thrombocytocrit [(0.14±0.01)% vs. (0.19±0.01)%, P = 0.001] were significantly lower than those in the survival group. (3) The level of IL-6 in the death group was significantly higher than that in the survival group (ng/L: 577.66±143.16 vs. 99.74±33.84, P < 0.001). There were no statistically significant differences in other cytokines, IL-2, IL-4, IL-10, TNF-α, IFN-γ and IL-17 between the death group and the survival group [IL-2 (ng/L): 2.44±0.38 vs. 2.63±0.27, P = 0.708; IL-4 (ng/L): 3.26±0.67 vs. 3.18±0.34, P = 0.913; IL-10 (ng/L): 33.22±5.13 vs. 39.43±2.85, P = 0.262; TNF-α (ng/L): 59.33±19.21 vs. 48.79±29.87, P = 0.839; IFN-γ (ng/L): 6.69±5.18 vs. 1.81±0.16, P = 0.100; IL-17 (ng/L): 2.05±0.29 vs. 2.58±0.33, P = 0.369]. (4) Binary Logistic regression analysis showed that APACHE II and IL-6 were independent risk factors affecting the prognosis of patients with sepsis [odds ratio (OR) and 95% confidence interval (95%CI) were 1.050 (1.008-1.093) and 1.001 (1.000-1.002), P values were 0.019 and 0.026, respectively]. (5) ROC curve analysis showed that APACHE II and IL-6 had certain predictive value for the prognosis of patients with sepsis, the area under the ROC curve (AUC) was 0.754 (95%CI was 0.681-0.827) and 0.592 (95%CI was 0.511-0.673), P values were < 0.001 and 0.035, respectively. When the optimal cut-off value of APACHE II was 16.50 score, the sensitivity was 72.6% and the specificity was 69.9%. When the optimal cut-off value of IL-6 was 27.87 ng/L, the sensitivity was 67.2% and the specificity was 52.8%.@*CONCLUSIONS@#APACHE II score and IL-6 level have certain predictive value for the prognosis of patients with sepsis, the higher APACHE II score and IL-6 level, the greater the probability of death in patients with sepsis.

Comparison of four early warning scores in predicting the prognosis of critically ill patients in secondary hospitals / 中华危重病急救医学

OBJECTIVE@#To explore the predictive value of acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), quick sequential organ failure assessment (qSOFA) and modified early warning score (MEWS) in evaluating the prognosis of patients in intensive care unit (ICU) of secondary hospitals, and to provide guidance for clinical application.@*METHODS@#The clinical data of adult critical patients admitted to the ICU of Wanzhou District First People's Hospital from October 2022 to April 2023 were retrospectively analyzed. According to the clinical outcome of ICU, the patients were divided into improvement group and death group. The general information, blood routine, heart, liver and kidney function indicators, coagulation indicators, blood gas analysis, APACHE II score, SOFA score, qSOFA score, MEWS score at the time of admission to the ICU, the number of cases of invasive mechanical ventilation (IMV) and continuous blood purification (CBP) were compared between the two groups. Univariate analysis was performed, and multivariate Logistic regression analysis was used to analyze the related factors of death. Receiver operator characteristic curve (ROC curve) was used to analyze the predictive value of the four scores in ICU patients.@*RESULTS@#A total of 126 patients were included, of which 45 patients died in the ICU and 81 patients improved and transferred out. Univariate analysis of death-related critically ill patients showed that procalcitonin (PCT), serum creatinine (SCr), blood urea nitrogen (BUN), albumin (ALB), prothrombin time (PT), activated partial prothrombin time (APTT), D-dimer, pH value, HCO3-, blood lactic acid (Lac), number of patients treated with IMV and CBP, APACHE II score, SOFA score, qSOFA score and MEWS score were significantly different between the two groups (all P < 0.05). Multivariate Logistic regression analysis showed that the APACHE II score [odds ratio (OR) = 1.115, 95% confidence interval (95%CI) was 1.025-1.213, P = 0.011], SOFA score (OR = 1.204, 95%CI was 1.037-1.398, P = 0.015), MEWS score (OR = 1.464, 95%CI was 1.102-1.946, P = 0.009), and APTT (OR = 1.081, 95%CI was 1.015-1.152, P = 0.016) were independent risk factors affecting the mortality of critically ill patients in the ICU. ROC curve analysis showed that APACHE II, SOFA, qSOFA, and MEWS scores could predict the prognosis of critically ill ICU patients, among which SOFA score had the strongest predictive effect, and the area under the curve (AUC) was 0.808. There was a statistically significant difference in the time required for the four scores (F = 117.333, P < 0.001), among which the MEWS scoring required the shortest time [(1.03±0.39) minutes], and the APACHE II scoring required the longest time [(2.81±1.04) minutes].@*CONCLUSIONS@#APACHE II, SOFA, qSOFA, and MEWS scores can be used to assess the severity of critically ill patients and predict in-hospital mortality. The SOFA score is superior to other scores in predicting severity. The MEWS is preferred because its assessment time is shortest. Early warning score can help secondary hospitals to detect potentially critical patients early and provide help for clinical rapid urgent emergency decision-making.

Short-term survival survey and risk factors analysis of death in sepsis patients in intensive care unit / 中华危重病急救医学

OBJECTIVE@#To analyze the 28-day survival status and influencing factors of adult patients with sepsis, providing reference for early diagnosis of sepsis prognosis and reducing sepsis mortality.@*METHODS@#A retrospective cohort study was conducted. A total of 160 adult patients with sepsis in the department of intensive care unit of the 940th Hospital of Joint Logistic Support Force of PLA from January 2021 to December 2022 were enrolled. The general information, laboratory examination results within 24 hours after admission, clinical treatment measures, and prognosis of patients were collected. Univariate analysis and binary multivariate Logistic regression were performed to screen the risk factors that might affect the short-term prognosis of patients with sepsis. Receiver operator characteristic curve (ROC curve) was plotted to analyze the predictive value of various risk factors on the short-term death risk of sepsis patients.@*RESULTS@#A total of 160 patients with sepsis were enrolled, of whom 91 survived in 28 days, and 69 died with a mortality of 43.12%. Compared with the survival group, the patients in the death group were older, more severe, had higher blood lactic acid (Lac) level, and had more complications such as hypertension and multiple organ dysfunction syndrome (MODS). A total of 22 related factors were statistically significant: age, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, length of hospital stay, Lac, interleukin-6 (IL-6), fibrinogen (FIB), international normalized ratio (INR), ratio of prothrombin time (PT) to healthy people, prothrombin activity (PTA), PT, thrombin time (TT), oxygenation index (PaO2/FiO2), aspartate aminotransferase (AST), ratio of AST to alanine amninotransferase (ALT), serum creatinine (SCr), blood urea nitrogen (BUN), site of infection, history of hypertension, concurrent MODS, implementation of continuous renal replacement therapy (CRRT), and treatment with vasoactive drugs. Combined with the results of the univariate analysis, variables that might affect the short-term prognosis of septic patients were included in the multivariate Logistic regression analysis. The results showed that APACHE II score ≥ 20 [odds ratio (OR) = 1.106, 95% confidence interval (95%CI) was 1.003-1.219], Lac ≥ 5 mmol/L (OR = 1.430, 95%CI was 1.041-1.964), combined with hypertension (OR = 13.879, 95%CI was 1.082-178.016), concurrent MODS (OR = 98.139, 95%CI was 18.252-527.672) was an independent risk factor for 28-day death in adult septic patients (all P < 0.05). ROC curve analysis showed that the combination of the four indicators including APACHE II score, Lac, combined with hypertension, concurrent MODS, had predictive value for short-term outcomes in patients with sepsis. The area under the ROC curve (AUC) was higher than that of the 4 indicators alone [AUC (95%CI): 0.952 (0.918-0.986) vs. 0.745 (0.670-0.820), 0.816 (0.748-0.883), 0.608 (0.518-0.699), 0.868 (0.810-0.927)], the sensitivity was 94.2%, and the specificity was 90.1%.@*CONCLUSIONS@#APACHE II score within 24 hours, Lac, combined with hypertension, and concurrent MODS are independent risk factors for short-term mortality in adult septic patients in ICU. The combination of these indicators can make meaningful early clinical judgments on the short-term prognosis of septic patients, thereby reducing the mortality.

Predictive effect of combined procalcitonin, interleukin-6 and antithrombin III on the severity and prognosis of patients with sepsis / 中华危重病急救医学

OBJECTIVE@#To investigate the correlation of procalcitonin (PCT), interleukin-6 (IL-6) and antithrombin III (AT III) with the severity of sepsis, and to compare the predictive value of the above indicators alone or in combination.@*METHODS@#A retrospective cohort study was conducted. Eighty-five patients with sepsis admitted to the department of intensive care medicine of Shandong Provincial Hospital Affiliated to Shandong First Medical University from April 2021 to September 2022 were enrolled. General information, sequential organ failure assessment (SOFA) score and acute physiology and chronic health evaluation II (APACHE II) score within 24 hours of admission, inflammatory indicators [PCT, IL-6, serum amyloid A (SAA), neutrophil to lymphocyte ratio (NLR), and C-reactive protein (CRP)] and coagulation indicators (D-dimer and AT III) levels at admission, and 28-day prognosis were collected. The differences of the above indicators were compared among patients with different prognosis at 28 days and different severity of sepsis. The correlation between PCT, IL-6, AT III and the severity of sepsis was analyzed by Spearman rank correlation method. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of PCT, IL-6 and AT III alone or in combination on the 28-day death of patients with sepsis.@*RESULTS@#Eighty-five patients were enrolled finally, 67 cases survived and 18 cases died at 28 days. The mortality was 21.2%. There were no statistical significant differences in gender, age and other general data between the two groups. The patients in the death group were more serious than those in the survival group, and PCT, IL-6, and CRP levels were significantly higher than those in the survival group [PCT (μg/L): 4.34 (1.99, 14.42) vs. 1.17 (0.31, 3.94), IL-6 (ng/L): 332.40 (50.08, 590.18) vs. 61.95 (31.64, 194.20), CRP (mg/L): 149.28 (75.34, 218.60) vs. 83.23 (48.22, 174.96), all P < 0.05], and AT III activity was significantly lower than that in the survival group [(53.67±28.57)% vs. (80.96±24.18)%, P < 0.01]. However, there were no significant differences in D-dimer, NLR and SAA between the two groups. Among the 85 patients, 36 had sepsis with single organ dysfunction, 29 had sepsis with multiple organ dysfunction, and 20 had septic shock with multiple organ dysfunction. With the increase of the severity of sepsis, PCT and IL-6 levels gradually increased [PCT (μg/L): 0.36 (0.19, 1.10), 3.00 (1.22, 9.94), 4.34 (2.18, 8.86); IL-6 (ng/L): 43.99 (20.73, 111.13), 100.00 (45.37, 273.00), 332.40 (124.4, 693.65)], and the activity of AT III decreased gradually [(89.81±21.42)%, (71.97±24.88)%, and (53.50±25.41)%], all with statistically significant differences (all P < 0.01). Spearman rank correlation analysis showed that PCT and IL-6 levels in sepsis patients were significantly positively correlated with the severity of the disease (r values were 0.562 and 0.517, respectively, both P < 0.01), and AT III activity was significantly negatively correlated with the severity of the disease (r = -0.523, P < 0.01). ROC curve analysis showed that PCT, IL-6, and AT III alone or in combination had some predictive value for the death of sepsis patients at 28 days. The area under the ROC curve (AUC) of the above three indicators in combination was higher than that of the individual tests (0.818 vs. 0.722, 0.725, and 0.770), with a sensitivity of 83.3% and a specificity of 73.1%.@*CONCLUSIONS@#PCT, IL-6, and AT III were significantly correlated with the severity of sepsis patients. The combined assay of the above three indicators can effectively improve the prediction of the prognosis of sepsis patients.

Prognostic value of hemoglobin-to-red cell distribution width ratio in patients with cardiopulmonary resuscitation after out-of-hospital cardiac arrest / 中华危重病急救医学

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.

Clinical value of new coagulation biomarkers in pediatric sepsis / 中华儿科杂志

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.

Perfil de pacientes com sepse e choque séptico em um hospital de trauma: estudo transversal / Profile of patients with sepsis and septic shock in a trauma hospital: cross-sectional study / Perfil de pacientes con sepsis y shock séptico en un hospital de trauma: estudio transversal

Objetivo: Avaliar o perfil e o desfecho clínico de pacientes com sepse e choque séptico em um hospital de trauma de Belo Horizonte. Métodos: Trata-se de estudo transversal, retrospectivo e descritivo dos casos de sepse e choque séptico. Resultados: Constatou-se que 97 (73%) pacientes eram do sexo masculino, com idade entre 19 e 59 anos, 87 (65%) previamente hígidos. Principal motivo de internação foi queda da própria altura com 23 (17%) casos e 37 (28%) diagnósticos de trauma crânio encefálico. Oitenta e seis (65%) pacientes tiveram diagnóstico de sepse e 47 (35%) choque séptico, o principal foco de infecção foi pulmonar 83 (62%). Obtivemos 88 (66%) resultados de hemoculturas negativas e 45 (34%) positivas, 64 (48%) pacientes receberam antibiótico em até 60 minutos após o diagnóstico e o tempo de internação mais prevalente após o evento séptico foi de 7 dias, representado por 31 (46%) pacientes que já estavam internados em uma Unidade de Terapia Intensiva (UTI). Mortalidade de 37 (28%) e 96 (72%) sobreviventes da população estudada. Conclusão: O evento séptico é um problema de saúde pública e tem alta letalidade em pacientes traumatizados. (AU)

Objective: To evaluate the profile and clinical outcome of patients with sepsis and septic shock in a trauma hospital in Belo Horizonte. Methods: This is a cross-sectional, retrospective and descriptive study of cases of sepsis and septic shock. Results: It was found that 97 (73%) patients were male, aged 19 to 59 years, 87 (65%) were previously healthy. Main reason for hospitalization was a fall from one's own height with 23 (17%) cases and 37 (28%) diagnosis of traumatic brain injury. Eighty-six (65%) patients were diagnosed with sepsis and 47 (35%) septic shock, the main focus of infection was pulmonary 83 (62%). We got 88 (66%) negative blood culture results and 45 (34%) positive, 64 (48%) patients received antibiotics within 60 minutes after diagnosis and the most prevalent hospital stay after septic event was 7 days, represented by 31 (46%) patients who were already hospitalized in an Intensive Care Unit (ICU). Mortality of 37 (28%) and 96 (72%) survivors of the population studied. Conclusion: Septic event is a public health problem and has high lethality in traumatized patients. (AU)

Objetivo: Evaluar el perfil y el resultado clínico de los pacientes con sepsis y shock séptico en un hospital de trauma en Belo Horizonte. Métodos: Este es un estudio transversal, retrospectivo y descriptivo de casos de sepsis y shock séptico. Resultados: Se encontró que 97 (73%) pacientes eran hombres, de 19 a 59 años, 87 (65%) antes estaban sanos. La razón principal de la hospitalización fue una caída desde la propia altura con 23 (17%) casos y 37 (28%) diagnóstico de lesión cerebral traumática. Ochenta y seis (65%) pacientes fueron diagnosticados con sepsis y 47 (35%) shock séptico, el foco principal de infección fue pulmonar 83 (62%). Tenemos 88 (66%) resultados negativos del cultivo sanguíneo y 45 (34%) positivo, 64 (48%) los pacientes recibieron antibióticos dentro de los 60 minutos posteriores al diagnóstico y la estancia hospitalaria más frecuente después del evento séptico fue de 7 días, representados por 31 (46%) pacientes que ya estaban hospitalizados en una Unidad de Cuidados Intensivos (UCI). Mortalidad de 37 (28%) y 96 (72%) sobrevivientes de la población estudiada. Conclusión: El evento séptico es un problema de salud pública y tiene alta letalidad en pacientes traumatizados. (AU)

Epidemiology and treatment of sepsis at a public pediatric emergency department

ABSTRACT Objective To describe the clinical characteristics and treatment of children with sepsis, severe sepsis, and septic shock at a pediatric emergency department of a public hospital. Methods A retrospective, observational study. The medical records of patients included in the hospital Pediatric Sepsis Protocol and patients with discharge ICD-10 A41.9 (sepsis, unspecified), R57 (shock) and A39 (meningococcal meningitis) were evaluated. Results A total of 399 patients were included. The prevalence of sepsis, severe sepsis, and septic shock at the emergency room were 0.41%, 0.14% and 0.014%, respectively. The median age was 21.5 months for sepsis, 12 months for severe sepsis, and 20.5 months for septic shock. Sepsis, severe sepsis, and septic shock were more often associated with respiratory diseases. The Respiratory Syncytial Virus was the most common agent. The median time to antibiotic and fluid administration was 3 hours in patients with sepsis and severe sepsis. In patients with septic shock, the median times to administer antibiotics, fluid and vasoactive drugs were 2 hours, 2.5 hours and 6 hours, respectively. The median length of hospital stay for patients with sepsis, severe sepsis and septic shock were 3 days, 4 days and 1 day, respectively. The overall mortality was 2%. Conclusion Sepsis had a low prevalence. Early diagnosis and recognition are a challenge for the emergency care pediatrician, the first place of admission.