Objective:To explore the current situation and influencing factors of quality of life of septic patients in intensive care unit (ICU) after discharge, and to provide theoretical basis for clinical early psychological intervention and continuity of care.Methods:A prospective observational study was conducted. The septic patients who were hospitalized in the department of critical care medicine of the Affiliated Hospital of Jining Medical University and discharged with improvement from January 1 to December 31, 2022 were selected as the research objects. The demographic information, basic diseases, infection site, vital signs at ICU admission, severity scores of the condition within 24 hours after ICU admission, various biochemical indexes, treatment process, and prognostic indexes of all the patients were recorded. All patients were assessed by questionnaire at 3 months of discharge using the 36-item short-form health survey scale (SF-36 scale), the activities of daily living scale (ADL scale), and the Montreal cognitive assessment scale (MoCA scale). Multiple linear regression was used to analyze the factors influencing the quality of life of septic patients after discharge from the hospital.Results:A total of 200 septic patients were discharged with improvement and followed up at 3 months of discharge, of which 150 completed the questionnaire. Of the 150 patients, 57 had sepsis and 93 had septic shock. The total SF-36 scale score of septic patients at 3 months of discharge was 81.4±23.0, and the scores of dimensions were, in descending order, role-emotional (83.4±23.0), mental health (82.9±23.6), bodily pain (82.8±23.3), vitality (81.6±23.2), physical function (81.4±23.5), general health (81.1±23.3), role-physical (79.5±27.0), and social function (78.8±25.2). There was no statistically significant difference in the total SF-36 scale score between the patients with sepsis and septic shock (82.6±22.0 vs. 80.7±23.6, P > 0.05). Incorporating the statistically significant indicators from linear univariate analysis into multiple linear regression analysis, and the results showed that the factors influencing the quality of life of septic patients at 3 months after discharge included ADL scale score at 3 months after discharge [ β= 0.741, 95% confidence interval (95% CI) was 0.606 to 0.791, P < 0.001], length of ICU stay ( β= -0.209, 95% CI was -0.733 to -0.208, P = 0.001), duration of mechanical ventilation ( β= 0.147, 95% CI was 0.122 to 0.978, P = 0.012), total dosage of norepinephrine ( β= -0.111, 95% CI was -0.044 to -0.002, P = 0.028), mean arterial pressure (MAP) at ICU admission ( β= -0.102, 95% CI was -0.203 to -0.007, P = 0.036) and body weight ( β= 0.097, 95% CI was 0.005 to 0.345, P = 0.044). Conclusions:The quality of life of patients with sepsis at 3 months after discharge is at a moderately high level. The influencing factors of the quality of life of patients with sepsis at 3 months after discharge include the ADL scale score at 3 months after discharge, the length of ICU stay, the duration of mechanical ventilation, the total dosage of norepinephrine, MAP at ICU admission and body weight, and healthcare professionals should enhance the treatment and care of the patients during their hospitalization based on the above influencing factors, and pay attention to early psychological intervention and continued care for such patients.

Here, we report a previously unrecognized syndromic neurodevelopmental disorder associated with biallelic loss-of-function variants in the RBM42 gene. The patient is a 2-year-old female with severe central nervous system (CNS) abnormalities, hypotonia, hearing loss, congenital heart defects, and dysmorphic facial features. Familial whole-exome sequencing (WES) reveals that the patient has two compound heterozygous variants, c.304C>T (p.R102*) and c.1312G>A (p.A438T), in the RBM42 gene which encodes an integral component of splicing complex in the RNA-binding motif protein family. The p.A438T variant is in the RRM domain which impairs RBM42 protein stability in vivo. Additionally, p.A438T disrupts the interaction of RBM42 with hnRNP K, which is the causative gene for Au-Kline syndrome with overlapping disease characteristics seen in the index patient. The human R102* or A438T mutant protein failed to fully rescue the growth defects of RBM42 ortholog knockout ΔFgRbp1 in Fusarium while it was rescued by the wild-type (WT) human RBM42. A mouse model carrying Rbm42 compound heterozygous variants, c.280C>T (p.Q94*) and c.1306_1308delinsACA (p.A436T), demonstrated gross fetal developmental defects and most of the double mutant animals died by E13.5. RNA-seq data confirmed that Rbm42 was involved in neurological and myocardial functions with an essential role in alternative splicing (AS). Overall, we present clinical, genetic, and functional data to demonstrate that defects in RBM42 constitute the underlying etiology of a new neurodevelopmental disease which links the dysregulation of global AS to abnormal embryonic development.
Female ; Animals ; Mice ; Humans ; Child, Preschool ; Intellectual Disability/genetics* ; Heart Defects, Congenital/genetics* ; Facies ; Cleft Palate ; Muscle Hypotonia

Objective:To investigate the risk factors of lower extremity deep venous thrombosis (LEDVT) in patients with sepsis during hospitalization in intensive care unit (ICU), and to construct a nomogram prediction model of LEDVT in sepsis patients in the ICU based on the critical care scores combined with inflammatory markers, and to validate its effectiveness in early prediction.Methods:726 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2015 to December 2021 were retrospectively included as the training set to construct the prediction model. In addition, 213 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2022 to June 2023 were retrospectively included as the validation set to verify the performance of the prediction model. Clinical data of patients were collected, such as demographic information, vital signs at the time of admission to the ICU, underlying diseases, past history, various types of scores within 24 hours of admission to the ICU, the first laboratory indexes of admission to the ICU, lower extremity venous ultrasound results, treatment, and prognostic indexes. Lasso regression analysis was used to screen the influencing factors for the occurrence of LEDVT in sepsis patients, and the results of Logistic regression analysis were synthesized to construct a nomogram model. The nomogram model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve, clinical impact curve (CIC) and decision curve analysis (DCA).Results:The incidence of LEDVT after ICU admission was 21.5% (156/726) in the training set of sepsis patients and 21.6% (46/213) in the validation set of sepsis patients. The baseline data of patients in both training and validation sets were comparable. Lasso regression analysis showed that seven independent variables were screened from 67 parameters to be associated with the occurrence of LEDVT in patients with sepsis. Logistic regression analysis showed that the age [odds ratio ( OR) = 1.03, 95% confidence interval (95% CI) was 1.01 to 1.04, P < 0.001], body mass index (BMI: OR = 1.05, 95% CI was 1.01 to 1.09, P = 0.009), venous thromboembolism (VTE) score ( OR = 1.20, 95% CI was 1.11 to 1.29, P < 0.001), activated partial thromboplastin time (APTT: OR = 0.98, 95% CI was 0.97 to 0.99, P = 0.009), D-dimer ( OR = 1.03, 95% CI was 1.01 to 1.04, P < 0.001), skin or soft-tissue infection ( OR = 2.53, 95% CI was 1.29 to 4.98, P = 0.007), and femoral venous cannulation ( OR = 3.72, 95% CI was 2.50 to 5.54, P < 0.001) were the independent influences on the occurrence of LEDVT in patients with sepsis. The nomogram model was constructed by combining the above variables, and the ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the occurrence of LEDVT in patients with sepsis was 0.793 (95% CI was 0.746 to 0.841), and the AUC in the validation set was 0.844 (95% CI was 0.786 to 0.901). The calibration curve showed that its predicted probability was in good agreement with the actual probabilities were in good agreement, and both CIC and DCA curves suggested a favorable net clinical benefit. Conclusion:The nomogram model based on the critical illness scores combined with inflammatory markers can be used for early prediction of LEDVT in ICU sepsis patients, which helps clinicians to identify the risk factors for LEDVT in sepsis patients earlier, so as to achieve early treatment.

Objective:To analyze and explore the independent risk factors of 28-day mortality in patients with septic myocardial injury.Methods:A retrospective cohort study was conducted to collect clinical data of 505 patients diagnosed with sepsis related myocardial injury admitted to the intensive care unit (ICU) of the Affiliated Hospital of Jining Medical University from January 2015 to December 2020. According to the 28-day survival status of patients, they were divided into survival group and death group. COX multivariate regression analysis was used to analyze the influencing factors of the 28-day mortality rate of sepsis related myocardial injury patients, and receiver operating characteristic (ROC) curves were drawn to evaluate the effectiveness of independent risk factors in predicting the 28-day mortality rate of sepsis related myocardial injury patients.Results:A total of 505 patients with sepsis myocardial injury were included, of which 282 survived on 28 days and 223 died, with a mortality rate of 44.16%. COX multivariate regression analysis showed that Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, blood lactate (LAC), oxygenation index (PaO 2/FiO 2), admission heart rate, and albumin were independent risk factors for sepsis associated myocardial injury mortality at 28 days (all P<0.05). ROC curve analysis showed that the area under the ROC curve (AUC) of SOFA score was 0.766 2, and the 95% confidence interval (95% CI) was 0.724 5-0.807 9; The predictive value of 28-day mortality in sepsis associated myocardial injury patients was superior to APACHE Ⅱ score, LAC, PaO 2/FiO 2, admission heart rate, and albumin [The AUC values were 0.754 1(0.711 5-0.796 7), 0.752 6(0.710 1-0.795 1), 0.697 0(0.649 7-0.744 2), 0.623 2(0.573 7-0.672 7), and 0.620 3(0.570 8-0.669 7), respectively]. Conclusions:SOFA score, APACHE Ⅱ score, LAC, PaO 2/FiO 2, admission heart rate, and albumin are independent risk factors for the 28-day mortality rate of sepsis related myocardial injury. Clinical practice should identify these factors early, intervene early, and improve patient prognosis.

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 construct a nomogram model predicting the occurrence of acute kidney injury (AKI) in patients with sepsis in the intensive care unit (ICU), and to verify its validity for early prediction.Methods:Sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to December 2021 were retrospectively included, and those who met the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Univariate and multivariate logistic regression models were used to identify independent risk factors for AKI in patients with sepsis, and a nomogram was constructed based on the independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model.Results:741 patients with sepsis were included in the study, 335 patients developed AKI within 7 d of ICU admission, with an AKI incidence of 45.1%. Randomization was performed in the training set ( n=519) and internal validation set ( n=222). Multivariate logistic analysis revealed that acute physiology and chronic health status score Ⅱ, sequential organ failure score, serum lactate, calcitoninogen, norepinephrine dose, urea nitrogen, and neutrophil percentage were independent factors influencing the occurrence of AKI, and a nomogram model was constructed by combining these variables. In the training set, the AUC of the nomogram model ROC was 0.875 (95% CI: 0.767-0.835), the calibration curve showed consistency between the predicted and actual probabilities, and the DCA showed a good net clinical benefit. In the internal validation set, the nomogram model had a similar predictive value for AKI (AUC=0.871, 95% CI: 0.734-0.854). Conclusions:A nomogram model constructed based on the critical care score at admission combined with inflammatory markers can be used for the early prediction of AKI in sepsis patients in the ICU. The model is helpful for clinicians early identify AKI in sepsis patients.

Objective:To analyze the risk factors of acute kidney injury (AKI) in patients with septic shock in intensive care unit (ICU), construct a predictive model, and explore the predictive value of the predictive model.Methods:The clinical data of patients with septic shock who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical College from April 2015 to June 2019 were retrospectively analyzed. According to whether the patients had AKI within 7 days of admission to the ICU, they were divided into AKI group and non-AKI group. 70% of the cases were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. XGBoost model was used to integrate relevant parameters to predict the risk of AKI in patients with septic shock. The predictive ability was assessed through receiver operator characteristic curve (ROC curve), and was correlated with acute physiology and chronic health evaluationⅡ(APACHEⅡ), sequential organ failure assessment (SOFA), procalcitonin (PCT) and other comparative verification models to verify the predictive value.Results:A total of 303 patients with septic shock were enrolled, including 153 patients with AKI and 150 patients without AKI. The incidence of AKI was 50.50%. Compared with the non-AKI group, the AKI group had higher APACHEⅡscore, SOFA score and blood lactate (Lac), higher dose of norepinephrine (NE), higher proportion of mechanical ventilation, and tachycardiac. In the XGBoost prediction model of AKI risk in septic shock patients, the top 10 features were serum creatinine (SCr) level at ICU admission, NE use, drinking history, albumin, serum sodium, C-reactive protein (CRP), Lac, body mass index (BMI), platelet count (PLT), and blood urea nitrogen (BUN) levels. Area under the ROC curve (AUC) of the XGBoost model for predicting the risk of AKI in patients with septic shock was 0.816, with a sensitivity of 73.3%, a specificity of 71.7%, and an accuracy of 72.5%. Compared with the APACHEⅡscore, SOFA score and PCT, the performance of the model improved significantly. The calibration curve of the model showed that the goodness of fit of the XGBoost model was higher than the other scores (the calibration curve had the lowest score, with a score of 0.205).Conclusion:Compared with the commonly used clinical scores, the XGBoost model can more accurately predict the risk of AKI in patients with septic shock, which helps to make appropriate diagnosis, treatment and follow-up strategies while predicting the prognosis of patients.

Objective:To analyze independent influencing factors of surgical textbook outcome (TO) in patients with gallbladder carcinoma, and to establish a nomogram for predicting TO and evaluated the predictive ability.Methods:Patients with gallbladder carcinoma who underwent surgery in Department of Hepatobiliary and Pancreatic Surgery at Dongfang Hospital Affiliated to Shanghai Tongji University and Department of Biliary Tract Surgery Ⅰ, Third Affiliated Hospital of Naval Medical University (Shanghai Eastern Hepatobiliary Surgery Hospital) from January 2013 to December 2018 were included and the clinical features were retrospectively analyzed. A total of 232 patients were included, including 114 males and 118 females, aged (61.0±9.8) years. According to whether TO reached or not, they were divided into TO group ( n=86) and non-TO group ( n=146). Univariate and multivariate logistic regression were used to analyze the independent influencing factors of TO. The predictive nomogram model of TO was constructed. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive ability of the model, and the consistency of the predictive model was evaluated by the consistency curve graph and the Hosmer-Lemeshow test. Results:The 1-year and 3-years cumulative survival rates of patients with gallbladder carcinoma in the TO group (86.0% and 62.8%) were better than those in the non-TO group (46.6% and 27.3%), and the difference was statistically significant (χ 2=60.74, P<0.001). In multivariate analysis, higher T stage ( OR=0.16, 95% CI: 0.03-0.79, P<0.001) and cervical gallbladder cancer ( OR=0.14, 95% CI: 0.02-0.94, P=0.004) had the greatest negative association with a TO, and the higher the degree of tumor differentiation ( OR=7.08, 95% CI: 1.34-37.56, P=0.001), the easier it is to achieve TO. The ROC curve showed that the area under the curve of the predictive model was 0.84 (95% CI: 0.79-0.90), suggesting that the model had good predictive performance. A nomogram to assess the probability of TO was developed and had good accuracy in both the consistency curve and Hosmer-Lemeshow test (χ 2=5.77, P=0.673). Conclusion:Tumor T stage, tumor differentiation degree and tumor location are independent influencing factors for achieving TO in patients with gallbladder carcinoma after surgery. The nomogram model constructed according to the above conclusions could accurately predict the probability of reaching TO.

Objective:To investigate the changes of quadriceps femoris thickness with the length of stay in intensive care unit (ICU) in patients with sepsis, and to evaluate the diagnostic value of muscle changes in mortality.Methods:A prospective study was conducted, and 92 patients with sepsis who were admitted to the ICU of the Affiliated Hospital of Jining Medical College from January 2020 to December 2021 were enrolled. The thickness of quadriceps femoris [including the quadriceps femoris muscle thickness at the midpoint of the anterior superior iliac spine and the upper edge of the patella (M-QMLT), and at the middle and lower 1/3 of the patella (T-QMLT)] measured by ultrasound 1 day (D1), 3 days (D3), and 7 days (D7) after admission to the ICU were collected. The atrophy rate of quadriceps femoris was calculated 3 and 7 days after admission to the ICU compared with 1 day [(D3-D1)/D1 and (D7-D1)/D1, (TD3-TD1)/TD1 and (TD7-TD1)/TD1, respectively]. The demographic information, underlying diseases, vital signs when admission to the ICU and in-hospital mortality of all patients were recorded, and the differences of the above indicators between the two groupswere compared. Multivariate Logistic regression was used to analyze the influence of quadriceps femoris muscle thickness and atrophy rate on in-hospital mortality of septic patients. The receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of quadriceps femoris muscle thickness and atrophy rate on in-hospital mortality of septic patients.Results:A total of 92 patients with severe sepsis were included, of which 41 patients died in hospital, 51 patients discharged. The in-hospital mortality was 44.6%. The muscle thickness of quadriceps femoris in severe septic patients decreased with the prolongation of ICU stay, and there was no significant difference between the two groups at the first and third day of ICU admission. The muscle thickness of quadriceps femoris at different measuring positions in the survival group was significantly greater than those in the death group 7 days after admission to the ICU [M-QMLT D7 (cm): 0.50±0.26 vs. 0.39±0.19, T-QMLT D7 (cm): 0.58±0.29 vs. 0.45±0.21, both P < 0.05]. The atrophy rate of quadriceps femoris muscle thickness at different measuring positions 3 and 7 days after admission to ICU in the survival group was significantly lower than those in the death group [(D3-D1)/D1: (8.33±3.44)% vs. (9.74±3.91)%, (D7-D1)/D1: (12.21±4.76)% vs. (19.80±6.15)%, (TD3-TD1)/TD1: (7.83±4.26)% vs. (10.51±4.75)%, (TD7-TD1)/TD1: (11.10±5.46)% vs. (20.22±6.05)%, all P < 0.05]. Multivariate Logistic regression analysis showed that M-QMLT D7, T-QMLT D7, (D3-D1)/D1, (D7-D1)/D1, (TD3-TD1)/TD1, (TD7-TD1)/TD1 were independent risk factors for in-hospital mortality (all P < 0.05). The results were stable after adjusting for confounding factors. ROC curve analysis showed that (TD7-TD1)/TD1 [area under the ROC curve (AUC) was 0.853, 95% confidence interval (95% CI) was 0.773-0.934] was superior to (D7-D1)/D1, T-QMLT D7, M-QMLT D7, (TD3-TD1)/TD1 and (D3-D1)/D1 [AUC was 0.821 (0.725-0.917), 0.692 (0.582-0.802), 0.683 (0.573-0.794), 0.680 (0.569-0.791), 0.622 (0.502-0.742)]. Conclusions:For septic patients in ICU, bedside ultrasound monitoring of quadriceps femoris muscle thickness and atrophy rate has a certain predictive value for in-hospital mortality, and a certain guiding significance in clinical treatment and predicting the prognosis of sepsis.

Objective:To investigate the relationship between the arterial blood lactic acid level after entering the intensive care unit (ICU) and the 28-day mortality of patients with septic shock.Methods:The clinical data of 303 patients with septic shock hospitalized in the department of critical medicine of the Affiliated Hospital of Jining Medical College from April 2015 to June 2019 were analyzed retrospectively. According to the blood lactate (Lac) level, the patients were divided into <4 mmol/L group ( n=203), 4-10 mmol/L group ( n=69) and >10 mmol/L group ( n=31). The baseline characteristics of the patients were analyzed. Multiple logistic regression analysis was used to analyze the independent influencing factors of the 28-day mortality of patients with septic shock. The receiver operating characteristic (ROC) curve was used to analyze the predictive value of the Lac level after entering the ICU for 28-day mortality, and Kaplan-Meier survival curve was performed according to the best cut-off value. Results:A total of 303 patients with septic shock were included, with 179 died in 28 days, and the total mortality was 59.08%. There were 203, 69, 31 patients in Lac<4 mmol/L, 4-10 mmol/L and >10 mmol/L group, respectively. There were significant differences in Acute Physiology and Chronic Health Evalution Ⅱ (APACHE Ⅱ), Sequential Organ Failure Assessment (SOFA), oxygenation index (PaO 2/FiO 2), abdominal infection, the proportion of vasoactive drugs use among the three groups ( P<0.05). Multiple logistic regression analysis showed that the independent influencing factor of the 28-day mortality of septic shock were age, SOFA, use of mechanical ventilation, lactic acid (Lac). ROC curve analysis showed that the area under the ROC curve (AUC) for predicting 28-day mortality of patients with septic shock was 0.604 5 (95% CI: 0.540 8-0.668 2). When the optimal cut-off value was 3.55 mmol/L, the sensitivity was 0.508 4, the specificity was 0.733 9, the positive likelihood ratio was 1.910 3 and the negative likelihood ratio was 0.669 9. According to the best cut-off value of entrance Lac, patients were divided into high Lac group (≥3.55 mmol/L) and low Lac group (<3.55 mmol/L), and their 28-day mortality rates were 73.39%(91/124) and 49.16%(88/179). Kaplan-Meier survival curve showed that the 28-day cumulative survival rate of the high Lac group was significantly lower than that of the low Lac group ( P<0.001). Multiple logistic regression analysis showed that after adjusting for confounding factors, the 28 d mortality increased to 1.22 times for each increase of 1 mmol/L of Lac [odds ratio ( OR)=1.22, 95% confidence interval (95% CI) was 1.08-1.37, P=0.001 4]. The 28 d mortality in high Lac group was 3.53 times higher than that in low Lac group ( OR=3.53, 95% CI was 1.36-7.09, P=0.000 4). Conclusions:In patients with ICU septic shock, the arterial blood Lac level after admission was associated with 28-day mortality. Patients with septic shock whose arterial blood Lac level exceeded 3.55 mmol/L within 1 hour of entering the room had a significantly increased risk of death.