Objective To investigate the effects of Chinese herbal medication combined with nutrition intervention on perioperative nutrition in patients with colon carcinoma.Methods Ninety-six patients with colon carcinoma were divided according to their odd or even number into the control group and the intervention group.The former were given routine nutrition and the latter Chinese herbal medication combined with nutrition intervention.Then the mini-nutritional assessment(MNA)was used to assess the changes before and after operation and the complications.Results The intervention group was significantly better than the control one in terms of all nutrition factors but hematoglobin(P<0.05).The incidences of complication and dystrophy in the intervention group were lower than in control group(P<0.05).Conclusion The application of Chinese herbal medication combined with nursing intervention is important for the improvement of their nutrition and the reduction of postoperative complications.

Objective:To explore the value of lactic acid (Lac), procalcitonin (PCT), sequential organ failure assessment (SOFA) score and acute physiology and chronic health evaluationⅡ (APACHEⅡ) score in assessing the severity and predicting the prognosis in sepsis shock.Methods:A retrospectively study was conducted. Patients with septic shock hospitalized in the department of critical care medicine of the Affiliated Hospital of Jining Medical University from April 2015 to June 2019 were enrrolled. The patient's gender, age, body mass index (BMI), infection site, organ dysfunction status; Lac, PCT, C-reactive protein (CRP), heart rate and body temperature immediately after admission to the intensive care unit (ICU); APACHEⅡ and SOFA scores within 24 hours, and 28-day prognosis were collected. According to the 28-day prognosis, the patients with septic shock were divided into the survival group and the death group, and the differences in the indicators between the groups were compared. Multivariate Logistic regression analysis was used to screen the risk factors of 28-day death in patients with septic shock; receiver operating characteristic curve (ROC curve) was used to analyze the value of Lac, PCT, SOFA, APACHEⅡ, and age in predicting the 28-day prognosis of patients with septic shock.Results:A total of 303 septic shock patients were enrolled, of which 124 cases survived and 179 died on the 28th day, and the 28-day mortality was 59.08%. ① Compared with the survival group, patients in the death group were older (years old: 66.58±15.22 vs. 61.15±15.68), APACHEⅡ, SOFA, proportion of lung infections, Lac increased [APACHEⅡ score: 22.79±7.62 vs. 17.98±6.88, SOFA score: 9.42±3.51 vs. 5.65±1.59, proportion of lung infections: 53.63% (96/179) vs. 39.52% (49/124), Lac (mmol/L): 5.10±3.72 vs. 3.71±2.56], oxygenation index (PaO 2/FiO 2) and ICU body temperature decreased [PaO 2/FiO 2 (mmHg, 1 mmHg = 0.133 kPa): 198.94±80.15 vs. 220.68±72.06, ICU body temperature (℃): 37.47±1.08 vs. 37.80±1.14], and the differences were statistically significant (all P < 0.05).②Multivariate Logistic regression analysis results: after adjusted for potential confounding factors, APACHEⅡ, PCT, Lac, age and SOFA were independent risk factors for death in patients with septic shock [APACHEⅡ: odds ratio ( OR) =1.05, 95% confidence interval (95% CI) was 1.01-1.10, P = 0.039; PCT: OR = 0.99, 95% CI was 0.98-1.00, P =0.012; Lac: OR = 1.23, 95% CI was 1.08-1.40, P = 0.002; age: OR = 1.03, 95% CI was 1.01-1.05, P =0.009; SOFA score: OR =1.88, 95% CI was 1.59-2.22, P < 0.001]. ③ROC curve analysis showed that APACHEⅡ, Lac, age and SOFA could predict the prognosis of patients with septic shock [APACHEⅡ: the area under the ROC curve (AUC) = 0.682 4, 95% CI was 0.621 7-0.743 1, P = 0.000; when the best cut-off value was 18.500, its sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 72.63%, 54.84%, 69.89%, 58.12%, 1.608 1 and 0.499 2, respectively. Lac: AUC = 0.604 5, 95% CI was 0.540 8-0.668 2, P = 0.002; when the best cut-off value was 3.550 mmol/L, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 50.84%, 73.39%, 73.39%, 50.94%, 1.910 3 and 0.669 9, respectively. Age: AUC = 0.599 1, 95% CI was 0.535 4-0.662 7, P = 0.003; when the best cut-off value was 72.500 years old, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 42.46%, 75.00%, 71.03%, 47.45%, 1.698 3 and 0.767 2, respectively. SOFA: AUC =0.822 3, 95% CI was 0.776 7-0.867 9, P = 0.000; when the best cut-off value was 7.500, its sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 68.72%, 87.90%, 89.13%, 66.06%, 5.680 4, 0.355 9 respectively]. The combined prediction had a good sensitivity (72.63%) and specificity (84.86%), and AUC (0.876 5) was higher than that of a single variable, suggested that the multivariate combination was more accurate in predicting the short-term outcome of septic shock. Conclusion:Lac, PCT, SOFA score, APACHEⅡ score and age were independent risk factors for death in patients with septic shock, and the accuracy of Lac, SOFA score, APACHEⅡ score and age in predicting short-term prognosis of septic shock was better than that of single variable, and the diagnostic value was higher.

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 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 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 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.

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 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.

The gut microbiota of intensive care unit (ICU) patients displays extreme dysbiosis asso-ciated with increased susceptibility to organ failure, sepsis, and septic shock. However, such dysbio-sis is difficult to characterize owing to the high dimensional complexity of the gut microbiota. We tested whether the concept of enterotype can be applied to the gut microbiota of ICU patients to describe the dysbiosis. We collected 131 fecal samples from 64 ICU patients diagnosed with sepsis or septic shock and performed 16S rRNA gene sequencing to dissect their gut microbiota compo-sitions. During the development of sepsis or septic shock and during various medical treatments, the ICU patients always exhibited two dysbiotic microbiota patterns, or ICU-enterotypes, which could not be explained by host properties such as age, sex, and body mass index, or external stressors such as infection site and antibiotic use. ICU-enterotype I (ICU E1) comprised predominantly Bac-teroides and an unclassified genus of Enterobacteriaceae, while ICU-enterotype Ⅱ(ICU E2) com-prised predominantly Enterococcus. Among more critically ill patients with Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ) scores > 18, septic shock was more likely to occur with ICU E1 (P = 0.041). Additionally, ICU E1 was correlated with high serum lactate levels (P = 0.007). Therefore, different patterns of dysbiosis were correlated with different clinicaloutcomes, suggesting that ICU-enterotypes should be diagnosed as independent clinical indices. Thus, the microbial-based human index classifier we propose is precise and effective for timely mon-itoring of ICU-enterotypes of individual patients. This work is a first step toward precision medicine for septic patients based on their gut microbiota profiles.

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.