[Analysis of the current quality of life status and influencing factors of sepsis survivors in intensive care unit].

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 [ß= 0.741, 95% confidence interval (95%CI) was 0.606 to 0.791, P < 0.001], length of ICU stay (ß= -0.209, 95%CI was -0.733 to -0.208, P = 0.001), duration of mechanical ventilation (ß= 0.147, 95%CI was 0.122 to 0.978, P = 0.012), total dosage of norepinephrine (ß= -0.111, 95%CI was -0.044 to -0.002, P = 0.028), mean arterial pressure (MAP) at ICU admission (ß= -0.102, 95%CI was -0.203 to -0.007, P = 0.036) and body weight (ß= 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.

[Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].

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.

Association between neutrophil percentage-to-albumin ratio and 28-day mortality in Chinese patients with sepsis.

OBJECTIVE: To assess the association between neutrophil percentage-to-albumin ratio (NPAR) and 28-day mortality in severely ill Chinese patients with sepsis. METHODS: In this retrospective, single-centre study, sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University between May 2015 and December 2021 were retrospectively analysed. The relationship between NPAR and 28-day mortality was examined using Cox proportional-hazards model. RESULTS: In total, 741 patients with sepsis were included. Multivariate analysis, adjusted for age, sex, body mass index (BMI), smoking and alcohol drinking history, showed an association between elevated NPAR and a high risk of 28-day mortality. After removal of additional confounders, moderate and high NPAR values remained significantly associated with 28-day mortality in comparison with low NPAR values (tertile 2 vs 1: HR, 95% CI: 1.42, 1.06-1.90; tertile 3 vs 1: HR, 95% CI: 1.35, 1.00-1.82). Survival curves stratified by NPAR groups showed that high NPAR levels had lower survival probabilities than lower NPAR levels. Subgroup analysis did not show any significant interactions between NPAR and 28-day mortality. CONCLUSIONS: Elevated NPAR values were linked to increased 28-day mortality in severely ill Chinese patients with sepsis. The findings require verification by large, prospective, multi-centre studies.

[Diagnostic value of early bedside ultrasound measurement of quadriceps femoris on in-hospital mortality of septic patients].

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.

[Construction of a predictive model for early acute kidney injury risk in intensive care unit septic shock patients based on machine learning].

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 evaluation II (APACHE II), 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 APACHE II score, 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 APACHE II score, 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). CONCLUSIONS: 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.

Association between admission serum procalcitonin and the occurrence of acute kidney injury in patients with septic shock: A retrospective cohort study.

BACKGROUND: Procalcitonin (PCT) is an effective and sensitive diagnostic biomarker that can facilitate the early detection of infection and septicemia, but whether it can similarly be utilized to predict the development of acute kidney injury (AKI) in patients suffering from septic shock remains to be established. Herein, the relationship between serum PCT at admission and the onset of AKI in septic shock patients was thus evaluated following adjustment for other potential covariates. METHODS: This was a retrospective cohort study of 303 septic shock patients treated in a Chinese hospital between May 2015 and May 2019. All patients in whom PCT levels were measured on admission and who did not exhibit AKI or chronic kidney disease at the time of admission were assessed for AKI development within one week following intensive care unit (ICU) admission as per the KDIGO criteria. The relationship between serum PCT at admission and AKI incidence was then assessed for these patients. RESULTS: These 303 patients were an average of 64 years old, and were 59.7% male. Of these patients, 50.5% developed AKI within the first 7 days following ICU admission. A dully-adjusted binary logistic regression analysis revealed PCT levels at admission to be associated with AKI following adjustment for potential confounding factors (odds ratio (OR) = 1.01, 95%CI (1.01,1.02), p = 0.0007). Receiver operating characteristic curve analysis further indicated that a PCT cutoff level of 52.59 ng/ml at admission was able to predict the incidence of AKI with respective sensitivity and specificity values of 50% and 84%. Interaction analysis revealed no significant interactive relationship between PCT and AKI, suggesting that serum PCT levels represent an early predictor of AKI incidence in septic shock patients. CONCLUSIONS: Serum PCT at the time of admission can be used as a predictor of AKI in patients suffering from septic shock.

[Combined prognostic value of serum lactic acid, procalcitonin and severity score for short-term prognosis of septic shock patients].

OBJECTIVE: To explore the value of lactic acid (Lac), procalcitonin (PCT), sequential organ failure assessment (SOFA) score and acute physiology and chronic health evaluation II (APACHE II) 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); APACHE II 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, APACHE II, 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), APACHE II, SOFA, proportion of lung infections, Lac increased [APACHE II 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 (PaO2/FiO2) and ICU body temperature decreased [PaO2/FiO2 (mmHg, 1 mmHg = 0.133 kPa): 198.94±80.15 vs. 220.68±72.06, ICU body temperature (centigrade): 37.47±1.08 vs. 37.80±1.14], and the differences were statistically significant (all P < 0.05). (2) Multivariate Logistic regression analysis results: after adjusted for potential confounding factors, APACHE II, PCT, Lac, age and SOFA were independent risk factors for death in patients with septic shock [APACHE II: 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]. (3) ROC curve analysis showed that APACHE II, Lac, age and SOFA could predict the prognosis of patients with septic shock [APACHE II: 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. CONCLUSIONS: Lac, PCT, SOFA score, APACHE II score and age were independent risk factors for death in patients with septic shock, and the accuracy of Lac, SOFA score, APACHE II score and age in predicting short-term prognosis of septic shock was better than that of single variable, and the diagnostic value was higher.

From sepsis to acute respiratory distress syndrome (ARDS): emerging preventive strategies based on molecular and genetic researches.

A healthy body activates the immune response to target invading pathogens (i.e. viruses, bacteria, fungi, and parasites) and avoid further systemic infection. The activation of immunological mechanisms includes several components of the immune system, such as innate and acquired immunity. Once any component of the immune response to infections is aberrantly altered or dysregulated, resulting in a failure to clear infection, sepsis will develop through a pro-inflammatory immunological mechanism. Furthermore, the severe inflammatory responses induced by sepsis also increase vascular permeability, leading to acute pulmonary edema and resulting in acute respiratory distress syndrome (ARDS). Apparently, potential for improvement exists in the management of the transition from sepsis to ARDS; thus, this article presents an exhaustive review that highlights the previously unrecognized relationship between sepsis and ARDS and suggests a direction for future therapeutic developments, including plasma and genetic pre-diagnostic strategies and interference with proinflammatory signaling.

Serial Multiple Mediation of Professional Identity, and Psychological Capital in the Relationship Between Work-Related Stress and Work-Related Well-Being of ICU Nurses in China: A Cross-Sectional Questionnaire Survey.

This study aimed to investigate the serial-multiple mediation effect of professional identity, psychological capital (PsyCap), work-related stress, and work-related wellbeing among intensive care unit (ICU) nurses in China. The cross-sectional survey was conducted from January 2017 to May 2017 in two Grade III A general hospitals (with more than 2000 beds) in Jining, Shandong Province, China. Cluster sampling was used to recruit participants from the two hospitals. A total of 330 ICU nurses participated in the study. The nurses' work stress scale, Chinese nurse's professional identity scale, the PsyCap questionnaire, and Chinese work-related wellbeing scale were used to collect the data. Descriptive analysis, independent-samples t-test, one-way analysis of variance, Pearson correlation analysis, linear regression analysis, and structural equation modeling were used to analyze the data (P < 0.05 was considered statistically significant). The average score for the work-related wellbeing of ICU nurses was 85.91 ± 13.94. Work-related stress, professional identity, and PsyCap correlated significantly with work-related wellbeing. The major predictors of work-related wellbeing were PsyCap, work-related stress, professional identity, and monthly salary. The serial-multiple mediation effects of professional identity and PsyCap in the relationship between work-related stress and work-related wellbeing were statistically significant. Positive professional identity and PsyCap were sequentially associated with decreased work-related stress, which in turn was related to increased work-related wellbeing among ICU nurses. Therefore, this study aims to explore the impact of ICU nurses' work-related stress on work-related wellbeing, as well as the mediating effect of professional identity and PsyCap. It is hoped that hospital care managers will pay attention to the mental health of ICU nurses, increase their professional identity, and reduce work-related stress to improve the quality of the ICU nursing service and stabilize nursing work.