PURPOSE: To compare the efficacy of continuous renal replacement therapy (CRRT) using either oXiris or conventional hemopurification filters in the treatment of intra-abdominal sepsis. METHODS: We conducted a retrospective analysis of septic patients with severe intra-abdominal infections admitted to our hospital from October 2019 to August 2023. Patients who meet the criteria for intra-abdominal sepsis based on medical history, symptoms, physical examination, and laboratory/imaging findings were included. EXCLUSION CRITERIA: pregnancy, terminal malignancy, prior CRRT before intensive care unit admission, pre-existing liver or renal failure. Heart rate (HR), mean arterial pressure, oxygenation index, lactic acid level (Lac), platelet count (PLT), neutrophil percentage, serum levels of procalcitonin, C-reactive protein, interleukin (IL)-6, norepinephrine dosage, acute physiology and chronic health evaluation II (APACHE II), and sequential organ failure assessment (SOFA) scores before and after 24 h and 72 h of treatment, as well as ventilator use time, hemopurification treatment time, intensive care unit and hospital lengths of stay, and 14-day and 28-day mortality were compared between patients receiving CRRT using either oXiris or conventional hemofiltration. Statistical analysis was performed using SPSS Statistics 26.0 software, including the construction of predictive models via logistic regression equations and repeated measures ANOVA. RESULTS: Baseline values including time to antibiotic administration, time to source control, and time to initiation of CRRT were similar between the 2 groups (all p>0.05). Patients receiving conventional CRRT exhibited significant changes in HR but of none of the other indexes at the 24 h and 72 h time points (p=0.041, p=0.026, respectively). The oXiris group showed significant improvements in HR, Lac, IL-6, and APACHE II score 24 h after treatment (p<0.05); after 72 h, all indexes were improved except PLT (all p<0.05). Intergroup comparison disclosed significant differences in HR, Lac, norepinephrine dose, APACHE II, SOFA, neutrophil percentage, and IL-6 after 24 h of treatment (p<0.05). Mean arterial pressure, serum levels of procalcitonin, C-reactive protein, SOFA score, and norepinephrine dosage were similar between the 2 groups at 24 h (p>0.05). Except for HR, oxygenation index, and PLT, post-treatment change rates of △ (%) were significantly greater in the oXiris group (p < 0.05). Duration of ventilator use, CRRT time, and intensive care unit and hospital lengths of stay were similar between the 2 groups (p>0.05). The 14-day mortality rates of the 2 groups were similar (p=0.091). After excluding patients whose CRRT was interrupted, 28-day mortality was significantly lower in the oXiris than in the conventional group (25.0% vs. 54.2%; p=0.050). The 28-day mortality rate increased by 9.6% for each additional hour required for source control and by 21.3% for each 1-point increase in APACHE II score. CONCLUSIONS In severe abdominal infections, the oXiris filter may have advantages over conventional CRRT, which may provide an alternative to clinical treatment. Meanwhile, early active infection source control may reduce the case mortality rate of patients with severe abdominal infections.
Humans ; Retrospective Studies ; Female ; Male ; Middle Aged ; Sepsis/mortality* ; Aged ; Adult ; Continuous Renal Replacement Therapy/methods* ; Intraabdominal Infections/mortality* ; APACHE ; Organ Dysfunction Scores ; Intensive Care Units ; Treatment Outcome

OBJECTIVE: To investigate the predictive value of early lactate/albumin ratio (LAR) combined with quick sequential organ failure assessment (qSOFA) for the 28-day prognosis of patients with sepsis caused by emergency community-acquired pneumonia (CAP). METHODS: The clinical data of patients with sepsis caused by CAP admitted to the department of emergency of Beijing Haidian Hospital from June 2021 to August 2022 were retrospectively analyzed, including gender, age, comorbidities, lactic acid (Lac), serum albumin (Alb), LAR, procalcitonin (PCT) within 1 hour, and 28-day prognosis. Patients were divided into two groups based on 28-day prognosis, and risk factors affecting patients' prognosis were analyzed using univariate and multivariate Cox regression methods. Patients were divided into two groups according to the best cut-off value of LAR, and Kaplan-Meier survival curves were used to analyze the 28-day cumulative survival of patients in each group. Time-dependent receiver operator characteristic curve (ROC curve) were plotted to analyze the predictive value of sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), and qSOFA+LAR score on the prognosis of patients with sepsis caused by CAP at 28 days. The area under the curve (AUC) was calculated and compared. RESULTS: A total of 116 patients with sepsis caused by CAP were included, of whom 80 survived at 28 days and 36 died, 28-day mortality of 31.0%. There were no statistically significant differences in age, gender, comorbidities, pH, platelet count, and fibrinogen between the survival and death groups, and there were significantly differences in blood urea nitrogen (BUN), white blood cell count (WBC), hemoglobin, Lac, Alb, PCT, D-dimer, LAR, as well as qSOFA score, SOFA score, and APACHE II score. Univariate Cox regression analyses showed that BUN, WBC, pH, Lac, Alb, PCT, LAR, qSOFA score, SOFA score, and APACHE II score were associated with mortality outcome. Multifactorial Cox regression analysis of the above variables showed that BUN, WBC, PCT, and APACHE II score were independent risk factors for 28-day death in the emergency department in patients with sepsis caused by CAP [hazard ratio (HR) were 1.081, 0.892, 1.034, and 1.135, respectively, all P < 0.05]. The best cut-off value of early LAR for predicting the 28-day prognosis of sepsis patients was 0.088, the Kaplan-Meier survival curve showed that the 28-day cumulative survival rate of sepsis patients in the LAR ≤ 0.088 group was significantly higher than that in the LAR > 0.088 group [82.9% (63/76) vs. 42.5% (17/40), Log-Rank test: χ² = 22.51, P < 0.001]. The qSOFA+LAR score was calculated based on the LAR cut-off value and qSOFA score, and ROC curve analysis showed that the AUCs of SOFA score, APACHE II score, and qSOFA+LAR score for predicting 28-day death of patients with sepsis caued by CAP were 0.741, 0.774, and 0.709, respectively, with the AUC of qSOFA+LAR score slightly lower than those of SOFA score and APACHE II score, but there were no significantly differences. When the best cut-off value of qSOFA+LAR score was 1, the sensitivity was 63.9% and the specificity was 80.0%. CONCLUSION The qSOFA+LAR score has predictive value for the 28-day prognosis of patients with sepsis caused by CAP in the emergency department, its predictive value is comparable to the SOFA score and the APACHE II score, and it is more convenient for early use in the emergency department.
Emergency Service, Hospital/statistics & numerical data* ; Sepsis/etiology* ; Prognosis ; Community-Acquired Pneumonia/mortality* ; Organ Dysfunction Scores ; Predictive Value of Tests ; Lactic Acid/blood* ; Serum Albumin, Human/analysis* ; Biomarkers/blood* ; Retrospective Studies ; Hospital Mortality ; Kaplan-Meier Estimate ; APACHE ; Procalcitonin/blood* ; ROC Curve ; Area Under Curve ; Humans

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

OBJECTIVE: To develop a method for identifying high-risk patients among septic populations requiring mechanical ventilation, and to conduct phenotypic analysis based on this method. METHODS: Data from four sources were utilized: the Medical Information Mart for Intensive Care (MIMIC-IV 2.0, MIMIC-III 1.4), the Philips eICU-Collaborative Research Database 2.0 (eICU-CRD 2.0), and the Anhui Medical University Second Affiliated Hospital dataset. The adult patients in intensive care unit (ICU) who met Sepsis-3 and received invasive mechanical ventilation (IMV) on the first day of first admission were enrolled. The MIMIC-IV dataset with the highest data integrity was divided into a training set and a test set at a 6:1 ratio, while the remaining datasets were served as validation sets. The demographic information, comorbidities, laboratory indicators, commonly used ICU scores, and treatment measures of patients were extracted. Clinical data collected within first day of ICU admission were used to calculate the sequential organ failure assessment (SOFA) score. K-means clustering was applied to cluster SOFA score components, and the sum of squared errors (SSE) and Davies-Bouldin index (DBI) were used to determine the optimal number of disease subtypes. For clustering results, normalized methods were employed to compare baseline characteristics by visualization, and Kaplan-Meier curves were used to analyze clinical outcomes across phenotypes. RESULTS: This study enrolled patients from MIMIC-IV dataset (n = 11 166), MIMIC-III dataset (n = 4 821), eICU-CRD dataset (n = 6 624), and a local dataset (n = 110), with the four datasets showing similar median ages and male proportions exceeding 50%; using 85% of the MIMIC-IV dataset as the training set, 15% as the test set, and the rest dataset as the validation set. K-means clustering based on the six-item SOFA score was performed to determine the optimal number of clusters as 3, and patients were finally classified into three phenotypes. In the training set, compared with the patients with phenotype II and phenotype III, those with phenotype I had the more severe circulatory and respiratory dysfunction, a higher proportion of vasoactive drug usage, more obvious metabolic acidosis and hypoxia, and a higher incidence of congestive heart failure. The patients with phenotype II was dominated by respiratory dysfunction with higher visceral injury. The patients with phenotype III had relatively stable organ function. The above characteristics were consistent in both the test and validation sets. Analysis of infection-related indicators showed that the patients with phenotype I had the highest SOFA score within 7 days after ICU admission, initial decreases and later increases in platelet count (PLT), and higher counts of neutrophils, lymphocytes, and monocytes as compared with those with phenotype II and phenotype III, their blood cultures had a higher positivity rates for Gram-positive bacteria, Gram-negative bacteria and fungi as compared with those with phenotype II and phenotype III. The Kaplan-Meier curve indicated that in the training, test, and validation sets, the 28-day cumulative mortality of patients with phenotype I was significantly higher than that of patients with phenotypes II and phenotype III. CONCLUSIONS Three distinct phenotypes in septic patients receiving IMV based on unsupervised machine learning is derived, among which phenotype I, characterized by cardiorespiratory failure, can be used for the early identification of high-risk patients in this population. Moreover, this population is more prone to bloodstream infections, posing a high risk and having a poor prognosis.
Humans ; Machine Learning ; Sepsis/therapy* ; Respiration, Artificial ; Intensive Care Units ; Organ Dysfunction Scores ; Male ; Female ; Middle Aged ; Adult

OBJECTIVE: To evaluate the predictive value of plasma heparin-binding protein (HBP) combined with albumin (Alb) for predicting 28-day mortality in patients with sepsis. METHODS: The clinical data of patients with sepsis admitted to the emergency intensive care unit (EICU) of the People's Hospital of Shenzhen Baoan District from March 2020 to March 2024 were retrospectively analyzed. The study began at the time of the first diagnosis of sepsis upon EICU admission and ended upon patient death or at 28 days. The gender, age, length of stay in EICU, underlying diseases, and infection sites were recorded. Within 24 hours of sepsis diagnosis, blood culture results, white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), blood lactate acid (Lac), HBP, Alb, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), mortality in emergency department sepsis score (MEDS), modified early warning score (MEWS), number of organ failures, use of vasopressors, application of mechanical ventilation, renal replacement therapy, and 28-day prognosis were recorded, the differences in these indicators between two groups were compared. Univariate and multivariate Logistic regression analyses were used to analyze the risk factors of 28-day mortality in patients with sepsis. Receiver operator characteristic curve (ROC curve) was drawn, and the area under the ROC curve (AUC) was calculated to evaluate the early predictive value of various risk factors for 28-day mortality in patients with sepsis. RESULTS: A total of 300 patients with sepsis were included, with 16 excluded, resulting in 284 patients being analyzed. Among them, 191 survived and 93 died within 28 days. There were no statistically significant differences between the two groups in terms of gender, age, underlying diseases, infection sites, blood culture positivity rate, number of organ failures, and length of stay in EICU. Univariate analysis showed that the rate of vasopressor use, the rate of mechanical ventilation, HBP, PCT, CRP, Lac, SOFA score, APACHE II score, MEDS score, and MEWS score were significantly higher in the death group than those in the survival group, while Alb was significantly lower in the death group than that in the survival group. Multivariate Logistic regression analysis showed that HBP and Alb were independent risk factors for predicting 28-day mortality in patients with sepsis [odds ratio (OR) and 95% confidence interval (95%CI) were 1.093 (0.989-1.128) and 1.174 (1.095-1.259), both P < 0.05]. ROC curve analysis showed that both HBP and Alb had certain predictive value for 28-day mortality in patients with sepsis [AUC and 95%CI were 0.820 (0.717-0.923) and 0.786 (0.682-0.890), both P < 0.05]. When the critical value of HBP was 117.50 μg/L, the sensitivity was 85.90%, and the specificity was 70.50%. When the critical value of Alb was 28.30 g/L, the sensitivity was 69.30%, and the specificity was 81.20%. When the two indexes were combined for diagnosis, the AUC was 0.881 (95%CI was 0.817-0.945, P < 0.001), the sensitivity was 92.70%, and the specificity was 76.80%. CONCLUSIONS HBP and Alb are independent risk factors for predicting 28-day mortality in patients with sepsis. The combined prediction efficiency of HBP and Alb for 28-day mortality in patients with sepsis is superior to a single indicator.
Humans ; Sepsis/diagnosis* ; Retrospective Studies ; Predictive Value of Tests ; Intensive Care Units ; Blood Proteins/analysis* ; Prognosis ; Antimicrobial Cationic Peptides/blood* ; APACHE ; Male ; Female ; Organ Dysfunction Scores ; ROC Curve ; Middle Aged ; C-Reactive Protein/analysis* ; Emergency Service, Hospital ; Aged ; Hospital Mortality ; Serum Albumin/analysis*

Humans ; Fasciitis, Necrotizing/diagnosis* ; Prospective Studies ; Organ Dysfunction Scores ; Prognosis

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.
Adult ; Humans ; Sepsis/diagnosis* ; ROC Curve ; Retrospective Studies ; Critical Illness ; Early Warning Score ; Organ Dysfunction Scores ; Intensive Care Units ; Prognosis ; Hospitals

OBJECTIVE: To explore the prognostic value of early multiple detection indicators in combination with sequential organ failure assessment (SOFA) in sepsis patients. METHODS: A retrospective analysis was conducted. Patients with sepsis admitted to the department of critical care medicine of Huanggang Central Hospital of Yangtze University from May 2020 to May 2022 were selected as the research subjects. Coagulation indicators, inflammatory factors, blood routine, liver and kidney function, and blood gas analysis were collected at admission. Organ dysfunction was assessed based on the SOFA score within 24 hours after admission. Patients were divided into a survival group and a death group according to the outcome of 28 days in ICU. Differences in the above indicators between the two groups were compared. Multifactorial Logistic regression analysis was used to analyze prognostic factors of 28-day mortality in sepsis patients. Receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive performance of various indicators, the SOFA score, and the combine model for the 28-day outcome in patients with sepsis. RESULTS: A total of 101 patients with sepsis were enrolled, 56 patients survived and 45 patients died. Compared to the survival group, patients in the death group were older, the proportion of patients with septic shock was larger, the SOFA score, and the proportion of pulmonary infection were higher, the prothrombin time (PT) and activated partial thromboplastin time (APTT) were significantly prolonged, the prothrombin activity (PTA) was significantly shortened, and antithrombin (AT) was significantly decreased, the levels of hypersensitivity C-reactive protein (hs-CRP), blood urea nitrogen (BUN), total bilirubin (TBil), and lactic acid (Lac) were significantly increased, while the platelet count (PLT) was significantly decreased. Multifactorial Logistic regression analysis showed that pulmonary infection [odds ratio (OR) = 0.010, 95% confidence interval (95%CI) was 0.001-0.164, P = 0.001], AT (OR = 0.944, 95%CI was 0.910-0.978, P = 0.002), hs-CRP (OR = 1.008, 95%CI was 1.001-1.015, P = 0.017), Lac (OR = 1.619, 95%CI was 1.195-2.193, P = 0.002), and SOFA score (OR = 1.363, 95%CI was 1.076-1.727, P = 0.010) were independent prognostic factors for 28-day mortality in patients. A combined model was constructed using pulmonary infection, AT, hs-CRP, Lac, and SOFA score. ROC curve analysis showed that the area under the ROC curve (AUC) for the combine model in predicting sepsis prognosis was 0.936 (95%CI was 0.869-0.975, P < 0.001), which was higher in value compared to single indicators (AUC of AT, hs-CRP, Lac, and SOFA score were 0.775, 0.666, 0.802, 0.796, respectively, all P < 0.05). CONCLUSIONS The predictive ability of the SOFA score for sepsis patient outcomes is limited. The combine model combining infection site, AT, hs-CRP, and Lac shows better predictive ability.
Humans ; Organ Dysfunction Scores ; Retrospective Studies ; C-Reactive Protein ; ROC Curve ; Sepsis/metabolism* ; Prognosis ; Anticoagulants ; Antithrombin III ; Intensive Care Units

BACKGROUND: Hospital-acquired pneumonia (HAP) is the most common hospital-acquired infection in China with substantial morbidity and mortality. But no specific risk assessment model has been well validated in patients with HAP. The aim of this study was to investigate the published risk assessment models that could potentially be used to predict 30-day mortality in HAP patients in non-surgical departments. METHODS: This study was a single-center, retrospective study. In total, 223 patients diagnosed with HAP from 2012 to 2017 were included in this study. Clinical and laboratory data during the initial 24 hours after HAP diagnosis were collected to calculate the pneumonia severity index (PSI); consciousness, urea nitrogen, respiratory rate, blood pressure, and age ≥65 years (CURB-65); Acute Physiology and Chronic Health Evaluation II (APACHE II); Sequential Organ Failure Assessment (SOFA); and Quick Sequential Organ Failure Assessment (qSOFA) scores. The discriminatory power was tested by constructing receiver operating characteristic (ROC) curves, and the areas under the curve (AUCs) were calculated. RESULTS: The all-cause 30-day mortality rate was 18.4% (41/223). The PSI, CURB-65, SOFA, APACHE II, and qSOFA scores were significantly higher in non-survivors than in survivors (all P < 0.001). The discriminatory abilities of the APACHE II and SOFA scores were better than those of the CURB-65 and qSOFA scores (ROC AUC: APACHE II vs. CURB-65, 0.863 vs. 0.744, Z = 3.055, P = 0.002; APACHE II vs. qSOFA, 0.863 vs. 0.767, Z = 3.017, P = 0.003; SOFA vs. CURB-65, 0.856 vs. 0.744, Z = 2.589, P = 0.010; SOFA vs. qSOFA, 0.856 vs. 0.767, Z = 2.170, P = 0.030). The cut-off values we defined for the SOFA, APACHE II, and qSOFA scores were 4, 14, and 1. CONCLUSIONS These results suggest that the APACHE II and SOFA scores determined during the initial 24 h after HAP diagnosis may be useful for the prediction of 30-day mortality in HAP patients in non-surgical departments. The qSOFA score may be a simple tool that can be used to quickly identify severe infections.
Aged ; China ; Hospital Mortality ; Hospitals ; Humans ; Intensive Care Units ; Organ Dysfunction Scores ; Pneumonia ; Prognosis ; ROC Curve ; Retrospective Studies ; Sepsis

OBJECTIVE: To study the significance of the level of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) in serum and bronchoalveolar lavage fluid (BALF), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and Sequential Organ Failure Assessment (SOFA) score in evaluating the conditions and prognosis of children with severe pneumonia. METHODS: A total of 76 children with severe pneumonia who were admitted from August 2017 to October 2019 were enrolled as the severe pneumonia group. According to the treatment outcome, they were divided into a non-response group with 34 children and a response group with 42 children. Ninety-four children with common pneumonia who were admitted during the same period of time were enrolled as the common pneumonia group. One hundred healthy children who underwent physical examination in the outpatient service during the same period of time were enrolled as the control group. The serum level of sTREM-1, APACHE II score, and SOFA score were measured for each group, and the level of sTREM-1 in BALF was measured for children with severe pneumonia. The correlation of the above indices with the severity and prognosis of severe pneumonia in children was analyzed. RESULTS: The severe pneumonia group had significantly higher serum sTREM-1 level, APACHEII score, and SOFA score than the common pneumonia group and the control group (P<0.05). For the children with severe pneumonia, the non-response group had significant increases in the levels of sTREM-1 in serum and BALF and SOFA score on day 7 after admission, while the response group had significant reductions in these indices, and there were significant differences between the two groups (P<0.05). Positive correlation was found between any two of serum sTREM-1, BALF sTREM-1, and SOFA score (P<0.05). APACHE II score was not correlated with serum sTREM-1, BALF sTREM-1, and SOFA score (P>0.05). CONCLUSIONS The level of sTREM-1 in serum and BALF and SOFA score can be used to evaluate the severity and prognosis of severe pneumonia in children.
APACHE ; Bronchoalveolar Lavage Fluid ; Child ; Humans ; Organ Dysfunction Scores ; Pneumonia ; Prognosis ; ROC Curve ; Sepsis ; Triggering Receptor Expressed on Myeloid Cells-1