Objective:To investigate the differences and prognostic value of serum myeloid-related proteins 8 and 14 (Mrp 8/14) in patients with acute respiratory distress syndrome (ARDS) induced by pulmonary and extrapulmonary sepsis.Methods:A retrospective cohort study was conducted to collect the general clinical data of septic ARDS patients admitted to the intensive care unit (ICU) of Nanjing Hospital of Nanjing Medical University from August 2021 to February 2024. The serum levels of Mrp 8/14 were detected within 24 hours after admission. According to whether the patients died during ICU stay, the patients with pulmonary ARDS and extrapulmonary ARDS induced by sepsis were divided into survival and death groups, respectively. The differences of Mrp 8/14 and other clinical data between the two groups were compared. Logistic regression analysis was used to analyze the prognostic factors of septic patients with pulmonary ARDS and extrapulmonary ARDS. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive value of serum Mrp 8/14 for the prognosis of patients with pulmonary ARDS and extrapulmonary ARDS.Results:A total of 138 patients with sepsis-induced ARDS were enrolled in this study, including 79 patients with pulmonary ARDS and 59 patients with extrapulmonary ARDS. Compared to the death group, the level of serum Mrp 8/14 was significantly lower [Mrp 8/14: 22.90 (17.91, 30.88) μg/mL vs. 10.73 (7.15, 17.20) μg/mL, P<0.05]. Multivariate logistic regression analysis demonstrated that the serum Mrp 8/14 level was an independent risk factor for the prognosis of pulmonary ARDS patients only during the ICU stay ( OR=1.253, 95% CI: 1.110-1.414, P<0.05). ROC curve analysis showed that the area under the curve of serum Mrp 8/14 for the prediction of death in ARDS patients with pulmonary sepsis during ICU stay was 0.855 (95% CI: 0.773-0.938, P<0.05), the cut-off value was 19.230 μg/mL, the sensitivity was 0.718, the specificity was 0.925, and the Yonden index was 0.405. Conclusion:Serum Mrp 8/14 is an effective prognostic indicator for the mortality of ARDS patients with pulmonary sepsis during ICU hospitalization.

Objective:To investigate the prognostic value of the combined use of central venous-arterial carbon dioxide partial pressure difference/arterial-central venous oxygen content difference ratio (Pv-aCO 2/Ca-vO 2) combined with peripheral perfusion index (PI) for prognosis in middle-aged and elderly patients with acute heart failure (AHF) complicated by hypoperfusion. Methods:A case-control study was conducted, enrolling middle-aged and elderly AHF patients with tissue hypoperfusion admitted to the Emergency Intensive Care Unit of Nanjing First Hospital from May 2022 to May 2024. The primary endpoint was 28-day all-cause mortality. Patients were divided into survival and death groups based on prognosis. Baseline characteristics and clinical data were compared between groups. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of risk factors. Stratified analysis based on optimal cutoff values was performed, and Kaplan-Meier survival curves were used to compare prognostic differences between subgroups.Results:A total of 70 patients with AHF and hypoperfusion were enrolled, with 36 deaths (28-day mortality rate: 51.43%). No significant differences were observed in baseline characteristics, N-terminal B-type natriuretic peptide precursor, creatine kinase, creatine kinase-myocardial band, cardiac troponin I,central venous pressure, or left ventricular ejection fraction between groups(all P>0.05). Compared with the survival group, the death group exhibited significantly higher APACHEⅡ scores, lactate levels, and Pv-aCO 2/Ca-vO 2 ratios, along with lower PI values (all P<0.05). The area under the ROC curve (AUCs) for PI, Pv-aCO 2/Ca-vO 2, and their combination in predicting 28-day mortality were 0.804 (95% CI: 0.701-0.908), 0.848 (95% CI: 0.758-0.938), and 0.922 (95% CI: 0.859-0.985), respectively. The optimal cutoff value for PI was 1.17 (sensitivity 83.3% and specificity 67.6%), and for Pv-aCO 2/Ca-vO 2 was 1.59 (sensitivity 77.8% and specificity 79.4%). Stratified analysis revealed that the PI≤1.17 group had a significantly higher 28-day mortality rate than the PI>1.17 group ( P<0.01), and the Pv-aCO 2/Ca-vO 2>1.59 group had a markedly higher mortality rate than the Pv-aCO 2/Ca-vO 2≤1.59 group ( P<0.01) ,consistent with Kaplan-Meier survival analysis. Conclusion:Early assessment of Pv-aCO 2/Ca-vO 2 combined with PI demonstrates superior predictive performance for prognosis in AHF patients with hypoperfusion.

Objective:To investigate the correlation between sepsis-induced coagulopathy (SIC) scores, soluble thrombomodulin (sTM) levels, and disease severity in septic patients, and to evaluate their individual and combined predictive value for patient prognosis.Methods:This retrospective cohort study included 212 sepsis patients admitted to the ICU of Nanjing Hospital affiliated with Nanjing Medical University from April 2021 to April 2024. Clinical data collected within 24 hours of admission included demographics, laboratory parameters (routine blood tests, coagulation profiles, biochemistry, inflammatory markers), sTM levels, SOFA scores, APACHEⅡ scores, and SIC scores. Patients were stratified into survival ( n=124) and non-survival ( n=88) groups based on 30-day mortality. Intergroup comparisons were performed, and Spearman correlation analysis assessed relationships between SIC scores, sTM, and APACHEⅡ scores. Multivariate logistic regression identified prognostic factors, while ROC curve analysis evaluated the predictive performance of SIC scores, sTM, and their combination for 30-day mortality. Results:The non-survival group exhibited significantly higher levels of PCT [25.3 (10.8, 87.4)μg/L vs. 11.7 (7.0, 18.9) μg/L], APACHEⅡ scores (27.48±7.01 vs. 21.75±5.68), SIC scores [4.0 (3.5, 5.0) vs. 2.5 (1.0, 4.0)], and sTM [17.2 (11.6, 36.2) TU/mL vs. 10.3 (8.7, 14.6) TU/mL] compared to survivors (all P<0.05). Both SIC scores ( r=0.482) and sTM ( r=0.379) correlated positively with APACHE II scores ( P<0.05). Multivariate analysis identified APACHEⅡ score, PCT, sTM, and SIC score as independent prognostic predictors (all P<0.05). ROC analysis demonstrated predictive utility for SIC scores (AUC=0.733) and sTM (AUC=0.592), with SIC scores approximating APACHE II performance (AUC=0.755). The combined SIC-sTM model showed superior predictive accuracy (AUC=0.887; sensitivity=92.5%; specificity=76.5%) versus individual markers. Conclusions:SIC scores and sTM levels correlate significantly with sepsis severity. Their combined use enhances prognostic assessment, offering clinically valuable predictive performance for 30-day mortality in septic patients.

Sepsis is one of the most common critical conditions in clinical practice,with a persistently high mortali-ty rate.As the core pathological mechanism of sepsis,cytokine storm,the continuous activation of the immune sys-tem and the disordered release of a large number of cytokines cause severe damage to the patient's tissues and or-gans,leading to systemic inflammatory response syndrome and even multiple organ dysfunction syndrome,posing a significant threat to patient health.Therefore,this article reviews the role of cytokine storm in sepsis and current therapeutic strategies.

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.

Objective:To investigate the risk factors for combined coronary microvascular dysfunction (CMD) in patients with ischemia and non-obstructive coronary artery disease (INOCA).Methods:From October 2020 to May 2022, 100 INOCA patients with myocardial ischemic symptoms who underwent coronary angiography (CAG) suggestive of <50% stenosis in all three coronary arteries at the Tenth People′s Hospital of Tongji University were prospectively recruited. Myocardial perfusion imaging (MPI), transthoracic echocardiography and cadmium-zinc-telluride (CZT) SPECT coronary flow quantification were performed in the same month, and 93 INOCA patients (36 males and 57 females, age (63.0±10.9) years) were finally included. CMD was defined as coronary flow reserve (CFR)<2.5. Independent-sample t test, Mann-Whitney U test and χ2 test were used to compare MPI results and left ventricular volume parameters between CMD and non-CMD groups. ROC curve analysis was used to analyze the efficacy of each index in predicting CMD, and independent risk factors for CMD were screened by multivariate logistic regression analysis. Results:Among 93 INOCA patients, 29 were in the CMD group and 64 were in the non-CMD group. The age, proportion of hypertension, left ventricular mass index (LVMI), summed stress score (SSS), summed difference score (SDS), left ventricular internal diameter systolic (LVIDS), interventricular septum thickness (IVST), and left ventricular posterior wall thickness (LVPWT) in the CMD group were higher than those in the non-CMD group ( t values: 2.42-3.76, χ2=8.94, z values: -3.31, -3.41, all P<0.05). ROC curve analysis showed that LVMI, SSS, SDS, LVPWT, IVST and age were significant in predicting CMD (AUCs: 0.67-0.72). Multivariate logistic regression analysis showed that LVMI (odds ratio ( OR)=1.08, 95% CI: 1.01-1.17), SDS ( OR=5.37, 95% CI: 1.95-14.78), hypertension ( OR=5.68, 95% CI: 1.34-24.18) and age ( OR=1.10, 95% CI: 1.03-1.18) were risk factors for CMD. Conclusion:LVMI, SDS, hypertension and age are strongly associated with combined CMD in INOCA patients, which can be used for early risk stratification of INOCA patients.

Objective:We aimed to explore a colorectal cancer risk prediction model through machine learning algorithm based on the big data in laboratory medicine.Methods:According to the labeling of colonoscopy combined with pathology or referring to the ICD-10 code, the colonoscopy patients in Shanghai Changhai Hospital from 2013.1.1 to 2019.6.30 and the outpatients and inpatients from 2010.1.1 to 2019.6.30 were divided into colorectal cancer groups and non-colorectal cancer group. Four machine learning algorithms, Extreme gradient boosting(Xgboost),Artificial Neural Network(ANN),Support Vector Machine(SVM),Random Forest(RF), are used to mine all routine laboratory test item data of the enrolled patients, select model features and establish a classification model for colorectal cancer. And the effectiveness of the model was prospectively verified in patients in the whole hospital of Changhai Hospital from 2019.7.1 to 2020.8.31.Result:A colorectal cancer risk prediction model (CRC-Lab7) including 7 characteristics of fecal occult blood, carcinoembryonic antigen, red blood cell distribution width, lymphocyte count, albumin/globulin, high-density lipoprotein cholesterol and hepatitis B virus core antibody was constructed by the XgBoost algorithm. The AUC of the model in the validation set and prospective validation set were 0.799 and 0.816, respectively, which was significantly higher than that of fecal occult blood (AUC was 0.68 and 0.706, respectively). It also has high diagnostic accuracy for colorectal cancer with negative fecal occult blood or under 50 years old.Conclusion:In this study, a colorectal cancer risk prediction model was established by mining routine laboratory big data. The model′s performance is better than fecal occult blood, and it has high diagnostic accuracy for colorectal cancer in patients with negative fecal occult blood and younger than 50 years old.

Objective:To evaluate the feasibility of a predictire model composed of non-specific test indexes in early diagnosis of gastric cancer.Methods:From the database of electronic medical record system of Shanghai Changhai Hospital, a total of 24 615 case records were included from January 1, 2010 to April 30, 2019, including 10 497 cases of gastric cancer, 5 198 cases of precancerous diseases, and 8 920 cases of health examination. Through stratified random sampling, the study population was divided into validation set, training set and test set. After data processing and quality control for all laboratory variables, the optimal machine learning algorithm and diagnostic efficiency grouping were selected through four machine learning algorithms, induding the gradient boosting decision tree, random forest, support vector machine, and artificial neural network, and the data were trained by backward stepwise regression method to build the best feature model.Result:In this study, a diagnostic model V22 consisting of 22 routine testing parameters was established. V22 could distinguish early gastric cancer from control group composed of healthy group and precancerous disease, AUC was 0.808, the sensitivity was 85.7%, and the specificity was 91.9%. For CEA negative gastric cancer, V22 also showed high diagnostic accuracy, AUC was 0.801.Conclusion:V22 was a valuable model for the diagnosis of gastric cancer. V22 was an auxiliary diagnostic model of gastric cancer with clinical application value, which could well distinguish early gastric cancer from the control group composed of healthy group and precancerous disease, and the detection rate of early gastric cancer was better than the traditional tumor marker CEA.

Objectives:To explore the value of heparin-binding protein (HBP) in the diagnosis and prognosis of patients with respiratory viral infections.Methods:The patients who were admitted to Emergency Department of Nanjing Hospital Affiliated to Nanjing Medical University from November 2018 to November 2020 were selected as the viral infection group, and the non-infected patients admitted in the same period as the non-viral infection group. Data of all patients’ general clinical information, peripheral white blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), and HBP in 24 h were collected. The differences in various indicators were compared between the two groups of patients, the receiver operating characteristic (ROC) curves were drawn, and the diagnostic value of each indicator for patients with respiratory virus infection were evaluated. The prognostic indicators such as sequential organ failure assessment (SOFA) score, the acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score within 24 h were recorded, and duration of ICU stay, antiviral treatment, ventilator and vasoactive agents to total length of hospital stay of patients in the viral infection group were calculated. The Spearman correlation analysis of HBP and the above indicators was performed to determine the prognostic value of HBP in patients with respiratory virus infections.Results:A total of 106 patients were included in the viral infection group, and 107 in the non-viral infection group. There were no significant differences in sex, age, and body mass index (BMI) between the two groups of patients (P>0.05). Compared with the non-viral infection group, the serum CRP and HBP of the viral infection group were significantly higher (P<0.05), while the WBC and NLR levels were significantly lower (P<0.05). There was no statistical difference in PCT between the two groups (P>0.05). HBP had the best diagnosis efficiency for respiratory viral infections, the areas under the ROC was 0.895, the optimal cut-off point was 13.625 μg/L, the sensitivity was 92.50% and the specificity was 76.60%. Correlation analysis showed that serum HBP levels within 24 h in the viral infection group were positively correlated with SOFA score and APACHEⅡ score in 24 h after admission (r = 0.756, P<0.05; r = 0.747, P<0.05). In the viral infection group, duration of ICU stay, antiviral treatment, and ventilator and vasoactive agents to total length of hospital stay were also positively correlated with serum HBP level (r = 0.873, 0.748, 0.830, and 0.794, P<0.05).Conclusions:HBP can be used as a favorable diagnostic indicator for patients with respiratory virus infections and has a good evaluation value for the prognosis.