OBJECTIVES: Global epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability. METHODS: This multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV; n=2 235), the eICU Collaborative Research Database (eICU-CRD; n=1 274), and the Patient Admission Dataset from the ICU of Third Xiangya Hospital, Central South University (CSU-XYS-ICU; n=107). MIMIC-IV was split into a training set (n=1 584) and internal validation set (n=651), with the remaining datasets used for external validation. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and Bayesian Information Criterion (BIC), and a nomogram model was constructed. An extreme gradient boosting (XGBoost) algorithm was used to build the machine learning model. Model performance was assessed by the concordance index (C-index), calibration curves, Brier score, decision curve analysis (DCA), and net reclassification improvement index (NRI). RESULTS: Five key predictors, age [odds ratio (OR)=1.02, 95% CI 1.01 to 1.03, P<0.001], minimum activated partial thromboplastin (APTT; OR=1.09, 95% CI 1.08 to 1.11, P<0.001), maximum APTT (OR=1.01, 95% CI 1.00 to 1.01, P<0.001), maximum lactate (OR=1.56, 95% CI 1.39 to 1.75, P<0.001), and maximum serum creatinine (OR=2.03, 95% CI 1.79 to 2.30, P<0.001), were included in the nomogram. The model showed robust performance in internal validation (C-index=0.845, 95% CI 0.811 to 0.879) and external validation (eICU-CRD: C-index=0.827, 95% CI 0.800 to 0.854; CSU-XYS-ICU: C-index=0.779, 95% CI 0.687 to 0.871). Calibration curves indicated good agreement between predicted and observed outcomes (Brier score<0.25), and DCA confirmed clinical benefit. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI 0.969 to 0.985) in the training set, but performance declined in external validation (eICU-CRD, AUC=0.825, 95% CI 0.817 to 0.861; CSU-XYS-ICU, AUC=0.766, 95% CI 0.700 to 0.873), though it remained above clinical thresholds. Net reclassification improvement was slightly lower for XGBoost compared with the nomogram (NRI=0.58). CONCLUSIONS Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
Humans ; Sepsis/complications* ; Machine Learning ; Nomograms ; Venous Thrombosis/complications* ; Retrospective Studies ; Hospital Mortality ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Prognosis ; Bayes Theorem

OBJECTIVE: To explore the risk factors affecting the prognosis of patients with sepsis complicated with acute pulmonary embolism, and to construct and validate a nomogram predictive model for in-hospital mortality risk. METHODS: Based on the American Medical Information Mart for Intensive Care (MIMIC-III, MIMIC-IV) databases, the data were collected on patients with sepsis complicated with acute pulmonary embolism from 2001 to 2019, including baseline characteristics, and vital signs, disease scores, laboratory tests within 24 hours of admission to the intensive care unit (ICU), and interventions. In-hospital mortality was the outcome event. The total samples were divided into training and testing sets in a 7:3 ratio by random sampling. Univariate Cox regression analysis was used to verify the impact of all variables on the risk of in-hospital mortality, thereby screen potential influencing factors. Subsequently, a stepwise bi-directional regression method was applied to select factors one by one, leading to the construction of a nomogram prediction model. Collinearity testing was used to demonstrate the absence of strong multicollinearity among the influencing factors in the nomogram prediction model. The discrimination of the nomogram model, sequential organ failure assessment (SOFA), and simplified pulmonary embolism severity index (sPESI) was evaluated using C-index in the test set. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of various models for in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. RESULTS: A total of 562 patients with sepsis complicated with acute pulmonary embolism were included, including 393 in the training set and 169 in the testing set. Univariate Cox regression analysis showed that 30 factors associated with in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. Through stepwise bi-directional regression, 12 variables were ultimately selected, including gender, presence of malignant tumors, body temperature, red cell distribution width (RDW), blood urea nitrogen (BUN), serum potassium, prothrombin time (PT), 24-hour urine output, mechanical ventilation, vasoactive drugs, warfarin use, and sepsis-induced coagulopathy (SIC). Collinearity testing indicated no strong multicollinearity among the influencing factors [all variance inflation factor (VIF) > 10]. A nomogram model was constructed using the 12 variables mentioned above. The nomogram model predicted the C-index and its 95% confidence interval (95%CI) of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism better than SOFA score and sPESI [0.771 (0.725-0.816) vs. 0.579 (0.519-0.639), 0.608 (0.554-0.663)]. The ROC curve showed that the area under the curve (AUC) and its 95%CI of the nomogram model were higher than those of the SOFA score and sPESI [0.811 (0.766-0.857) vs. 0.630 (0.568-0.691), 0.623 (0.566-0.680)]. These findings were consistently replicated in the internal validation of the testing set. In both the training and testing sets, Delong's test showed that the AUC of the nomogram model was significantly higher than the SOFA score and sPESI (both P < 0.05). CONCLUSION The nomogram model demonstrated good predictive effectiveness for the risk of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism, enabling clinicians to predict mortality risk in advance and take timely interventions to reduce mortality.
Humans ; Pulmonary Embolism/mortality* ; Hospital Mortality ; Nomograms ; Sepsis/complications* ; Prognosis ; Risk Factors ; Intensive Care Units ; Male ; Female ; Middle Aged ; Aged

OBJECTIVE: To develop and validate a nomogram model for predicting 30-day mortality among elderly patients with sepsis-associated liver dysfunction (SALD), to identify high-risk patients and improve prognosis. METHODS: A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database for elderly patients with SALD who were first admitted to the intensive care unit (ICU) of Beth Israel Deaconess Medical Center between 2008 and 2019, including basic characteristics, severity scores, underlying diseases, infection foci, 24-hour vital signs, initial laboratory indicators, 24-hour complications, and prognosis related indicators. Patients were randomly assigned to training group and validation group in a ratio of 7 : 3. The training group used the LASSO regression analysis, as well as multivariate Logistic regression analysis to screen for independent risk factors for 30-day mortality. A nomogram prediction model was constructed, and receiver operator characteristic curve (ROC curve), calibration curves, and decision curve analysis (DCA) were used to evaluate the model, and validate the model using the validation cohort. RESULTS: A total of 630 elderly patients with SLAD were included in the study, including 441 in the training group and 189 in the validation group. Oxford acute severity of illness score (OASIS) for training group [odds ratio (OR) = 1.060, 95% confidence interval (95%CI) was 1.034-1.086], 24-hour pulse oxygen saturation (SpO₂; OR = 0.876, 95%CI was 0.797-0.962), initial mean corpuscular volume (MCV; OR = 1.043, 95%CI was 1.009-1.077), initial red blood cell distribution width (RDW; OR = 1.237, 95%CI was 1.123-1.362), initial blood glucose (OR = 1.008, 95%CI was 1.004-1.013), and initial aspartate aminotransferase (AST; OR = 1.000, 95%CI was 1.000-1.001) were independent risk factors for 30-day mortality in patients (all P < 0.05). Based on the above variables, a nomogram model was constructed, and the ROC curve showed that the area under the curve (AUC) of the model in the training group was 0.757 (95%CI was 0.712-0.803), with a sensitivity of 65.05% and a specificity of 74.90%; the AUC of the model in the validation group was 0.712 (95%CI was 0.631-0.792), with a sensitivity of 58.67% and a specificity of 81.58%. The calibration curves of the training and validation groups show that both the fitted curves were close to the standard curves. The Hosmer-Lemeshow test: the training group (χ ² = 6.729, P = 0.566), the validation group (χ ² = 13.889, P = 0.085), indicating that the model can fit the observed data well. The DCA curve shows that when the threshold probability of the training group was 16% to 94% and the threshold probability of the validation group was 27% to 99%, the net benefit of the model was good. CONCLUSIONS OASIS, 24-hour SpO₂, initial MCV, initial RDW, initial blood glucose and initial AST are independent risk factors for 30-day mortality in elderly patients with SALD. The nomogram based on these six variables demonstrates good predictive performance.
Humans ; Sepsis/complications* ; Retrospective Studies ; Nomograms ; Aged ; Prognosis ; Risk Factors ; Liver Diseases/mortality* ; Intensive Care Units ; ROC Curve ; Male ; Female ; Logistic Models

OBJECTIVE: To assess the relationship between blood pressure reactivity index (BPRI) and in-hospital mortality risk in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A retrospective cohort study was conducted to collect data from patients admitted to the intensive care unit (ICU) and clinically diagnosed with SA-AKI between 2008 and 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database in the United States. The collected data included demographic characteristics, comorbidities, vital signs, laboratory parameters, sequential organ failure assessment (SOFA) and simplified acute physiology scoreII(SAPSII) within 48 hours of SA-AKI diagnosis, stages of AKI, treatment regimens, mean BPRI during the first and second 24 hours (BPRI_0_24, BPRI_24_48), and outcome measures including primary outcome (in-hospital mortality) and secondary outcomes (ICU length of stay and total hospital length of stay). Variables with statistical significance in univariate analysis were included in LASSO regression analysis for variable selection, and the selected variables were subsequently incorporated into multivariate Logistic regression analysis to identify independent predictors associated with in-hospital mortality in SA-AKI patients. Restricted cubic spline (RCS) analysis was employed to examine whether there was a linear relationship between BPRI within 48 hours and in-hospital mortality in SA-AKI patients. Basic prediction models were constructed based on the independent predictors identified through multivariate Logistic regression analysis, and receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of each basic prediction model before and after incorporating BPRI. RESULTS: A total of 3 517 SA-AKI patients admitted to the ICU were included, of whom 826 died during hospitalization and 2 691 survived. The BPRI values within 48 hours of SA-AKI diagnosis were significantly lower in the death group compared with the survival group [BPRI_0_24: 4.53 (1.81, 8.11) vs. 17.39 (5.16, 52.43); BPRI_24_48: 4.76 (2.42, 12.44) vs. 32.23 (8.85, 85.52), all P < 0.05]. LASSO regression analysis identified 20 variables with non-zero coefficients that were included in the multivariate Logistic regression analysis. The results showed that respiratory rate, temperature, pulse oxygen saturation (SpO₂), white blood cell count (WBC), hematocrit (HCT), activated partial thromboplastin time (APTT), lactate, oxygenation index, SOFA score, fluid balance (FB), BPRI_0_24, and BPRI_24_48 were all independent predictors for in-hospital mortality in SA-AKI patients (all P < 0.05). RCS analysis revealed that both BPRI showed "L"-shaped non-linear relationships with the risk of in-hospital mortality in SA-AKI patients. When BPRI_0_24 ≤ 14.47 or BPRI_24_48 ≤ 24.21, the risk of in-hospital mortality in SA-AKI increased as BPRI values decreased. Three basic prediction models were constructed based on the identified independent predictors: Model 1 (physiological indicator model) included respiratory rate, temperature, SpO₂, and oxygenation index; Model 2 (laboratory indicator model) included WBC, HCT, APTT, and lactate; Model 3 (scoring indicator model) included SOFA score and FB. ROC curve analysis showed that the predictive performance of the basic models ranked from high to low as follows: Model 3, Model 2, and Model 1, with area under the curve (AUC) values of 0.755, 0.661, and 0.655, respectively. The incorporation of BPRI indicators resulted in significant improvement in the discriminative ability of each model (all P < 0.05), with AUC values increasing to 0.832 for Model 3+BPRI, 0.805 for Model 2+BPRI, and 0.808 for Model 1+BPRI. CONCLUSIONS BPRI is an independent predictor factor for in-hospital mortality in SA-AKI patients. Incorporating BPRI into the prediction model for in-hospital mortality risk in SA-AKI can significantly improve its predictive capability.
Humans ; Acute Kidney Injury/mortality* ; Sepsis/complications* ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Blood Pressure ; Intensive Care Units ; Male ; Female ; Length of Stay ; Middle Aged ; Aged ; Adult ; Logistic Models

OBJECTIVE: To analyze the pathogenic bacterial spectrum, drug resistance, and risk factors associated with multidrug-resistant bacterial infection and mortality in patients with hematologic diseases complicated by bloodstream infections, so as to provide reference for rational drug use and improving prognosis. METHODS: Positive blood culture specimens of patients with hematologic diseases in two Class A tertiary hospitals of Shanxi province from January 2019 to December 2021 were retrospectively analyzed. Pathogen distribution, drug resistance and outcomes of patients with bloodstream infection were investigated, then the multivariate logistic analysis was performed to analyze the risk factors of multidrug-resistant bacterial infection and factors affecting prognosis. RESULTS: 203 strains of pathogens were identified, mainly Gram-negative bacteria (GNB) (69.46%, 141/203), of which Escherichia coli (E.coli) had the highest incidence (41.13%, 58/141), followed by Klebsiella pneumoniae (20.57%, 29/141) and Pseudomonas aeruginosa (12.77%, 18/141). Extended-spectrum beta-lactamase (ESBL)-producing E.coli and Klebsiella pneumoniae were 46.55% (27/58) and 37.93% (11/29), respectively. Carbapenem-resistant Gram-negative bacteria accounted for 10.64% (15/141). And Gram-positive bacteria accounted for 27.59% (56/203), Staphylococcus epidermidis, Streptococcus pneumoniae, and Staphylococcus aureus were the most frequently isolated pathogen among Gram-positive bacteria (14.29%, 12.50% and 10.71%, respectively), of which methicillin-resistant Staphylococcus aureus accounted for 33.33% (2/6), coagulase-negative staphylococci accounted for 87.50% (7/8), without vancomycin- or linezolid-resistant strain. Additionally, fungi accounted for 2.95% (6/203), all of which were Candida. Multidrug-resistant Gram-negative bacteria (MDR-GNB) accounted for 53.90% (76/141). Duration of neutropenia >14 days was a risk factor for developing MDR-GNB infection. The 30-day all-cause mortality was 10.84%. Multivariate logistic regression analysis showed that the significant independent risk factors for mortality were age≥60 years (P <0.01, OR =5.85, 95% CI: 1.80-19.07) and use of vasopressor drugs (P <0.01, OR =5.89, 95% CI: 1.83-18.94). CONCLUSION The pathogenic bacteria of bloodstream infection in patients with hematological diseases are widely distributed, and the detection rate of multidrug-resistant bacteria is high. The clinicians should choose suitable antibiotics according to the results of bacterial culture and antibiotic susceptibility test.
Humans ; Middle Aged ; Bacteremia/mortality* ; Bacteria/isolation & purification* ; Drug Resistance ; Drug Resistance, Bacterial ; Gram-Negative Bacteria ; Hematologic Diseases/complications* ; Methicillin-Resistant Staphylococcus aureus ; Retrospective Studies ; Risk Factors ; Sepsis/mortality*

PURPOSE: Colonic perforation is a lethal condition presenting high morbidity and mortality in spite of urgent surgical treatment. This study investigated the surgical outcome of patients with colonic perforation associated with retroperitoneal contamination. METHODS: Retrospective analysis was performed for 30 patients diagnosed with colonic perforation caused by either inflammation or ischemia who underwent urgent surgical treatment in our facility from January 2005 to December 2014. Patient characteristics were analyzed to find risk factors correlated with increased postoperative mortality. Using the Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (POSSUM) audit system, the mortality and morbidity rates were estimated to verify the surgical outcomes. Patients with retroperitoneal contamination, defined by the presence of retroperitoneal air in the preoperative abdominopelvic CT, were compared to those without retroperitoneal contamination. RESULTS: Eight out of 30 patients (26.7%) with colonic perforation had died after urgent surgical treatment. Factors associated with mortality included age, American Society of Anesthesiologists (ASA) physical status classification, and the ischemic cause of colonic perforation. Three out of 6 patients (50%) who presented retroperitoneal contamination were deceased. Although the patients with retroperitoneal contamination did not show significant increase in the mortality rate, they showed significantly higher ASA physical status classification than those without retroperitoneal contamination. The mortality rate predicted from Portsmouth POSSUM was higher in the patients with retroperitoneal contamination. CONCLUSION: Patients presenting colonic perforation along with retroperitoneal contamination demonstrated severe comorbidity. However, retroperitoneal contamination was not found to be correlated with the mortality rate.
Classification ; Colon* ; Comorbidity ; Humans ; Inflammation ; Intestinal Perforation ; Ischemia ; Mortality* ; Postoperative Complications ; Retroperitoneal Space ; Retrospective Studies ; Risk Factors* ; Sepsis

BACKGROUND/AIMS: Patients with liver cirrhosis (LC) are at risk for critical events leading to Intensive Care Unit (ICU) admission. Coagulopathy in cirrhotic patients is complex and can lead to bleeding as well as thrombosis. The aim of this study was to investigate bleeding complications in critically ill patients with LC admitted to a medical ICU (MICU). METHODS: All adult patients admitted to our MICU with a diagnosis of LC from January 2006 to December 2012 were retrospectively assessed. Patients with major bleeding at the time of MICU admission were excluded from the analysis. RESULTS: A total of 205 patients were included in the analysis. The median patient age was 62 years, and 69.3% of the patients were male. The most common reason for MICU admission was acute respiratory failure (45.4%), followed by sepsis (27.3%). Major bleeding occurred in 25 patients (12.2%). The gastrointestinal tract was the most common site of bleeding (64%), followed by the respiratory tract (20%). In a multivariate analysis, a low platelet count at MICU admission (odds ratio [OR], 0.98; 95% confidence interval [CI], 0.97 to 0.99) and sepsis (OR, 8.35; 95% CI, 1.04 to 67.05) were independent risk factors for major bleeding. The ICU fatality rate was significantly greater among patients with major bleeding (84.0% vs. 58.9%, respectively; p = 0.015). CONCLUSIONS: Major bleeding occurred in 12.2% of critically ill cirrhotic patients admitted to the MICU. A low platelet count at MICU admission and sepsis were associated with an increased risk of major bleeding during the MICU stay. Further study is needed to better understand hemostasis in critically ill patients with LC.
Aged ; Blood Platelets ; Critical Illness ; Female ; Gastrointestinal Hemorrhage/blood/diagnosis/*etiology/mortality ; Hospital Mortality ; Humans ; Intensive Care Units ; Liver Cirrhosis/blood/*complications/diagnosis/mortality ; Male ; Middle Aged ; Multivariate Analysis ; Odds Ratio ; Platelet Count ; Prognosis ; Republic of Korea ; Respiratory Tract Diseases/blood/diagnosis/*etiology/mortality ; Retrospective Studies ; Risk Factors ; Sepsis/blood/complications ; Time Factors

Patients with cirrhosis who are hospitalized for an acute decompensation (AD) and also have organ failure(s) are at high risk of short-term death. These patients have a syndrome called Acute-on-Chronic Liver Failure (ACLF). ACLF is now considered as a new syndrome that it is distinct from "mere" AD not only because of the presence of organ failure(s) and high short-term mortality but also because of younger age, higher prevalence of alcoholic etiology of cirrhosis, higher prevalence of some precipitants (such as bacterial infections, active alcoholism), and more intense systemic inflammatory response. ACLF is a new syndrome also because severe sepsis or severe alcoholic hepatitis do not account for 100% of the observed cases; in fact, almost 50% of the cases are of "unknown" origin. In other words, severe sepsis, severe alcoholic hepatitis and ACLF of "unknown origin" are subcategories of the syndrome.
Acute-On-Chronic Liver Failure/complications/mortality/*pathology ; Age Factors ; Cytokines/metabolism ; Hepatitis, Alcoholic/complications ; Humans ; Liver Cirrhosis/*complications/diagnosis ; Sepsis/complications ; Severity of Illness Index ; Survival Rate

Wound sepsis is one of the main causes of death in patients with severe burn and trauma. The high incidence of burn wound sepsis in children is attributed to their imperfect immune system function, poor resistance against infection, and the weakened skin barrier function after burn. The key to reduce the mortality of pediatric patients with burn wound sepsis is to enhance the understanding of its etiology, epidemiology, pathogenesis, and diagnostic criteria, in order to improve its early diagnosis and treatment.
Burns ; complications ; prevention & control ; therapy ; Child ; Humans ; Sepsis ; diagnosis ; etiology ; mortality ; therapy ; Skin ; microbiology ; pathology ; Survival Rate ; Wound Infection ; mortality ; prevention & control ; therapy

BACKGROUND AND OBJECTIVES: Esophageal perforation is relatively uncommon and requires careful diagnostic evaluation and expert management. It has a high mortality due to significant mediastinal and pleural contamination leading to sepsis and multiple organ failure. We reviewed our experience of esophageal perforation to determine how to better recognize such a lesion and facilitate its correct management. SUBJECTS AND METHOD: A retrospective chart review was performed on all patients treated for esophageal perforation from January 2000 to March 2016. These patients have been studied with respect to gender and age distribution, causes, sites, clinical manifestation, complications, management and postoperative complications. RESULTS: Patients ranged in age from 21 to 87 years, with an average age of 57.6±12.4 years. Fifty of the patients were men and 21 were women. The causes of the perforations were as follows: foreign body retention (18 patients), trauma (17 patients), Boerhaave's syndrome (22 patients), and iatrogenic (14 patients). The sites of esophageal perforation were: the cervical esophagus (25 patients), thoracic esophagus (44 patients) and abdominal esophagus (2 patients). Primary repair only was performed in seven (9.9%) patients, whereas 32 (45%) patients were treated with primary repair & patch, seven (9.9%) patients with T-tube drainage. Exclusion & division were performed in three (4.2%) patients and esophagectomy was performed in two (2.8%) patients. Twenty (28.2%) patients were treated conservatively. CONCLUSION: Early recognition and appropriate management of esophageal perforation are essential for reduction of morbidity and mortality.
Age Distribution ; Drainage ; Esophageal Perforation* ; Esophagectomy ; Esophagus ; Female ; Foreign Bodies ; Humans ; Male ; Methods ; Mortality ; Multiple Organ Failure ; Postoperative Complications ; Retrospective Studies ; Sepsis