Background: The quick Sequential Organ Failure Assessment (qSOFA) score was introduced by Sepsis-3 or the Third International Consensus Definitions for Sepsis and Septic Shock to help physicians in identifying patients outside the intensive care unit with suspected infection who are at high risk for in-hospital mortality. However, sepsis is not a homogenous entity and the outcomes vary based on several factors. This study aimed to determine the predictive accuracy of qSOFA in identifying those at high-risk of in-hospital mortality among adult patients with sepsis secondary to urinary tract infection. Methodology: A retrospective cohort study was done involving the use of qSOFA score to predict in-hospital mortality of adult patients with a diagnosis of sepsis secondary to urinary tract infection, admitted in the hospital from January 1, 2013 to December 31, 2020. qSOFA is computed based on the following independent variables: systolic blood pressure (SBP), respiratory rate (RR), and Glasgow Coma Scale (GCS). Results: Of the 128 charts retrieved, 121 patients were included in the study. Fifteen (12.40%) died while 106 (87.60%) survived. Mean age was 60.76 years old, with more females (71.90%) than males (28.10%). Hypertension and Diabetes Mellitus Type 2 were the most frequent comorbidities. Complicated UTI was the most frequent source of infection. Mean length of stay was 8.29 days. Forty (33.06%) patients had qSOFA ≥ 2 wherein 11 (27.5%) died. Diagnostic performance results revealed: sensitivity (73.33%), specificity (72.64%), positive (27.5%) and negative (95.06%) predictive values, and positive (2.68) and negative (0.37) likelihood ratios. qSOFA accuracy was 72.73% with an AUROC of 0.76. Conclusion Among the admitted adult patients with sepsis secondary to a UTI, qSOFA had a good prognostic accuracy for in-hospital mortality.
Sepsis ; Urinary Tract Infections ; Hospital Mortality

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*

OBJECTIVE: To explore the effect of thrombocytopenia on the prognosis of patients with septic shock and its mechanism in leading to death. METHODS: A retrospective case-control study was conducted. Patients with septic shock admitted to emergency intensive care unit (EICU) and intensive care unit (ICU) in Peking University People's Hospital from April 1, 2015 to January 31, 2023 were enrolled. Patients were divided into the thrombocytopenia group and the non-thrombocytopenia group, according to whether the minimum platelet count was less than 100×10⁹/L within 24 hours after admission to ICU. The outcome index was the mortality during ICU stay. The baseline data, hospitalization information and laboratory test results of the two groups were compared, and the risk factors of in-hospital death were analyzed by Logistic regression, and the mediation effect was performed by Bootstrap method. RESULTS: A total of 301 patients with septic shock were enrolled, of which 172 (57.1%) had thrombocytopenia and 129 (42.9%) did not. There were significant differences between the two groups in age, mortality, disseminated intravascular coagulation (DIC), continuous renal replacement therapy, and level of creatinine, urea nitrogen, total bilirubin, white blood cell count, lymphocyte count, prothrombin time (PT) and activated partial thromboplastin time (APTT). Univariate Logistic regression analysis showed thrombocytopenia [odds ratio (OR) = 4.478], continuous renal replacement therapy (OR = 4.601), DIC (OR = 6.248), serum creatinine (OR = 1.005), urea nitrogen (OR = 1.126), total bilirubin (OR = 1.006) and PT (OR = 1.126) were risk factors of death during hospitalization in patients with septic shock (all P < 0.05). Multivariate Logistic regression analysis showed that thrombocytopenia [OR = 3.338, 95% confidence interval (95%CI) was 1.910-5.834, P = 0.000], continuous renal replacement therapy (OR = 3.175, 95%CI was 1.576-6.395, P = 0.001) and PT (OR = 1.077, 95%CI was 1.011-1.147, P = 0.021) were independent risk factors for in-hospital mortality in patients with septic shock. Mediation analysis showed that 51% of the deaths due to thrombocytopenia in patients with septic shock were due to coagulopathy. CONCLUSIONS Thrombocytopenia is a powerful predictor of death in septic shock patients, and half of all thrombocytopenia-related deaths may be due to abnormal coagulation function.
Humans ; Shock, Septic ; Retrospective Studies ; Case-Control Studies ; Hospital Mortality ; Prognosis ; Thrombocytopenia ; Intensive Care Units ; Bilirubin ; Nitrogen ; Urea ; Sepsis

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.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective: To investigate dose-response associations between fluid overload (FO) and hospital mortality in patients with sepsis. Methods: The current cohort study was prospective and multicenter. Data were derived from the China Critical Care Sepsis Trial, which was conducted from January 2013 to August 2014. Patients aged≥18 years who were admitted to intensive care units (ICUs) for at least 3 days were included. Fluid input/output, fluid balance, fluid overload (FO), and maximum FO (MFO) were calculated during the first 3 days of ICU admission. The patients were divided into three groups based on MFO values: MFO<5%L/kg, MFO 5%-10%L/kg, and MFO≥10% L/kg. Kaplan-Meier analysis was used to predict time to death in hospital in the three groups. Associations between MFO and in-hospital mortality were evaluated via multivariable Cox regression models with restricted cubic splines. Results: A total of 2 070 patients were included in the study, of which 1 339 were male and 731 were female, and the mean age was (62.6±17.9) years. Of 696 (33.6%) who died in hospital, 968 (46.8%) were in the MFO<5%L/kg group, 530 (25.6%) were in the MFO 5%-10%L/kg group, and 572 (27.6%) were in the MFO≥10%L/kg group. Deceased patients had significantly higher fluid input than surviving patients during the first 3 days [7 642.0 (2 874.3, 13 639.5) ml vs. 5 738.0 (1 489.0, 7 153.5)ml], and lower fluid output [4 086.0 (1 367.0, 6 354.5) ml vs. 6 130.0 (2 046.0, 11 762.0) ml]. The cumulative survival rates in the three groups gradually decreased with length of ICU stay, and they were 74.9% (725/968) in the MFO<5% L/kg group, 67.7% (359/530) in the MFO 5%-10%L/kg group, and 51.6% (295/572) in the MFO≥10%L/kg group. Compared with the MFO<5%L/kg group, the MFO≥10%L/kg group had a 49% increased risk of inhospital mortality (HR=1.49, 95%CI 1.28-1.73). For each 1% L/kg increase in MFO, the risk of in-hospital mortality increased by 7% (HR=1.07, 95% CI 1.05-1.09). There was a"J-shaped"non-linear association between MFO and in-hospital mortality with a nadir of 4.1% L/kg. Conclusion: Higher and lower optimum fluid balance levels were associated with an increased risk of in-hospital mortality, as reflected by the observed J-shaped non-linear association between fluid overload and inhospital mortality.
Humans ; Male ; Female ; Adult ; Middle Aged ; Aged ; Aged, 80 and over ; Hospital Mortality ; Cohort Studies ; Prospective Studies ; Water-Electrolyte Imbalance ; Sepsis ; Intensive Care Units ; Retrospective Studies

PURPOSE: COVID-19 is also referred to as a typical viral septic pulmonary infection by 2019-nCoV. However, little is known regarding its characteristics in terms of systemic inflammation and organ injury, especially compared with classical bacterial sepsis. This article aims to investigate the clinical characteristics and prognosis between COVID-19-associated sepsis and classic bacterial-induced sepsis. METHODS: In this retrospective cohort study, septic patients with COVID-19 in the intensive care unit (ICU) of a government-designed therapy center in Shenzhen, China between January 14, 2020 and March 10, 2020, and septic patients induced by carbapenem-resistant klebsiella pneumonia (CrKP) admitted to the ICU of the Second People's Hospital of Shenzhen, China between January 1, 2014 and October 30, 2019 were enrolled. Demographic and clinical parameters including comorbidities, critical illness scores, treatment, and laboratory data, as well as prognosis were compared between the two groups. Risk factors for mortality and survival rate were analyzed using multivariable logistic regression and survival curve, respectively. RESULTS: A total of 107 patients with COVID-19 and 63 patients with CrKP were enrolled. A direct comparison between the two groups demonstrated more serious degrees of primary lung injury following 2019-nCoV infection (indicated by lower PaO CONCLUSION Critical COVID-19 shares clinical characteristics with classical bacterial sepsis, but the degree of systemic inflammatory response, secondary organ damage and mortality rate are less severe. However, following 2019-nCoV infection, the level of immunosuppression may be increased and thus induce in more death at the later stage of patients' hospitalstay.
COVID-19 ; Carbapenems ; Hospital Mortality ; Humans ; Intensive Care Units ; Klebsiella ; Prognosis ; Retrospective Studies ; SARS-CoV-2 ; Sepsis

INTRODUCTION: There is paucity of data on the occurrence of cardiovascular events (CVEs) in critically ill patients with sepsis. We aimed to describe the incidence, risk factors and impact on mortality of CVEs in these patients. METHODS: This was a retrospective cohort study of critically ill patients admitted to the medical intensive care unit (ICU) between July 2015 and October 2016. The primary outcome was intra-hospital CVEs, while the secondary outcomes were in-hospital mortality, ICU and hospital length of stay. RESULTS: Patients with sepsis (n=662) had significantly more CVEs compared to those without (52.9% versus 23.0%, P<0.001). Among sepsis patients, 350 (52.9%) had 1 or more CVEs: 59 (8.9%) acute coronary syndrome; 198 (29.9%) type 2 myocardial infarction; 124 (18.7%) incident atrial fibrillation; 76 (11.5%) new or worsening heart failure; 32 (4.8%) cerebrovascular accident; and 33 (5.0%) cardiovascular death. Factors associated with an increased risk of CVEs (adjusted relative risk [95% confidence interval]) included age (1.013 [1.007-1.019]); ethnicity-Malay (1.214 [1.005-1.465]) and Indian (1.240 [1.030-1.494]) when compared to Chinese; and comorbidity of ischaemic heart disease (1.317 [1.137-1.527]). There were 278 patients (79.4%) who developed CVEs within the first week of hospitalisation. Sepsis patients with CVEs had a longer median (interquartile range [IQR]) length of stay in the ICU (6 [3-12] vs 4 [2-9] days, P<0.001), and hospital (21 [10-42] vs 15 [7-30] days, P<0.001) compared to sepsis patients without CVEs. There was no difference in in-hospital mortality between the 2 groups (46.9% vs 45.8%, P=0.792). CONCLUSION CVEs complicate half of the critically ill patients with sepsis, with 79.4% of patients developing CVEs within the first week of hospitalisation, resulting in longer ICU and hospital length of stay.
Cardiovascular Diseases/epidemiology* ; Critical Illness/epidemiology* ; Hospital Mortality ; Humans ; Intensive Care Units ; Length of Stay ; Retrospective Studies ; Risk Factors ; Sepsis/epidemiology*

Objective: To investigate the incidence and trend of short-term outcomes among preterm infants born <34 weeks' gestation. Methods: A secondary analysis of data from the standardized database established by a multicenter cluster-randomized controlled study "reduction of infection in neonatal intensive care units (NICU) using the evidence-based practice for improving quality (REIN-EPIQ) study". This study was conducted in 25 tertiary NICU. A total of 27 192 infants with gestational age <34 weeks at birth and admitted to NICU within the first 7 days of life from May 2015 to April 2018 were enrolled. Infants with severe congenital malformation were excluded. Descriptive analyses were used to describe the mortality and major morbidities of preterm infants by gestational age groups and different admission year groups. Cochran-Armitage test and Jonckheere-Terpstra test were used to analyze the trend of incidences of mortality and morbidities in 3 study-years. Multiple Logistic regression model was constructed to analyze the differences of outcomes in 3 study-years adjusting for confounders. Results: A total of 27 192 preterm infants were enrolled with gestational age of (31.3±2.0) weeks at birth and weight of (1 617±415) g at birth. Overall, 9.5% (2 594/27 192) of infants were discharged against medical advice, and the overall mortality rate was 10.7% (2 907/27 192). Mortality for infants who received complete care was 4.7% (1 147/24 598), and mortality or any major morbidity was 26.2% (6 452/24 598). The incidences of moderate to severe bronchopulmonary dysplasia, sepsis, severe intraventricular hemorrhage or periventricular leukomalacia, proven necrotizing enterocolitis, and severe retinopathy of prematurity were 16.0% (4 342/27 192), 11.9% (3 225/27 192), 6.8% (1 641/24 206), 3.6% (939/25 762) and 1.5% (214/13 868), respectively. There was a decreasing of the overall mortality (P<0.001) during the 3 years. Also, the incidences for sepsis and severe retinopathy of prematurity both decreased (both P<0.001). However, there were no significant differences in the major morbidity in preterm infants who received complete care during the 3-year study period (P=0.230). After adjusting for confounders, infants admitted during the third study year showed significantly lower risk of overall mortality (adjust OR=0.62, 95%CI 0.55-0.69, P<0.001), mortality or major morbidity, moderate to severe bronchopulmonary dysplasia, sepsis and severe retinopathy of prematurity, compared to those admitted in the first study year (all P<0.05). Conclusions: From 2015 to 2018, the mortality and major morbidities among preterm infants in Chinese NICU decreased, but there is still space for further efforts. Further targeted quality improvement is needed to improve the overall outcome of preterm infants.
Bronchopulmonary Dysplasia/epidemiology* ; Gestational Age ; Humans ; Infant ; Infant Mortality/trends* ; Infant, Newborn ; Infant, Premature ; Infant, Premature, Diseases/epidemiology* ; Patient Discharge ; Retinopathy of Prematurity/epidemiology* ; Sepsis/epidemiology*

mortality, in-hospital mortality, 28-day mortality, or ICU length of stay between the groups.CONCLUSION: Discontinuing NE prior to AVP was associated with a lower incidence of hypotension in patients recovering from septic shock. However, our results should be interpreted with caution, due to the considerable between-study heterogeneity.]]>
Arginine Vasopressin ; Arginine ; Bias (Epidemiology) ; Consensus ; Hospital Mortality ; Humans ; Hypotension ; Incidence ; Intensive Care Units ; Length of Stay ; Mortality ; Norepinephrine ; Odds Ratio ; Population Characteristics ; Sepsis ; Shock, Septic ; Treatment Outcome ; Vasoconstrictor Agents

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