OBJECTIVE: To analyze the clinical characteristics of elderly patients with sepsis, identify the key factors affecting their clinical outcomes, construct a death risk assessment scale for elderly patients with sepsis, and evaluate its predictive value. METHODS: A retrospective case-control study was conducted. The clinical data of sepsis patients admitted to intensive care unit (ICU) of the First Affiliated Hospital of Wenzhou Medical University from September 2021 to September 2023 were collected, including basic information, clinical characteristics, and clinical outcomes. The patients were divided into non-elderly group (age ≥ 65 years old) and elderly group (age < 65 years old) based on age. Additionally, the elderly patients were divided into survival group and death group based on their 30-day survival status. The clinical characteristics of elderly patients with sepsis were analyzed. Univariate and multivariate Logistic regression analyses were used to screen the independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed. The regression equation was simplified, and the death risk assessment scale was established. The predictive value of different scores for the prognosis of elderly patients with sepsis was compared. RESULTS: (1) A total of 833 patients with sepsis were finally enrolled, including 485 in the elderly group and 348 in the non-elderly group. Compared with the non-elderly group, the elderly group showed significantly lower counts of lymphocyte, T cell, CD8⁺ T cell, and the ratio of T cells and CD8⁺ T cells [lymphocyte count (×10⁹/L): 0.71 (0.43, 1.06) vs. 0.83 (0.53, 1.26), T cell count (cells/μL): 394.0 (216.0, 648.0) vs. 490.5 (270.5, 793.0), CD8⁺ T cell count (cells/μL): 126.0 (62.0, 223.5) vs. 180.0 (101.0, 312.0), T cell ratio: 0.60 (0.48, 0.70) vs. 0.64 (0.51, 0.75), CD8⁺ T cell ratio: 0.19 (0.13, 0.28) vs. 0.24 (0.16, 0.34), all P < 0.01], higher natural killer cell (NK cell) count, acute physiology and chronic health evaluation II (APACHE II) score, ratio of invasive mechanical ventilation (IMV) during hospitalization, and 30-day mortality [NK cell count (cells/μL): 112.0 (61.0, 187.5) vs. 95.0 (53.0, 151.0), APACHE II score: 16.00 (12.00, 21.00) vs. 13.00 (8.00, 17.00), IMV ratio: 40.6% (197/485) vs. 31.9% (111/348), 30-day mortality: 28.9% (140/485) vs. 19.5% (68/348), all P < 0.05], and longer length of ICU stay [days: 5.5 (3.0, 10.0) vs. 5.0 (3.0, 8.0), P < 0.05]. There were no statistically significant differences in the levels of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and interleukins (IL-2, IL-4, IL-6, IL-10) between the two groups. (2) In 485 elderly patients with sepsis, 345 survived in 30 days, and 140 died with the 30-day mortality of 28.9%. Compared with the survival group, the patients in the death group were older, and had lower body mass index (BMI), white blood cell count (WBC), PCT, platelet count (PLT) and higher IL-6, IL-10, N-terminal pro-brain natriuretic peptide (NT-proBNP), total bilirubin (TBil), blood lactic acid (Lac), and ratio of in-hospital IMV and continuous renal replacement therapy (CRRT). Multivariate Logistic regression analysis indicated that BMI [odds ratio (OR) = 0.783, 95% confidence interval (95%CI) was 0.678-0.905, P = 0.001], IL-6 (OR = 1.073, 95%CI was 1.004-1.146, P = 0.036), TBil (OR = 1.009, 95%CI was 1.000-1.018, P = 0.045), Lac (OR = 1.211, 95%CI was 1.072-1.367, P = 0.002), and IMV during hospitalization (OR = 6.181, 95%CI was 2.214-17.256, P = 0.001) were independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed (Logit P = 1.012-0.244×BMI+0.070×IL-6+0.009×TBil+0.190×Lac+1.822×IMV). The regression equation was simplified to construct a death risk assessment scale, namely BITLI score. Receiver operator characteristic curve (ROC curve) analysis showed that the area under the ROC curve (AUC) of BITLI score for predicting death risk was 0.852 (95%CI was 0.769-0.935), and it was higher than APACHE II score (AUC = 0.714, 95%CI was 0.623-0.805) and sequential organ failure assessment (SOFA) score (AUC = 0.685, 95%CI was 0.578-0.793). The determined cut-off value of BITLI score was 1.50, while achieving a sensitivity of 83.3% and specificity of 74.0%. CONCLUSIONS Elderly patients with sepsis often have reduced lymphocyte counts, severe conditions, and poor prognosis. BMI, IL-6, TBil, Lac, and IMV during hospitalization were independent risk factors for 30-day death in elderly patients with sepsis. The BITLI score constructed based above risk factors is more precise and reliable than traditional APACHE II and SOFA scores in predicting the outcomes of elderly patients with sepsis.
Humans ; Sepsis/mortality* ; Aged ; Retrospective Studies ; Risk Assessment ; Case-Control Studies ; Prognosis ; Male ; Female ; Intensive Care Units ; Risk Factors ; Aged, 80 and over ; Logistic Models ; Middle Aged

OBJECTIVE: To explore the implementation effect of hour-1 bundle for sepsis patients based on crisis resource management (CRM) system. METHODS: A historical control study was conducted. The hour-1 bundle for sepsis based on CRM was used to train 24 nurses in the emergency department from October 2022 to March 2023. Clinical data of sepsis patients admitted to the emergency department of the First People's Hospital of Zunyi from April 2022 to September 2023 were collected. The patients were divided into three groups based on different stages of CRM system construction: control group (before construction, from April to September in 2022), improvement group (during construction, from October 2022 to March 2023) and observation group (after construction, from April to September in 2023). The baseline data, implementation rate of hour-1 bundle [including blood culture, antibiotic usage, blood lactic acid (Lac) detection, fluid resuscitation, hypertensors usage], identification and diagnosis time, and prognosis parameters [including correction rate of hypoxemia, intensive care unit (ICU) occupancy rate, and 28-day survival rate]. Sepsis cognition survey and non-technical skill (NTS) evaluation of nurses in emergency department were conducted before and after training. RESULTS: Finally 43 cases were enrolled in the control group, improvement group and observation group, respectively. There was no statistically significant difference in baseline data including the gender, age, primary site, heart rate, systolic blood pressure, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, mechanical ventilation ratio among the three groups with comparability. With the gradual improvement of the CRM system, the implementation rate of 1-hour bundle was gradually increased, and the implementation rate in the control group, improvement group and observation group were 65.12% (28/43), 74.42% (32/43) and 88.37% (38/43), respectively, with statistically significant difference (P < 0.05). It was mainly reflected in the completion rate of blood culture, antibiotic usage rate, Lac detection rate and hypertensors usage rate within 1 hour, which were significantly higher in the observation group than those in the control group [completion rate of blood culture: 90.70% (39/43) vs. 62.79% (27/43), antibiotic usage rate: 88.37% (38/43) vs. 60.47% (26/43), Lac detection rate: 93.02% (40/43) vs. 72.09% (31/43), hypertensors usage rate: 88.37% (38/43) vs. 60.47% (26/43), all P < 0.05]. The fluid resuscitation rates within 1 hour in the three groups were all over 90%, with no statistically significant difference among the three groups. The recognition and diagnosis time in the observation group was significantly shorter than that in the control group and the improvement group (hours: 0.41±0.15 vs. 0.61±0.21, 0.51±0.18, both P < 0.05), the correction rate of hypoxemia and 28-day survival rate were significantly higher than those in the control group [correction rate of hypoxemia: 95.35% (41/43) vs. 74.42% (32/43), 28-day survival rate: 83.72% (36/43) vs. 60.47% (26/43), both P < 0.05], and ICU occupancy rate was significantly lower than that in the control group [72.09% (31/43) vs. 93.02% (40/43), P < 0.05]. After training in the CRM system, the score of the sepsis awareness survey questionnaire for emergency department nurses was significantly increased as compared with before training (60.42±5.29 vs. 44.17±9.21, P < 0.01), and NTS also showed significant improvement. CONCLUSION CRM plays a significant role in promoting the implementation of sepsis hour-1 bundle, which can improve the implementation rate of hour-1 bundle and NTS of medical staff, effectively improve patients' hypoxemia, reduce patients' ICU occupancy rate and 28-day risk of death.
Humans ; Sepsis/therapy* ; Emergency Service, Hospital ; Patient Care Bundles ; Intensive Care Units ; Female ; Male ; Middle Aged

OBJECTIVE: To explore the correlation between mechanical power normalized to dynamic lung compliance (Cdyn-MP) and weaning outcomes and prognosis in mechanically ventilated patients. METHODS: A prospective, observational cohort study was conducted. Patients who underwent invasive mechanical ventilation (IMV) for more than 24 hours and used a T-tube ventilation strategy for extubation in the intensive care unit (ICU) of Lianyungang First People's Hospital and Lianyungang Second People's Hospital between January 2022 and December 2023 were enrolled. The collected data encompassed patients' baseline characteristics, primary causes of ICU admission, vital signs and laboratory indicators during the initial spontaneous breathing trial (SBT), respiratory mechanics parameters within the 4-hour period prior to the SBT, weaning outcomes and prognostic indicators. Mechanical power (MP) and Cdyn-MP were calculated using a simplified MP equation. Univariate and multivariate Logistic regression analyses were utilized to determine the independent risk factors associated with weaning failure in patients undergoing mechanical ventilation. Restricted cubic spline (RCS) analysis and Spearman rank-sum test were employed to investigate the correlation between Cdyn-MP and weaning outcomes as well as prognosis. Receiver operator characteristic curve (ROC curve) was constructed, and the area under the ROC curve (AUC) was computed to evaluate the predictive accuracy of Cdyn-MP for weaning outcomes in mechanically ventilated patients. RESULTS: A total of 366 patients undergoing IMV were enrolled in this study, with 243 cases classified as successful weaning and 123 cases classified as failed weaning. Among them, 23 patients underwent re-intubation within 48 hours after the successful withdrawal of the first SBT, non-invasive ventilation, or died. Compared with the successful weaning group, the patients in the failed weaning group had significantly increased levels of sequential organ failure assessment (SOFA) score, body temperature and respiratory rate (RR) during SBT, and respiratory mechanical parameters within the 4-hour period prior to the SBT [ventilation frequency, positive end-expiratory pressure (PEEP), platform pressure (Pplat), peak inspiratory pressure (Ppeak), dynamic driving pressure (ΔPaw), fraction of inspired oxygen (FiO₂), MP, and Cdyn-MP], dynamic lung compliance (Cdyn) was significantly reduced, and duration of IMV, ICU length of stay, and total length of hospital stay were significantly prolonged. However, there were no statistically significant differences in age, gender, body mass index (BMI), smoking history, main causes of ICU admission, other vital signs [heart rate (HR), mean arterial pressure (MAP), saturation of peripheral oxygen (SpO₂)] and laboratory indicators [white blood cell count (WBC), albumin (Alb), serum creatinine (SCr)] during SBT of patients between the two groups. Univariate Logistic regression analysis was conducted, and variables with P < 0.05 and no multicollinearity with Cdyn-MP were selected for inclusion in the multivariate Logistic regression model. The results demonstrated that SOFA score [odds ratio (OR) = 1.081, 95% confidence interval (95%CI) was 1.008-1.160, P = 0.030], and PEEP (OR = 1.191, 95%CI was 1.075-1.329, P = 0.001), FiO₂ (OR = 1.035, 95%CI was 1.006-1.068, P = 0.021) and Cdyn-MP (OR = 1.190, 95%CI was 1.086-1.309, P < 0.001) within the 4-hour period prior to the SBT were independent risk factors for weaning failure in patients undergoing IMV. The RCS analysis after adjusting for confounding factors showed that as Cdyn-MP within the 4-hour period prior to the SBT increased, the risk of weaning failure in patients undergoing IMV significantly increased (P < 0.001). The Spearman rank correlation test showed that Cdyn-MP within the 4-hour period prior to the SBT was positively correlated with respiratory mechanical parameters including ΔPaw and MP (r values were 0.773 and 0.865, both P < 0.01), and negatively correlated with Cdyn (r = -0.587, P < 0.01). Cdyn-MP within the 4-hour period prior to the SBT was positively correlated with prognostic indicators such as duration of IMV, length of ICU stay, and total length of hospital stay (r values were 0.295, 0.196, and 0.120, all P < 0.05). ROC curve analysis demonstrated that, within the 4-hour period preceding the SBT, Cdyn-MP, MP, Cdyn, and ΔPaw possessed predictive value for weaning failure in patients undergoing IMV. Notably, Cdyn-MP exhibited superior predictive capability, evidenced by an AUC of 0.761, with a 95%CI ranging from 0.712 to 0.810 (P < 0.001). At the optimal cut-off value of 408.5 J/min×cmH₂O/mL×10^-3, the sensitivity was 68.29%, and the specificity was 71.19%. CONCLUSION Cdyn-MP is related to weaning outcomes and prognosis in mechanically ventilated patients, and has good predictive ability in assessing the risk of weaning failure.
Humans ; Prospective Studies ; Ventilator Weaning ; Prognosis ; Respiration, Artificial ; Intensive Care Units ; Lung Compliance ; Female ; Male ; Middle Aged ; Aged

OBJECTIVE: To analyze the risk factors of hypophosphatemia in patients with acute respiratory distress syndrome (ARDS). METHODS: A retrospective case-control study was conducted. The clinical data of the patients with ARDS admitted to Yanbian University Affiliated Hospital from January 2018 to October 2022 were collected. According to the 1-day serum phosphorus level after intensive care unit (ICU) admission, the patients with normal (0.80-1.45 mmol/L) or elevated (> 1.45 mmol/L) serum phosphorus levels were included in the non-hypophosphatemia group, while those with phosphorus levels lower than 0.80 mmol/L were included in the hypophosphatemia group. The differences in the inflammatory indicators [neutrophils percentage (NEU%), neutrophil count (NEU), lymphocyte count (LYM), high-sensitivity C-reactive protein (hs-CRP)], proteins [total protein (TP), albumin (Alb), prealbumin (PA)], blood lactic acid (Lac), neutrophil/lymphocyte ratio (NLR), neutrophil/albumin ratio (NAR), and blood lactic acid/albumin ratio (L/A) at 1, 2, 4, 6 and 8 days after ICU admission were compared between the two groups. The partial correlation method was used to analyze the correlation between the 1-day serum phosphorus level after ICU admission and the above indicators. Multivariate Logistic regression analysis was adopted to explore the risk factors of hypophosphatemia in patients with ARDS. RESULTS: All 110 patients were enrolled in the final analysis, among which there were 56 cases in the hypophosphatemia group and 54 cases in the non-hypophosphatemia group. At 1 day and 2 days after ICU admission, NEU% in the hypophosphatemia group were significantly higher than those in the non-hypophosphatemia group (1 day: 0.87±0.08 vs. 0.82±0.12, 2 days: 0.87±0.05 vs. 0.83±0.11, both P < 0.05). As the ICU admission time prolonged, LYM in the hypophosphatemia group was basically on the rise, and NEU%, hs-CRP, and NLR were first decreased and then increased. At 1 day after ICU admission, TP, Alb and PA in the hypophosphatemia group were significantly lower than those in the non-hypophosphatemia group [TP (g/L): 52.96±8.42 vs. 56.47±8.36, Alb (g/L): 29.73±5.83 vs. 33.08±7.35, PA (g/L): 69.95±50.72 vs. 121.50±82.42, all P < 0.05]. As the ICU admission time prolonged, TP and Alb in the hypophosphatemia group were basically showed a trend of first decreasing and then increasing, but at 8 days, Alb was still lower than that at 1 day, and PA basically showed an upward trend. In the non-hypophosphatemia group, the change trends of TP and Alb were consistent with those in the hypophosphatemia group. Lac and L/A both showed a downward trend in the two groups. Partial correlation analysis showed that 1-day serum phosphorus level after ICU admission was significantly negatively correlated with NEU% and hs-CRP (r value was -0.229 and -0.286, respectively, both P < 0.05), and significantly positively correlated with LYM and PA (r value was 0.231 and 0.311, respectively, both P < 0.05). Multivariate Logistic regression analysis showed that 1-day NEU% [odds ratio (OR) = 0.932, 95% confidence interval (95%CI) was 0.873-0.996, P = 0.038] and Alb (OR = 1.167, 95%CI was 1.040-1.308, P = 0.008) were the independent risk factors for hypophosphatemia in ARDS patients. CONCLUSION NEU% and Alb at 1 day after ICU admission are independent risk factors for hypophosphatemia in patients with ARDS.
Humans ; Hypophosphatemia/etiology* ; Respiratory Distress Syndrome/blood* ; Risk Factors ; Retrospective Studies ; Case-Control Studies ; Intensive Care Units ; Male ; Female ; Phosphorus/blood* ; Middle Aged ; Neutrophils ; Aged ; C-Reactive Protein

With the development of critical medical emergency technology, the success rate of sepsis treatment has been significantly improved, and the improvement of the long-term quality of life of sepsis survivors has also attracted more and more attention. ICU-acquired weakness (ICU-AW) refers to a group of syndromes with systemic and symmetrical muscle weakness during the intensive care unit (ICU) hospitalization and cannot be explained by the patient's own disease, which often involve diaphragm and skeletal muscle, resulting in difficulty in weaning and nosocomial infection. The incidence of ICU-AW in sepsis patients is over 50%, making it an important factor affecting the prognosis of these patients. The occurrence of sepsis ICU-AW is related to many factors, which can be summarized into two categories, including sepsis-related factors such as sepsis-associated inflammatory response, sepsis-associated encephalopathy (SAE), and treatment-related factors such as physical immobilization and insufficient nutritional support. The current ICU-AW risk assessment tools are mainly on subjective assessment scales, but there are some limitations in clinical application, and objective assessment tools including predictive model and imaging assessment, which are still in the research stage. "ABCDEF bundle strategy" is an important measure to prevent ICU-AW, in which early rehabilitation is the core element. This review of the literature from the risk factors, risk assessment and early rehabilitation of ICU-AW, and focuses on the timing, content, method and safety assessment of early rehabilitation, aims to improve the understanding of ICU-AW, strengthen the prevention of sepsis with ICU-AW, and improve the prognosis of sepsis patients, not only survive, but also live better.
Humans ; Sepsis/complications* ; Muscle Weakness/etiology* ; Intensive Care Units ; Prognosis ; Quality of Life

OBJECTIVE: To investigate the predictive value of oxygenation index (PaO₂/FiO₂) at intensive care unit (ICU) admission on 30-day mortality in patients with sepsis. METHODS: A retrospective study was conducted. Patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to October 2023 were enrolled. The demographic information, comorbidities, sites of infection, vital signs and laboratory test indicators at the time of admission to the ICU, disease severity scores within 24 hours of admission to the ICU, treatment process and prognostic indicators were collected. According to the PaO₂/FiO₂ at ICU admission, patients were divided into Q1 group (PaO₂/FiO₂ of 4.1-16.4 cmHg, 1 cmHg ≈ 1.33 kPa), Q2 group (PaO₂/FiO₂ of 16.5-22.6 cmHg), Q3 group (PaO₂/FiO₂ of 22.7-32.9 cmHg), and Q4 group (PaO₂/FiO₂ of 33.0-94.8 cmHg). Differences in the indicators across the four groups were compared. Multifactorial Cox regression analysis was used to assess the relationship between PaO₂/FiO₂ and 30-day mortality of patients with sepsis. The predictive value of PaO₂/FiO₂, sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) on 30-day prognosis of patients with sepsis was analyzed by receiver operator characteristic curve (ROC curve). RESULTS: A total of 1 711 patients with sepsis were enrolled, including 428 patients in Q1 group, 424 patients in Q2 group, 425 patients in Q3 group, and 434 patients in Q4 group. 622 patients died at 30-day, the overall 30-day mortality was 36.35%. There were statistically significant differences in age, body mass index (BMI), history of smoking, history of alcohol consumption, admission heart rate, respiratory rate, APACHE II score, SOFA score, Glasgow coma score (GCS), site of infection, Combined chronic obstructive pulmonary disease (COPD), blood lactic acid (Lac), prothrombin time (PT), albumin (Alb), total bilirubin (TBil), pH, proportion of mechanical ventilation, duration of mechanical ventilation, proportion of vasoactive medication used, and maximal concentration, length of ICU stay, hospital stay, incidence of acute kidney injury, in-hospital mortality, 30-day mortality among the four groups. Multivariate Cox regression analysis showed that after adjusting for confounding factors, for every 1 cmHg increase in PaO₂/FiO₂ at ICU admission, the 30-day mortality risk decreased by 2% [hazard ratio (HR) = 0.98, 95% confidence interval (95%CI) was 0.98-0.99, P < 0.001]. The 30-day mortality risk in the Q4 group was reduced compared with the Q1 group by 41% (HR = 0.59, 95%CI was 0.46-0.76, P < 0.001). The fitted curve showed that a curvilinear relationship between PaO₂/FiO₂ and 30-day mortality after adjustment for confounders. In the inflection point analysis, for every 1 cmHg increase in PaO₂/FiO₂ at PaO₂/FiO₂ < 28.55 cmHg, the risk of 30-day death in sepsis patients was reduced by 5% (HR = 0.95, 95%CI was 0.94-0.97, P < 0.001); when PaO₂/FiO₂ ≥ 28.55 cmHg, there was no statistically significant association between PaO2/FiO2 and the increase in the risk of 30-day death in sepsis (HR = 1.01, 95%CI was 0.99-1.02, P = 0.512). ROC curve analysis showed that the area under the curve (AUC) for the prediction of 30-day mortality by admission PaO₂/FiO₂ in ICU sepsis patients was 0.650, which was lower than the predictive ability of the SOFA score (AUC = 0.698) and APACHE II score (AUC = 0.723). CONCLUSION In patients with sepsis, PaO₂/FiO₂ at ICU admission is strongly associated with 30-day mortality risk, alerting healthcare professionals to pay attention to patients with low PaO₂/FiO₂ for timely interventions.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Retrospective Studies ; Prognosis ; Hospital Mortality ; Oxygen ; Male ; Predictive Value of Tests ; Female ; Middle Aged ; Aged

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 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

Central line associated bloodstream infection (CLABSI) is the most severe complication of indwelling intravascular catheters and one of the most common causes of intensive care unit (ICU)- or hospital-acquired infections. Once CLABSI occurs, it significantly increases the risk of mortality, long of hospital stay, and healthcare economic burden. In recent years, multiple large-scale clinical studies on the diagnosis, treatment, and prevention of CLABSI have been completed, providing evidence-based medical support for related practices. Additionally, evolving global trends in antibiotic resistance epidemiology and the development of novel antimicrobial agents necessitate adjustments in clinical management strategies. Based on these developments, the Chinese Society of Critical Care Medicine has updated and revised the Guideline on the Prevention and Treatment of Intravascular Catheter-Related Infections (2007). This guideline was developed following the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system for evidence quality assessment. Guided by clinical questions, the working group initiated the process by defining key clinical issues, conducting literature searches, screening studies, performing meta-analyses, and synthesizing evidence-based findings to draft preliminary recommendations. These recommendations underwent iterative revisions through expert panel reviews, remote and in-person meetings, and two rounds of voting by the Standing Committee of the Chinese Society of Critical Care Medicine before finalization. The guideline comprises 52 recommendations, focusing on adult patients with central venous catheters in ICU. Key areas addressed include: selection of catheter insertion sites and techniques, catheter type and design, catheter management, prevention, diagnosis, and treatment of CLABSI. The guideline aims to provide ICU healthcare professionals with best practices for central line management, ensuring standardized clinical protocols for adult CLABSI.
Humans ; Catheter-Related Infections/therapy* ; Catheterization, Central Venous/adverse effects* ; Bacteremia/therapy* ; Intensive Care Units ; Cross Infection/prevention & control*

OBJECTIVE: To analyze the clinical characteristics, microbiological analysis, and drug resistance patterns of intensive care unit (ICU) bloodstream infection. METHODS: A prospective cohort study method was employed to collect clinical data from patients suspected of bloodstream infection (BSI) during their stay in ICUs across 67 hospitals in 16 provinces and cities nationwide, from July 1, 2021, to December 31, 2022. Electronic data collection technology was used to gather general information on ICU patients, including gender, age, length of hospital stay, as well as diagnostic results, laboratory tests, imaging studies, microbiological results (including smear, culture results, and pathogen high-throughput testing), and prognosis. Patients were divided into a BSI group and a non-BSI group based on the presence or absence of BSI; further, patients with BSI were categorized into a drug-resistant group and a non-drug-resistant group based on the presence or absence of drug resistance. Differences in the aforementioned indicators between groups were analyzed and compared; variables with P < 0.10 in the univariate analysis were included in a multivariate Logistic regression analysis to identify risk factors for mortality and drug resistance in ICU patients with BSI. RESULTS: A total of 2 962 ICU patients suspected of BSI participated in the study, including 790 in the BSI group and 2 172 in the non-BSI group. Patients in the BSI group were mainly from East China and Southwest China, with significantly higher age and mortality rates than those in the non-BSI group. Among ICU patients with BSI, Staphylococcus had the highest detection rate (8.10%), followed by Klebsiella pneumoniae (7.47%); there were 169 cases in the drug-resistant group and 621 cases in the non-drug-resistant group; 666 cases survived, and 124 cases died (mortality was 15.70%). There were statistically significant differences between the death group and the survival group in terms of age, regional distribution, and bloodstream infections caused by Gram negative (G^-) bacilli, Enterococcus faecium, Aspergillus, and Klebsiella pneumoniae; multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.01, 95% confidence interval (95%CI) was 1.00-1.03], regional distribution (OR = 4.07, 95%CI was 1.02-1.34), Enterococcus faecium infection (OR = 3.64, 95%CI was 1.16-11.45), and Klebsiella pneumoniae infection (OR = 2.64,95%CI was 1.45-4.80) were independent risk factors for death in ICU patients with BSI (all P < 0.05). There were statistically significant differences between the drug-resistant group and the non-drug-resistant group in terms of age and bloodstream infections caused by Gram positive (G⁺) cocci and G^- bacilli; multivariate Logistic regression analysis showed that age (OR = 1.01,95%CI was 1.00-1.03), G^- bacilli infection (OR = 2.18, 95%CI was 1.33-3.59), Escherichia coli infection (OR = 0.28,95%CI was 0.09-0.84), and Enterococcus faecium infection (OR = 3.35, 95%CI was 1.06-10.58) were independent risk factors for drug resistance in ICU patients with BSI (all P < 0.05). CONCLUSIONS Bloodstream infections may increase the mortality of ICU patients. Older age, regional distribution, Enterococcus faecium infection and Klebsiella pneumoniae infection can increase the mortality rate of ICU patients with BSI; bloodstream infections caused by G^- bacilli are prone to drug resistance, but have no significant impact on the mortality of ICU patients with BSI.
Adult ; Humans ; Bacteremia/microbiology* ; China/epidemiology* ; Cohort Studies ; Cross Infection/microbiology* ; Drug Resistance, Bacterial ; Intensive Care Units/statistics & numerical data* ; Prospective Studies ; Risk Factors ; Sepsis/microbiology*