Search Results

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

7.Early lactate/albumin ratio combined with quick sequential organ failure assessment for predicting the prognosis of sepsis caused by community-acquired pneumonia in the emergency department.

Xinyan ZHANG ; Yingbo AN ; Yezi DONG ; Min LI ; Ran LI ; Jinxing LI

Chinese Critical Care Medicine 2025;37(2):118-122

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

8.Construction and external validation of a machine learning-based prediction model for epilepsy one year after acute stroke.

Wenkao ZHOU ; Fangli ZHAO ; Xingqiang QIU ; Yujuan YANG ; Tingting WANG ; Lingyan HUANG

Chinese Critical Care Medicine 2025;37(5):445-451

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

9.Establishment and evaluation of a machine learning prediction model for sepsis-related encephalopathy in the elderly.

Xiao YUE ; Yiwen WANG ; Zhifang LI ; Lei WANG ; Li HUANG ; Shuo WANG ; Yiming HOU ; Shu ZHANG ; Zhengbin WANG

Chinese Critical Care Medicine 2025;37(10):937-943

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

10.Analysis of the application effect, access safety and infection-related factors of extracorporeal membrane oxygenation in series with continuous renal replacement therapy access in critically ill patients.

Xiangyu ZHU ; Yan SHI ; Peng XIE ; Jing FU ; Wenhan GE ; Haichen YANG

Chinese Critical Care Medicine 2025;37(10):962-967

OBJECTIVE: To analyze the efficacy and access safety of extracorporeal membrane oxygenation (ECMO) in series with continuous renal replacement therapy (CRRT) access for critically ill patients using propensity score matching analysis, and to explore the potential influencing factors of infection. METHODS: A total of 200 critically ill patients who received both ECMO and CRRT treatment in the intensive care unit (ICU) of Huai'an Second People's Hospital from December 2020 to December 2024 were retrospectively selected as the research subjects. They were divided into the independent operation group (72 cases) and the series system group (128 cases) according to the access connection mode of ECMO and CRRT. Propensity score matching analysis was used to perform 1 : 1 matching for patients of the two groups. The general data [age, gender, body mass index (BMI), clinical diagnosis, underlying disease, intubation method, intubation position, disease severity, ECMO support duration, catheter indwelling duration, oxygenation index (PaO₂/FiO₂) at 48 hours after ECMO initiation, serum creatinine (SCr), procalcitonin (PCT), hemoglobin (Hb), white blood cell count (WBC), platelet count (PLT)], treatment status [ECMO initiation duration, ECMO operation duration, ECMO flow, left ventricular ejection fraction (LVEF), CRRT initiation duration, CRRT catheter indwelling duration, inflow and outflow volume of replacement fluid], clinical outcome indicators (28-day survival rate, length of ICU stay, renal function recovery, fluid balance compliance rate), and access safety indicators (incidence of ECMO access thrombosis, incidence of infection, and incidence of bleeding events) of all the patients were collected. Subgroup analysis was conducted based on the occurrence of infection, and multivariate Logistic regression analysis was used to screen the potential risk factors for infection in critically ill patients receiving both ECMO and CRRT treatment. RESULTS: Finally, a total of 120 patients were successfully matched, with 60 patients in both the independent operation group and the series system group. No statistically significant differences were observed in the general data between the two groups, indicating comparability. Compared with the independent operation group, the ECMO flow at 48 hours after ECMO initiation, SCr, and alanine transaminase (ALT) of the patients in the series system group were significantly decreased, while the LVEF at 48 hours after ECMO initiation was significantly increased, additionally, the CRRT initiation duration, CRRT catheter indwelling duration, and the length of ICU stay were significantly shortened, and the inflow and outflow volume of replacement fluid were significantly increased. The incidence of infection and bleeding events in the series system group was significantly lower than that in the independent operation group [infection incidence: 11.67% (7/60) vs. 36.67% (22/60), bleeding event incidence: 8.33% (5/60) vs. 48.33% (29/60), both P < 0.05]. No significant difference was found in the other general data, treatment status, clinical outcome indicators, or access safety indicators between the two groups. Among the 120 patients, 29 cases developed infection (accounting for 24.17%), and 91 cases had no infection (accounting for 75.83%). Compared with the non-infection group, the catheter indwelling duration was significantly prolonged and PCT was significantly increased in the infection group, while the PLT and the proportion of patients with ECMO and CRRT access connected via the series system were significantly decreased. Multivariate Logistic regression analysis showed that catheter indwelling duration [odds ratio (OR) = 1.277, 95% confidence interval (95%CI) was 1.001-1.629, P = 0.049], PCT (OR = 1.529, 95%CI was 1.222-1.914, P < 0.001], PLT (OR = 0.953, 95%CI was 0.926-0.981, P = 0.001), and access connection mode (OR = 0.289, 95%CI was 0.090-0.930, P = 0.037) were potential risk factors for infection in critically ill patients. CONCLUSIONS The ECMO-in-series CRRT access can accelerate the initiation of CRRT, avoid local bleeding, stabilize patients' cardiac, hepatic and renal functions, reduce potential infection risks, and improve the prognosis of patients.
Humans ; Extracorporeal Membrane Oxygenation/adverse effects* ; Critical Illness/therapy* ; Retrospective Studies ; Continuous Renal Replacement Therapy ; Male ; Female ; Intensive Care Units ; Propensity Score ; Middle Aged ; Renal Replacement Therapy ; Adult ; Aged ; Risk Factors

1.Mental health status and academic performance of graduating nursing students during COVID-19 pandemic in a government school in Leyte, mental health program model: A correlational study.

2.Mental health status and academic performance of graduating nursing students during COVID-19 pandemic in a government school in Leyte, mental health program model: A correlational study.

3.The perceptions of AI use of Filipino occupational therapy students at the University of Santo Tomas: A study protocol.

4.Quality of care among patients with acute heart failure at the emergency room and adherence of physicians at the University of the Philippines – Philippine General Hospital to the division of cardiovascular medicine – heart failure pathway:A retrospective cohort study.

5.Psychometric evaluation of the Tagalog version of psoriatic arthritis quality of life questionnaire.

6.Gallstones, cholecystectomy, and cancer risk: an observational and Mendelian randomization study.

7.Early lactate/albumin ratio combined with quick sequential organ failure assessment for predicting the prognosis of sepsis caused by community-acquired pneumonia in the emergency department.

8.Construction and external validation of a machine learning-based prediction model for epilepsy one year after acute stroke.

9.Establishment and evaluation of a machine learning prediction model for sepsis-related encephalopathy in the elderly.

10.Analysis of the application effect, access safety and infection-related factors of extracorporeal membrane oxygenation in series with continuous renal replacement therapy access in critically ill patients.

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