OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

OBJECTIVE: To investigate the distribution characteristics of polymorphonuclear neutrophil (PMN) in the lungs during the early stage of severe burns and the mechanism of neutrophil elastase (NE) promoting lung injury. METHODS: 6-8-week-old male C57BL/6J mice were selected for the experiments. A 30% total body surface area (TBSA) III degree burn mouse model was established (severe burn group); the Sham-injury group was treated with 37 centigrade water. In the sodium sivelestat intervention group (SV intervention group), NE competitive inhibitor, sivelestat, 100 mg/kg, was injected via tail vein immediately after injury, while other groups received an equal volume of saline. Ten mice were harvested from each group to observe survival for 72 hours. Respiratory function tests were tested at 0 (immediate), 3, 6, 12, and 24 hours after molding. hematoxylin-eosin (HE) and immunohistochemical staining were used to observe lung tissue structure, inflammatory changes and PMN infiltration. The PMN absolute count in mice lung tissue was detected buy flow cytometry. At 6, 12, and 24 hours after molding, PMN counts and the concentration of NE [enzyme linked immunosorbent assay (ELISA)] in peripheral blood plasma, lung tissue, and bronchoalveolar lavage fluid (BALF) were detected. RESULTS: (1) HE staining results showed that compared with the Sham-injury group, the lungs of mice in the severe burn group showed inflammatory changes and PMN infiltration, with more significant changes at 6 hours. Immunohistochemistry results also confirmed that the expression of NE protein released from PMN significantly increased after 6 hours of severe burn injury [(3.79±0.62)% vs. (0.18±0.05)%, t = 11.56, P < 0.01]. (2) Compared with the Sham-injury group, the number of PMN and the concentration of NE in the peripheral blood and lung tissues in the severe burn group were significantly increased (F values were 13.709, 55.350 and 29.890, 13.286, respectively, all P < 0.01), peaking at 6 hours [plasma PMN count (×10⁹/L): 2.92±1.01 vs. 0.92±0.29, lung tissue PMN absolute count (cells): 48 788.03±11 833.91 vs. 1 516.72±415.35, plasma NE (ng/L): 24 522.71±3 842.92 vs. 7 009.34±4 067.86, lung tissue NE (ng/L): 262 189.04±9 695.13 vs. 65 026.03± 16 016.31, all P < 0.01]. The number of PMN in the lung of severely burned mice was highly correlated with NE concentration (r = 0.892, P < 0.001). There was no significantly difference in the PMN absolute count in the BALF of mice between the Sham-injury group and severe burn group (F = 1.403, P > 0.05). The Sham-injury group and severe burn group contained a small amount of NE in the BALF, and the concentration of NE in the BALF of the severely burned 6 hours and 12 hours groups were significantly higher than those of the Sham-injury group (ng/L: 328.58±158.10, 415.30±240.89 vs. 61.95±15.80, both P < 0.05). (3) Kaplan-Meier survival curve showed that the 72-hour survival rate of mice in the SV intervention group was significantly higher than that in the severe burn group (100% vs. 10%, Log-Rank test: χ² = 19.12, P < 0.001). (4) Compared with the Sham-injury group, all lung function indices of the severe burn group decreased significantly. All lung function indices of SV intervention group improved gradually over time, which were significantly better than those of the severe burn group. (5) Compared with the Sham-injury group, the PMN absolute count in lung tissue and the concentration of NE in plasma and lung tissue were significantly higher in the SV intervention group (F values were 46.709, 3.535, 32.701, respectively, all P < 0.05), with a peak at 6 hours. Compared with the severe burn group, the SV intervention group had a higher PMN absolute count in lung tissue (cells: 8 870.80±7 013.89 vs. 25 974.92±22 240.8, P < 0.05), and higher plasma and lung tissue NE concentrations (ng/L: 14 955.94±3 944.41 vs. 21 972.75±4 573.05, 81 956.87±38 658.35 vs. 168 182.30±83 513.91, both P < 0.01) were significantly decreased. CONCLUSIONS In the early stage of severe burns, there is a significant infiltration of PMN into the lungs. The NE promotes lung injury in the early stage of severe burn, and improve lung injury by inhibiting the action of NE.
Animals ; Burns/metabolism* ; Leukocyte Elastase/metabolism* ; Male ; Mice, Inbred C57BL ; Mice ; Neutrophils/metabolism* ; Lung/metabolism* ; Disease Models, Animal ; Neutrophil Infiltration ; Lung Injury/metabolism* ; Glycine/analogs & derivatives* ; Sulfonamides

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

OBJECTIVE: To develop and compare risk prediction models for in-hospital post-cardiac arrest brain injury (PCABI) in critically ill patients using nomograms and random forest algorithms, aiming to identify the optimal model for early identification of high-risk PCABI patients and providing evidence for precise treatment. METHODS: A retrospective cohort study was used to collect the first-time in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU) from 2008 to 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) as the study population, and the patients' age, gender, body mass, health insurance utilization, first vital signs and laboratory tests within 24 hours of ICU admission, mechanical ventilation, and critical care scores were extracted. Independent influencing factors of PCABI were identified through univariate and multivariate Logistic regression analyses. The included patients were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio, and the PCABI risk prediction model was constructed by the nomogram and random forest algorithm, respectively, and the model was evaluated by receiver operator characteristic curve (ROC curve), the calibration curve, and the decision curve analysis (DCA), and after the better model was selected, 179 patients admitted to Tianjin Medical University General Hospital as the external validation cohort for external evaluation were collected by using the same inclusion and exclusion criteria. RESULTS: A total of 1 419 patients with without traumatic brain injury who had their first-time IHCA were enrolled, including 995 in the training cohort (including 176 PCABI and 819 non-PCABI) and 424 in the internal validation cohort (including 74 PCABI and 350 non-PCABI). Univariate and multivariate analysis showed that age, potassium, urea nitrogen, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation III (APACHE III), and mechanical ventilation were independent influences on the occurrence of PCABI in patients with IHCA (all P < 0.05). Combining the above variables, we constructed a nomogram model and a random forest model for comparison, and the results show that the nomogram model has better predictive efficacy than the random forest model [nomogram model: area under the ROC curve (AUC) of the training cohort = 0.776, with a 95% credible interval (95%CI) of 0.741-0.811; internal validation cohort AUC = 0.776, with a 95%CI of 0.718-0.833; random forest model: AUC = 0.720, with a 95%CI of 0.653-0.787], and they performed similarly in terms of calibration curves, but the nomogram performed better in terms of decision curve analysis (DCA); at the same time, the nomogram model was robust in terms of external validation cohort (external validation cohort AUC = 0.784, 95%CI was 0.692-0.876). CONCLUSIONS A nomogram risk prediction model for the occurrence of PCABI in critically ill patients was successfully constructed, which performs better than the random forest model, helps clinicians to identify the risk of PCABI in critically ill patients at an early stage and provides a theoretical basis for early intervention.
Humans ; Critical Illness ; Retrospective Studies ; Heart Arrest/complications* ; Nomograms ; Brain Injuries/etiology* ; Intensive Care Units ; Algorithms ; Male ; Female ; Middle Aged ; ROC Curve ; Risk Factors ; Risk Assessment ; Logistic Models ; Aged

OBJECTIVE: To observe the neuroprotective effect of 6-shogaol (6-SH) in global cerebral ischemia/reperfusion injury (CIRI) following cardiac arrest (CA) and cardiopulmonary resuscitation (CPR) in rats. METHODS: Computer-aided molecular docking was used to determine whether 6-SH could spontaneously bind to death-associated protein kinase 1 (DAPK1). SPF-grade male SD rats were randomly divided into a sham group (n = 5), a CPR group (n = 7), and a CPR+6-SH group (n = 7). The CPR group and CPR+6-SH group were further divided into 12-, 24-, and 48-hour subgroups based on observation time points. A rat model of global CIRI after CA-CPR was established by asphyxiation. In the sham group, only tracheal and vascular intubation was performed without asphyxia and CPR induction. The CPR group was intraperitoneally injected with 1 mL of normal saline immediately after successful modeling. The CPR+6-SH group received an intraperitoneal injection of 20 mg/kg 6-SH (1 mL) immediately after successful modeling, followed by administration every 12 hours until the endpoint. Neurological Deficit Score (NDS) was recorded at each time point after modeling. After completion of observation at each time point, rats were anesthetized and sacrificed, and brain tissue specimens were collected. Histopathological changes of neurons were observed under light microscopy after hematoxylin-eosin (HE) staining. Ultrastructural changes of hippocampal neurons and autophagy were observed by transmission electron microscopy (TEM). Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect mRNA expression levels of DAPK1, vacuolar protein sorting 34 (VPS34), Beclin1, and microtubule-associated protein 1 light chain 3 (LC3) in brain tissues. Western blotting was used to detect protein expression levels of DAPK1, phosphorylated DAPK1 at serine 308 (p-DAPK1 ser308), VPS34, Beclin1, and LC3. Immunofluorescence was used to observe Beclin1 and LC3 expression in brain tissues under a fluorescence microscope. RESULTS: Molecular docking results indicated that 6-SH could spontaneously bind to DAPK1. Compared with the sham group, the NDS scores of the CPR group rats were significantly increased at all modeling time points; under light microscopy, disordered cell arrangement, widened intercellular spaces, and edema were observed in brain tissues, with pyknotic and necrotic nuclei in some areas; under TEM, mitochondria were markedly swollen with intact membranes, dissolved matrix, reduced or disappeared cristae, vacuolization, and increased autophagosomes. Compared with the CPR group, the NDS scores of the CPR+6-SH group rats were significantly decreased at all modeling time points; under light microscopy, local neuronal edema and widened perinuclear space were observed; under TEM, mitochondria were mostly mildly swollen with intact membranes, fewer autophagosomes, and alleviated injury. RT-qPCR results showed that compared with the sham group, mRNA expression levels of DAPK1, VPS34, Beclin1, and LC3 in brain tissues were significantly upregulated in all CPR subgroups, with the most pronounced changes at 24 hours. Compared with the CPR group, the CPR+6-SH group showed significantly lower mRNA expression of the above indicators at each time point [24 hours post-modeling (relative expression): DAPK1 mRNA: 3.41±0.68 vs. 4.48±0.62; VPS34 mRNA: 3.63±0.49 vs. 4.66±1.18; Beclin1 mRNA: 3.08±0.49 vs. 4.04±0.22; LC3 mRNA: 2.60±0.36 vs. 3.67±0.62; all P < 0.05]. Western blotting results showed that compared with the sham group, the protein expression levels of DAPK1, VPS34, Beclin1, and LC3 in all CPR subgroups were significantly increased, while the expression of p-DAPK1 ser308 was significantly decreased, with the most pronounced changes observed in the CPR 24-hour subgroup. Compared with the CPR group, the CPR+6-SH subgroups exhibited significantly reduced protein expression of DAPK1, VPS34, Beclin1, and LC3 [24-hour post-modeling: DAPK1/β-actin: 1.88±0.22 vs. 2.47±0.22; VPS34/β-actin: 2.55±0.06 vs. 3.46±0.05; Beclin1/β-actin: 2.12±0.03 vs. 2.87±0.03; LC3/β-actin: 2.03±0.24 vs. 3.17±0.23; all P < 0.05]. Conversely, the expression of p-DAPK1 ser308 was significantly upregulated in the CPR+6-SH group compared to the CPR group [24-hour post-modeling: p-DAPK1 ser308/β-actin: 0.40±0.02 vs. 0.20±0.07, P < 0.05]. Under the fluorescence microscope, fluorescence intensities of Beclin1 and LC3 in the CPR 24-hour group were significantly higher than those in the sham 24-hour group; compared with the CPR 24-hour group, the CPR+6-SH 24-hour group showed significantly reduced fluorescence intensities of Beclin1 and LC3. CONCLUSION 6-SH inhibited the expression of DAPK1, alleviated excessive autophagy after global CIRI following CA-CPR in rats, and exerted neuroprotective effects. The mechanism may be related to phosphorylation at the DAPK1 ser308 site.
Animals ; Rats, Sprague-Dawley ; Male ; Rats ; Cardiopulmonary Resuscitation ; Autophagy/drug effects* ; Heart Arrest/therapy* ; Death-Associated Protein Kinases/metabolism* ; Reperfusion Injury/metabolism* ; Disease Models, Animal ; Neuroprotective Agents/pharmacology* ; Brain Ischemia/metabolism*

OBJECTIVE: To investigate the role and mechanism of the cyclic guanosine monophosphate-adenosine monophosphate synthase/stimulator of interferon gene (cGAS/STING) pathway in oleic acid-induced acute lung injury (ALI) in mice. METHODS: Male wild-type C57BL/6J mice were randomly divided into five groups (each n = 10): normal control group, ALI model group, and 5, 50, 500 μg/kg inhibitor pretreatment groups. The ALI model was established by tail vein injection of oleic acid (7 mL/kg), while the normal control group received no intervention. The inhibitor pretreatment groups were intraperitoneally injected with the corresponding doses of cGAS inhibitor RU.521 respectively 1 hour before modeling. At 24 hours post-modeling, blood was collected, and mice were sacrificed. Lung tissue pathological changes were observed under light microscopy after hematoxylin-eosin (HE) staining, and pathological scores were assessed. Western blotting was used to detect the protein expressions of cGAS, STING, phosphorylated TANK-binding kinase 1 (p-TBK1), phosphorylated interferon regulatory factor 3 (p-IRF3), and phosphorylated nuclear factor-κB p65 (p-NF-κB p65) in lung tissue. Immunohistochemistry was performed to observe STING and p-NF-κB positive expressions in lung tissue. Serum interferon-β (IFN-β) levels were measured by enzyme-linked immunosorbent assay (ELISA). RESULTS: Compared with the normal control group, the ALI model group exhibited significant focal alveolar thickening, intra-alveolar hemorrhage, pulmonary capillary congestion, and neutrophil infiltration in the pulmonary interstitium and alveoli, along with markedly increased pathological scores (10.33±0.58 vs. 1.33±0.58, P < 0.05). Protein expressions of cGAS, STING, p-TBK1, p-IRF3, and p-NF-κB p65 in lung tissue significantly increased [cGAS protein (cGAS/β-actin): 1.24±0.02 vs. 0.56±0.02, STING protein (STING/β-actin): 1.27±0.01 vs. 0.55±0.01, p-TBK1 protin (p-TBK1/β-actin): 1.34±0.03 vs. 0.22±0.01, p-IRF3 protein (p-IRF3/β-actin): 1.23±0.02 vs. 0.36±0.01, p-NF-κB p65 protein (p-NF-κB p65/β-actin): 1.30±0.02 vs. 0.53±0.02, all P < 0.05], positive expressions of STING and p-NF-κB in lung tissue were significantly elevated [STING (A value): 0.51±0.03 vs. 0.30±0.07, p-NF-κB (A value): 0.57±0.05 vs. 0.31±0.03, both P < 0.05], and serum IFN-β levels were also significantly higher (ng/L: 256.02±3.84 vs. 64.15±1.17, P < 0.05). The cGAS inhibitor pretreatment groups showed restored alveolar structural integrity, reduced inflammatory cell infiltration, and decreased hemorrhage area, along with dose-dependent lower pathological scores as well as the protein expressions of cGAS, STING, p-TBK1, p-IRF3 and p-NF-κB p65 in lung tissue, with significant differences between the 500 μg/kg inhibitor group and ALI model group [pathological score: 2.67±0.58 vs. 10.33±0.58, cGAS protein (cGAS/β-actin): 0.56±0.03 vs. 1.24±0.02, STING protein (STING/β-actin): 0.67±0.03 vs. 1.27±0.01, p-TBK1 protein (p-TBK1/β-actin): 0.28±0.01 vs. 1.34±0.03, p-IRF3 protein (p-IRF3/β-actin): 0.32±0.01 vs. 1.23±0.02, p-NF-κB p65 protein (p-NF-κB p65/β-actin): 0.63±0.01 vs. 1.30±0.02, all P < 0.05]. Compared with the ALI model group, positive expressions of STING and p-NF-κB in lung tissue were significantly reduced in the 500 μg/kg inhibitor group [STING (A value): 0.40±0.01 vs. 0.51±0.03, p-NF-κB (A value): 0.43±0.02 vs. 0.57±0.05, both P < 0.05], and serum IFN-β levels were also markedly reduced (ng/L: 150.03±6.19 vs. 256.02±3.84, P < 0.05). CONCLUSIONS The cGAS/STING pathway is activated in oleic acid-induced ALI, leading to exacerbated inflammatory responses and increased lung damage. RU.521 can inhibit cGAS, thereby down-regulating the expression of pathway proteins and cytokines, and providing protection to lung tissue.
Animals ; Acute Lung Injury/chemically induced* ; Male ; Nucleotidyltransferases/metabolism* ; Mice ; Signal Transduction ; Mice, Inbred C57BL ; Membrane Proteins/metabolism* ; Oleic Acid/adverse effects* ; Transcription Factor RelA/metabolism* ; Lung/pathology* ; Interferon Regulatory Factor-3/metabolism* ; Disease Models, Animal

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 construct and validate a prognostic prediction model for children with sepsis using the Phoenix sepsis score (PSS). METHODS: A retrospective case series study was conducted to collect clinical data of children with sepsis admitted to the pediatric intensive care unit (PICU) of the Affiliated Hospital of Guizhou Medical University from January 2022 to April 2024. The data included general information, the worst values of laboratory indicators within the first 24 hours of PICU admission, PSS score, pediatric critical illness score (PCIS), and the survival status of the children within 30 days of admission. The statistically significant indicators in univariate Logistic regression analysis were included in multivariate Logistic regression analysis to screen the risk factors affecting the prognosis of children with sepsis and construct a nomogram model. The receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive performance of the model. The Bootstrap method was used to perform 1 000 repeated sampling internal verification and draw the calibration curve of the model. RESULTS: A total of 199 children with sepsis were included, of which 32 died and 167 survived 30 days after admission. In the univariate Logistic regression analysis, shock, white blood cell count (WBC), international normalized ratio (INR), lactic acid (Lac), PSS score, and PCIS score were identified as statistically significant predictors. These variables were then included in the multivariate Logistic regression analysis, which demonstrated that shock [odds ratio (OR) = 4.258, 95% confidence interval (95%CI) was 1.049-17.288], WBC (OR = 1.124, 95%CI was 1.052-1.210), and PSS score (OR = 1.977, 95%CI was 1.298-3.012) were independent risk factors for mortality in pediatric patients with sepsis (all P < 0.05). A nomogram model was constructed based on these three risk factors, with the model equation as follows: -4.809+1.449×shock+0.682×PSS score+0.117×WBC. The calibration curve results showed that the model's predictions were highly consistent with the actual observations. The ROC curve showed that when the Youden index of the prediction model was 0.792, the sensitivity and specificity were 90.6% and 88.6%, respectively, and the area under the curve (AUC) was 0.957 (95%CI was 0.930-0.984), which was higher than the AUC of shock, WBC, and PSS score alone (0.808, 0.667, 0.908, respectively). CONCLUSIONS Shock, WBC, and PSS score have demonstrated certain predictive value for mortality in children with sepsis. The nomogram model based on the above indicators has important clinical significance for evaluating the prognosis and guiding treatment of children with sepsis.
Humans ; Sepsis/diagnosis* ; Prognosis ; Retrospective Studies ; Logistic Models ; Intensive Care Units, Pediatric ; Nomograms ; Child ; ROC Curve ; Risk Factors ; Male ; Female ; Child, Preschool ; Infant

OBJECTIVE: To explore the timing of weaning extracorporeal membrane oxygenation (ECMO) and analyze the risk factors that affect survival outcomes before weaning. METHODS: A retrospective case-control study was conducted. Patients who received ECMO treatment and were weaned according to physicians' orders at the Second Xiangya Hospital of Central South University from January 2020 to June 2024 were enrolled as the study subjects. The general information, underlying diseases, indications and processes of ECMO, vital signs and arterial blood gas analysis 1 hour before weaning test, and biochemical indicators 24 hours before weaning test were collected through the hospital electronic medical record system. The primary outcome measure was the hospital mortality. The variables with P < 0.1 in univariate analysis and correlation analysis were included into binary Logistic regression analysis to identify risk factors. A nomogram model was constructed to predict the risk of weaning death in patients with ECMO, and receiver operator characteristic curve (ROC curve) and calibration curve were drawn to evaluate the model. Decision curve analysis (DCA) was used to evaluate the clinical net benefit rate of the model. RESULTS: A total of 32 ECMO patients were included, among whom 10 received veno-arterial ECMO (VA-ECMO) and 22 received veno-venous ECMO (VV-ECMO). During the hospitalization period, 23 patients survived, while 9 died. The time from mechanical ventilation to ECMO activation in the death group was significantly longer than that in the survival group, and the time from ECMO cessation to discharge was significantly shorter than that in the survival group. The levels of diastolic blood pressure (DBP) and albumin (Alb) before weaning were significantly lower than those in the survival group, and the level of procalcitonin (PCT) was significantly higher than that in the survival group (all P < 0.05). Spearman correlation analysis showed that DBP, PCT, Alb, and thrombin time (TT) were correlated with the weaning outcomes of ECMO patients (r values were -0.450, 0.373, -0.376, -0.346, all P < 0.1). Binary Logistic regression analysis showed that the final indicators entering the regression equation included DBP [odds ratio (OR) = 0.864, 95% confidence interval (95%CI) was 0.756-0.982], PCT (OR = 1.157, 95%CI was 0.679-1.973), and TT (OR = 0.852, 95%CI was 0.693-1.049), and a nomogram model was constructed to predict the weaning outcomes of ECMO patients. ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the weaning outcome of ECMO patients was 0.831, with a sensitivity of 77.8% and a specificity of 65.2%. Its predictive value was better than that of single indicators DBP, PCT, and TT (AUC of 0.787, 0.739, and 0.722, respectively). The calibration curve showed that the prediction probability of the model was in good consistency with the actual observed results, the Hosmer-Lemeshow goodness of fit test showed that, χ ² = 8.3521, P = 0.400, indicating that the model fits well. DCA showed that across risk threshold of 0-0.8, the net benefit rate was greater than 0, which was significantly better than that of single indicator. CONCLUSIONS The nomogram model constructed with DBP, PCT, and TT has certain predictive value for the weaning outcomes of ECMO patients and can be used as a screening indicator for ECMO weaning timing.
Humans ; Extracorporeal Membrane Oxygenation ; Retrospective Studies ; Risk Factors ; Case-Control Studies ; Hospital Mortality ; Male ; Female ; Nomograms ; Logistic Models ; ROC Curve ; Middle Aged ; Adult ; Ventilator Weaning ; Time Factors

OBJECTIVE: To identify the risk factors for acute respiratory distress syndrome (ARDS) in geriatric patients following gastrointestinal perforation surgery, and constructed a model to validate its predictive value. METHODS: A retrospective analysis was conducted. The clinical data of geriatric patients (aged ≥ 60 years) after gastrointestinal perforation surgery admitted to the intensive care unit (ICU) of Hebei General Hospital from October 2017 to October 2024 were enrolled. Two groups were divided according to whether ARDS occurred postoperatively, and the differences in each index between the groups were compared. Lasso regression and multifactorial Logistic regression analyses were used to identify independent risk factors for the development of ARDS, and a prediction model was constructed based on these, which was presented using a nomogram. The receiver operator characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) were plotted to evaluate the discrimination, accuracy, and clinical practicability of the model. RESULTS: A total of 155 geriatric patients following gastrointestinal perforation surgery were ultimately included in the analysis, among whom 43 developed ARDS, with an incidence rate of 27.7%. There were significantly differences in age, body mass index (BMI), acute kidney injury comorbidity, heart rate, onset time, the duration of surgery, the site of perforation, seroperitoneum, amount of bleeding, shock comorbidity, central venous pressure (CVP), C-reactive protein, and albumin between ARDS and non-ARDS groups. Lasso regression identified nine significant predictors: age, BMI, acute kidney injury comorbidity, onset time, seroperitoneum, shock comorbidity, CVP, hemoglobin, and albumin. Multivariate Logistic regression analysis identified BMI [odds ratio (OR) = 1.310, P < 0.001], hemoglobin (OR = 1.019, P = 0.045), seroperitoneum (OR = 1.001, P = 0.017), and albumin (OR = 0.871, P < 0.001) as independent risk factors for the occurrence of ARDS. A prediction model was constructed based on the above four independent risk factors, and the ROC curve showed that the area under the curve (AUC) of the model for predicting the occurrence of ARDS was 0.885 [95% confidence interval (95%CI) was 0.824-0.946], and internal validation was performed using bootstrap resampling (Bootstrap 500 times), which showed that the AUC value of the model was 0.886 (95%CI was 0.883-0.889). Calibration curves revealed excellent concordance between observed outcomes and model predictions. DCA indicated a high net benefit value for the model, which has good clinical utility. CONCLUSIONS BMI, hemoglobin, seroperitoneum, and albumin were identified as independent risk factors for ARDS in geriatric patients following gastrointestinal perforation surgery. The prediction model constructed using these four indicators facilitates early identification of high-risk individuals by clinicians.
Humans ; Respiratory Distress Syndrome/etiology* ; Retrospective Studies ; Aged ; Risk Factors ; Logistic Models ; Postoperative Complications ; Intestinal Perforation/surgery* ; Male ; ROC Curve ; Female ; Middle Aged ; Intensive Care Units ; Nomograms