Journal of intensive medicine最新文献

{"title":"Title Page","authors":"","doi":"10.1016/S2667-100X(26)00064-2","DOIUrl":"10.1016/S2667-100X(26)00064-2","url":null,"abstract":"","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages i-ii"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Itaconate modifications: Mechanisms and applications","authors":"Yingyi Yang ,&nbsp;Rui Kang ,&nbsp;Huiting Zhou ,&nbsp;Daolin Tang","doi":"10.1016/j.jointm.2025.10.002","DOIUrl":"10.1016/j.jointm.2025.10.002","url":null,"abstract":"<div><div>Itaconate is a mitochondrial metabolite generated from the tricarboxylic acid cycle intermediate cis-aconitate by the enzyme aconitate decarboxylase 1 (ACOD1). Beyond its metabolic role, itaconate has emerged as a critical regulator of immune and inflammatory signaling. Together with its electrophilic derivatives (e.g., 4-octyl itaconate, dimethyl itaconate), it modulates diverse cellular processes through covalent post-translational modifications. These include S-itaconation, a cysteine-directed Michael addition primarily mediated by electrophilic derivatives, and K-itaconation, a lysine-targeted, reversible acylation involving an itaconyl-CoA intermediate derived from itaconate. Such modifications influence multiple immune regulators – including Kelch-like ECH-associated protein 1 (KEAP1), stimulator of interferon response cGAMP interactor 1 (STING1), NLR family pyrin domain containing 3 (NLRP3), glutathione peroxidase 4 (GPX4), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) – thereby modulating inflammation, oxidative stress, and cell death pathways such as pyroptosis and ferroptosis. Preclinical studies demonstrate that itaconate derivatives confer therapeutic benefits in sepsis, colitis, neurodegeneration, autoimmunity, and cancer. By contrast, endogenous itaconate exhibits context-dependent effects, acting as either a pro-resolving or immunostimulatory metabolite. This review integrates current insights into itaconate biosynthesis, molecular targets, post-translational modifications, detection technologies, and translational potential, underscoring its emerging role as a master regulator of immunometabolic reprogramming and inflammatory control.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 114-127"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of CD4+T-cell reduction and pulmonary infections in critically ill immunocompromised patients","authors":"Yizhu Chen ,&nbsp;Lidi Zhang ,&nbsp;Tao Wang,&nbsp;Xiaojun Pan,&nbsp;Dechang Chen,&nbsp;Jiao Liu","doi":"10.1016/j.jointm.2025.10.007","DOIUrl":"10.1016/j.jointm.2025.10.007","url":null,"abstract":"<div><h3>Background</h3><div>The CD4⁺T-cell count is a key indicator for evaluating immunosuppression. Infections significantly influence the survival and prognosis of critically ill patients. This study aims to systematically evaluate the association between reduced CD4⁺ T-cell counts and lung infections in immunosuppressed ICU patients, offering clinical evidence to guide the management of lung infections in this population.</div></div><div><h3>Methods</h3><div>This retrospective, single-center study included 40 immunocompromised patients admitted to the ICU from January 1, 2021, to June 30, 2023. All participants underwent metagenomic next-generation sequencing. Patients with suspected lung infections based on their CD4⁺T-cell counts were divided into mild (350/µL&lt;CD4⁺T-cell count&lt;500/µL), moderate (200 /µL&lt;CD4⁺T-cell count ≤ 350/µL), and severe (CD4⁺T-cell count ≤200/µL) groups. Microecological analysis was performed on each group. Intergroup comparisons were conducted for 28-day mortality, hospital length of stay, and ICU length of stay.</div></div><div><h3>Results</h3><div>Amang these forty immunosuppressed patients, 8 were assigned to the mild group, 16 to the moderate group, and 16 to the severe group. <em>Streptococcus pneumoniae</em> was almost all distributed in moderate patients (75.0%), while severe patients had a higher proportion of fungi detected (25.7%). Respiratory microbiome analysis identified <em>Acinetobacter baumannii</em>, Human alphaherpesvirus 1, and <em>Klebsiella pneumoniae</em> as the most abundant species. Although no significant difference in the alpha diversity index was found among the groups, index values were lower in the severe group than in the moderate group. Beta diversity analysis showed that the microbial community structure did not significantly differ among the three groups. A total of 27 microbial markers were obtained, with multiple streptococcal species showing enrichment in moderate group and <em>Candida tropicalis</em> in severe group. By day 28, four patients (50.0%) in the mild group had died compared with six (37.5%) in the moderate group and nine (56.3%) in the severe group. There were no significant difference in the duration of ICU or hospital stays.</div></div><div><h3>Conclusions</h3><div>This study on ICU-admitted immunocompromised patients identified the prevalent pathogens and microbiome features associated with pulmonary infections, as well as their relationship with CD4⁺T-cell depletion. These findings are valuable for optimizing clinical diagnosis and treatment strategies and may contribute to improving patient outcomes.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 157-165"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Effect of timing of norepinephrine administration on prognosis of patients with septic shock: A prospective cohort study” [Journal of Intensive Medicine volume 2 (2025) 160–166]","authors":"Yuting Li,&nbsp;Deyou Zhang,&nbsp;Hongxiang Li,&nbsp;Youquan Wang,&nbsp;Dong Zhang","doi":"10.1016/j.jointm.2025.11.002","DOIUrl":"10.1016/j.jointm.2025.11.002","url":null,"abstract":"","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Page 196"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical models for predicting 30-day mortality in ARDS: A focus on ventilatory ratio-defined subgroups","authors":"Zhangwei Liang ,&nbsp;Xinyi Luo ,&nbsp;Ya Wang ,&nbsp;Weilin Wang,&nbsp;Yuelin Liu,&nbsp;Ying Zhu,&nbsp;Juan Ouyang,&nbsp;Jie Zhang,&nbsp;Yin Xi,&nbsp;Yimin Li,&nbsp;Yonghao Xu","doi":"10.1016/j.jointm.2025.09.002","DOIUrl":"10.1016/j.jointm.2025.09.002","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;It is supposed that acute respiratory distress syndrome (ARDS) patients with increased dead space, indicated by elevated ventilatory ratio (VR), had higher mortality. The difference in mortality predictors among ARDS patients categorized by VR remains unclear, so we aimed to investigate the risk factors for mortality prediction in subgroups defined by VR and develop risk models to predict the 30-day mortality in distinct ARDS subgroups.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This study performed a retrospective analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD) databases, as well as data from NHLBI ARDS Clinical Trials Network (ARDSnet). Patients were divided into high VR (VR ≥2) and low VR (VR &lt;2) subgroups based on baseline VR. In ARDSnet cohort, two 30-day mortality risk prediction models were constructed using logistic regression, internally validated using the bootstrap method, and externally validated in the MIMIC-IV and eICU-CRD cohorts. The performance of models was evaluated using receiver operating characteristic curves, Brier scores, calibration curves, and decision curve analysis (DCA) curves, and DeLong test was used to compare the predictive efficacy of different models in each subgroup.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;This study included a total of 2977 ARDS patients: 1031 in the high VR training cohort, 1506 in the low VR training cohort, 159 in the high VR external validation cohort, and 281 in the low VR external validation cohort. Through stepwise regression analysis, 11 predictors were finally selected to construct high VR prediction model including age, body mass index (BMI), heart rate, mean arterial pressure, body temperature, blood urea nitrogen (BUN), bilirubin, minute ventilation, peak inspiratory pressure, inspired oxygen fraction (FiO&lt;sub&gt;2&lt;/sub&gt;), and peripheral oxygen saturation (SpO&lt;sub&gt;2&lt;/sub&gt;), and 13 predictors to construct low VR prediction model including age, heart rate, body temperature, platelet count, BUN, bilirubin, respiratory rate, peak inspiratory pressure, FiO&lt;sub&gt;2&lt;/sub&gt;, hypercapnia, hypocapnia, acidemia, and alkalemia. The area under the curve (AUC) of high VR model in the training cohort was 0.76 (95 % CI: 0.73 to 0.79), and the AUC of low VR model in training cohort was 0.76 (95 % CI: 0.74 to 0.79). The Brier scores for high VR model and low VR model in training cohort were 0.171 and 0.139, respectively. Decision curve analysis (DCA) showed that the DCA curve for high VR model waspositioned away from two extreme curves across a threshold probability range of 0.2 to 0.8, and the curve for low VR model was positioned away from two extreme curves across a threshold probability range of 0.1 to 0.6. The AUC of high VR model in low VR subgroup training set was 0.74, significantly lower than the low VR model (DeLong test, &lt;em&gt;P&lt;/em&gt;=0.024). The AUC of low VR model in high VR subgroup training ","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 185-195"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrolyte imbalance and post-open-heart surgery complications: Is there a link?","authors":"Uria Shaul ,&nbsp;Pierre Singer ,&nbsp;Ruth Mishali ,&nbsp;Liran Statlender ,&nbsp;Chen Seidenberg ,&nbsp;Michal Slevin Kish ,&nbsp;Hila Vidal ,&nbsp;Eric Setton","doi":"10.1016/j.jointm.2025.12.007","DOIUrl":"10.1016/j.jointm.2025.12.007","url":null,"abstract":"<div><h3>Background</h3><div>Electrolyte disturbances are common after cardiac surgery involving cardiopulmonary bypass (CPB) and may influence postoperative outcomes. Although postoperative atrial fibrillation (POAF), atelectasis, and pleural effusion are frequently encountered complications, the extent to which electrolyte imbalances contribute to their development remains unclear. We investigate the association between perioperative electrolyte imbalances and the incidence of POAF, atelectasis, and pleural effusion in patients undergoing open-heart surgery with CPB.</div></div><div><h3>Methods</h3><div>This retrospective descriptive study analyzed 1392 adult patients who underwent elective coronary artery bypass grafting (CABG), valve surgery, or aortic repair at Herzliya Medical Center between January 2017 and August 2023. Serum electrolyte levels (sodium, potassium, magnesium, and phosphorus) were recorded during the immediate postoperative period. Complications were defined using clinical and imaging criteria. Associations between electrolyte abnormalities and complications were evaluated using Chi-squared tests, independent <em>t</em>-tests, and logistic regression models.</div></div><div><h3>Results</h3><div>The most common complication was POAF (249 cases, 17.9%); postoperative pulmonary complications were observed in 124 (8.9%) cases. Hyponatremia was not associated with complications, but hypernatremia was associated with complications in 24% of patients, while only 8% of patients without hypernatremia developed complications (odds ratio [OR]=3.500, 95% confidence interval [CI]: 1.673–7.323, <em>P</em> &lt;0.001). Hypokalemia was not associated with pulmonary complications, but hyperkalemia was significantly associated with respiratory complications (OR=2.113, 95% CI: 1.055 to 4.230, <em>P</em>=0.035). Hyperphosphatemia was also independently associated with an elevated risk of atelectasis and pleural effusion (OR=1.523, 95% CI: 1.015 to 2.284, <em>P</em>=0.042). Hypermagnesemia showed a significant difference (12.4% <em>vs.</em> 6.5%) in terms of pulmonary complications (OR=1.636, 95% CI: 1.100 to 2.434, <em>P</em>=0.015). In contrast, no significant associations were observed between electrolyte imbalances and POAF, although hyperphosphatemia showed a borderline association (<em>P</em>=0.051).</div></div><div><h3>Conclusions</h3><div>Electrolyte imbalances—particularly involving potassium, sodium, and phosphorus—are strongly associated with postoperative pulmonary complications following cardiac surgery. These findings highlight the importance of vigilant perioperative electrolyte monitoring and targeted correction protocols to mitigate the risk of atelectasis and pleural effusion. The role of electrolyte disturbances in the development of POAF appears less pronounced, suggesting a multifactorial etiology.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 150-156"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cast nephropathy in the ICU: Early recognition and extracorporeal strategies to improve outcomes","authors":"Silvia De Rosa ,&nbsp;Fiorenza Ferrari ,&nbsp;Diana Zarantonello ,&nbsp;Alessia Dalpiaz ,&nbsp;Sergio Lassola","doi":"10.1016/j.jointm.2025.10.006","DOIUrl":"10.1016/j.jointm.2025.10.006","url":null,"abstract":"<div><div>Cast-induced acute kidney injury (AKI) is a frequent yet under-recognized cause of kidney dysfunction in the intensive care unit. It arises when filtered proteins or pigments – free light chains (FLCs) in multiple myeloma, myoglobin in rhabdomyolysis, bilirubin in severe cholestasis, or hemoglobin in intravascular hemolysis – precipitate within kidney tubules, forming obstructive casts and triggering oxidative and inflammatory injury. Early recognition is essential because traditional markers (creatinine, urine output) rise late. Emerging biomarkers, including neutrophil gelatinase-associated lipocalin (NGAL), cystatin C, and the tissue inhibitor of metalloproteinases-2 · insulin-like growth factor binding protein-7 ([TIMP-2]·[IGFBP7]) panel, detect tubular stress earlier and can guide timely intervention. This narrative review summarizes pathophysiology, diagnostic tools, and extracorporeal strategies tailored to the offending molecule and patient stability. For myeloma cast nephropathy, high cut-off membranes provide robust early FLC clearance but require albumin monitoring; medium cut-off and polymethylmethacrylate membranes offer sustained removal with lower albumin loss. In rhabdomyolysis, continuous kidney replacement therapy with high-flux or newer membranes supports hemodynamic stability and myoglobin clearance; hemoadsorption may be considered in severe cases. In bile cast nephropathy, artificial extracorporeal liver support (e.g., molecular adsorbent recirculating system, fractional plasma separation and adsorption), single-pass albumin dialysis, and hemoadsorption reduce bilirubin and bile acids, while plasma exchange remains reserved mainly for hyperviscosity syndromes. Across etiologies, extracorporeal approaches are most effective when combined with disease-specific treatments, such as chemotherapy for myeloma or targeted therapy for hemolysis. Emerging evidence suggests that integrating artificial intelligence-driven diagnostic tools with these therapeutic strategies may further enhance early recognition and individualized management of renal injury. A patient-centered, pathophysiology-driven strategy can shift extracorporeal therapies from rescue measures to proactive tools that improve kidney recovery and survival. Prospective studies should refine timing, modality selection, and biomarker-based algorithms to optimize outcomes in cast-induced AKI.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 93-104"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic inflammatory trajectories and 28-day mortality in patients in the intensive care unit: An exploratory observational study","authors":"Xinyuan Ding ,&nbsp;Shangzhong Chen ,&nbsp;Lihong Zhu ,&nbsp;Chengcheng Zheng ,&nbsp;Hang Mou ,&nbsp;Guolong Cai ,&nbsp;Yanfei Shen","doi":"10.1016/j.jointm.2025.12.011","DOIUrl":"10.1016/j.jointm.2025.12.011","url":null,"abstract":"<div><h3>Background</h3><div>Inflammatory dynamics profoundly influence outcomes in critically ill patients; however, the prognostic significance of early inflammatory trajectories remains unclear. This study aimed to characterize the distinct trajectories of inflammatory mediators and evaluate their association with 28-day hospital mortality in patients in the intensive care unit (ICU).</div></div><div><h3>Methods</h3><div>A retrospective cohort of 511 critically ill patients in the ICU (July 2018–June 2024) was analyzed using group-based multitrajectory modeling for longitudinal measurements of C-reactive protein, procalcitonin (PCT), interleukin (IL)-2, IL-4, IL-6, and IL-10 during the first five ICU days (log-transformed as <em>Y</em>=ln[mediator + 1] for standardization). Multivariable Cox regression, inverse probability weighting, and treatment interaction analyses were performed to assess mortality risk and therapeutic responses across trajectories.</div></div><div><h3>Results</h3><div>Three inflammatory trajectories were identified. The mortality rate was 9.5% in the first trajectory (Traj. 1), characterized by persistent mild inflammation (18.6%). In the second trajectory (Traj. 2), a shift toward moderate inflammation was observed with subsequent resolution (55.6%), leading to a mortality rate of 12.3%. Compared with Traj. 1, this trajectory did not demonstrate a substantial increase in death risk (adjusted hazard ratio [HR]=1.14, 95% confidence interval [CI]: 0.54–2.42). The third trajectory (Traj. 3) exhibited sustained hyperinflammation (25.8%), resulting in a mortality rate of 28.0%, which was significantly associated with an elevated risk of death compared with Traj. 1 (adjusted HR=3.41, 95% CI: 1.57 to 7.41). Inverse probability weighting analysis confirmed that compared to Traj. 1, the absolute mortality increase for Traj. 2 was 4.3% (<em>P</em>=0.234), whereas that for Traj. 3 was 10.5% (<em>P</em>=0.031). Notably, corticosteroid use reduced mortality, specifically in Traj. 3 patients (<em>P</em>=0.031), whereas vasopressors and ulinastatin showed no trajectory-specific benefits.</div></div><div><h3>Conclusions</h3><div>Early inflammatory trajectories robustly predict ICU mortality, with sustained hyperinflammation (Traj. 3) conferring the highest risk. Dynamic biomarker profiling may facilitate the development of targeted immunomodulatory strategies in high-risk subgroups.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 166-174"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A potential role for trace amines in the treatment of septic shock","authors":"Kenneth J. Broadley ,&nbsp;Alexander C. Voisey ,&nbsp;William R. Ford ,&nbsp;Harrison D. Broadley","doi":"10.1016/j.jointm.2025.09.003","DOIUrl":"10.1016/j.jointm.2025.09.003","url":null,"abstract":"<div><div>Sepsis involves an unregulated response to bacterial infection, the inflammatory response leading to persistent hypotension, leading to a precipitous fall in arterial blood pressure. The major trigger for the hypotension in sepsis and septic shock is the release of lipopolysaccharide (LPS) from the cell wall of gram-negative bacteria, which generates a major inflammatory response. LPS activates toll-like receptors (TLR4), leading to the release of an array of inflammatory mediators, which most notably activate inducible nitric oxide (NO) synthase and release copious amounts of NO. This is responsible for the hypotension. Despite the development of a large number of newer drugs for treating sepsis, none has emerged as superior to existing treatments. Early vasopressor therapy remains an integral life-saving strategy to treat the hypotension. Noradrenaline remains the vasopressor of choice; however, it has a number of limitations which are discussed. Improvements in vasopressor therapies are required and research from the authors is used to advance the case for using trace amines, such as β-phenylethylamine (PEA). Although usually regarded as sympathomimetic amines, PEA and related amines such as amphetamine and ephedrine exert vasoconstrictive effects via trace amine-associated receptors (TAARs), particularly when administered by infusion. The sympathomimetic and TAAR vasoconstrictor actions are demonstrated on anesthetized rat blood pressure. Unlike noradrenaline, PEA is not a universal vasoconstrictor; it also dilates other vessels, including mesenteric vascular beds. This would provide a superior profile of activity for use in restoring blood pressure in sepsis. The ability of PEA to reverse the vasodilator action of LPS is demonstrated in a simple <em>in vitro</em> blood vessel model. This review therefore opens the possibility of using trace amines for restoring blood pressure and organ perfusion in septic shock.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 105-113"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU","authors":"Yunpeng Xu ,&nbsp;Ting Lei ,&nbsp;Zi Yang ,&nbsp;Hong Guo ,&nbsp;Lei Zhu ,&nbsp;Jianlin Wang ,&nbsp;Hao Liu ,&nbsp;Tianhu Liang ,&nbsp;Qinglin Lin ,&nbsp;Guang Yao ,&nbsp;Zhiqiang Yao ,&nbsp;Jian Liu","doi":"10.1016/j.jointm.2025.10.010","DOIUrl":"10.1016/j.jointm.2025.10.010","url":null,"abstract":"<div><h3>Background</h3><div>Sepsis complicated by acute respiratory failure (ARF) is a common and severe condition among patients admitted to the intensive care unit (ICU), associated with high mortality. Accurate prediction of short-term outcomes is crucial for optimizing clinical decisions and treatment strategies. Therefore, we aimed to develop and validate an interpretable machine learning (ML) model to predict the 28-day mortality risk in ICU patients with sepsis complicated by ARF.</div></div><div><h3>Methods</h3><div>This retrospective study included ICU patients with sepsis complicated by ARF from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1) database as the training cohort, and patients from the eICU-CRD (v2.0) database as the external validation cohort. Candidate predictors were initially identified based on clinical guidelines and expert consensus, and the Boruta algorithm was applied to determine the optimal feature set. Seven ML algorithms, namely random forest, XGBoost, logistic regression, AdaBoost, gradient boosting, CatBoost, and neural network, were compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. To enhance interpretability, feature importance was assessed using SHapley Additive exPlanations (SHAP) analysis. These results were integrated to construct a practical prognostic prediction platform.</div></div><div><h3>Results</h3><div>The training cohort (2975 deaths) comprised 12,597 ICU patients with sepsis complicated by ARF from the MIMIC-IV (v3.1) database, while 890 patients from the eICU-CRD (v2.0) database (102 deaths) were included for external validation. Among the ML models, XGBoost achieved the best performance in the training cohort (AUC: 0.812; accuracy: 0.772; sensitivity: 0.605; specificity: 0.823). In the external validation cohort, XGBoost demonstrated good generalizability (AUC: 0.714). SHAP analysis identified PaO₂, alanine aminotransferase, albumin, age, Acute Physiology Score (APS) III, lactate, urine output, and respiratory rate as the most influential predictors of 28-day mortality. Accordingly, a short-term mortality prediction platform was developed.</div></div><div><h3>Conclusions</h3><div>We successfully developed an efficient, interpretable predictive model based on the XGBoost algorithm, accurately predicting 28-day mortality risk for ICU patients with sepsis complicated by ARF. Demonstrating stable performance and strong generalizability, this model holds promise as a clinical decision-support tool for the early identification of high-risk patients and optimization of personalized treatments.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"6 2","pages":"Pages 175-184"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}