Pub Date : 2026-01-01DOI: 10.1016/j.medine.2025.502290
Yueqi Wang , Yan Cui , Moxuan Han , Donghui Yue
{"title":"Enhancing methodological rigor in mechanical insufflation-exsufflation weaning studies: Commentary on patient selection, long-term outcomes, and psychological assessment","authors":"Yueqi Wang , Yan Cui , Moxuan Han , Donghui Yue","doi":"10.1016/j.medine.2025.502290","DOIUrl":"10.1016/j.medine.2025.502290","url":null,"abstract":"","PeriodicalId":94139,"journal":{"name":"Medicina intensiva","volume":"50 1","pages":"Article 502290"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805473","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}
Pub Date : 2026-01-01DOI: 10.1016/j.medine.2025.502251
Seo Hee Yoon, Sohyun Eun
Objective
Sepsis remains a major cause of mortality worldwide. While neutrophil CD64 (nCD64) has demonstrated superior performance in detecting sepsis compared to conventional biomarkers, its prognostic value remains unclear. This meta-analysis evaluates the performance of nCD64 in predicting mortality in patients with sepsis.
Design
Systematic review and meta-analysis.
Settings
A systematic search of PubMed, Embase, the Cochrane Library, and Web of Science was conducted up to January 28, 2025, to identify relevant studies.
Patients
Patients aged 16 years or older diagnosed with sepsis based on Sepsis-1, Sepsis-2, or Sepsis-3 criteria.
Interventions
Studies assessing the predictive accuracy of nCD64 for mortality and providing sufficient data for contingency table construction were included.
Main variables of interest
Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated with 95% confidence intervals (CIs). Overall predictive accuracy was assessed using the area under the summary receiver operating characteristic curve.
Results
Eight studies involving 756 patients were included. The pooled sensitivity, specificity, and DOR of nCD64 for predicting mortality were 0.79 (95% CI: 0.68–0.87), 0.67 (95% CI: 0.56–0.77), and 7.71 (95% CI: 4.38–13.57), respectively. The predictive accuracy was 0.80.
Conclusions
Our findings suggest that nCD64 may serve as a valuable auxiliary biomarker for identifying patients with sepsis at higher risk of mortality.
{"title":"Neutrophil CD64 as a prognostic biomarker for mortality in sepsis: A systematic review and meta-analysis","authors":"Seo Hee Yoon, Sohyun Eun","doi":"10.1016/j.medine.2025.502251","DOIUrl":"10.1016/j.medine.2025.502251","url":null,"abstract":"<div><h3>Objective</h3><div>Sepsis remains a major cause of mortality worldwide. While neutrophil CD64 (nCD64) has demonstrated superior performance in detecting sepsis compared to conventional biomarkers, its prognostic value remains unclear. This meta-analysis evaluates the performance of nCD64 in predicting mortality in patients with sepsis.</div></div><div><h3>Design</h3><div>Systematic review and meta-analysis.</div></div><div><h3>Settings</h3><div>A systematic search of PubMed, Embase, the Cochrane Library, and Web of Science was conducted up to January 28, 2025, to identify relevant studies.</div></div><div><h3>Patients</h3><div>Patients aged 16 years or older diagnosed with sepsis based on Sepsis-1, Sepsis-2, or Sepsis-3 criteria.</div></div><div><h3>Interventions</h3><div>Studies assessing the predictive accuracy of nCD64 for mortality and providing sufficient data for contingency table construction were included.</div></div><div><h3>Main variables of interest</h3><div>Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated with 95% confidence intervals (CIs). Overall predictive accuracy was assessed using the area under the summary receiver operating characteristic curve.</div></div><div><h3>Results</h3><div>Eight studies involving 756 patients were included. The pooled sensitivity, specificity, and DOR of nCD64 for predicting mortality were 0.79 (95% CI: 0.68–0.87), 0.67 (95% CI: 0.56–0.77), and 7.71 (95% CI: 4.38–13.57), respectively. The predictive accuracy was 0.80.</div></div><div><h3>Conclusions</h3><div>Our findings suggest that nCD64 may serve as a valuable auxiliary biomarker for identifying patients with sepsis at higher risk of mortality.</div></div>","PeriodicalId":94139,"journal":{"name":"Medicina intensiva","volume":"50 1","pages":"Article 502251"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304142","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}
Pub Date : 2026-01-01DOI: 10.1016/j.medine.2025.502226
Alberto García-Salido , Yolanda López-Fernández , Alberto Medina Villanueva , María José Santiago Lozano
{"title":"Introduction to the series “Update in Pediatric Intensive Care”","authors":"Alberto García-Salido , Yolanda López-Fernández , Alberto Medina Villanueva , María José Santiago Lozano","doi":"10.1016/j.medine.2025.502226","DOIUrl":"10.1016/j.medine.2025.502226","url":null,"abstract":"","PeriodicalId":94139,"journal":{"name":"Medicina intensiva","volume":"50 1","pages":"Article 502226"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602680","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}
Pub Date : 2026-01-01DOI: 10.1016/j.medine.2025.502344
Elena Cuenca Fito , Eric Mayor-Vázquez , Cándido Díaz Lagares , Bárbara Vidal Tegedor , Noelia Isabel Lázaro Martín , Alba López Fernández , Laura Sanchez Montori , Íñigo Isern , Amparo Cabanillas Carrillo , Jorge Sánchez Gómez , Maria Luisa Gómez Grande , Alba Fernández Rodríguez , Anastasio Espejo , Domingo Díaz Díaz , Alejandra García Roche , Margarita Márquez , Mireya Molina Cortés , Natalia Valero , Manuel Gracia Romero , Fernando Eiras Abalde , Inés Gómez-Acebo
Objective
The present study compares the clinical, functional and oncological characteristics of cancer patients assessed for admission to the Intensive Care Unit (ICU), with the aim of identifying factors associated with admission and of developing specific predictive models.
Design
A prospective, multicenter observational study was conducted.
Setting
Thirty-three ICUs across Spain.
Patients or participants
Patients aged 18 years or older with solid tumors or hematological malignancies who were assessed for ICU admission between January and June 2024 were included.
Interventions
None.
Main variables of interest
Demographic, clinical, functional, oncological, and severity variables were collected. Differences between admitted and non-admitted patients were analyzed using multivariate logistic regression and LASSO-type predictive models.
Results
A total of 1341 patients were included, of whom 1177 (87.8%) were admitted to the ICU. Neutropenia, younger age, and recent oncologic treatment, among other factors, were associated with a higher likelihood of ICU admission. Patients with metastasis or progression of the hematological disease were less likely to be admitted. The predictive models demonstrated high discriminative capacity for both solid tumors (AUC 0.79) and hematological malignancies (AUC 0.82).
Conclusions
Prognostic models for ICU admission were developed by applying a multivariate approach and selecting variables based on their joint contribution to overall predictive accuracy rather than their isolated contribution. The full model (Model 1) demonstrated the best predictive capacity, with an AUC of 0.79 (95%CI: 0.75−0.84) for solid and an AUC of 0.82 (95%CI: 0.76−0.88) for hematological tumors.
{"title":"Determinants in the decision of intensive care admission of cancer patients: A Spanish multicenter prospective study","authors":"Elena Cuenca Fito , Eric Mayor-Vázquez , Cándido Díaz Lagares , Bárbara Vidal Tegedor , Noelia Isabel Lázaro Martín , Alba López Fernández , Laura Sanchez Montori , Íñigo Isern , Amparo Cabanillas Carrillo , Jorge Sánchez Gómez , Maria Luisa Gómez Grande , Alba Fernández Rodríguez , Anastasio Espejo , Domingo Díaz Díaz , Alejandra García Roche , Margarita Márquez , Mireya Molina Cortés , Natalia Valero , Manuel Gracia Romero , Fernando Eiras Abalde , Inés Gómez-Acebo","doi":"10.1016/j.medine.2025.502344","DOIUrl":"10.1016/j.medine.2025.502344","url":null,"abstract":"<div><h3>Objective</h3><div>The present study compares the clinical, functional and oncological characteristics of cancer patients assessed for admission to the Intensive Care Unit (ICU), with the aim of identifying factors associated with admission and of developing specific predictive models.</div></div><div><h3>Design</h3><div>A prospective, multicenter observational study was conducted.</div></div><div><h3>Setting</h3><div>Thirty-three ICUs across Spain.</div></div><div><h3>Patients or participants</h3><div>Patients aged 18 years or older with solid tumors or hematological malignancies who were assessed for ICU admission between January and June 2024 were included.</div></div><div><h3>Interventions</h3><div>None.</div></div><div><h3>Main variables of interest</h3><div>Demographic, clinical, functional, oncological, and severity variables were collected. Differences between admitted and non-admitted patients were analyzed using multivariate logistic regression and LASSO-type predictive models.</div></div><div><h3>Results</h3><div>A total of 1341 patients were included, of whom 1177 (87.8%) were admitted to the ICU. Neutropenia, younger age, and recent oncologic treatment, among other factors, were associated with a higher likelihood of ICU admission. Patients with metastasis or progression of the hematological disease were less likely to be admitted. The predictive models demonstrated high discriminative capacity for both solid tumors (AUC 0.79) and hematological malignancies (AUC 0.82).</div></div><div><h3>Conclusions</h3><div>Prognostic models for ICU admission were developed by applying a multivariate approach and selecting variables based on their joint contribution to overall predictive accuracy rather than their isolated contribution. The full model (Model 1) demonstrated the best predictive capacity, with an AUC of 0.79 (95%CI: 0.75−0.84) for solid and an AUC of 0.82 (95%CI: 0.76−0.88) for hematological tumors.</div></div>","PeriodicalId":94139,"journal":{"name":"Medicina intensiva","volume":"50 1","pages":"Article 502344"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145919441","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}
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