Indian Journal of Critical Care Medicine最新文献

{"title":"Impact of Frailty (Clinical Frailty Scale) on Weaning from Mechanical Ventilation: A Systematic Review and Meta-analysis.","authors":"Shilpi S Mehra, Saurabh K Das, Dwipan Das, Ajay Gupta","doi":"10.5005/jp-journals-10071-25180","DOIUrl":"10.5005/jp-journals-10071-25180","url":null,"abstract":"<p><strong>Background and aims: </strong>Frailty is a multidimensional syndrome marked by reduced physiological reserve and increased vulnerability to stress, often seen in critically ill patients. It may affect outcomes such as weaning from mechanical ventilation. It has been associated with adverse intensive care unit (ICU) outcomes, but its relationship with liberation from mechanical ventilation remains unclear. This study systematically reviewed and meta-analyzed the association between frailty [Clinical Frailty Scale (CFS)] and weaning outcomes in mechanically ventilated patients, hypothesizing worse outcomes with higher frailty.</p><p><strong>Methods: </strong>We systematically searched PubMed, Cochrane Library, ScienceDirect, KoreaMed, Google Scholar, and Semantic Scholar from inception for studies assessing frailty using the CFS and reporting weaning outcomes in mechanically ventilated ICU patients. Effect sizes were pooled as log odds ratios (OR) using a random-effects model. Risk of bias and certainty of evidence were evaluated using risk of bias in nonrandomized studies - of exposure (ROBINS-E) and Grading of Recommendations Assessment, Development and Evaluation (GRADE) tools.</p><p><strong>Results: </strong>Seven observational studies, including 13,746 patients, met the inclusion criteria. Frailty was associated with higher odds of weaning failure (OR: 2.52, 95% confidence interval: 1.46-4.34; <i>p</i> = 0.001; <i>k</i> = 7; prediction interval: 0.75-8.47). Substantial heterogeneity (<i>I²</i> = 92.9%), high risk of bias, inconsistency, and potential publication bias were noted. Associations were directionally consistent across subgroups and sensitivity analyses. The certainty of evidence was very low.</p><p><strong>Conclusion: </strong>Frailty appears to be associated with an increased risk of weaning failure, with approximately 2.5-fold higher odds observed in pooled analysis, supporting its role as a potential prognostic marker in clinical decision-making. However, the certainty of evidence is very low, with substantial heterogeneity, risk of bias, and possible residual confounding. These findings should be considered hypothesis-generating and underscore the need for standardized weaning definitions and well-designed prospective studies to better evaluate frailty's predictive value.</p><p><strong>Trial registration: </strong>PROSPERO CRD420251028375.</p><p><strong>How to cite this article: </strong>Mehra SS, Das SK, Das D, Gupta A. Impact of Frailty (Clinical Frailty Scale) on Weaning from Mechanical Ventilation: A Systematic Review and Meta-analysis. Indian J Crit Care Med 2026;30(4):324-334.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"324-334"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13178137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Author Response: NETosis Markers such as Citrullinated Histones Myeloperoxidase and Elastase should not be Recommended as Predictors of COVID-19 Severity.","authors":"Abhyuday Kumar","doi":"10.5005/jp-journals-10071-25183","DOIUrl":"10.5005/jp-journals-10071-25183","url":null,"abstract":"<p><p><b>How to cite this article:</b> Kumar A. Author Response: NETosis Markers such as Citrullinated Histones Myeloperoxidase and Elastase should not be Recommended as Predictors of COVID-19 Severity. Indian J Crit Care Med 2026;30(4):349.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"349"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13178122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Meta-analysis of the Efficacy of Different Music Therapy Interventions for Delirium in Adult Intensive Care Unit Patients.","authors":"Yunfan Hao, Ruihan Song, Aixin Wang","doi":"10.5005/jp-journals-10071-25178","DOIUrl":"10.5005/jp-journals-10071-25178","url":null,"abstract":"<p><strong>Background and aims: </strong>Delirium is common in adult intensive care unit (ICU) patients and is associated with worse outcomes. Music-based interventions are increasingly used, but the relative effectiveness of different programs remains unclear. We conducted a network meta-analysis (NMA) to compare multiple music therapy interventions for ICU delirium.</p><p><strong>Data sources: </strong>We searched major Chinese and English databases (e.g., CNKI, PubMed, and the Cochrane Library) from inception to 21 August 2025.</p><p><strong>Study selection: </strong>Randomized controlled trials comparing music-based interventions with usual care (UC) in adults (≥18 years) in the ICU were eligible. Interventions were categorized as simple music (SM) therapy, traditional Chinese medicine (TCM) five-element music, family voice stimulation combined with music (FS), and combined music (CM) therapy (music combined with another nonpharmacological component).</p><p><strong>Data synthesis: </strong>A frequentist random-effects NMA (R, netmeta) was performed. Compared with UC, all music-based interventions were associated with a reduced delirium incidence [CM: Risk ratio (RR) = 0.43; TCM: RR = 0.45; FS: RR = 0.48; SM: RR = 0.51]. For delirium duration, CM [mean difference (MD) = -3.21 days] and TCM (MD = -2.89 days) showed statistically significant reductions, whereas SM and FS did not. The network ranking suggested CM had the highest probability of benefit; however, between-intervention differences were mainly supported by indirect comparisons and small trials. Heterogeneity and potential small-study effects were observed.</p><p><strong>Conclusion: </strong>Music-based interventions may reduce ICU delirium incidence, and some programs may shorten delirium duration. Because evidence is limited by heterogeneity, possible publication bias, and a paucity of head-to-head trials, rankings should be interpreted cautiously. Well-designed, multicenter RCTs with standardized protocols are needed before firm recommendations can be made.</p><p><strong>How to cite this article: </strong>Hao Y, Song R, Wang A. Network Meta-analysis of the Efficacy of Different Music Therapy Interventions for Delirium in Adult Intensive Care Unit Patients. Indian J Crit Care Med 2026;30(4):335-343.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"335-343"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13178120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frail to Fail: More Support for Less Reserve-Can Clinical Frailty Scale Predict Path to Liberation?","authors":"Gunchan Paul, Sai Rama D Dodda","doi":"10.5005/jp-journals-10071-25190","DOIUrl":"10.5005/jp-journals-10071-25190","url":null,"abstract":"<p><p><b>How to cite this article:</b> Paul G, Dodda SRD. Frail to Fail: More Support for Less Reserve-Can Clinical Frailty Scale Predict Path to Liberation? Indian J Crit Care Med 2026;30(4):268-269.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"268-269"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13177756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gastrointestinal Tolerance to <i>Maranta arundinacea</i> (Arrowroot) Powder Added to Formula Feeds in Intensive Care Unit Patients: A Nonrandomized Controlled Quasi-experimental Trial.","authors":"Manju Mathew, Shiny Verghese","doi":"10.5005/jp-journals-10071-25166","DOIUrl":"10.5005/jp-journals-10071-25166","url":null,"abstract":"<p><strong>Background and aims: </strong>Diarrhea is common in critically ill patients receiving enteral nutrition and often leads to feed interruption. Arrowroot (<i>Maranta arundinacea</i>) is rich in soluble fiber, and may improve gastrointestinal (GI) tolerance. This study evaluated the safety and GI tolerance of adding arrowroot powder to enteral formula (EF) feeds.</p><p><strong>Patients and methods: </strong>A nonrandomized controlled open labeled, quasi-experimental, posttest-only trial was conducted in intensive care unit (ICU) patients receiving nasogastric enteral feeding. The intervention group received 100 gm of arrowroot powder added to formula feeds for 5 days; controls received standard formula feeds. Gastrointestinal intolerance to the specified weight of arrowroot powder was observed as incidence of new-onset noninfectious diarrhea, vomiting, and abdominal distension. Clinical outcomes included hospital-acquired infections, ICU/hospital length of stay (LOS) and ICU mortality.</p><p><strong>Results: </strong>Ninety-four patients were included (34 intervention, 60 control). Arrowroot supplementation was well tolerated, with no adverse GI effects. New-onset diarrhea was lower in the intervention group (8.8 vs 26.7%), though not statistically significant. Relative risk reduction was 0.67. No differences in nosocomial infections, mortality, or LOS were observed.</p><p><strong>Conclusion: </strong>Arrowroot supplementation in enteral feeds appears feasible and, safe in critically ill patients. Its potential as a supplement to enteral feeds to reduce diarrhea needs to be explored with larger studies.</p><p><strong>How to cite this article: </strong>Mathew M, Verghese S. Gastrointestinal Tolerance to <i>Maranta arundinacea</i> (Arrowroot) Powder Added to Formula Feeds in Intensive Care Unit Patients: A Nonrandomized Controlled Quasi-experimental Trial. Indian J Crit Care Med 2026;30(4):291-297.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"291-297"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13177963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Descriptive Hemodynamics to Decision-grade Vasoplegia Phenotyping: The Next Translational Step for Temporal Diastolic Shock Index.","authors":"Mulavagili Vijayasimha, Mulavagili Srikanth, Deepika Kapoor, Anjali Dhillon, Vanshika Verma, Saristhi Mishra","doi":"10.5005/jp-journals-10071-25186","DOIUrl":"10.5005/jp-journals-10071-25186","url":null,"abstract":"<p><p><b>How to cite this article:</b> Vijayasimha M, Srikanth M, Kapoor D, Dhillon A, Verma V, Mishra S. From Descriptive Hemodynamics to Decision-grade Vasoplegia Phenotyping: The Next Translational Step for Temporal Diastolic Shock Index. Indian J Crit Care Med 2026;30(4):354-355.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"354-355"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13178134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Comprehensive Acute Encephalitis Syndrome Severity Score in Children with Acute Encephalitis Syndrome.","authors":"Indar K Sharawat, Prateek K Panda, Vyas K Rathaur, Achanya Palayullakandi, Suthiraj Sopanam, Diksha Gupta, Poonam Sherwani, Lokesh K Tiwari","doi":"10.5005/jp-journals-10071-25185","DOIUrl":"10.5005/jp-journals-10071-25185","url":null,"abstract":"<p><strong>Background and aims: </strong>No comprehensive scoring system is currently available to predict functional neurological outcomes in children with acute encephalitis syndrome (AES). We therefore developed and validated a score specifically tailored for these children.</p><p><strong>Patients and methods: </strong>We developed the comprehensive acute encephalitis syndrome severity score (CAESS), which consists of five items [Glasgow Coma Scale (GCS)], focal deficit, status epilepticus, features of raised intracranial pressure (ICP), and pediatric logistic organ dysfunction-2 (PELOD-2) score selected after reviewing published literature. The content validity was assessed through the content validity index (CVI) and content validity ratio (CVR). The model's fit was examined using confirmatory factor analysis (CFA). Functional outcome was determined as good or poor at 6 months, based on the pediatric cerebral performance category (PCPC) score. Construct validity was established by correlating CAESS scores with the functional status scale (FSS), the intelligence quotient (IQ), and the gross motor function measure (GMFM-88).</p><p><strong>Results: </strong>Comprehensive acute encephalitis syndrome severity score was evaluated in 159 children with AES; 114 (71%) had favorable outcomes at 6 months. Inter-rater reliability was good to excellent across items [intraclass correlation coefficient (ICC) range 0.82-0.94; kappa 0.80-0.92]. Internal consistency was good (Cronbach's alpha = 0.80). Content validity was also good (I-CVI 0.9-1.0; CVR 0.8-1.0). Confirmatory factor analysis supported a unidimensional structure, demonstrating good model fit [Tucker-Lewis index (TLI) = 0.98, comparative fit index (CFI) = 0.99, and root mean square error of approximation (RMSEA) = 0.05]. Comprehensive acute encephalitis syndrome severity score demonstrated strong discriminant validity (AUC = 0.91). At a cut-off ≥6, CAESS showed sensitivity of 88.9%, specificity of 84.2%, and a Youden index of 0.73. Strong correlations were observed with FSS (<i>r</i> = 0.82), PCPC (<i>r</i> = 0.84), GMFM-88 (<i>r</i> = -0.83), IQ (<i>r</i> = -0.71), and pediatric quality of life inventory (PedsQL^TM) score (<i>r</i> = -0.74).</p><p><strong>Conclusion: </strong>The CAESS score may be a valid and reliable tool for predicting functional outcomes in children with acute encephalitis syndrome. However, given the absence of internal split-sample validation and external validation in an independent population, further prospective studies with robust internal validation and external replication are required before it can be recommended for routine clinical use.</p><p><strong>How to cite this article: </strong>Sharawat IK, Panda PK, Rathaur VK, Palayullakandi A, Sopanam S, Gupta D, <i>et al</i>. Development and Validation of a Comprehensive Acute Encephalitis Syndrome Severity Score in Children with Acute Encephalitis Syndrome. Indian J Crit Care Med 2026;30(4):311-318.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"311-318"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13177895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.","authors":"Abhilash Dash, Kalpana Majhi, Nimisha Ghosh, Pragati Jena, Satyajit Choudhury, S Anushapreethi, Samir Samal, Sagarika Panda, Shantanu K Patra, Shakti B Mishra","doi":"10.5005/jp-journals-10071-25157","DOIUrl":"10.5005/jp-journals-10071-25157","url":null,"abstract":"<p><strong>Background and aims: </strong>Precise forecasting of mortality in intensive care units (ICUs) is essential for enhancing patient management and resource distribution. Traditional scoring methods like the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) are prevalent yet may inadequately encompass the intricacies of critical illness. The aim of this work was to create and internally test machine learning models for predicting mortality in the ICU, utilizing routinely gathered electronic health record data.</p><p><strong>Patients and methods: </strong>This retrospective cohort analysis encompassed 5,553 adult ICU hospitalizations from September 2021 to December 2023. Patients were randomly allocated into development (80%) and test (20%) cohorts. Three predictive models-Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost)-were developed utilizing demographic information, APACHE II and SOFA values, comorbidities, and ventilatory support status. The evaluation of model performance was conducted utilizing the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score.</p><p><strong>Results: </strong>Among the 5,553 analyzed ICU admissions, 1,869 patients succumbed during their ICU stay, yielding an ICU mortality rate of 33.6% (95% CI: 32.4-34.9). The Random Forest model had the superior discriminative capability for mortality prediction, achieving an AUROC of 0.842, followed by XGBoost with an AUROC of 0.835, and Logistic Regression with an AUROC of 0.833. Although Logistic Regression demonstrated slightly superior overall accuracy, ensemble models more effectively identified non-linear correlations among predictors. Acute Physiology and Chronic Health Evaluation II and SOFA values proved to be the most significant predictors in all models. Temporal validation and sensitivity analysis produced consistent outcomes, demonstrating the resilience of model performance.</p><p><strong>Conclusion: </strong>Machine learning models exhibited strong efficacy in predicting ICU mortality, with Random Forest and XGBoost displaying slight advantages over Logistic Regression. The incorporation of machine learning algorithms into existing ICU scoring systems may improve risk categorization and facilitate clinical decision-making.</p><p><strong>How to cite this article: </strong>Dash A, Majhi K, Ghosh N, Jena P, Choudhury S, Anushapreethi S, <i>et al</i>. Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients. Indian J Crit Care Med 2026;30(4):282-290.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"282-290"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13177833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electroencephalography and Bispectral Index Reactivity to Predict Outcome in Unconscious Patients with Acute Severe Traumatic Brain Injury: A Prospective Observational Study.","authors":"Gayatri Sakrikar, Rohini M Surve, Ramesh Venkatapura, Dhritiman Chakrabarti, Ravindra Mundlamuri, Dhaval Shukla","doi":"10.5005/jp-journals-10071-25165","DOIUrl":"10.5005/jp-journals-10071-25165","url":null,"abstract":"<p><strong>Background and aims: </strong>This observational study aimed to assess electroencephalographic reactivity (EEG-R) and bispectral index reactivity (BIS-R) as early prognostic indicators and to evaluate their association with functional outcomes in patients with acute severe traumatic brain injury (sTBI).</p><p><strong>Patients and methods: </strong>Patients aged over 18 years with a Glasgow Coma Scale Score ≤8 were enrolled between days 3 and 5 postinjury. Baseline pan cranial electroencephalography (EEG) and BIS (from the hemisphere contralateral to injury) were recorded, followed by two painful and two auditory stimuli on each side at two-minute intervals. Electroencephalographic reactivity was independently rated as present or absent by three assessors. Bispectral index reactivity was defined as a change of ten or more points from baseline within 20 seconds of stimulation. Functional outcome was assessed at 3 and 6 months using the Glasgow Outcome Scale Extended (GOSE).</p><p><strong>Results: </strong>Among 40 patients analyzed, EEG-R was present in 18 (45%) and absent in 22 (55%). Absence of EEG-R was significantly associated with lower GOSE scores at both 3 months (<i>p</i> = 0.001) and 6 months (<i>p</i> = 0.016). Absence of EEG-R demonstrated specificity and positive predictive value of 100% for non-recovery of consciousness at 3 months. Bispectral index reactivity was observed in 21 patients (52.5%) and absent in 19 (47.5%). Bispectral index reactivity did not show a significant association with functional outcomes and demonstrated poor concordance with EEG-R.</p><p><strong>Conclusion: </strong>The absence of EEG-R in the acute phase of sTBI was a strong predictor of poor clinical outcome. Bispectral index reactivity showed limited prognostic value in this study.</p><p><strong>How to cite this article: </strong>Sakrikar G, Surve RM, Venkatapura R, Chakrabarti D, Mundlamuri R, Shukla D. Electroencephalography and Bispectral Index Reactivity to Predict Outcome in Unconscious Patients with Acute Severe Traumatic Brain Injury: A Prospective Observational Study. Indian J Crit Care Med 2026;30(4):275-281.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"275-281"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13177776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Literacy in Intensive Care: From Algorithmic Fluency to Clinical Accountability.","authors":"Tanmoy Ghatak","doi":"10.5005/jp-journals-10071-25188","DOIUrl":"10.5005/jp-journals-10071-25188","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI), particularly large language models (LLMs), is increasingly being used in intensive care environments to assist with documentation, literature synthesis, and clinical information retrieval. While these tools offer efficiency and cognitive support in high-pressure settings such as the intensive care unit (ICU), they also introduce risks related to hallucination, automation bias, and a lack of explainability.</p><p><strong>Aim: </strong>To examine the emerging challenges associated with AI use in critical care-especially hallucination and algorithmic overconfidence-and to highlight the need for structured AI literacy and governance frameworks for intensivists.</p><p><strong>Findings: </strong>Recent analyses show that AI hallucination-the generation of fabricated or distorted information-is an intrinsic feature of probabilistic language models rather than a rare technical error. In critical care contexts, this may manifest as fabricated references, distorted clinical reasoning, or overconfident recommendations. Additional concerns include automation bias, where clinicians may inadvertently rely on algorithmic outputs, and opacity in AI decision-making processes. Emerging evidence demonstrates that while AI tools may enhance efficiency in tasks such as documentation and knowledge summarization, they remain unreliable for unsupervised clinical reasoning.</p><p><strong>Conclusion: </strong>The integration of AI into intensive care practice should be guided by structured AI literacy, institutional governance frameworks, and continued human oversight. Artificial intelligence can serve as a supportive cognitive tool, but responsibility for clinical judgment, patient safety, and ethical decision-making must remain firmly with the clinician.</p><p><strong>How to cite this article: </strong>Ghatak T. Artificial Intelligence Literacy in Intensive Care: From Algorithmic Fluency to Clinical Accountability. Indian J Crit Care Med 2026;30(4):272-274.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"30 4","pages":"272-274"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13177757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}