Indian Journal of Critical Care Medicine最新文献

{"title":"Author Response: Bridging Gaps in Fluid Choices and Management in Critically Ill Patients.","authors":"Sachin Gupta","doi":"10.5005/jp-journals-10071-24977","DOIUrl":"https://doi.org/10.5005/jp-journals-10071-24977","url":null,"abstract":"","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"536-537"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498409","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":"What Every Intensivist Should Know About Remimazolam.","authors":"Rhythm Mathur, Konica Chittoria, Ankur Sharma, Shilpa Goyal, Nikhil Kothari","doi":"10.5005/jp-journals-10071-24993","DOIUrl":"10.5005/jp-journals-10071-24993","url":null,"abstract":"<p><p>Remimazolam, a recently developed benzodiazepine, possesses characteristics that make it an optimal sedative drug. These characteristics include a quicker onset, improved recovery, organ-independent metabolism, absence of accumulation, and rapid reversal with Flumazenil.</p><p><strong>How to cite this article: </strong>Mathur R, Chittoria K, Sharma A, Goyal S, Kothari N. What Every Intensivist Should Know About Remimazolam. Indian J Crit Care Med 2025;29(6):531-533.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"531-533"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498426","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: Critical Appraisal of the Bayesian Application of Modified Nutrition Risk in Critically Ill Score: Caution Against Overextension as a Mortality Predictor.","authors":"Jay Prakash, Vivek Verma, Pradip K Bhattacharya","doi":"10.5005/jp-journals-10071-24989","DOIUrl":"10.5005/jp-journals-10071-24989","url":null,"abstract":"","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"547-548"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498411","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":"Bridging Gaps in Fluid Choices and Management in Critically Ill Patients.","authors":"Vijay Sundarsingh, Manjunath Kulkarni, Manoj Kumar","doi":"10.5005/jp-journals-10071-24860","DOIUrl":"10.5005/jp-journals-10071-24860","url":null,"abstract":"","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"534-535"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498414","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":"Comparative Accuracy of Critical Care Pain Observation Tool (CPOT), Behavioral Pain Scale (BPS), and Non-verbal Pain Scale (NVPS) for Pain Assessment in Mechanically Ventilated Intensive Care Unit Patients: A Prospective Observational Study.","authors":"Derlin Thomas, Anoob J Kuruppasseril, Rahmath A Samad, Edwin J George","doi":"10.5005/jp-journals-10071-24984","DOIUrl":"10.5005/jp-journals-10071-24984","url":null,"abstract":"<p><strong>Aim and background: </strong>Evaluating pain in critically ill patients on mechanical ventilation poses a unique clinical challenge, mainly because of their inability to self-report. Hence, our study aimed to compare the effectiveness of the critical care pain observation tool (CPOT) and the behavioral pain scale (BPS) in these patients. Additionally, we determined their individual and combined accuracy against the nonverbal pain scale (NVPS) and examined their correlation with physiological indicators, specifically heart rate (HR) and blood pressure (BP).</p><p><strong>Patients and methods: </strong>Fifty mechanically ventilated patients were enrolled, with eight subsequently dropping out. Sedation was maintained using morphine and midazolam infusions, targeting a Ramsay Sedation Score above 3. Pain evaluation with CPOT, BPS, and NVPS were done at rest and during two painful stimuli: tracheal suctioning and patient repositioning, along with HR and BP measurements. Data were collected at four time points: At rest, during suctioning, post-repositioning, and finally again at rest.</p><p><strong>Results: </strong>Combined CPOT and BPS displayed superior diagnostic performance, showing the highest sensitivity (0.88), specificity (0.85), and AUC (0.87). Individually, both BPS (sensitivity 0.85, specificity 0.80) and CPOT (sensitivity 0.83, specificity 0.82) showed considerable accuracy, while NVPS (sensitivity 0.80, specificity 0.78) revealed comparatively lower sensitivity and specificity.</p><p><strong>Conclusion: </strong>The combined use of CPOT and BPS offers the most reliable method for assessing pain in ventilated ICU patients. Even though NVPS can be an ancillary tool, a multimodal pain assessment strategy ensures optimal patient comfort and care.</p><p><strong>How to cite this article: </strong>Thomas D, Kuruppasseril AJ, Samad RA, George EJ. Comparative Accuracy of Critical Care Pain Observation Tool (CPOT), Behavioral Pain Scale (BPS), and Non-verbal Pain Scale (NVPS) for Pain Assessment in Mechanically Ventilated Intensive Care Unit Patients: A Prospective Observational Study. Indian J Crit Care Med 2025;29(6):492-497.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"492-497"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498416","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":"Machine Learning and Deep Learning Models for Early Sepsis Prediction: A Scoping Review.","authors":"Hemalatha Shanmugam, Lavanya Airen, Saumya Rawat","doi":"10.5005/jp-journals-10071-24986","DOIUrl":"10.5005/jp-journals-10071-24986","url":null,"abstract":"<p><strong>Background and aims: </strong>Sepsis, a dangerous condition where infection triggers an abnormal host response, requires quick detection to save lives. While traditional detection methods often fall short, artificial intelligence (AI) and its subsets, machine learning (ML) and deep learning (DL), offer new hope. This scoping review inspects the ML and DL models that are published in the period from 2022 to 2025 for sepsis prediction using electronic health records (EHRs). It aims to provide a comprehensive update for clinicians on the proposed sepsis prediction models, features used, data processing methods, model performance and clinical integration.</p><p><strong>Methods: </strong>Our March 11, 2025, PubMed search identified thirteen relevant studies that developed ML or DL models for predicting adult sepsis.</p><p><strong>Results: </strong>Most researchers used supervised ML, with some exploring DL and hybrid approaches. The models relied on standard clinical data like vital signs and laboratory results, similar to traditional scoring methods. Some models utilized demographic information and electrocardiographic (ECG) readings as features to predict sepsis. Performance metrics such as area under the receiver operating characteristic (AUROC) curve, specificity, and sensitivity showed that these ML and DL models often surpassed the ability of both human clinicians and traditional scoring systems in predicting sepsis. Notable innovations included federated learning and model integration with EHR systems and physiological sensors.</p><p><strong>Conclusion: </strong>While AI shows promise for early sepsis detection, successful clinical adoption will require real-world testing and clear model interpretability. Future work should focus on standardizing these tools for practical medical use.</p><p><strong>How to cite this article: </strong>Shanmugam H, Airen L, Rawat S. Machine Learning and Deep Learning Models for Early Sepsis Prediction: A Scoping Review. Indian J Crit Care Med 2025;29(6):516-524.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"516-524"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498423","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":"Spectrum and Outcome of Acute Kidney Injury in Nonsurgical Cardiac Intensive Care Unit Patients: A Prospective Observational Study.","authors":"Neeraj Saini, Koushik Bhattacharjee","doi":"10.5005/jp-journals-10071-24990","DOIUrl":"10.5005/jp-journals-10071-24990","url":null,"abstract":"<p><strong>Background and aims: </strong>This study has been conducted to investigate the spectrum, outcome and prognostic factors of acute kidney injury (AKI) in nonsurgical cardiac intensive care unit (CICU).</p><p><strong>Patients and methods: </strong>Hospital-based single center prospective observational study with duration of 9 months (January/2023 to September/2023). Data recorded at baseline, 72 hours, day 7, at discharge and at 1 and 3 months after discharge.</p><p><strong>Results: </strong>194 AKI patients (incidence 15.45%), had mean hospital stay 9 ± 4 days, mean age 59.71 years, 84.0% male, 52.1% hypertensive and 47.9% diabetes mellitus. 73% had chest pain, 2.1% anuria, 40.20% shock and 8.2% required inotrope support. AKI was mainly community-acquired, nonoliguric, stage 2; due to type 1 cardiorenal syndrome secondary to acute myocardial infarction and heart failure. 92.3% had LV systolic dysfunction, 52.1% received diuretics and 16% had thrombolysis. 24 subjects received hemodialysis (HD) with mortality 5.6%. Major outcome was nonrecovery at discharge (50.5%), complete remission at 3 months (63.4%) and progression to chronic kidney disease (CKD) (27.8%,). Acute kidney injury staging and outcome was unaffected by discharge cardiac diagnosis. Severe AKI and HD requirement had significantly affected progression to CKD and mortality. Total leukocyte count and serum creatinine had significant connection with mortality. Moderate to severe AKI showed significant risk of subsequent cardiovascular events (<i>p</i> = 0.0008).</p><p><strong>Conclusion: </strong>Acute kidney injury in cardiac ICU is mostly community-acquired and due to cardiorenal syndrome type 1. Majority achieved complete remission on follow-up. Moderate to severe AKI, often multifactorial, is significantly associated with progression to CKD, patient mortality and subsequent cardiovascular events.</p><p><strong>How to cite this article: </strong>Saini N, Bhattacharjee K. Spectrum and Outcome of Acute Kidney Injury in Nonsurgical Cardiac Intensive Care Unit Patients: A Prospective Observational Study. Indian J Crit Care Med 2025;29(6):479-485.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"479-485"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498425","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":"Blockchain in Critical Care.","authors":"Prakash Gondode, Christopher Dass, Shailendra Kumar, Amit Malviya, M Ashwin, Puneet Khanna","doi":"10.5005/jp-journals-10071-24991","DOIUrl":"10.5005/jp-journals-10071-24991","url":null,"abstract":"<p><strong>Background and aims: </strong>Intensive care units (ICUs) face growing challenges with cybersecurity, data interoperability, medication safety, and resource management in an increasingly digital healthcare environment. This review explores how blockchain technology can address these issues and improve critical care delivery.</p><p><strong>Data sources: </strong>Relevant literature was sourced from peer-reviewed journals, healthcare cybersecurity reports, and studies on blockchain applications in medical settings.</p><p><strong>Study selection: </strong>Included works focused on blockchain's role in enhancing data security, drug traceability, consent management, and integration with AI tools in ICU contexts.</p><p><strong>Data synthesis: </strong>Blockchain offers tamper-proof health records, decentralized data sharing, and automated smart contracts, potentially transforming ICU operations. Benefits include improved patient safety, resource efficiency, and decision support. However, challenges such as scalability, regulatory concerns, and implementation costs remain.</p><p><strong>Conclusion: </strong>Blockchain holds strong potential to enhance ICU workflows and patient outcomes. Realizing its impact will require collaborative efforts and further research to overcome current limitations.</p><p><strong>How to cite this article: </strong>Gondode P, Dass C, Kumar S, Malviya A, Ashwin M, Khanna P. Blockchain in Critical Care. Indian J Crit Care Med 2025;29(6):525-530.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"525-530"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498413","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":"Epidemiology and Clinical Outcome of Common Multi-drug Resistant Gram-negative Bacterial Infections in a Network of Hospitals in India (IMPRES): A Multicenter Intensive Care Unit-based Prospective Clinical Study.","authors":"Saurabh K Das, Ziyokov Joshi, Deepak Govil, Mehul S Shah, Gunavathy N Jakaraddi, Sharmili Sinha, Ankit Singhal, Ravikumar Krupanandan, Manish Gupta, Shubha Sharma, Shweta R Chandankhede, Dharma J Samantaray, Lakshmikanthcharan Saravanabavan, Shilpa Gundlapally, Aniket A Kurhade, Manish Goyal, Nupur Gupta, Deepak R Jeswani, Anil Kumar, Rakesh Periwal, Ashit Hegde, Ajay Gupta, Jasvir Kaur, Sweta J Patel, Simranjit Nokewal, Ayesha Shaikh, Priyabrat Karan, Sudeep K Kapalavai, Meraj Ahmed, Guduru Sharab Raviraj, Brinda Kolar, Deepti Jeswani, Kanwalpreet Sodhi","doi":"10.5005/jp-journals-10071-24988","DOIUrl":"10.5005/jp-journals-10071-24988","url":null,"abstract":"<p><strong>Background and aims: </strong>India witnessed the exponential rise of antibiotic resistance due to the high burden of communicable disease. The Indian Council of Medical Research reported <i>Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii</i>, and <i>Klebsiella pneumoniae</i> (PEAK organisms) as the most common gram-negative isolates, constituting 65.5% of total isolates. The present study aimed to observe the demographics and clinical outcomes of patients infected with these four common gram-negative bacteria in ICUs across India.</p><p><strong>Patients and methods: </strong>This prospective multicentric observational study was conducted in ICUs of 19 hospitals across India. The data collected for each patient included: demography, diagnosis, disease severity score, site of infection, PEAK organism, risk factors for multidrug resistance, antibiotic sensitivity, resistance pattern, total ventilator days, and 28-day mortality. Subgroup analysis of 28-day mortality was done for community-acquired vs hospital-acquired infection, appropriate empirical antibiotic, Carbapenem- and Colistin-resistant infections.</p><p><strong>Results: </strong>A total of 936 patients were included in the analysis. Resistance to Cephalosporin, Fluroquinolones, Piperacillin Tazobactam, Carbapenem, Aminoglycosides, and Colistin was observed in 84, 68, 55, 47, 37, and 4.2% of patients, respectively. The 28-day crude mortality rate was 23.5%, which was higher in the subgroup with isolates resistant to empiric antibiotics compared to those with sensitive isolates (29.6 vs 21.4%, <i>p</i> > 0.05). Moreover, 32 and 27% mortality rates were observed in patients who were infected with Carbapenem-resistant and Colistin-resistant PEAK organisms, respectively.</p><p><strong>Conclusion: </strong>The present study observed a high prevalence of antibiotic resistance in Indian ICUs, contributing to a crude mortality rate of 23.5%. Patients with Carbapenem and Colistin resistance may exhibit higher 28-day crude mortality.</p><p><strong>How to cite this article: </strong>Das SK, Joshi Z, Govil D, Shah MS, Jakaraddi GN, Sinha S, <i>et al</i>. Epidemiology and Clinical Outcome of Common Multi-drug Resistant Gram-negative Bacterial Infections in a Network of Hospitals in India (IMPRES): A Multicenter Intensive Care Unit-based Prospective Clinical Study. Indian J Crit Care Med 2025;29(6):504-509.CTRI identifier: CTRI/2023/01/049121.</p>","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"504-509"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498422","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: Comment on: Nucleotidase as a Clinical Prognostic Marker in Snakebites: A Prospective Study.","authors":"Ram S Kaulgud, Tousif Hasan, Muragendraswami Astagimath, Gulamnabi Vanti, S Veeresh, Mahantesh M Kurjogi, Arun K Shettar, Shivakumar Belur","doi":"10.5005/jp-journals-10071-24980","DOIUrl":"https://doi.org/10.5005/jp-journals-10071-24980","url":null,"abstract":"","PeriodicalId":47664,"journal":{"name":"Indian Journal of Critical Care Medicine","volume":"29 6","pages":"544-545"},"PeriodicalIF":1.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498410","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}