Indian Journal of Critical Care Medicine最新文献

Background and aims: Medication errors (MEs) are among the most common preventable errors that affect patient care. Despite a growing body of research on MEs in India, no comprehensive systematic literature review (SLR) has been conducted on their epidemiology. This SLR aims to identify and critically assess the incidence rates, frequency, and severity of MEs across various hospitals in India.

Methods: This SLR was conducted in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It included studies (January 2014-April 2025) from electronic databases such as Medline, Cochrane, ScienceDirect, and Google Scholar. The inclusion criteria focused on studies reporting MEs in hospitalized patients in India. The quality of the studies was assessed using a validated quality appraisal tool.

Results: A total of 40 studies were included in the analysis, wherein 31 studies (77.5%) were rated as moderate, 5 (12.5%) as low, and 4 (10%) as high quality. The median incidence rate of MEs was 34.11% (95% CI: 20.45-54.56) in 10 studies, with rates ranging from 6.11% to 43.60% in ICU patients. Medication error frequency rate was 26.74% (95% CI: 17.69-35.80), reported in 28 studies. Most MEs were less severe; however, 8.9% of MEs required monitoring, 2.2% caused temporary harm, necessitating intervention, and 0.1-1.2% of MEs caused prolonged hospitalization.

Conclusions: The SLR emphasizes the significant challenges MEs pose to patient safety in Indian hospitals. The findings underscore the critical need for targeted interventions to mitigate MEs, particularly in severe categories.

How to cite this article: Govil D, Khan RA, Agarwal A, Ghosh P. Epidemiology of Medication Errors in Indian Hospital Settings: A Systematic Literature Review. Indian J Crit Care Med 2025;29(11):954-966.

How to cite this article: Vijayasimha M. Bias-aware Oxygen Saturation Index in Critical Care: A Standards-first Framework to Make a Useful Tool Truly Fair. Indian J Crit Care Med 2025;29(11):970-971.

Aim and background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high mortality rate despite advances in supportive care. Aviptadil, a synthetic analogue of vasoactive intestinal peptide (VIP), exhibits anti-inflammatory potential and cytoprotective effects that may improve pulmonary function. However, its role in improving survival among ARDS patients remains uncertain. This systematic review and meta-analysis aimed to evaluate the effectiveness of aviptadil in improving survival and oxygenation outcomes in ARDS.

Methodology: A comprehensive search was conducted across six databases-PubMed, Scopus, Embase, Google Scholar, Cochrane Library, and Web of Science-up to October 2025. Two investigators independently screened eligible studies. Descriptive synthesis and meta-analysis were performed using a random-effects model. The risk of bias (RoB) was assessed with the RoB 2 tool for randomized controlled trials (RCTs) and the Joanna Briggs Institute (JBI) tool for case series.

Results: The systematic review included nine studies (two RCTs and seven case series) with a total of 665 patients, 361 of whom received aviptadil. Meta-analysis of the two RCTs yielded a pooled prevalence of survival of 0.71 [95% confidence interval (CI): 0.53-0.87]. The survival odds ratio (OR) comparing aviptadil to placebo was 1.01 (95% CI: 0.72-1.42, p = 0.93), indicating no significant benefit. Across the case series, 48 of 54 patients (88.9%) survived following aviptadil therapy, demonstrating consistent improvements in oxygenation and reductions in inflammatory markers.

Conclusion: Aviptadil may have a physiological role in improving oxygenation and reducing inflammation in ARDS; however, current evidence does not indicate a significant survival benefit. Larger, well-designed RCTs are needed to clarify its therapeutic potential.

How to cite this article: Udupa AA, Todur P, Chaudhuri S, Gupta N, Bhat VR, Nagendra D, et al. Aviptadil Therapy in Acute Respiratory Distress Syndrome Patients: A Systematic Review and Meta-analysis. Indian J Crit Care Med 2025;29(11):942-953.

How to cite this article: Kumar N. Medication Errors in Critical Care. Indian J Crit Care Med 2025;29(11):899-901.

How to cite this article: Palanisamy S, Parameswaran N. Tools for Monitoring Respiratory Distress in Heated Humidified High-flow Nasal Cannula in Children: How do We Score the Scores? Indian J Crit Care Med 2025;29(11):897-898.

Background and aims: Veno-venous extracorporeal membrane oxygenation (VV-ECMO) is a life-saving treatment for patients with severe respiratory failure. However, predicting the survival chances of these patients remains difficult. Two commonly used scoring systems, the respiratory ECMO survival prediction (RESP) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score, help in estimating the risk of mortality. This study aimed to compare how well these two scoring systems predict mortality in VV-ECMO patients.

Patients and methods: This was a retrospective study involving patients who received VV-ECMO from 2015 to 2022. We looked at factors like patient age, existing health conditions, the duration of extracorporeal membrane oxygenation (ECMO) treatment, and whether they survived or not. We also compared the actual mortality rate with the predictions made by the RESP and APACHE II scores, using the area under the curve (AUC) to evaluate how accurate each system was.

Results: Out of all the patients, the actual mortality rate was 41.4%. The RESP score predicted a mortality rate of 51.1%, while APACHE II predicted 48.1%. Acute Physiology and Chronic Health Evaluation II (APACHE II) proved to be a better predictor (AUC = 0.722) compared to RESP (AUC = 0.649). Sepsis and difficulty in weaning off ECMO were strongly associated with higher mortality rates, while factors like age, comorbidities, and complications like bleeding or stroke didn't seem to have much of an impact.

Conclusion: Our study found that APACHE II is a more reliable tool than RESP when it comes to predicting mortality in VV-ECMO patients. It can help doctors make more informed decisions about patient care and predict mortality.

How to cite this article: Prasad J, Maqbool K, Chauhan M, Dewan S. Which Score Works Better? Comparing Respiratory Extra-Corporeal Membrane Oxygenation Survival Prediction and Acute Physiology and Chronic Health Evaluation II in Predicting Mortality for Veno-venous Extracorporeal Membrane Oxygenation Patients. Indian J Crit Care Med 2025;29(11):930-935.

How to cite this article: Koshy A, Kurian GP, Jacob JM, Vincent D, Rao SV, Kandasamy S, et al. Author Response: Bias-aware Oxygen Saturation Index in Critical Care: A Standards-first Framework to Make a Useful Tool Truly Fair. Indian J Crit Care Med 2025;29(11):972-973.

Objective: Delayed recognition of treatment failure with heated humidified high-flow nasal cannula (HFNC) in pediatric acute respiratory distress can lead to adverse outcomes. This study presents and verifies a pediatric-modified [heart rate, acidosis, consciousness, oxygenation, respiratory rate (HACOR)] (p-HACOR) score as an instrument for the early prediction of HFNC therapy failure in children experiencing acute hypoxemic respiratory distress.

Design: Single-center prospective observational study.

Setting: Pediatric intensive care unit (PICU) of a tertiary care teaching hospital in Pune, India.

Patients: One hundred and forty-six children (aged 1 month-18 years) admitted to PICU with acute hypoxemic respiratory distress necessitating HFNC therapy.

Interventions/measurements: The p-HACOR score-a composite metric incorporating heart rate, acidity, consciousness, oxygenation, and the respiratory rate adjusted for age-specific criteria was assessed at designated intervals: Before initiation and at 1, 6, 12, 24, 36, and 48 hours following commencement of HFNC therapy.

Results: Treatment failure was observed in 23 patients (15.8%), with 83% necessitating intubation within 1-2 hours after therapy commencement. The p-HACOR score exhibited superior predictive ability at 1 hour postinitiation, with 91.3% sensitivity, 83.74% specificity, and 84.93% diagnostic accuracy [area under the curve (AUC) 0.951, p < 0.01]. A score threshold exceeding 10.5 at 1 hour proved to be the most dependable predictor of failure. The p-HACOR score showed a high correlation with recognized clinical metrics, exceeding the prognostic accuracy of individual markers.

Conclusions: The p-HACOR score may be useful as an early indicator of HFNC therapy failure. Its predictive ability was noted to be best at 1 hour postinitiation of HFNC, suggesting its potential for contributing to early decision-making regarding HFNC failure.

How to cite this article: Vora H, Sarangi BU, Walimbe A. Early Prediction of High-flow Nasal Cannula Failure Using Pediatric-modified HACOR Score in Children with Acute Hypoxemic Respiratory Distress: A Prospective Observational Study. Indian J Crit Care Med 2025;29(11):902-906.

Background and aims: This study compares traditional emergency department (ED) triage systems with artificial intelligence (AI)-assisted triage to assess their impact on time to treatment (TTT) and patient outcomes. Emergency departments (EDs) manage high patient volumes and time-critical decisions, necessitating efficient triage. Traditional methods such as the emergency severity index (ESI) and Manchester triage system (MTS) rely on human judgment and may introduce variability. Artificial intelligence (AI)-based systems use machine learning (ML) algorithms to analyze patient data in real time, offering the potential for faster and more consistent decisions.

Patients and methods: We conducted a single-center randomized controlled trial (RCT) in a high-volume tertiary hospital in Pune, India. One hundred and five patients were randomized to traditional triage (Group A) or AI-assisted triage (Group B). The primary outcome was TTT, defined as arrival at first medical intervention. Mean TTT was 31.02 minutes with AI vs 44.12 minutes with traditional triage (p < 0.001); variability was lower with AI (standard deviation 7.75 vs 11.69).

Results: Intensive care unit (ICU) admission rates did not differ. Clinician ratings favored AI in terms of accuracy, workload reduction, and perceived impact. Multiple linear regression estimated an adjusted -13.1-minute effect of AI on TTT, independent of severity (p < 0.001).

Conclusion: Artificial intelligence (AI)-assisted triage improves ED efficiency by reducing TTT without altering ICU admission rates.

How to cite this article: Nadaf H, Jabade MV, Jamadar K, Jogdeo B, Jamdade V. Improving Emergency Response: A Comparative Analysis of Traditional vs Artificial Intelligence-assisted Triage Systems in Health Care and Their Impact. Indian J Crit Care Med 2025;29(11):925-929.

Background and aims: Obtaining informed consent (IC) for tracheostomy is a frequent and essential process in the intensive care unit (ICU). With the increasing use of artificial intelligence (AI) in health care, chatbots such as ChatGPT and Google Gemini (GG) are being explored as potential tools to assist in drafting IC documents.

Methods: In this cross-sectional study, IC drafts for tracheostomy were generated by ChatGPT and GG. Fifteen experienced intensivists independently evaluated these drafts for accuracy, completeness, readability, and sentiment. Readability was measured using the Flesch Reading Ease (FRE) score, while sentiment analysis assessed the emotional tone of the text.

Results: No statistically significant differences were observed in terms of accuracy or completeness between the two chatbots. The inter-rater reliability was assessed using the intraclass correlation (ICC). The ICC for completeness and accuracy ratings between ChatGPT and GG were 0.85 (95% CI: 0.75-0.92) and 0.80 (95% CI: 0.68-0.89), respectively, suggesting excellent to good inter-rater reliability between the two Chatbots. However, ChatGPT drafts had higher FRE scores (76.46 vs 60.04), indicating better readability. Sentiment analysis revealed that both drafts were predominantly neutral, with GG incorporating slightly more positive expressions.

Conclusion: Both ChatGPT and GG can generate clinically appropriate IC content for tracheostomy. ChatGPT appears to have an advantage in producing more readable and patient-friendly material, highlighting its potential utility in clinical communication.

How to cite this article: Chilkoti GT, Jain S, Gondode PG. Comparison of Artificial Intelligence Chatbots (ChatGPT vs Google Gemini) for Informed Consent Quality: A Cross-sectional Evaluation. Indian J Crit Care Med 2025;29(11):967-969.