Sirohiya P, Maurya P, Arora S, Ratre BK, Singh R, Kumar B. Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care. World J Crit Care Med 2026; 15(3): 120560 [DOI: 10.5492/wjccm.120560]
Corresponding Author of This Article
Prashant Sirohiya, Assistant Professor, Department of Onco-Anaesthesia and Palliative Medicine, National Cancer Institute (Jhajjar), All India Institute of Medical Sciences, Badsa, New Delhi 110029, Delhi, India. prashantsirohiya@aiims.edu
Research Domain of This Article
Critical Care Medicine
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review-article
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Sirohiya P, Maurya P, Arora S, Ratre BK, Singh R, Kumar B. Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care. World J Crit Care Med 2026; 15(3): 120560 [DOI: 10.5492/wjccm.120560]
Prashant Sirohiya, Prateek Maurya, Brajesh Kumar Ratre, Balbir Kumar, Department of Onco-Anaesthesia and Palliative Medicine, National Cancer Institute (Jhajjar), All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Sakshi Arora, Department of Anaesthesia, Ananta Institute of Medical Sciences, Udaipur 313001, Rajasthan, India
Ram Singh, Department of Anaesthesiology, Pain Medicine and Critical Care, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Author contributions: Sirohiya P conceptualized the study, supervised the project, and critically revised the manuscript for important intellectual content; Maurya P contributed to literature search, data extraction; Maurya P and Arora S contributed to drafting of the manuscript; Arora S assisted in data interpretation; Ratre BK contributed to critical revision of the manuscript and provided subject-matter expertise; Singh R contributed to clinical insights and manuscript editing; Sirohiya P and Kumar B contributed to study design; Kumar B contributed to manuscript review, and final approval.
AI contribution statement: AI tools such as ChatGPT and Grammarly are used for language refinement and clarity of expression. AI tools were not used for data analysis or generation of scientific content.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Prashant Sirohiya, Assistant Professor, Department of Onco-Anaesthesia and Palliative Medicine, National Cancer Institute (Jhajjar), All India Institute of Medical Sciences, Badsa, New Delhi 110029, Delhi, India. prashantsirohiya@aiims.edu
Received: March 2, 2026 Revised: April 9, 2026 Accepted: June 2, 2026 Published online: September 9, 2026 Processing time: 172 Days and 17.8 Hours
Abstract
Critically ill cancer patients have a unique physiological profile marked by severe immunosuppression, frailty, and multimorbidity, making traditional tools like Acute Physiology and Chronic Health Evaluation II or Sequential Organ Failure Assessment often inadequate for accurate risk assessment. This review explores artificial intelligence’s potential to transform onco-critical care from reactive to predictive management. We will synthesize literature on two key applications: Early sepsis detection in critically ill cancer patients and refining mortality prediction models to guide ethical care. The analysis will show how dynamic machine learning models, unlike static scores, use vital signs, lab trends, and unstructured machine learning data to detect deterioration hours before clinical decompensation. The review will also examine barriers to adoption, highlighting the need for explainable artificial intelligence to build clinician trust and address data heterogeneity across cancer populations. Ultimately, it will suggest that analytics can transform oncology intensive care unit care, balancing aggressive treatments and palliative care. While focusing on cancer patients, some evidence from general intensive care unit populations will be identified as extrapolated with caution, given different pathophysiology.
Core Tip: Integrating artificial intelligence (AI) and machine learning into onco-critical care could enable earlier sepsis detection and better risk assessment in critically ill cancer patients. Traditional scoring systems like Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II may be limited due to physiological traits like immunosuppression and treatment side effects. Emerging AI models that use real-time health data, vital signs, and unstructured clinical info show promise. Still, most evidence comes from retrospective studies and general intensive care unit data, and their use in oncology settings needs more validation. Explainable AI aids interpretability and clinician trust but should supplement, not replace, clinical judgment. More studies are needed for widespread adoption.
