Development of a Reinforcement Learning Algorithm to Optimize Corticosteroid Therapy in Critically Ill Patients with Sepsis

Early identification of high-risk patients admitted to emergency departments using vital signs and machine learning

BACKGROUND

Rapid and accurate identification of high-risk patients in the emergency departments (EDs) is crucial for optimizing resource allocation and improving patient outcomes. This study aimed to develop an early prediction model for identifying high-risk patients in EDs using initial vital sign measurements.

METHODS

This retrospective cohort study analyzed initial vital signs from the Chinese Emergency Triage, Assessment, and Treatment (CETAT) database, which was collected between January 1st, 2020, and June 25th, 2023. The primary outcome was the identification of high-risk patients needing immediate treatment. Various machine learning methods, including a deep-learning-based multilayer perceptron (MLP) classifier were evaluated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). AUC- ROC values were reported for three scenarios: a default case, a scenario requiring sensitivity greater than 0.8 (Scenario I), and a scenario requiring specificity greater than 0.8 (Scenario II). SHAP values were calculated to determine the importance of each predictor within the MLP model.

RESULTS

A total of 38,797 patients were analyzed, of whom 18.2% were identified as high-risk. Comparative analysis of the predictive models for high-risk patients showed AUC-ROC values ranging from 0.717 to 0.738, with the MLP model outperforming logistic regression (LR), Gaussian Naive Bayes (GNB), and the National Early Warning Score (NEWS). SHAP value analysis identified coma state, peripheral capillary oxygen saturation (SpO2), and systolic blood pressure as the top three predictive factors in the MLP model, with coma state exerting the most contribution.

CONCLUSION

Compared with other methods, the MLP model with initial vital signs demonstrated optimal prediction accuracy, highlighting its potential to enhance clinical decision-making in triage in the EDs.

Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches

Sepsis, a life-threatening medical condition, manifests as new or worsening organ failures due to a dysregulated host response to infection. Many patients with sepsis have manifested a hyperinflammatory phenotype leading to the identification of inflammatory modulation by corticosteroids as a key treatment modality. However, the optimal use of corticosteroids in sepsis treatment remains a contentious subject, necessitating a deeper understanding of their physiological and pharmacological effects. Our study conducts a comprehensive review of randomized controlled trials (RCTs) focusing on traditional corticosteroid treatment in sepsis, alongside an analysis of evolving clinical guidelines. Additionally, we explore the emerging role of artificial intelligence (AI) in medicine, particularly in diagnosing, prognosticating, and treating sepsis. AI's advanced data processing capabilities reveal new avenues for enhancing corticosteroid therapeutic strategies in sepsis. The integration of AI in sepsis treatment has the potential to address existing gaps in knowledge, especially in the application of corticosteroids. Our findings suggest that combining corticosteroid therapy with AI-driven insights could lead to more personalized and effective sepsis treatments. This approach holds promise for improving clinical outcomes and presents a significant advancement in the management of this complex and often fatal condition.

Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury

Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.

[Artificial intelligence in intensive care medicine]

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

Artificial intelligence & clinical nutrition: What the future might have in store

Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interventions for patients. Clinical nutrition is a critical aspect of patient care, and AI can help clinicians make more informed decisions regarding patients' nutritional requirements, disease prevention, and management. AI algorithms can analyse large datasets to identify novel associations between diet and disease outcomes, enabling clinicians to make evidence-based nutritional recommendations. AI-powered devices and applications can also assist in tracking dietary intake, providing feedback, and motivating patients towards healthier food choices. However, the adoption of AI in clinical nutrition raises several ethical and regulatory concerns, such as data privacy and bias. Further research is needed to assess the clinical effectiveness and safety of AI-powered nutrition interventions. In conclusion, AI has the potential to transform clinical nutrition, but its integration into clinical practice should be carefully monitored to ensure patient safety and benefit. This article discusses the current and future applications of AI in clinical nutrition and highlights its potential benefits.