Dutch Poison Information Centre, University Medical Centre Utrecht, Utrecht University, CX, Utrecht, The Netherlands.
Clin Toxicol (Phila). 2024 Mar;62(3):139-144. doi: 10.1080/15563650.2024.2334820. Epub 2024 Apr 29.
When predicting future events, we often rely on analyzing past occurrences and projecting them forward. This methodology is crucial in various fields, including toxicology, in which predicting outcomes in poisoned patients plays a vital role in guiding treatment decisions and improving patient care.
In cases of poisoning, understanding a patient's medical history, current physiological status, and the toxicokinetics of the ingested substance is essential for predicting potential outcomes and determining appropriate interventions.
WHAT TO PREDICT?: Predicting whether an intoxicated patient needs (further) treatment or even admission to the hospital is one of the most difficult decisions a clinician needs to make. The prediction of the course of an intoxication often lacks crucial information, leaving physicians with a sense of uncertainty in treating and advising patients. A significant source of this uncertainty stems from patients' limited awareness of the specific chemical(s) causing their symptoms, making a targeted approach challenging. Adding to the complexity, both patients and physicians frequently lack knowledge of the exposure dose, onset time, and potential interactions, further complicating the prediction of symptom progression. Patients are commonly placed in observation wards until the pharmacodynamic effects have diminished, leading to extended observation periods and unnecessary healthcare utilization and costs. Therefore, a key objective of a predictive model is to determine the necessity for intensive care unit admission.
Factors such as age, Glasgow Coma Scale, and specific comorbidities like dysrhythmias and chronic respiratory insufficiency significantly influence the likelihood of intensive care unit admission. By examining a patient's trajectory based on past medical history and organ function deterioration, clinicians can better anticipate the need for critical care support.
To enhance prediction models, leveraging modern methodologies like machine learning on large datasets (big data) are crucial. These advanced techniques can uncover previously unknown patient groups with similar outcomes or treatment responses, leading to more personalized and effective interventions. Regular updates to clustering, discrimination, and calibration processes ensure that predictive models remain accurate and relevant as new data emerges.
The field of clinical toxicology stands to benefit greatly from the creation and integration of large datasets to advance toxicological prognostication. By embracing innovative approaches and incorporating diverse data sources, clinicians can enhance their ability to predict outcomes in poisoned patients and improve overall patient management strategies.
在预测未来事件时，我们通常依赖于分析过去的事件并将其向前推断。这种方法在各个领域都至关重要，包括毒理学，在毒理学中，预测中毒患者的结果对于指导治疗决策和改善患者护理至关重要。
在中毒情况下，了解患者的病史、当前生理状况和摄入物质的毒代动力学对于预测潜在结果和确定适当的干预措施至关重要。
需要预测什么？：预测中毒患者是否需要（进一步）治疗甚至住院是临床医生需要做出的最困难的决策之一。中毒过程的预测往往缺乏关键信息，使医生在治疗和咨询患者时感到不确定。这种不确定性的一个重要来源是患者对导致其症状的特定化学物质的意识有限，这使得采用有针对性的方法具有挑战性。此外，患者和医生通常缺乏对暴露剂量、发病时间和潜在相互作用的了解，这进一步增加了症状进展预测的复杂性。患者通常被安置在观察病房中，直到药效学效应减弱，导致观察期延长和不必要的医疗保健利用和成本增加。因此，预测模型的一个关键目标是确定是否需要入住重症监护病房。
年龄、格拉斯哥昏迷量表和特定的合并症（如心律失常和慢性呼吸功能不全）等因素显著影响入住重症监护病房的可能性。通过检查患者基于既往病史和器官功能恶化的轨迹，临床医生可以更好地预测对重症监护支持的需求。
为了增强预测模型，在大型数据集（大数据）上利用机器学习等现代方法至关重要。这些先进技术可以发现具有相似结局或治疗反应的以前未知的患者群体，从而实现更个性化和有效的干预措施。定期更新聚类、区分和校准过程可确保预测模型在新数据出现时保持准确和相关。
临床毒理学领域通过创建和整合大型数据集来推进毒理学预后将受益匪浅。通过采用创新方法并整合多种数据源，临床医生可以提高预测中毒患者结局的能力，并改善整体患者管理策略。
