Baumbach Linda, Hötzendorfer Walter, Baumbach Jan
Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.
Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg, Germany, 49 407410590.
J Med Internet Res. 2025 May 19;27:e69341. doi: 10.2196/69341.
Prognostic models in medicine have garnered significant attention, with established guidelines governing their development. However, there remains a lack of clarity regarding the appropriate circumstances for (1) creating and (2) implementing tools based on models with limited performance. This commentary addresses this gap by analyzing the pros and cons of tool development and providing a structured outline that includes critical questions to consider in the decision-making process, based on an example of patients with osteoarthritis. We propose three general justifications for the implementation of survey-based models: (1) mitigation of expectation bias among patients and clinicians, (2) advancement of personalized medicine, and (3) enhancement of existing predictive information sources. Nevertheless, it is crucial to acknowledge that implementing such models is always context-dependent and may harm certain patients, necessitating careful consideration of the withdrawal of tool development and implementation in specific cases. To facilitate the identification of these scenarios, we delineate 16 possibilities following the implementation of a personalized prognostic model and compare the consequences to a current one-size-fits-all treatment recommendation at a population level. Our analysis encompasses the possible patient benefits and harms resulting from implementing or not implementing personalized prognostic models and summarizes them. These findings, together with context-related factors, are important to consider when deciding if, how, and for whom a personalized prognostic tool should be created and implemented. We present a checklist of questions and an Excel sheet calculation table, allowing researchers to weigh the benefits and harms of creating and implementing a personalized prognostic model at a population level against one-size-fits-all standard care in a structured and standardized manner. We condense this into a single value using a uniform Benefit-Risk Score formula. Together with context-related factors, the calculation table and formula are designed to aid researchers in their decision-making process on providing a personalized prognostic tool and deciding for or against its complete or partial implementation. This work serves as a foundation for further discourse and refinement of tool development decisions for prognostic models in health care.
医学中的预后模型已引起广泛关注，并有既定的指南来指导其开发。然而，对于在（1）创建和（2）实施基于性能有限的模型的工具时的适当情形，仍缺乏明确性。本评论通过分析工具开发的利弊，并以骨关节炎患者为例，提供一个结构化大纲，其中包括在决策过程中要考虑的关键问题，来解决这一差距。我们提出了实施基于调查的模型的三个一般理由：（1）减轻患者和临床医生的期望偏差，（2）推进个性化医疗，以及（3）增强现有的预测信息来源。然而，必须认识到，实施此类模型总是取决于具体情况，可能会对某些患者造成伤害，因此在特定情况下需要仔细考虑是否停止工具开发和实施。为便于识别这些情况，我们在实施个性化预后模型后描绘了16种可能性，并在人群层面将其后果与当前的一刀切治疗建议进行比较。我们的分析涵盖了实施或不实施个性化预后模型可能给患者带来的益处和危害，并进行了总结。这些发现以及与背景相关的因素，对于决定是否、如何以及为谁创建和实施个性化预后工具非常重要。我们提供了一份问题清单和一个Excel表格计算表，使研究人员能够以结构化和标准化的方式权衡在人群层面创建和实施个性化预后模型相对于一刀切标准护理的利弊。我们使用统一的效益风险评分公式将其浓缩为一个单一值。连同与背景相关的因素，计算表和公式旨在帮助研究人员在提供个性化预后工具以及决定是否完全或部分实施该工具的决策过程中提供帮助。这项工作为进一步探讨和完善医疗保健中预后模型的工具开发决策奠定了基础。
