PDF(Pubmed)
Abstract：
UNASSIGNED: Belantamab mafodotin (belamaf) has demonstrated clinically meaningful antimyeloma activity in patients with heavily pretreated multiple myeloma. However, it is highly active against dividing cells, contributing to off-target adverse events, particularly ocular toxicity. Changes in best corrected visual acuity (BCVA) and corneal examination findings are routinely monitored to determine Keratopathy Visual Acuity (KVA) grade to inform belamaf dose modification.
UNASSIGNED: We aimed to develop a semiautomated mobile app to facilitate the grading of ocular events in clinical trials involving belamaf.
UNASSIGNED: The paper process was semiautomated by creating a library of finite-state automaton (FSA) models to represent all permutations of KVA grade changes from baseline BCVA readings. The transition states in the FSA models operated independently of eye measurement units (e.g., Snellen, logMAR, decimal) and provided a uniform approach to determining KVA grade changes. Together with the FSA, the complex decision tree for determining the grade change based on corneal examination findings was converted into logical statements for accurate and efficient overall KVA grade computation. First, a web-based user interface, conforming to clinical practice settings, was developed to simplify the input of key KVA grading criteria. Subsequently, a mobile app was developed that included additional guided steps to assist in clinical decision-making.
UNASSIGNED: The app underwent a robust Good Clinical Practice validation process. Outcomes were reviewed by key stakeholders, our belamaf medical lead, and the systems integration team. The time to compute a patient\'s overall KVA grade using the Belamaf Eye Exam (BEE) app was reduced from a 20- to 30-min process to <1-2 min. The BEE app was well received, with most investigators surveyed selecting \"satisfied\" or \"highly satisfied\" for its accuracy and time efficiency.
UNASSIGNED: Our semiautomated approach provides for an accurate, simplified method of assessment of patients\' corneal status that reduces errors and quickly delivers information critical for potential belamaf dose modifications. The app is currently available on the Apple iOS and Android platforms for use by investigators of the DREAMM clinical trials, and its use could easily be extended to the clinic to support healthcare providers who need to make informed belamaf treatment decisions.
UNASSIGNED: We aimed to develop a semiautomated mobile app to facilitate the grading of ocular events in clinical trials involving belamaf.
UNASSIGNED: The paper process was semiautomated by creating a library of finite-state automaton (FSA) models to represent all permutations of KVA grade changes from baseline BCVA readings. The transition states in the FSA models operated independently of eye measurement units (e.g., Snellen, logMAR, decimal) and provided a uniform approach to determining KVA grade changes. Together with the FSA, the complex decision tree for determining the grade change based on corneal examination findings was converted into logical statements for accurate and efficient overall KVA grade computation. First, a web-based user interface, conforming to clinical practice settings, was developed to simplify the input of key KVA grading criteria. Subsequently, a mobile app was developed that included additional guided steps to assist in clinical decision-making.
UNASSIGNED: The app underwent a robust Good Clinical Practice validation process. Outcomes were reviewed by key stakeholders, our belamaf medical lead, and the systems integration team. The time to compute a patient\'s overall KVA grade using the Belamaf Eye Exam (BEE) app was reduced from a 20- to 30-min process to <1-2 min. The BEE app was well received, with most investigators surveyed selecting \"satisfied\" or \"highly satisfied\" for its accuracy and time efficiency.
UNASSIGNED: Our semiautomated approach provides for an accurate, simplified method of assessment of patients\' corneal status that reduces errors and quickly delivers information critical for potential belamaf dose modifications. The app is currently available on the Apple iOS and Android platforms for use by investigators of the DREAMM clinical trials, and its use could easily be extended to the clinic to support healthcare providers who need to make informed belamaf treatment decisions.
摘要：
■Belantamabmafodotin（belamaf）已证明对严重预处理的多发性骨髓瘤患者具有临床意义的抗骨髓瘤活性。然而,它对分裂的细胞非常活跃，导致脱靶不良事件，特别是眼部毒性。常规监测最佳矫正视力（BCVA）和角膜检查结果的变化，以确定角膜病变视力（KVA）等级，以告知belamaf剂量修改。
■我们旨在开发一种半自动移动应用程序，以促进涉及belamaf的临床试验中眼部事件的分级。
■通过创建有限状态自动机（FSA）模型库来表示来自基线BCVA读数的KVA等级变化的所有排列，纸张过程是半自动的。FSA模型中的过渡状态独立于眼睛测量单元(例如，Snellen,logmar,十进制)，并提供了确定KVA等级变化的统一方法。与FSA一起，将根据角膜检查结果确定等级变化的复杂决策树转换为逻辑语句,以进行准确有效的总体KVA等级计算.首先,基于Web的用户界面，符合临床实践设置，是为了简化关键KVA分级标准的输入而开发的。随后，我们开发了一个移动应用程序,其中包括额外的指导步骤,以协助临床决策.
■该应用经过了强有力的良好临床实践验证过程。主要利益攸关方审查了成果，我们的Belamaf医疗线索,和系统集成团队。使用Belamaf眼科检查（BEE）应用程序计算患者总体KVA等级的时间从20至30分钟减少到<1-2分钟。BEE应用程序很受欢迎，大多数调查人员选择“满意”或“高度满意”的准确性和时间效率。
■我们的半自动方法提供了准确的，简化的患者角膜状态评估方法，可减少错误并快速提供对潜在的belamaf剂量修改至关重要的信息。该应用程序目前可在AppleiOS和Android平台上使用，供DREAMM临床试验的研究人员使用，它的使用可以很容易地扩展到诊所，以支持需要做出知情的belamaf治疗决定的医疗保健提供者。
■我们旨在开发一种半自动移动应用程序，以促进涉及belamaf的临床试验中眼部事件的分级。
■通过创建有限状态自动机（FSA）模型库来表示来自基线BCVA读数的KVA等级变化的所有排列，纸张过程是半自动的。FSA模型中的过渡状态独立于眼睛测量单元(例如，Snellen,logmar,十进制)，并提供了确定KVA等级变化的统一方法。与FSA一起，将根据角膜检查结果确定等级变化的复杂决策树转换为逻辑语句,以进行准确有效的总体KVA等级计算.首先,基于Web的用户界面，符合临床实践设置，是为了简化关键KVA分级标准的输入而开发的。随后，我们开发了一个移动应用程序,其中包括额外的指导步骤,以协助临床决策.
■该应用经过了强有力的良好临床实践验证过程。主要利益攸关方审查了成果，我们的Belamaf医疗线索,和系统集成团队。使用Belamaf眼科检查（BEE）应用程序计算患者总体KVA等级的时间从20至30分钟减少到<1-2分钟。BEE应用程序很受欢迎，大多数调查人员选择“满意”或“高度满意”的准确性和时间效率。
■我们的半自动方法提供了准确的，简化的患者角膜状态评估方法，可减少错误并快速提供对潜在的belamaf剂量修改至关重要的信息。该应用程序目前可在AppleiOS和Android平台上使用，供DREAMM临床试验的研究人员使用，它的使用可以很容易地扩展到诊所，以支持需要做出知情的belamaf治疗决定的医疗保健提供者。
