Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK.
Department of Psychology, University of Sheffield, Sheffield, UK.
BMC Med. 2022 Feb 1;20(1):45. doi: 10.1186/s12916-022-02250-2.
Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information.
Six thousand eight hundred four patients aged 59-102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external validation, respectively. A personalised prescription model based on the Recurrent Neural Network machine learning architecture was developed to predict the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores post-drug initiation. The drug that resulted in the smallest decline in cognitive scores between prescription and the next visit was selected as the treatment of choice. Change of cognitive scores up to 2 years after treatment initiation was compared for model evaluation.
Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only.
It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years.
多奈哌齐、加兰他敏、利伐斯的明和美金刚对于痴呆患者的认知障碍可能具有潜在的治疗效果,但这些药物尚未针对个体患者进行个体化治疗。我们研究了人工智能推荐能否利用常规收集的患者信息来确定最佳治疗方案。
使用来自英国两个国民保健服务基金会信托基金的年龄在 59-102 岁之间的 6804 名被诊断为痴呆症的患者,分别用于模型训练/内部验证和外部验证。开发了一种基于递归神经网络机器学习架构的个性化处方模型,用于预测药物起始后 MMSE 和 MoCA 评分。选择药物处方和下一次就诊之间认知评分下降最小的药物作为首选治疗药物。比较治疗开始后 2 年内认知评分的变化,以评估模型。
总体而言,内部验证组共纳入 1343 名 MMSE 评分患者,其中 285 名(21.22%)患者接受了推荐药物治疗。2 年后,该组的 MMSE 评分平均(标准偏差)下降幅度明显小于其余 1058 名(78.78%)患者(0.60[0.26] vs 2.80[0.28];P=0.02)。在外部验证队列(N=1772)中,222 名(12.53%)患者接受了推荐药物治疗,与未接受推荐药物治疗的 1550 名(87.47%)患者相比,MMSE 评分下降幅度较小(1.01[0.49] vs 4.23[0.60];P=0.01)。当仅在接受 AChEI 治疗的患者中测试模型时,也出现了类似的性能差距。
可以在个体患者层面上确定治疗痴呆认知障碍的最有效药物。根据模型选择最合适药物的常规护理患者,在 2 年后认知表现更好。