小柯机器人

英国伯明翰大学Dipak Kotecha团队联合英国健康数据研究中心Georgios V Gkoutos团队重新定义了窦性心律和心房颤动心力衰竭患者的β受体阻滞剂反应。2021年8月30日出版的《柳叶刀》杂志发表了这项成果。

尽管在治疗方面取得了进展，但心力衰竭和左室射血分数（LVEF）降低患者的死亡率仍然高得令人无法接受。研究人员假设一种新的人工智能方法可以更好地评估多重和高维的合并症相互作用，并确定β-受体阻滞剂在窦性心律和房颤患者中的疗效群。

研究组将基于神经网络的变异自动编码器和分级聚类应用于从9个双盲、随机、安慰剂对照的β受体阻滞剂试验汇集的个体患者数据。通过意向治疗评估中位随访1.3年的全因死亡率，并通过心电图心率进行分层。聚类和维度的数量是客观确定的，结果使用留一法进行验证。

该研究共纳入15659例心衰患者，LVEF小于50%，中位年龄65岁， 中位LVEF为27%。3708例（24%）为女性。在窦性心律患者（12822名）中，大多数群集显示β受体阻滞剂的总体死亡率受益一致，优势比（OR）为0.54-0.74。一组症状较轻的窦性心律老年患者无显著疗效，OR为0.86。

在房颤患者（2837）中，5个组中有4个组与安慰剂的整体中性效应一致，OR为0.92。一组死亡风险较低但LVEF与平均水平相似的年轻房颤患者，使用β受体阻滞剂后死亡率有所降低，差异具有统计学意义。所有模型的稳健性和聚类一致性都得到了确认，并通过9个独立试验外部验证了聚类成员关系。

研究结果表明，一种基于人工智能的聚类方法能够区分心力衰竭和LVEF降低患者使用β受体阻滞剂的预后反应。

附：英文原文

Title: Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Author: Andreas Karwath, Karina V Bunting, Simrat K Gill, Otilia Tica, Samantha Pendleton, Furqan Aziz, Andrey D Barsky, Saisakul Chernbumroong, Jinming Duan, Alastair R Mobley, Victor Roth Cardoso, Luke Slater, John A Williams, Emma-Jane Bruce, Xiaoxia Wang, Marcus D Flather, Andrew J S Coats, Georgios V Gkoutos, Dipak Kotecha

Issue&Volume: 2021-08-30

Abstract:

Background

Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation.

Methods

Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012).

Findings

15659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials.

Interpretation

An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality.

DOI: 10.1016/S0140-6736(21)01638-X

Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)01638-X/fulltext

LANCET：《柳叶刀》，创刊于1823年。隶属于爱思唯尔出版社，最新IF：202.731
官方网址：http://www.thelancet.com/
投稿链接：http://ees.elsevier.com/thelancet