Abstract：
OBJECTIVE: To assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.
METHODS: Systematic review and meta-analysis.
METHODS: Eligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.
METHODS: We searched MEDLINE, EMBASE and Scopus from 2005 to 2023.Risk-of-bias (RoB) assessment: We assessed RoB using the Cochrane Risk of Bias Tool 2 for RCTs and cluster RCT and the ROBINS-I tool for quasi-experimental studies.
RESULTS: We used random effects meta-analysis to synthesise decisional conflict and patient knowledge outcomes reported in RCTs. We performed narrative synthesis for all outcomes. The main outcomes of interest were decisional conflict and patient knowledge.
RESULTS: 13 articles, reporting on 11 studies (4 RCTs, 1 cluster RCT and 6 quasi-experimental) met the inclusion criteria. There were 2714 participants across all studies (2372 in RCTs), of which 26% were women and the mean age was 71 years. Socioeconomically disadvantaged groups were poorly represented in the included studies. Seven studies (n=2508) focused on non-valvular AF and the mean CHAD2DS2-VASc across studies was 3.2 and for HAS-BLED 1.9. All tools focused on decisions regarding thromboembolic stroke prevention and most enabled calculation of individualised stroke risk. Tools were heterogeneous in features and functions; four tools were patient decision aids. The readability of content was reported in one study. Meta-analyses showed a reduction in decisional conflict (4 RCTs (n=2167); standardised mean difference -0.19; 95% CI -0.30 to -0.08; p=0.001; I2=26.5%; moderate certainty evidence) corresponding to a decrease in 12.4 units on a scale of 0 to 100 (95% CI -19.5 to -5.2) and improvement in patient knowledge (2 RCTs (n=1057); risk difference 0.72, 95% CI 0.68, 0.76, p<0.001; I2=0%; low certainty evidence) favouring digital patient decision-support tools compared with usual care. Four of the 11 tools were publicly available and 3 had been implemented in healthcare delivery.
CONCLUSIONS: In the context of stroke prevention in AF, digital patient decision-support tools likely reduce decisional conflict and may result in little to no change in patient knowledge, compared with usual care. Future studies should leverage digital capabilities for increased personalisation and interactivity of the tools, with better consideration of health literacy and equity aspects. Additional robust trials and implementation studies are warranted.
UNASSIGNED: CRD42020218025.
METHODS: Systematic review and meta-analysis.
METHODS: Eligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.
METHODS: We searched MEDLINE, EMBASE and Scopus from 2005 to 2023.Risk-of-bias (RoB) assessment: We assessed RoB using the Cochrane Risk of Bias Tool 2 for RCTs and cluster RCT and the ROBINS-I tool for quasi-experimental studies.
RESULTS: We used random effects meta-analysis to synthesise decisional conflict and patient knowledge outcomes reported in RCTs. We performed narrative synthesis for all outcomes. The main outcomes of interest were decisional conflict and patient knowledge.
RESULTS: 13 articles, reporting on 11 studies (4 RCTs, 1 cluster RCT and 6 quasi-experimental) met the inclusion criteria. There were 2714 participants across all studies (2372 in RCTs), of which 26% were women and the mean age was 71 years. Socioeconomically disadvantaged groups were poorly represented in the included studies. Seven studies (n=2508) focused on non-valvular AF and the mean CHAD2DS2-VASc across studies was 3.2 and for HAS-BLED 1.9. All tools focused on decisions regarding thromboembolic stroke prevention and most enabled calculation of individualised stroke risk. Tools were heterogeneous in features and functions; four tools were patient decision aids. The readability of content was reported in one study. Meta-analyses showed a reduction in decisional conflict (4 RCTs (n=2167); standardised mean difference -0.19; 95% CI -0.30 to -0.08; p=0.001; I2=26.5%; moderate certainty evidence) corresponding to a decrease in 12.4 units on a scale of 0 to 100 (95% CI -19.5 to -5.2) and improvement in patient knowledge (2 RCTs (n=1057); risk difference 0.72, 95% CI 0.68, 0.76, p<0.001; I2=0%; low certainty evidence) favouring digital patient decision-support tools compared with usual care. Four of the 11 tools were publicly available and 3 had been implemented in healthcare delivery.
CONCLUSIONS: In the context of stroke prevention in AF, digital patient decision-support tools likely reduce decisional conflict and may result in little to no change in patient knowledge, compared with usual care. Future studies should leverage digital capabilities for increased personalisation and interactivity of the tools, with better consideration of health literacy and equity aspects. Additional robust trials and implementation studies are warranted.
UNASSIGNED: CRD42020218025.
摘要：
目的：评估数字患者决策支持工具对成人房颤患者房颤（AF）治疗决策的影响。
方法：系统评价和荟萃分析。
方法：合格的随机对照试验（RCT）评估了成人房颤患者治疗决策的数字患者决策支持工具。
方法：我们搜索了MEDLINE，EMBASE和Scopus从2005年到2023年。偏倚风险（RoB）评估：我们使用Cochrane偏倚风险工具2进行RCT和集群RCT，并使用ROBINS-I工具进行准实验研究。
结果：我们使用随机效应荟萃分析来综合RCT中报告的决策冲突和患者知识结果。我们对所有结果进行了叙事综合。感兴趣的主要结果是决策冲突和患者知识。
结果：13篇文章，报告11项研究(4项RCT，1组RCT和6组准实验)符合纳入标准。所有研究中有2714名参与者(RCT中的2372名)，其中26%为女性，平均年龄为71岁。在纳入的研究中，社会经济弱势群体的代表性很低。7项研究（n=2508）集中于非瓣膜性房颤，所有研究的CHAD2DS2-VASc平均值为3.2，HAS-BLED平均值为1.9。所有工具都集中在有关血栓栓塞性中风预防和最有效的个性化中风风险计算的决策上。工具的特征和功能各不相同；四个工具是患者决策辅助工具。在一项研究中报告了内容的可读性。荟萃分析显示决策冲突减少（4个RCT（n=2167）；标准化平均差异-0.19；95％CI-0.30至-0.08；p=0.001；I2=26.5％；中度确定性证据）对应于在0至100的量表上减少了12.4个单位（95％CI-19.5至-5.2）；患者知识的改善（2个RCT（n=1072％），患者支持率<0，011种工具中有4种是公开可用的，3种已在医疗保健服务中实施。
结论：在房颤预防中风的背景下，数字患者决策支持工具可能会减少决策冲突，并可能导致患者知识几乎没有变化，与平时的护理相比。未来的研究应该利用数字功能来提高工具的个性化和交互性，更好地考虑健康素养和公平方面。需要进行其他可靠的试验和实施研究。
■CRD42020218025。
方法：系统评价和荟萃分析。
方法：合格的随机对照试验（RCT）评估了成人房颤患者治疗决策的数字患者决策支持工具。
方法：我们搜索了MEDLINE，EMBASE和Scopus从2005年到2023年。偏倚风险（RoB）评估：我们使用Cochrane偏倚风险工具2进行RCT和集群RCT，并使用ROBINS-I工具进行准实验研究。
结果：我们使用随机效应荟萃分析来综合RCT中报告的决策冲突和患者知识结果。我们对所有结果进行了叙事综合。感兴趣的主要结果是决策冲突和患者知识。
结果：13篇文章，报告11项研究(4项RCT，1组RCT和6组准实验)符合纳入标准。所有研究中有2714名参与者(RCT中的2372名)，其中26%为女性，平均年龄为71岁。在纳入的研究中，社会经济弱势群体的代表性很低。7项研究（n=2508）集中于非瓣膜性房颤，所有研究的CHAD2DS2-VASc平均值为3.2，HAS-BLED平均值为1.9。所有工具都集中在有关血栓栓塞性中风预防和最有效的个性化中风风险计算的决策上。工具的特征和功能各不相同；四个工具是患者决策辅助工具。在一项研究中报告了内容的可读性。荟萃分析显示决策冲突减少（4个RCT（n=2167）；标准化平均差异-0.19；95％CI-0.30至-0.08；p=0.001；I2=26.5％；中度确定性证据）对应于在0至100的量表上减少了12.4个单位（95％CI-19.5至-5.2）；患者知识的改善（2个RCT（n=1072％），患者支持率<0，011种工具中有4种是公开可用的，3种已在医疗保健服务中实施。
结论：在房颤预防中风的背景下，数字患者决策支持工具可能会减少决策冲突，并可能导致患者知识几乎没有变化，与平时的护理相比。未来的研究应该利用数字功能来提高工具的个性化和交互性，更好地考虑健康素养和公平方面。需要进行其他可靠的试验和实施研究。
■CRD42020218025。
