哮喘联盟

   摘要
   背景：计算机决策支持能为预防性护理提供有效预警。我们旨在确定个性化的计算机哮喘决策支持能否提高哮喘管理的质量并降低哮喘失控事件的发生率。
   方法：来自加拿大魁北克的81位初级护理医师和他们的4447名哮喘患者进行本项集群随机试验。患者在初次随访后继续随访3～33个月。医师对照组采用21世纪医师办公（MOXXI）系统，后者是一项集成电子健康记录系统。一项个性化的哮喘决策系统嵌入MOXXI，并在干预组由医师操作。
   结果：初次随访时，9.8%（干预组）-12.9%（对照组）的患者存在哮喘未控制，哮喘未控制是指患者在过去的3个月内由于呼吸相关问题急诊就诊或住院治疗，和/或使用超过250喷以上的速效β受体激动剂（FABA）治疗。在试验结束时，干预组的吸入性糖皮质激素与速效β受体激动剂的剂量比率明显增加（0.93 vs. 0.69: 差异: 0.27; 95% 可信区间: 0.02-0.51; P = 0.03）。总体哮喘未控制率为54.7（对照组）和46.2（干预组）/100例患者/年(100 PY)，无显著差异率为-8.7(95% 可信区间: -24.7, 7.3; P = 0.29)。对研究之初为未控制哮喘者干预效果较大，与干预组可控哮喘患者相比(-0.08每100PY，[95% 可信区间: -10.3, 8.6; P = 0.86].，干预组哮喘未控制者得到哮喘控制更好，在随访中出现的5597次哮喘未控制事件中这组患者占44.7%，与研究之初为哮喘控制者相比(-0.08[95% 可信区间: -10.3, 8.6; P = 0.86]其哮喘未控制事件发生率下降-28.4每100患者年(95%可信区间I: -55.6, -1.2; P = 0.04)。
   讨论：本研究利用系统性监测哮喘控制状态的计算机辅助ADS系统，对哮喘未控制患者进行随访的效果评估，提出了循证医学和个性化哮喘治疗推荐方案。我们发现医师更愿意对哮喘未控制患者使用ADS，大多数此类患者都被建议使用吸入性糖皮质激素或白三烯拮抗剂进行治疗，因此在随访过程中干预组的吸入性糖皮质激素/FABA的均值比显著增加。这也降低了初始哮喘未控制患者在整个研究中的哮喘未控制事件发生率。未来的研究应该对耦合个体化治疗、个性化随访和能对护理质量和效果进行反馈的家庭护理是否能促进指南的采用和对护理效果进行评估。
   结论：2015年由牛津大学代表美国医药信息协会发表。

 

（杨冬 审校）
J Am Med Inform Assoc. 2015 Feb 10. pii: ocu009. doi: 10.1093/jamia/ocu009. [Epub ahead of print]


 

Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial.

Tamblyn R1, Ernst P2, Winslade N3, Huang A4, Grad R5, Platt RW6, Ahmed S7, Moraga T3, Eguale T8.

ABSTRACT
BACKGROUND: Computer-based decision support has been effective in providing alerts for preventive care. Our objective was to determine whether a personalized asthma management computer-based decision support increases the quality of asthma management and reduces the rate of out-of-control episodes.
METHODS: A cluster-randomized trial was conducted in Quebec, Canada among 81 primary care physicians and 4447 of their asthmatic patients. Patients were followed from the first visit for 3-33 months. The physician control group used the Medical Office of the 21st century (MOXXI) system, an integrated electronic health record. A custom-developed asthma decision support system was integrated within MOXXI and was activated for physicians in the intervention group.
RESULTS: At the first visit, 9.8% (intervention) to 12.9% (control) of patients had out-of-control asthma, which was defined as a patient having had an emergency room visit or hospitalization for respiratory-related problems and/or more than 250 doses of fast-acting β-agonist (FABA) dispensed in the past 3 months. By the end of the trial, there was a significant increase in the ratio of doses of inhaled corticosteroid use to fast-acting β-agonist (0.93 vs. 0.69: difference: 0.27; 95% CI: 0.02-0.51; P = 0.03) in the intervention group. The overall out-of-control asthma rate was 54.7 (control) and 46.2 (intervention) per 100 patients per year (100 PY), a non-significant rate difference of -8.7 (95% CI: -24.7, 7.3; P = 0.29). The intervention's effect was greater for patients with out-of-control asthma at the beginning of the study, a group who accounted for 44.7% of the 5597 out-of-control asthma events during follow-up, as there was a reduction in the event rate of -28.4 per 100 PY (95% CI: -55.6, -1.2; P = 0.04) compared to patients with in-control asthma at the beginning of the study (-0.08 [95% CI: -10.3, 8.6; P = 0.86]).
DISCUSSION: This study evaluated the effectiveness of a novel computer-assisted ADS system that facilitates systematic monitoring of asthma control status, follow-up of patients with out of control asthma, and evidence-based, patient-specific treatment recommendations. We found that physicians were more likely to use ADS for out-of-control patients, that in the majority of these patients, they were advised to add an inhaled corticosteroid or a leukotriene inhibitor to the patient s treatment regimen, and the intervention significantly increased the mean ratio of inhaled corticosteroids to FABA during follow-up. It also reduced the rate of out-of-control episodes during follow up among patients whose asthma was out-of-control at the time of study entry. Future research should assess whether coupling patient-specific treatment recommendations, automated follow-up, and home care with comparative feedback on quality and outcomes of care can improve guideline adoption and care outcomes.
CONCLUSIONS: The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

J Am Med Inform Assoc. 2015 Feb 10. pii: ocu009. doi: 10.1093/jamia/ocu009. [Epub ahead of print]