Efficacy and cost-effectiveness of an automated screening algorithm in an inpatient clinical trial
Efficacy and cost-effectiveness of an automated screening algorithm in an inpatient clinical trial
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DOI：
10.1177/1740774511434844
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发表时间：
2012-04-01
期刊：
影响因子：
2.7
通讯作者：
Wexler, Deborah J.
中科院分区：
文献类型：
--
作者：
Beauharnais, Catherine C.;Larkin, Mary E.;Wexler, Deborah J.
临床试验的筛选和招募既昂贵又耗时。住院试验带来了额外的挑战，因为登记是基于住院时间长短的时间敏感的。我们假设使用自动预筛选算法来确定符合条件的受试者将提高筛选效率和入学率，并且与人工审查每日入院名单相比更具成本效益。方法采用前后设计，我们比较了在马萨诸塞州总医院进行的一项住院糖尿病试验中，每种筛查方法的筛查时间、筛查患者数量、入组率和成本效益。手工图表审查（CR）包括审查每日入院患者名单以确定符合条件的受试者。自动预筛选（APS）方法使用一种算法生成每日血糖水平>= 180 mg/dL、胰岛素顺序和/或入院诊断为糖尿病的患者名单。然后对生成的人口普查进行手动筛选，以确认合格并排除符合我们排除标准的患者。我们根据研究样本量确定了筛查率和入组率以及每种方法的成本效益。结果总筛查时间（预筛查和筛查）从4小时减少到2小时，允许受试者在住院期间更早地接触。每天预筛查的患者平均人数从13 +/- 4人增加到30 +/- 16人（P < 0.0001）。每个筛查日的入组率从0.17例增加到0.32例。开发计算机算法为这项研究增加了3000美元的固定成本。根据我们的筛查和入组率，该算法在入组12例患者后成本中性。更大的样本量进一步支持用算法进行筛选。相比之下，较高的招募率有利于个体cr。局限性由于本研究的前后设计，未测量的因素可能导致了入学率的增加。结论：使用计算机算法在住院环境中识别符合临床试验条件的患者增加了筛选和入组的患者数量，减少了入组所需的时间，并且成本更低。即使对于相对较小的试验，特别是在预期招募率较低的情况下，前期投资开发计算机化算法以改进筛选也可能具有成本效益。临床试验2012；9: 198 - 203。http://ctj.sagepub.com
Introduction Screening and recruitment for clinical trials can be costly and time-consuming. Inpatient trials present additional challenges because enrollment is time sensitive based on length of stay. We hypothesized that using an automated prescreening algorithm to identify eligible subjects would increase screening efficiency and enrollment and be cost-effective compared to manual review of a daily admission list.Methods Using a before-and-after design, we compared time spent screening, number of patients screened, enrollment rate, and cost-effectiveness of each screening method in an inpatient diabetes trial conducted at Massachusetts General Hospital. Manual chart review (CR) involved reviewing a daily list of admitted patients to identify eligible subjects. The automated prescreening (APS) method used an algorithm to generate a daily list of patients with glucose levels >= 180 mg/dL, an insulin order, and/or admission diagnosis of diabetes mellitus. The census generated was then manually screened to confirm eligibility and eliminate patients who met our exclusion criteria. We determined rates of screening and enrollment and cost-effectiveness of each method based on study sample size.Results Total screening time (prescreening and screening) decreased from 4 to 2 h, allowing subjects to be approached earlier in the course of the hospital stay. The average number of patients prescreened per day increased from 13 +/- 4 to 30 +/- 16 (P < 0.0001). Rate of enrollment increased from 0.17 to 0.32 patients per screening day. Developing the computer algorithm added a fixed cost of US$3000 to the study. Based on our screening and enrollment rates, the algorithm was cost-neutral after enrolling 12 patients. Larger sample sizes further favored screening with an algorithm. By contrast, higher recruitment rates favored individual CR.Limitations Because of the before-and-after design of this study, it is possible that unmeasured factors contributed to increased enrollment.Conclusion Using a computer algorithm to identify eligible patients for a clinical trial in the inpatient setting increased the number of patients screened and enrolled, decreased the time required to enroll them, and was less expensive. Upfront investment in developing a computerized algorithm to improve screening may be cost-effective even for relatively small trials, especially when the recruitment rate is expected to be low. Clinical Trials 2012; 9: 198-203. http://ctj.sagepub.com