Massachusetts General Hospital Diabetes Center, Boston, MA 02114, USA.
Clin Trials. 2012 Apr;9(2):198-203. doi: 10.1177/1740774511434844. Epub 2012 Feb 3.
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.
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.
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.
Because of the before-and-after design of this study, it is possible that unmeasured factors contributed to increased enrollment.
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.
临床试验的筛选和招募可能既昂贵又耗时。住院试验带来了额外的挑战，因为入组是基于住院时间的敏感的。我们假设使用自动预筛选算法来识别合格的受试者将提高筛选效率和入组率，并与手动审查每日入院清单相比具有成本效益。
使用前后设计，我们比较了在麻省总医院进行的一项住院糖尿病试验中，每种筛选方法的筛选时间、筛选的患者数量、入组率和成本效益。手动图表审查（CR）涉及审查每日入院患者名单以确定合格的受试者。自动预筛选（APS）方法使用算法生成每日血糖水平≥180mg/dL、胰岛素医嘱和/或入院诊断为糖尿病的患者名单。然后手动筛选生成的普查结果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每种方法的筛选和入组率以及成本效益。
总筛选时间（预筛选和筛选）从 4 小时减少到 2 小时，使受试者能够在住院期间的早期阶段得到关注。每天预筛选的患者平均数量从 13±4 增加到 30±16（P<0.0001）。入组率从 0.17 增加到 0.32 名患者/筛选日。开发计算机算法增加了研究的固定成本 3000 美元。根据我们的筛选和入组率，在入组 12 名患者后，算法的成本是中性的。更大的样本量更有利于使用算法进行筛选。相比之下，更高的招募率有利于个人 CR。
由于这项研究的前后设计，可能有未测量的因素导致入组增加。
在住院环境中使用计算机算法识别临床试验的合格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，预先投资开发一种用于改善筛选的计算机算法也可能具有成本效益，尤其是当预期招募率较低时。
