Clinical Trial Management

BACKGROUND Early detection or notification of adverse event (AE) occurrences during clinical trials is essential to ensure patient safety. Clinical trials take advantage of innovative strategies, clinical designs, and state-of-the-art technologies to evaluate efficacy and safety, however, early awareness of AE occurrences by investigators still needs to be systematically improved. OBJECTIVE This study aimed to build a system to promptly inform investigators when clinical trial participants make unscheduled visits to the emergency room or other departments within the hospital. METHODS We developed the Adverse Event Awareness System (AEAS), which promptly informs investigators and study coordinators of AE occurrences by automatically sending text messages when study participants make unscheduled visits to the emergency department or other clinics at our center. We established the AEAS in July 2015 in the clinical trial management system. We compared the AE reporting timeline data of 305 AE occurrences from 74 clinical trials between the preinitiative period (December 2014-June 2015) and the postinitiative period (July 2015-June 2016) in terms of three AE awareness performance indicators: onset to awareness, awareness to reporting, and onset to reporting. RESULTS A total of 305 initial AE reports from 74 clinical trials were included. All three AE awareness performance indicators were significantly lower in the postinitiative period. Specifically, the onset-to-reporting times were significantly shorter in the postinitiative period (median 1 day [IQR 0-1], mean rank 140.04 [SD 75.35]) than in the preinitiative period (median 1 day [IQR 0-4], mean rank 173.82 [SD 91.07], P≤.001). In the phase subgroup analysis, the awareness-to-reporting and onset-to-reporting indicators of phase 1 studies were significantly lower in the postinitiative than in the preinitiative period (preinitiative: median 1 day, mean rank of awareness to reporting 47.94, vs postinitiative: median 0 days, mean rank of awareness to reporting 35.75, P=.01; and preinitiative: median 1 day, mean rank of onset to reporting 47.4, vs postinitiative: median 1 day, mean rank of onset to reporting 35.99, P=.03). The risk-level subgroup analysis found that the onset-to-reporting time for low- and high-risk studies significantly decreased postinitiative (preinitiative: median 4 days, mean rank of low-risk studies 18.73, vs postinitiative: median 1 day, mean rank of low-risk studies 11.76, P=.02; and preinitiative: median 1 day, mean rank of high-risk studies 117.36, vs postinitiative: median 1 day, mean rank of high-risk studies 97.27, P=.01). In particular, onset to reporting was reduced more in the low-risk trial than in the high-risk trial (low-risk: median 4-0 days, vs high-risk: median 1-1 day). CONCLUSIONS We demonstrated that a real-time automatic alert system can effectively improve safety reporting timelines. The improvements were prominent in phase 1 and in low- and high-risk clinical trials. These findings suggest that an information technology-driven automatic alert system effectively improves safety reporting timelines, which may enhance patient safety.

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Mobile access to virtual randomization for investigator-initiated trials

Clinical Trials ◽

10.1177/1740774517706509 ◽

2017 ◽

Vol 14 (4) ◽

pp. 396-405

Author(s):

Thomas M Deserno ◽

András P Keszei

Keyword(s):

Clinical Trial ◽

Data Capture ◽

Electronic Data Capture ◽

Trial Management ◽

The Core ◽

Mobile Access ◽

Electronic Data Capture System ◽

Investigator Initiated Trials ◽

The Subject ◽

Clinical Trial Management

Background/aims Randomization is indispensable in clinical trials in order to provide unbiased treatment allocation and a valid statistical inference. Improper handling of allocation lists can be avoided using central systems, for example, human-based services. However, central systems are unaffordable for investigator-initiated trials and might be inaccessible from some places, where study subjects need allocations. We propose mobile access to virtual randomization, where the randomization lists are non-existent and the appropriate allocation is computed on demand. Methods The core of the system architecture is an electronic data capture system or a clinical trial management system, which is extended by an R interface connecting the R server using the Java R Interface. Mobile devices communicate via the representational state transfer web services. Furthermore, a simple web-based setup allows configuring the appropriate statistics by non-statisticians. Our comprehensive R script supports simple randomization, restricted randomization using a random allocation rule, block randomization, and stratified randomization for un-blinded, single-blinded, and double-blinded trials. For each trial, the electronic data capture system or the clinical trial management system stores the randomization parameters and the subject assignments. Results Apps are provided for iOS and Android and subjects are randomized using smartphones. After logging onto the system, the user selects the trial and the subject, and the allocation number and treatment arm are displayed instantaneously and stored in the core system. So far, 156 subjects have been allocated from mobile devices serving five investigator-initiated trials. Conclusion Transforming pre-printed allocation lists into virtual ones ensures the correct conduct of trials and guarantees a strictly sequential processing in all trial sites. Covering 88% of all randomization models that are used in recent trials, virtual randomization becomes available for investigator-initiated trials and potentially for large multi-center trials.

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