Aggregated EMR: Mitigating Trial Risk through Quality by Design Protocols - Applied Clinical Trials


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Aggregated EMR: Mitigating Trial Risk through Quality by Design Protocols

Source: Applied Clinical Trials

Many biopharmaceutical and medical device sponsors design protocols that focus on accomplishing study endpoints, enrolling specific patient populations, and meeting payer/formulary requirements. However, cultural factors embedded in clinical operations groups, such as focusing on First Patient in (FPI) deadlines takes away from the quality of a robustly designed protocol.

Jean Mulinde, Senior Advisor of CDER at the FDA recently indicated that the FDA considers protocol design as the blueprint for quality, and demonstrated that poorly designed protocols resulted in FDA audits. Moreover, study sites have expressed concerns about accepting protocols that do not enroll because inclusion/exclusion criteria are too stringent to enroll qualified subjects. In my experience, sponsors oftentimes rush to achieve FPI deadlines, and realize after study launch that enrollment rates did not meet expectations. 

Correspondingly, sponsors tend to amend their studies soon after launch to maximize enrollment potential; an activity that costs more than $454,000/amendment, with Phase III trials exceeding an average of 3.5 amendments per trial.1*  Further, the literature suggests that protocol design is becoming less efficient, as illustrated in Table 1 .2

Table 1: Protocol Design Statistics2

Many clinical operations personnel tend to focus on monitoring, audits, and endpoints when ‘quality by design’ is mentioned, and dismiss other factors, such as subject enrollment, and adapting protocols to natural patient behaviors to improve subject retention.  In this article, we will demonstrate how biopharmaceutical sponsors can use aggregated Electronic Medical Records (EMR) to create analytically optimized protocols, which enhance clinical trial subject enrollment, and minimize protocol amendments.

Leveraging Aggregated EMR for Study Enrollment Verification

Leveraging EMR querying during the protocol design phase enables study teams to evaluate whether their protocol will enroll based on actual patient population medical data.  We conducted an EMR analysis on a Phase II diabetes trial in order to determine its enrollment viability, and we conducted the analysis on a hospital system in the Aggregated EMR network.  Table 2 demonstrates the impact of each inclusion/exclusion criterion on qualified candidates at a medical system.

Table 2: Impact of inclusion/exclusion criteria

Through my own experience and data analysis, I have found clinical trials that have exhibited a higher number of inclusion/exclusion criteria tended to be associated with lower subject enrollment rates.  Table 2 shows this impact as we added more inclusion/exclusion criteria towards the query.  In this case, we observed a 2% qualification rate in diabetic patients, with the HbgA1C inclusion criterion having the highest impact on excluding patients.  Figure 1 illustrates why.

Figure 1: HbA1C Level Trends in Patients

Granted, the HbgA1C criterion could have been an important inclusion criterion for this trial’s endpoints, however, visualizing enrollment potential allows study teams to realistically evaluate the clinical trial’s enrollment potential, which can affect financial expectations and study completion forecasts.  It is important to balance expertise in trial design with patient data in order to generate favorable trial results.

Analytical Enrollment Verification Minimizes Timeline Slippage and Protocol Amendments

The example above demonstrates that study teams can avoid timeline slippage and protocol amendments related to subject enrollment by verifying protocol design against real-time patient data.  It is strongly advised that study teams acquire statistical significance in their analyses in order to ensure high-quality outcomes and predictability in patient population trends.

Study Sites Should Be Cautious of Poorly Designed Protocols

Study sites oftentimes run across poorly designed protocols that do not enroll, which causes study sites to lose capital on maintaining regulatory requirements, conducting screening activities, and increasing the risk for an FDA audit.  By running queries against protocols during the protocol evaluation/qualification process, study sites can protect themselves from accepting to execute poorly designed studies.

Analytical Study Optimization is an Art of Balance

Study teams currently leverage study design expertise and existing medical data from insurance and other databases in order to design their protocols.  Analytical protocol optimization through Aggregated EMR sheds light on a missing piece that is essential towards enhancing quality by design, as Aggregated EMR adds a depth of understanding that other data sources cannot offer.  Study teams should not only leverage medical and protocol design expertise, but also verify protocol design with aggregated EMR.  Doing so is known to enhance subject enrollment, minimize timeline slippage, improve financial forecasting, and reduce enrollment-related protocol amendments.



[1] Kenneth Getz, et al. Measuring the Incidence, Causes, and Repercussions of Protocol Amendments. Drug Information Journal, May 2011 45:265

*Excludes internal resources utilized to amend protocol, costs/fees with language translation, and resubmission.   Figure is not statistically significant

[2] Tufts CSDD; Medidata


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As it creates a plan to implement the US biosimilar pathway, should FDA:
Borrow heavily from EMA's pathway program?
Borrow lightly from EMA's pathway program?
Create entirely its own pathway program?
Borrow heavily from EMA's pathway program?
Borrow lightly from EMA's pathway program?
Create entirely its own pathway program?
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