# Work smart, not hard: analysis of delays faced by clinical trials investigating spinal fusion using Protocol AI

## TL;DR

This study uses Protocol AI to analyze delays and failures in spinal fusion clinical trials, finding that 25% are terminated early, often due to recruitment issues.

## Contribution

The study introduces the use of Protocol AI to analyze spinal fusion trial performance and highlights recruitment as a key challenge.

## Key findings

- 25% of spinal fusion clinical trials are terminated early.
- Completed trials face an average delay of 10.6 months, extending anticipated duration by 25%.
- Patient recruitment issues are the primary cause of delays and failures.

## Abstract

Degenerative diseases of the spine are increasingly prevalent with age. Spinal fusion is a common treatment if non-invasive or less-invasive treatment approaches have not been successful. Numerous clinical trials on spinal fusion are started every year to investigate novel technologies worldwide. However, a substiantial amount of trials are terminated prior to completion.

In this study, we analyzed the historical performance of all clinical trials on spinal fusion since 2010.

The identification of related trials was carried out using Protocol AI, which is the Risklick's software. It collects and updates clinical trial data from various sources, including clinical trial registries and datasets from the World Health Organization. Protocol AI has automatically extracted the data on trial, categorized them, and clustered them in trial phases.

The historical probability of early termination for a clinical trial investigating spinal fusion was approximately 25%. The average trial delay for completed trials was 10.6 months. With an average anticipated trial duration approaching 40 months, the observed delay represents an extension of 25% of the anticipated trial duration for completed trials. Trials facing delay and failure predominantly reported critical issues with patient recruitment.

This study emphasizes the importance of implementing a strict risk management plan and recruitment plans, while suggesting professionals to implement standardized enrollment monitoring analyzes during the course of the trial. The amelioration of recruitment policies could substantially maximize the performance of trials within the field, benefiting patients and all stakeholders involved.