Analysis of health trajectories leading to adverse opioid-related events.

Identifying patient risk factors leading to adverse opioid-related events (AOEs) may enable targeted risk-based interventions, uncover potential causal mechanisms, and enhance prognosis. In this study, part of CTN-0099, the authors aimed to discover patient diagnosis, procedure, and medication event trajectories associated with AOEs using large-scale data mining methods. The individual temporally preceding factors associated with the highest relative risk (RR) for AOEs were opioid withdrawal therapy agents, toxic encephalopathy, problems related to housing and economic circumstances, and unspecified viral hepatitis, with RR of 33.4, 26.1, 19.9, and 18.7, respectively. Patient cohorts with a socioeconomic or mental health code had a larger RR for over 75% of all identified trajectories compared to the average population. By analyzing health trajectories leading to AOEs, researchers discover novel, temporally-connected combinations of diagnoses and health service events that significantly increase risk of AOEs, including natural histories marked by socioeconomic and mental health diagnoses.

Conclusion: In this study, researchers examined the temporal sequencing of diagnoses, procedures and prescriptions as risk factors leading to an adverse opioid event. Using a data driven approach, they show how large-scale healthcare records can be leveraged to extract risk factors for future research, inform guidelines for practitioner prescribing of opioids and importantly highlights the incidences where further assessments and services are needed to address the patient’s overall health.

Related protocols: CTN-0099