ML Determines The OUD Treatment Discontinuation Likelihood


One of the research teams from the University of Florida has gone ahead and, as a matter of fact, come up with a machine learning framework so as to identify which opioid use disorder- OUD patients may happen to be at high risk in terms of prematurely going ahead with the discontinuation of buprenorphine treatment.

As per the researchers, buprenorphine happens to be one of the three FDA-approved treatments when it comes to OUD, and it has been shown to treat effectively the addiction as well as pain phases. But premature treatment discontinuation can go on to undermine the effectiveness thereby elevating the risk as far as overdose and death in patients are concerned.

Identifying the at-risk OUD patients can as well help the healthcare providers to come up with a patient support system and, at the same time, also make sure to enhance the treatment retention rates, however, the research investigating the predictive value within the terms of the known treatment continuation risk element happens to be pretty limited.

One happens to be well aware of the fact that sticking to a buprenorphine treatment plan is indeed beneficial. The premature discontinuation of the same can as well raise the hospitalization risk, it can result in a situation of a drug overdose, and in the worst-case scenario, mortality cannot be stroked out, says assistant professor, UF College of Pharmacy department of pharmaceutical outcomes and policy, Md. Mahmudul Hasan, PhD. He adds that if one can make use of AI to forecast which patients happen to be at higher risk pertaining to this behavior, the practitioners can as well get to the root of it, thereby making much smarter and more informed decisions, and at the same time, they can also devise some targeted interventions within that set of patients. With regards to that, the research team has gone on to develop a machine learning-based clinical decision support element so as to predict the discontinuation of buprenorphine within the first year of the initiation of the treatment.

The analysis went on to pull MarketScan commercial claims data between 2018 and 2021 so as to come up with a study group of individuals who are insured and have OUD between the ages of 18 and 64. Apparently, these patients went on to initiate buprenorphine treatment right from 2018 to 2020 and happened to have no prescription for drugs six months before the period of the study.

The researchers made use of the data from there so as to measure the buprenorphine prescription discontinuation gap of 30 or more days in the first year after initiating the treatment. There were multiple predictive models that were developed so as to get the analysis done, like the decision tree classifier, Adaboost, random forest, logistic regression, XGBoost as well as random forest- XGBoost ensemble.

By way of making use of these models, the research team evaluated the risk of discontinuation at the time of the start of the treatment and also after a gap of one and three months later. The group was thereafter stratified in the risk subgroups. It is well to be noted that over 30,000 patients went on to initiate buprenorphine, and out of them, almost 15% did not finish their recommended yearlong treatment. Moreover, 46% of the group went on to discontinue the treatment just in the first three months of starting it.

Numerous elements happened to be found that were associated with the discontinuation of treatment, such as age and gender, usage of stimulants as well as antipsychotics, early adherence to the treatment and the number of days of prescription of buprenorphine for a patient.

As per Hasan, the younger patients are indeed at a higher risk when it comes to prematurely stopping the treatment, in addition to those who have a history of stimulant usage, which includes the likes of nicotine. They also happened to find patients having a lower adherence to buprenorphine at the early stage of treatment are more at risk when it comes to the premature discontinuation of the treatment.

Patients who happen to be facing barriers to access the treatment such as staying in the rural areas where there are no treatment facilities nearby, also happen to be at an increased risk when it comes to discontinuation of buprenorphine. The researchers went ahead and stressed on the point that the usage of predictive technologies such as theirs can as well help flag those who may as well struggle with the treatment of buprenorphine, thereby enabling the clinicians to gauge better which are the patients who are likely to respond in a better way while at the same time developing strategies for the rest who may as well need to have that additional support so as to enhance the adherence to the treatment.

Hasan adds that the primary care physicians already happen to be overburdened as well as overworked, and that they happen to have resources that are indeed limited. A tool such as this can, in a much more dependent way, go on to forecast which patient is going to be at a higher risk, and this can turn out to be very helpful.

The point is that within a very short span of time and without increasing any of the workload whatsoever, healthcare providers can go on to identify the interventions that are required for each and every patient, thereby allowing them to best allot the limited set of resources.

It is worth noting that this research can very well be said to be as a result of the dipping of deaths because of overdose in the US and that too for the very first time in the past five years. This is indeed a very positive sign, as healthcare organizations happen to struggle to keep in check the effects pertaining to the ongoing opioid crisis.