Analysis of umbilical cord samples revealed that 44% were positive for drugs, with cannabinoids being the most common.

RT’s Three Key Takeaways

1. High Prenatal Cannabis Exposure: Analysis of umbilical cord samples revealed that 44% were positive for drugs, with cannabinoids being the most common, indicating higher-than-expected cannabis use during pregnancy.
2. Health Implications: Researchers say the findings underscore the need for increased vigilance in testing for cannabis exposure in pregnant individuals and educating patients about its potential risks to fetal health.
3. Opioid Use Prediction: Machine learning models demonstrated high accuracy in predicting the duration of postoperative hydrocodone use, suggesting a new approach to personalized pain management and reducing the risk of opioid dependence.

Rates of cannabis use during pregnancy are far higher than previously thought, according to research being presented at ADLM 2024. Additionally, a second study is being presented on a machine-learning model that predicts the duration of opioid use after surgery.

Cannabis Use During Pregnancy

Cannabis is rapidly becoming legalized in more and more states across the United States, and its recreational use is skyrocketing. But its effects on the developing fetus are not fully understood. Current recommendations advise against cannabis use and exposure during pregnancy due to its association with negative outcomes, such as preterm birth, fetal growth restriction, low birth weight, and developmental deficits.

A research team from NMS Labs led by Alexandria Reinhart, PhD, sought to investigate the prevalence of cannabinoid exposure in utero during 2019-2023 by analyzing umbilical cord samples submitted for testing. Of the 90,384 samples tested, 44% were positive for at least one of approximately 60 analytes included in the testing panel, and cannabinoids accounted for 59%-63% of all positive results, making them the most common drug found.

“The sheer amount of cannabinoid positivity we found in comparison to all the drugs that we run on our umbilical cord toxicity testing was pretty astounding,” says Reinhart in a release.

As the effects of cannabinoids on health continue to be studied, the clinical laboratory should be vigilant in testing for them in pregnant individuals, according to Reinhart. This, in turn, will enable clinicians to educate these patients about the potential harm that cannabis can do to a fetus.

Predicting Duration of Opioid Use

Hydrocodone is the most commonly prescribed opioid in the United States and is often used for pain management following surgery. It is a known potential drug of abuse and can result in dependence and addiction. 

There is significant variability in patients’ response to hydrocodone therapy, though, including how long after surgery they require the drug to manage their pain.

Hunter Miller, PhD, along with colleagues from the University of Louisville and a researcher from ARUP Institute for Clinical and Experimental Pathology, evaluated whether machine learning models could predict postoperative hydrocodone use duration in patients who had undergone orthopedic surgery. 

They developed two different models—a fast and frugal tree (FFTree) and a second that used an extreme gradient boosting approach (xgBoost)—that incorporated patient demographics, genetic test results, concurrently prescribed medications, and other clinical laboratory test results.

The researchers evaluated the two models by using them to predict the duration of hydrocodone use for 79 patients for whom they already had hydrocodone use duration information. Both models demonstrated good to excellent performance when classifying patients as either “short” or “long” duration users. Specifically, the FFTree model classified patients with 0.80 sensitivity and 0.76 specificity, while the xgBoost model achieved a sensitivity and specificity of 0.87 and 0.63, respectively.

“Currently, when it comes to pain management, most clinicians are kind of just taking a shot in the dark, because they don’t really know how a patient is going to respond to a drug,” says Miller in a release. In the future, “a physician could theoretically put a patient’s information into the model, estimate a probability for how long a patient is going to be on hydrocodone, and potentially switch them to a different therapeutic strategy if they have a high risk of prolonged use.”   

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