Machine learning to detect recent recreational drug use in intensive cardiac care units

Abstract

Background

Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this issue, machine learning methods could assist in the detection of recreational drug use.

Aims

To investigate the accuracy of a machine learning model using clinical, biological and echocardiographic data for detecting recreational drug use in patients admitted to intensive cardiac care units.

Methods

From 07 to 22 April 2021, systematic screening for all traditional recreational drugs (cannabis, opioids, cocaine, amphetamines, 3,4-methylenedioxymethamphetamine) was performed by urinary testing in all consecutive patients admitted to intensive cardiac care units in 39 French centres. The primary outcome was recreational drug detection by urinary testing. The framework involved automated variable selection by eXtreme Gradient Boosting (XGBoost) and model building with multiple algorithms, using 31 centres as the derivation cohort and eight other centres as the validation cohort.

Results

Among the 1499 patients undergoing urinary testing for drugs (mean age 63 ± 15 years; 70% male), 161 (11%) tested positive (cannabis: 9.1%; opioids: 2.1%; cocaine: 1.7%; amphetamines: 0.7%; 3,4-methylenedioxymethamphetamine: 0.6%). Of these, only 57% had reported drug use. Using nine variables, the best machine learning model (random forest) showed good performance in the derivation cohort (area under the receiver operating characteristic curve = 0.82) and in the validation cohort (area under the receiver operating characteristic curve = 0.76).

Conclusions

In a large intensive cardiac care unit cohort, a comprehensive machine learning model exhibited good performance in detecting recreational drug use, and provided valuable insights into the relationships between clinical variables and drug use through explainable machine learning techniques.