Development and validation of an overdose risk prediction tool using prescription drug monitoring program data.

Gellad WF, Yang Q, Adamson KM, Kuza CC, Buchanich JM, Bolton AL, Murzynski SM, Goetz CT, Washington T, Lann MF, Chang CH, Suda KJ, Tang L. Development and validation of an overdose risk prediction tool using prescription drug monitoring program data. Drug and Alcohol Dependence. 2023 May 1; 246:109856.

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Abstract:

OBJECTIVES: To develop and validate a machine-learning algorithm to predict fatal overdose using Pennsylvania Prescription Drug Monitoring Program (PDMP) data. METHODS: The training/testing (n  =  3020,748) and validation (n  =  2237,701) cohorts included Pennsylvania residents with a prescription dispensing from February 2018-September 2021. Potential predictors (n  =  222) were measured in the 6 months prior to a random index date. Using a gradient boosting machine, we developed a 20-variable model to predict risk of fatal drug overdose in the 6 months after the index date. RESULTS: Beneficiaries in the training (n  =  1,812,448), testing (n  =  1,208,300), and validation (n  =  2,237,701) samples had similar age, with low rates of fatal overdose during 6-month follow up (0.12%, 0.12%, 0.04%, respectively). The validation c-statistic was 0.86 for predicting fatal overdose using 20 PDMP variables. When ranking individuals based on risk score, the prediction model more accurately identified fatal overdose at 6 months compared to using opioid dosage or opioid/benzodiazepine overlap, although the percentage of individuals in the highest risk percentile who died at 6 months was less than 1%. CONCLUSIONS AND POLICY IMPLICATIONS: A gradient boosting machine algorithm predicting fatal overdose derived from twenty variables performed well in discriminating risk across testing and validation samples, improving on single factor risk measures like opioid dosage.