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• W4360989852 abstract "To develop and validate a machine-learning algorithm to predict fatal overdose using Pennsylvania Prescription Drug Monitoring Program (PDMP) data.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.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%.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." @default.
• W4360989852 created "2023-03-30" @default.
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• W4360989852 date "2023-05-01" @default.
• W4360989852 modified "2023-09-27" @default.
• W4360989852 title "Development and validation of an overdose risk prediction tool using prescription drug monitoring program data" @default.
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• W4360989852 doi "https://doi.org/10.1016/j.drugalcdep.2023.109856" @default.
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