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首页> 外文期刊>Pharmacology Research & Perspectives >Utility of accumulated opioid supply days and individual patient factors in predicting probability of transitioning to long‐term opioid use: An observational study in the Veterans Health Administration
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Utility of accumulated opioid supply days and individual patient factors in predicting probability of transitioning to long‐term opioid use: An observational study in the Veterans Health Administration

机译:累积阿片类药物供应日的效用和个体患者因素预测过渡到长期阿片类药物的使用概率:退伍军人健康管理局的观察研究

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Initial supply days dispensed to new users is strongly predictive of future long-term opioid use (LTO). The objective was to examine whether a model integrating additional clinical variables conferred meaningful improvement in predicting LTO, beyond a simple approach using only accumulated supply. Three cohorts were created using Veteran's Health Administration data based on accumulated supply days during the 90?days following opioid initiation: (a) 30?days, (b) ≥30?days, (c) ≥60?days. A base, unadjusted probability of subsequent LTO (days 91-365) was calculated for each cohort, along with an associated risk range based on midpoint values between cohorts. Within each cohort, log-binomial regression modeled the probability of subsequent LTO, using demographic, diagnostic, and medication characteristics. Each patient's LTO probability was determined using their individual characteristic values and model parameter estimates, where values falling outside the cohort's risk range were considered a clinically meaningful change in predictive value. Base probabilities for subsequent LTO and associated risk ranges by cohort were as follows: (a) 3.92% (0%-10.75%), (b) 17.59% (10.76%-28.05%), (c) 38.53% (28.06%-47.55%). The proportion of patients whose individual probability fell outside their cohort's risk range was as follows: 1.5%, 4.6%, and 9.2% for cohorts 1, 2, and 3, respectively. The strong relationship between accumulated supply days and future LTO offers an opportunity to leverage electronic healthcare records for decision support in preventing the initiation of inappropriate LTO through early intervention. More complex models are unlikely to meaningfully guide decision making beyond the single variable of accumulated supply days.? 2020 The Authors. Pharmacology Research & Perspectives published by John Wiley & Sons Ltd, British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics.
机译:分配给新用户的初始供应天是强烈的预测未来长期阿片类药物(LTO)。目的是检查一体化额外临床变量的模型是否赋予了预测LTO的有意义的改进,超出了仅累计供应的简单方法。使用Veteran的健康管理数据根据阿片类药物启动后90.累累的供应日使用退伍军人的健康管理数据来创建三个群组:(a)<30?天,(b)≥30?天,(c)≥60?天。为每个群组计算基础,随后的LTO(第91-365天)的概率,以及基于群组之间的中点值的相关风险范围。在每个群组内,使用人口统计学,诊断和药物特征,对Log-Xinomial回归建模了后续LTO的概率。每个患者的LTO概率使用其个性的特征值和模型参数估计确定,其中落在群组的风险范围之外的值被认为是预测值的临床有意义的变化。群组随后的LTO和相关风险范围的基本概率如下:(a)3.92%(0%-10.75%),(b)17.59%(10.76%-28.05%),(c)38.53%(28.06% - 47.55%)。个人概率落在队列的风险范围之外的患者的比例如下:队列1,2和3分别为1.5%,4.6%和9.2%。积累供应日与未来LTO之间的良好关系提供了利用电子医疗保健记录来决策支持的机会,防止通过提前干预启动不当LTO。更复杂的模型不太可能对累计供应天的单一变量超出有意义的指导决策。 2020作者。 John Wiley&Sons Ltd,英国药理学会和美国药理学学会和实验治疗学士发表的药理学研究与观点。

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