Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
Naval Medical Center Portsmouth, Portsmouth, Virginia, USA.
PM R. 2024 Jan;16(1):14-24. doi: 10.1002/pmrj.12994. Epub 2023 Jul 26.
Over-prescription of opioids has diminished in recent years; however, certain populations remain at high risk. There is a dearth of research evaluating prescription rates using specific multimorbidity patterns.
To identify distinct clinical profiles associated with opioid prescription and evaluate their relative odds of receiving long-term opioid therapy.
Retrospective analysis of the complete military electronic health record. We assessed demographics and 26 physiological, psychological, and pain conditions present during initial opioid prescription. Latent class analysis (LCA) identified unique clinical profiles using diagnostic data. Logistic regression measured the odds of these classes receiving long-term opioid therapy.
All electronic health data under the TRICARE network.
All servicemembers on active duty during fiscal years 2016 through 2019 who filled at least one opioid prescription.
Number and qualitative characteristics of LCA classes; odds ratios (ORs) from logistic regression. We hypothesized that LCA classes characterized by high-risk contraindications would have significantly higher odds of long-term opioid therapy.
A total of N = 714,446 active duty servicemembers were prescribed an opioid during the study window, with 12,940 (1.8%) receiving long-term opioid therapy. LCA identified five classes: Relatively Healthy (82%); Musculoskeletal Acute Pain and Substance Use Disorders (6%); High Pain, Low Mental Health Burden (9%); Low Pain, High Mental Health Burden (2%), and Multisystem Multimorbid (1%). Logistic regression found that, compared to the Relatively Healthy reference, the Multisystem Multimorbid class, characterized by multiple opioid contraindications, had the highest odds of receiving long-term opioid therapy (OR = 9.24; p < .001; 95% confidence interval [CI]: 8.56, 9.98).
Analyses demonstrated that classes with greater multimorbidity at the time of prescription, particularly co-occurring psychiatric and pain disorders, had higher likelihood of long-term opioid therapy. Overall, this study helps identify patients most at risk for long-term opioid therapy and has implications for health care policy and patient care.
近年来,阿片类药物的过度处方有所减少;然而,某些人群仍面临高风险。使用特定的多种合并症模式评估处方率的研究很少。
确定与阿片类药物处方相关的独特临床特征,并评估它们接受长期阿片类药物治疗的相对可能性。
对完整的军事电子健康记录进行回顾性分析。我们评估了初始阿片类药物处方时存在的人口统计学和 26 种生理、心理和疼痛状况。潜在类别分析 (LCA) 使用诊断数据确定独特的临床特征。逻辑回归衡量这些类别接受长期阿片类药物治疗的可能性。
TRICARE 网络下的所有电子健康数据。
在 2016 年至 2019 年财政年度期间现役的所有现役军人,他们至少开了一种阿片类药物处方。
潜在类别分析 (LCA) 类别的数量和定性特征;逻辑回归的比值比 (OR)。我们假设,具有高风险禁忌症特征的 LCA 类别的长期阿片类药物治疗的可能性显著更高。
在研究期间,共有 714446 名现役军人开具了阿片类药物处方,其中 12940 人(1.8%)接受了长期阿片类药物治疗。潜在类别分析确定了五类:相对健康(82%);肌肉骨骼急性疼痛和物质使用障碍(6%);高疼痛、低心理健康负担(9%);低疼痛、高心理健康负担(2%)和多系统多合并症(1%)。逻辑回归发现,与相对健康的参考组相比,多系统多合并症类别的可能性最高,该类别的特征是多种阿片类药物禁忌,接受长期阿片类药物治疗的可能性最高(OR=9.24;p<0.001;95%置信区间[CI]:8.56,9.98)。
分析表明,在处方时合并症较多的人群,特别是同时存在精神和疼痛障碍的人群,长期接受阿片类药物治疗的可能性更高。总体而言,这项研究有助于确定最有可能接受长期阿片类药物治疗的患者,并对医疗保健政策和患者护理具有影响。