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[索赔数据中的疼痛特异性诊断模式——通过分类与回归树(CART)进行识别]

[Pain-specific diagnosis patterns in claims data - Identification by means of classification and regression trees (CART)].

作者信息

Schiffhorst G, Freytag A, Höer A, Häussler B, Gothe H

机构信息

IGES - Institut GmbH, Berlin, Germany.

出版信息

Gesundheitswesen. 2010 Jun;72(6):347-55. doi: 10.1055/s-0030-1249692. Epub 2010 May 17.

Abstract

The identification of beneficiaries with persistent, recurrent or chronic pain in claims data by means of individual diagnoses or analgesic prescription is not sufficient and reliable. By using CLASSIFICATION AND REGRESSION TREES (CART) it was possible to identify specific diagnosis patterns for patients suffering from pain. Diagnosis patterns are considered as specific if they occur more frequently among beneficiaries with at least two opioid prescriptions within one year compared with beneficiaries who did not receive any analgesic therapy. Diagnosis and prescription data of 2006 were provided by the German sickness fund DAK. As a result, 65 diagnosis patterns occurred more frequently among beneficiaries treated with opioids than among the control group. These 65 patterns can be classified as follows: cancer-related pain (4), specific back pain/osteoporosis (8), spine-related pain (6), arthritis-related pain/rheumatoid arthritis (22), pain after traumatic fractures (5), pain in multimorbid, dependent patients (3), neuropathic pain (7), headache (5), non-specific back pain (5). The derived diagnosis patterns showed high predictive values (sensitivity: 78%, specificity: 66%) and are suitable for the identification of beneficiaries suffering from pain - the first step towards health services research in pain-based on claims data.

摘要

通过个体诊断或镇痛处方在理赔数据中识别患有持续性、复发性或慢性疼痛的受益人是不够可靠的。利用分类与回归树(CART)能够识别疼痛患者的特定诊断模式。如果与未接受任何镇痛治疗的受益人相比,这些诊断模式在一年内至少有两份阿片类药物处方的受益人中出现得更为频繁,那么它们就被视为特定模式。2006年的诊断和处方数据由德国疾病基金DAK提供。结果显示,在接受阿片类药物治疗的受益人中,有65种诊断模式比对照组出现得更为频繁。这65种模式可分类如下:癌症相关疼痛(4种)、特定背痛/骨质疏松症(8种)、脊柱相关疼痛(6种)、关节炎相关疼痛/类风湿性关节炎(22种)、创伤性骨折后疼痛(5种)、多病共存的依赖患者的疼痛(3种)、神经性疼痛(7种)、头痛(5种)、非特异性背痛(5种)。得出的诊断模式显示出较高的预测价值(敏感性:78%,特异性:66%),适用于识别疼痛患者——这是以理赔数据为基础进行疼痛相关卫生服务研究的第一步。

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