使用诊断代码对虚弱评估的风险分析指数进行调整。

Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes.

机构信息

Department of Neurology, New York Presbyterian-Weill Cornell Medical Center, New York, New York.

Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico.

出版信息

JAMA Netw Open. 2024 May 1;7(5):e2413166. doi: 10.1001/jamanetworkopen.2024.13166.

Abstract

IMPORTANCE

Frailty is associated with adverse outcomes after even minor physiologic stressors. The validated Risk Analysis Index (RAI) quantifies frailty; however, existing methods limit application to in-person interview (clinical RAI) and quality improvement datasets (administrative RAI).

OBJECTIVE

To expand the utility of the RAI utility to available International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) administrative data, using the National Inpatient Sample (NIS).

DESIGN, SETTING, AND PARTICIPANTS: RAI parameters were systematically adapted to ICD-10-CM codes (RAI-ICD) and were derived (NIS 2019) and validated (NIS 2020). The primary analysis included survey-weighed discharge data among adults undergoing major surgical procedures. Additional external validation occurred by including all operative and nonoperative hospitalizations in the NIS (2020) and in a multihospital health care system (UPMC, 2021-2022). Data analysis was conducted from January to May 2023.

EXPOSURES

RAI parameters and in-hospital mortality.

MAIN OUTCOMES AND MEASURES

The association of RAI parameters with in-hospital mortality was calculated and weighted using logistic regression, generating an integerized RAI-ICD score. After initial validation, thresholds defining categories of frailty were selected by a full complement of test statistics. Rates of elective admission, length of stay, hospital charges, and in-hospital mortality were compared across frailty categories. C statistics estimated model discrimination.

RESULTS

RAI-ICD parameters were weighted in the 9 548 206 patients who were hospitalized (mean [SE] age, 55.4 (0.1) years; 3 742 330 male [weighted percentage, 39.2%] and 5 804 431 female [weighted percentage, 60.8%]), modeling in-hospital mortality (2.1%; 95% CI, 2.1%-2.2%) with excellent derivation discrimination (C statistic, 0.810; 95% CI, 0.808-0.813). The 11 RAI-ICD parameters were adapted to 323 ICD-10-CM codes. The operative validation population of 8 113 950 patients (mean [SE] age, 54.4 (0.1) years; 3 148 273 male [weighted percentage, 38.8%] and 4 965 737 female [weighted percentage, 61.2%]; in-hospital mortality, 2.5% [95% CI, 2.4%-2.5%]) mirrored the derivation population. In validation, the weighted and integerized RAI-ICD yielded good to excellent discrimination in the NIS operative sample (C statistic, 0.784; 95% CI, 0.782-0.786), NIS operative and nonoperative sample (C statistic, 0.778; 95% CI, 0.777-0.779), and the UPMC operative and nonoperative sample (C statistic, 0.860; 95% CI, 0.857-0.862). Thresholds defining robust (RAI-ICD <27), normal (RAI-ICD, 27-35), frail (RAI-ICD, 36-45), and very frail (RAI-ICD >45) strata of frailty maximized precision (F1 = 0.33) and sensitivity and specificity (Matthews correlation coefficient = 0.26). Adverse outcomes increased with increasing frailty.

CONCLUSION AND RELEVANCE

In this cohort study of hospitalized adults, the RAI-ICD was rigorously adapted, derived, and validated. These findings suggest that the RAI-ICD can extend the quantification of frailty to inpatient adult ICD-10-CM-coded patient care datasets.

摘要

重要性

即使是轻微的生理应激源,虚弱也与不良结局相关。经过验证的风险分析指数 (RAI) 可量化虚弱;然而,现有的方法将其应用限制在面对面访谈(临床 RAI)和质量改进数据集(行政 RAI)。

目的

通过使用国家住院患者样本 (NIS),将 RAI 的效用扩展到可用的国际疾病分类,第十版,临床修正版 (ICD-10-CM) 行政数据。

设计、设置和参与者:RAI 参数被系统地适应 ICD-10-CM 代码(RAI-ICD),并从 NIS 2019 中得出并验证(NIS 2020)。主要分析包括对接受主要手术的成年人进行调查加权的出院数据。通过包括 NIS(2020)和多医院医疗保健系统(UPMC,2021-2022)中的所有手术和非手术住院治疗,进行了额外的外部验证。数据分析于 2023 年 1 月至 5 月进行。

暴露

RAI 参数和院内死亡率。

主要结果和测量

使用逻辑回归计算和加权 RAI 参数与院内死亡率的关联,生成整数化 RAI-ICD 评分。经过初步验证,通过全套检验统计量选择了定义虚弱类别的阈值。比较了虚弱类别的择期入院率、住院时间、医院费用和院内死亡率。C 统计量估计了模型的判别能力。

结果

9548206 名住院患者(平均[SE]年龄为 55.4[0.1]岁;3742330 名男性[加权百分比为 39.2%]和 5804431 名女性[加权百分比为 60.8%])进行了 RAI-ICD 参数加权,对院内死亡率(2.1%;95%CI,2.1%-2.2%)进行建模,具有出色的推导判别能力(C 统计量,0.810;95%CI,0.808-0.813)。11 个 RAI-ICD 参数适应了 323 个 ICD-10-CM 代码。8113950 名手术患者的验证人群(平均[SE]年龄为 54.4[0.1]岁;3148273 名男性[加权百分比为 38.8%]和 4965737 名女性[加权百分比为 61.2%];院内死亡率为 2.5%[95%CI,2.4%-2.5%])反映了推导人群。在验证中,加权和整数化的 RAI-ICD 在 NIS 手术样本(C 统计量,0.784;95%CI,0.782-0.786)、NIS 手术和非手术样本(C 统计量,0.778;95%CI,0.777-0.779)和 UPMC 手术和非手术样本(C 统计量,0.860;95%CI,0.857-0.862)中产生了良好到优秀的判别能力。定义虚弱的稳健(RAI-ICD<27)、正常(RAI-ICD,27-35)、虚弱(RAI-ICD,36-45)和非常虚弱(RAI-ICD>45)分层的阈值最大限度地提高了精度(F1=0.33)和敏感性和特异性(马修斯相关系数=0.26)。不良结局随着虚弱程度的增加而增加。

结论和相关性

在这项对住院成年人的队列研究中,RAI-ICD 经过严格的改编、推导和验证。这些发现表明,RAI-ICD 可以将虚弱的量化扩展到住院成年 ICD-10-CM 编码的患者护理数据集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c67/11127118/ace61959ffab/jamanetwopen-e2413166-g001.jpg

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