Frailty is a common geriatric syndrome linked to negative clinical outcomes. Current assessments of frailty predominantly depend on various scales, lacking a standardized gold standard. Chronic inflammation is a key pathophysiological mechanism of frailty, and examination of inflammatory markers in routine blood tests is easy and simple-to-use. Nevertheless, the correlation between these inflammatory markers and frailty has not been fully elucidated.

To explore the correlation between inflammatory markers in routine blood tests and frailty in the elderly, analyze the influencing factors of frailty and construct a risk prediction model for the risk of frailty or pre-frailty.

Elderly individuals receiving physical examinations in the Healthcare Center, Beijing Friendship Hospital, Capital Medical University from August 2020 to September 2022 were recruited. Baseline characteristics and laboratory test results were collected. The frailty was assessed using the Simple Frailty Questionnaire (FRAIL) . Univariate and multivariate Logistic regression analyses were applied to identify risk factors for frailty/pre-frailty in the elderly. A nomogram was then created, followed by an internal validation of its performance via Bootstrap. Finally, the receiver operating characteristic (ROC) curves, calibration curve and decision curve analysis (DCA) were used to evaluate the identification ability, accuracy and clinical applicability of the nomogram.

A total of 554 elderly individuals were included in the study, of whom 213 (38.4%) were identified as frail or pre-frail. Multivariate Logistic regression analysis showed that age-adjusted Charlson Comorbidity Index (ACCI, OR=1.42, 95%CI=1.21-1.66) , Mini-nutritional Assessment-short Form (MNA-SF, OR=0.71, 95%CI=0.61-0.83) , hemoglobin/red cell distribution width ratio (HRR, OR=0.44, 95%CI=0.23-0.86) , and medication of multiple drugs (OR=0.54, 95%CI=0.36-0.81) were independent influencing factors of frailty or pre-frailty in the elderly (P<0.05) . The predictive nomogram was established by employing the above-mentioned variables. The area under the curve (AUC) of the nomogram for identifying frailty or pre-frailty in the elderly was 0.719 (95%CI=0.675-0.764) . The nomogram had a high goodness-of-fit after internal validation using the Bootstrap resampling method. The nomogram was found with a high goodness-of-fit by the Hosmer-Lemeshow test (P>0.05) . DCA showed that when the threshold probability of patients ranging from 0.15 to 0.95, the nomogram resulted in higher net benefit of predicting the risk of frailty or pre-frailty.

Comorbidities, medication of multiple drugs, malnutrition, and HRR are influencing factors of frailty or pre-frailty in the elderly. The constructed predictive nomogram shows strong discrimination, consistency, and clinical utility, offering valuable guidance for the early screening of frailty or pre-frailty.

衰弱是一种常见的老年综合征，与不良临床结局密切相关。目前评估主要依赖各种量表，缺乏统一的金标准。慢性炎症作为衰弱的病理生理机制之一，血常规炎性指标简单易获得，关于血常规炎性指标和衰弱之间的相关研究较少。

探讨体检老年人血常规炎性指标和衰弱的相关性，分析衰弱的影响因素并构建衰弱发生风险的预测模型。

选取2020年8月—2022年9月于首都医科大学附属北京友谊医院医疗保健中心行健康体检的老年人。收集研究对象的一般资料、体检实验室检查数据，并采用FRAIL量表评估衰弱。采用单因素及多因素Logistic回归分析探讨衰弱的影响因素并建立列线图预测模型，采用Bootstrap进行模型内部验证。使用受试者工作特征（ROC）曲线、Hosmer-Lemeshow校准曲线和临床决策曲线分析（DCA）评价预测模型的区分度、校准度及预测模型的临床有效性。

共纳入554例老年人，其中衰弱/衰弱前期213例（38.4%）。多因素Logistic回归分析结果显示，年龄校正的查尔森合并症指数（ACCI）（OR=1.42，95%CI=1.21~1.66）、简易营养筛查量表（MNA-SF）评分（OR=0.71，95%CI=0.61~0.83）、血红蛋白与红细胞体积分布宽度比值（HRR）（OR=0.44，95%CI=0.23~0.86）及多重用药（OR=0.54，95%CI=0.36~0.81）是老年人衰弱/衰弱前期的独立影响因素（P<0.05）。基于多因素Logistic回归分析中的影响因素构建衰弱预测模型，该模型预测老年人衰弱/衰弱前期的ROC曲线下面积（AUC）为0.719（95%CI=0.675~0.764），Bootstrap重抽样法进行内部验证后，列线图模型拟合度较好；Hosmer-Lemeshow校准曲线拟合度较好（P>0.05）；DCA显示当患者的阈值概率为0.15~0.95时，使用列线图模型预测衰弱发生风险更有益。

共病、多重用药、营养不良及HRR是老年人衰弱/衰弱前期的影响因素，构建的预测模型具有良好的区分度、一致性与临床实用性，可为衰弱/衰弱前期早期筛查提供指导。