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.