Development and Validation of a Nomogram for Adverse Outcomes of Geriatric Trauma Patients Based on Frailty Syndrome

Abstract

Purpose Currently, assessing trauma severity alone in geriatric trauma patients (GTPs) cannot accurately predict the risk of serious adverse outcomes during hospitalization. As an emerging concept in recent years, frailty syndrome is closely related to the poor prognosis of many diseases in elderly patients, including trauma. A logistic model for predicting adverse outcomes in elderly trauma patients during hospitalization was constructed in elderly patients, and the predictive efficacy of the model was verified. Patients and Methods Trauma patients aged ≥65 years between June 2020 and September 2021 were selected and randomly divided into a training set and validation set at a ratio of 3:1. Mid arm muscle circumference (MAMC) was measured to determine the degree of frailty. LASSO regression was used to screen appropriate variables for the construction of a prognostic model. The logistic regression model was established and presented in the form of a nomogram. Calibration curves and ROC curves were used to verify the performance of the model. Results A total of 209 patients were enrolled, including 143 (68.4%) males and 66 (31.6%) females, with an average age of 70.8 ± 4.8 years. Ageless Charlson comorbidity index, BT unit, ISS, GCS, MAMC, prealbumin and lactic acid levels were screened by LASSO regression to construct a prognostic model. The AUC of the ROC analysis prediction model was 0.89 (95% CI 0.80–0.97) in the validation set. The results of the Hosmer–Lemeshow test for the validation set were χ2 = 11.23, P = 0.189. Conclusion The prognostic model of adverse outcomes in GTPs has good accuracy and differentiation, which can improve the prediction results of risk stratification of GTPs during hospitalization by medical staff and provide a new idea for prognostic prediction.

Development and validation of a nomogram for predicting persistent inflammation, immunosuppression, and catabolism syndrome in trauma patients

“…Different models have been developed for predicting fatigue syndrome and poor outcomes in elderly trauma patients ( 22 ), which have demonstrated good predictive and evaluative capabilities in trauma patients. By combining multiple variables and their respective weights, the nomogram provides a visualized model for risk prediction ( 23 , 24 ), enabling clinicians to make effective predictions of the probability of PIICS occurrence in individual patients. This can aid in identifying high-risk patients and implementing targeted interventions, such as immunomodulatory therapy or nutritional support ( 25 ), to mitigate the progression of PIICS and its associated complications.…”

“…The top three body regions with the most severe injuries were the head and neck (26.5%), abdomen (20.1%), and chest (20%). The median NRS 2002 and ISS scores were 3 (1-3) and 25 (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33), respectively.…”

Post-discharge nutritional management for patients with coronary heart disease and frailty: a qualitative study

“…In elderly patients with coronary heart disease, this restricted activity leads to loss of muscle mass and abnormalities in body metabolism, features that are closely associated with the development of frailty in old age [ 2 ]. Studies have shown that the incidence of frailty in elderly patients with coronary artery disease ranges from 18.8% to 57.8%, which can increase their readmission rates by a factor of 4.3 [ 3 ] and the incidence of malnutrition among such patients by a factor of 6.841 [ 4 ]. Leading to challenges in the quality of life of discharged elderly patients with coronary heart disease and frailty.…”