Meltzer-Bruhn Ariana T, Esper Garrett W, Herbosa Christopher G, Ganta Abhishek, Egol Kenneth A, Konda Sanjit R
Orthopedic Surgery, New York University Langone Health, New York, USA.
Orthopedic Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
Cureus. 2022 Jul 8;14(7):e26666. doi: 10.7759/cureus.26666. eCollection 2022 Jul.
Background Smoking, obesity, and being below a healthy body weight are known to increase all-cause mortality rates and are considered modifiable risk factors. The purpose of this study is to assess whether adding these risk factors to a validated geriatric inpatient mortality risk tool will improve the predictive capacity for hip fracture patients. We hypothesize that the predictive capacity of the Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) tool will improve. Methodology Between October 2014 and August 2021, 2,421 patients >55-years-old treated for hip fractures caused by low-energy mechanisms were analyzed for demographics, injury details, hospital quality measures, and mortality. Smoking status was recorded as a current every-day smoker, former smoker, or never smoker. Smokers (current and former) were compared to non-smokers (never smokers). Body mass index (BMI) was defined as underweight (<18.5 kg/m), healthy weight (18.5-24.9 kg/m), overweight (25.0-24.9 kg/m), or obese (>30 kg/m). The baseline STTGMA tool for hip fractures (STTGMAHIP_FX_SCORE) was modified to include patients' BMI and smoking status (STTGMA_MODIFIABLE), and new mortality risk scores were calculated. Each model's predictive ability was compared using DeLong's test by analyzing the area under the receiver operating curves (AUROCs). Comparative analyses were conducted on each risk quartile. Results A comparison of smokers versus non-smokers demonstrated that smokers experienced higher rates of inpatient (p = 0.025) and 30-day (p = 0.048) mortality, myocardial infarction (p < 0.01), acute respiratory failure (p < 0.01), and a longer length of stay (p = 0.014). Comparison among BMI cohorts demonstrated that underweight patients experienced higher rates of pneumonia (p = 0.033), decubitus ulcers (p = 0.046), and the need for an intensive care unit (ICU) (p < 0.01). AUROC comparison demonstrated that STTGMA_MODIFIABLE significantly improved the predictive capacity for inpatient mortality compared to STTGMAHIP_FX_SCORE (0.792 vs. 0.672, p = 0.0445). Quartile stratification demonstrated the highest risk cohort had a longer length of stay (p < 0.01), higher rates of inpatient (p < 0.01) and 30-day mortality (p < 0.01), and need for an ICU (p < 0.01) compared to the minimal risk cohort. Patients in the lowest risk quartile were most likely to be discharged home (p < 0.01). Conclusions Smoking, obesity, and being below a healthy body weight increase the risk of perioperative complications and poor outcomes. Including smoking and BMI improves the STTGMAHIP_FX_SCORE tool to predict mortality and risk stratify patient outcomes. Because smoking, obesity, and being below a healthy body weight are modifiable patient factors, providers can counsel patients and implement lifestyle changes to potentially decrease their risk of longer-term poor outcomes, especially in the setting of another fracture. For patients who are former smokers, providers can use this information to encourage continued restraint and healthy choices.
背景 吸烟、肥胖以及体重低于健康水平均会增加全因死亡率,被视为可改变的风险因素。本研究旨在评估将这些风险因素添加到经过验证的老年住院患者死亡风险工具中是否会提高对髋部骨折患者的预测能力。我们假设老年和中年创伤分诊评分(STTGMA)工具的预测能力将会提高。方法 在2014年10月至2021年8月期间,对2421名年龄大于55岁、因低能量机制导致髋部骨折接受治疗的患者进行了人口统计学、损伤细节、医院质量指标及死亡率分析。吸烟状况记录为当前每日吸烟者、既往吸烟者或从不吸烟者。将吸烟者(当前吸烟者和既往吸烟者)与非吸烟者(从不吸烟者)进行比较。体重指数(BMI)定义为体重过轻(<18.5kg/m²)、健康体重(18.5 - 24.9kg/m²)、超重(25.0 - 29.9kg/m²)或肥胖(>30kg/m²)。对髋部骨折的基线STTGMA工具(STTGMAHIP_FX_SCORE)进行修改,纳入患者的BMI和吸烟状况(STTGMA_MODIFIABLE),并计算新的死亡风险评分。通过分析受试者工作曲线下面积(AUROC),使用德龙检验比较每个模型的预测能力。对每个风险四分位数进行比较分析。结果 吸烟者与非吸烟者的比较表明,吸烟者的住院死亡率(p = 0.025)、30天死亡率(p = 0.048)、心肌梗死发生率(p < 0.01)、急性呼吸衰竭发生率(p < 0.01)更高,住院时间更长(p = 0.014)。BMI队列之间的比较表明,体重过轻的患者肺炎发生率(p = 0.033)、压疮发生率(p = 0.046)以及入住重症监护病房(ICU)的需求(p < 0.01)更高。AUROC比较表明,与STTGMAHIP_FX_SCORE相比,STTGMA_MODIFIABLE显著提高了对住院死亡率的预测能力(0.792对0.672,p = 0.0445)。四分位数分层表明,与最低风险队列相比,最高风险队列的住院时间更长(p < 0.01)、住院死亡率(p < 0.01)和30天死亡率(p < 0.01)更高,入住ICU的需求也更高(p < 0.01)。最低风险四分位数的患者最有可能出院回家(p <