背景:来自成像扫描的骨骼肌密度(SMD)测量结果可识别肌肉骨化,并可筛查患者进行老年评估。我们评估了SMD性能作为一种筛查工具,以识别患有癌症的老年人可能是虚弱的,谁可以从深入评估中受益;我们比较了性别和糖尿病状态的表现。
方法:我们分析了癌症和衰老弹性评估(CARE)注册表中的患者。使用患者报告的老年评估(CARE工具)捕获虚弱和糖尿病。根据赤字积累原理,使用CARE脆弱指数(CARE-FI)定义脆弱。根据计算机断层扫描(L3椎骨)计算SMD。按性别和糖尿病状态进行分析。散点图和线性回归描述了SMD和脆弱评分之间的粗略关联。分类性能(脆弱与非虚弱)用(1)受试者工作特征曲线下面积(AUC)和置信区间(CI)进行分析;(2)性别特异性SMD四分位数截止值的敏感性/特异性(Q1,中位数,Q3)。使用差异和估计CIs(2000次自举重复)比较了有糖尿病和无糖尿病患者的表现。我们还计算了正负似然比(LR+,LR-)。
结果:分析队列包括872名患者(39%为女性,中位年龄68岁,27%患有糖尿病),主要是III/IV期胃肠道癌;>60%计划开始一线化疗。SMD与虚弱评分呈负相关;模型最适合男性糖尿病患者。女性(范围:0.58-0.62)和男性(0.58-0.68)患者的AUC估计值较低。Q3截止点灵敏度高(范围:0.76-0.89),但特异性差(0.25-0.34)。糖尿病并不影响女性患者的估计。与没有糖尿病的男性患者相比,男性糖尿病患者的敏感性估计更高(敏感性差异:0.23[0.07,0.38],0.08[-0.07,0.24],第一季度为0.11[0.00,0.22],中位数,Q3分别)。男性糖尿病患者的LR估计值最为显着(LR=2.92,Q1截止值;LR-=0.46,Q3截止值)。
结论:仅使用SMD筛查老年患者进行老年评估需要改进。高灵敏度的截止点可能会错过11-24%的虚弱患者,许多不虚弱的患者可能会被标记。使用SMD进行筛查是可行的,但需要努力了解不同截止点的临床和资源影响。未来的研究应该通过其他临床数据和亚组来评估性能。
BACKGROUND: Skeletal muscle density (SMD) measurements from imaging scans identify myosteatosis and could screen patients for geriatric assessment. We assessed SMD performance as a screening tool to identify older adults with cancer likely to be frail and who could benefit from in-depth assessment; we compared performance by sex and diabetes status.
METHODS: We analyzed patients in the Cancer & Aging Resilience Evaluation (CARE) Registry. Frailty and diabetes were captured using a patient-reported geriatric assessment (CARE tool). Frailty was defined using CARE frailty index (CARE-FI) based on principles of deficit accumulation. SMD was calculated from computed tomography scans (L3 vertebrae). Analyses were conducted by sex and diabetes status. Scatterplots and linear regression described crude associations between SMD and frailty score. Classification performance (frail vs. non-frail) was analyzed with (1) area under the receiver operating characteristic curves (AUC) and confidence intervals (CIs); and (2) sensitivity/specificity for sex-specific SMD quartile cut-offs (Q1, median, Q3). Performance was compared between patients with and without diabetes using differences and estimated CIs (2000 bootstrap replicates). We additionally calculated positive and negative likelihood ratios (LR+, LR-).
RESULTS: The analytic cohort included 872 patients (39% female, median age 68 years, 27% with diabetes) with predominately stage III/IV gastrointestinal cancer; >60% planning to initiate first-line chemotherapy. SMD was negatively associated with frailty score; models were best fit in male patients with diabetes. AUC estimates for female (range: 0.58-0.62) and male (0.58-0.68) patients were low. Q3 cut-offs had high sensitivity (range: 0.76-0.89), but poor specificity (0.25-0.34). Diabetes did not impact estimates for female patients. Male patients with diabetes had greater sensitivity estimates compared to those without (sensitivity differences: 0.23 [0.07, 0.38], 0.08 [-0.07, 0.24], and 0.11 [0.00, 0.22] for Q1, median, Q3, respectively). LR estimates were most notable for male patients with diabetes (LR+ = 2.92, Q1 cut-off; LR- = 0.46, Q3 cut-off).
CONCLUSIONS: Using SMD alone to screen older patients for geriatric assessment requires improvement. High-sensitivity cut-off points could miss 11-24% of patients with frailty, and many non-frail patients may be flagged. Screening with SMD is practical but work is needed to understand clinical andresource impacts of different cut-off points. Future research should evaluate performance with additional clinical data and in subgroups.