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To date, surgeons have yet to operationalize a decade of research showing that frailty correlates with worse surgical outcomes. Despite the development of many scoring systems, there is no universally accepted and easy-to-calculate pointof-care assessment tool to accurately identify patients with frailty who are at risk for poor outcomes, nor are there proven programs to improve the fitness of the patients to prevent complications. Sarcopenia, progressive and generalized loss of muscle mass and function, is a risk factor for adverse outcomes, functional decline, and frailty in aging populations, but its assessment and management have faced similar challenges for integration into routine clinical practice.In this issue, Fumagalli et al 1 propose that artificial intelligence (AI) software can analyze muscle quantity and quality on preoperative computed tomography scans to provide automated and objective assessments of frailty to streamline clinical risk stratification. They retrospectively evaluated scans from 48 444 patients who underwent abdominal operations, using principal component analysis to develop weighted body composition scores. They found that muscle quantity and quality scores correlated with a validated frailty assessment 2 and were associated with 30-day morbidity, mortality, and readmission after adjusting for relevant factors (reduced risk of morbidity for highest vs lowest quartile: relative risk, 0.59 [95% CI, 0.52-0.67]; and reduced risk of readmission or mor-
To date, surgeons have yet to operationalize a decade of research showing that frailty correlates with worse surgical outcomes. Despite the development of many scoring systems, there is no universally accepted and easy-to-calculate pointof-care assessment tool to accurately identify patients with frailty who are at risk for poor outcomes, nor are there proven programs to improve the fitness of the patients to prevent complications. Sarcopenia, progressive and generalized loss of muscle mass and function, is a risk factor for adverse outcomes, functional decline, and frailty in aging populations, but its assessment and management have faced similar challenges for integration into routine clinical practice.In this issue, Fumagalli et al 1 propose that artificial intelligence (AI) software can analyze muscle quantity and quality on preoperative computed tomography scans to provide automated and objective assessments of frailty to streamline clinical risk stratification. They retrospectively evaluated scans from 48 444 patients who underwent abdominal operations, using principal component analysis to develop weighted body composition scores. They found that muscle quantity and quality scores correlated with a validated frailty assessment 2 and were associated with 30-day morbidity, mortality, and readmission after adjusting for relevant factors (reduced risk of morbidity for highest vs lowest quartile: relative risk, 0.59 [95% CI, 0.52-0.67]; and reduced risk of readmission or mor-
ImportancePhysical biomarkers for stratifying patients with lung cancer into subtypes suggestive of outcomes are underexplored.ObjectiveTo investigate the clinical utility of respiratory sarcopenia for optimizing postoperative risk stratification in patients with non–small cell lung cancer (NSCLC).Design, Setting, and ParticipantsThis retrospective cohort study reviewed consecutive patients undergoing lobectomy and mediastinal lymph node dissection for NSCLC at 2 institutions in Tokyo, Japan, between 2009 and 2018. Eligible patients underwent electronic computed tomography image analysis. Follow-up began at the date of surgery and continued until death, the last contact, or March 2022. Data analysis was performed from April 2022 to March 2023.Main Outcomes and MeasuresRespiratory sarcopenia was identified by poor respiratory strength (peak expiratory flow rate) and was confirmed by a low pectoralis muscle index (PMI; pectoralis muscle area/body mass index). Patients with poor peak expiratory flow rate but normal PMI received a diagnosis of pre–respiratory sarcopenia. Short-term and long-term postoperative outcomes were compared among patients with a normal status, pre–respiratory sarcopenia, and respiratory sarcopenia. Group differences were analyzed using the Kruskal-Wallis test and Pearson χ2 test for continuous and categorical data, respectively. Survival differences were compared using the log-rank test. Univariable and multivariable analyses were conducted using the Cox proportional hazards model.ResultsOf a total of 1016 patients, 806 (497 men [61.7%]; median [IQR] age, 69 [64-76] years) were eligible for electronic computed tomography image analysis. The median (IQR) duration of follow-up for survival was 5.2 (3.6-6.4) years. Respiratory strength was more closely correlated with PMI than pectoralis muscle radiodensity (Pearson r2, 0.58 vs 0.29). Respiratory strength and PMI declined with aging simultaneously (both P for trend < .001). Pre–respiratory sarcopenia was present in 177 patients (22.0%), and respiratory sarcopenia was present in 130 patients (16.1%). The risk of postoperative complications escalated from 82 patients (16.4%) with normal status to 39 patients (22.0%) with pre–respiratory sarcopenia to 39 patients (30.0%) with respiratory sarcopenia (P for trend < .001), as did the risk of delayed recovery after surgery (P for trend < .001). Compared with patients with normal status or pre–respiratory sarcopenia, patients with respiratory sarcopenia exhibited worse 5-year overall survival (438 patients [87.2%] vs 133 patients [72.9%] vs 85 patients [62.5%]; P for trend < .001). Multivariable analysis identified respiratory sarcopenia as a factor independently associated with increased risk of mortality (hazard ratio, 1.83; 95% CI, 1.15-2.89; P = .01) after adjustment for sex, age, smoking status, performance status, chronic heart disease, forced expiratory volume in 1 second, diffusing capacity for carbon monoxide, C-reactive protein, albumin, carcinoembryonic antigen, histology, and pathologic stage.Conclusions and RelevanceThis study identified individuals at higher risk of poor outcomes by screening and staging respiratory sarcopenia. The early diagnosis of respiratory sarcopenia could optimize management strategies and facilitate longitudinal care in patients with NSCLC.
ObjectivesTo compare the characteristics of body compositions between metabolic syndrome (MetS) and frailty, and determine the independent and overlapping of MetS and frailty with postoperative complications among older patients with gastric cancer.DesignA prospectively observational study.Setting and ParticipantsTwo hundred and eighty six older patients from 60 to 80 years undergoing radical gastrectomy for the first time.MeasurementsMetS was diagnosed by the criteria from the 2020 edition of Chinese guideline for the prevention and treatment of type 2 diabetes mellitus, and frailty was defined by frailty phenotype. An InBody770 impedance analyzer was used to measure body compositions and with 10 fat‐ and muscle‐related indicators being included in this study. Based on the presence of frailty and MetS, patients were divided into the frailty group, MetS group, frailty+MetS group, and normal group, and the body compositions indicators of these groups were compared. Clavien–Dindo classification was used to grade the severity of postoperative complications. Univariate and multivariate regression models were performed to explore the independent and joint association of MetS and frailty with postoperative complications.ResultsThe incidence rate of MetS, frailty, and frailty+MetS being 20.3%, 15.7%, and 4.2% respectively. Compared with the normal group, both fat and muscle compositions were decreased significantly in the frailty group (p < 0.05), while the statistically significant difference of fat‐to‐muscle mass ratio (FMR) and skeletal muscle mass to visceral fat area ratio (SVR) were not observed (p > 0.05). In contrast, except SVR, the other indicators of the MetS group were higher than the normal group (p < 0.05). As to the frailty+MetS group, there was a significant increase in fat compositions and FMR, as well as a significant decline in SVR (p < 0.05), while the difference of muscle compositions was not statistically significant (p > 0.05). There was an association of frailty with postoperative total (OR = 3.068, 95% CI: 1.402–6.713) and severe (OR = 9.423, 95% CI: 2.725–32.589) complications, but no association was found of MetS alone. MetS coexisting with frailty was associated with the highest risk of both total (OR = 3.852, 95% CI: 1.020–14.539) and severe (OR = 12.096, 95% CI: 2.183–67.024) complications.ConclusionsBoth frailty and MetS coexisting with frailty had adverse effects on postoperative complications, which appeared greatly different characteristics in body compositions and therefore reinforced the importance of targeted nutritional or metabolic intervention. Although MetS alone were not significantly associated with postoperative complications, it is essential to focus on the causal relationship and development trend between MetS and frailty to prevent MetS from shifting into frailty, considering the highest risk in their coexistence state.
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