SOUTHERN SURGICAL ASSOCIATION ARTICLE
Inclusion of Sarcopenia Outperforms the Modified Frailty Index in Predicting 1-Year Mortality among 1,326 Patients Undergoing Gastrointestinal Surgery for a Malignant Indication Stefan Buettner, BS, Doris Wagner, MD, Yuhree Kim, MD, MPH, Georgios A Margonis, MD, PhD, Martin A Makary, MD, MPH, Ana Wilson, DO, Kazunari Sasaki, MD, Neda Amini, MD, Faiz Gani, MBBS, Timothy M Pawlik, MD, MPH, PhD, FACS Although it is a useful metric for preoperative risk stratification, frailty can be difficult to identify in patients before surgery. We sought to develop a preoperative frailty-risk model combining sarcopenia with clinical parameters to predict 1-year mortality using a cohort of patients undergoing gastrointestinal cancer surgery. STUDY DESIGN: We identified 1,326 patients undergoing hepatobiliary, pancreatic, or colorectal surgery between 2011 and 2014. Sarcopenia defined by psoas density was measured using preoperative cross-sectional imaging. Multivariable Cox regression analysis was performed to identify preoperative risk factors associated with 1-year mortality and used to develop a preoperative risk-stratification score. RESULTS: Among all patients identified, 640 (48.3%) patients underwent pancreatic surgery, 347 (26.2%) underwent a hepatobiliary procedure, and 339 (25.5%) a colorectal procedure. Using sex-specific cut-offs, 398 (30.0%) patients were categorized as sarcopenic. Sarcopenic patients were more likely to develop postoperative complications vs non-sarcopenic patients (odds ratio [OR] 1.80, 95% CI 1.42 to 2.29; p < 0.001). Overall 1-year mortality was 9.4%. On multivariable analysis, independent risk factors for 1-year mortality included increasing age (65 to 75 years: [hazard ratio (HR) 1.81, 95% CI 1.05 to 3.14] greater than 75 years [HR 2.79, 95% CI 1.55 to 5.02]), preoperative anemia hemoglobin < 12.5 g/dL (HR 1.68, 95% CI 1.17 to 2.40), and preoperative sarcopenia (HR 1.98, 95% CI 1.36 to 2.88; all p < 0.05). Using these variables, a 28-point weighed composite score was able to stratify patients by their risk for mortality 1 year after surgery (C-statistic ¼ 0.70). The proposed score outperformed other indices of frailty including the modified Frailty Index (C-statistic ¼ 0.55) and the Eastern Cooperative Oncology Group (ECOG) performance score (C-statistic ¼ 0.57) (both p < 0.05). CONCLUSION: Sarcopenia was combined with clinical factors to generate a composite risk-score that can be used to identify frail patients at greatest risk for 1-year mortality after gastrointestinal cancer surgery. (J Am Coll Surg 2016;-:1e11. 2016 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved.)
BACKGROUND:
Given advances in surgical technique and medical therapy, an increasing number of patients are being considered as surgical candidates for a wide array of gastrointestinal cancers.1-4 Although perioperative mortality is relatively low, many patients are at risk for adverse postoperative outcomes due to the often complex nature of these procedures.4-6 Furthermore, with an estimated 70 million patients expected to be 65 years or older by 2030, preoperative risk assessment and appropriate
Disclosure Information: Nothing to disclose. Presented at the Southern Surgical Association 127th Annual Meeting, Hot Springs, VA, December 2015. Received November 30, 2015; Accepted December 10, 2015. From the Department of Surgery, Johns Hopkins Hospital, Baltimore, MD. Correspondence address: Timothy M Pawlik, MD, MPH, PhD, FACS, Department of Surgery, Johns Hopkins Hospital, 600 N Wolfe St, Blalock 688, Baltimore, MD 21287. email: [email protected]
ª 2016 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved.
1
http://dx.doi.org/10.1016/j.jamcollsurg.2015.12.020 ISSN 1072-7515/15
2
Buettner et al
Abbreviations and Acronyms
ASA CCI ECOG HR HUAC IQR mFI OR TPA TPV
¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼
J Am Coll Surg
Frailty to Predict 1-Year Mortality
American Society of Anesthesiologists Charlson Comorbidity Index Eastern Cooperative Oncology Group hazard ratio Hounsfield unit average calculation interquartile range modified Frailty Index odds ratio total psoas area total psoas volume
patient selection for these complex procedures have taken on increased importance.7 Several studies have noted that physiologic, rather than chronologic, age is more strongly associated with perioperative outcomes.8-10 Specifically, the evaluation of patient frailtyda physiologic syndrome characterized by a cumulative decline across multiple physiological systemsdhas been proposed as an important metric to assess perioperative risk.11-14 A standard objective assessment of frailty to measure a patient’s physiologic reserve can be difficult to define.15 Frailty can be measured by combining information from a patient’s medical history, physical examination, and assessment of physical and functional status.16 These proposed composite measures are, however, often timeconsuming, cumbersome to record, and reliant on multiple subjective measurements.17,18 For example, the Frailty Index (FI) developed by the Canadian Study of Health and Aging (CSHA) consists of a 70-item scale derived from patient history and physical examination.19,20 A more recent modified iteration of the frailty index proposed by Obeid and colleagues21 maps 11 characteristics from the FI to data from the National Surgical Quality Improvement Program (NSQIP). Other groups, including our own, have proposed the use of sarcopenia (muscle wasting) as an alternative, objective, and easy to measure marker for patient frailty.22-25 To date, most data on patient physiologic reserve, frailty, and sarcopenia have focused exclusively on shortterm outcomes.13,14,26 Specifically, data on the use of the modified frailty index (mFI), as well as sarcopenia, to determine patient outcomes have been limited to reports on perioperative morbidity and mortality within the first 30 to 90 days after surgery.21,23 Although information on immediate short-term outcomes is important, data to predict death within 1 year of surgery are also relevant to patients and providers. Given that major gastrointestinal surgery can be associated with some degree of morbidity and loss of quality of life, accurate identification of patients who are the least likely to benefit from surgery
would be valuable.27 Therefore, the objective of this study was to identify factors, as well as to assess the prognostic accuracy of the mFI, in predicting 1-year mortality after hepato-pancreatico-biliary and colorectal surgery. Specifically, we sought to develop a preoperative frailty-risk model using both clinical and morphometric parameters to predict 1-year outcomes of patients after major surgery.
METHODS Data sources and patient population Patients undergoing a hepatobiliary, pancreatic, or colorectal resection for malignant disease between January 1, 2011 and December 31, 2014 at the Johns Hopkins Hospital were identified using relevant International Classification of DiseaseeClinical Modification (ICD-9-CM) procedure and diagnosis codes. Patients aged less than 18 years and patients undergoing emergent procedures were excluded from the study. For each patient record, detailed sociodemographic, clinicopathologic, and laboratory data were extracted from hospital records. Specifically, sociodemographic and clinicopathologic data that were collected included age, sex, and race, as well as preoperative comorbidity, preoperative functional and performance status, BMI, smoking status, procedure type, year of procedure, duration of ICU stay, length of stay for the index admission, and development of postoperative complications. Preoperative comorbidity was classified according to the Charlson Comorbidity Index (CCI) (CCI ¼ 0 to 2 and CCI 3).28 Functional and performance status were categorized according to the American Society of Anesthesiologists (ASA) physical classification grade and the Eastern Cooperative Oncology Group (ECOG) performance score, respectively.29,30 To assess preoperative frailty, the mFI score was calculated for each patient using a composite score derived from 11 conditions identified by the Canadian Study of Health and Aging mapped to the American College of Surgeons NSQIP database.21 Conditions included diabetes mellitus, COPD, active pneumonia infection, heart disease (defined as either a history of congestive heart failure within 30 days before surgery, or a history of myocardial infarction within the 6 months preceding surgery), hypertension requiring medical treatment, peripheral vascular disease, altered sensorium, cerebrovascular disease (with and without neurologic impairment), and impaired functional status.21 Using previously described methodology, an mFI score was calculated for each patient as the proportion of the total number of conditions present from the 11 conditions that were measured.21 For example, if a patient presented with a history of diabetes mellitus and a history of
Vol.
-,
No.
-, -
2016
congestive heart failure within 30 days before surgery, his or her calculated mFI would be 0.18 (2 of 11 conditions).21 To limit spurious analysis with low numbers, patients with an mFI > 0.36 were grouped together and represented a high mFI score.21 Image analysis and calculating sarcopenia For all patients who met inclusion criteria, preoperative abdominal CT images within 90 days of surgery were reviewed, and morphometric measurements of sarcopenia, including total psoas area (TPA), total psoas volume (TPV), and total psoas density (HUAC: Hounsfield unit average calculation) were calculated. Using the Ultravisual software package (Merge Emageon), TPA was measured in a semi-automated fashion with a manual outlining of the psoas muscle borders at the level of the third lumbar vertebra (L3), where both iliac crests were clearly visible.24,25 Similarly, TPV was calculated using the AW Workstation Volume Viewer Software (GE Healthcare) by 3 manual measurements at the level of the L3 vertebra on the first image where both iliac crests are clearly visible.22,31 To reduce potential bias due to vascular and/ or fatty infiltration, all measurements were performed with a density threshold setting between 30 and 110 Hounsfield units (HU). For greater comparability, all measurements for TPA and TPV were normalized for height calculated as (height [m] height [m]). A measure of muscle density and fatty infiltration, HUAC, was calculated for both right and left psoas muscles using the methodology described by Joglekar and colleagues.23 Right and left psoas muscles were evaluated, and the average psoas density was used to calculate the final HUAC: right Hounsfield unit calculation (RHUC) ¼ (right Hounsfield unit*right psoas area)/(total psoas area); left Hounsfield unit calculation (LHUC) ¼ (left Hounsfield unit*left psoas area)/(total psoas area); and final HUAC ¼ (right Hounsfield unit calculation þ left Hounsfield unit calculation)/2.23 Optimum stratification based on sensitivity analyses was performed using logrank statistics to define the optimal sex-specific cut-offs for TPA, TPV, and HUAC associated with the primary outcome of interest (1-year mortality). Statistical analysis Continuous variables were reported as medians with interquartile range (IQR); categorical variables were reported as whole numbers and percentages. Univariable comparisons for continuous variables were performed using the Kruskal-Wallis test, and for categorical variables using the Pearson chi-square test. Multiple imputations were performed using the MICE package for R version 3.0.3 (www.r-project.net) to account for missing data
Buettner et al
Frailty to Predict 1-Year Mortality
3
for preoperative hemoglobin (14.8%), ECOG score (16.9%), CCI (1.3%), and ASA (3.8%). The primary outcome of the study was 1-year mortality, calculated from the date of surgery to the date of death or last available follow-up, as appropriate. Oneyear mortality was estimated using the Kaplan-Meier method, and differences in survival between patient groups were compared using the log-rank test. To identify preoperative risk factors for 1-year mortality, multivariable Cox-proportional hazards regression analysis was performed using a backward stepwise selection based on Akaike Information Criterion (AIC). Model performance was assessed using Harrell’s concordance index (C-index), and bootstrap resampling was performed to quantify model overfit. Regression coefficients from multivariable regression analysis were reported as hazard ratios (HR) with 95% confidence intervals (95% CI); beta coefficients from the multivariable model were subsequently used to develop a nomogram to predict the probability of 1-year mortality after surgery. All statistical analyses were performed using STATA version 14.0 (StataCorp) or R version 3.0.3 (http://www.r-project.org). Statistical significance was defined as p < 0.05. The study was approved by the Johns Hopkins University Institutional Review Board.
RESULTS Baseline demographic and clinicopathologic characteristics We identified a total of 1,326 patients who met inclusion criteria (Table 1). The median age of the study cohort was 62.5 years (IQR 53 to 70 years); the majority were male (n ¼ 730, 55.1%) and Caucasian (n ¼ 1,115, 84.1%). The median BMI for all patients was 26.1 kg/m2 (IQR 23.1 to 29.9 kg/m2); 9.8% (n ¼ 111) of the cohort were either active smokers or had a previous smoking history. Comorbidity was common, as 42.7% (n ¼ 559) of patients presented with an age-adjusted CCI 3. All patients had a malignant indication for surgery and were operated on with curative intent. At the time of surgery, 347 (26.2%) patients underwent a hepatectomy, 640 (48.3%) had a pancreatic resection, and 339 (25.5%) a colorectal resection (Table 2). More than one-third of patients presented with an mFI score of 0 (n ¼ 501, 37.8%), with approximately 29.1% of patients presenting with an mFI score 0.18. Among all patients, the median TPA, TPV, and HUAC were 7.8 cm2/m2 (IQR 6.4 to 9.5 cm2/m2), 27.8 cm3/m2 (IQR 21.8 to 34.3 cm3/m2), and 45.0 HU (IQR 36.7 to 51.0), respectively (Supplemental Fig. 1). Given that HUAC performed slightly better on sensitivity analyses, and is
4
Buettner et al
Table 1.
Clinicopathologic and Operative Characteristics of the Cohort by Sarcopenia
Characteristic
J Am Coll Surg
Frailty to Predict 1-Year Mortality
All patients (N ¼ 1,326)
Age, y, median (IQR) 62.5 (53.0e70.0) Sex, n (%) Female 596 (44.9) Male 730 (55.1) Race, n (%) White 1115 (84.1) Black 104 (7.8) Others 107 (8.1) BMI, kg/m2, median (IQR) 26.1 (23.1e29.9) Smoking status (n ¼ 1,138), n (%) Nonsmoker 1027 (90.2) Smoker 111 (9.8) Charlson Index (n ¼ 1,309), median (IQR) 2.0 (1.0e3.0) 0e2, n (%) 750 (57.3) 3, n (%) 559 (42.7) ASA (n ¼ 1,275), n (%) 1 8 (0.6) 2 366 (28.7) 3 866 (67.9) 4 34 (2.7) 5 1 (0.1) Modified Frailty Score, n (%) 0.00 501 (37.8) 0.09 439 (33.1) 0.18 270 (20.4) 0.27 82 (6.2) 0.36 23 (1.7) 0.45 11 (0.8) ECOG functional status (n ¼ 1,102), n (%) 0 587 (53.3) 1 491 (44.6) 2 21 (1.9) 3 2 (0.2) 4 1 (0.1) TPA, cm2/m2, median (IQR) 7.8 (6.4e9.5) TPV, cm3/m2, median (IQR) 27.8 (21.8e34.3) HUAC, HU, median (IQR) 45.0 (36.7e51.0) Metastasis, n (%) M0 1050 (79.2) M1 276 (20.8)
Patients without sarcopenia* (n ¼ 928)
Patients with sarcopenia* (n ¼ 398)
59.0 (51.0e68.0)
68.0 (61.0e75.0)
417 (44.9) 511 (55.1)
179 (45.0) 219 (55.0)
761 84 83 26.0
(82.0) (9.1) (8.9) (22.9e29.9)
354 20 24 26.5
(88.9) (5.0) (6.0) (23.5e30.3)
723 68 2.0 610 307
(91.4) (8.6) (1.0e3.0) (66.5) (33.5)
304 43 3.0 140 252
(87.6) (12.4) (2.0e4.0) (35.7) (64.3)
6 299 566 14
(0.7) (33.8) (64.0) (1.6) 0
2 67 300 20 1
(0.5) (17.2) (76.9) (5.1) (0.3)
398 306 169 38 13 4
(42.9) (33.0) (18.2) (4.1) (1.4) (0.4)
103 133 101 44 10 7
(25.9) (33.4) (25.4) (11.1) (2.5) (1.8)
433 336 7 0 1 8.0 29.1 48.3
(55.7) (43.2) (0.9) (0.0) (0.1) (6.6e9.8) (22.8e35.7) (44.4e53.4)
154 155 14 2 0 7.3 24.3 32.7
(47.4) (47.7) (4.3) (0.6) (0.0) (6.0e9.0) (20.2e30.5) (27.2e35.9)
p Value
Inclusion of Sarcopenia Outperforms the Modified Frailty Index in Predicting 1-Year Mortality among 1,326 Patients Undergoing Gastrointestinal Surgery for a Malignant Indication Stefan Buettner, BS, Doris Wagner, MD, Yuhree Kim, MD, MPH, Georgios A Margonis, MD, PhD, Martin A Makary, MD, MPH, Ana Wilson, DO, Kazunari Sasaki, MD, Neda Amini, MD, Faiz Gani, MBBS, Timothy M Pawlik, MD, MPH, PhD, FACS Although it is a useful metric for preoperative risk stratification, frailty can be difficult to identify in patients before surgery. We sought to develop a preoperative frailty-risk model combining sarcopenia with clinical parameters to predict 1-year mortality using a cohort of patients undergoing gastrointestinal cancer surgery. STUDY DESIGN: We identified 1,326 patients undergoing hepatobiliary, pancreatic, or colorectal surgery between 2011 and 2014. Sarcopenia defined by psoas density was measured using preoperative cross-sectional imaging. Multivariable Cox regression analysis was performed to identify preoperative risk factors associated with 1-year mortality and used to develop a preoperative risk-stratification score. RESULTS: Among all patients identified, 640 (48.3%) patients underwent pancreatic surgery, 347 (26.2%) underwent a hepatobiliary procedure, and 339 (25.5%) a colorectal procedure. Using sex-specific cut-offs, 398 (30.0%) patients were categorized as sarcopenic. Sarcopenic patients were more likely to develop postoperative complications vs non-sarcopenic patients (odds ratio [OR] 1.80, 95% CI 1.42 to 2.29; p < 0.001). Overall 1-year mortality was 9.4%. On multivariable analysis, independent risk factors for 1-year mortality included increasing age (65 to 75 years: [hazard ratio (HR) 1.81, 95% CI 1.05 to 3.14] greater than 75 years [HR 2.79, 95% CI 1.55 to 5.02]), preoperative anemia hemoglobin < 12.5 g/dL (HR 1.68, 95% CI 1.17 to 2.40), and preoperative sarcopenia (HR 1.98, 95% CI 1.36 to 2.88; all p < 0.05). Using these variables, a 28-point weighed composite score was able to stratify patients by their risk for mortality 1 year after surgery (C-statistic ¼ 0.70). The proposed score outperformed other indices of frailty including the modified Frailty Index (C-statistic ¼ 0.55) and the Eastern Cooperative Oncology Group (ECOG) performance score (C-statistic ¼ 0.57) (both p < 0.05). CONCLUSION: Sarcopenia was combined with clinical factors to generate a composite risk-score that can be used to identify frail patients at greatest risk for 1-year mortality after gastrointestinal cancer surgery. (J Am Coll Surg 2016;-:1e11. 2016 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved.)
BACKGROUND:
Given advances in surgical technique and medical therapy, an increasing number of patients are being considered as surgical candidates for a wide array of gastrointestinal cancers.1-4 Although perioperative mortality is relatively low, many patients are at risk for adverse postoperative outcomes due to the often complex nature of these procedures.4-6 Furthermore, with an estimated 70 million patients expected to be 65 years or older by 2030, preoperative risk assessment and appropriate
Disclosure Information: Nothing to disclose. Presented at the Southern Surgical Association 127th Annual Meeting, Hot Springs, VA, December 2015. Received November 30, 2015; Accepted December 10, 2015. From the Department of Surgery, Johns Hopkins Hospital, Baltimore, MD. Correspondence address: Timothy M Pawlik, MD, MPH, PhD, FACS, Department of Surgery, Johns Hopkins Hospital, 600 N Wolfe St, Blalock 688, Baltimore, MD 21287. email: [email protected]
ª 2016 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved.
1
http://dx.doi.org/10.1016/j.jamcollsurg.2015.12.020 ISSN 1072-7515/15
2
Buettner et al
Abbreviations and Acronyms
ASA CCI ECOG HR HUAC IQR mFI OR TPA TPV
¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼
J Am Coll Surg
Frailty to Predict 1-Year Mortality
American Society of Anesthesiologists Charlson Comorbidity Index Eastern Cooperative Oncology Group hazard ratio Hounsfield unit average calculation interquartile range modified Frailty Index odds ratio total psoas area total psoas volume
patient selection for these complex procedures have taken on increased importance.7 Several studies have noted that physiologic, rather than chronologic, age is more strongly associated with perioperative outcomes.8-10 Specifically, the evaluation of patient frailtyda physiologic syndrome characterized by a cumulative decline across multiple physiological systemsdhas been proposed as an important metric to assess perioperative risk.11-14 A standard objective assessment of frailty to measure a patient’s physiologic reserve can be difficult to define.15 Frailty can be measured by combining information from a patient’s medical history, physical examination, and assessment of physical and functional status.16 These proposed composite measures are, however, often timeconsuming, cumbersome to record, and reliant on multiple subjective measurements.17,18 For example, the Frailty Index (FI) developed by the Canadian Study of Health and Aging (CSHA) consists of a 70-item scale derived from patient history and physical examination.19,20 A more recent modified iteration of the frailty index proposed by Obeid and colleagues21 maps 11 characteristics from the FI to data from the National Surgical Quality Improvement Program (NSQIP). Other groups, including our own, have proposed the use of sarcopenia (muscle wasting) as an alternative, objective, and easy to measure marker for patient frailty.22-25 To date, most data on patient physiologic reserve, frailty, and sarcopenia have focused exclusively on shortterm outcomes.13,14,26 Specifically, data on the use of the modified frailty index (mFI), as well as sarcopenia, to determine patient outcomes have been limited to reports on perioperative morbidity and mortality within the first 30 to 90 days after surgery.21,23 Although information on immediate short-term outcomes is important, data to predict death within 1 year of surgery are also relevant to patients and providers. Given that major gastrointestinal surgery can be associated with some degree of morbidity and loss of quality of life, accurate identification of patients who are the least likely to benefit from surgery
would be valuable.27 Therefore, the objective of this study was to identify factors, as well as to assess the prognostic accuracy of the mFI, in predicting 1-year mortality after hepato-pancreatico-biliary and colorectal surgery. Specifically, we sought to develop a preoperative frailty-risk model using both clinical and morphometric parameters to predict 1-year outcomes of patients after major surgery.
METHODS Data sources and patient population Patients undergoing a hepatobiliary, pancreatic, or colorectal resection for malignant disease between January 1, 2011 and December 31, 2014 at the Johns Hopkins Hospital were identified using relevant International Classification of DiseaseeClinical Modification (ICD-9-CM) procedure and diagnosis codes. Patients aged less than 18 years and patients undergoing emergent procedures were excluded from the study. For each patient record, detailed sociodemographic, clinicopathologic, and laboratory data were extracted from hospital records. Specifically, sociodemographic and clinicopathologic data that were collected included age, sex, and race, as well as preoperative comorbidity, preoperative functional and performance status, BMI, smoking status, procedure type, year of procedure, duration of ICU stay, length of stay for the index admission, and development of postoperative complications. Preoperative comorbidity was classified according to the Charlson Comorbidity Index (CCI) (CCI ¼ 0 to 2 and CCI 3).28 Functional and performance status were categorized according to the American Society of Anesthesiologists (ASA) physical classification grade and the Eastern Cooperative Oncology Group (ECOG) performance score, respectively.29,30 To assess preoperative frailty, the mFI score was calculated for each patient using a composite score derived from 11 conditions identified by the Canadian Study of Health and Aging mapped to the American College of Surgeons NSQIP database.21 Conditions included diabetes mellitus, COPD, active pneumonia infection, heart disease (defined as either a history of congestive heart failure within 30 days before surgery, or a history of myocardial infarction within the 6 months preceding surgery), hypertension requiring medical treatment, peripheral vascular disease, altered sensorium, cerebrovascular disease (with and without neurologic impairment), and impaired functional status.21 Using previously described methodology, an mFI score was calculated for each patient as the proportion of the total number of conditions present from the 11 conditions that were measured.21 For example, if a patient presented with a history of diabetes mellitus and a history of
Vol.
-,
No.
-, -
2016
congestive heart failure within 30 days before surgery, his or her calculated mFI would be 0.18 (2 of 11 conditions).21 To limit spurious analysis with low numbers, patients with an mFI > 0.36 were grouped together and represented a high mFI score.21 Image analysis and calculating sarcopenia For all patients who met inclusion criteria, preoperative abdominal CT images within 90 days of surgery were reviewed, and morphometric measurements of sarcopenia, including total psoas area (TPA), total psoas volume (TPV), and total psoas density (HUAC: Hounsfield unit average calculation) were calculated. Using the Ultravisual software package (Merge Emageon), TPA was measured in a semi-automated fashion with a manual outlining of the psoas muscle borders at the level of the third lumbar vertebra (L3), where both iliac crests were clearly visible.24,25 Similarly, TPV was calculated using the AW Workstation Volume Viewer Software (GE Healthcare) by 3 manual measurements at the level of the L3 vertebra on the first image where both iliac crests are clearly visible.22,31 To reduce potential bias due to vascular and/ or fatty infiltration, all measurements were performed with a density threshold setting between 30 and 110 Hounsfield units (HU). For greater comparability, all measurements for TPA and TPV were normalized for height calculated as (height [m] height [m]). A measure of muscle density and fatty infiltration, HUAC, was calculated for both right and left psoas muscles using the methodology described by Joglekar and colleagues.23 Right and left psoas muscles were evaluated, and the average psoas density was used to calculate the final HUAC: right Hounsfield unit calculation (RHUC) ¼ (right Hounsfield unit*right psoas area)/(total psoas area); left Hounsfield unit calculation (LHUC) ¼ (left Hounsfield unit*left psoas area)/(total psoas area); and final HUAC ¼ (right Hounsfield unit calculation þ left Hounsfield unit calculation)/2.23 Optimum stratification based on sensitivity analyses was performed using logrank statistics to define the optimal sex-specific cut-offs for TPA, TPV, and HUAC associated with the primary outcome of interest (1-year mortality). Statistical analysis Continuous variables were reported as medians with interquartile range (IQR); categorical variables were reported as whole numbers and percentages. Univariable comparisons for continuous variables were performed using the Kruskal-Wallis test, and for categorical variables using the Pearson chi-square test. Multiple imputations were performed using the MICE package for R version 3.0.3 (www.r-project.net) to account for missing data
Buettner et al
Frailty to Predict 1-Year Mortality
3
for preoperative hemoglobin (14.8%), ECOG score (16.9%), CCI (1.3%), and ASA (3.8%). The primary outcome of the study was 1-year mortality, calculated from the date of surgery to the date of death or last available follow-up, as appropriate. Oneyear mortality was estimated using the Kaplan-Meier method, and differences in survival between patient groups were compared using the log-rank test. To identify preoperative risk factors for 1-year mortality, multivariable Cox-proportional hazards regression analysis was performed using a backward stepwise selection based on Akaike Information Criterion (AIC). Model performance was assessed using Harrell’s concordance index (C-index), and bootstrap resampling was performed to quantify model overfit. Regression coefficients from multivariable regression analysis were reported as hazard ratios (HR) with 95% confidence intervals (95% CI); beta coefficients from the multivariable model were subsequently used to develop a nomogram to predict the probability of 1-year mortality after surgery. All statistical analyses were performed using STATA version 14.0 (StataCorp) or R version 3.0.3 (http://www.r-project.org). Statistical significance was defined as p < 0.05. The study was approved by the Johns Hopkins University Institutional Review Board.
RESULTS Baseline demographic and clinicopathologic characteristics We identified a total of 1,326 patients who met inclusion criteria (Table 1). The median age of the study cohort was 62.5 years (IQR 53 to 70 years); the majority were male (n ¼ 730, 55.1%) and Caucasian (n ¼ 1,115, 84.1%). The median BMI for all patients was 26.1 kg/m2 (IQR 23.1 to 29.9 kg/m2); 9.8% (n ¼ 111) of the cohort were either active smokers or had a previous smoking history. Comorbidity was common, as 42.7% (n ¼ 559) of patients presented with an age-adjusted CCI 3. All patients had a malignant indication for surgery and were operated on with curative intent. At the time of surgery, 347 (26.2%) patients underwent a hepatectomy, 640 (48.3%) had a pancreatic resection, and 339 (25.5%) a colorectal resection (Table 2). More than one-third of patients presented with an mFI score of 0 (n ¼ 501, 37.8%), with approximately 29.1% of patients presenting with an mFI score 0.18. Among all patients, the median TPA, TPV, and HUAC were 7.8 cm2/m2 (IQR 6.4 to 9.5 cm2/m2), 27.8 cm3/m2 (IQR 21.8 to 34.3 cm3/m2), and 45.0 HU (IQR 36.7 to 51.0), respectively (Supplemental Fig. 1). Given that HUAC performed slightly better on sensitivity analyses, and is
4
Buettner et al
Table 1.
Clinicopathologic and Operative Characteristics of the Cohort by Sarcopenia
Characteristic
J Am Coll Surg
Frailty to Predict 1-Year Mortality
All patients (N ¼ 1,326)
Age, y, median (IQR) 62.5 (53.0e70.0) Sex, n (%) Female 596 (44.9) Male 730 (55.1) Race, n (%) White 1115 (84.1) Black 104 (7.8) Others 107 (8.1) BMI, kg/m2, median (IQR) 26.1 (23.1e29.9) Smoking status (n ¼ 1,138), n (%) Nonsmoker 1027 (90.2) Smoker 111 (9.8) Charlson Index (n ¼ 1,309), median (IQR) 2.0 (1.0e3.0) 0e2, n (%) 750 (57.3) 3, n (%) 559 (42.7) ASA (n ¼ 1,275), n (%) 1 8 (0.6) 2 366 (28.7) 3 866 (67.9) 4 34 (2.7) 5 1 (0.1) Modified Frailty Score, n (%) 0.00 501 (37.8) 0.09 439 (33.1) 0.18 270 (20.4) 0.27 82 (6.2) 0.36 23 (1.7) 0.45 11 (0.8) ECOG functional status (n ¼ 1,102), n (%) 0 587 (53.3) 1 491 (44.6) 2 21 (1.9) 3 2 (0.2) 4 1 (0.1) TPA, cm2/m2, median (IQR) 7.8 (6.4e9.5) TPV, cm3/m2, median (IQR) 27.8 (21.8e34.3) HUAC, HU, median (IQR) 45.0 (36.7e51.0) Metastasis, n (%) M0 1050 (79.2) M1 276 (20.8)
Patients without sarcopenia* (n ¼ 928)
Patients with sarcopenia* (n ¼ 398)
59.0 (51.0e68.0)
68.0 (61.0e75.0)
417 (44.9) 511 (55.1)
179 (45.0) 219 (55.0)
761 84 83 26.0
(82.0) (9.1) (8.9) (22.9e29.9)
354 20 24 26.5
(88.9) (5.0) (6.0) (23.5e30.3)
723 68 2.0 610 307
(91.4) (8.6) (1.0e3.0) (66.5) (33.5)
304 43 3.0 140 252
(87.6) (12.4) (2.0e4.0) (35.7) (64.3)
6 299 566 14
(0.7) (33.8) (64.0) (1.6) 0
2 67 300 20 1
(0.5) (17.2) (76.9) (5.1) (0.3)
398 306 169 38 13 4
(42.9) (33.0) (18.2) (4.1) (1.4) (0.4)
103 133 101 44 10 7
(25.9) (33.4) (25.4) (11.1) (2.5) (1.8)
433 336 7 0 1 8.0 29.1 48.3
(55.7) (43.2) (0.9) (0.0) (0.1) (6.6e9.8) (22.8e35.7) (44.4e53.4)
154 155 14 2 0 7.3 24.3 32.7
(47.4) (47.7) (4.3) (0.6) (0.0) (6.0e9.0) (20.2e30.5) (27.2e35.9)
p Value
