Abstract
Background
Frailty is increasingly becoming a powerful prognostic factor for cancer patients. The purpose of this study was to investigate the prognostic effect of 5-modified frailty index (mFI-5) on adverse outcomes after surgery in older patients with esophageal cancer over 65 years of age.
Methods
Patients over 65 years old who underwent esophagectomy between January 1, 2014 and January 31, 2017 were included in the study analysis. The mFI-5 variables include hypertension, type Ⅱ diabetes, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and independent functional status. Patients were divided into 3 groups: robust group (mFI-5, 0), prefrail group (mFI-5, 1) and frail group (mFI-5, 2 ~ 5). Primary outcome was 30-day mortality. Secondary outcomes were postoperative delirium and pneumonia. Logistic regression analyzes and COX analyzes were used to identify independent risk factors for outcomes. The receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive efficacy and clinical net benefit of different variables in predicting outcomes. Delong test was used to compare and discern the effectiveness of two ROC curves in a classification setting.
Results
A total of 699 patients were included in this retrospective cohort study, of which 342 (48.93%) in the robust group, 184 (26.32%) patients in the prefrail group, and 173 (24.75%) patients were in the frail group. Frail patients identified by mFI-5 had the highest incidence of postoperative 30-day mortality (frailty: 5.8% vs. prefrail: 1.6% vs. robust: 1.2%), delirium (22.5% vs. 14.7% vs. 2.9%, P < 0.001) and pneumonia (13.3% vs. 9.8% vs. 3.8%, P < 0.05). Multivariate stepwise regression analysis found that frailty was associated with a significantly increased risk of postoperative 30-day mortality [adjusted Odds Ratio (aOR) = 14.30, 95%CI: 4.87–42.03, P < 0.001], delirium (aOR = 6.82, 95%CI: 3.12–14.89, P < 0.001), and pneumonia (aOR = 4.12, 95%CI: 2.52–6.72, P < 0.001). mFI-5 combined with Age and ASA classification had the highest predictive value in predicting postoperative adverse outcomes in older patients with esophageal cancer [30-day mortality area under curve (AUC): 0.84; delirium AUC: 0.78, and pneumonia AUC: 0.67].
Conclusions
The 5-modified frailty index is a convenient and effective tool to predict postoperative 30-day mortality, delirium and pneumonia in older patients over 65 years old undergoing esophagectomy. The mFI-5 could guide clinical decision-making and become a highly promising prognostic scale for risk stratification of esophageal cancer older patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-025-06452-0.
Keywords: 5-modified frailty index, Esophageal cancer, Adverse outcomes, Older patients
Background
Esophageal cancer is the eighth most common cancer globally, with approximately 240,000 new cases diagnosed each year, primarily affecting the older population [1]. With an aging global population that continues to grow, esophageal cancer poses a considerable threat to the older population [2, 3]. While esophagectomy is a common treatment option for many esophageal cancer patients, it is often associated with high mortality rates, reduced quality of life, and increased medical expenses [4]. Consequently, optimizing perioperative management strategies for older patients with esophageal cancer remains a significant challenge for clinical frontline doctors.
The significance of symptom management following a cancer diagnosis and perioperative risk stratification has gained increasing attention in recent clinical cancer research [5]. Frailty is a condition characterized by the loss of physiological reserves in multiple organ systems, making patients more susceptible to homeostasis disorders and organ dysfunction when exposed to postoperative stressors [6, 7]. Clinically, frailty is often described as a state of reduced physiological reserve, manifesting as a decline in physical ability, metabolic function, and cognition. A systematic review of general surgery found prevalence estimates for patients ranging between 21.3% and 45.8% for those who are prefrail, and between 10.4% and 37.0% for those who are frail [8]. It is inevitably becoming a trend for frailty to increasingly establish itself as a risk factor, particularly in the context of postoperative adverse outcomes [9]. There may be a correlation between preoperative frailty and the risk of complication, readmission, and mortality, particularly after both inpatient and outpatient surgery in multiple surgical disciplines [10]. The incidence of complications and frailty is higher in older patients who undergo esophagectomy, which has a negative impact on both postoperative course and survival. Although frailty has primarily been examined as a clinical manifestation in older adults, there is currently no widely utilized and standardized clinical “gold-standard” assessment tool to quantify frailty.
The concept of the cumulative defect model has persistently evolved using the National Surgical Quality Improvement Program (NSQIP) database, leading to the introduction of the modified Frailty Index-5 (mFI-5) as a proposed measure [11]. mFI-5 has only been validated in a few surgical preoperative risk stratification studies [12–14]. Nonetheless, the significance of preoperative frailty identification remains inadequately validated in cohorts undergoing radical esophagectomy. Our study aimed at determining the predictive effect of the mFI-5 on short-term survival and adverse events in older patients who underwent radical esophagectomy. Furthermore, a secondary objective of our study is to evaluate the feasibility of combining a comprehensive frailty assessment with a standard preoperative workup in the field of thoracic surgery.
Method
Study population
We retrospectively analyzed the case data of 699 patients (aged ≥ 65 years) who underwent radical surgery for esophageal cancer from January 1, 2014 to January 31, 2017 in the perioperative database of geriatric thoracic surgery in the First Medical Center of Chinese PLA General Hospital. Esophageal cancer diagnoses were verified via manual chart review of electronic medical records. This observational study was approved by the Ethics Committee of Chinese PLA General Hospital (S2021-342-01).
Patients need to meet the following conditions for inclusion in the study: (1) age ≥ 65 years, (2) tissue confirmed diagnosis of esophageal cancer and underwent an esophagectomy (comprising partial and total esophagectomy), and (3) the surgical time took more than 2 h and the postoperative hospital stay was longer than 3 days. Exclude the following patients: (1) patients with ASA stage V, (2) patients with delirium or coma before the operation, (3) pneumonia and pulmonary infection in the patient’s preoperative medical records, and (4) missing covariate or accurate outcome during follow-up.
Data sources and covariates
Electronic data is derived from patient records systems, clinical examination systems, medical imaging management systems, radio-information management systems, transfusion management systems and nursing workstations. Access to data in electronic medical record systems using SQL servers (Microsoft, United States). From the patient record integrated management system (PRIDE 2.1.2.193, Heren Health, China), we extracted the preoperative data of eligible patients, including gender, age, American society of anesthesiologists (ASA) classification, body mass index (BMI), smoking history, drinking history, comorbidities and mFI-5 variables. Intraoperative data obtained from anesthesia and medical records included anesthesia method, estimated blood loss, operative duration, anesthesia duration, surgical procedure, pathological diagnosis type, degree of tumor invasion and lymph node metastasis. Laboratory testing includes hemoglobin, red blood cell, serum albumin, leukocyte count, serum potassium, blood sodium, total bilirubin, and preoperative mean artery pressure. Research members manually checked electronic anesthesia record and laboratory report for all procedures and compared the results with electronic data extractions for these cases to ensure data accuracy. All data was then reviewed and confirmed by six clinical professionals (Xu ZY, Guo YX, Liu JJ, Tian JY, Cao FY, and Song YP), and conflicting data was double-checked to ensure data quality.
5-Modified Frailty Index
The deficits model considers frailty to be a burden of risk factors leading to adverse events. The more deficits that a person has, the frailer they are. Data development from the Canadian Health and Aging Study (CSHA) forms the basis of the mFI-5 scale [15]. Unlike previous frailty index, mFI-5 uses a small number of variables readily available in a patient’s history, including functional status (partial or complete dependence), type 2 diabetes mellitus (T2DM), chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and hypertension requiring medication [16]. One point is assigned to each variable. The mFI-5 score was calculated by increasing the number of variables per patient [17]. Patients were divided into 3 groups based on their mFI-5: frail group (mFI-5, 2 ~ 5), prefrail group (mFI-5, 1) and robust group (mFI-5, 0) [18]. The range of the mFI-5 is from 0 to 5 with increments of 1, and increasing the mFI-5 implies increasing frailty. We compared characteristics including patient demographics, intraoperative information, comorbidities, and laboratory test factors between robust, prefrail, and frail groups. Covariates were included based on clinical relevance, previous studies of surgical outcomes for esophageal cancer surgical outcomes, and availability in the database (Table 1).
Table 1.
Comparison of preoperative and intraoperative data for robust, prefrail, and frail groups defined by mFI-5 in older patients with esophageal cancer (N = 699)
| Variables | Total (n = 699) | Robust (n = 342) | Prefrail (n = 184) | Frail (n = 173) | P ¶ |
|---|---|---|---|---|---|
| Demographics | |||||
| Gender, Male | 577 (82.5) | 280 (81.9) | 139 (75.5) | 158 (91.3) | < 0.001 |
| Age (years) | |||||
| 65–75 | 638 (91.3) | 310 (90.6) | 169 (91.8) | 159 (91.9) | 0.846 |
| > 75 | 61 (8.7) | 32 (9.4) | 15 (8.2) | 14 (8.1) | |
| ASA classification | |||||
| ≤ Ⅱ | 427 (61.1) | 295 (86.3) | 73 (39.7) | 59 (34.1) | < 0.001 |
| Ⅲ | 231 (33.0) | 40 (11.7) | 98 (53.3) | 93 (53.8) | |
| Ⅳ | 41 (5.9) | 7 (2.0) | 13 (7.1) | 21 (12.1) | |
| BMI (kg/m2) | |||||
| ≤ 25 | 490 (70.1) | 257 (75.1) | 108 (58.7) | 125 (72.3) | 0.001 |
| 25–30 | 190 (27.2) | 80 (23.4) | 66 (35.9) | 44 (25.4) | |
| > 30 | 19 (2.7) | 5 (1.5) | 10 (5.4) | 4 (2.3) | |
| Current smoking status | 161 (23.0) | 68 (19.9) | 40 (21.7) | 53 (30.6) | 0.021 |
| Current drinking status | 181 (25.9) | 82 (24.0) | 47 (25.5) | 52 (30.1) | 0.328 |
| mFI-5 | |||||
| Hypertension | 217 (31.0) | 0 (0.0) | 138 (75.0) | 79 (45.7) | < 0.001 |
| Type Ⅱ diabetes | 94 (13.4) | 0 (0.0) | 36 (19.6) | 58 (33.5) | < 0.001 |
| CHF | 8 (1.1) | 0 (0.0) | 0 (0.0) | 8 (4.6) | < 0.001 |
| COPD | 24 (3.4) | 0 (0.0) | 0 (0.0) | 24 (13.9) | < 0.001 |
| Dependent functional status | 58 (8.3) | 0 (0.0) | 10 (5.4) | 48 (27.7) | < 0.001 |
| Comorbidity | |||||
| Coronary heart disease | 58 (8.3) | 11 (3.2) | 24 (13.0) | 23 (13.3) | < 0.001 |
| Myocardial infarction | 6 (0.9) | 1 (0.3) | 1 (0.5) | 4 (2.3) | 0.055 |
| Cerebrovascular disease | 71 (10.2) | 24 (7.0) | 23 (12.5) | 24 (13.9) | 0.024 |
| Asthma | 12 (1.7) | 3 (0.9) | 6 (3.3) | 3 (1.7) | 0.133 |
| Malignant tumor | 17 (2.4) | 10 (2.9) | 4 (2.2) | 3 (1.7) | 0.685 |
| Renal insufficiency | 5 (0.7) | 1 (0.3) | 2 (1.1) | 2 (1.2) | 0.429 |
| Laboratory testing | |||||
| Hemoglobin, g/L | 137.0 (127.0-147.0) | 138.0 (127.0-147.0) | 137.0 (124.0-149.0) | 136.0 (128.0-145.0) | 0.600 |
| RBC, 1012/L | 4.5 (4.2–4.8) | 4.5 (4.2–4.7) | 4.5 (4.2–4.8) | 4.5 (4.1–4.7) | 0.964 |
| Serum albumin, g/L | 40.5 (38.2–43.0) | 40.6 (38.0-43.1) | 40.8 (38.6–43.7) | 40.2 (38.2–42.0) | 0.091 |
| Leukocyte count, 109/L | 6.0 (5.0-7.2) | 5.8 (4.8-7.0) | 6.0 (5.1–7.2) | 6.3 (5.4–7.7) | 0.002 |
| Serum potassium, mmol/L | 4.1 (3.9–4.4) | 4.1 (3.9–4.4) | 4.1 (3.9–4.3) | 4.2 (3.9–4.4) | 0.009 |
| Blood sodium, mmol/L | 142 (140.3-143.5) | 142.3 (140.5-143.7) | 141.8 (140.0-143.4) | 141.7 (140.2-143.2) | 0.017 |
| TBIL, µmol/L | 10.8 (8.6–13.8) | 10.5 (8.6–13.4) | 11.3 (9.0-14.8) | 10.7 (8.3–13.6) | 0.173 |
| Intraoperative information | |||||
| Anesthesia method | |||||
| Intravenous general anesthesia | 18 (2.6) | 10 (2.9) | 5 (2.7) | 3 (1.7) | 0.716 |
| Intravenous inhalation anesthesia | 681 (97.4) | 332 (97.1) | 179 (97.3) | 170 (98.3) | |
| Estimated blood loss, ml | 150.0 (100.0-200.0) | 200.0 (100.0-200.0) | 175.0 (100.0-200.0) | 150.0 (100.0-200.0) | 0.143 |
| Operative duration, min | 240.0 (193.0-310.0) | 238.0 (190.0-306.3) | 250.0 (205.3–304.0) | 243.0 (186.0-314.0) | 0.450 |
| Anesthesia duration, min | 290.0 (240.0-367.0) | 285.0 (239.3-364.75) | 300.0 (252.0-357.5) | 291.0 (230.0-370.0) | 0.647 |
| Preoperative MAP, mmHg | 94.5 (11.0) | 93.1 (10.3) | 97.8 (11.4) | 93.7 (11.3) | < 0.001 |
| Surgical procedure | |||||
| Thoracotomy | 485 (69.4) | 234 (68.4) | 130 (70.7) | 121 (69.9) | 0.855 |
| Minimally invasive surgery | 214 (30.6) | 108 (31.6) | 54 (29.3) | 52 (30.1) | |
| Pathological diagnosis type | |||||
| Squamous cell carcinoma | 455 (65.1) | 223 (65.2) | 113 (61.4) | 119 (68.8) | 0.587 |
| Adenocarcinoma | 222 (31.8) | 109 (31.9) | 63 (34.2) | 50 (28.9) | |
| Other | 22 (3.1) | 10 (2.9) | 8 (4.3) | 4 (2.3) | |
| Surgical approaches | 0.596 | ||||
| McKeow | 74 (10.6) | 33 (9.6) | 23 (12.5) | 18 (10.4) | |
| Ivor Lewis | 625 (89.4) | 309 (90.4) | 161 (87.5) | 155 (89.6) | |
| Extent of lymphadenectomy | 0.902 | ||||
| Three-field | 41(5.9) | 19 (5.6) | 12 (6.5) | 10 (5.8) | |
| Two field | 658(94.1) | 323 (94.4) | 172 (93.5) | 163 (94.2) | |
| Resection margin | 0.362 | ||||
| R0 | 657 (94.0) | 324 (94.7) | 169 (91.8) | 164 (94.8) | |
| R1 | 42 (6.0) | 18 (5.3) | 15 (8.2) | 9 (5.2) | |
| Location of cancer | 0.566 | ||||
| Upper thoracic esophagus | 31 (4.4) | 14 (4.1) | 7 (3.8) | 10 (5.8) | |
| Middle thoracic esophagus | 316 (45.3) | 147 (43.1) | 91 (49.5) | 78 (45.1) | |
| Lower thoracic esophagus | 351 (50.3) | 180 (52.8) | 86 (46.7) | 85 (49.1) | |
| Tumour stage | 0.421 | ||||
| T1 | 129 (18.5) | 66 (19.4) | 28 (15.2) | 35 (20.2) | |
| T2 | 284 (40.5) | 142 (41.5) | 82 (44.6) | 60 (34.7) | |
| T3 | 273 (39.1) | 126 (36.8) | 72 (39.1) | 75 (43.4) | |
| T4 | 13 (1.9) | 8 (2.3) | 2 (1.1) | 3 (1.7) | |
| Node stage | 0.835 | ||||
| N0 | 114 (16.3) | 59 (17.3) | 25 (13.6) | 30 (17.3) | |
| N1 | 419 (59.9) | 204 (59.6) | 114 (62.0) | 101 (58.4) | |
| N2 | 166 (23.8) | 79 (23.1) | 45 (24.4) | 42 (24.3) | |
Data are presented as n (%) or mean (SD) or median (IQR); ¶, comparison of patients in different mFI-5 groups
Abbreviations: ASA American society of anesthesiologists; mFI modified Frailty Index; CHF congestive heart failure; RBC red blood cell; BMI body mass index; TBIL total bilirubin; COPD chronic obstructive pulmonary disease; MAP mean artery pressure; SD standard deviation; IQR interquartile range. CNY Chinese Yuan
Functional status refers to needing some or all of the assistance of others in daily activities, including bathing, eating, dressing, going to the toilet, moving, traveling, and more [19]. T2DM are based on blood glucose levels, which are as follows: fasting blood glucose level ≥ 7.0 mmol/L, 2-hour postprandial blood glucose level ≥ 11.1 mmol/L, blood glucose levels measured ≥ 11.1 mmol/L, and glycosylated hemoglobin (HbA1c) levels typically ≥ 6.5% [20, 21]. Diagnosis of COPD requires the following: smoking history, clinical symptoms: cough, phlegm, wheezing and chest tightness, and pulmonary function tests: FEV1/FVC < 0.70, reflecting the presence of pulmonary ventilation disorder [22]. CHF is detected through an electrocardiogram, X-ray examination, echocardiography, and heart failure biomarkers (natriuretic peptide and N-terminal B-type pronatriuretic peptide (NT proBNP)) [23]. Hypertension requiring medication is authentic blood pressure (measured 3 times different from the same day) ≥ 140/90mmHg with ineffective lifestyle changes or target organ damage [24].
Primary and secondary outcomes
Primary outcome was 30-day mortality. Secondary outcomes were postoperative delirium and pneumonia. Delirium was defined as the acute dysfunction of attention and cognition, which seriously affected the long-term quality of life of patients [25]. Delirium was recorded in medical and nursing records by describing text, including: altered mental state, confusion, disorientation, agitation, delirium, inappropriate behavior, attention deficit, hallucinations and aggressive behavior, disorganized thinking, altered level of consciousness, memory impairment, cognitive impairment, psychomotor disorder, awakening disorders and sleep cycle disorders [26]. Secondly, the patients preliminarily diagnosed by a computer were rechecked by neurologists using the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria [27]. Clinical suspicion of pneumonia-infiltrated chest radiography and at least two clinical standards below [28, 29] :1) patients had a vital sign of fever > 38.3 °C, 2) leucocytosis > 12 × 109/mL, 3) patients had new or progressive respiratory symptoms, such as coughing and expectoration, 4) isolated pathogen from blood culture or sputum and 5) the presence of purulent tracheobronchial secretions. Survival data was obtained through telephone interviews with clinical professionals.
Statistical analysis
Continuous variables conforming to normal distribution were expressed in mean standard deviation (SD) and compared with the student’s t test. While continuous variables that did not fit normal distribution were expressed as median (interquartile range, IQR), and compared using the Mann-Whitney U test. Categorical variables were expressed as frequency and percentage (%) and compared using the Chi-squared test or Fisher’s exact test. Since there are too few people (n = 4) in ASA Ⅰ, the combination of Ⅰ and Ⅱ is expressed as ≤ Ⅱ.
Logistic regression analyzes and COX analyzes were used to identify independent risk factors for postoperative outcomes. Multivariate regression analysis uses stepwise to screen for independent risk factors. Stepwise regression, a feature selection strategy, optimizes model performance by iteratively selecting the most significant features and progressively adding or removing them. The receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive efficacy and clinical net benefit of different variables in predicting postoperative 30-day mortality, delirium and pneumonia. The Delong test serves as a rigorous statistical tool to compare and identify the validity of two ROC curves, which fundamentally assesses the comparative advantages and limitations of competing models. This test therefore provides a nuanced evaluation of model performance, preventing the misinterpretation of minor AUC variations.
All tests were double-tailed and P < 0.05 was considered statistically significant. Data analyzing was performed using the SPSS software (version 26.0, IBM, Armonk, New York, USA), and R program (Version 3.6.3, R Foundation for Statistical Computing, Vienna, Austria). Packages used in the R environment included “rmda”, “survival”, “rms”, “survminer”, “pROC” and “ggplot2”.
Results
Patient characteristics
From January 1, 2012 to January 31, 2017, 842 patients in total over 65 years old who underwent elective radical resection for esophageal cancer were enrolled in our research center. Figure 1 shows the inclusion and exclusion criteria for the study population of 699 individuals. Excluding 18 cases with ASA stage Ⅴ, 70 cases with preoperative pneumonia or pulmonary infection, 12 cases with preoperative delirium and 43 cases with missing covariate data. A total of 699 patients were included in this retrospective cohort study, of which 173 (24.75%) were in the frail group, 184 (26.32%) in the prefrail group, and 342 (48.93%) in the robust group (Fig. 1).
Fig. 1.
Research flowchart. ASA, American society of anesthesiologists. mFI-5, 5-modified frailty index
The detailed baseline characteristics of robust, prefrail, and frail patients are shown in Table 1. Results showed that 82.5% of the study participants were male, 91.3% were men in the frail group and 75.5% were men in the prefrail group. The overall study subjects were concentrated in 65 ~ 75 years old (91.3%). 12.1% of patients in the frail group had ASA Ⅳ, 53.3% of patients in the prefrail group had ASA Ⅲ and 86.3% of patients in the robust group had ASA ≤ Ⅱ. Among the 173 frail patients, 45.7% patients had hypertension, 33.5% patients had diabetes, 4.6% patients had CHF, 13.9% patients had COPD, and 27.7% patients had dependent functional status. Importantly, the comparison of variables of mFI-5 among three groups of patients showed statistical differences (P < 0.001).
Postoperative adverse outcomes
Frail patients have highest incidences of postoperative adverse outcomes (Fig. 2). The incidence of 30-day mortality for frail, prefrail and robust patients were 5.8%, 1.6%, and 1.2% (P < 0.001), respectively. The incidence of postoperative delirium for frail, prefrail and robust patients were 22.5%, 14.7%, and 2.9% (P < 0.001), respectively. The incidence of postoperative pneumonia for frail, prefrail and robust patients were 13.3%, 9.8%, and 3.8% (P < 0.001), respectively.
Fig. 2.
Comparison of the incidence of 30-day mortality, delirium, and pneumonia in robust, prefrail, and frail older patients with esophageal cancer. A shows the incidence of postoperative 30-day mortality in the robust, prefrail and frail groups. B shows the incidence of postoperative delirium in the robust, prefrail and frail groups. C shows the incidence of postoperative pneumonia in the robust, prefrail and frail groups. *** means P < 0.001; ** means P < 0.01; ns means P > 0.05
Univariable and multivariable logistic regression
Table 2 summarized the results of multivariate stepwise regression analysis for postoperative adverse outcomes in older patients with esophageal cancer. Supplement Table 1 showed the univariate and multivariate COX regression analysis of 30-day mortality. Frail was associated with significantly increased risk of 30-day mortality compared with robust patients (HR = 14.946, 95%CI:5.125–43.587, P < 0.001; aHR = 14.303, 95%CI: 4.867–42.031, P < 0.001). Supplement Table 2 showed univariate and multivariate logistic regression analysis for postoperative delirium. Results suggested that both prefrail and frail were associated with significantly increased risks of delirium compared with robust patients (frail: OR = 9.663, 95%CI: 4.689 − 19.913, P < 0.001; prefrail: OR = 5.710, 95% CI: 2.697 − 12.087, P < 0.001). After adjusting for multiple variables, it was found that frail and prefrail were also associated with significantly increased risks of postoperative delirium (frail: aOR = 6.815, 95% CI:3.119–14.888, P < 0.001; prefrail: aOR = 3.869, 95% CI:1.732–8.640, P = 0.001). Supplement Table 3 showed univariate and multivariate regression analyzes for pneumonia, which showed that frail was associated with a significantly increased risk of pneumonia (OR = 4.248, 95% CI:2.681–6.730, P < 0.001; aOR = 4.118, 95% CI:2.523–6.722, P < 0.001).
Table 2.
Multivariable Stepwise regression analysis for postoperative 30-day mortality, delirium and pneumonia in older patients with esophageal cancer underwent radical surgery (N = 699)
| Variables | 30-day mortality | Postoperative delirium | Postoperative pneumonia | |||
|---|---|---|---|---|---|---|
| HR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| Gender, female vs. male | 0.766 (0.277–2.855) | 0.744 | 1.524 (0.455–3.421) | 0.767 | 15.632 (3.759–65.003) | < 0.001 |
| ASA, Ⅲ vs. ≤ Ⅱ | 1.042 (0.972–2.812) | 0.923 | 2.524 (1.420–4.487) | 0.002 | 1.022 (0.911–3.914) | 0.092 |
| ASA, Ⅳ vs. ≤ Ⅱ | 2.431 (0.568–4.619) | 0.713 | 0.408 (0.090–1.851) | 0.245 | 1.842 (0.831–3.179) | 0.061 |
| Drinking, yes vs. no | 0.623 (0.365–1.764) | 0.376 | 0.986 (0.456–1.876) | 0.637 | 1.532 (1.033–2.012) | 0.043 |
| Anesthesia duration, per min | 0.545 (0.058–2.766) | 0.978 | 1.002 (0.999–1.004) | 0.877 | 1.006 (1.004–1.009) | < 0.001 |
| MIS vs. thoracotomy | 0.256 (0.084–0.782) | 0.017 | 0.844 (0.477–1.867) | 0.888 | 1.342 (0.798–3.639) | 0.659 |
| MI, yes vs. no | 7.477 (1.010-55.335) | 0.049 | 4.542 (0.452–25.574) | 0.756 | 4.455 (0.864–21.643) | 0.065 |
| Prefrail vs. robust | 1.328 (0.292–6.031) | 0.714 | 3.869 (1.732–8.640) | 0.001 | 2.536 (1.510–4.261) | < 0.001 |
| Frail vs. robust | 14.303 (4.867–42.031) | < 0.001 | 6.815 (3.119–14.888) | < 0.001 | 4.118 (2.523–6.722) | < 0.001 |
| Leukocyte count, per 109/L | 1.325 (0.439–4.630) | 0.757 | 1.163 (0.700-1.699) | 0.655 | 1.134 (1.009–1.275) | 0.035 |
Abbreviations: ASA American society of anesthesiologists; MIS minimally invasive surgery; MI myocardial infarction; HR hazard ratio; CI confidential interval; OR odds ratio
Predictive values and clinical efficacy
Figure 3 showcased the prediction of postoperative 30-day mortality, delirium, and pneumonia by different risk indicators. ASA stage and Age were traditional indicators of preoperative comprehensive status assessment of patients. mFI-5 has the largest area under the curve (AUC) when compared to ASA stage and Age alone (30-day mortality AUC: 0.790,delirium AUC: 0.727 and pneumonia AUC: 0.660). It was worth noting that the AUCs of ASA stage + Age + mFI-5 were the largest (30-day mortality AUC: 0.838 and delirium AUC: 0.778) which provided an important reference for a clinical recommendation of preoperative frailty screening. ASA stage + mFI-5 AUC was largest in predicting postoperative pneumonia in older patients with esophageal cancer (ASA stage + mFI-5: 0.665 vs. ASA stage + Age + mFI-5: 0.661). Supplement Table 4 compared the effects of different variables (combinations) on predicting postoperative 30 day mortality, delirium, and pneumonia in older patients with esophageal cancer. In predicting the 30 day mortality, it was found that the ASA + Age + mFI-5 group had statistical differences in AUC compared to mFI-5 and ASA + Age (P < 0.001). In predicting delirium outcomes, it was found that, there are statistical differences between ASA + Age + mFI-5 and the other three groups (P < 0.05). In predicting pneumonia results, it was found that ASA + Age + mFI-5 only shows statistical differences compared to ASA + Age (P = 0.034).
Fig. 3.
Predictive value of different predictive indicators for postoperative 30-day mortality, delirium, and pneumonia in older patients with esophageal cancer. A Predictive value of 6 clinical predictors (combinations) for postoperative 30-day mortality. B Predictive value of 6 clinical predictors (combinations) for postoperative delirium. C Predictive value of 6 clinical predictors (combinations) for postoperative pneumonia. ASA, American society of anesthesiologists. mFI-5, 5-modified frailty index
Evaluation of predictive models and diagnostic test results revealed that, ASA + Age + mFI-5 and mFI-5 have a high net clinical benefit in predicting the 30 day postoperative mortality prognosis of older patients with esophageal cancer in the range of 0.05 to 0.2 (Fig. 4). ASA + Age + mFI-5 had the highest net clinical benefit in predicting postoperative delirium prognosis in older patients with esophageal cancer in the range of 0.05 to 0.25. mFI-5 had the highest net clinical benefit in predicting postoperative pneumonia prognosis in the range of 0.1 to 0.35 (Fig. 4).
Fig. 4.
Comparison of different predictive indicators on the net benefit risk of 30-day mortality, delirium, and pneumonia in older patients with esophageal cancer. A Comparison of the net benefit of different models in predicting postoperative 30-day mortality. B Comparison of the net benefit of different models in predicting postoperative delirium. C Comparison of the net benefit of different models in predicting postoperative pneumonia. ASA, American society of anesthesiologists. mFI-5, 5-modified Frailty Index
Discussion
We focused on exploring the prognostic value of the mFI-5 scale for adverse outcomes and survival in older patients underwent radical esophagectomy in China. After adjusting for all relevant clinical characteristics and confounding factors, frailty as determined by mFI-5 was an independent risk factor for postoperative 30-day mortality, delirium and pneumonia in patients over 65 with esophageal cancer. Our results indicated that the use of the mFI-5 scale has better prognostic value when combined with clinical ASA stage and Age.
Increasing burden of chronic disease in older patients affects overall health and well-being [30]. Comprehensive Geriatric Assessment (CGA), as a multidimensional evaluation tool for older adults, encompasses core domains including physical function, comorbidity status, cognitive and psychological health, nutrition, and social support, among which frailty screening serves as a pivotal component. Frailty is a hallmark of diminished physiological reserve and increased vulnerability to stress in older patients and could help explain why some older patients recover better than expected after surgery, while others fare much worse than expected [31]. In this study, frailty was systematically assessed using the standardized mFI-5 tool within the CGA framework, with integration of oncology-specific parameters to identify high-risk patients and guide clinical decision-making. Assessment of frailty is usually assessed for people who are at higher risk of developing frail syndrome, such as older adults or people with chronic medical conditions. These populations are more likely to experience decline in their physiological functions and health due to age or disease. mFI-5 assessment sensitively detects small changes in a patient’s physical strength, mobility and nutritional status, which may not be obvious or problematic in healthy people.
Older patients undergoing surgery have unique physiologic vulnerabilities that require systematic and objective preoperative screening to assess frailty. According to the latest research, frailty is considered a significant risk factor for poor clinical outcomes and increased postoperative mortality [32]. A recent review concluded that mortality risk increased in a graded manner with an increasing number of phenotypic components or deficits present [33]. The link between frailty and mortality has been demonstrated in many studies and in a variety of settings and subpopulations. Shen et al. showed that the frail group in the Chinese hip fracture older population had nearly twice the risk of adverse postoperative outcomes compared with normal patients [34]. A previous risk stratification study of colorectal surgery patients using mFI-5 found patients with higher scores were more likely to experience postoperative complications [35, 36].
In this study, frailty further improved the ability of ASA stage and Age to predict delirium, pneumonia, and mortality. The most intuitive explanation is that the AUCs after adding the frailty score is the highest, which provides strong evidence and data support for clinical application of preoperative frailty assessment. This study demonstrated that frailty was quite common in older patients undergoing radical esophagectomy and could provide prognostic information for patients. mFI-5 could be implemented rapidly and easily using information from medical history, which may be useful in acute or time-sensitive situations.
Patients undergoing esophagectomy for cancer often suffer from malnutrition and reduced physiological function due to chronic dysphagia and induction therapy regimens [37]. The field of frailty research should focus more on patients with esophageal cancer. As a result of the growing interest in accurate risk stratification, the surgical community has largely shifted from assessments based on subjective clinical judgment (i.e. ASA stage) to more objective analytical methods (i.e. mFI-5) [38]. Several characteristics of mFI-5 make it a predictive value screening tool. First of all, it is free and simple to score mFI-5 in a short time (1 ~ 2 min) and has been shown to be robust in populations of varying ethnicity and educational levels. Frailty assessment is feasible in preoperative outpatient settings and has high acceptance among healthcare workers and patients [39]. Finally, after a thorough and professional frailty assessment, frail patients can receive personalized and optimized preoperative care, including medical optimization, functional and nutritional evaluation, as well as emotional problems and preoperative smoking cessation counseling.
Once a frailty diagnosis is confirmed, perioperative intervention zones could improve outcomes for patients: shared decision-making, rehabilitation, and interdisciplinary combined geriatric management. In addition, preoperative scores could be obtained without intraoperative variables, allowing optimal preoperative management and a rational approach to perioperative measures such as BIS-guided anesthesia and opioid-sparing multimodal analgesia in frail patients [40]. For surgical tumor patients, exercise training in a new or assisted surgical environment is safe and feasible, and measures to improve physical health and health-related quality of life [41]. In terms of intervention for frail older patients, we can implement tailored interventions such as exercise and nutrition plans, prevention of falls or mental disorders, and changes in perioperative medication treatment.
This study had several limitations. Firstly, it was a retrospective study. Because assessing delirium in a retrospective dataset using the confusion assessment method (CAM) or Delirium Detection Score was not achievable, patients with delirium were identified based on medical records using DSM-IV criteria. But only retrospective datasets could provide a large sample size. Prospective studies may provide a more accurate assessment of whether frailty is a risk factor for postoperative delirium. We are currently conducting a multi-center prospective external validation study to identify delirium patients using CAM, CAM-ICU, and 3D-CAM. Secondly, this study explored frailty in older patients with esophageal cancer who underwent surgery, and did not focus on patients who were not eligible for surgery. Future evidence-based studies comparing the outcomes of frailty in surgical versus non-surgical management methods. Thirdly, the database lacks information on targeted therapy and immunotherapy, which may have some level of bias. In addition, frailty is multidimensional, with physical and psychosocial factors playing a part in its development. Future prospective studies will focus on the association of preoperative frailty with long-term postoperative quality of life and mood changes (such as anxiety and depression) in older patients.
Conclusion
The mFI-5 is a convenient and effective tool to predict postoperative 30-day mortality, delirium, and pneumonia in older patients over 65 years old undergoing esophagectomy. Targeted improvement of frailty status may help to accelerate postoperative prognosis and recovery in older patients with esophageal cancer. Future prospective studies with longer follow-ups are warranted to accumulate higher quality evidence in this field.
Supplementary Information
Acknowledgements
We would like to thank Wei-Wei of Hangzhou Le9 Healthcare Technology Co., Ltd., for help in the clinical data collection of this study.
Abbreviations
- OR
Odds ratio
- CI
Confidence interval
- BMI
Body mass index
- COPD
Chronic obstructive pulmonary disease
- mFI-5
5-modified frailty index
- NSQIP
National Surgical Quality Improvement Program
- ASA
American Society of Anesthesiologists
- CSHA
Canadian Health and Aging Study
- DSM-IV
Diagnostic and Statistical Manual of Mental Disorders, fourth edition
- SD
Standard deviation
- IQR
Interquartile range
- ROC
Receiver operating characteristic
- DCA
Decision curve analysis
- CNY
Chinese Yuan
- CAM
Confusion assessment method
Authors’ contributions
XYH, ZYX, GYX and JJL conceived this clinical study, performed the statistical analysis of the study and drafted the manuscript. JJL, FYC, JYT and YPS extracted study data from all clinical records of patients and prepared the database. YHL, QF and JBC drafted the clinical protocol to be submitted to the hospital Ethics Committee. WDM and LT revised it critically for important intellectual content.
Funding
This work was supported by the National Key Research and Development Program of China (2018YFC2001901).
Data availability
The datasets generated and/or analyzed during the current study are not publicly available as individual privacy could be compromised but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted following the Declaration of Helsinki. The study was approved by the Ethics Committee Board of the First Medical Center of Chinese PLA General Hospital (No. S2021-342–01). Patient consent was waived because the study was retrospective, and all data were anonymized before analysis.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
The original online version of this article was revised: authors identified an error in the author name of Li Tong.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xinyu Hao, Ziyao Xu and Yongxin Guo contributed equally to this work.
Change history
12/11/2024
The original online version of this article was revised: authors identified an error in the author name of Li Tong.
Change history
12/11/2025
A Correction to this paper has been published: 10.1186/s12877-025-06886-6
Contributor Information
Weidong Mi, Email: wwdd1962@163.com.
Li Tong, Email: tongli301@aliyun.com.
References
- 1.Torpy JM, Burke AE, Glass RM. JAMA patient page. Esophageal cancer. JAMA. 2010;30 4(6):704. [Google Scholar]
- 2.Eads JR, Haller DG. Primary chemoradiotherapy for older patients with esophageal cancer. JAMA Oncol. 2021;7(10):1451–2. [DOI] [PubMed] [Google Scholar]
- 3.Hashimoto T, Makino T, Yamasaki M, et al. The pattern of residual tumor after neoadjuvant chemotherapy for locally advanced esophageal cancer and its clinical significance. Ann Surg. 2020;271(5):875–84. [DOI] [PubMed] [Google Scholar]
- 4.Xia R, Li H, Shi J, et al. Cost-effectiveness of risk-stratified endoscopic screening for esophageal cancer in high-risk areas of china: a modeling study. Gastrointest Endosc. 2022;95(2):225–e23520. [DOI] [PubMed] [Google Scholar]
- 5.Shaw JF, Budiansky D, Sharif F, McIsaac DI. The association of frailty with outcomes after cancer surgery: a systematic review and metaanalysis. Ann Surg Oncol. 2022;29(8):4690–704. [DOI] [PubMed] [Google Scholar]
- 6.Audisio RA, van Leeuwen BL. Beyond age: frailty assessment strategies improve care of older patients with cancer. Ann Surg Oncol. 2015;22(12):3774–5. [DOI] [PubMed] [Google Scholar]
- 7.Veronese N, Custodero C, Cella A, et al. Prevalence of multidimensional frailty and pre-frailty in older people in different settings: a systematic review and meta-analysis. Ageing Res Rev. 2021;72:101498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hewitt J, Moug SJ, Middleton M, et al. Prevalence of frailty and its association with mortality in general surgery. Am J Surg. 2015;209:254e9. [DOI] [PubMed] [Google Scholar]
- 9.He L, He R, Huang J, Zou C, Fan Y. Impact of frailty on all-cause mortality and major bleeding in patients with atrial fibrillation: a meta-analysis. Ageing Res Rev. 2022;73:101527. [DOI] [PubMed] [Google Scholar]
- 10.Kojima G, Iliffe S, Walters K. Frailty index as a predictor of mortality: a systematic review and meta-analysis. Age Ageing. 2018;47(2):193–200. [DOI] [PubMed] [Google Scholar]
- 11.Medendorp AR, Liu H, Kwan L, et al. The impact of frailty on outcomes of sling surgery with and without prolapse repair. J Urol. 2021;206(2):382–9. [DOI] [PubMed] [Google Scholar]
- 12.Tatar C, Benlice C, Delaney CP, et al. Modified frailty index predicts high-risk patients for readmission after colorectal surgery for cancer. Am J Surg. 2020;220(1):187–90. [DOI] [PubMed] [Google Scholar]
- 13.Chimukangara M, Helm MC, Frelich MJ, et al. A 5-item frailty index based on NSQIP data correlates with outcomes following paraesophageal hernia repair. Surg Endosc. 2017;3(16):2509–19. [Google Scholar]
- 14.Osaki T, Saito H, Shimizu S, et al. Modified frailty index is useful in predicting nonhome discharge in elderly patients with gastric cancer who undergo gastrectomy. World J Surg. 2020;44(11):3837–44. [DOI] [PubMed] [Google Scholar]
- 15.El-Sharkawy AM, Watson P, Neal KR, et al. Hydration and outcome in older patients admitted to hospital (the HOOP prospective cohort study). Age Ageing. 2015;44(6):943–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hubbard RE, Andrew MK, Rockwood K. Effect of parental age at birth on the accumulation of deficits, frailty and survival in older adults. Age Ageing. 2009;38(4):380–5. [DOI] [PubMed] [Google Scholar]
- 17.Shin JI, Keswani A, Lovy AJ, et al. Simplified frailty index as a predictor of adverse outcomes in total hip and knee arthroplasty. J Arthroplasty. 2016;31(11):2389–94. [DOI] [PubMed] [Google Scholar]
- 18.Williams AM, Krull KR, Howell CR, et al. Physiologic frailty and neurocognitive decline among young-adult childhood cancer survivors: a prospective study from the St Jude lifetime cohort. J Clin Oncol. 2021;39(31):3485–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wan MA, Clark JM, Nuño M, et al. Can the risk analysis index for frailty predict morbidity and mortality in patients undergoing high-risk surgery?? Ann Surg. 2020(6). 10.1097/SLA.0000000000004626.
- 20.Faselis C, Katsimardou A, Imprialos K, et al. Microvascular complications of type 2 diabetes mellitus. Curr Vasc Pharmacol. 2020;18(2):117–24. [DOI] [PubMed] [Google Scholar]
- 21.Arnold SV, Bhatt DL, Barsness GW, et al. Clinical management of stable coronary artery disease in patients with type 2 diabetes mellitus: a scientific statement from the American Heart Association. Circulation. 2020;141(19):e779–806. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Aaron SD, Vandemheen KL, Whitmore GA, et al. Early diagnosis and treatment of COPD and asthma - a randomized, controlled trial. N Engl J Med. 2024;390(22):2061–2073.
- 23.Heidenreich PA, Bozkurt B, Aguilar D et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: A report of the American college of cardiology/american heart association joint committee on clinical practice guidelines [published correction appears in circulation. 2022;145(18):e1033.
- 24.Kim JH, Thiruvengadam R. Hypertension in an ageing population: diagnosis, mechanisms, collateral health risks, treatments, and clinical challenges. Ageing Res Rev. 2024;98:102344. [DOI] [PubMed] [Google Scholar]
- 25.Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014;383(9920):911–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zhang LM, Hornor MA, Robinson T, et al. Evaluation of postoperative functional health status decline among older adults. JAMA Surg. 2020;155(10):950–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Kuhn E, Du X, McGrath K, et al. Validation of a consensus method for identifying delirium from hospital records. PLoS ONE. 2014;9(11):e111823. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Kanda M, Koike M, Tanaka C, et al. Risk prediction of postoperative pneumonia after subtotal esophagectomy based on preoperative serum cholinesterase concentrations. Ann Surg Oncol. 2019;26(11):3718–26. [DOI] [PubMed] [Google Scholar]
- 29.Fàbregas N, Ewig S, Torres A, et al. Clinical diagnosis of ventilator associated pneumonia revisited: comparative validation using immediate post-mortem lung biopsies. Thorax. 1999;54(10):867–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Prince MJ, Wu F, Guo Y, et al. The burden of disease in older people and implications for health policy and practice. Lancet. 2015;385(9967):549–62. [DOI] [PubMed] [Google Scholar]
- 31.Walston J, Buta B, Xue QL. Frailty screening and interventions: considerations for clinical practice. Clin Geriatr Med. 2018;34(1):25–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Stolz E, Mayerl H, Freidl W. Fluctuations in frailty among older adults. Age Ageing. 2019;48(4):547–52. [DOI] [PubMed] [Google Scholar]
- 33.Traven SA, Reeves RA, Althoff AD, Slone HS, Walton ZJ. New five-factor modified frailty index predicts morbidity and mortality in geriatric hip fractures. J Orthop Trauma. 2019;33(7):319–23. [DOI] [PubMed] [Google Scholar]
- 34.Carli F, Bousquet-Dion G, Awasthi R, et al. Effect of multimodal prehabilitation vs postoperative rehabilitation on 30-Day postoperative complications for frail patients undergoing resection of colorectal cancer: A randomized clinical trial [published correction appears in. JAMA Surg. 2020;155(3):269. [Google Scholar]
- 35.Thompson MQ, Theou O, Ratcliffe J, et al. Frailty state utility and minimally important difference: findings from the North West Adelaide health study. Age Ageing. 2021;50(2):565–9. [DOI] [PubMed] [Google Scholar]
- 36.Stirrups R. Minimally invasive oesophagectomy for oesophageal cancer. Lancet Oncol. 2019;20(2):e74. [DOI] [PubMed] [Google Scholar]
- 37.Simon HL, Reif de Paula T, Profeta da Luz MM, et al. Frailty in older patients undergoing emergency colorectal surgery: USA National surgical quality improvement program analysis. Br J Surg. 2020;107(10):1363–71. [DOI] [PubMed] [Google Scholar]
- 38.Lu J, Cao LL, Zheng CH, et al. The preoperative frailty versus inflammation-based prognostic score: which is better as an objective predictor for gastric cancer patients 80 years and older?? Ann Surg Oncol. 2017;24(3):754–62. [DOI] [PubMed] [Google Scholar]
- 39.Raad M, Amin R, Puvanesarajah V, et al. The CARDE-B scoring system predicts 30-day mortality after revision total joint arthroplasty. J Bone Joint Surg Am. 2021;103(5):424–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Elliott A, Hull L, Conroy SP. Frailty identification in the emergency department-a systematic review focussing on feasibility. Age Ageing. 2017;46(3):509–13. [DOI] [PubMed] [Google Scholar]
- 41.Alvarez-Nebreda ML, Bentov N, Urman RD, et al. Recommendations for preoperative management of frailty from the society for perioperative assessment and quality improvement (SPAQI). J Clin Anesth. 2018;47:33–42. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available as individual privacy could be compromised but are available from the corresponding author on reasonable request.