Abstract
Multiple pre-operative risk assessment scores are available for risk stratification of cancer patients undergoing surgery. This is the first study comparing commonly used preoperative risk assessment tools of Eastern Cooperative Oncology Group Performance Scale (ECOG) and American Society of Anaesthesiologists Physical Status Scale (ASA PS) with frailty scores of Modified Frailty Index (MFI) and Clinical Frailty Scale (CFS). This is a prospective observational study of adult cancer patients undergoing oncosurgery in a tertiary cancer center over one year. Pre-operative risk stratification was done using CFS, MFI, ASA PS, and ECOG scales. All patients were followed up postoperatively for 30 days, and complications were documented. Univariate and multivariate analyses were performed. p value of ≤0.05 was considered significant. Of the 4107 patients studied, 12.6% had prolonged hospitalization, 6.1% had morbidity, 0.9% had readmission, and mortality was 0.6%. ASA PS, ECOG, and CFS were significantly associated with prolonged hospitalization, morbidity, and mortality. MFI was significantly associated with prolonged hospitalization and morbidity. No score could predict readmission. On multivariate analysis, morbidity and readmission were significantly associated with neoadjuvant therapy (p=0.001), mortality with emergency surgery (p=0.001), and prolonged hospitalization with stage III and IV cancer (p=0.001). In adult patients undergoing oncosurgery, ASA PS, ECOG, and CFS are predictors of prolonged hospitalization, morbidity, and mortality. MFI is predictive of prolonged hospitalization and morbidity. None of the studied pre-operative risk scores predict readmission. Newer predictive tools with cancer-specific factors are required for better risk stratification of cancer patients undergoing surgery.
Keywords: Modified Frailty Index (MFI), Clinical Frailty Scale (CFS), American Society of Anaesthesiologists Physical Status Classification System (ASA PS), Eastern Cooperative Oncology Group Performance Scale (ECOG), Postoperative complications, Cancer surgery
Introduction
The incidence of cancer and its associated burden is ever growing globally and in India [1]. Solid tumors are the major portion of the cancer burden, for which surgery is the primary modality of curative treatment. Unlike noncancer surgeries, surgery in a cancer patient is more complex and may be associated with multiple pre-existing cancer-related factors such as cancer cachexia, immune suppression, and neoadjuvant therapy like chemotherapy or radiation therapy. The presence of multiple co-morbidities and the older age group of usual presentation along with the psychosocial implications of the diagnosis and treatment add to the further burden on the patient’s health. Early and smooth postoperative recovery is of utmost importance in these patients as they may receive further adjuvant therapy which must be delivered in a time bound fashion for the completion of cancer treatment and improve outcomes. The accurate prediction of postoperative outcomes may also assist in the treatment planning, as poor surgical candidates may be offered non-surgical therapy as per their risk benefit analysis. There is a growing need to identify factors which can accurately predict increased risk of postoperative complications in cancer patients. The pre-operative risk stratification may alter the treatment plan of the patient.
There are multiple conventional pre-operative risk assessment scoring systems in use such as the American Society of Anaesthesiologists Physical Status Classification System (ASA PS) score [2] used by the Anaesthesiologists and the Eastern Cooperative Oncology Group Performance Scale (ECOG) used by the oncologist [3]. ASA PS considers the general condition of the patient, habits, and the co-morbidities of the patient and its duration and severity in the score. ECOG score represents a patient’s level of functioning with reference to their ability of selfcare, everyday activity, and physical ability such as walking. These scoring systems have proven to be reliable in predicting the postoperative outcomes; however, they are subjective in nature.
Frailty is a recent concept in assessing patients’ fitness for tolerating surgery or adjuvant therapy by not considering chronological age as the only benchmark, but considering a whole plethora of diseases and patients’ functional status [4]. This helps to gain a more realistic picture of patient outcome following treatment regime. As the average life expectancy rises, the burden of frailty and cancer is predicted to rise leading to increase in the number of frail cancer patients [5]. Along with age, co-morbidities, and disabilities, frailty adds another dimension in assessing the patient fitness for undergoing treatment for any disease [6]. The concept of frailty is gradually being integrated into clinical practice. Frailty assessment, irrespective of disease cohort, helps to improve objectivity on the influence of a patient's aging on clinical decisions [4].
Oncologic patients due to their systemic nature of the disease, concurrent immunosuppressive treatment and associated nutritional depletion are more prone to be frail and tend to have a higher perioperative morbidity. Frailty has been causally associated with the older adults [7]. A frailty index assesses comorbidities and functional status, which helps to identify pre-operatively the patient’s risk for complications irrespective of age. The Canadian Study of Health and Aging Frailty Index (CSHA-FI) is an exhaustive list of 70 items that considers all aspects of a patient’s life to determine frailty [7]. The CSHA–FI for ease of use has been simplified into a Modified Frailty Index (MFI) with 11 indices of comorbidities and functional status to help risk stratify patients and predict adverse events [8]. The Clinical Frailty Scale (CFS) is a 9-point frailty score which uses clinical descriptors and pictographs. Clinical Frailty Scale is an easily applicable tool which helps to stratify older adults according to level of vulnerability [9, 10].
There are currently very few prospective studies which assess the role of frailty scores in predicting the postoperative complications in cancer patients undergoing surgery. This study was conducted to compare the established preoperative risk assessment tools of ECOG and ASA PS with frailty scores of MFI and CFS. As per our research, this is the first prospective study which compares frailty scores of MFI and CFS with the standard preoperative risk stratification of ECOG and ASA PS in patients undergoing oncosurgery.
Material and Methods
This prospective observational study was conducted after Institutional Review Board clearance (IRB No. 01/2018/03). Ethics clearance was waived as it was an observational study. This study was conducted from 1st March 2018 to 28th February 2019 at a tertiary cancer care center in Kerala, India.
Data Collection
All adult patients undergoing surgery for cancers during the study period were recruited. The pre-operative risk stratification was done using the CFS, MFI, ASA PS, and ECOG scale. The ECOG score (normal range from 0 to 5) was entered by the treating surgeons while the ASA PS score (normal range from 1 to 6) was entered by the assessing anesthesiologist. MFI (normal range from 0 to 11) and CFS (normal range from 1 to 9) scores were entered by a single anesthesiologist (corresponding author). An ECOG score and ASA score of greater than 2 were considered high-risk for surgery, and less than or equal to 2 was considered low-risk for surgery [2, 3]. Patients with CFS >3 were considered frail while a CFS score of less than or equal to 3 was considered non-frail [10]. An MFI score of 3 or more was considered frail and high-risk, and less than 3 was considered non-frail and low-risk [11]. The data collected included age, sex, stage of the cancer, neoadjuvant therapy (chemotherapy or radiotherapy), nature of the surgery (emergency or elective), postoperative complications, duration of hospitalization, and readmission. The stage of cancer was categorized as early (stages I and II) and advanced (stages III and IV). All patients were followed up for 30 days immediately postoperatively. The 30-day morbidity was recorded and scored as per Common Terminology Criteria for Adverse Events (CTCAE) version 4.03 [12]. The 30-day mortality occurring during this period was noted. Prolonged hospitalization was defined as more than three days postoperative hospitalization in breast cancer surgery patients and more than seven days postoperative hospitalization in all other patients.
Statistical Analysis
Statistical evaluation was done using SPSS software, Version SPSS 11.0.1, (LEAD Technologies, Inc., US). Descriptive statistics were generated using frequency, median, and mean. Univariate analysis was done for age, stage, neoadjuvant therapy received, nature of the surgery (emergency/elective), ECOG score, ASA PS score, MFI score, and CFS score with respect to 30-day mortality, 30-day morbidity, 30-day readmission, and prolonged hospitalization. Association between categorical variables was assessed using Chi-Square or Fisher's exact test as appropriate. Risk assessment for postoperative adverse events was done using univariate and multivariate logistic regression analyses. The variables which had skewed data due to increased numbers in one category resulted in biased analysis and were omitted from the analysis. Multivariate analysis was done only for those variables which were significant on univariate analysis. A p value of ≤0.05 was considered statistically significant.
Results
Patient Characteristics
Four thousand one hundred and seven patients underwent surgery during the study period. Patient demographics, tumor, and treatment parameters are depicted in Table 1. Females constituted 63.2% of the patients who underwent surgery during the study period. Patients younger than 65 years of age consisted of 83.3%. The median age was 54 years with a range from 16 to 84 years.
Table 1.
Patient, cancer, and treatment data
| Parameter | Number of patients | Percentage |
|---|---|---|
| Patient characteristics | ||
| Gender | ||
| Male | 1510 | 36.8 |
| Female | 2597 | 63.2 |
| Age | ||
| <65 years | 3420 | 83.3 |
| ≥65 years | 687 | 16.7 |
| Tumor type | ||
| Head and neck | 1319 | 32.1 |
| Gastrointestinal | 694 | 16.9 |
| Lung | 40 | 1.0 |
| Sarcoma | 166 | 4.0 |
| Breast | 1309 | 31.9 |
| Gynecological | 407 | 9.8 |
| Urological | 87 | 2.1 |
| Miscellaneous | 85 | 2.1 |
| Tumor characteristics | ||
| Stage | ||
| I | 1134 | 27.6 |
| II | 881 | 21.5 |
| III | 1523 | 37.1 |
| IV | 415 | 10.1 |
| Recurrence | 153 | 3.7 |
| Neoadjuvant therapy | ||
| Present | 1194 | 29.1 |
| Absent | 2913 | 70.9 |
| Type of surgery | ||
| Elective | 3892 | 94.8 |
| Emergency | 215 | 5.2 |
Tumor and Surgery Characteristics
The most common site was the head and neck cancers (32.1%) closely followed by breast cancer (31.9%). The commonest stage of presentation was stage III (37.1%) followed by stage I (27.6%). Patients presenting with a recurrence and undergoing surgery constituted 3.7% of the patients. Neoadjuvant therapy was received by 29.1% of the cases while the rest of the patients underwent primary surgery. Out of the patients receiving neoadjuvant therapy, 27.5% of the patients received chemotherapy and 8.1% of the patients received preoperative radiotherapy with 6.5% of the patients receiving both modalities of neoadjuvant therapy. Most of the surgeries (94.8%) were elective surgeries while the rest of the patients underwent surgery on an emergency basis.
Preoperative Score Characteristics
The preoperative assessment scores of the 4 scales studied are depicted in Table 2. The MFI score ranged from 0 to 3 with 0 being the most common score (51.4%) followed by score 1 (30.4%). The CFS ranged from 0 to 5 with the most common being score 3 (86.9%) followed by score 2 (6.0%). On stratification, 5.5% of patients had CFS of greater than 3. The ECOG score ranged from 0 to 2 with ECOG 1 being the most common (92.6%) followed by ECOG 2 (4.4%). The ASA PS score ranged from 2 to 3 with ASA PS score 2 (95.3%) being the most common followed by ASA PS score 3 (4.7%).
Table 2.
Risk Stratification scores of patients
| Scoring system | Number of patients | Percentage |
|---|---|---|
| Modified Frailty Index | ||
| 0 | 2111 | 51.4 |
| 1 | 1246 | 30.4 |
| 2 | 638 | 15.5 |
| 3 | 112 | 2.7 |
| ≥4 | 0 | 0 |
| Clinical Frailty Scale | ||
| 0 | 1 | .0 |
| 1 | 67 | 1.6 |
| 2 | 247 | 6.0 |
| 3 | 3563 | 86.9 |
| 4 | 218 | 5.3 |
| 5 | 11 | 0.2 |
| ≥6 | 0 | 0 |
| ≤3 | 3878 | 94.5 |
| >3 | 229 | 5.5 |
| Eastern Cooperative Oncology Group | ||
| 0 | 126 | 3.0 |
| 1 | 3801 | 92.6 |
| 2 | 180 | 4.4 |
| ≥3 | 0 | 0 |
| American Society of Anesthesia Physical Status | ||
| 1 | 0 | 0 |
| 2 | 3918 | 95.3 |
| 3 | 199 | 4.7 |
| ≥4 | 0 | 0 |
Postoperative Characteristics
Postoperative complications are depicted in Table 3. Prolonged hospitalization was present in 12.6% of the patients. The median duration of postoperative hospital stay was 4 days with a range from 1 to 40 days. Thirty-day morbidity was 6.1%. The CTCAE grade of the morbidity ranged from 0 to 5 with grade 2 being the most common (2.2%) among patients with morbidity. Readmission occurred in 0.9% of patients, and postoperative 30-day mortality was 0.6%.
Table 3.
Postoperative complications
| Variable | Number of patients | Percentage |
|---|---|---|
| Prolonged hospitalization | ||
| Present | 3589 | 87.4 |
| Absent | 518 | 12.6 |
| Morbidity (30 days) | ||
| Present | 3857 | 93.9 |
| Absent | 250 | 6.1 |
| Common Terminology Criteria for Adverse Events (CTCAE) grade of morbidity | ||
| 0 | 3857 | 93.9 |
| 1 | 7 | 0.2 |
| 2 | 92 | 2.2 |
| 3 | 72 | 1.9 |
| 4 | 55 | 1.2 |
| 5 | 24 | 0.6 |
| Readmission (30 days) | ||
| Present | 4069 | 99.1 |
| Absent | 38 | 0.9 |
| Mortality (30 days) | ||
| Present | 4083 | 99.4 |
| Absent | 24 | 0.6 |
Statistical Analysis
The association between the 4 preoperative risk assessment scores and the postoperative complications are depicted in Tables 4, 5, 6, and 7. On statistical analysis, there was significant association between ECOG, CFS and ASA PS with mortality (p = 0.01). MFI was not significantly associated with mortality (p=0.834). All the four assessed scoring systems had significant association with prolonged postoperative stay (p = 0.001). Morbidity within 30 days of surgery was also significantly associated with ASA PS and ECOG (p = 0.001), CFS (p=0.01), and MFI (p = 0.032). Readmission was not significantly associated with any of the 4 scoring systems.
Table 4.
Association of postoperative complications and Eastern Cooperative Oncology Group Performance Scale (ECOG)
| ECOG score | Postoperative variables | Total | p value | |
|---|---|---|---|---|
| Present | Absent | |||
| Prolonged hospitalization | ||||
| 0 | 7 | 119 | 126 | |
| 1 | 472 | 3329 | 3801 | 0.001 |
| 2 | 39 | 141 | 180 | |
| ≥3 | 0 | 0 | 0 | |
| Morbidity (30 days) | ||||
| 0 | 3 | 123 | 126 | |
| 1 | 224 | 3577 | 3801 | 0.001 |
| 2 | 23 | 157 | 180 | |
| ≥3 | 0 | 0 | 0 | |
| Readmission (30 days) | ||||
| 0 | 1 | 125 | 126 | |
| 1 | 33 | 3768 | 3801 | |
| 2 | 4 | 176 | 180 | 0.181 |
| ≥3 | 0 | 0 | 0 | |
| Mortality (30 days) | ||||
| 0 | 0 | 126 | 126 | |
| 1 | 18 | 3783 | 3801 | 0.001 |
| 2 | 6 | 174 | 180 | |
| ≥3 | 0 | 0 | 0 | |
Table 5.
Association of postoperative complications and American Society of Anaesthesiologists Physical Status Classification System (ASA PS)
| ASA PS score | Postoperative variables | Total | p value | |
|---|---|---|---|---|
| Present | Absent | |||
| Prolonged hospitalization | ||||
| 1 | 0 | 0 | 0 | 0.001 |
| 2 | 474 | 3437 | 3911 | |
| 3 | 44 | 151 | 195 | |
| ≥4 | 0 | 0 | 0 | |
| Morbidity (30 days) | ||||
| 1 | 0 | 0 | 0 | |
| 2 | 3686 | 225 | 3911 | 0.001 |
| 3 | 171 | 24 | 195 | |
| ≥4 | 0 | 0 | 0 | |
| Readmission (30 days) | ||||
| 1 | 0 | 0 | 0 | |
| 2 | 35 | 3876 | 3911 | 0.425 |
| 3 | 3 | 192 | 195 | |
| ≥4 | 0 | 0 | 0 | |
| Mortality (30 days) | ||||
| 1 | 0 | 0 | 0 | |
| 2 | 17 | 3894 | 3911 | 0.001 |
| 3 | 6 | 189 | 195 | |
| ≥4 | 0 | 0 | 0 | |
Table 6.
Association of postoperative complications and American Society of Modified Frailty Index (MFI)
| MFI Score | Postoperative variables | Total | P value | |
|---|---|---|---|---|
| Present | Absent | |||
| Prolonged hospitalization | ||||
| 0 | 229 | 1882 | 2111 | |
| 1 | 177 | 1069 | 1246 | 0.001 |
| 2 | 87 | 551 | 638 | |
| 3 | 25 | 87 | 112 | |
| ≥4 | 0 | 0 | 0 | |
| Morbidity (30 days) | ||||
| 0 | 114 | 1997 | 2111 | |
| 1 | 82 | 1164 | 1246 | |
| 2 | 40 | 598 | 638 | 0.032 |
| 3 | 14 | 98 | 112 | |
| ≥4 | 0 | 0 | 0 | |
| Readmission (30 days) | ||||
| 0 | 21 | 2090 | 2111 | |
| 1 | 13 | 1233 | 1246 | |
| 2 | 3 | 635 | 638 | 0.610 |
| 3 | 1 | 111 | 112 | |
| ≥4 | 0 | 0 | 0 | |
| Mortality (30 days) | ||||
| 0 | 13 | 2098 | 2111 | |
| 1 | 7 | 1238 | 1245 | 0.834 |
| 2 | 3 | 635 | 638 | |
| 3 | 1 | 111 | 112 | |
| ≥4 | 0 | 0 | 0 | |
Table 7.
Association of postoperative complications and Clinical Frailty Scale (CFS)
| CFS score | Postoperative variables | Total | p value | |
|---|---|---|---|---|
| Present | Absent | |||
| Prolonged hospitalization | ||||
| ≤3 | 470 | 3408 | 3878 | 0.001 |
| >3 | 48 | 181 | 229 | |
| Morbidity (30 days) | ||||
| ≤3 | 3651 | 227 | 3878 | |
| > | 206 | 23 | 229 | 0.010 |
| Readmission (30 days) | ||||
| ≤3 | 35 | 3843 | 3878 | 0.468 |
| >3 | 3 | 226 | 229 | |
| Mortality (30 days) | ||||
| ≤3 | 18 | 3860 | 3878 | |
| >3 | 6 | 223 | 229 | 0.001 |
On univariate analysis, morbidity was significantly associated with ECOG score of 2 (p = 0.004), ASA PS of score 3 (p = 0.001), MFI score of 3 (p = 0.002), CFS score of >3 (p = 0.011), age ≥ 65 years (p=0.001), female sex (p=0.001), stage III and IV cancer at diagnosis (p=0.001), neoadjuvant therapy (p=0.001), and emergency surgery (p=0.001). On multivariate analysis, morbidity was significantly associated with neoadjuvant therapy (p=0.001) only.
On univariate analysis, prolonged hospitalization was significantly associated with stages III and IV (p=0.001), neoadjuvant chemotherapy (p=0.002), ECOG score of 2 (p=0.001), ASA PS score of >2 (p=0.001), MFI score of 3 (p=0.001), CFS score of > 3 (p=0.001), age ≥ 65 years (p=0.001), female sex (p=0.001), and emergency surgery (p=0.001). On multivariate analysis there was a significant association between prolonged hospitalization and stages III and IV at detection (p=0.001).
On univariate and multivariate analysis, readmission was significantly associated with neoadjuvant therapy (p=0.001). On univariate analysis, mortality was significantly associated with CFS score of >3 (p=0.001), age ≥ 65 years (p=0.009), female sex (p=0.012), and emergency surgery (p=0.001). On multivariate analysis, emergency surgery (p=0.001) was found to be significantly associated with mortality.
Discussion
The incidence of cancer is rising globally. Surgery is the mainstay of the curative treatment of most solid cancers. Early recovery from surgery is crucial for the timely delivery of adjuvant therapy like chemotherapy and radiotherapy. Postoperative complications which lead to a delay in the initiation of adjuvant therapy may cause decrease in the survival of the patient [13, 14]. It is important to pre-operatively stratify the risk of the surgery patients as the further treatment of the patient may differ based on the risk stratification. If the patients have a high risk of postoperative morbidity and mortality, the same must be conveyed to the rest of the treating team and to the patient and bystanders. The multidisciplinary plan of management may change from radical to less radical or even to non-surgical management as found to be best suitable for the patient. When such drastic changes in the treatment plan are made, it is very essential to have an accurate and objective preoperative risk stratification system.
The current commonly used scales are of ASA PS and ECOG. Both are time tested but subjective scores. A previous study by Young et al., in colorectal malignancies, stated that both ASA PS and ECOG perform similarly with additional predictive benefit if both are used to predict chances for prolonged hospitalization [15]. Frailty scores assess patients from a multidimensional viewpoint. MFI score is an objective scoring system with an equal score assigned to each co-morbidity while CFS is a more subjective and pictorial scale which assesses the patient’s level of activity and independence. A recent overview by Simcock and Wright has highlighted the strengths and weaknesses of ECOG and has recommended the use of CFS for more optimal triaging and treatment planning of cancer patients [16]. GOSAFE trial demonstrated that the older adults’ pre-operative quality of life was preserved 3 months after cancer surgery, independent of their age [17]. Frailty screening tools, patient-reported outcomes, and goals-of-care discussions can guide decisions to pursue surgery and direct patients’ expectations [17].
Our study compared the four pre-operative risk scores of ECOG, ASA PS, MFI, and CFS in predicting the 30-day postoperative morbidity, readmission rate, prolonged hospitalization, and mortality in cancer patients undergoing surgery. On statistical analysis there was significant association between ECOG, CFS and ASA PS with 30-day mortality. All the four assessed scoring systems had significant association with prolonged postoperative hospitalization and 30-day morbidity. Readmission was not significantly associated with any of the 4 scoring systems. Thus, ECOG, ASA PS, and CFS were better predictors of 30-day postoperative mortality than MFI. In a recent study by Gu et al., frailty was found to be a better predictor of postoperative 30-day morbidity in elderly than ASA PS [18]. In another study by Vermillion et al., MFI was found to be predictive of mortality and postoperative complications in patients undergoing surgery for colorectal cancer [19]. In a study by Pichatechaiyoot et al., MFI was found to be predictive of postoperative complications in endometrial cancer surgery patients [20]. All these studies were database analysis and retrospective in nature. In a study by Hewitt et al., higher CFS score was found to be associated with poorer outcomes including mortality in emergency surgical patients [21]. A meta-analysis and systematic review also found frailty to be predictive of postoperative outcomes [22]. These studies encourage the inclusion of frailty into the pre-operative assessment of the patient. In a retrospective study by Cihoric et al., ECOG score was found to be predictive of postoperative mortality in patients undergoing emergency laparotomy surgery [23]. In a recent study of Indian cancer patients, ECOG score correlated moderately well with abnormalities in function and falls, psychological assessment, and cognition, while it poorly correlates with nutritional status and comorbidities [24]. A study by Rosa et al. concluded that ASA PS is insufficient to predict operative risk in patients with gastric cancer [25]. The current evidence of frailty in cancer patients is limited to site specific analysis. This study is the first prospective large study comparing various pre-operative risk factors in cancer patients. As per our study, ECOG, ASA PS, and CFS are better predictors of postoperative outcomes of 30-day mortality and morbidity and prolonged hospitalization. These outcomes can alter the course of further treatment and overall result of the therapy in cancer patients and thus have an important role in the risk benefit analysis of surgery of the patient.
Our study identified certain factors which were associated with postoperative outcomes which are specific to cancer patients. On multivariate analysis, morbidity was significantly associated with preoperative neoadjuvant therapy, and advanced stage at detection, readmission was significantly associated with neoadjuvant therapy, prolonged hospitalization with advanced stage of cancer, and mortality with emergency surgery. These factors are not considered in any of the pre-operative risk scores. Cancer patients differ from the rest of the surgery patients as they may be immunocompromised and malnourished and may have received neoadjuvant toxic debilitating therapy. Cancer is a systemic disease which affects the patient both physically and psychologically. These patients usually have comorbidities such as diabetes mellitus and cardiac illness which further affects their physical status in a negative manner. Diabetes mellitus has been suggested to increase the mortality in cancer patients [26]. The likelihood of concomitant coronary heart disease is higher in cancer patients than in the general population without cancer [27]. A pre-operative risk assessment score specifically for cancer patients undergoing surgery incorporating all these factors should be developed.
There are some limitations of our study. This study consists of patients who underwent surgery for cancer. The patients who refused surgery due to high risk for postoperative complications have not been studied. The proportion of high-risk patients in our cohort is low, and so are postoperative complications. The quality of life, postoperative functional decline, and long-term outcomes have not been assessed. Further follow-up of this cohort is planned to study the long-term outcomes with respect to pre-operative risk assessment tools.
Conclusion
In adult patients undergoing cancer surgery, ASA PS, ECOG, and CFS are predictors of prolonged postoperative hospitalization, 30-day morbidity, and 30-day mortality. MFI is predictive of prolonged postoperative hospitalization and 30-day morbidity. None of the studied pre-operative risk scores predict readmission. Newer predictive tools which include cancer-specific factors are required for better risk stratification of cancer patients undergoing surgery.
Declarations
Ethics Approval
The study was cleared by the Institutional Review Board of our Cancer Centre (IRB No. 01/2018/03).
Conflict of Interest
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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