Head and neck cancer (HNC) is the sixth most common cancer worldwide. With 890,000 new cases and 450,000 deaths in 2018, it is predicted to continue to rise by 30% by 2030 [1-3]. The survival for HNC has improved modestly over the past three decades as is evident from the analysis of the Surveillance, Epidemiology, and End Results (SEER) Registry. HNC constitutes 30-40% of all the cancers in India.[4] Surgery is the mainstay of treatment in HNC with perioperative chemotherapy and radiotherapy added as indicated. Cancer patients are at higher surgical risk due to their malignancy, immunosuppressive treatment and associated nutritional depletion. Unlike non-cancer patients, early recovery is more important in cancer patients as they have to undergo subsequent adjuvant treatment. Postoperative morbidity and prolonged hospitalization is associated with increased physical, psychological and economic stress on the patient and the hospital. There is a growing need to identify factors which can accurately predict increased morbidity in these patients, especially so in the elderly [5]. There are many scoring systems available for preoperative risk stratification of cancer patients. The most commonly followed scores are American Society of Anesthesiologists Physical Status (ASA) and Eastern Cooperative Oncology Group Performance Scale (ECOG) [6,7]. As these scores are very subjective, there is a need for more objective means of preoperative risk stratification.

Frailty is a marker of physiological decline. Frailty is a multidimensional syndrome characterized by a decline in physiologic and cognitive homeostatic reserve that increases susceptibility to adverse events and unfavorable outcomes, often following relatively minor stressors.[8] Frailty has been causally associated with the elderly [9]. A frailty index assesses comorbidities and functional status to identify patients preoperatively who are at risk for complications after surgical procedures regardless of age. The Canadian Study of Health and Aging Frailty Index (CSHA-FI) is a comprehensive list of 70 items that assesses all facets of a patient’s life to determine frailty 10]. This comprehensive list has been simplified into a Modified Frailty Index (MFI) which consists of 11 indices of comorbidities and functional status to risk stratify and predict adverse events [11]. Though MFI has been used as an effective predictor of postoperative complications in several other surgical specialties, there is scant prospective data in HNC [12-14]. The Clinical Frailty Scale (CFS) is a 9-point frailty score which uses clinical descriptors and pictographs. It was developed to provide clinicians with an easily applicable tool to stratify older adults according to level of vulnerability.[10] Recent studies reported that CFS was a strong predictor of in-hospital mortality, prolonged hospitalisation and readmission [15-18]. There are currently very few prospective studies which assess the role of frailty scores in predicting postoperative morbidity, readmission rates, prolonged hospitalisaton and mortality in HNC patients undergoing surgery. This study was conducted to compare the established preoperative risk assessment tools of ECOG and ASA with frailty scores of MFI and CFS. We believe ours is the first prospective study that compares frailty scores such as MFI and CFS with the standard preoperative risk stratification of ECOG and ASA in HNC patients undergoing oncosurgery.

This prospective observational study was conducted after obtaining clearance from the Institutional Review Board. The study was conducted from 1st March 2018 to 28th February 2019 at tertiary Cancer Care Centre in South India. All adult patients undergoing surgery for head and neck cancers during the study period were included in the study. Patients were stratified during their preanaesthetic evaluation as per CFS, MFI, ASA and ECOG scales. The ECOG score was entered by the treating surgeons while the ASA score was entered by the assessing anaesthesiologist and this data was collected prospectively from the files. MFI was scored from 0 to 11 and CFS was score from 1 to 9 by a single anaesthesiologist (corresponding author). Patients with CFS > 4 were considered vulnerable while a CFS score of less than or equal to 3 were considered fit [19]. Data collected included age, gender, stage of the cancer, neoadjuvant therapy (chemotherapy or radiotherapy), nature of the surgery (emergency or elective), prolonged hospital stay, and readmission and 30-day mortality. All patients were followed up for 30 days postoperatively. The 30-day morbidity was recorded and scored as per common terminology criteria for adverse events (CTCAE) version 4.03 [20].

Statistical analysis

Statistical evaluation was done using SPSS 11.0. Descriptive statistics were generated. Association between categorical variables were assessed using Chi-square or Fisher’s exact test, whichever was appropriate. Risk factors for morbidity were evaluated using univariate and multivariate logistic regression analysis. A p value of ≤ 0.05 was considered statistically significant.

Patient characteristics

Of the 4107 patients who underwent surgery during the study period, 1319 i.e., 32% patients were for cancers of the head and neck region. The patient, tumour and treatment parameters are depicted in (Table 1).  Males constituted 63.6% of the patients. 81.9% of the patients were younger than 65 years of age. The median age was 54 years with a range from 16 to 84 years.

Table 1: Patient, tumor and treatment characteristics.

Tumour and surgery characteristics

The most common site of cancer was the oral cavity (68.8%) followed by thyroid (19.9%) (Table 1). The commonest stage was stage I (43.2%) followed by stage III (22%). Surgery for recurrent cancer was done in 6.6% patients. Neoadjuvant therapy was received by 19% of the cases while the remaining patients underwent primary surgery. Most of the surgeries (99.2%) were planned elective surgeries while the rest were emergency surgeries.

Preoperative score characteristics

The preoperative assessment scores of the 4 scales studied are depicted in (Table 2). The Modified Frailty Index (MFI) score ranged from 0 to 3 with 0 being the most common score (53.8%) followed by score 1 (29.3%). The Clinical Frailty Scale (CFS) ranged from 1 to 4 with the most common being score 3 (85.8%) followed by score 2 (7.4%).  On stratification into CFS less than or equal to 3 and greater than 3, only 4.5% patients had a CFS greater than 3.  The ECOG score ranged from 0 to 2 with ECOG 1 being the most common (92.4%) followed by ECOG 0 (4.3%). The ASA score ranged from 1 to 3 with ASA score 2 (90.8%) being the most common followed by ASA score 1 (5.2%).

Table 2: Frequency of preoperative assessment scores in patients.

Postoperative characteristics

Prolonged hospital stay was present in 7.3% 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 2.8%. The CTCAE grade of the morbidity ranged from 1 to 4 with grade 3 being the most common (40.5%).  Readmission was present in 0.5% of the patients. There was no postoperative 30-day mortality during the study period.

Table 3: Postoperative complications.

Table 4: Cross tables of Modified Frailty Index and postoperative variables.

Table 5:  Clinical Frailty Score and postoperative variables.

Table 6:  American Society of Anesthesiologists (ASA) Physical Status and postoperative variables.

Statistical analysis

Statistical analysis revealed no significant association between MFI, CFS and ASA, and prolonged postoperative stay, 30-day morbidity and readmission as depicted in Tables 4, 5 and 6. There was a significant association between ECOG and prolonged hospital stay (p = 0.028) but not with 30-day morbidity and readmission (Table 7).   On univariate analysis, 30-day morbidity was significantly associated with advanced stage (p = 0.011), prolonged hospital stay (0.001) and readmission (0.001). On multivariate analysis, 30-day morbidity was significantly associated only with prolonged hospital stay (0.001) and readmission (0.001). On univariate analysis, prolonged hospital stay was significantly associated with advanced stage (p = 0.001), neoadjuvant chemotherapy (p = 0.028), ECOG (p = 0.028) and morbidity (p = 0.001). On multivariate analysis, there was a significant association between prolonged hospital stay and advanced stage (p = 0.001), ECOG (p = 0.028) and morbidity (p = 0.001). On univariate and multivariate analysis, readmission was significantly associated with neoadjuvant chemotherapy (p = 0.008), emergency surgery (p = 0.004) and morbidity (p = 0.001).

Table 7: European Cooperative Oncology Group (ECOG) score and postoperative variables.

Head and neck cancers are among the commonest cancers in our country. Surgery is the mainstay of treatment.  As the surgery in locally advanced HNC is complex, there is a high risk of postoperative morbidity. The current perioperative mortality rate of HNC is 1% as per a recent study [21]. There are many scales to predict perioperative morbidity.  The most commonly used scales in oncological surgery are ECOG and ASA. A previous study by Young et.al, stated that both ASA and ECOG perform similarly with additional benefit if both are used concurrently [22]. A recent overview by Simcock and Wright has highlighted the strengths and weaknesses of ECOG and has recommended the use of Clinical Frailty Score for triaging and treatment planning for cancer [23]. Frailty scores assess patient from a multidimensional viewpoint. MFI score is an objective scoring system with a score assigned to each comorbidity while CFS is a more subjective and pictorial scale.

This study compared the standard scores of ASA and ECOG with the frailty scores MFI and CFS.  The ECOG score was calculated by the surgeons and the ASA, MFI and CFS scales were entered by the anaesthesiologists. Data of all patients who underwent surgery during the study duration was collected prospectively and they were followed up for 1 month postoperatively. Postoperative data including prolonged hospital stay, 30-day morbidity, CTCAE grade of morbidity, readmission and mortality was collected. Statistical analysis revealed no significant association between MFI, CFS and ASA and prolonged postoperative stay, 30-day morbidity and readmission. ECOG was significant associated with prolonged hospital stay but not associated with 30-day morbidity and readmission. There was no mortality during the study period. Our study indicates that MFI and CFS are not superior to ASA and ECOG in predicting postoperative outcomes. ECOG was the only score which significantly predicted prolonged hospital stay but failed to predict other postoperative outcomes. There are multiple studies which have demonstrated the superiority of frailty scores, [11-19] but many of these studies are retrospective analysis of large databanks [24]. A retrospective study by Pitts et al of 410 patients compared 2 frailty scores (American College of Surgeons National Surgical Quality Improvement Program modified frailty index and the Johns Hopkins Adjusted Clinical Groups frailty index) and ASA as predictor of perioperative morbidity, mortality, 30-day readmission, and length of stay [13]. The MFI score significantly increased the odds of mortality, readmission, length of stay and perioperative morbidity. ASA was also significantly associated with readmission, length of stay, and perioperative morbidity. A study by de Varies et al concluded that frail patients with HNC have an increased risk for decline in health-related quality of life and the risk further increases after curative treatme [14].  Goldstein et al conducted a prospective study of 241 HNC patients aged 50 years or older and concluded that frailty is a predictor of type and severity of postoperative complications [12]. The results of our study are contrary to most of the previous studies as frailty scores in our study failed to predict postoperative complications. Our study is a prospective and includes a large sample size. On multivariate analysis, 30-day morbidity was significantly associated with prolonged hospital stay and readmission, while neoadjuvant chemotherapy was associated with prolonged hospital stay (univariate) and readmission (multivariate). Advanced stage of cancer was associated with prolonged hospital stay (multivariate) and morbidity (univariate) whereas emergency surgery was associated with readmission (multivariate). All these factors are cancer-specific and are not included in any of the predictive scales. Development of a frailty scale which includes these cancer-specific factors may provide a better predictor of frailty in HNC patients.

There are some limitations of our study. We were fortunate to have no mortality in our study. Hence, the role of the scores to predict mortality could not be studied. A subset analysis based on age and site of the HNC was not done. Any significant correlation with specific site of HNC was not studied. Our study included patients of all age groups with only 18.1% patients aged 65 years or greater. The significance of frailty in this subset was not assessed separately as the study is not adequately powered for the same.

Our study questions the growing evidence of the role of frailty scores in predicting postoperative complications in HNC. The varying definition of frailty in different studies makes it difficult to assess the exact prevalence of frailty in cancer patients [25]. A systematic review states the prevalence of frailty in cancer to be between 6% and 86%, while the prevalence of pre-frailty is between 13% and 79% [26]. Newer frailty models including cancer specific factors like neoadjuvant chemotherapy, stage of the cancer and emergency or elective status of the surgery may be more sensitive in predicting postoperative complications in HNC patients.

There is no significant association between MFI, CFS and ASA and prolonged postoperative hospital stay, 30-day morbidity and readmission. ECOG could predict prolonged hospital stay but not 30-day morbidity or readmission. Newer frailty models including cancer-specific factors may be more sensitive in predicting the postoperative complications in HNC patients.

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