ECOG performance status as a representative of deficits in older Indian patients with cancer: A cross-sectional analysis from a large cohort study : Cancer Research, Statistics, and Treatment

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Original Article: Geriatric Oncology Section

ECOG performance status as a representative of deficits in older Indian patients with cancer

A cross-sectional analysis from a large cohort study

Gattani, Shreya; Ramaswamy, Anant; Noronha, Vanita; Castelino, Renita1; Kumar, Sharath1; Rao, Abhijith Rajaram; Dhekale, Ratan2; Krishnamurthy, Jyoti3; Kannan, Sadhana4; Gota, Vikram1; Prabhash, Kumar; Banavali, Shripad; Badwe, Rajendra A.5

Author Information
Cancer Research, Statistics, and Treatment: Apr–Jun 2022 - Volume 5 - Issue 2 - p 256-262
doi: 10.4103/crst.crst_127_22
  • Open

Abstract

INTRODUCTION

Older patients with cancer have a specific set of requirements and attributes that set them apart from younger patients. Besides a lack of clarity on how the treatment modalities used in younger patients should be extrapolated to older patients, there is also the question of the relevance of a geriatric assessment (GA) in all older patients.[1] While the GA provides complete information in a systematic manner, it is an intensive and time-consuming process that may not be feasible in all scenarios. Although tools such as the VES-13 and G8 are well established for screening, the most commonly used assessment in oncology practice in terms of general well-being evaluation is the performance status (PS) as measured by the Eastern Cooperative Oncology Group (ECOG PS) scale or the Karnofsky Performance Status (KPS) scale.[23]

Some of the advantages of using a standardized barometer like the ECOG PS include its widespread use in clinical practice and clinical trials, its correlation with oncological outcomes, as well as being a reasonable stand-alone estimate of the functional status. What has been evaluated to a lesser extent, especially in the Indian population, is the correlation of the ECOG with the individual components of the GA as well as the correlation with the burden of deficits as estimated in a GA.[4] Such information would provide greater information on the benefit of using the ECOG PS alone as a tool for screening patients for a GA as well as to identify deficits. With this background, the current study was conducted in Indian patients evaluated at the geriatric oncology clinic in a tertiary care hospital.

MATERIALS AND METHODS

General study details

This cross-sectional observational study was conducted between May 2018 and January 2021 in the geriatric oncology clinic of the Tata Memorial Hospital, a tertiary care cancer center in Mumbai, India. This is a multidisciplinary clinic, which includes a medical oncologist, geriatrician, clinical pharmacologist, psychiatrist, dietician, physiotherapist, occupational therapist, and social worker. The study was approved by the Institutional Ethics Committee (IEC-III; Project No. 900596, approved on Mar 20, 2020; Supplementary appendix 1) as a retrospective cum prospective study, along with a waiver for the requirement of taking written informed consent for the retrospective portion of the study. Written informed consent was taken from Mar 2020 onward as a part of the prospective portion of the study. The study was registered with the Clinical Trials Registry – India (CTRI/2020/04/024675). The study was conducted according to the Good Clinical Practice guidelines, and the ethical principles as per the Declaration of Helsinki were followed. There was no funding for the study.

Participants

The data of patients aged ≥60 years with solid tumors analyzed in the geriatric oncology clinic were evaluated in the study. Patients had an ECOG PS of 0–3, as these were the patients usually considered for cancer-directed therapy, who were referred to the geriatric oncology clinic. Patients with ECOG PS 4 were not evaluated with a GA and were therefore not a part of the study.

Aims/objectives

We aimed to study the correlation of the ECOG PS with the individual components of the GA as well as with the burden of deficits as estimated in the GA. The primary outcome variable was defined as the presence of ≥2 abnormalities on the tested 5 domains of the GA (Yes or No). Secondary outcome variables included the presence of at least one abnormality on the 5 tested domains of the GA and the presence of ≥3 abnormalities on the tested domains of the GA. The independent variable was ECOG PS (0–3), analyzed as a discrete variable. Other variables assessed included age, sex, primary sites of disease, and stage.

Study methodology

A multidisciplinary GA assessing multiple geriatric domains was conducted, and the details were recorded in the geriatric oncology clinic database. For the purpose of the study, the details extracted from the database included the baseline sociodemographic variables, ECOG PS, and the assessments in the specific geriatric domains of function and falls (Lawton Instrumental Activities of Daily Living [IADL]: abnormal <8 for women and <5 for men,[5] Katz Index of Independence in Activities of Daily Living [Katz ADL]: abnormal <6,[6] history of falls: presence of a fall in the preceding 6 months counting as a deficit, and Time to Get-up-and-Go [TUG]: abnormal ≥12 s),[7] comorbidities (Charlson Comorbidity Index [CCI]: abnormal ≥2,[8] Cumulative Illness Rating Scale-Geriatric [CIRS-G]: abnormal >6),[9] nutritional status (BMI [body mass index, abnormal < 21], MNA: at risk of undernutrition <23),[10] psychological status (Geriatric Depression Score [GDS]: at risk of depression ≥5,[11] Generalized Anxiety Disorder Screener [GAD-7]: risk of mild anxiety >10),[12] and cognition (Mini Mental Status Examination [MMSE]: abnormal <24).[13] The presence of at least one deficit in any of the tests conducted in a domain was coded as an abnormality in that particular domain.

Statistics

As this was a cross-sectional observational study, we included all patients who had undergone a GA during the study period; no sample size calculation was performed. Descriptive analysis was used to describe the baseline characteristics of the patients in the study. To evaluate the discriminatory power of the ECOG PS for identifying ≥2 or ≥3 geriatric abnormalities as well deficits in individual geriatric domains, a logistic regression receiver operating characteristic (ROC) curve was calculated using area under the ROC curve (AU-ROC) with 95% confidence intervals (CI) to predict the accuracy. AU-ROCs were divided into ≥0.8 to indicate excellent, <0.8–≥0.65 to signify moderate, and <0.65 to signify poor discriminant abilities. Data entry and analyses were performed using the Statistical Program for the Social Sciences (released 2012, IBM SPSS Statistics for Windows, version 21.0; IBM Corp., Armonk, NY, USA).

RESULTS

Baseline characteristics

A total of 638 patients were evaluated during the study period, of whom 594 [Figure 1] were included and their baseline characteristics are presented in Table 1. The median age was 69 years (range, 60–100), with the most common sites of primary cancer being lung (n = 242, 41%) and gastrointestinal (n = 195, 33%). Approximately half the patients (n = 303, 51%) had stage IV cancer. The majority of patients had an ECOG PS of 1 or 2 (n = 476, 80%).

F1-11
Figure 1:
Schema of inclusion of participants in the study
T1-11
Table 1:
Baseline characteristics and results of the geriatric assessment

Prevalence of deficits in the geriatric domains

The prevalence of overall deficits in the GA is detailed in Table 1. Approximately three-fourths of patients (n = 458, 77%) had deficits in at least 2 of the 5 domains assessed, while 280 (47%) patients had deficits in at least 3 domains. The prevalence of abnormalities in individual geriatric domains was as follows: nutrition (n = 428, 72%), comorbidities (n = 481, 81%), cognition (n = 101, 17%), function and falls (n = 297, 50%), and psychological status (n = 155, 26%). The number of impaired geriatric domains increased with an increasing PS score; patients with PS 0 had a median of 1 (interquartile range [IQR], 1–2) impaired domain, those with PS 1 had a median of 2 (IQR, 1–3) impaired domains, those with PS 2 had a median of 3 (IQR, 2–4) impaired domains, and those with PS 3 had a median of 4 (IQR, 3–4) impaired domains.

Correlation of ECOG PS with abnormalities in geriatric domains

An ECOG PS of ≥1 was predictive of ≥2 geriatric abnormalities with an AU-ROC of 0.69 (95% CI, 0.64–0.74), sensitivity of 95.4%, and specificity of 18.4%, while at a cut-off of ≥3 geriatric abnormalities, the AU-ROC was 0.73 (95% CI, 0.69–0.77) with a sensitivity of 98.6% and specificity of 13.4% [Figure 2]. With each 1 unit increase in the ECOG PS score, the odds of having ≥ 2 geriatric abnormalities increased by 4.69 (95% CI, 2.53–8.68). With each 1 unit increase in the ECOG score, the odds of having ≥3 geriatric abnormalities increased by 10.65 (95% CI, 3.77–30.12) [Table 2].

T2-11
Table 2:
Odds ratio and receiver operating curve characteristics of the ECOG PS in patients with =2 and =3 geriatric abnormalities on the geriatric assessment
F2-11
Figure 2:
Area under the ROC curves comparing the discriminatory ability of the ECOG PS scale ≥1 in identifying ≥2 and ≥3 geriatric abnormalities. ECOG = Eastern Cooperative Oncology Group, PS = performance status, ROC = receiver operating characteristic

The ECOG PS correlated moderately well with deficits in cognition (AUC = 0.66 [95% CI, 0.61–0.72]), function and falls (AUC = 0.73 [95% CI, 0.69–0.77]), and psychological domains (AUC = 0.65 [95% CI, 0.60–0.70]), while the correlation of the ECOG PS was less robust with the nutritional status (AUC = 0.63 [95% CI, 0.58–0.68]) and comorbidities (AUC = 0.55 [95% CI, 0.49–0.61]) [Figure 3]. The prevalence of impairments in each of the geriatric domains across each PS is represented in Figure 4.

F3-11
Figure 3:
Area under the ROC curves comparing the discriminatory ability of the ECOG PS scale in identifying vulnerabilities in nutrition, comorbidities, cognition, function and falls, and psychological domains. ECOG = Eastern Cooperative Oncology Group, PS = performance status, ROC = receiver operating characteristic
F4-11
Figure 4:
The bar graph shows the prevalence (in percentage) of abnormal geriatric domains at various scores of the performance status

DISCUSSION

The incidence of the majority of cancers increases with age, with some cancers almost exclusively occurring in the sixth or seventh decades of life. Available data from India suggest that certain cancers have a peak incidence that occurs a decade earlier than that in the high-income countries. Nonetheless, the proportion of the older population as well as the incidence of certain cancers is growing in India.[1415] Additionally, there is a significant paucity of trained geriatricians and geriatric oncologists and a general lack of awareness of geriatric oncologic-related issues in India.[16] The above-mentioned facts entail the need to systematically evaluate all aspects of GA in Indian patients with cancer to ensure that appropriate methods are used where required.

Ninety-eight percent of older Indian patients with cancer have deficits in at least one geriatric domain assessed, as per previously published data from our clinic.[4] The corollary of this finding is that screening tools have limited validity in such a scenario and almost all older Indian patients are candidates for a GA.[4] A similar finding was noted in our present study, with 96% of patients having a deficit in at least 1 domain and 77% having deficits in at least 2 domains. Such a finding is indicative of the increased burden of deficits in older Indian patients with cancer compared to their western counterparts.[1718] However, given the logistic issues associated with the conduct of a GA in the Indian scenario coupled with the large patient volumes at most centers, we need to identify abbreviated forms of the GA without depriving older patients with cancer of the benefits of the same. A short GA has been developed previously, but does not have widespread usage in India.[19]

In this context, evaluation of the ECOG PS and its correlation with deficits in various domains of the GA is a valuable exercise. The ECOG PS could serve reasonably well as a general trigger to identify which older patients with cancer require a GA. In the current study of 594 older patients with cancer, the ECOG PS correlated moderately well with deficits in cognition, function and falls, and psychological deficits and poorly correlated with nutritional status and comorbidities. Additionally, an ECOG PS ≥1 has a very high sensitivity in identifying patients with deficits in ≥2 geriatric domains in a cohort in which a significant proportion of patients have deficits in the GA. These findings offer a snapshot of the pros and cons of using the ECOG PS as a screening tool to identify which older patient with cancer requires a GA. Firstly, Indian patients with ECOG PS 1 were also noted to have significant deficits on the GA, and thus, a good PS (ECOG PS 0 or 1) should not preclude a GA in such patients. This is an extension of the near-universal presence of deficits in older Indian patients with cancer, as discussed previously. The traditional thought process that patients with ECOG PS 1 would be unlikely to have deficits in the various geriatric domains needs to be modified in the Indian context. Secondly, patients with a poor PS have an exponential increase in the number of deficits in various geriatric domains and need additional evaluation to identify and actively manage these problems. Thirdly, the lack of correlation of the ECOG PS with the presence of comorbidities and nutritional deficits indicates an additional need for assessment of these aspects, either via a GA or separately. These findings are in contrast to previously published data.[20] One major reason for this lack of correlation is the high proportion of abnormalities on the nutritional assessment (72%) and comorbidities (81%), as opposed to the other abnormalities in the current study cohort. This would limit the discriminatory power of the ECOG PS or any other variable in such a population. From a practical standpoint, in centers which do not have the wherewithal to perform a GA for every patient, specific attention should be paid to these aspects of care in older Indian patients with cancer.

Going forward, based on these data in the Indian scenario, the usefulness and limitations of the ECOG PS as a screening tool in older patients with cancer need to be promulgated. The high prevalence of comorbidities and nutritional deficits in older patients with a “good” ECOG PS needs to be recognized and addressed before and during cancer-directed treatment. Additional screening tools with possibly greater discriminant ability should be evaluated in the Indian context for wider application in older Indian patients with cancer.

While the current study has important findings in the Indian context, certain limitations need to be acknowledged. Our study cohort included a high proportion of patients (almost 50%) with stage IV cancers. These patients have a higher burden of disease, which will likely reflect on various aspects of the GA. Whether similar findings will be present in patients with limited stage cancers needs to be evaluated. Another limitation was the high proportion of patients with thoracic and gastrointestinal malignancies in comparison to other malignancies. Whether the ECOG PS correlates similarly with abnormalities in the geriatric domains in other tumor types cannot be commented upon. The proportion of patients with ECOG PS 0 was small (8%) in our cohort and the need for a GA in this population cannot be commented on, based on the findings of this study. We did not correlate the data on the presence of polypharmacy, the use of inappropriate medications, geriatric syndromes, visual and hearing loss, and social support with the ECOG PS in the current study. These domains are important aspects of a GA, and correlation of the ECOG PS with these domains would have further clarified the use of ECOG PS. We did not calculate the sample size a priori. Finally, our cohort did not include patients with ECOG PS 4; hence, we did not provide any data on the correlation of the GA in patients with ECOG PS 4.

CONCLUSION

Almost all older Indian patients with cancer have significant abnormalities on the GA, and a higher ECOG PS predicts for an increased number of abnormalities in the GA. There is an exponential increase in the specificity of the ECOG PS for detecting the presence of ≥ 2 deficits in geriatric domains. The ECOG PS correlates moderately well with abnormalities in function and falls, psychological assessments, and cognition, while evaluation of nutritional status and comorbidities needs additional assessments, as they are not well represented by ECOG PS.

Data sharing statement

The deidentified individual patient data are available on reasonable request from Dr. Anant Ramaswamy by email ([email protected]), immediately after publication and ending 5 years after article publication.

Author contributions

Study conception and design: SG, AR, VN; data collection: SG, AR, VN, RC, SK, RD, JK; analysis and interpretation: SG, AR, VN, RC, SK, ARR, RD, JK, SK, VG, SB, RB, KP; manuscript writing: SG, AR, VN, RC, SK, ARR, RD, JK, SK, VG, SB, RB, KP; approval of final article: all authors; accountability for all aspects of the work: all authors

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

CLINICAL STUDY PROTOCOL

RETROSPECTIVE AND PROSPECTIVE ANALYSIS OF THE RESULTS OF A COMPREHENSIVE GERIATRIC ASSESSMENT OF OLDER PATIENTS WITH CANCER WHO HAVE BEEN EVALUATED IN THE GERIATRIC ONCOLOGY CLINIC AT TATA MEMORIAL HOSPITAL

STUDY PROTOCOL INTRODUCTION

The world's older population continues to grow at an unprecedented rate. Today, 8.5% of people worldwide (617 million) are aged 65 and over. 'An Aging World: 2015' reported that this proportion will increase to almost 17% of the world's population by 2050, or 1.6 billion [1]. According to a 2016 report by the Ministry for Statistics and Programme Implementation, India has 103.9 million elderly (people above age 60 years), about 8.5% of the population. These numbers are based on the 2011 census [2]. In 2018, the government stated in the Parliament that India will have 34 crore people above 60 years of age by 2050, which would be more than the total population of the United States of America [3].

Advancing age is a risk factor for the development of malignancy, with persons over 65 years accounting for 60% of newly diagnosed malignancies and 70% of all cancer deaths, mainly cancers of the breast, lung, prostate, cervix, esophagus and ovary [4]. It has been predicted that in 2026, in India, there will be approximately 4,50,000 men and 3,70,000 women with cancer in the ≥60 years age population [5].

Management of the older patient with cancer is a challenge. These patients have comorbidities, are on multiple medications, are often frail, and may have social, economic and psychological problems. Many times, the older patients with cancer are not considered for a curative treatment approach, despite the tumor being amenable to radical treatment, just by virtue of their age. Even when these geriatric oncology patients are treated with chemotherapy, radiation and/or surgery, they may experience more toxicity, and higher chance of morbidity and mortality. Some fit older patients may be denied aggressive therapy for fear of excessive morbidity and some frail older patients may receive therapy that is overly aggressive, leading to morbidity. Striking the right balance is a challenge. Many of the landmark clinical trials have excluded geriatric oncology patients, making the management decision even more difficult. The American Society of Clinical Oncology (ASCO) [6] and the International Society of Geriatric Oncology (SIOG) [7] have official guidelines that detail the recommended evaluation of an older patient with cancer, and include evaluation for various vulnerabilities, a thorough check of the medications and assessment for the presence of polypharmacy or the presence of potentially inappropriate medications, assessment of the risk of chemotherapy toxicity and evaluation of the life-expectancy. This comprehensive evaluation can be time and resource-consuming and most often, our older patients with cancer at Tata Memorial Hospital are evaluated and treated based on clinician discretion without the use of a comprehensive geriatric assessment and without the use of the various standard validated tools.

In order to improve the care of our older patients with cancer, we started the geriatric oncology clinic at Tata Memorial Center on 15th June 2018. We would like to retrospectively and prospectively analyze the data gathered in this geriatric oncology clinic.

AIMS AND OBJECTIVES:

PRIMARY OBJECTIVE:

To describe the profile of the patients evaluated at the geriatric oncology clinic at Tata Memorial Center. This description will include the:

  1. The incidence of vulnerabilities in the domains of function, falls, comorbidities, social, psychological, cognition and nutrition.
  2. The incidence of polypharmacy and of potentially inappropriate medications.

Secondary objectives:

To describe the:

  1. Time taken to perform the comprehensive geriatric assessment.
  2. Acceptability and feasibility of the various self-administered questionnaires for our patients.
  3. To assess if various short screening tools like G8, VAS and TRST can predict for the presence of an abnormality in the comprehensive geriatric assessment.
  4. The chemotherapy risk assessment profile of the patients.
  5. Quality of life of geriatric oncology patients and caregivers.
  6. The prevalence of fatigue, falls, insomnia, constipation, urinary obstruction and various other symptoms in geriatric oncology patients.
  7. To evaluate whether the vulnerabilities of the geriatric oncology patients were recognized and/or addressed during the preceding routine oncology assessment and workup, prior to referral to the geriatric oncology clinic.
  8. The prevalence of anemia, hypoalbuminemia, renal dysfunction, hyponatremia and various other laboratory abnormalities.
  9. The ability of the chemotherapy risk assessment calculator to predict actual therapy toxicity in the patients who went on to receive systemic therapy.
  10. To evaluate the correlation of the presence of vulnerabilities in the various domains to the development of chemotherapy toxicity.
  11. To evaluate the PFS and OS in our geriatric oncology patients.
  12. To evaluate the correlation of the presence of vulnerabilities to overall survival.
  13. To evaluate the correlation of the presence of polypharmacy and/or potentially inappropriate medications to the development of chemotherapy toxicity and/or overall survival.
  14. To evaluate if any other factors like social factors, laboratory abnormalities, type of primary, tumor stage, presence of various symptoms like fatigue, etc. may impact on the overall survival.

INCLUSION CRITERIA: The inclusion criteria for evaluation in the geriatric oncology clinic were as follows:

  1. Age 60 years and over
  2. Diagnosis of malignancy
  3. ECOG performance status 0 to 3, i.e. patient not completely disabled.

For the retrospective analysis, we will include all patients whose details have been entered in the prospectively maintained database of the geriatric oncology clinicfrom15th June 2018 onwards, until date.

EXCLUSION CRITERIA

The only exclusion criterion for evaluation in the geriatric oncology clinic was patient or caregiver refusal to undergo assessment.

METHODS

The geriatric oncology clinic was started on 15th June 2018 and was held once a week in theoutpatient department of medical oncology at Tata Memorial Hospital, Parel, Mumbai. The study will be divided into two portions- the retrospective portion will consist of the details of the patients who have already undergone evaluation in the geriatric oncology clinic. Once the IEC grants approval, the second portion of the study, i.e. the prospective portion will start, in which patients will be prospectively evaluated and the data analyzed after obtaining written informed consent. Patients will undergo a comprehensive geriatric assessment and the information was prospectively entered into a Microsoft Excel sheet. For the first portion of the study, there was no written informed consent for being evaluated in the geriatric clinic, however, verbal consent was taken. Once IEC approval is obtained, the second prospective portion of the study will begin, and the data will be collected prospectively in a database, after obtaining written informed consent.

Process of geriatric assessment: Patients were explained the need for a geriatric assessment and were asked if they were willing to proceed. The assessment was carried out by one to three (depending on availability) medical oncologists with the help of a social worker. We followed the ASCO and SIOG guidelines for comprehensive geriatric assessment (CGA) [6,7]. Patients who were willing were interviewed, asked to fill out the various questionnaires (help was provided by the oncologist or the social worker, if necessary), timed get-up-and-go was checked in the clinic room and height, weight, mid-arm and mid-thigh circumference were measured. Disease-related and treatment-related information were obtained from the electronic medical records. Patients filled out the QOL form (EORTC QLQ C30) and caregivers who were willing filled out the Caregiver Burden Scale. No additional testing- either blood tests or imaging was done. The details of the results of the geriatric assessment were made available to the patient's treating oncologists through the electronic medical records along with recommendations for possible interventions to tackle vulnerabilities in non-oncologic problem areas. In patients who were noted to have deficits in functions or history of falls, referral was made to the physiotherapy and occupational therapy department. Similarly, patients with deficits in nutrition or depression/anxiety were referred to the dietitian and to the psychiatrist/counsellor respectively. No change in oncologic management of the patient was made. The time taken for the assessment was recorded, however this did not include the time spent by the patient filling out the self-administered questionnaires for ADL, IADL, GDS-Short Form, GAD-7, OARS-MSS or MOS-SSS and QOL forms and the time spent assessing the MNA by the social worker.

Domains assessed: We recorded the patient's demographic details (age, gender, education, address, living situation and number of caregivers, profession, smoking history) and disease- related features (primary tumor, stage, intent of therapy and therapy planned). We documented the medications that the patient was taking, both prescription and over-the- counter, including the use of alternative medicines (Ayurvedic/Homeopathic/Naturopathic/other), and the use of potentially inappropriate medicines as per Beer's criteria [8]. We asked the patients about the presence of symptoms like insomnia, constipation, falls, fatigue (fatigue was quantified using the MOB-T and MOB-H scales) [9], urinary incontinence, acidity/gastric ulcers and the presence of sensory deficits like vision/hearing impairment.

Not all domains were assessed in all patients. Our understanding of the process of a comprehensive geriatric assessment evolved with time and we added/changed assessments. We assessed the following domains:

  1. Function: We documented the ECOG performance status, Activities of Daily Living (Katz Index of Independence in Activities of Daily Living) [10], IADL (Lawton Scale) [11], and one performance-based measure of mobility (Timed-Up-and-Go test,
  2. TUG, in which the patient was asked to sit in a chair, the timer was started and the patient was asked to get up, walk a distance of 3 meters, turn around, walk back and sit down on the chair again, at which point the timer was stopped) [12].
  3. Falls: We asked the patient if they had experienced a fall(s) in the preceding year.
  4. Nutrition: We used the height and weight to calculate the body mass index (BMI- weight in kilograms divided by the height in meterssquared) and we asked whether the patient had experienced any unintentional weight loss. We realized that several patients had a low BMI without unintentional weight loss, perhaps reflecting that some Indians may be constitutionally thinner than global standards. Several other patients had never weighed themselves and were unable to quantify the degree of weight loss. We therefore started administering the MNA as well, for which we measured the mid-arm and the mid-thigh circumference with a measuring tape [13].
  5. Psychological: We screened for the presence of depression with the GDS-Short Form[14] and for the presence of anxiety with the GAD-7 [15].
  6. Comorbidities: These were assessed using the Charlson Comorbidity Index (CCI) [16] and the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) [17].
  7. Cognition: We used the full Mini-Mental State Examination (MMSE) [18]. Although this took a longer time than the short cognitive screening tools like the Mini-Cog Test[19] and Blessed Orientation-Memory-Concentration (BOMC) Test [20], early on we found that these were not culturally appropriate in the Indian setting. Many of the patients did not know how to read, or how to draw time on a clock and many patients did not know the exact time, or the months of the year as per the Gregorian calendar.
  8. Social: We recorded the living situation of the patient, how many persons he/she was living with, how many caregivers were available and who exactly the caregivers were. We evaluated the social support with the use of OARS-MSS (21) or the RAND Medical Outcomes Study Social Support Survey Resources (MOS-SSS) (22).
  9. Screening tools: We scored the patients using the G8 questionnaire (23), VES-13(24) and the Triage Risk Screening Tool (TRST) (25), in an attempt to assess whether these screening tools could reliably predict a deficit in the comprehensive geriatric assessment in our Indian patients.

Scoring of the geriatric scales: We used the standard scoring systems described with the tools [Table 1].

Table 1: Details of the scoring system for the various domains tested as part of the comprehensive geriatric assessment. The tests used, the range of possible scores and what values were considered abnormal are enumerated.

Chemotherapy risk assessment: We used either the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) tool (26) or the Cancer and Aging Research Group (CARG) online toxicity tool(27,28) to assess the chemotherapy toxicity risk for the patients. CARG Score risk groups were per the derivation study: low-risk (score 0–5), intermediaterisk

(score 6–9), and high-risk (score ≥ 10).

Determination of non-cancer life expectancy: We used the ePrognosis website to determine the Lee and the Schonberg index for each patient (29).

Quality of life: From July 2019 onwards, we requested patients to fill out QoL forms (European Organisation for Research and Treatment of Cancer QLQ-C30, v.3.0) (30). The QLQ-C30 questionnaire consists of thirty questions, including five multi-item functioning scales (physical, role, social, emotional and cognitive functioning), nine symptom scales (pain, fatigue, nausea/vomiting, constipation, sleep, appetite, dyspnea and financial impact) and two items that measure overall health/QoL. We asked the patient to identify their primary caregiver, and we requested that caregiver to fill out the Caregiver Burden Scale, if they were willing. The Caregiver Burden Scale consists of a 22-item self-administered questionnaire that assesses the experience of the caregiver in terms of caring for the patient. Each question has 5 possible answers ranging from never (0 points) to nearly always (4 points). Total score ranges from 0 to 88, with a score of 0 to 20 signifying little or no burden; 21 to 40 = mild to moderate burden; 41 to 60 = moderate to severe burden; 61 to 88 = severe burden(31).

STATISTICAL ANALYSIS

Sample size:

Sample size for the study is approximately 5000. For the analysis, we will include all patients who have been included in the geriatric oncology database.

STATISTICAL ANALYSIS-

Data were prospectively entered in a Microsoft Excel database. Data will be entered into SPSS v.20 for the purpose of analysis.

Demographics, clinical details, deficits in the various domains, polypharmacy and the presence of potentially inappropriate medications will be presented with descriptive statistics, using absolute numbers, simple percentages, median, range and interquartile range (IQR). If even a single test was used to assess a particular domain, then that domain will be considered to have been tested in that patient. To calculate the proportion of patients with a deficit in a particular domain, the denominator will be all the patients in whom that domain was tested.

To evaluate the screening tests-G8, VES and TRST, we will calculate the sensitivity, specificity, positive predictive value, negative predictive value (NPV), likelihood ratio of a positive and negative test, overall accuracy, and area under the receiver operating characteristic curve (ROC AUC) for each screening tool.

For toxicity assessment, the details of toxicity of chemotherapy will be captured from the electronic medical record and the patient's clinical file and will be scored according to the common toxicity criteria for adverse events (CTCAE), v.5. Simple percentages will be used to describe toxicity. Association between the CARG chemotherapy risk score and the development of severe toxicity will be tested using Chi-tests of association. Univariate and multivariate logistic regression will be used to explore potential associations between toxicity and covariates, with the CARG Score treated as a continuous variable.

For evaluation of PFS (calculated as the date of diagnosis to date of disease progression either objectively on scan or date of symptomatic deterioration in the absence of objective evidence of PD or date of death from any cause) and OS (calculated as the date of diagnosis to date of death from any cause), survival analysis will be done by Kaplan-Meier method. To evaluate the factors that affect survival and toxicity, log rank test and Cox proportional hazard model will be used.

The mean scores and standard deviations of HRQoL scores will be calculated according to the EORTC QLQ scoring manual and using percentages, means, standard deviations, t-test and Chi-squared test.

ETHICAL CONSIDERATIONS

This is a standard assessment of patients who have undergone routine comprehensive geriatric assessment. Once IEC approval has been obtained, we will include patients after obtaining written informed consent. Since the geriatric evaluation is part of routine care, there are no ethical considerations. At the time of analysis and publication, the patient data will be anonymized, and no form of patient identity will be revealed.

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Keywords:

ECOG PS; geriatric domains; GA; India

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