To Treat or Not to Treat? Dilemmas when Deciding on Antineoplastic Treatment in Patients With Far Advanced Cancers

Abstract

Patients with advanced cancers and their oncologists are often faced with difficult treatment decisions, especially when there are borderline situations of expected benefit or increased risk of complications. In this narrative review, we will explore the decision-making process for patients with advanced cancers and provide insights on how to approach this complex task, while didactically dividing the oncologist’s assessments according to a mnemonic rule of the ABCDE of therapeutic decision-making. Part A (advanced cancer) recalls that the rule is to be used specifically for advanced cancers. Parts B (potential benefits) and C (clinical conditions and risks) represents the traditional risk vs benefit scale. In Part D, we discuss ways to identify and understand patients’ desires, values, preferences, and beliefs. The prognostic estimation, from Part E, may function as an “adjust” for the antineoplastic treatment decision-making. Treatment decisions need to be conducted by skilled oncologists, in a patient-centered care, aiming to promote valuable oncology with lower rates of aggressive care.

Keywords

neoplasms palliative care decision-making toxicity cancer therapy prognosis advance care planning

Introduction

Clinical oncology has come a long way in recent decades, moving from a phase focused on cytotoxic antineoplastics to the era of personalized medicine, which includes numerous new molecularly targeted drugs, such as a focus on several driver mutations and signaling pathways (such as BRAF, HER2, ALK, PI3K, PTEN, FGF).¹ Other examples are the effective use of ADP ribose polymerase inhibitors (PARPi) for the treatment of ovarian and breast BRCA-mutated cancers² and the new antibody-drug conjugate drugs,³ such as Trastuzumab Entamsine, Sacituzumab Govitecan, and Trastuzumab deruxtecan, for the treatment of breast cancer. In addition, the use of immune checkpoint inhibitors is revolutionizing the treatment of several types of malignant neoplasms and keeps moving forward.⁴ Despite enormous therapeutic advances, most treatments are still offered to prolong and facilitate patient survival, hence called palliative antineoplastic treatment.

In this context of multiple systemic therapeutic options, the oncologist, who has an optimistic nature and often personal difficulty in dealing with situations of finitude, finds himself in the difficult situation of suspending an antineoplastic treatment. The “availability bias,”⁵ a mental shortcut that relies on immediate examples that come to mind, for example with the memory of anecdotal cases of significant clinical improvement with chemotherapy, probably interferes in the medical decision to try a new cancer treatment. This situation is often stimulated by the patient’s inclination to want to be treated and to seek a new source of hope. The communication of the absence of new systemic antineoplastic treatment is often complex, demanding a lot from the oncologist, who may not have had adequate training for this task.⁶

Chemotherapy in the last days of life may cause harm by decreasing quality of life and raising costs, and it does not improve survival times.^7-9 As a result, death within 14 and 30 days of chemotherapy has been used as a quality indicator for cancer care.^10,11 Although not a well-established definition, overly aggressive care is associated with more than 10% of patients who have received chemotherapy in the past 14 days.¹² When a patient receives chemotherapy treatment at the end of life, it suggests that the oncology service has failed to adequately integrate the philosophy of palliative care into cancer care.

There has been a significant advance in oncology support strategies during antineoplastic treatment and after it has ceased. Several clinical trials have shown the benefit of early inclusion of palliative care concomitant to oncologic treatment.^13-17 There are improved health-related quality of life indices, reduced symptoms of depression, improved symptom control, and greater satisfaction with care by patients and caregivers, as well as reduced costs and less aggressive end-of-life care.^18,19 Thus, when integrated early into a service with well-structured palliative care and adequately trained staff, it is possible for patients to receive more time in oncology treatment, including more intensity of treatment, without significant impact on their qualities of life. It is important to note that with the advance of new cancer therapies, for example, with the advent of oral therapies that can be used for a prolonged time and with high efficacy, active and palliative care have blurred boundaries.

In this article, we will didactically divide the oncologist’s assessments according to a mnemonic rule of the ABCDE of therapeutic decision-making (Figure 1).

Figure 1.

Conceptual flowchart of the suggested therapeutic decision-making process. A: Advanced cancer. The first step in applying these concepts is to identify whether the disease in question is indeed an advanced and incurable cancer. B: potential benefits. Discuss and share with the patient the potential benefits of the antineoplastic treatment. C: clinical conditions and risks. Discuss and share with the patient the clinical conditions (functionality, comorbidities) and the potential risks of the antineoplastic treatment. D: desire, values, preferences, and beliefs. Identify and understand patients’ desires, values, preferences, and beliefs. E: prognostic estimation. The prognostic estimation may function as an “adjust” for the antineoplastic treatment decision-making.

A: Advanced cancer

B: Benefits (principle of beneficence)

C: Clinical conditions and risks (principle of non-maleficence)

D: Desire, values, preferences, and beliefs (principle of autonomy)

E: Prognostic Estimation

What Is the Disease? (A: Advanced Cancer)

The first step in applying these concepts is to identify whether the disease in question is indeed an advanced and incurable cancer. Throughout this article, we will consider as advanced disease that which is locally recurrent or with distant metastases, not amenable to surgical resection. Thus, the mnemonic rule below was designed to be used where the expected clinical outcomes of treatment are symptom control, improved quality of life, and/or improved survival times. In early disease cancers, the primary focus is on increasing cure rates and survival times; thus, often with “acceptable” increases in treatment toxicity. In incurable disease, the focus is ultimately on promoting quality of life and, if possible, increasing lifespan.

What Are the Potential Clinical Benefits of This Treatment? (B: Potential Benefits)

This is perhaps the most important step to consider during the process of deciding whether or not to treat a patient with an advanced cancer. The medical literature is extensive regarding new anticancer therapies. However, most clinical trials have not included real-life patients, but rather patients in good clinical condition and not fully representative of the general patient population.²⁰ When a patient perfectly fits the eligibility criteria of a previous positive clinical trial, the decision about treatment is often easy since the potential benefit can be generalized and explained to the patient during the shared decision process. The difficulty, which often translates into the art of oncology, is treatment decisions in situations outside the contexts of clinical trials, for example, in patients with comorbidities or inadequate functional performance.

Currently, there are tools developed to help measure the potential benefit of a treatment, such as the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS)²¹ and the American Society of Clinical Oncology (ASCO) Value Framework—Advanced Disease.²² In the ASCO Value Framework—Advanced Disease, for instance, 4 sections are evaluated: Clinical Benefit; Toxicity; Bonus Points; and Net Health Benefit. In the Clinical Benefit section, overall survival (OS), progression-free survival (PFS), and response rate (RR) are measured with distinct weights; the authors suggest multiplying OS, PFS and RR benefits by 16, 11, and 8, respectively. Interestingly, in the Bonus Point section, symptom palliation and treatment-free intervals are measured. These tools were developed considering the vast universe of new cancer therapies, with both patients’ and healthcare systems’ economic impact. Although the tools cited are extremely useful in defining therapeutic protocols, they are probably of little use in real-life shared decision-making processes.

Palliative antineoplastic treatments can vary significantly depending on the economic region of the world in which a patient lives, and the type of access to public or private healthcare services. Factors such as the availability of specific drug protocols, medical expertise, and funding can impact the type and quality of care provided.²³ In some countries or regions, access to palliative antineoplastic therapies may be limited, while in others, patients may have a wider range of treatment options available to them.²⁴ In addition, racial and ethnic minorities are underrepresented in oncology clinical trials, potentially reducing the generalizability of findings.²⁵ These differences highlight the importance of considering individual circumstances when determining the most appropriate treatment plan for patients with advanced cancer.

In daily practice, what the clinical oncologist needs is to know the details of clinical trials that guide his/her patients’ treatment indications and know how to conduct shared decision-making using good communication skills. In addition to defining the potential benefit of treatment based on the literature using tools that measure value in health care, it is of utmost importance to identify the value of treatment from the patient’s perspective.²⁶

What Are the Clinical Conditions and Potential Risks of the Treatment in Question? (C: Clinical Conditions and Risks)

Weighting the risks and benefits of prescribing an antineoplastic treatment for older adults, with comorbidities, or borderline performance status is challenging for the oncologist.

Previous studies in the general population, have suggested an association between measures of physical performance and all-cause mortality.^27,28 As in the general population, physical functioning is associated with the prognosis of patients with advanced cancer.^29-31 One of the traditional ways of evaluating the functionality of a cancer patient is through the measurement of his functional performance by means of the Karnofsky Performance Status³² and the Eastern Cooperative Oncology Group Performance Status (ECOG-PS).³³

Most clinical trials have included patients in better clinical condition, with ECOG-PS 0 or 1 and, in less than 10% of the time, ECOG-PS of 2³⁰. In the meta-analysis by Cheng et al,³⁴ only one clinical trial out of 62 evaluated (<2%), included patients with an ECOG-PS of 3. Thus, day-to-day therapeutic decisions in patients with poor functional conditions (ECOG-PS 2 or 3) are mostly made outside of clinical trial best evidence. While prognosis is clearly associated with patient performance status (PS), the benefit of cancer treatment may not be related. The relative benefit of treatment appears to have no interaction with performance status, both in studies that included older treatments regimens³⁰ and studies with targeted drugs included.³⁴ In contrast, non-small cell lung cancer patients with impaired PS status were, on average, twice less likely to achieve a clinical response when exposed to immune checkpoint inhibitors when compared with ECOG-PS ≤1 population.³⁵

The association of antineoplastic treatment with the risk of adverse events in the different PS subgroups needs to be further explored in new studies. Severe nausea and vomiting, for instance, were much more pronounced in ECOG-PS 2 patients than in ECOG-PS 0 to 1 patients.³⁰ In addition, it is important to remember that cancer patients may have their PS influenced by the cancer itself, but also as a function of some comorbidity. Such information can be important for the oncologist while defining the treatment. For example, in patients with cancer symptoms and reduced PS, antineoplastic treatment may be indicated in an attempt to therapeutic response, improvement of symptoms and PS; however, patients with poor PS due to comorbidity may even worsen the PS by oncologic treatment.

Another variable that interferes in the decision about cancer treatment is advanced age and the consequent vulnerability of the patient. In patients aged 65 and older, receiving antineoplastic treatments, geriatric assessment (GA) should be used to identify vulnerabilities or geriatric impairments. The GA comprises the evaluation of functional status, falls, comorbidities, depression, social activity/support, nutritional status, and cognition.³⁶ Elderly cancer patients are particularly prone to “over-treatment,” with a high likelihood of complications/toxicity, or “undertreatment,” that is, not receiving desirable treatment that they would be fit for, for fear (on the part of the oncologist or the patient themselves) of complications.^37,38 Furthermore, traditional oncology performance measurement tools, such as KPS or ECOG-PS, are not accurate in predicting risk of complications in elderly patients.³⁹

To assist the oncologist, several practical tools are available. Among the tools that can provide estimates of the risk of chemotherapy toxicity are the Cancer and Aging Research Group (CARG)^39,40 and the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH)⁴¹ toxicity tools (Table 1).

Table 1.

Validated Geriatric Assessment Tools.

Tool	Aim	Variables	How to use
CARG^37,38	Toxicity prediction	11 items: 5 geriatric assessment items and 6 items from routine daily practice (tumor type, treatment, and laboratory values)	Scoring system ranging from 0 to 19 Risk score: Incidence (%) of grades 3–5 chemotherapy toxicity in older adults at low (0–5 points) = 30%; intermediate (6–9 points) = 52%; or high risk (10–19 points) = 83%
CRASH³⁹	Toxicity prediction	8 items divided in 2 scores: Hematological (H) (diastolic blood pressure, IADLs, LDH, Chemotox score) and non-hematological (NH) (ECOG, MMSE, MNA, Chemotox score)	The predictors of H toxicity were IADLs, lactate dehydrogenase, diastolic blood pressure, and Chemotox. Risk categories: low, 7%; medium-low, 23%; medium-high, 54%; and high, 100%, respectively. The predictors of NH toxicity were ECOG, MMSE, MNA, and Chemotoxicity. Risk categories: low, 33%; medium-low, 46%; medium-high, 67%; and high, 93%, respectively.
VES-13³⁹	Geriatric screening tool	13 items, including age, self-rated health, limitations in physical function, and disability	Sensitivity and specificity of 68.7% and 74.3%, respectively. VES-13 score ≥3 predicts a 68% risk of abnormality in 2 or more domains on subsequent CGA and is associated with a 4.2-fold increased risk of death and functional decline.
G8⁴⁰	Geriatric screening tool	15 items, including food intake, weight loss, BMI, mobility, neuropsychological problems, medication intake, and age	Sensitivity and specificity of 76.5% and 64.4%, respectively. A score ≤14 is associated with ≥80% risk of abnormalities in one or more domains on subsequent CGA.
aCGA⁴¹	Geriatric screening tool	15 items that were divided into 4 domains: ADL (3 items), IADL (4 items), MMSE (4 items), and GDS (4 items)	Each domain has an individual cutoff point. Two positive “yes” items on the aCGA indicates the use of the full GDS (specificity .89; sensitivity .81). In the ADL domain, a positive item for “need for assistance” should be screened using the entire instrument. In the IADL topic, a positive item for “need for assistance” should be screened using the entire instrument. For the dementia topic, a score ≥6 indicates screening with the MMSE (sensitivity .91; specificity .82)
GFI⁴²	Geriatric screening tool	15 items, including physical (mobility, fatigue, vision, and hearing), cognitive, social (emotional isolation), and psychological (depression and anxiety) domains	A cutoff of ≤4: Absence of a geriatric risk profile
fTRST^43,44	Geriatric screening tool	5 items assessing the presence of cognitive decline (yes, 2 point), living alone or without help (yes, 1 point), reduced mobility or falling (yes, 1 point), hospitalization in the last 3 months (yes, 1 point), and polypharmacy (yes, 1 point)	The total score ranges from 0 to 6. Cutoff ≥1 point

Abbreviations: CARG, Cancer and Aging Research Group; CRASH, Chemotherapy Risk Assessment Scale for High-Age Patients; VES-13, The Vulnerable Elders Survey; G8, Geriatric 8; SAOP3, Senior Adult Oncology Program 3 screening questionnaire; fTRST, Flemish version of the Triage Risk Screening Tool; GFI, Groningen Frailty Index; aCGA, Abbreviated Comprehensive Geriatric Assessment; IADLs, Instrumental Activities of Daily Living; ADL, Activities of Daily Living; LDH, Lactase Dehydrogenase; ECOG, Eastern Cooperative Oncology Group; H, Hematology; NH, Non-Hematology; MMSE, Mini Mental Status Examination; MNA, Mini Nutritional Assessment; CGA, Comprehensive geriatric assessment; BMI, Body Mass Index; GDS, Geriatric Depression Scale.

The Geriatric 8 (G8),⁴² the Vulnerable Elders Survey-13 (VES-13),⁴³ the abbreviated Comprehensive Geriatric Assessment (aCGA),⁴⁴ the Groningen Frailty Index (GFI),⁴⁵ and the Flemish version of the Triage Risk Screening Tool (fTRST)^46,47 are among the screening tools that have been associated with adverse outcomes. These tools should be used to identify frail and/or vulnerable patients who are most likely to benefit from a geriatric assessment.⁴⁸ The G8 and the VES-13 are the screening tools with the most scientific evidence for clinical use⁴⁹ (Table 1).

Regardless of PS and age, the presence of significant comorbidities is of utmost importance in the setting of antineoplastic treatment, influencing the patient’s course of disease and the choice of antineoplastic treatment.^50–54 In 1987, Charlson and colleagues⁵⁵ developed a method to classify nineteen patient comorbidities to predict risk of death within 1 year of hospitalization. The comorbidities represent different weights in the final score (Table 2), ranging from 1 to 6. Since then, the Charlson Commorbidity Index (CCI) has been the most widely used tool in the studies to measure the impact of comorbidities on clinical outcomes of patients with different cancer types (for a detailed review, please see 56).

Table 2.

Classic Comorbidity Assessment Tools and its Weighed Cancer-Specific Versions.

Comorbid Conditions	CCI⁵²	NCI-Breast⁵⁶	NCI-Prostate⁵⁶	NCI-Colorectal⁵⁶	ECI⁵⁷	ECI-Breast⁶¹	ECI-Prostate⁶¹	ECI-Lung⁶¹	ECI-Colorectal⁶¹
Myocardial infarction	●1	●0.40	●0.05	●0.28
Congestive heart failure	●1	●0.85	●0.87	●0.84	●1	●7	●9	●1	●3
Cardiac arrhythmias					●1
Peripheral vascular disease	●1	●0.22	●0.36	●0.28	●1	●4	●3	●2	●2
Cerebrovascular disease	●1	●0.71	●0.27	●0.55
Hypertension					●1	●-1	●-1	●0	●0
Fluid and electrolyte disorders					●1	●2	●4	●0	●3
Anemia (Blood loss)					●1	●2	●-6	●0	●1
Deficiency anemia					●1	●1	●2	●0	●1
Dementia	●1	●1.19	●0.78	●0.60
Chronic pulmonary disease	●1	●0.47	●0.73	●0.47	●1	●5	●5	●3	●5
Pulmonary circulation disorders					●1	●5	●−2	●3	●1
Connective tissue disease	●1	●0.75	●0.09	●−0.02	●1	●0	●0	●3	●1
Peptic ulcer disease	●1	●0.23	●−0.25	●0.08	●1
Liver disease					●1	●6	●−4	●0	●6
Mild liver disease	●1	●2	●3
Moderate or severe liver disease	●3				●1	●6	●−4	●0	●6
Diabetes	●1	●0.45	●0.24	●0.44	●1	●3	●2	●0	●1
Diabetes (with end-organ damage)	●2	●0.02	●0.44	●0.30	●1	●-1	●2	●1	●2
Hemiplegia (or paraplegia)/Paralysis	●2	●0.21	●0.39	●0.30	●1	●6	●3	●2	●-2
Neurologic or degenerative disorder					●1	●5	●2	●3	●2
Psychoses					●1	●4	●4	●1	●2
Depression					●1	●2	●0	●2	●3
Renal failure					●1	●3	●1	●2	●1
Moderate or severe chronic renal failure	●2	●1.19	●0.68	●0.97
Any tumor without metastasis	●2				●1
Metastatic solid tumor	●6				●1
Leukemia	●2
AIDS/HIV	●6				●1
Lymphoma	●2				●1
Previous malignancy
Hypothyroidism					●1	●1	●−4	●1	●1
Coagulopathy					●1	●2	●0	●−1	●2
Valvular disease					●1	●1	●1	●1	●1
Obesity					●1	●−6	●−7	●−1	●−3
Weight loss					●1	●3	●1	●3	●3
Alcohol abuse					●1	●2	●9	●0	●−1
Drug abuse					●1

Abbreviations: CCI, Charlson Comorbidity Index; NCI, National Cancer Institute Combined Index; ECI, Elixhauser comorbidity index.

Considering that a comorbidity measure developed or adapted for a specific disease population is likely to perform better than a generic score,⁵⁷ the CCI has been further reviewed and adapted for use in oncology populations. Of note, the original CCI includes as comorbidities some hematological neoplasms and metastatic cancer. Thus, in 2000, a cancer-specific National Cancer Institute Comorbidity Index (NCI-CI) was developed by using SEER derived breast and prostate cancer cases cohorts, excluding solid tumors, leukemias, and lymphomas as comorbid conditions.⁵⁸ Subsequently, the NCI comorbidity score evolved into the NCI Combined Index Models, which outperformed the original CCI in predicting 2-year survival in cancer patients.⁵⁹

Another tool for assessing the impact of comorbidities is the Elixhauser comorbidity index (ECI), which includes a total of 30 comorbidities.⁶⁰ Originally, each comorbidity has equal weight; modifications of the ECI with different weights for each comorbidity have already been described.^61,62 A previous systematic review⁶³ found that the ECI performs better than the CCI in predicting mortality beyond 30 days. It has already been adapted for use in cancer patients,⁶⁴ achieving higher C-Index values when compared to the NCI Combined Index Model.

In patients with lung cancer, the Scottish Comorbidity Scoring System (SCSS),⁶⁵ which classifies 9 commodities according to their severity (nil: 0; low: 1–6; severe: 7 or more), proved more useful than the CCI in predicting overall survival in patients with localized (stages I–IIIA) and advanced disease (stages IIIB and IV).

As mentioned earlier, the most commonly used way to assess comorbidities in studies is through the CCI or one of its variations. However, there is no gold standard instrument for this.⁶⁶ As a practical suggestion, there are easy-to-use Excel applications that can be obtained for free and used to calculate comorbidity scores.^67,68 Despite the availability of the tools mentioned, in practice, the oncologist generally evaluates comorbidities in an individualized manner within a typical anamnesis. When planning systemic antineoplastic therapy, hepatic, renal, cardiovascular, and hematologic functions need to be carefully considered. In addition, uncontrolled active infections should be ruled out before the initiation of antineoplastic therapies since most of treatments can course with a drop in WBC and neutrophil levels and changes in the immune system.

Although not well defined, the decision to treat with antineoplastics can differ significantly among various medical oncology subspecialties, particularly those that specialize in specific tumor types, such as breast, gynecologic, lung, or colorectal vs those that treat a wide range of tumors in general oncology. Interdisciplinary communication and collaboration among all involved disciplines, including palliative care, general practitioners, and geriatric oncology, are crucial to ensuring that patients receive the most appropriate care for their unique circumstances.^69–71

Patients with hematological malignancies carry a significant burden of illness, often receiving more aggressive end-of-life treatments and experiencing a high level of symptom distress. Despite this, they tend to underutilize palliative care, especially those with leukemia.^72,73 It has been reported that hemato-oncologists are more inclined than oncologists to prescribe systemic therapies that carry moderate toxicity and do not confer any survival benefits to patients who present low performance status and an expected survival of one month.⁷⁴

Physicians without specialized knowledge in palliative medicine tend to make more aggressive decisions regarding end-of-life care, such as administering higher levels of intravenous hydration, nasogastric tube insertion, and blood transfusions.⁷⁵ Additionally, a previous systematic review found that younger and less experienced oncologists were more likely to continue palliative antineoplastic treatment.⁶

In the decision to treat with antineoplastics, and particularly in the choice of the type of antineoplastic, the oncologist needs to measure the possible adverse events of the treatment, even if not severe enough to be life-threatening, but that may interfere with the quality of life of the patients. For example, a professional pianist with peripheral neuropathy may have to stop work. Similarly, patients who need their body image intact to work (such as models, TV journalists) will strongly wish to avoid alopecia. Thus, just as treatment needs to be customized considering the molecular characteristics of the tumor, it is also necessary to customize treatment considering the treatment’s potential interference with the patient’s personal and work life.

D: How can we identify the Patients’ Wishes, Preferences, Beliefs, and Values That May Influence the Choice of Treatment? (D: Desires, Values, Preferences, and Beliefs)

In our mnemonic model of therapeutic decision, after evaluating parts B and C, which basically defines a decision scale (risks vs benefits), we move to the next phase that uses the patient’s perception of the treatment, taking into consideration their personal wishes, values, and beliefs; part D is broad and interferes with the discussion on parts B and C (Figure 1). Here it is important to discuss ways of extracting such information from the patient. The question “do you want to try a new treatment?” is generally inappropriate, as most patients wish to live and be cured and will answer the question in the affirmative.

Patients with cancer must cope with a variety of stressors, including the need for treatment on an urgent basis, the management of adverse effects of treatment, the sharing decision-making processes, the numerous physical and emotional challenges while dealing with a life-threatening illness, and having to tolerate uncertainty regarding their future.^76,77 Thus, one cannot expect patients to be consciously positive if they don’t have a space to meet and talk about their care preferences and express their distress, or without providing them the necessary time to adapt to the cancer experience and enhance their repertoire of coping strategies,⁷⁷ a gap that can be reduced with the help of an Advance Care Planning (ACP).

ACP is a process of planning for future health and personal care that helps patients make decisions and identify their values and preferences for care at a time when they are unable to make or communicate their decisions. This type of planning is intended to align the care the patient will receive with their preferences and priorities.⁷⁸ ACP develops in an interactive and communicative process where patients, healthcare staff, and family members have conversations about preferences for care, particularly those at the end-of-life. These preferences may even be recorded in a legal document, called advance directives.^79,80

Advance care planning is a crucial aspect of cancer care, especially when making decisions about new anticancer treatments. A previous ACP can help patients and their families make informed decisions about treatment options, considering the potential benefits and harms of each intervention, as well as the patient’s overall health status and prognosis. However, patients with advanced cancer often have not previously participated in ACP discussions. In such situations, serious illness goals of care conversations need to be conducted by trained oncologists. While ACP focuses on preparing the patient for future healthcare decisions, goals of care discussions focus on current healthcare decision-making.

In order to help patients with advanced cancer articulate their goals of care, oncologists need to assess patients’ knowledge of their disease and prognosis. Often, patients with advanced cancers undergoing chemotherapy report an inaccurate perception of curability.^81,82 Prognostication is a dynamic and multi-step process encompassing the sequence of prognostic prediction, prognostic disclosure, prognostic awareness, prognostic acceptance, and prognostic-based decision-making.⁸³ In fact, when patients are aware of their disease stage and prognosis and, preferably, emotionally accept the situation, the clinical decisions to treat or not to treat them are facilitated by sharing decisions in a mature way and with potentially less distress.

Below are some examples of open, in-depth questions, oncologists may use to identify and understand the patient’s wishes, preferences, beliefs, and values^84–86:

Identifying Patient Values

“What does ‘living well’ mean to you? What are your goals right now?”

“What are your biggest fears and worries about the future with your health?”

“What gives you strength as you think about the future with your illness?”

“What abilities are so critical to your life that you can’t imagine living without them?”

“How much does your family know about your priorities and wishes?”

Exploring Disease Understanding and Treatment Goals

“What are your most important goals if your health situation worsens?”

“What do you understand about the current status of your illness?”

“What is your understanding now of where you are with your illness?”

“How much information about what is likely to be ahead with your illness would you like from me?”

Identifying Future Care and Treatment Preferences

“If you become sicker, how much are you willing to go through for the possibility of gaining more time?”

“Tell me a little bit about your main future wishes”

“Would you like to actively participate in your advance care planning?”

“When you no longer have the ability to make decisions, what care or treatment would you not want in your end-of-life process?”

To understand the profile of patient care preferences and to serve as a stimulus for further discussion about cancer treatment, we have been using in practice the following trade-off item from a previous publication⁸¹: “If you could choose only one of the options, would you prefer... (A): a treatment that was designed to prolong life as much as possible, even if the treatment caused more pain and discomfort; or (B): a care plan to relieve pain and discomfort as much as possible, even if it meant not living as long.” Other ways of assessing health care preference in a similar way, particularly in the elderly with cancer, have been described.^87,88

To take into consideration the wishes and values of patients is to respect their autonomy, and consequently, their dignity.^89,90 The conduction of dignity-promoting activities in the process of illness and end-of-life of cancer patients brings to the family members the possibility of discussions about the resolution of pending issues, whether affective, financial, and even bureaucratic (insurance, contracts, inventories), deliberation of care for dependents, better development of resources for coping with the imminent loss of loved ones, and elaboration of the bereavement process.⁹¹

The practical use of tools that allow the health team to identify the wishes of patients during the dying process and subsequent development of conducts that promote their dignity, is a promising strategy to improve the care of cancer patients. A potentially useful strategy to facilitate end-of-life conversations is the Go Wish Cards Game (GWCG), composed of 36 cards, which display a variety of end-of-life wish preferences. The cards are separated into 3 piles considering the ten not so important, the ten more or less important, and the ten very important wishes; the most important wishes are further discussed with the patients. The GWCG method is one of the ways of optimization in dignity therapy for patients in advanced disease process. The use of the cards brings important ways to initiate dialogues and broaden engagements between patient, family members, and multidisciplinary team, through simple vocabulary and variety of possible wishes that extend to the essence of well-being of the human being.^92,93 A study carried out with patients with advanced cancer compared a random wish list developed by the patients and the GWCG and found that 62% of the patients preferred the GWCG for its ease of understanding and for presenting a wider range of wish options. Additionally, it was easy to apply.⁹⁴

In general, the oncologist needs to be open and trained to identify patients’ wishes, values, beliefs, and preferences. The introduction of processes into routine care (such as ACP and goals of care discussions) and the use of practical tools such as the GWCG can be extremely helpful to the oncologist in the therapeutic decision process. It is important to note that the treatment decision does not necessarily have to occur in a single moment but is often the result of sequential medical consultations.

E - What Is the Patient’s Estimated Lifetime? (E: Prognosis Estimation)

After evaluating the potential benefits (part B), the clinical conditions/risks of the treatment (part C), and the importance of a new therapeutic attempt in the patient’s view (part D), it is essential to “adjust” the clinical decision considering the patient’s life expectancy (part E) (Figure 1). As mentioned earlier, the parts are divided here from a didactic point of view. Thus, prognostication is intrinsically related to part D and can be omitted from the process. We suggest part D as an adjustment in the shared decision, particularly in situations where treatment decisions are incongruent from the oncologist’s point of view. For example, patients where it is proposed to treat with new chemotherapy, but the estimated prognosis is only a few months.

The prognosis estimation is not able to independently establish the indication of a new treatment, however it can assist in the decision making. Furthermore, expectations are not usually communicated to patients routinely, but only in specific situations. The prognosis is somewhat dynamic and can be modified by the treatment itself.⁹⁵ We strongly suggest that its communication, when provided to the patient, should be in the form of intervals and never with specific time values or mean/medians, such as “hours to days,” “days to weeks,” “weeks to months,” “months to years.”

Clinical Prediction of Survival

Among the strategies for estimating the prognosis of an advanced cancer patient, one of the most classic and instituted ways is the clinical prediction of survival (CPS), in which the opinion of the health professional, mainly the physician, is considered. It is often overestimated, due to excessive optimism in predicting the survival, particularly those patients who die early in the terminal phase and could benefit from earlier palliative care.⁹⁶

The low concordance between clinical predictions and actual survival times is evidenced in systematic reviews of the literature, with 83% of prediction errors being overestimated by physicians⁹⁷ and an estimate of 4 weeks beyond observed in 27% of cases.⁹⁸ The accuracy of physicians in estimating the prognosis of advanced cancer patients in survival intervals, for example, varies around 60% and the professional’s experience does not seem to interfere on the accuracy of prediction.⁹⁹ Even palliative care specialists are unable to predict survival in 32% of cases if a patient is alive or dead at 4 weeks.¹⁰⁰

Functionality Scales

The functionality assessment is another way to predict prognosis, using functional performance scales, such as the Eastern Cooperative Oncology Group Performance Status (ECOG-PS),³³ the Karnofsky Performance Status (KPS)³² and the Palliative Performance Scale (PPS).¹⁰¹ These instruments have been widely used as prognostic tools, with an important role in the scenario of survival estimates, through the interpretation of a patient’s dependence on the environment in which they live, measuring their ability to perform activities of daily living, without assistance.

Symptoms of Advanced Disease and Laboratory Parameters

With the awareness of the potential limitations of functional performance scales (such as, low inter-observer reproducibility and cross-sectional evaluation), the assessment of clinical symptoms in combination with KPS and physicians’ clinical estimates may improve survival prediction. Symptoms such as anorexia, cachexia, weight loss, dysphagia, and pain may be independently associated with a worse prognosis in advanced cancer patients. In exclusive palliative care setting, the mental confusion, fatigue, cachexia, weight loss, dyspnea, dysphagia and nausea predict a shorter survival.¹⁰² It is important to point out that the clinical assessment of symptoms is also subjective and often influenced by psychosocial factors, which leads to some inaccuracy in its measurement, even with the use of visual scales.^103,104

Laboratory parameters are possible prognostic markers in patients with advanced-stage cancer. The presence of leukocytosis, as well as lymphopenia, is associated with a worse prognosis in patients with advanced cancer.¹⁰⁵ Lactic dehydrogenases (LDH), a serum marker that rises in cases with high disease volume, and the c-reactive protein, a marker related to inflammation, are more nonspecific, but in the context of advanced cancer they can be associated with progressive disease.^106,107 Hypoalbuminemia, in addition to reflecting the nutritional status of patients, also expresses a negative influence on the prognosis of critically ill patients.¹⁰⁵

Prognostic Tools

Knowing the limitations of predicting survival based on clinical estimates by physicians, assessment of symptoms and biochemical parameters, there is robust evidence that the combination of all parameters in one model may improve the prediction of survival in advanced cancer patients.⁹⁶ Prognostic models have been created in the palliative care setting, improving the accuracy, and helping healthcare professionals to plan the patient journey.

The Palliative Performance Scale (PPS),¹⁰¹ Palliative Prognostic Score (PaP),^108,109 Palliative Prognostic Index (PPI),¹¹⁰ Objective Prognostic Score (OPS)¹¹¹ and Prognosis in Palliative Care Study Predictor (PiPS)¹¹² were developed and validated for patients with few months of life expectancy. In contrast, the modified Glasgow Prognostic Score (mGPS),^113,114 the PRONOPALL prognostic tool,¹¹⁵ the Spanish Prognostic Nomogram (SPN),¹¹⁶ the Barretos Prognostic Nomogram (NPB)¹¹⁷ and the HAprog (clinical and full versions)¹¹⁸ were developed in patients with advanced cancer with an expectation of several months of survival (Table 3). It is interesting to note that the prediction accuracies increase the closer the patient is to the end of life.

There may be a myriad of combinations between prognostic variables; each model has its peculiarity, according to the profile of patients evaluated. However, some variables are more frequently contemplated, such as the functional status of the patient. Almost all the main models, regardless of life expectancy, include performance status as a clinical variable, with KPS being the most used way. Only models related to a shorter life expectancy (days to weeks) include symptoms and clinical signs as prognostic factors, with “delirium,” “dyspnea,” and “anorexia” being the most frequent observed.

Inflammatory laboratory variables were included in some prognostic models, among which “total leukocytes,” “serum albumin,” and “LDH” stand out as the most frequently related. Among these models with longer life expectancy, only the NRF and HAprog clinical version models did not assess laboratory variables.^118,119 On the other hand, the mGPS is the only model that presents only laboratory variables in its composition (c-reactive protein, albumin), however better accuracy is achieved when evaluated together with the ECOG-PS.^113,114 Laboratory variables can indeed increase the prognostic ability of a tool, but it can be a barrier to its applicability in the clinical routine, regarding the request for complementary tests in patients under palliative care.

The quick access to prognostic tools for use in clinical practice is essential. PredictSurvival.com is an online platform, free to use, which includes grouped classic prognostic tools, being particularly useful for cases of patients in the last 6 months of life.¹²⁰ Its use by health professionals has increased prognostic confidence and accessibility to discuss the prognosis with patients.¹²¹ In this same context, the HAprog models have been transcribed into a digital version for use in smartphones.¹¹⁸ Interestingly, in addition to calculating the probabilities of survival at 30, 90, and 180 days, HAprog shows the probable survival times in days in the expected, worst-case, and best-case scenarios. The practical implication of these results still needs to be evaluated in subsequent studies.

Despite the availability of validated predictive tools in the current literature, there are still barriers to their use in daily clinical practice. Knowing the importance of estimating an accurate prognosis, choosing which tool to use for each patient, knowing how to interpret and communicate the estimated survival value and managing time during patient care are issues that must be still on the discussion.^120,122 It is important to emphasize that the use of a prognostic tool does not have the intention of standardizing or “mechanizing” the process of evaluating the patients’ survival, but it increases the clinical evaluation of the physician with less subjectivity and greater accuracy.

Future Perspective

Communicating difficult news in oncology is a complex and stressful task for oncologists¹²³ that should be taught, learned, and retained. The SPIKES protocol,¹²⁴ the elements of which are Setting, Perception, Invitation, Knowledge, Emotions, and Strategy, is the most commonly used framework in the education of medical students, residents, and oncologists.¹²⁵ A recent systematic review¹²⁶ identified 3 communication models validated through randomized clinical trials; that is, the Serious Illness Conversation Guide,¹²⁷ the 4 Habits Model,¹²⁸ and the acronym ADAPT.¹²⁹ Another very useful mnemonic when discussing goals of care is the framework REMAP (Reframe, Expect emotion, Map outpatient goals, Align with goals, and Propose a plan).¹³⁰

In this article, we present the ABCDE mnemonic rule of therapeutic decision-making, which can assist oncologists not only in the direct communication process but also with a conceptual model of decision-making to be considered and discussed with patients with far advanced cancers when deciding about continuing or discontinuing antineoplastic treatment during the shared decision-making process. Future studies are needed to test whether new communication skills training programs that include our decision model are able to reduce aggressive end-of-life treatment and promote decision-making that aligns with the preferences of well-informed patients regarding their disease and prognosis.

Table 3.

Prognostic Tools for Use in Advanced Cancer Patients.

Prognostic tool	Life Expectancy	Patient Profile	Variables (N, Description)	Practical Implications
PPS⁹²	Weeks to days	Patients under palliative care	1: Performance status	KPS adaptation is ideal application for use in palliative care patients, especially those with worse clinical conditions
PaP⁹⁹	Weeks to days	Cancer patients under exclusive palliative care and a life expectancy lower than 30 days	6: KPS, dyspnea, anorexia, clinical prediction of survival, leukocytes, lymphocytes	Used in the last month of patient’s life. Clinical variables of symptoms and clinical prediction of survival are considered
D-PaP¹⁰⁰	Weeks to days	Cancer patients under exclusive palliative care and a life expectancy lower than 30 days	7: KPS, dyspnea, anorexia, delirium, clinical prediction of survival, leukocytes, lymphocytes	Similar to PaP, with the inclusion of “delirum” as a variable, which slightly improved the prognostic accuracy of the tool
PPI¹⁰⁰	Weeks to days	Advanced cancer patients and a life expectancy lower than 3 weeks	5: KPS, dyspnea, anorexia, delirium, edema	Similar to D-PaP but without laboratory variables and with the inclusion of the “edema” variable. There is an alternate variation of this model that substitutes KPS for ECOG (PPI-ECOG)
OPS¹⁰¹	Weeks to days	Advanced cancer inpatients for hospice care in terminal stage and a life expectancy lower than 3 weeks	7: ECOG, oral intake, dyspnea, leukocytosis, serum bilirubin, serum creatinine, and lactate dehydrogenase	Only model that uses non-inflammatory laboratory variables
mGPS^103,104	Months	Advanced cancer patients and a life expectancy lower than 3 months	2: C-reactive protein and albumin	The combination of 2 laboratory variables could represent the patient’s inflammatory state, however without evaluating clinical variables. It can be applied to patients with a longer life expectancy, but its use in conjunction with the ECOG-PS is suggested for greater accuracy.
NRF¹⁰⁹	Months	Cancer patients and a life expectancy lower than 60 weeks	3: KPS, site of primary neoplasm (breast vs non-breast), site of metastases (bone vs non-bone	Specific profile of patients from a radiotherapy service.
PRONOPALL¹⁰⁵	Months	Advanced cancer patients and a life expectancy lower than 2 months	4: KPS, number of distant metastasis sites, albumin, lactic dehydrogenase	Dichotomic variables. Results presented in 3 groups of possible survival at 2 months
SPN¹⁰⁶	Months	Advanced cancer patients in exclusive palliative care and a life expectancy lower than 60 days	4: ECOG, time from diagnosis to palliation, lymphocytes, albumin, lactic dehydrogenase	Functional performance status, duration of illness, and laboratory variables
BNP¹⁰⁷	Months	Advanced cancer outpatients when referred for the first time to palliative care and a life expectancy lower than 180 days	5: Gender, KPS, presence of distant metastases, leukocytes, albumin	The only evaluated model that uses gender as a variable and was developed for patients in an outpatient setting. Presents the KPS as a continuous variable, but restricted above 40%
HAprog full version¹⁰⁸	Months	Advanced cancer outpatients when referred for the first time to palliative care and a life expectancy lower than 180 days	6: Gender, ECOG, sites with distant metastases, presence of locoregional disease, leukocytes, albumin	Refinement of the BPN with the replacement of KPS by ECOG and optimization of the variable “metastasis” (previously evaluated in a dichotomous way). In addition, an application for use on smartphones was developed.
HAprog clinical version¹⁰⁸	Months	Advanced cancer outpatients when referred for the first time to palliative care and a life expectancy lower than 180 days	5: Gender, ECOG, sites with distant metastases, presence of locoregional disease, ongoing antineoplastic treatment	Variation of the HAprog full version model, without considering laboratory variables and including ongoing antineoplastic treatment

Abbreviations: PPS, Palliative Performance Scale; PaP, Palliative Prognostic Score; D-PaP, Palliative Prognostic Score with Delirium; PPI, Palliative Prognostic Index; OPS, Objective Prognostic Score; mGPS, Modified Glasgow Prognostic Score; NRF, Number of Risk Factors; SPN, Spanish Prognostic Nomogram; BPN, Barretos Prognostic Nomogram; ECOG, Eastern Cooperative Oncology Group; KPS, Karnofsky Performance Status.

Conclusions

The therapeutic decision process of whether or not to treat patients with advanced cancer with systemic antineoplastic therapies is complex. Oncologists need to know in detail the therapeutic protocols to which they will submit their patients, mainly its potential clinical benefits and risks. In addition, they need to know how to capture and adequately comprehend their patients’ wishes, values, and personal beliefs. Accurate prognostic estimates can also help in “fine-tuning” therapeutic decisions.

Footnotes

Appendix

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Authors’ Note

CEP and BSRP are Research Productivity Fellows Level 2 from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil).

ORCID iD

Carlos Eduardo Paiva

References

Anand

Dey

Chandel

AKS

, et al. Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics. Genes Dis. 2022. doi:10.1016/J.GENDIS.2022.02.007

Mateo

Lord

Serra

, et al. A decade of clinical development of PARP inhibitors in perspective. Ann Oncol. 2019;30(9):1437-1447. doi:10.1093/ANNONC/MDZ192

Zhang

Shen

, et al. Is antibody-drug conjugate a rising star for clinical treatment of solid tumors? A systematic review and meta-analysis. Crit Rev Oncol Hematol. 2022;177:103758. doi:10.1016/J.CRITREVONC.2022.103758

Mazzarella

Duso

Trapani

, et al. The evolving landscape of “next-generation” immune checkpoint inhibitors: A review. Eur J Cancer. 2019;117:14-31. doi:10.1016/J.EJCA.2019.04.035

Kahneman

. Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux. Cited on. Published online 2013:15, 1st bpk.

Clarke

Johnston

Corrie

Kuhn

Barclay

. Withdrawal of anticancer therapy in advanced disease: A systematic literature review. BMC Cancer. 2015;15(1):892. doi:10.1186/S12885-015-1862-0

Saito

Landrum

Neville

Ayanian

Earle

. The effect on survival of continuing chemotherapy to near death. BMC Palliat Care. 2011;10:14. doi:10.1186/1472-684X-10-14

Prigerson

Bao

Shah

, et al. Chemotherapy use, performance status, and quality of life at the end of life. JAMA Oncol. 2015;1(6):778-784. doi:10.1001/JAMAONCOL.2015.2378

Bao

Maciejewski

Garrido

Shah

Maciejewski

Prigerson

. Chemotherapy use, end-of-life care, and costs of care among patients diagnosed with stage IV pancreatic cancer. J Pain Symptom Manag. 2018;55(4):1113-1121. doi:10.1016/J.JPAINSYMMAN.2017.12.335

10.

O’Brien

MER

Borthwick

Rigg

, et al. Mortality within 30 days of chemotherapy: A clinical governance benchmarking issue for oncology patients. Br J Cancer. 2006;95(12):1632-1636. doi:10.1038/SJ.BJC.6603498

11.

Earle

Park

Lai

Weeks

Ayanian

Block

. Identifying potential indicators of the quality of end-of-life cancer care from administrative data. J Clin Oncol. 2003;21(6):1133-1138. doi:10.1200/JCO.2003.03.059

12.

Earle

Neville

Landrum

, et al. Evaluating claims-based indicators of the intensity of end-of-life cancer care. Int J Qual Health Care. 2005;17(6):505-509. doi:10.1093/INTQHC/MZI061

13.

Temel

Greer

Muzikansky

, et al. Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med. 2010;363(8):733-742. doi:10.1056/NEJMoa1000678

14.

McDonald

Swami

Hannon

, et al. Impact of early palliative care on caregivers of patients with advanced cancer: Cluster randomised trial. Ann Oncol. 2017;28:163-168. Published online September 29. doi:10.1093/annonc/mdw438

15.

Zimmermann

Swami

Krzyzanowska

, et al. Early palliative care for patients with advanced cancer: A cluster-randomised controlled trial. Lancet. 2014;383(9930):1721-1730. doi:10.1016/S0140-6736(13)62416-2

16.

Bakitas

Lyons

Hegel

, et al. Effects of a palliative care intervention on clinical outcomes in patients with advanced cancer: The Project ENABLE II randomized controlled trial. JAMA. 2009;302(7):741-749. doi:10.1001/jama.2009.1198

17.

Temel

Greer

El-Jawahri

, et al. Effects of early integrated palliative care in patients with lung and GI Cancer: A randomized clinical trial. J Clin Oncol. 2017;35(8):834-841. doi:10.1200/JCO.2016.70.5046

18.

Ferrell

Temel

Temin

, et al. Integration of palliative care into standard oncology care: American society of clinical oncology clinical practice guideline update. J Clin Oncol. 2017;35(1):96-112. doi:10.1200/JCO.2016.70.1474

19.

Kavalieratos

Corbelli

Zhang

, et al. Association between palliative care and patient and caregiver outcomes: A systematic review and meta-analysis. JAMA. 2016;316(20):2104-2114. doi:10.1001/jama.2016.16840

20.

Ioannidis

JPA

. Why most clinical research is not useful. PLoS Med. 2016;13(6):e1002049. doi:10.1371/JOURNAL.PMED.1002049

21.

Cherny

Sullivan

Dafni

, et al. A standardised, generic, validated approach to stratify the magnitude of clinical benefit that can be anticipated from anti-cancer therapies: The European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). Ann Oncol. 2017;28(11):2901-2905. doi:10.1093/ANNONC/MDW258

22.

Schnipper

Davidson

Wollins

, et al. American society of clinical oncology statement: A conceptual framework to assess the value of cancer treatment options. J Clin Oncol. 2015;33(23):2563-2577. doi:10.1200/JCO.2015.61.6706

23.

Eniu

Martei

Trimble

Shulman

. Cancer care and control as a human right: Recognizing global oncology as an academic field. Am Soc Clin Oncol Educ Book. 2017;37(37):409-415. doi:10.1200/EDBK_175251

24.

Cherny

Sullivan

Torode

Saar

Eniu

. ESMO European consortium Study on the availability, out-of-pocket costs and accessibility of antineoplastic medicines in Europe. Ann Oncol. 2016;27(8):1423-1443. doi:10.1093/ANNONC/MDW213

25.

Hamel

Penner

Albrecht

Heath

Gwede

Eggly

. Barriers to clinical trial enrollment in racial and ethnic minority patients with cancer. Cancer Control. 2016;23(4):327-337. doi:10.1177/107327481602300404

26.

Ersek

Nadler

Freeman-Daily

Mazharuddin

Kim

. Clinical pathways and the patient perspective in the pursuit of value-based oncology care. Am Soc Clin Oncol Educ Book. 2017;37(37):597-606. doi:10.1200/EDBK_174794

27.

Pavasini

Guralnik

Brown

, et al. Short physical performance battery and all-cause mortality: Systematic review and meta-analysis. doi:10.1186/s12916-016-0763-7.

28.

Studenski

Perera

Patel

, et al. Gait speed and survival in older adults. JAMA. 2011;305(1):50-58. doi:10.1001/JAMA.2010.1923

29.

Rocha

Ross

Paiva

, et al. Inflammation and performance status: The cornerstones of prognosis in advanced cancer. J Pain Symptom Manag. 2022;0(0). doi:10.1016/J.JPAINSYMMAN.2022.11.021

30.

Sargent

Köhne

Sanoff

, et al. Pooled safety and efficacy analysis examining the effect of performance status on outcomes in nine first-line treatment trials using individual data from patients with metastatic colorectal cancer. J Clin Oncol. 2009;27(12):1948-1955. doi:10.1200/JCO.2008.20.2879

31.

Zhang

Wang

Kang

Liu

. Prognostic value of performance status in metastatic renal cell carcinoma patients receiving tyrosine kinase inhibitors: A systematic review and meta-analysis. BMC Cancer. 2019;19(1):168. doi:10.1186/S12885-019-5375-0

32.

Karnofsky

Burchenal

. The clinical evaluation of chemotherapeutic agents in cancer. In: MacLeod

, ed. Evaluation of Chemotherapeutic Agents. New York, NY: Columbia University Press; 1949:191-205.

33.

Oken

Creech

Tormey

, et al. Toxicity and response criteria of the Eastern cooperative oncology group. Am J Clin Oncol. 1982;5(6):649-655. http://www.ncbi.nlm.nih.gov/pubmed/7165009. Accessed September 24, 2014.

34.

Cheng

Qureshi

Pullenayegum

Haynes

Chan

KKW

. Do patients with reduced or excellent performance status derive the same clinical benefit from novel systemic cancer therapies? A systematic review and meta-analysis. ESMO Open. 2017;2(4):e000225. doi:10.1136/ESMOOPEN-2017-000225

35.

Tomasik

Bieńkowski

Braun

Popat

Dziadziuszko

. Effectiveness and safety of immunotherapy in NSCLC patients with ECOG PS score ≥2 - Systematic review and meta-analysis. Lung Cancer. 2021;158:97-106. doi:10.1016/J.LUNGCAN.2021.06.004

36.

Mohile

Dale

Somerfield

, et al. Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology. J Clin Oncol. 2018;36(22):2326-2347. doi:10.1200/JCO.2018.78.8687

37.

Mohile

Dale

Hurria

. Geriatric oncology research to improve clinical care. Nat Rev Clin Oncol. 2012;9(10):571-578. doi:10.1038/NRCLINONC.2012.125

38.

Zeng

Wen

Morgans

Pao

Shu

Zheng

. Disparities by race, age, and sex in the improvement of survival for major cancers: results from the national cancer institute surveillance, epidemiology, and end results (SEER) Program in the United States, 1990 to 2010. JAMA Oncol. 2015;1(1):88-96. doi:10.1001/JAMAONCOL.2014.161

39.

Hurria

Mohile

Gajra

, et al. Validation of a prediction tool for chemotherapy toxicity in older adults with cancer. J Clin Oncol. 2016;34(20):2366-2371. doi:10.1200/JCO.2015.65.4327

40.

Hurria

Togawa

Mohile

, et al. Predicting chemotherapy toxicity in older adults with cancer: A prospective multicenter study. J Clin Oncol. 2011;29(25):3457-3465. doi:10.1200/JCO.2011.34.7625

41.

Extermann

Boler

Reich

, et al. Predicting the risk of chemotherapy toxicity in older patients: The chemotherapy risk assessment scale for high-age patients (CRASH) score. Cancer. 2012;118(13):3377-3386. doi:10.1002/CNCR.26646

42.

Bellera

Rainfray

Mathoulin-Pélissier

, et al. Screening older cancer patients: First evaluation of the G-8 geriatric screening tool. Ann Oncol. 2012;23(8):2166-2172. doi:10.1093/ANNONC/MDR587

43.

Min

Wenger

Reuben

Saliba

. A short functional survey is responsive to changes in functional status in vulnerable older people. J Am Geriatr Soc. 2008;56(10):1932-1936. doi:10.1111/J.1532-5415.2008.01921.X

44.

Overcash

Beckstead

Moody

Extermann

Cobb

. The abbreviated comprehensive geriatric assessment (aCGA) for use in the older cancer patient as a prescreen: Scoring and interpretation. Crit Rev Oncol Hematol. 2006;59(3):205-210. doi:10.1016/J.CRITREVONC.2006.04.003

45.

Slaets

JPJ

. Vulnerability in the elderly: Frailty. Med Clin North Am. 2006;90(4):593-601. doi:10.1016/J.MCNA.2006.05.008

46.

Meldon

Mion

Palmer

, et al. A brief risk-stratification tool to predict repeat emergency department visits and hospitalizations in older patients discharged from the emergency department. Acad Emerg Med. 2003;10(3):224-232. doi:10.1111/J.1553-2712.2003.TB01996.X

47.

Kenis

Decoster

Van Puyvelde

, et al. Performance of two geriatric screening tools in older patients with cancer. J Clin Oncol. 2014;32(1):19-26. doi:10.1200/JCO.2013.51.1345

48.

Loh

Soto-Perez-De-Celis

Hsu

, et al. What every oncologist should know about geriatric assessment for older patients with cancer: young international society of geriatric oncology position paper. J Oncol Pract. 2018;14(2):85-94. doi:10.1200/JOP.2017.026435

49.

Garcia

Agar

Soo

Phillips

. Screening tools for identifying older adults with cancer who may benefit from a geriatric assessment: A systematic review. JAMA Oncol. 2021;7(4):616-627. doi:10.1001/JAMAONCOL.2020.6736

50.

Lehtonen

Heiskanen

Reinikainen

Kellokumpu-Lehtinen

. Both comorbidity and worse performance status are associated with poorer overall survival after external beam radiotherapy for prostate cancer. BMC Cancer. 2020;20(1):324. doi:10.1186/S12885-020-06812-6

51.

Curry

Munker

, et al. Performance status, comorbidities, and cycles of methotrexate exert the greatest influence on outcomes of primary and secondary CNS lymphomas: The Lexington experience. Ann Hematol. 2023;102:141-154. doi:10.1007/S00277-022-05018-Z

52.

Ogata

Hatta

Ohara

, et al. Predictors of early and late mortality after the treatment for early gastric cancers. Dig Endosc. 2022;34(4):816-825. doi:10.1111/DEN.14172

53.

Bauml

Mick

Zhang

, et al. Determinants of survival in advanced non--small-cell lung cancer in the era of targeted therapies. Clin Lung Cancer. 2013;14(5):581-591. doi:10.1016/J.CLLC.2013.05.002

54.

Wright

Doan

McBride

Jacobson

Hershman

. Variability in chemotherapy delivery for elderly women with advanced stage ovarian cancer and its impact on survival. Br J Cancer. 2008;98(7):1197-1203. doi:10.1038/SJ.BJC.6604298

55.

Charlson

Pompei

Ales

MacKenzie

. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chron Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8

56.

Charlson

Carrozzino

Guidi

Patierno

. Charlson comorbidity index: A critical review of clinimetric properties. Psychother Psychosom. 2022;91(1):8-35. doi:10.1159/000521288

57.

Iezzoni

. Risk adjustment for measuring healthcare outcomes; 2013. Published online.

58.

Klabunde

Potosky

Legler

Warren

. Development of a comorbidity index using physician claims data. J Clin Epidemiol. 2000;53(12):1258-1267. doi:10.1016/S0895-4356(00)00256-0

59.

Klabunde

Legler

Warren

Baldwin

Schrag

. A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. Ann Epidemiol. 2007;17(8):584-590. doi:10.1016/J.ANNEPIDEM.2007.03.011

60.

Elixhauser

Steiner

Harris

Coffey

. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27. doi:10.1097/00005650-199801000-00004

61.

Van Walraven

Austin

Jennings

Quan

Forster

. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47(6):626-633. doi:10.1097/MLR.0B013E31819432E5

62.

Moore

White

Washington

Coenen

Elixhauser

. Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: The AHRQ elixhauser comorbidity index. Med Care. 2017;55(7):698-705. doi:10.1097/MLR.0000000000000735

63.

Sharabiani

MTA

Aylin

Bottle

. Systematic review of comorbidity indices for administrative data. Med Care. 2012;50(12):1109-1118. doi:10.1097/MLR.0B013E31825F64D0

64.

Mehta

Sura

Adhikari

, et al. Adapting the Elixhauser comorbidity index for cancer patients. Cancer. 2018;124(9):2018-2025. doi:10.1002/CNCR.31269

65.

Grose

Morrison

Devereux

, et al. The impact of comorbidity upon determinants of outcome in patients with lung cancer. Lung Cancer. 2015;87(2):186-192. doi:10.1016/J.LUNGCAN.2014.11.012

66.

Sarfati

. Review of methods used to measure comorbidity in cancer populations: No gold standard exists. J Clin Epidemiol. 2012;65(9):924-933. doi:10.1016/J.JCLINEPI.2012.02.017

67.

Prommik

Tootsi

Saluse

Strauss

Kolk

Märtson

. Simple Excel and ICD-10 based dataset calculator for the Charlson and Elixhauser comorbidity indices. BMC Med Res Methodol. 2022;22(1):4. doi:10.1186/S12874-021-01492-7

68.

Hall

Ramachandran

Narayan

Jani

Vijayakumar

. An electronic application for rapidly calculating Charlson comorbidity score. BMC Cancer. 2004;4:94. doi:10.1186/1471-2407-4-94

69.

Taplin

Weaver

Salas

, et al. Reviewing cancer care team effectiveness. J Oncol Pract. 2015;11(3):239-246. doi:10.1200/JOP.2014.003350

70.

Leiter

Bischoff

Carey

, et al. Top ten tips palliative care clinicians should know about delivering specialty-aligned palliative care. J Palliat Med. 2023;31:2023. doi:10.1089/JPM.2023.0116

71.

Kaasa

Loge

Aapro

, et al. Integration of oncology and palliative care: A Lancet Oncology Commission. Lancet Oncol. 2018;19(11):e588-e653. doi:10.1016/S1470-2045(18)30415-7

72.

Hui

Didwaniya

Vidal

, et al. Quality of end-of-life care in patients with hematologic malignancies: A retrospective cohort study. Cancer. 2014;120(10):1572-1578. doi:10.1002/CNCR.28614

73.

LeBlanc

Abernethy

Casarett

. What is different about patients with hematologic malignancies? A retrospective cohort study of cancer patients referred to a hospice research network. J Pain Symptom Manag. 2015;49(3):505-512. doi:10.1016/J.JPAINSYMMAN.2014.07.003

74.

Hui

Bansal

Park

, et al. Differences in attitudes and beliefs toward end-of-life care between hematologic and solid tumor oncology specialists. Ann Oncol. 2015;26(7):1440-1446. doi:10.1093/ANNONC/MDV028

75.

Piili

Lehto

Luukkaala

Hinkka

Kellokumpu-Lehtinen

PLI

. Does special education in palliative medicine make a difference in end-of-life decision-making? BMC Palliat Care. 2018;17(1):94. doi:10.1186/S12904-018-0349-6

76.

Nipp

Greer

El-Jawahri

, et al. Coping and prognostic awareness in patients with advanced Cancer. J Clin Oncol. 2017;35(22):2551-2557. doi:10.1200/JCO.2016.71.3404

77.

Nipp

El-Jawahri

Fishbein

, et al. The relationship between coping strategies, quality of life, and mood in patients with incurable cancer. Cancer. 2016;122(13):2110-2116. doi:10.1002/CNCR.30025

78.

Rodi

Detering

Sellars

, et al. Exploring advance care planning awareness, experiences, and preferences of people with cancer and support people: An Australian online cross-sectional study. Support Care Cancer. 2021;29(7):3677-3688. doi:10.1007/S00520-020-05878-Z

79.

Kubi

Istl

Lee

Conca-Cheng

Johnston

. Advance care planning in cancer: Patient preferences for personnel and timing. JCO Oncol Pract. 2020;16(9):e875-e883. doi:10.1200/JOP.19.00367

80.

Sudore

Lum

You

, et al. Defining advance care planning for adults: A consensus definition from a multidisciplinary delphi panel. J Pain Symptom Manag. 2017;53(5):821-832. doi:10.1016/J.JPAINSYMMAN.2016.12.331

81.

Paiva

Teixeira

Minto Lourenço

, et al. Anticancer Treatment Goals and Prognostic Misperceptions among Advanced Cancer Outpatients. Int J Environ Res Publ Health. 2022;19(10):6272. doi:10.3390/IJERPH19106272

82.

Weeks

Catalano

Cronin

, et al. Patients’ expectations about effects of chemotherapy for advanced cancer. N Engl J Med. 2012;367(17):1616-1625. doi:10.1056/NEJMOA1204410

83.

Hui

Paiva

. The importance of prognostication: Impact of prognostic predictions, disclosures, awareness, and acceptance on patient outcomes. Curr Treat Options Oncol. 2021;22(2):12. doi:10.1007/S11864-020-00810-3

84.

McMahan

Tellez

Sudore

. Deconstructing the complexities of advance care planning outcomes: What do we know and where do we go? A scoping review. J Am Geriatr Soc. 2021;69(1):234-244. doi:10.1111/JGS.16801

85.

Serious Illness Conversation Guide. Published 2015. Accessed March 19, 2023.www.ariadnelabs.org.

86.

Daly

Schmidt

Thoma

Levy

. How well are serious illness conversations documented and what are patient and physician perceptions of these conversations? J Palliat Care. 2022;37(3):332-340. doi:10.1177/08258597221090387

87.

Festen

Van Twisk

Van Munster

De Graeff

. What matters to you?” Health outcome prioritisation in treatment decision-making for older patients. Age Ageing. 2021;50(6):2264-2269. doi:10.1093/AGEING/AFAB160

88.

Fried

Tinetti

Agostini

Iannone

Towle

. Health outcome prioritization to elicit preferences of older persons with multiple health conditions. Patient Educ Couns. 2011;83(2):278-282. doi:10.1016/J.PEC.2010.04.032

89.

Jacobson

. Dignity violation in health care. Qual Health Res. 2009;19(11):1536-1547. doi:10.1177/1049732309349809

90.

Tad

Bayer

Dieppe

. Dignity in health care: Reality or rhetoric? Rev Clin Gerontol. 2002;12(1):1-4.

91.

Brent

Santy-Tomlinson

Hertz

. Family partnerships, palliative care and end of life. Published online 2018:137-145. doi:10.1007/978-3-319-76681-2_11.

92.

Osman

El Jurdi

Sabra

Arawi

. Respecting patient choices: using the “Go Wish” cards as a teaching tool. BMJ Support Palliat Care. 2018;8(2):194-197. doi:10.1136/BMJSPCARE-2017-001342

93.

Litzelman

Inui

Schmitt-Wendholt

, et al. Clarifying values and preferences for care near the end of life: The role of a new lay workforce. J Community Health. 2017;42(5):926-934. doi:10.1007/S10900-017-0336-5

94.

Delgado-Guay

Rodriguez-Nunez

De la Cruz

, et al. Advanced cancer patients’ reported wishes at the end of life: A randomized controlled trial. Support Care Cancer. 2016;24(10):4273-4281. doi:10.1007/S00520-016-3260-9

95.

Hui

. Prognostication of survival in patients with advanced cancer: Predicting the unpredictable? Cancer Control. 2015;22(4):489-497. doi:10.1177/107327481502200415

96.

Chow

Harth

Hruby

Finkelstein

Danjoux

. How accurate are physicians’ clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review. Clin Oncol. 2001;13(3):209-218. doi:10.1053/CLON.2001.9256

97.

Cheon

Agarwal

Popovic

, et al. The accuracy of clinicians’ predictions of survival in advanced cancer: a review. Ann Palliat Med. 2016;5(1):22-29. doi:10.3978/J.ISSN.2224-5820.2015.08.04

98.

Glare

Virik

Jones

, et al. A systematic review of physicians’ survival predictions in terminally ill cancer patients. BMJ. 2003;327(7408):195-198. doi:10.1136/bmj.327.7408.195

99.

Gripp

Moeller

Bölke

, et al. Survival prediction in terminally ill cancer patients by clinical estimates, laboratory tests, and self-rated anxiety and depression. J Clin Oncol. 2007;25(22):3313-3320. doi:10.1200/JCO.2006.10.5411

100.

Bruera

Miller

Kuehn

MacEachern

Hanson

. Estimate of survival of patients admitted to a palliative care unit: A prospective study. J Pain Symptom Manag. 1992;7(2):82-86. doi:10.1016/0885-3924(92)90118-2

101.

Anderson

Downing

Hill

Casorso

Lerch

. Palliative performance scale (PPS): A new tool. J Palliat Care. 1996;12(1):5-11. http://www.ncbi.nlm.nih.gov/pubmed/8857241. Accessed October 1, 2014.

102.

Trajkovic-Vidakovic

de Graeff

Voest

Teunissen

SCCM

. Symptoms tell it all: A systematic review of the value of symptom assessment to predict survival in advanced cancer patients. Crit Rev Oncol Hematol. 2012;84(1):130-148. doi:10.1016/J.CRITREVONC.2012.02.011

103.

Rana

Kanatas

Herzberg

, et al. Prospective study of the influence of psychological and medical factors on quality of life and severity of symptoms among patients with oral squamous cell carcinoma. Br J Oral Maxillofac Surg. 2015;53(4):364-370. doi:10.1016/J.BJOMS.2015.01.019

104.

Bruera

Kuehn

Miller

Selmser

Macmillan

. The Edmonton Symptom Assessment System (ESAS): A simple method for the assessment of palliative care patients. J Palliat Care. 1991;7(2):6-9. http://www.ncbi.nlm.nih.gov/pubmed/1714502. Accessed October 28, 2014.

105.

Maltoni

Pirovano

Nanni

, et al. Biological indices predictive of survival in 519 Italian terminally ill cancer patients. Italian multicenter study group on palliative care. J Pain Symptom Manag. 1997;13(1):1-9. doi:10.1016/S0885-3924(96)00265-5

106.

Suh

Ahn

. Lactate dehydrogenase as a prognostic factor for survival time of terminally ill cancer patients: A preliminary study. Eur J Cancer. 2007;43(6):1051-1059. doi:10.1016/J.EJCA.2007.01.031

107.

Suh

Ahn

. A prospective study on C-reactive protein as a prognostic factor for survival time of terminally ill cancer patients. Support Care Cancer. 2007;15(6):613-620. doi:10.1007/S00520-006-0208-5

108.

Pirovano

Maltoni

Nanni

, et al. A new palliative prognostic score: A first step for the staging of terminally ill cancer patients. Italian Multicenter and Study Group on Palliative Care. J Pain Symptom Manag. 1999;17(4):231-239. http://www.ncbi.nlm.nih.gov/pubmed/10203875. Accessed February 20, 2015.

109.

Scarpi

Maltoni

Miceli

, et al. Survival prediction for terminally ill cancer patients: Revision of the palliative prognostic score with incorporation of delirium. Oncol. 2011;16(12):1793-1799. doi:10.1634/THEONCOLOGIST.2011-0130

110.

Morita

Tsunoda

Inoue

Chihara

. The Palliative prognostic index: A scoring system for survival prediction of terminally ill cancer patients. Support Care Cancer. 1999;7(3):128-133. doi:10.1007/s005200050242

111.

Suh

Choi

Shim

, et al. Construction of a new, objective prognostic score for terminally ill cancer patients: A multicenter study. Support Care Cancer. 2010;18(2):151-157. doi:10.1007/S00520-009-0639-X

112.

Gwilliam

Keeley

Todd

, et al. Development of prognosis in palliative care study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study. BMJ Support Palliat Care. 2015;5(4):390-398. doi:10.1136/bmjspcare-2012-d4020rep

113.

McMillan

. An inflammation-based prognostic score and its role in the nutrition-based management of patients with cancer. Proc Nutr Soc. 2008;67(3):257-262. doi:10.1017/S0029665108007131

114.

Laird

Kaasa

McMillan

, et al. Prognostic factors in patients with advanced cancer: A comparison of clinicopathological factors and the development of an inflammation-based prognostic system. Clin Cancer Res. 2013;19(19):5456-5464. doi:10.1158/1078-0432.CCR-13-1066

115.

Bourgeois

Grudé

Solal-Céligny

, et al. Clinical validation of a prognostic tool in a population of outpatients treated for incurable cancer undergoing anticancer therapy: PRONOPALL study. Ann Oncol. 2017;28(7):1612-1617. doi:10.1093/annonc/mdx172

116.

Feliu

Jiménez-Gordo

Madero

, et al. Development and validation of a prognostic nomogram for terminally ill cancer patients. J Natl Cancer Inst. 2011;103(21):1613-1620. doi:10.1093/jnci/djr388

117.

Paiva

BSR

de Paula Pântano

, et al. Development and validation of a prognostic nomogram for ambulatory patients with advanced cancer. Cancer Med. 2018;7:3003-3010. doi:10.1002/cam4.1582

118.

Preto

DDA

Paiva

BSR

Hui

Bruera

Paiva

. HAprog: A new prognostic application to assist oncologists in routine care. J Pain Symptom Manag. 2022;63(6):1014-1021.e4. doi:10.1016/J.JPAINSYMMAN.2022.02.004

119.

Chow

Abdolell

Panzarella

, et al. Predictive model for survival in patients with advanced cancer. J Clin Oncol. 2008;26(36):5863-5869. doi:10.1200/JCO.2008.17.1363

120.

Hui

Maxwell

Paiva

. Dealing with prognostic uncertainty: The role of prognostic models and websites for patients with advanced cancer. Curr Opin Support Palliat Care. 2019;13(4):360-368. doi:10.1097/SPC.0000000000000459

121.

Hui

De La Rosa

Shelal

, et al. The impact of a web-based prognostic intervention on physicians’ prognostic confidence. J Clin Oncol. 2021;39(suppl l-15):12106-12106. doi:10.1200/JCO.2021.39.15_suppl.12106

122.

Gill

. The central role of prognosis in clinical decision making. JAMA. 2012;307(2):199-200. doi:10.1001/JAMA.2011.1992

123.

Studer

Danuser

Gomez

. Physicians’ psychophysiological stress reaction in medical communication of bad news: A critical literature review. Int J Psychophysiol. 2017;120:14-22. doi:10.1016/J.IJPSYCHO.2017.06.006

124.

Baile

Buckman

Lenzi

Glober

Beale

Kudelka

. SPIKES-A six-step protocol for delivering bad news: Application to the patient with cancer. Oncol. 2000;5(4):302-311. doi:10.1634/THEONCOLOGIST.5-4-302

125.

Mahendiran

Yeung

Rossi

Khosravani

Perri

. Evaluating the effectiveness of the SPIKES Model to break bad news - A systematic review. Am J Hosp Palliat Care. 2023:104990912211462. Published online. doi:10.1177/10499091221146296

126.

Bloom

Marshall

Rodriguez-Russo

Martin

Jones

Dharmarajan

. Prognostic disclosure in oncology - Current communication models: A scoping review. BMJ Support Palliat Care. 2022;12(2):167-177. doi:10.1136/BMJSPCARE-2021-003313

127.

Paladino

Bernacki

Neville

, et al. Evaluating an intervention to improve communication between oncology clinicians and patients with life-limiting Cancer: A cluster randomized clinical trial of the serious illness care program. JAMA Oncol. 2019;5(6):801-809. doi:10.1001/JAMAONCOL.2019.0292

128.

Fossli Jensen

Gulbrandsen

Dahl

Krupat

Frankel

Finset

. Effectiveness of a short course in clinical communication skills for hospital doctors: Results of a crossover randomized controlled trial (ISRCTN22153332). Patient Educ Couns. 2011;84(2):163-169. doi:10.1016/J.PEC.2010.08.028

129.

Curtis

Downey

Back

, et al. Effect of a Patient and clinician communication-priming intervention on patient-reported goals-of-care discussions between patients with serious illness and clinicians: A randomized clinical trial. JAMA Intern Med. 2018;178(7):930-940. doi:10.1001/JAMAINTERNMED.2018.2317

130.

Childers

Back

Tulsky

Arnold

. REMAP: A framework for goals of care conversations. J Oncol Pract. 2017;13(10):e844-e850. doi:10.1200/JOP.2016.018796