Prognostication in Serious Illness

Personalized medicine: An alternative for cancer treatment

The incidence of cancer continues to increase worldwide, resulting in significant physical, emotional, and financial challenges for individuals, families, communities, and healthcare systems. Cancer is projected to be responsible for approximately 10 million deaths in 2020, accounting for one in six deaths globally. Prostate, colon, lung, and breast cancers are the most common types of cancer. In India, it is estimated that there will be around 2.7 million cancer patients by 2020. Personalized medicine has the potential to offer an alternative approach to cancer treatment. Precision medicine, often known as personalized medicine, is a new cancer treatment technique that focuses on tailoring medication to each patient's specific genetic, biochemical, and lifestyle factors. The goal is to optimize tumor response while minimizing therapy side effects, resulting in improved patient care and quality of life. Personalized medicine allows for the creation of focused medicines that address specific gene mutations by leveraging knowledge about a patient's cancer, including its genetic makeup. Ongoing research seeks to detect gene modifications in diverse cancer types, produce novel diagnostic tools, and develop treatments that particularly target these genetic changes. In recent years, personalized medicine has achieved major advances in the treatment of solid tumors, with the promise to improve treatment precision, reduce side effects, as well as enhance outcomes for patients in cancer therapy. This review aims to objectively evaluate the transformation of cancer treatment, emphasizing the shift towards a more precise methodology.

Depression, performance status, and discontinued treatment mediate an association of curability belief with prognosis in advanced cancer patients

Individuals diagnosed with advanced cancer often experience stress and depression, factors linked to worse survival. Curability belief-defined as the hope and expectation of cure through treatment, based on affective forecasting-may differ from the patient's actual life expectancy (i.e., likelihood estimation) and has shown variable associations with cancer survival. In this study, multivariate Cox regression analyses were used to examine the effect of curability belief and depression on 1-year survival after adjustment for physical factors. Additionally, regularized partial correlations among physical and psychological factors were assessed using mixed graphical models to elucidate their roles in mediating the relationship between curability belief and 1-year survival. This multi-center cohort study, conducted across 13 tertiary hospitals (including four ranked among the 'World's Best Specialized Hospitals 2025' in oncology), involved 382 adults with stage IV advanced cancer and an oncologist-estimated survival of more than 6 months. Baseline data included demographics, primary tumor site, number of metastatic sites, symptom burdens (EORTC QLQ-C15-PAL), performance status (ECOG-PS), depression levels (PHQ-9), anti-cancer treatment type, patient's life expectancy estimation, and curability belief. Follow-up data included 1-year survival and end-of-life care (place of death) for deceased patients. Multivariate Cox proportional hazards models were used to assess adjusted hazard ratios (aHRs) for curability belief, depression, and their interaction on 1-year survival, adjusting for significant demographic and clinical factors from univariate Cox regressions. The Kaplan-Meier method was used to plot survival probability by curability belief and depression interaction. Mixed graphical models estimated regularized partial correlations among 1-year survival, curability belief, patient's life expectancy, depression, primary tumor site, anti-cancer treatment type, performance status, and symptom burden. In terms of healthcare utilization, patients with curability belief were more likely to receive standard or advanced anti-cancer therapy, while those without curability belief tended to suspend or discontinue therapy (P < 0.001). Among patients who did not survive the 1-year follow-up (N = 161), end-of-life care settings differed significantly between those with curability belief (predominantly nursing homes and home settings) and those without (primarily hospice and tertiary/secondary hospitals; P = 0.036). In multivariate Cox regression, curability belief (P = 0.003), depression (PHQ-9 score ≥ 10; P = 0.003), and their interaction (P = 0.040) were significantly associated with 1-year survival, after adjusting for sex, residential area, primary tumor site, performance status, anti-cancer treatment type, and symptom burdens (fatigue and appetite loss). The relationship between curability belief and 1-year survival was significant only in patients without depression [PHQ-9 score < 10; aHR (95% CI) = 2.20 (1.31-3.70); P = 0.003]. In the mixed graphical model, node predictability values for curability belief, depression, and 1-year survival were 0.68, 0.50, and 0.70, respectively, with curability belief showing partial correlations with depression (r = 0.30) and patients' life expectancy (r = 0.20); depression correlated with fatigue (r = 0.53), anorexia (r = 0.16), life expectancy (r = 0.24), performance status (r = 0.23), and curability belief; and 1-year survival correlated with suspended/stopped anti-cancer treatment (r = 0.45), primary tumor site (r = 0.24), and performance status (r = 0.15). Partial correlations of performance status with depression and discontinued treatment mediated the association between curability belief and 1-year survival. Curability belief among stage IV advanced cancer patients with an oncologist-estimated survival of over 6 months was associated with depression levels and patients' perceived life expectancy estimations. Performance status, depression, and anti-cancer treatment status mediate the relationship between curability belief and improved 1-year survival in patients without depression. Further research using longitudinal modeling of depression, performance status, and healthcare utilization, with curability belief and primary tumor site as covariates, is warranted.Trial registration: Clinical Trial Number (ClinicalTrials.gov): NCT03222258; Study Registration Dates (First submitted: 2017-06-05; First submitted following the QC criteria: 2017-07-16; First posted: 2017-07-19).

Hospital admissions in the last month of life for patients with advanced cancer residing in nursing homes

INTRODUCTION

Overtreatment and frequent hospital admissions in patients at end-of-life has been shown in several studies, including in the nursing home (NH) setting. However, overtreatment in NH residents with advanced cancer has not been studied before. Our objective was to compare emergency room (ER) visits and hospital admissions in the last month of life and place of death among NH residents with or without advanced cancer.

MATERIALS AND METHODS

This retrospective study is based on Region Stockholm's administrative database from 2015 to 2019. Demographic data, ER visits, and Hospital Frailty Risk Score (HFRS) were extracted for each subject. Univariable and multivariable analyses were performed, and odds ratios (OR) calculated.

RESULTS

In total, 30,324 NH residents were identified, of which 1807 had advanced cancer. Advanced cancer was associated with more ER visits in the last month of life in the univariable analysis but not when adjusted for age, sex, and risk of frailty with an adjusted aOR of 1.07 (95 %CI 0.97-1.19). Having a cancer diagnosis was significantly associated with acute hospital admissions, although the association was rather weak with an aOR 1.13 (95 %CI 1.02-1.26). NH residents with advanced cancer were less likely to die in hospital than non-cancer residents: aOR 0.63 (95 %CI 0.54-0.73).

DISCUSSION

NH residents with advanced cancer have a slightly higher risk of hospital admissions in the last month of life compared to non-cancer residents but are less likely to die in hospital. The probability of ER visits during the last month of life was similar between NH residents with and without cancer.

Neuron-specific enolase as a prognostic biomarker in acute ischemic stroke patients treated with reperfusion therapies

Introduction

Ischemic stroke is a significant global health concern, with reperfusion therapies playing a vital role in patient management. Neuron-specific enolase (NSE) has been suggested as a potential biomarker for assessing stroke severity and prognosis, however, the role of NSE in predicting long-term outcomes in patients undergoing reperfusion therapies is still scarce.

Aim

To investigate the association between serum NSE levels at admission and 48 h after reperfusion therapies, and functional outcomes at 90 days in ischemic stroke patients.

Methods

This study conducted a prospective cross-sectional analysis on consecutive acute ischemic stroke patients undergoing intravenous fibrinolysis and/or endovascular thrombectomy. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days post-stroke and two groups were defined according to having unfavorable (mRS3-6) or favorable (mRS0-2) outcome. Demographic, clinical, radiological, and laboratory data were collected, including NSE levels at admission and 48 h. Spearman's coefficient evaluated the correlation between analyzed variables. Logistic regression analysis was performed to verify which variables were independently associated with unfavorable outcome. Two ROC curves determined the cut-off points for NSE at admission and 48 h, being compared by Delong test.

Results

Analysis of 79 patients undergoing reperfusion treatment following acute stroke revealed that patients with mRS 3-6 had higher NIHSS at admission (p < 0.0001), higher NIHSS at 24 h (p < 0.0001), and higher NSE levels at 48 h (p = 0.008) when compared to those with mRS 0-2. Optimal cut-off values for NSE0 (>14.2 ng/mL) and NSE48h (>26.3 ng/mL) were identified, showing associations with worse clinical outcomes. Adjusted analyses demonstrated that patients with NSE48h > 26.3 ng/mL had a 13.5 times higher risk of unfavorable outcome, while each unit increase in NIHSS24h score was associated with a 22% increase in unfavorable outcome. Receiver operating characteristic analysis indicated similar predictive abilities of NSE levels at admission and 48 h (p = 0.298). Additionally, a strong positive correlation was observed between NSE48h levels and mRS at 90 days (r = 0.400 and p < 0.0001), suggesting that higher NSE levels indicate worse neurological disability post-stroke.

Conclusion

Serum NSE levels at 48 h post-reperfusion therapies are associated with functional outcomes in ischemic stroke patients, serving as potential tool for patient long-term prognosis.

Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis

BACKGROUND

The palliative prognostic score is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of palliative prognostic score tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of palliative prognostic score, delirium-palliative prognostic score, and palliative prognostic score without clinician prediction in predicting 30-day survival of cancer patients and to compare their performance.

METHODS

Six databases were searched for peer-reviewed studies and grey literature published from inception to June 2, 2023. English studies must assess palliative prognostic score, delirium-palliative prognostic score, or palliative prognostic score without clinician-predicted survival for 30-day survival in adults aged 18 years and older with any stage or type of cancer. Outcomes were pooled using the random effects model or summarized narratively when meta-analysis was not possible.

RESULTS

A total of 39 studies (n = 10 617 patients) were included. Palliative prognostic score is an accurate prognostic tool (pooled area under the curve [AUC] = 0.82, 95% confidence interval [CI] = 0.79 to 0.84) and outperforms palliative prognostic score without clinician-predicted survival (pooled AUC = 0.74, 95% CI = 0.71 to 0.78), suggesting that the original palliative prognostic score should be preferred. The meta-analysis found palliative prognostic score and delirium-palliative prognostic score performance to be comparable. Most studies reported survival probabilities corresponding to the palliative prognostic score risk groups, and higher risk groups were statistically significantly associated with shorter survival.

CONCLUSIONS

Palliative prognostic score is a validated prognostic tool for cancer patients that can enhance clinicians' confidence and accuracy in predicting survival. Future studies should investigate if accuracy differs depending on clinician characteristics. Reporting of validation studies must be improved, as most studies were at high risk of bias, primarily because calibration was not assessed.

Improve Advance Care Planning: A Brief Report Discussing Goals of Care Interventions to Improve Communication Among Health Care Teams and Patients Maximizing the Use of the Electronic Health Record Tools

Introduction: A key element of advance care planning (ACP) is the goals of care (GOC) conversation between the provider and the patient. The value of meaningful GOC conversations for the patient, provider, and health care institution is well documented. However, if the GOC documentation is buried in the medical record, not well defined, or poorly documented, that value is squandered. The Improvement Process: Interventions were implemented with oncology physicians and nurse practitioners (NPs). These included education, system reform including improving the ease and consistency of documentation of ACP, and regular feedback. Results: Participants reported increased confidence in communication skills about GOC conversations postworkshops. Data results for the tracked metrics, health care power of attorney, code status, and GOC, all showed improvement. Conclusion: Physicians and NPs recognized the importance of GOC conversations as part of ACP. Considerable progress was made by focusing on GOC conversations, maximizing information technology, participating in coaching, and ongoing data monitoring.

Molecular biomarkers in prostate cancer tumorigenesis and clinical relevance

Prostate cancer (PCa) is the second most frequent type of cancer in men and assessing circulating tumor cells (CTCs) by liquid biopsy is a promising tool to help in cancer early detection, staging, risk of recurrence evaluation, treatment prediction and monitoring. Blood-based liquid biopsy approaches enable the enrichment, detection and characterization of CTCs by biomarker analysis. Hence, comprehending the molecular markers, their role on each stage of cancer development and progression is essential to provide information that can help in future implementation of these biomarkers in clinical assistance. In this review, we studied the molecular markers most associated with PCa CTCs to better understand their function on tumorigenesis and metastatic cascade, the methodologies utilized to analyze these biomarkers and their clinical significance, in order to summarize the available information to guide researchers in their investigations, new hypothesis formulation and target choice for the development of new diagnostic and treatment tools.

The applicability of the "surprise question" as a prognostic tool in patients with severe chronic comorbidities in a university teaching outpatient setting

BACKGROUND

Life expectancy in recent decades has increased the prevalence of chronic diseases in the population, requiring an approach to new health topics, such as discussions on quality of life and expectations about death and dying. The concept of advance directives (ADs) gives individuals the opportunity to make known their decisions about the treatments they would like to receive at the end of life. Despite the recognition of relevance in clinical practice, the applicability of the concept presents challenges, including establishing the appropriate prognosis for each patient and the ideal time to approach the patient. Some prognostic tools were developed, such as the surprise question (SQ): "Would you be surprised if your patient died in 12 months?", which is used in some clinical settings to predict patient deaths and to make decisions regarding ADs. The main objective of the present study was to evaluate the behavior of second-year resident physicians (PGY-2) when the SQ was applied.

METHOD

In our observational study, from July 1, 2016, to February 28, 2017, (PGY-2) in the Internal Medicine Residency Program (IMRP) applied SQ to all patients with multiple and varied chronic no communicable comorbidities, who were followed up at the general medicine outpatient clinic (GMOC) of a tertiary university hospital in São Paulo- Brazil. The frequency of the outcome (death or non-death within 12 months) was analyzed by correlating it with the clinical data (impact of the studied variables).

RESULTS

Eight hundred forty patients entered the study. Fitfty-two of them (6.2%) died within one year. PGY-2 predicted that two hundred and fourteen patients (25.5% of total) would die within a year (answer No to SQ), of which, 32 (14.9%) did so. The correct residents' prognosis for the subgroup of 626 patients (answer "Yes" to SQ) was NPV = 96.8% (CI = 95.4%-98.2%) and PPV = 14.9% (CI 10.1%-19, 6%). Answering "Yes" to SQ correlated negatively to addressing AD while the outcomes death and the answer No to SQ were positively correlated, according to the number of comorbidities.

CONCLUSION

The SQ, in addition to care, contributed to health education, communication and care planning shared by the doctor and patient.

Prognostic disclosure in oncology - current communication models: a scoping review

BACKGROUND

Prognostic disclosure is essential to informed decision making in oncology, yet many oncologists are unsure how to successfully facilitate this discussion. This scoping review determines what prognostic communication models exist, compares and contrasts these models, and explores the supporting evidence.

METHOD

A protocol was created for this study using the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols extension for Scoping Reviews. Comprehensive literature searches of electronic databases MEDLINE, EMBASE, PsycINFO and Cochrane CENTRAL were executed to identify relevant publications between 1971 and 2020.

RESULTS

In total, 1532 articles were identified, of which 78 met inclusion criteria and contained 5 communication models. Three of these have been validated in randomised controlled trials (the Serious Illness Conversation Guide, the Four Habits Model and the ADAPT acronym) and have demonstrated improved objective communication measures and patient reported outcomes. All three models emphasise the importance of exploring patients' illness understanding and treatment preferences, communicating prognosis and responding to emotion.

CONCLUSION

Communicating prognostic estimates is a core competency skill in advanced cancer care. This scoping review highlights available communication models and identifies areas in need of further assessment. Such areas include how to maintain learnt communication skills for lifelong practice, how to assess patient and caregiver understanding during and after these conversations, and how to best scale these protocols at the institutional and national levels.

Mortality in hospitalised older patients: the WHALES short-term predictive score

OBJECTIVE

To develop and validate the WHALES screening tool predicting short-term mortality (3 months) in older patients hospitalised in an acute geriatric unit.

METHODS

Older patients transferred to an acute geriatric ward from June 2017 to December 2018 were included. The cohort was divided into two groups: derivation (n=664) and validation (n=332) cohorts. Cause for admission in emergency room, hospitalisation history within the previous year, ongoing medical conditions, cognitive impairment, frailty status, living conditions, presence of proteinuria on a urine strip or urine albumin-to-creatinine ratio and abnormalities on an ECG were collected at baseline. Multiple logistic regressions were performed to identify independent variables associated with mortality at 3 months in the derivation cohort. The prediction score was then validated in the validation cohort.

RESULTS

Five independent variables available from medical history and clinical data were strongly predictive of short-term mortality in older adults including age, sex, living in a nursing home, unintentional weight loss and self-reported exhaustion. The screening tool was discriminative (C-statistic=0.74 (95% CI: 0.67 to 0.82)) and had a good fit (Hosmer-Lemeshow goodness-of-fit test (X2 (3)=0.55, p=0.908)). The area under the curve value for the final model was 0.74 (95% CI: 0.67 to 0.82).

CONCLUSIONS AND IMPLICATIONS

The WHALES screening tool is a short and rapid tool predicting 3-month mortality among hospitalised older patients. Early identification of end of life may help appropriate timing and implementation of palliative care.

'I Don't Have a Crystal Ball' - Why Do Doctors Tend to Avoid Prognostication?

Uncertainty, fear to harm the patient, discomfort handling the discussion and lack of time are the most cited barriers to prognostic disclosure. Physicians can be reassured that patients desire the truth about prognosis and can manage the discussion without harm, including the uncertainty of the information, if approached in a sensitive manner. Conversational guides could provide support in preparing such difficult conversations. Communicating 'with realism and hope' is possible, and anxiety is normal for both patients and clinicians during prognostic disclosure. As a clinician pointed out: 'I had asked a mentor once if it ever got easier. - No. But you get better at it.'

Can primary palliative care education change life-sustaining treatment intensity of older adults at the end of life? A retrospective study

Background

Palliative care education has been carried out in some hospitals and palliative care has gradually developed in mainland China. However, the clinical research is sparse and whether primary palliative care education influence treatment intensity of dying older adults is still unknown. This study aims to explore the changes to the intensity of end-of-life care in hospitalized older adults before and after the implementation of primary palliative care education.

Methods

A retrospective study was conducted. Two hundred three decedents were included from Beijing Tongren Hospital’s department of geriatrics between January 1, 2014 to December 31, 2019. Patients were split into two cohorts with regards to the start of palliative care education. Patient demographics and clinical characteristics as well as analgesia use, medical resources use and provision of life-sustaining treatments were compared. We used a chi-square test to compare categorical variables, a t test to compare continuous variables with normal distributions and a Mann–Whitney U test for continuous variables with skewed distributions.

Results

Of the total participants in the study, 157(77.3%) patients were male. The median age was 88 (interquartile range; Q1-Q3 83–93) and the majority of patients (N = 172, 84.7%) aged 80 years or older. The top 3 causes of death were malignant solid tumor (N = 74, 36.5%), infectious disease (N = 74, 36.5%), and cardiovascular disease (N = 23, 11.3%). Approximately two thirds died of non-cancer diseases. There was no significant difference in age, gender, cause of death and functional status between the two groups (p > 0.05). After primary palliative care education, pain controlling drugs were used more (p < 0.05), fewer patients received electric defibrillation, bag mask ventilation and vasopressors (p < 0.05). There was no change in the length of hospitalization, intensive care admissions, polypharmacy, use of broad-spectrum antibiotics, blood infusions, albumin infusions, nasogastric/nasoenteric tubes, parenteral nutrition, renal replacement and mechanical ventilation (p > 0.05).

Conclusions

Primary palliative care education may promotes pain controlling drug use and DNR implementation. More efforts should be put on education about symptom assessment, prognostication, advance care planning, code status discussion in order to reduce acute medical care resource use and apply life-sustaining treatment appropriately.

The Importance of Prognostication: Impact of Prognostic Predictions, Disclosures, Awareness, and Acceptance on Patient Outcomes

OPINION STATEMENT

In the advanced cancer setting, patients, families, and clinicians are often confronted with an uncertain future regarding treatment outcomes and survival. Greater certainty on what to expect can enhance decision-making for many personal and healthcare issues. Although 70-90% of patients with advanced cancer desire open and honest prognostic disclosure, a small proportion do not want to know. Approximately half of patients with advanced cancer have an inaccurate understanding of their illness, which could negatively impact their decision-making. In this review, we use a conceptual framework to highlight 5 key steps along the prognostic continuum, including (1) prognostic formulation, (2) prognostic disclosure, (3) prognostic awareness, (4) prognostic acceptance, and (5) prognosis-based decision-making. We shall summarize the impact of prognostic predictions, disclosure, awareness, and acceptance on various patient and caregiver outcomes, such as hope, trust, anxiety, depression, chemotherapy use, and care planning. Based on where the patient is at along the prognostic continuum, we propose 5 different subgroups (avoidance: "I don't want to know"; discordant, "I never wanted to know"; anxious, "I don't know what's happening"; concerned, "I don't like this"; acceptance, "I know how to plan ahead"). Although prognostication is not necessarily a linear process, recognizing where the patient is at cognitively and emotionally along the prognostic continuum may allow clinicians to provide personalized interventions, such as specialist palliative care and psychology referral, towards personalizing prognostic disclosure, enhancing prognostic awareness, increasing prognostic acceptance, and supporting decision-making and, ultimately, improving patient outcomes.