A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

Comparison of Simplified Palliative Prognostic Index and Palliative Performance Scale in Patients with Advanced Cancer in a Home Palliative Care Setting

Objective: The Palliative Performance Scale (PPS) has been reported to be as accurate as Palliative Prognostic Index (PPI). PPS is a component of the simplified PPI (sPPI). It is unknown whether PPS is as accurate as sPPI. This study aimed to compare the prognostic performance of the PPS and sPPI in patients with advanced cancer in a home palliative care setting in South Korea. Methods: This was a secondary analysis of a prospective cohort study that included Korean patients with advanced cancer who received home-based palliative care. We used the medical records maintained by specialized palliative care nurses. We computed the prognostic performance of PPS and sPPI using the area under the receiver operating characteristic curve (AUROC) and calibration plots for the 3- and 6-week survival. Results: A total of 80 patients were included, with a median overall survival of 47.0 days. The AUROCs of PPS were 0.71 and 0.69 at the 3- and 6-week survival predictions, respectively. The AUROCs of sPPI were 0.87 and 0.73 at the 3- and 6-week survival predictions, respectively. The calibration plot demonstrated satisfactory agreement across all score ranges for both the PPS and sPPI. Conclusions: This study showed that the sPPI assessed by nurses was more accurate than the PPS in a home palliative care setting in predicting the 3-week survival in patients with advanced cancer. The PPS can be used for a quick assessment.

Let Us Have the Conversation: Serious Illness Communication in Oncology: Definitions, Barriers, and Successful Approaches

Serious illness communications are crucial elements of care delivery for patients with cancer. High-quality serious illness communications are composed of open, honest discussions between patients, caregivers, and clinicians regarding patient's communication preferences, expected illness trajectory, prognosis, and risks and benefits of any recommended care. High-quality communication ideally starts at the time of a patients' cancer diagnosis, allows space for and response to patient emotions, elicits patients' values and care preferences, and is iterative and longitudinal. When integrated into cancer care, such communication can result in improved patient experiences with their care, care that matches patients' goals, and reduced care intensity at the end of life. Despite national recommendations for routine integration of these communication into cancer care, a minority of patients with cancer receive such communication. In this chapter, we describe elements of high-quality serious illness communication, patient-, clinician-, institution-, and payer-level barriers, and successful strategies that can routinely integrate such communication into cancer care delivery.

Predictors of Early Hospice or Death in Patients With Inoperable Lung Cancer Treated With Curative Intent

INTRODUCTION

Treatment for inoperable stage II to III non-small cell lung cancer (NSCLC) involves chemo-radiotherapy (CRT). However, some patients transition to hospice or die early during their treatment course. We present a model to prognosticate early poor outcomes in NSCLC patients treated with curative-intent CRT.

METHODS AND MATERIALS

Across a statewide consortium, data was prospectively collected on stage II to III NSCLC patients who received CRT between 2012 and 2019. Early poor outcomes included hospice enrollment or death within 3 months of completing CRT. Logistic regression models were used to assess predictors in prognostic models. LASSO regression with multiple imputation were used to build a final multivariate model, accounting for missing covariates.

RESULTS

Of the 2267 included patients, 128 experienced early poor outcomes. Mean age was 71 years and 59% received concurrent chemotherapy. The best predictive model, created parsimoniously from statistically significant univariate predictors, included age, ECOG, planning target volume (PTV), mean heart dose, pretreatment lack of energy, and cough. The estimated area under the ROC curve for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%.

CONCLUSIONS

This multivariate model identified a combination of clinical variables and patient reported factors that may identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at higher risk for early poor outcomes.

Time estimates in prognostic discussions: A conversation analytic study of hospice multidisciplinary team meetings

BACKGROUND

Recommendations state that multidisciplinary team expertise should be utilised for more accurate survival predictions. How the multidisciplinary team discusses prognoses during meetings and how they reference time, is yet to be explored.

AIM

To explore how temporality is conveyed in relation to patients' prognoses during hospice multidisciplinary team meetings.

DESIGN

Video-recordings of 24 hospice multidisciplinary team meetings were transcribed and analysed using Conversation Analysis.

SETTING/PARTICIPANTS

A total of 65 staff participating in multidisciplinary team meetings in a UK hospice from May to December 2021.

RESULTS

Team members conveyed temporality in three different ways. (i) Staff stated that a patient was dying as part of the patient's current health status. These formulations did not include a time reference per se but described the patient's current situation (as dying) instead. (ii) Staff used specific time period references where another specific reference had been provided previously that somehow constrained the timeframe. In these cases, the prognosis would conflict with other proposed care plans. (iii) Staff members used unspecific time period references where the reference appeared vague and there was greater uncertainty about when the patient was expected to die.

CONCLUSIONS

Unspecific time period references are sufficient for achieving meaningful prognostic talk in multidisciplinary teams. In-depth discussion and accurate prediction of patient prognoses are not deemed a priority nor a necessity of these meetings. Providing precise predictions may be too difficult due to uncertainty and accountability. The lack of staff pursuing more specific time references implies shared knowledge between staff and a context-specific use of prognostic estimates.

Comparison of Prognostic Abilities of Palliative Prognostic Index, Laboratory Prognostic Score, and Palliative Prognostic Score

CONTEXT

Few studies have compared the prognostic value of scoring systems based on physical and blood parameters in terminally ill patients with cancer.

OBJECTIVES

This study evaluated the prognostic abilities of Palliative Prognostic Index (PPI), Laboratory Prognostic Score (LPS), and Palliative Prognostic Score (PaP).

METHODS

We included 989 terminally ill patients with cancer who consulted for admission to our palliative care unit. We compared the discriminative abilities of PPI, LPS, and PaP for 7-, 14-, 30-, 60-, and 90-day mortality. Additionally, we compared the estimated median survival of PPI, LPS, and PaP with the actual survival (AS). The prediction accuracy was considered adequate if the ratio of estimated median survival in days to AS in days fell within the range of 0.66 to 1.33, optimistic when it exceeds 1.33, and pessimistic when it falls below 0.66.

RESULTS

The accuracies for 7-, 14-, 30-, 60-, and 90-day mortality were superior for PPI, LPS, LPS, PaP, and PaP (72%, 73%, 71%, 80%, and 82%), respectively, although the discriminative abilities for 7-, 14-, 30-, 60-, and 90-day mortality were similar among the three scoring systems. The prediction accuracy of survival (PAS) was similar among the three scoring systems with adequate, optimistic, and pessimistic rates of 36%-41%, 20%-46%, and 16%-38%, respectively. PAS was superior in actual survival for 14-59 days.

CONCLUSIONS

The prognostic abilities of PPI, LPS, and PaP were comparable. The most adequate estimation occurred for patients with AS for 14-59 days. A more accurate prognostic model is needed for patients with longer survival.

Are Surprise Questions and Probabilistic Questions by Nurses Useful in Home Palliative Care? A Prospective Study

Background: Surprise questions (SQs) are used as screening tools in palliative care. Probabilistic questions (PQs) are more accurate than temporal predictions. However, no study has examined the usefulness of SQs and PQs assessed by nurses. Objectives: To examine the accuracy of nurses' SQ and PQ assessments in patients with advanced cancer receiving home palliative care. Design: A prospective single-center cohort study. Setting/Subjects: Adult patients with advanced cancer who received palliative care at home in South Korea between 2019 and 2020. Measurements: Palliative care specialized nurses were asked the SQ, "Would you be surprised if the patient died in a specific timeframe?" and PQ, "What is the probability that this patient will be alive (0 to 100%) within a specific timeframe?" at the 1-, 2-, 4-, and 6-week timeframes at enrollment. We calculated the sensitivities and specificities of the SQs and PQs. Results: 81 patients were recruited with 47 days of median survival. The sensitivity, specificity, and overall accuracy (OA) of the 1-week SQ were 50.0, 93.2, and 88.9%, respectively. The accuracies for the 1-week PQ were 12.5, 100.0, and 91.3%, respectively. The 6-week SQ showed sensitivity, specificity, and OA of 84.6, 42.9, and 62.9%, respectively; the accuracies for the 6-week PQ were 59.0, 66.7, and 63.0%, respectively.Conclusion: The SQ and PQ showed acceptable accuracy in home palliative care patients. Interestingly, PQ showed higher specificity than SQ at all timeframes. The SQ and PQ assessed by nurses may be useful in providing additional prognostic information for home palliative care.

Tackling Guideline Non-concordance: Primary Care Barriers to Incorporating Life Expectancy into Lung Cancer Screening Decision-Making-A Qualitative Study

BACKGROUND

Primary care providers (PCPs) are often the first point of contact for discussing lung cancer screening (LCS) with patients. While guidelines recommend against screening people with limited life expectancy (LLE) who are less likely to benefit, these patients are regularly referred for LCS.

OBJECTIVE

We sought to understand barriers PCPs face to incorporating life expectancy into LCS decision-making for patients who otherwise meet eligibility criteria, and how a hypothetical point-of-care tool could support patient selection.

DESIGN

Qualitative study based on semi-structured telephone interviews.

PARTICIPANTS

Thirty-one PCPs who refer patients for LCS, from six Veterans Health Administration facilities.

APPROACH

We thematically analyzed interviews to understand how PCPs incorporated life expectancy into LCS decision-making and PCPs' receptivity to a point-of-care tool to support patient selection. Final themes were organized according to the Cabana et al. framework Why Don't Physicians Follow Clinical Practice Guidelines, capturing the influence of clinician knowledge, attitudes, and behavior on LCS appropriateness determinations.

KEY RESULTS

PCP referrals to LCS for patients with LLE were influenced by limited knowledge of the life expectancy threshold at which patients are less likely to benefit from LCS, discomfort estimating life expectancy, fear of missing cancer at the point of early detection, and prioritization of factors such as quality of life, patient values, clinician-patient relationship, and family support. PCPs were receptive to a decision support tool to inform and communicate LCS appropriateness decisions if easy to use and integrated into clinical workflows.

CONCLUSIONS

Our study suggests knowledge gaps and attitudes may drive decisions to offer screening despite LLE, a behavior counter to guideline recommendations. Integrating a LCS decision support tool that incorporates life expectancy within the electronic medical record and existing clinical workflows may be one acceptable solution to improve guideline concordance and increase confidence in selecting high benefit patients for LCS.

The utility of the surprise question by nurses to identify hospitalised older patients nearing the end-of-life and promotion of advance care planning: An interventional study

AIMS AND OBJECTIVES

To assess the prognostic accuracy of the surprise question (SQ) when used by nurses working in hospital wards to determine 1-year mortality in acutely hospitalised older patients.

BACKGROUND

The predictive accuracy of the SQ, when used by general nurses caring for older hospitalised patients, has not been comprehensively studied.

DESIGN

A prospective cohort study.

METHODS

This cohort study recruited consecutive 10,139 older patients (aged ≥65 years) who were admitted to Taipei City Hospital and were evaluated for the needs of palliative care in 2015. All patients were followed up for 12 months or until their death. The c-statistic value was calculated to indicate the predictive accuracy of the SQ and Palliative Care Screening Tool (PCST).

RESULTS

Of all participants, 18.8% and 18.6% had a SQ response of 'no' and a PCST score ≥4, respectively. After controlling for other covariates, an SQ response of 'no' (adjusted hazard ratio [aHR], 2.05; 95% confidence interval [CI], 1.83-2.31) and a PCST score ≥4 (AHR = 1.50; 95% CI: 1.29-1.75) were found to be the independent predictors for patients' 12-month mortality. The C-statistic values of the SQ and the PCST at recognising patients in their last year of life were .663 and .670, respectively. Moreover, there was moderate concordance (k = .44) between the SQ and the PCST in predicting 12-month mortality.

CONCLUSIONS

SQ response of 'no' and a PCST score ≥4 were independent predictors of 12-month mortality in older patients.

RELEVANCE TO CLINICAL PRACTICE

The SQ, when used by nurses working in hospital wards, is effective in identifying older patients nearing the end of life, as well as in providing advance care planning for patients.

PATIENT OR PUBLIC CONTRIBUTION

Patients' palliative care needs at admission were assessed by general nurses using the SQ and PCST.

Can We Make More Accurate Prognoses During Last Days of Life?

Background: Life expectancy prediction is important for end-of-life planning. Established methods (Palliative Performance Scale [PPS], Palliative Prognostic Index [PPI]) have been validated for intermediate- to long-term prognoses, but last-weeks-of-life prognosis has not been well studied. Patients admitted to a palliative care facility often have a life expectancy of less than three weeks. Reliable last-weeks-of-life prognostic tools are needed. Objective: To improve short-term survival prediction in terminally ill patients. Method: This prospective study included all patients admitted to a palliative care facility in Montreal, Canada, over one year. PPS and PPI were assessed until patients' death. Seven prognostic clinical signs of impending death (Short-Term Prognosis Signs [SPS]) were documented daily. Results: The analyses included 273 patients (76% cancer). The median survival time for a PPS ≤20% was 2.5 days, while for a PPS ≥50% it was 44.5 days, for a PPI >8 the median survival was 3.5 days and for a PPI ≤4 it was 38.5 days. Receiver operating characteristic curves showed a high accuracy in predicting survival. Median survival after the first occurrence of any SPS was below one week. Conclusions: This study demonstrated that the PPS and PPI perform well between one week and three months extending their usefulness to shorter term survival prediction. SPS items provided survival information during the last week of life. Using SPS along with PPS and PPI during the last weeks of life could enable a more precise short-term survival prediction across various end-of-life diagnoses. The translation of this research into clinical practice could lead to a better adapted treatment, the identification of a most appropriate care setting for patients, and improved communication of prognosis with patients and families.

A Prospective Study with Patients and Families on the Usefulness of Accurate Prognosis for Palliative Care Patients

Prediction of life expectancy in terminally ill patients is an important end-of-life care issue for patients, families and mental health workers during the last days of life. This study was conducted to examine the importance/usefulness for patients/families to have an accurate prognosis and its impact on planning their activities prior to death. All patients admitted during a period of one year were included. Patients' and families' viewpoints on the usefulness of an accurate prognosis was documented at admission. There were 285 patients in the cohort. The median time to death was 8 days. Most families (83%) rated the importance of an accurate prognosis as moderately (13%) to very much useful (70%). A total of 42% of patients were able to complete e the questionnaire. Among these, 58% found it moderately to very much useful. For families, having an accurate prognosis influenced the planning of visits (69%), communication/closure (42%) and spiritual needs/funeral arrangements (31%). Patients identified planning of visits (10%), communication/closure (12%), and goals/accomplishments (9%) as very important. Discussing the prognosis and its impact is very helpful for the mental health professionals to have open and honest conversations with patients/families to identify, prioritize and adapt treatment to achieve goals prior to death.

Aggressive anticancer treatment in the last 2 weeks of life

BACKGROUND

There is a concern that terminally ill cancer patients may be aggressively treated due to the rapidly growing possibilities of anticancer treatment. The aim of this study was to evaluate the use of anticancer treatment at the end of life (EoL).

MATERIALS AND METHODS

This retrospective study included adult patients with advanced solid cancers who were treated at the Institute of Oncology Ljubljana and died of cancer between January 2015 and December 2019. A multiple logistic regression model was used to assess an association between the aggressiveness of anticancer treatment (i.e. systemic therapy, radiotherapy and surgery) in the last 2 weeks of life and year of death, age at death, sex, prognosis of cancer and enrolment into the specialist palliative care (SPC).

RESULTS

We included 1736 patients in our analysis. Overall, 13.7% of patients were enrolled into the SPC and 14.4% received anticancer treatment in the last 2 weeks of life. The odds of receiving anticancer treatment significantly increased over time [odds ratio (OR) 1.15, 95% confidence interval (CI) 1.04-1.27]. There was an increased use of novel systemic therapy (e.g. small-molecule targeted therapy and immunotherapy) at the EoL. Older patients had significantly lower odds to receive anticancer treatment in the last 2 weeks of life as compared to younger patients (OR 0.96, 95% CI 0.95-0.98). As compared to patients receiving only a standard oncology care, those also enrolled into the SPC had significantly lower odds for anticancer treatment in the last 2 weeks of life (OR 0.22, 95% CI 0.12-0.43).

CONCLUSIONS

Terminally ill cancer patients have increased odds for receiving anticancer treatment, especially novel systemic therapies, in the last 2 weeks of life. Younger patients and those not enrolled into the SPC are at particular risk for anticancer treatment at the EoL.

Indian Society of Critical Care Medicine and Indian Association of Palliative Care Expert Consensus and Position Statements for End-of-life and Palliative Care in the Intensive Care Unit

End-of-life care (EOLC) exemplifies the joint mission of intensive and palliative care (PC) in their human-centeredness. The explosion of technological advances in medicine must be balanced with the culture of holistic care. Inevitably, it brings together the science and the art of medicine in their full expression. High-quality EOLC in the ICU is grounded in evidence, ethical principles, and professionalism within the framework of the Law. Expert professional statements over the last two decades in India were developed while the law was evolving. Recent landmark Supreme Court judgments have necessitated a review of the clinical pathway for EOLC outlined in the previous statements. Much empirical and interventional evidence has accumulated since the position statement in 2014. This iteration of the joint Indian Society of Critical Care Medicine-Indian Association of Palliative Care (ISCCM-IAPC) Position Statement for EOLC combines contemporary evidence, ethics, and law for decision support by the bedside in Indian ICUs.

How to cite this article

Mani RK, Bhatnagar S, Butola S, Gursahani R, Mehta D, Simha S, et al. Indian Society of Critical Care Medicine and Indian Association of Palliative Care Expert Consensus and Position Statements for End-of-life and Palliative Care in the Intensive Care Unit. Indian J Crit Care Med 2024;28(3):200-250.

Doctors and nurses subjective predictions of 6-month outcome compared to actual 6-month outcome for adult patients with spontaneous intracerebral haemorrhage (ICH) in neurocritical care: An observational study

Background

Acute spontaneous intracerebral haemorrhage is a devastating form of stroke. Prognostication after ICH may be influenced by clinicians' subjective opinions.

Purpose

To evaluate subjective predictions of 6-month outcome by clinicians' for ICH patients in a neurocritical care using the modified Rankin Scale (mRS) and compare these to actual 6-month outcome.

Method

We included clinicians' predictions of 6-month outcome in the first 48 h for 52 adults with ICH and compared to actual 6-month outcome using descriptive statistics and multilevel binomial logistic regression.

Results

35/52 patients (66%) had a poor 6-month outcome (mRS 4-6); 19/52 (36%) had died. 324 predictions were included. For good (mRS 0-3) versus poor (mRS 4-6), outcome, accuracy of predictions was 68% and exact agreement 29%. mRS 6 and mRS 4 received the most correct predictions. Comparing job roles, predictions of death were underestimated, by doctors (12%) and nurses (13%) compared with actual mortality (36%). Predictions of vital status showed no significant difference between doctors and nurses: OR = 1.24 {CI; 0.50-3.05}; (p = 0.64) or good versus poor outcome: OR = 1.65 {CI; 0.98-2.79}; (p = 0.06). When predicted and actual 6-month outcome were compared, job role did not significantly relate to correct predictions of good versus poor outcome: OR = 1.13 {CI;0.67-1.90}; (p = 0.65) or for vital status: OR = 1.11 {CI; 0.47-2.61}; p = 0.81).

Conclusions

Early prognostication is challenging. Doctors and nurses were most likely to correctly predict poor outcome but tended to err on the side of optimism for mortality, suggesting an absence of clinical nihilism in relation to ICH.

Estimated mortality risk and use of palliative care services among home care clients during the last 6 months of life: a retrospective cohort study

BACKGROUND

In Canada, only 15% of patients requiring palliative care receive such services in the year before death. We describe health care utilization patterns among home care users in their last 6 months of life to inform care planning for older people with varying mortality risks and evolving care needs as they decline.

METHODS

Using population health administrative data from Ontario, we performed a retrospective cohort study involving home care clients aged 50 years and older who received at least 1 interRAI (Resident Assessment Instrument) Home Care assessment between April 2018 and September 2019. We report the proportion of clients who used acute care, long-term care, and palliative home care services within 6 months of their assessment, stratified by their predicted 6-month mortality risk using a prognostic tool called the Risk Evaluation for Support: Predictions for Elder-life in their Communities Tool (RESPECT) and vital status.

RESULTS

The cohort included 247 377 adults, 11.9% of whom died within 6 months of an assessment. Among decedents, 50.6% of those with a RESPECT-estimated median survival of fewer than 3 months received at least 1 nonphysician palliative home care visit before death. This proportion declined to 38.7% and 29.5% among decedents with an estimated median survival between 3 and 6 months and between 6 and 12 months, respectively.

INTERPRETATION

Many older adults in Ontario do not receive any palliative home care before death. Prognostic tools such as RESPECT may improve recognition of reduced life expectancies and palliative care needs of individuals in their final years of life.

Prognostic model for overall survival of head and neck cancer patients in the palliative phase

BACKGROUND

Patients with head and neck squamous cell carcinoma (HNSCC) enter the palliative phase when cure is no longer possible or when they refuse curative treatment. The mean survival is five months, with a range of days until years. Realistic prognostic counseling enables patients to make well-considered end-of-life choices. However, physicians tend to overestimate survival. The aim of this study was to develop a prognostic model that calculates the overall survival (OS) probability of palliative HNSCC patients.

METHODS

Patients diagnosed with incurable HNSCC or patients who refused curative treatment for HNSCC between January 1st 2006 and June 3rd 2019 were included (n = 659). Three patients were lost to follow-up. Patients were considered to have incurable HNSCC due to tumor factors (e.g. inoperability with no other curative treatment options, distant metastasis) or patient factors (e.g. the presence of severe comorbidity and/or poor performance status).Tumor and patients factors accounted for 574 patients. An additional 82 patients refused curative treatment and were also considered palliative. The effect of 17 candidate predictors was estimated in the univariable cox proportional hazard regression model. Using backwards selection with a cut-off P-value < 0.10 resulted in a final multivariable prediction model. The C-statistic was calculated to determine the discriminative performance of the model. The final model was internally validated using bootstrapping techniques.

RESULTS

A total of 647 patients (98.6%) died during follow-up. Median OS time was 15.0 weeks (95% CI: 13.5;16.6). Of the 17 candidate predictors, seven were included in the final model: the reason for entering the palliative phase, the number of previous HNSCC, cT, cN, cM, weight loss in the 6 months before diagnosis, and the WHO performance status. The internally validated C-statistic was 0.66 indicating moderate discriminative ability. The model showed some optimism, with a shrinkage factor of 0.89.

CONCLUSION

This study enabled the development and internal validation of a prognostic model that predicts the OS probability in HNSCC patients in the palliative phase. This model facilitates personalized prognostic counseling in the palliative phase. External validation and qualitative research are necessary before widespread use in patient counseling and end-of-life care.

Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.

Identifying patients in need of palliative care: Adaptation of the Necesidades Paliativas CCOMS-ICO© (NECPAL) screening tool for use in Israel

OBJECTIVES

The Necesidades Paliativas CCOMS-ICO© (NECPAL) screening tool was developed to identify patients in need of palliative care and has been used in Israel without formal translation, reliability testing, or validation. Because cultural norms significantly affect subscales such as social vulnerability and health-care delivery, research is needed to comprehensively assess the NECPAL's components, adapt it, and validate it for an Israeli health-care setting. This study linguistically and culturally translated the NECPAL into Hebrew to examine cultural and contextual acceptability for use in the Israeli geriatric health sector. The newly adapted tool was measured for itemized and scale-level content validity, inter-rater reliability (IRR), and construct validity.

METHODS

The NECPAL was back-translated and its content validated by a 5-member expert panel for clarity and relevance, forming the Israeli-NECPAL (I-NECPAL). Six health-care professionals used the I-NECPAL with 25 post-acute geriatric patients to measure IRR. For construct validity, the known-groups method was used, as there is no "gold standard" method for identifying palliative needs for comparison with the NECPAL. The known groups were 2 fictitious cases, predetermined of palliative need. Thirty health-care professionals, blinded to the predetermined palliative status, used the I-NECPAL to determine whether a patient needs a palliative-centered plan of care.

RESULTS

The findings point to acceptable content and construct validity as well as IRR of the I-NECPAL for potential inclusion as a tool for identifying geriatric patients in need of palliative care. Content-validity assessment brought linguistic changes and the exclusion of the frailty parameter from the annex of chronic diseases. The kappa-adjusted scale-level content-validity index indicated a high level of content validity (0.96). IRR indicated a high level of agreement (all parameters with an "excellent-good" agreement level). The sensitivity (0.93), specificity (0.17), positive predictive value (0.53), and negative predictive value (0.71) revealed how heavily the scale weighed upon the surprise question. These metrics are improved when removing the surprise question from the instrument.

SIGNIFICANCE OF RESULTS

Similar to other countries, the Israeli health-care system is regulated by policies that portray the local beliefs and culture as well as evidence-based practice. The decision about when to switch a patient to a palliative-centered plan of care is one such example. It is thus of utmost importance that only locally adapted and vigorously tested screening tools be offered to health-care providers to assist in this decision. The I-NECPAL is the first psychometrically tested palliative needs identification tool for use in the geriatric population in Israel, on both a scale and an itemized level. The results indicate that it can immediately replace the current unvalidated version in use. Further research is needed to determine whether all parts of the scale are relevant for this patient population.

Examining assisted suicide and euthanasia through the lens of healthcare quality

Many people on both sides of the debate to legalise physician-hastened death are motivated by compassion and a desire to provide better end of life care for others. Assisted dying may include euthanasia and/or assisted suicide (EAS). It is legal in some jurisdictions and under debate in others including Ireland. EAS is a complex, sensitive and can be an emotive issue; detailed and nuanced examination of the subject is needed. To enhance this discussion, we examine EAS through the lens of quality. In examining EAS from this stance, we consider the action, along with the outcomes, the impact of the outcomes from other jurisdictions with legalised EAS, alongside the risks and the balancing measures used, in addition to considering the intervention itself. Progressive expansion of eligibility for EAS has occurred over time in the Netherlands, Belgium and Canada. Given the complexity of assessing coercion, the risks to persons in vulnerable groups (including older persons, persons with mental health conditions and persons with disabilities), the progressive expansion of eligibility for EAS, the lack of safety and the undermining of suicide prevention strategies, the current law is most protective of persons in vulnerable groups in the interest of social justice. Person-centred and compassionate care needs be prioritised with greater access and equitable access to primary and specialist palliative care and mental health care for persons with incurable and terminal illnesses and support for caregivers allowing patients to die naturally with optimised symptom control.

Racial and Ethnic Differences in Hospice Use Among Medicaid-Only and Dual-Eligible Decedents

Importance

Hospice care enhances quality of life for people with terminal illness and is most beneficial with longer length of stay (LOS). Most hospice research focuses on the Medicare-insured population. Little is known about hospice use for the racially and ethnically diverse, low-income Medicaid population.

Objective

To compare hospice use and hospice LOS by race and ethnicity among Medicaid-only individuals and those with dual eligibility for Medicare and Medicaid (duals) in the Connecticut Medicaid program who died over a 4-year period.

Design, Setting, and Participants

This retrospective population-based cohort study used Medicaid and traditional Medicare enrollment and claims data for 2015 to 2020. The study included Connecticut Medicaid recipients with at least 1 of 5 most common hospice diagnoses who died from 2017 to 2020.

Exposure

Race and ethnicity.

Main Outcomes and Measures

Hospice use (yes/no) and hospice LOS (1-7 days vs ≥8 days.) Covariates included sex, age, and nursing facility stay within 60 days of death.

Results

Overall, 2407 and 23 857 duals were included. Medicaid-only decedents were younger (13.8% ≥85 vs 52.5%), more likely to be male (50.6% vs 36.4%), more racially and ethnically diverse (48.7% non-Hispanic White vs 79.9%), and less likely to have a nursing facility stay (34.9% vs 56.1%). Race and ethnicity were significantly associated with hospice use and LOS in both populations: non-Hispanic Black and Hispanic decedents had lower odds of using hospice than non-Hispanic White decedents, and Hispanic decedents had higher odds of a short LOS. In both populations, older age and female sex were also associated with more hospice use. For duals only, higher age was associated with lower odds of short LOS. For decedents with nursing facility stays, compared with those without, Medicaid-only decedents had higher odds of using hospice (odds ratio [OR], 1.49; 95% CI, 1.24-1.78); duals had lower odds (OR, 0.60; 95% CI, 0.57-0.63). Compared with decedents without nursing facility stays, duals with a nursing facility stay had higher odds of short LOS (OR, 2.63; 95% CI, 2.43-2.85).

Conclusions and Relevance

Findings raise concerns about equity and timing of access to hospice for Hispanic and non-Hispanic Black individuals in these understudied Medicaid populations. Knowledge about, access to, and acceptance of hospice may be lacking for these low-income individuals. Further research is needed to understand barriers to and facilitators of hospice use for people with nursing facility stays.

A review of the utility of prognostic tools in predicting 6-month mortality in cancer patients, conducted in the context of voluntary assisted dying

BACKGROUND

Eligibility to access the Victorian voluntary assisted dying (VAD) legislation requires that people have a prognosis of 6 months or less (or 12 months or less in the setting of a neurodegenerative diagnosis). Yet prognostic determination is frequently inaccurate and prompts clinician discomfort. Based on functional capacity and clinical and biochemical markers, prognostic tools have been developed to increase the accuracy of life expectancy predictions.

AIMS

This review of prognostic tools explores their accuracy to determine 6-month mortality in adults when treated under palliative care with a primary diagnosis of cancer (the diagnosis of a large proportion of people who are requesting VAD).

METHODS

A systematic search of the literature was performed on electronic databases Medline, Embase and Cinahl.

RESULTS

Limitations of prognostication identified include the following: (i) prognostic tools still provide uncertain prognoses; (ii) prognostic tools have greater accuracy predicting shorter prognoses, such as weeks to months, rather than 6 months; and (iii) functionality was often weighted significantly when calculating prognoses. Challenges of prognostication identified include the following: (i) the area under the curve (a value that represents how well a model can distinguish between two outcomes) cannot be directly interpreted clinically and (ii) difficulties exist related to determining appropriate thresholds of accuracy in this context.

CONCLUSIONS

Prognostication is a significant aspect of VAD, and the utility of the currently available prognostic tools appears limited but may prompt discussions about prognosis and alternative means (other than prognostic estimates) to identify those eligible for VAD.

Endocrine treatment near the end of life among older women with metastatic breast cancer: a nationwide cohort study

Background

The appropriate time to discontinue chemotherapy at the end of life has been widely discussed. In contrast, few studies have investigated the patterns of endocrine treatment near death. In this study, we aimed to investigate the end-of-life endocrine treatment patterns of older women with metastatic breast cancer and explore characteristics associated with treatment.

Methods

A retrospective cohort study of all older women (age ≥65 years) with hormone receptor-positive breast cancer who died in Sweden, 2016 - 2020. We used routinely collected administrative and health data with national coverage. Treatment initiation was defined as dispensing during the last three months of life with a nine-month washout period, while continuation and discontinuation were assessed by previous use during the same period. We used log-binomial models to explore factors associated with the continuation and initiation of endocrine treatments.

Results

We included 3098 deceased older women with hormone receptor-positive breast cancer (median age 78). Overall, endocrine treatment was continued by 39% and initiated by 5% and of women during their last three months of life, while 31% discontinued and 24% did not use endocrine treatment during their last year of life. Endocrine treatment continuation was more likely among older and less educated women, and among women who had multi-dose drug dispensing, chemotherapy, and CDK4/6 use. Only treatment-related factors were associated with treatment initiation.

Conclusion

More than a third of women with metastatic breast cancer continue endocrine treatments potentially past the point of benefit, whereas late initiation is less frequent. Further research is warranted to determine whether our results reflect overtreatment at the end of life once patients' preferences and survival prognosis are considered.

Learning Individual Survival Models from PanCancer Whole Transcriptome Data

PURPOSE

Personalized medicine attempts to predict survival time for each patient, based on their individual tumor molecular profile. We investigate whether our survival learner in combination with a dimension reduction method can produce useful survival estimates for a variety of patients with cancer.

EXPERIMENTAL DESIGN

This article provides a method that learns a model for predicting the survival time for individual patients with cancer from the PanCancer Atlas: given the (16,335 dimensional) gene expression profiles from 10,173 patients, each having one of 33 cancers, this method uses unsupervised nonnegative matrix factorization (NMF) to reexpress the gene expression data for each patient in terms of 100 learned NMF factors. It then feeds these 100 factors into the Multi-Task Logistic Regression (MTLR) learner to produce cancer-specific models for each of 20 cancers (with >50 uncensored instances); this produces "individual survival distributions" (ISD), which provide survival probabilities at each future time for each individual patient, which provides a patient's risk score and estimated survival time.

RESULTS

Our NMF-MTLR concordance indices outperformed the VAECox benchmark by 14.9% overall. We achieved optimal survival prediction using pan-cancer NMF in combination with cancer-specific MTLR models. We provide biological interpretation of the NMF model and clinical implications of ISDs for prognosis and therapeutic response prediction.

CONCLUSIONS

NMF-MTLR provides many benefits over other models: superior model discrimination, superior calibration, meaningful survival time estimates, and accurate probabilistic estimates of survival over time for each individual patient. We advocate for the adoption of these cancer survival models in clinical and research settings.

What outcomes do studies use to measure the impact of prognostication on people with advanced cancer? Findings from a systematic review of quantitative and qualitative studies

BACKGROUND

Studies evaluating the impact of prognostication in advanced cancer patients vary in the outcomes they measure, and there is a lack of consensus about which outcomes are most important.

AIM

To identify outcomes previously reported in prognostic research with people with advanced cancer, as a first step towards constructing a core outcome set for prognostic impact studies.

DESIGN

A systematic review was conducted and analysed in two subsets: one qualitative and one quantitative. (PROSPERO ID: CRD42022320117; 29/03/2022).

DATA SOURCES

Six databases were searched from inception to September 2022. We extracted data describing (1) outcomes used to measure the impact of prognostication and (2) patients' and informal caregivers' experiences and perceptions of prognostication in advanced cancer. We classified findings using the Core Outcome Measures in Effectiveness Trials (COMET) initiative taxonomy, along with a narrative description. We appraised retrieved studies for quality, but quality was not a basis for exclusion.

RESULTS

We identified 42 eligible studies: 32 quantitative, 6 qualitative, 4 mixed methods. We extracted 70 outcomes of prognostication in advanced cancer and organised them into 12 domains: (1) survival; (2) psychiatric outcomes; (3) general outcomes; (4) spiritual/religious/existential functioning/wellbeing, (5) emotional functioning/wellbeing; (6) cognitive functioning; (7) social functioning; (8) global quality of life; (9) delivery of care; (10) perceived health status; (11) personal circumstances; and (12) hospital/hospice use.

CONCLUSION

Outcome reporting and measurement varied markedly across the studies. A standardised approach to outcome reporting in studies of prognosis is necessary to enhance data synthesis, improve clinical practice and better align with stakeholders' priorities.

Incorporating Value-Based Decisions in Breast Cancer Treatment Algorithms

Given the excellent prognosis and availability of evidence-based treatment, patients with early-stage breast cancer are at risk of overtreatment. In this review, we summarize key opportunities to incorporate value-based decisions to optimize the delivery of high-value treatment across the breast cancer care continuum.

Decoding biological age from face photographs using deep learning

Because humans age at different rates, a person's physical appearance may yield insights into their biological age and physiological health more reliably than their chronological age. In medicine, however, appearance is incorporated into medical judgments in a subjective and non-standardized fashion. In this study, we developed and validated FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs. FaceAge was trained on data from 58,851 healthy individuals, and clinical utility was evaluated on data from 6,196 patients with cancer diagnoses from two institutions in the United States and The Netherlands. To assess the prognostic relevance of FaceAge estimation, we performed Kaplan Meier survival analysis. To test a relevant clinical application of FaceAge, we assessed the performance of FaceAge in end-of-life patients with metastatic cancer who received palliative treatment by incorporating FaceAge into clinical prediction models. We found that, on average, cancer patients look older than their chronological age, and looking older is correlated with worse overall survival. FaceAge demonstrated significant independent prognostic performance in a range of cancer types and stages. We found that FaceAge can improve physicians' survival predictions in incurable patients receiving palliative treatments, highlighting the clinical utility of the algorithm to support end-of-life decision-making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, while age was not. These findings may extend to diseases beyond cancer, motivating using deep learning algorithms to translate a patient's visual appearance into objective, quantitative, and clinically useful measures.

Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice

PURPOSE

This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice.

METHODS

We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions.

RESULTS

Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population.

CONCLUSION

Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.

Performance of the Palliative Prognostic Index for cancer patients: A systematic review and meta-analysis

BACKGROUND

Clinician predicted survival for cancer patients is often inaccurate, and prognostic tools may be helpful, such as the Palliative Prognostic Index (PPI). The PPI development study reported that when PPI score is greater than 6, it predicted survival of less than 3 weeks with a sensitivity of 83% and specificity of 85%. When PPI score is greater than 4, it predicts survival of less than 6 weeks with a sensitivity of 79% and specificity of 77%. However, subsequent PPI validation studies have evaluated various thresholds and survival durations, and it is unclear which is most appropriate for use in clinical practice. With the development of numerous prognostic tools, it is also unclear which is most accurate and feasible for use in multiple care settings.

AIM

We evaluated PPI model performance in predicting survival of adult cancer patients based on different thresholds and survival durations and compared it to other prognostic tools.

DESIGN

This systematic review and meta-analysis was registered in PROSPERO (CRD42022302679). We calculated the pooled sensitivity and specificity of each threshold using bivariate random-effects meta-analysis and pooled diagnostic odds ratio of each survival duration using hierarchical summary receiver operating characteristic model. Meta-regression and subgroup analysis were used to compare PPI performance with clinician predicted survival and other prognostic tools. Findings which could not be included in meta-analyses were summarised narratively.

DATA SOURCES

PubMed, ScienceDirect, Web of Science, CINAHL, ProQuest and Google Scholar were searched for articles published from inception till 7 January 2022. Both retrospective and prospective observational studies evaluating PPI performance in predicting survival of adult cancer patients in any setting were included. The Prediction Model Risk of Bias Assessment Tool was used for quality appraisal.

RESULTS

Thirty-nine studies evaluating PPI performance in predicting survival of adult cancer patients were included (n = 19,714 patients). Across meta-analyses of 12 PPI score thresholds and survival durations, we found that PPI was most accurate for predicting survival of <3 weeks and <6 weeks. Survival prediction of <3 weeks was most accurate when PPI score>6 (pooled sensitivity = 0.68, 95% CI 0.60-0.75, specificity = 0.80, 95% CI 0.75-0.85). Survival prediction of <6 weeks was most accurate when PPI score>4 (pooled sensitivity = 0.72, 95% CI 0.65-0.78, specificity = 0.74, 95% CI 0.66-0.80). Comparative meta-analyses found that PPI performed similarly to Delirium-Palliative Prognostic Score and Palliative Prognostic Score in predicting <3-week survival, but less accurately in <30-day survival prediction. However, Delirium-Palliative Prognostic Score and Palliative Prognostic Score only provide <30-day survival probabilities, and it is uncertain how this would be helpful for patients and clinicians. PPI also performed similarly to clinician predicted survival in predicting <30-day survival. However, these findings should be interpreted with caution as limited studies were available for comparative meta-analyses. Risk of bias was high for all studies, mainly due to poor reporting of statistical analyses. while there were low applicability concerns for most (38/39) studies.

CONCLUSIONS

PPI score>6 should be used for <3-week survival prediction, and PPI score>4 for <6-week survival. PPI is easily scored and does not require invasive tests, and thus would be easily implemented in multiple care settings. Given the acceptable accuracy of PPI in predicting <3- and <6-week survival and its objective nature, it could be used to cross-check clinician predicted survival especially when clinicians have doubts about their own judgement, or when clinician estimates seem to be less reliable. Future studies should adhere to the reporting guidelines and provide comprehensive analyses of PPI model performance.

Developing a Core Outcome Set for Prognostic Research in Palliative Cancer Care: Protocol for a Mixed Methods Study

BACKGROUND

Studies exploring the impact of receiving end-of-life prognoses in patients with advanced cancer use a variety of different measures to evaluate the outcomes, and thus report often conflicting findings. The standardization of outcomes reported in studies of prognostication in palliative cancer care could enable uniform assessment and reporting, as well as intertrial comparisons. A core outcome set promotes consistency in outcome selection and reporting among studies within a particular population. We aim to develop a set of core outcomes to be used to measure the impact of end-of-life prognostication in palliative cancer care.

OBJECTIVE

This protocol outlines the proposed methodology to develop a core outcome set for measuring the impact of end-of-life prognostication in palliative cancer care.

METHODS

We will adopt a mixed methods approach consisting of 3 phases using methodology recommended by the Core Outcome Measure in Effectiveness Trials (COMET) initiative. In phase I, we will conduct a systematic review to identify existing outcomes that prognostic studies have previously used, so as to inform the development of items and domains for the proposed core outcome set. Phase II will consist of semistructured interviews with patients with advanced cancer who are receiving palliative care, informal caregivers, and clinicians, to explore their perceptions and experiences of end-of-life prognostication. Outcomes identified in the interviews will be combined with those found in existing literature and taken forward to phase III, a Delphi survey, in which we will ask patients, informal caregivers, clinicians, and relevant researchers to rate these outcomes until consensus is achieved as to which are considered to be the most important for inclusion in the core outcome set. The resulting, prioritized outcomes will be discussed in a consensus meeting to agree and endorse the final core outcome set.

RESULTS

Ethical approval was received for this study in September 2022. As of July 2023, we have completed and published the systematic review (phase I) and have started recruitment for phase II. Data analysis for phase II has not yet started. We expect to complete the study by October 2024.

CONCLUSIONS

This protocol presents the stepwise approach that will be taken to develop a core outcome set for measuring the impact of end-of-life prognostication in palliative cancer care. The final core outcome set has the potential for translation into clinical practice, allowing for consistent evaluation of emerging prognostic algorithms and improving communication of end-of-life prognostication. This study will also potentially facilitate the design of future clinical trials of the impact of end-of-life prognostication in palliative care that are acceptable to key stakeholders.

TRIAL REGISTRATION

Core Outcome Measures in Effectiveness Trials 2136; https://www.comet-initiative.org/Studies/Details/2136.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/49774.

Development and external validation of a predictive multivariable model for last-weeks survival of advanced cancer patients in the palliative home care setting (PACS)

PURPOSE

Various prognostic indexes have been proposed to improve physicians' ability to predict survival time in advanced cancer patients, admitted to palliative care (PC) with a survival probably to a few weeks of life, but no optimal score has been identified. The study aims therefore to develop and externally validate a new multivariable predictive model in this setting.

METHODS

We developed a model to predict short-term overall survival in cancer patients on the basis of clinical factors collected at PC admission. The model was developed on 1020 cancer patients prospectively enrolled to home palliative care at VIDAS Milan, Italy, between May 2018 and February 2020 and followed-up to June 2020, and validated in two separate samples of 544 home care and 247 hospice patients.

RESULTS

Among 68 clinical factors considered, five predictors were included in the predictive model, i.e., rattle, heart rate, anorexia, liver failure, and the Karnofsky performance status. Patient's survival probability at 5, 15, 30 and 45 days was estimated. The predictive model showed a good calibration and moderate discrimination (area under the receiver operating characteristic curve between 0.72 and 0.79) in the home care validation set, but model calibration was suboptimal in hospice patients.

CONCLUSIONS

The new multivariable predictive model for palliative cancer patients' survival (PACS model) includes clinical parameters routinely at patient's admission to PC and can be easily used to facilitate immediate and appropriate short-term clinical decisions for PC cancer patients in the home setting.

Triage Procedures for Critical Care Resource Allocation During Scarcity

Importance

During the COVID-19 pandemic, many US states issued or revised pandemic preparedness plans guiding allocation of critical care resources during crises. State plans vary in the factors used to triage patients and have faced criticism from advocacy groups due to the potential for discrimination.

Objective

To analyze the role of comorbidities and long-term prognosis in state triage procedures.

Design, Setting, and Participants

This cross-sectional study used data gathered from parallel internet searches for state-endorsed pandemic preparedness plans for the 50 US states, District of Columbia, and Puerto Rico (hereafter referred to as states), which were conducted between November 25, 2021, and June 16, 2023. Plans available on June 16, 2023, that provided step-by-step instructions for triaging critically ill patients were categorized for use of comorbidities and prognostication.

Main Outcomes and Measures

Prevalence and contents of lists of comorbidities and their stated function in triage and instructions to predict duration of postdischarge survival.

Results

Overall, 32 state-promulgated pandemic preparedness plans included triage procedures specific enough to guide triage in clinical practice. Twenty of these (63%) included lists of comorbidities that excluded (11 of 20 [55%]) or deprioritized (8 of 20 [40%]) patients during triage; one state's list was formulated to resolve ties between patients with equal triage scores. Most states with triage procedures (21 of 32 [66%]) considered predicted survival beyond hospital discharge. These states proposed different prognostic time horizons; 15 of 21 (71%) were numeric (ranging from 6 months to 5 years after hospital discharge), with the remaining 6 (29%) using descriptive terms, such as long-term.

Conclusions and Relevance

In this cross-sectional study of state-promulgated critical care triage policies, most plans restricted access to scarce critical care resources for patients with listed comorbidities and/or for patients with less-than-average expected postdischarge survival. This analysis raises concerns about access to care during a public health crisis for populations with high burdens of chronic illness, such as individuals with disabilities and minoritized racial and ethnic groups.

Validation of Modified Models of Objective Prognostic Score in Patients With Advanced Cancer

Background: The objective prognostic score (OPS) needs to be modified to reflect practical palliative care circumstances. Objectives: We aimed to validate modified models of OPS with few or no laboratory tests for patients with advanced cancer. Design: An observational study was performed. Setting/Subjects: A secondary analysis of an international, multicenter cohort study of patients in East Asia was performed. The subjects were inpatients with advanced cancer in the palliative care unit. Measurements: We developed two modified OPS (mOPS) models to predict two-week survival: mOPS-A consisted of two symptoms, two objective signs, and three laboratory results, while mOPS-B consisted of three symptoms, two signs, and no laboratory data. We compared the accuracy of the prognostic models using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Calibration plots for two-week survival and net reclassification indices (NRIs) were compared for the two models. Survival differences between higher and lower score groups of each model were identified by the log-rank test. Results: We included a total of 1796 subjects having median survival of 19.0 days. We found that mOPS-A had higher specificity (0.805-0.836) and higher AUROCs (0.791-0.797). In contrast, mOPS-B showed higher sensitivity (0.721-0.725) and acceptable AUROCs (0.740-0.751) for prediction of two-week survival. Two mOPSs showed good concordance in calibration plots. Considering NRIs, replacing the original OPS with mOPSs improved overall reclassification (absolute NRI: 0.47-4.15%). Higher score groups of mOPS-A and mOPS-B showed poorer survival than those of lower score groups (p < 0.001). Conclusions: mOPSs used reduced laboratory data and had relatively good accuracy for predicting survival in advanced cancer patients receiving palliative care.

Accuracy of clinical predictions of prognosis at the end-of-life: evidence from routinely collected data in urgent care records

BACKGROUND

The accuracy of prognostication has important implications for patients, families, and health services since it may be linked to clinical decision-making, patient experience and outcomes and resource allocation. Study aim is to evaluate the accuracy of temporal predictions of survival in patients with cancer, dementia, heart, or respiratory disease.

METHODS

Accuracy of clinical prediction was evaluated using retrospective, observational cohort study of 98,187 individuals with a Coordinate My Care record, the Electronic Palliative Care Coordination System serving London, 2010-2020. The survival times of patients were summarised using median and interquartile ranges. Kaplan Meier survival curves were created to describe and compare survival across prognostic categories and disease trajectories. The extent of agreement between estimated and actual prognosis was quantified using linear weighted Kappa statistic.

RESULTS

Overall, 3% were predicted to live "days"; 13% "weeks"; 28% "months"; and 56% "year/years". The agreement between estimated and actual prognosis using linear weighted Kappa statistic was highest for patients with dementia/frailty (0.75) and cancer (0.73). Clinicians' estimates were able to discriminate (log-rank p < 0.001) between groups of patients with differing survival prospects. Across all disease groups, the accuracy of survival estimates was high for patients who were likely to live for fewer than 14 days (74% accuracy) or for more than one year (83% accuracy), but less accurate at predicting survival of "weeks" or "months" (32% accuracy).

CONCLUSION

Clinicians are good at identifying individuals who will die imminently and those who will live for much longer. The accuracy of prognostication for these time frames differs across major disease categories, but remains acceptable even in non-cancer patients, including patients with dementia. Advance Care Planning and timely access to palliative care based on individual patient needs may be beneficial for those where there is significant prognostic uncertainty; those who are neither imminently dying nor expected to live for "years".

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital's palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.

Prognostic evaluation in patients with advanced cancer in the last months of life: ESMO Clinical Practice Guideline

•This ESMO Clinical Practice Guideline provides key recommendations for using prognostic estimates in advanced cancer. •The guideline covers recommendations for patients with cancer and an expected survival of months or less. •An algorithm for use of clinical predictions, prognostic factors and multivariable risk prediction models is presented. •The author group encompasses a multidisciplinary group of experts from different institutions in Europe, USA and Asia. •Recommendations are based on available scientific data and the authors’ collective expert opinion.

Clinicians' Prediction of Survival and Prognostic Confidence in Patients with Advanced Cancer in Three East Asian Countries

Background: Little is known about accuracy and confidence of clinicians' prediction of survival (CPS) in East-Asian countries. Objective: We aimed to examine accuracy of CPS for 7-, 21-, and 42-day survival in palliative inpatients and its association with prognostic confidence. Design: An international prospective cohort study in Japan (JP), Korea (KR), and Taiwan (TW). Setting/Subjects: Subjects were inpatients with advanced cancer admitted to 37 palliative care units in three countries. Measurements: Discrimination of CPS was investigated through sensitivity, specificity, overall accuracy, and area under the receiver operating characteristics curves (AUROCs) according to 7-, 21-, and 42-day survival. The accuracies of CPS were compared with those of Performance Status-based Palliative Prognostic Index (PS-PPI). Clinicians were instructed to rate confidence level on a 0-10-point scale. Results: A total of 2571 patients were analyzed. The specificity was highest at 93.2-100.0% for the 7-day CPS, and sensitivity was highest at 71.5-86.8% for the 42-day CPS. The AUROCs of the seven-day CPS were 0.88, 0.94, and 0.89, while those of PS-PPI were 0.77, 0.69, and 0.69 for JP, KR, and TW, respectively. As for 42-day prediction, sensitivities of PS-PPI were higher than those of CPS. Clinicians' confidence was strongly associated with the accuracy of prediction in all three countries (all p-values <0.01). Conclusions: CPS accuracies were highest (0.88-0.94) for the seven-day survival prediction. CPS was more accurate than PS-PPI in all timeframe prediction except 42-day prediction in KR. Prognostic confidence was significantly associated with the accuracy of CPS.

Prediction of Survival in Patients with Advanced Cancer: A Narrative Review and Future Research Priorities

This paper aimed to summarize the current situation of prognostication for patients with an expected survival of weeks or months, and to clarify future research priorities. Prognostic information is essential for patients, their families, and medical professionals to make end-of-life decisions. The clinician's prediction of survival is often used, but this may be inaccurate and optimistic. Many prognostic tools, such as the Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and Prognosis in Palliative Care Study, have been developed and validated to reduce the inaccuracy of the clinician's prediction of survival. To date, there is no consensus on the most appropriate method of comparing tools that use different formats to predict survival. Therefore, the feasibility of using prognostic scales in clinical practice and the information wanted by the end users can determine the appropriate prognostic tool to use. We propose four major themes for further prognostication research: (1) functional prognosis, (2) outcomes of prognostic communication, (3) artificial intelligence, and (4) education for clinicians.

Prognostic awareness in Japanese patients with advanced cancer: a follow-up cohort study

BACKGROUND

Patients with advanced cancer have been reported to be more likely to receive goal-concordant care if they have accurate prognostic awareness. However, many patients do not have this awareness. This study aimed to examine the prognostic awareness among Japanese patients with advanced cancer.

METHODS

This single-center, follow-up cohort study included Japanese patients with advanced cancer who received chemotherapy at Tohoku University Hospital between January 2015 and January 2016. Patients were surveyed at enrollment and followed up for clinical events for 5 years thereafter. We compared (i) the patients' prognostic awareness with both actual survival time and physician's prediction of survival and (ii) physician's prediction of survival time with actual survival. Factors associated with accurate prognostic awareness were identified by univariate analysis.

RESULTS

Of the 133 patients eligible for the study, 57 patients were analyzed. Only 10 (17.5%) patients had accurate prognostic awareness. Forty-three patients (75.4%) were optimistic about their prognosis; >80% of patients were more optimistic than their physicians about their prognosis. The physicians' predictions were accurate in for patients (37.5%). Accurate prognostic awareness was associated with physician's explanation of the prognosis and patients' perception of a good death.

CONCLUSIONS

A majority of the patients with advanced cancer in this study had prognostic awareness that was more optimistic in comparison with their actual survival, and most were more optimistic than their physicians about their prognosis. Further research is needed to develop programs to facilitate the discussion of life expectancy with patients in a manner that is consistent with their preferences.

Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study

Survival prediction is integral to oncology and palliative care, yet robust prognostic models remain elusive. We assessed the feasibility of combining actigraphy, sleep diary data, and routine clinical parameters to prognosticate. Fifty adult outpatients with advanced cancer and estimated prognosis of <1 year were recruited. Patients were required to wear an Actiwatch® (wrist actigraph) for 8 days, and complete a sleep diary. Univariate and regularised multivariate regression methods were used to identify predictors from 66 variables and construct predictive models of survival. A total of 49 patients completed the study, and 34 patients died within 1 year. Forty-two patients had disrupted rest-activity rhythms (dichotomy index (I < O ≤ 97.5%) but I < O did not have prognostic value in univariate analyses. The Lasso regularised derived algorithm was optimal and able to differentiate participants with shorter/longer survival (log rank p < 0.0001). Predictors associated with increased survival time were: time of awakening sleep efficiency, subjective sleep quality, clinician’s estimate of survival and global health status score, and haemoglobin. A shorter survival time was associated with self-reported sleep disturbance, neutrophil count, serum urea, creatinine, and C-reactive protein. Applying machine learning to actigraphy and sleep data combined with routine clinical data is a promising approach for the development of prognostic tools.

A Review of Clinical Signs and Symptoms of Imminent End-of-Life in Individuals With Advanced Illness

Background: World population is not only aging but suffering from serious chronic illnesses, requiring an increasing need for end-of-life care. However, studies show that many healthcare providers involved in the care of dying patients sometimes express challenges in knowing when to stop non-beneficial investigations and futile treatments that tend to prolong undue suffering for the dying person. Objective: To evaluate the clinical signs and symptoms that show end-of-life is imminent in individuals with advanced illness. Design: Narrative review. Methods: Computerized databases, including PubMed, Embase, Medline,CINAHL, PsycInfo, and Google Scholar were searched from 1992 to 2022 for relevant original papers written in or translated into English language that investigated clinical signs and symptoms of imminent death in individuals with advanced illness. Results: 185 articles identified were carefully reviewed and only those that met the inclusion criteria were included for review. Conclusion: While it is often difficult to predict the timing of death, the ability of healthcare providers to recognize the clinical signs and symptoms of imminent death in terminally-ill individuals may lead to earlier anticipation of care needs and better planning to provide care that is tailored to individual's needs, and ultimately results in better end-of-life care, as well as a better bereavement adjustment experience for the families.

Effect of a multidisciplinary ward-based intervention on end-of-life care for general medicine patients

OBJECTIVE

Providing good end-of-life (EOL) care for noncancer patients has been made a national priority in Singapore. A combined medical and nursing ward-based intervention known as the EOL care plan was piloted in a general medicine ward at our institution, aiming to guide key aspects of EOL care. The aim of this study is to assess the EOL care plan's effect on EOL care for general medicine patients.

METHOD

We conducted a retrospective cohort study on inpatients who died in a general ward under the discipline "General Medicine" from May to October 2019. We collected data around symptom management, rationalization of care and communication with families. The primary analysis compared care received by patients who died in the pilot ward with that of a control group of patients who died in other wards.

RESULTS

In total, 112 records were included in the analysis. Pain assessment was more common in the pilot ward compared with the control group (35.3% vs. 6.3%, p < 0.001), as were anti-psychotic prescriptions for delirium (64.7% vs. 24.4%, p = 0.001). Fewer patients received blood glucose monitoring in the last 48 h of life in the pilot ward (69.5% vs. 35.3%, p = 0.007). There were also less frequent parameters monitoring in the pilot ward (p < 0.004).

SIGNIFICANCE OF RESULTS

The implementation of the EOL care plan was associated with process-level indicators of better EOL care, suggesting that it could have a significant positive impact when implemented on a wider scale.

Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer

PURPOSE

Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer.

METHODS

We trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice.

RESULTS

Among 12,350 patients, 5,870 (47.5%) completed PRO assessments. Compared with EHR- and PRO-only algorithms, the EHR + PRO model improved predictive performance in both tertiary oncology (EHR + PRO v EHR v PRO: area under the curve [AUC] 0.86 [0.85-0.87] v 0.82 [0.81-0.83] v 0.74 [0.74-0.74]) and community oncology (area under the curve 0.89 [0.88-0.90] v 0.86 [0.85-0.88] v 0.77 [0.76-0.79]) practices.

CONCLUSION

Routinely collected PROs contain added prognostic information not captured by an EHR-based ML mortality risk algorithm. Augmenting an EHR-based algorithm with PROs resulted in a more accurate and clinically relevant model, which can facilitate earlier and targeted supportive care for patients with cancer.

The Surprise Question as a Trigger for Primary Palliative Care Interventions for Children with Advanced Heart Disease

There is significant uncertainty in describing prognosis and a lack of reliable entry criteria for palliative care studies in children with advanced heart disease (AHD). This study evaluates the utility of the surprise question-"Would you be surprised if this child died within the next year?"-to predict one-year mortality in children with AHD and assess its utility as entry criteria for future trials. This is a prospective cohort study of physicians and nurses caring for children (1 month-19 years) with AHD hospitalized ≥ 7 days. AHD was defined as single ventricle physiology, pulmonary vein stenosis or pulmonary hypertension, or any cardiac diagnosis with signs of advanced disease. Primary physicians were asked the surprise question and medical record review was performed. Forty-nine physicians responded to the surprise question for 152 patients. Physicians responded "No, I would not be surprised if this patient died" for 54 (36%) patients, 20 (37%) of whom died within 1 year, predicting one-year mortality with 77% sensitivity, 73% specificity, 37% positive predictive value, and 94% negative predictive value. Patients who received a "No" response had an increased 1-year risk of death (hazard ratio 7.25, p < 0.001). Physician years of experience, subspecialty, and self-rated competency were not associated with the accuracy of the surprise question. The surprise question offers promise as a bedside screening tool to identify children with AHD at high risk for mortality and help physicians identify patients who may benefit from palliative care and advance care planning discussions.

An online randomised controlled trial of prognosticating imminent death in advanced cancer patients: Clinicians give greater weight to advice from a prognostic algorithm than from another clinician with a different profession

BACKGROUND

A second opinion or a prognostic algorithm may increase prognostic accuracy. This study assessed the level to which clinicians integrate advice perceived to be coming from another clinician or a prognostic algorithm into their prognostic estimates, and how participant characteristics and nature of advice received affect this.

METHODS

An online double-blind randomised controlled trial was conducted. Palliative doctors, nurses and other types of healthcare professionals were randomised into study arms differing by perceived source of advice (algorithm or another clinician). In fact, the advice was the same in both arms (emanating from the PiPS-B14 prognostic model). Each participant reviewed five patient summaries. For each summary, participants: (1) provided an initial probability estimate of two-week survival (0% 'certain death'-100% 'certain survival'); (2) received advice (another estimate); (3) provided a final estimate. Weight of Advice (WOA) was calculated for each summary (0 '100% advice discounting' - 1 '0% discounting') and multilevel linear regression analyses were conducted.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04568629.

RESULTS

A total of 283 clinicians were included in the analysis. Clinicians integrated advice from the algorithm more than advice from another clinician (WOA difference = -0.12 [95% CI -0.18, -0.07], p < 0.001). There was no interaction between study arm and participant profession, years of palliative care or overall experience. Advice of intermediate strength (75%) was given a lower WOA (0.31) than advice received at either the 50% (WOA 0.40) or 90% level (WOA 0.43). The overall interaction between strength of advice and study arm on WOA was significant (p < 0.001).

CONCLUSION

Clinicians adjusted their prognostic estimates more when advice was perceived to come from a prognostic algorithm than from another clinician. Research is needed to understand how clinicians make prognostic decisions and how algorithms are used in clinical practice.

Impact of a prognostic model for overall survival on the decision-making process in a head and neck cancer multidisciplinary consultation meeting

BACKGROUND

Multidisciplinary decision-making in head and neck cancer care is complex and requires a tradeoff between prolonging survival and optimizing quality of life. To support prognostication and decision-making in head and neck cancer care, an individualized prognostic model for overall survival (OncologIQ) is available.

METHODS

By quantitative and qualitative research we have studied user value of OncologIQ and its impact on the decision-making process in a multidisciplinary consultation meeting.

RESULTS

Healthcare professionals experienced added value upon using prognostic estimates of survival from OncologIQ in half (47.5%) of the measurements. Significant impact on the decision making process was seen when OncologIQ was used for older patients, patients having a WHO performance score ≥ 2, or high tumor stage.

CONCLUSIONS

The prognostic model OncologIQ enables patient-centered decision-making in a multidisciplinary consultation meeting and was mostly valued in complex patients.

Time to End-of-Life of Patients Starting Specialised Palliative Care in Denmark: A Descriptive Register-Based Cohort Study

Increasing numbers of patients are being referred to specialised palliative care (SPC) which, in order to be beneficial, is recommended to last more than three months. This cohort study aimed to describe time to end-of-life after initiating SPC treatment and to explore potential regional variations. We used national register data from all Danish hospital SPC teams. We included patients who started SPC treatment from 2015-2018 to explore if time to end-of-life was longer than three months. Descriptive statistics were used to summarise the data and a generalised linear model was used to assess variations among the five Danish regions. A total of 27,724 patients were included, of whom 36.7% (95% CI 36.2-37.1%) had over three months to end-of-life. In the Capital Region of Denmark, 40.1% (95% CI 39.0-41.3%) had over three months to end-of-life versus 32.5% (95% CI 30.9-34.0%) in North Denmark Region. We conclude that most patients live for a shorter period of time than the recommended three months after initiating SPC treatment. This is neither optimal for patient care, nor the healthcare system. A geographical variation between regions was shown indicating different practices, patient groups or resources. These results warrant further investigation to promote optimal SPC treatment.

The interRAI CHESS scale is comparable to the palliative performance scale in predicting 90-day mortality in a palliative home care population

Background

Prognostic accuracy is important throughout all stages of the illness trajectory as it has implications for the timing of important conversations and decisions around care. Physicians often tend to over-estimate prognosis and may under-recognize palliative care (PC) needs. It is therefore essential that all relevant stakeholders have as much information available to them as possible when estimating prognosis.

Aims

The current study examined whether the interRAI Changes in Health, End-Stage Disease, Signs and Symptoms (CHESS) Scale is a good predictor of mortality in a known PC population and to see how it compares to the Palliative Performance Scale (PPS) in predicting 90-day mortality.

Methods

This retrospective cohort study used data from 2011 to 2018 on 80,261 unique individuals receiving palliative home care and assessed with both the interRAI Palliative Care instrument and the PPS. Logistic regression models were used to evaluate the relationship between the main outcome, 90-day mortality and were then replicated for a secondary outcome examining the number of nursing visits. Comparison of survival time was examined using Kaplan-Meier survival curves.

Results

The CHESS Scale was an acceptable predictor of 90-day mortality (c-statistic = 0.68; p < 0.0001) and was associated with the number of nursing days (c = 0.61; p < 0.0001) and had comparable performance to the PPS (c = 0.69; p < 0.0001). The CHESS Scale performed slightly better than the PPS in predicting 90-day mortality when combined with other interRAI PC items (c = 0.72; p < 0.0001).

Conclusion

The interRAI CHESS Scale is an additional decision-support tool available to clinicians that can be used alongside the PPS when estimating prognosis. This additional information can assist with the development of care plans, discussions, and referrals to specialist PC teams.

Are Prognostic Scores Better Than Clinician Judgment? A Prospective Study Using Three Models

CONTEXT

Several prognostic models such as the Palliative Performance Scale (PPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP) have been developed to complement clinician's prediction of survival (CPS). However, few studies with large scales have been conducted to show which prognostic tool had better performance than CPS in patients with weeks of survival.

OBJECTIVES

We aimed to compare the prognostic performance of the PPS, PPI, PaP, and CPS in inpatients admitted to palliative care units (PCUs).

METHODS

This study was part of a multi-center prospective observational study involving patients admitted to PCUs in Japan. We computed their prognostic performance using the area under the receiver operating characteristics curve (AUROC) and calibration plots for seven, 14-, 30- and 60-day survival.

RESULTS

We included 1896 patients with a median overall survival of 19 days. The AUROC was 73% to 84% for 60-day and 30-day survival, 75% to 84% for 14-day survival, and 80% to 87% for seven-day survival. The calibration plot demonstrated satisfactory agreement between the observational and predictive probability for the four indices in all timeframes. Therefore, all four prognostic indices showed good performance. CPS and PaP consistently had significantly better performance than the PPS and PPI from one-week to two-month timeframes.

CONCLUSION

The PPS, PPI, PaP, and CPS had relatively good performance in patients admitted to PCUs with weeks of survival. CPS and PaP had significantly better performance than the PPS and PPI. CPS may be sufficient for experienced clinicians while PPS may help to improve prognostic confidence for inexperienced clinicians.

Physicians' Conceptions of the Dying Patient: Scoping Review and Qualitative Content Analysis of the United Kingdom Medical Literature

Most people in high income countries experience dying while receiving healthcare, yet dying has no clear beginning, and contexts influence how dying is conceptualised. This study investigates how UK physicians conceptualise the dying patient. We employed Scoping Study Methodology to obtain medical literature from 2006-2021, and Qualitative Content Analysis to analyse stated and implied meanings of language used, informed by social-materialism. Our findings indicate physicians do not conceive a dichotomous distinction between dying and not dying, but construct conceptions of the dying patient in subjective ways linked to their practice. We argue that the focus of future research should be on exploring practice-based challenges in the workplace to understanding patient dying. Furthermore, pre-Covid-19 literature related dying to chronic illness, but analysis of literature published since the pandemic generated conceptions of dying from acute illness. Researchers should note the ongoing effects of Covid-19 on societal and medical awareness of dying.

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

Paracentesis for cancer-related ascites in palliative care: An international, prospective cohort study

BACKGROUND

Paracentesis is commonly undertaken in patients with cancer-related ascites.

AIM

To systematically investigate the symptomatic benefits and harms experienced by patients with cancer undergoing paracentesis using real-world data in the palliative care setting.

DESIGN

Prospective, multisite, observational, consecutive cohort study. Benefits and harms of paracentesis were assessed between 01/07/2018 and 31/02/2021 as part of routine clinical assessments by treating clinicians at four timepoints: (T0) before paracentesis; (T1) once drainage ceased; (T2) 24 h after T1 and (T3) 28 days after T1 or next paracentesis, if sooner.

SETTING/PARTICIPANTS

Data were collected from 11 participating sites across five countries (Australia, England, Hong Kong, Malaysia and New Zealand) on 111 patients undergoing paracentesis via a temporary (73%) or indwelling (21%) catheter: 51% male, median age 69 years, Australia-modified Karnofsky Performance Score 50.

RESULTS

At T1 (n = 100), symptoms had improved for most patients (81%), specifically abdominal distension (61%), abdominal pain (49%) and nausea (27%), with two-thirds experiencing improvement in ⩾2 symptoms. In the remaining patients, symptoms were unchanged (7%) or worse (12%). At least one harm occurred in 32% of patients, the most common being an ascitic leak (n = 14). By T3, 89% of patients had experienced some benefit and 36% some harm, including four patients who experienced serious harm, one of which was a fatal bowel perforation.

CONCLUSION

Most patients obtained rapid benefits from paracentesis. Harms were less frequent and generally mild, but occasionally serious and fatal. Our findings help inform clinician-patient discussions about the potential outcomes of paracentesis in this frail population.