The Prognosis in Palliative care Study II (PiPS2): A prospective observational validation study of a prognostic tool with an embedded qualitative evaluation

Examining Variability in Intra-Hospital Patient Referrals to Specialized Palliative Care: A Comprehensive Analysis of Disciplines and Mortality

Background: In Austria, specialized palliative care (SPC) access is limited, with unclear referral criteria, making it challenging to identify hospitalized patients requiring SPC and determine referral timing and mortality at the palliative care unit (PCU). Methods: This retrospective cohort study analyzed patients who underwent a palliative care (PC) needs assessment between March 2016 and November 2021 and were subsequently admitted to the PCU of Austria's largest academic hospital. Demographic, clinical, and standardized referral form data were used for analysis, employing descriptive statistics and logistic regression. Results: Out of the 903 assessed patients, 19% were admitted to the PCU, primarily cancer patients (94.7%), with lung (19%) and breast cancer (13%) being most prevalent. Common referral reasons included pain (61%) and nutritional problems (46%). Despite no significant differences in referral times, most patients (78.4%) died in the PCU, with varying outcomes based on cancer type. Referral reasons like pain (OR = 2.3), nutritional problems (OR = 2.4), and end-of-life care (OR = 6.5) were significantly associated with the outcome PCU mortality. Conclusions: This study underscores Austria's SPC access imbalance and emphasizes timely PC integration across disciplines for effective advance care planning and dignified end-of-life experiences in PCUs.

Predicting 60-Day Mortality in a Home-Care Service: Development of a New Inter-RAI 49-Frailty Index in Patients with Chronic Disease and without a Cancer Diagnosis

OBJECTIVE

Frailty Index (FI) is used to define the level of frailty in various clinical settings. Fifteen- and 26-item FIs have been demonstrated to predict 1-year mortality and intensity of care in home care (HC) and palliative home care (PHC). The objective of this study was to develop a new FI to predict the 60-day risk of death or transition to a PHC service after the initiation of an HC service in patients with chronic disease and without a cancer diagnosis.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

Patients 18 years and older followed in an HC service of a "Frailty Department-Local Palliative Care Network" from January 1, 2017, to October 31, 2021.

METHODS

A 49-item FI (FI-49) was developed selecting variables within the standardized international Residential Assessment Instrument assessments (interRAI-HC) and compared to existing FIs with 15 and 26 variables.

RESULTS

A total of 2099 patients were included in the study with a median age of 80.0 years (IQR: 72.0-86.0) and a predominantly female population (62.4%). Among these patients, 8% died or were transferred to PHC within the 60-day follow-up. The FI-49 demonstrated a higher ability to predict 60-day mortality (C index 0.8165, 95% CI 0.7848-0.8481) compared to the 26- and 15-item FI. An FI-49 cutoff of 0.33 was also selected to provide clinicians with a more practical approach (C-index of 0.7044, 95% CI 0.6796-0.7292).

CONCLUSION AND IMPLICATION

The FI-49 is a good predictor of short-term mortality or transition to palliative care among older patients referred to an HC service. The automatic calculation of this tool could facilitate more appropriate care planning and the correct allocation of healthcare resources, especially considering the rapid ageing of the population.

Quality of life assessment of patients undergoing palliative chemotherapy

BACKGROUND

Palliative chemotherapy uses systemic antineoplastic agents to treat an incurable malignancy. The results of cancer treatment need to be measured in terms of what physical and psychological limitations it brings to the patient. Therefore, there is a need to evaluate and establish the impact of the disease and its treatment on the patient's quality of life.

OBJECTIVE

To evaluate the quality of life of cancer patients undergoing palliative antineoplastic chemotherapy.

METHOD

A cross-sectional study with quali-quantitative unfolding. Three research instruments were applied: sociodemographic and clinical data; European Organization for Research and Treatment of Cancer quality of life questionnaire (EORTC QLQ-C30); interview script with predefined open questions.

RESULTS

The quality of life of the participants was considered good. Fatigue and financial difficulty were the main factors that interfered in daily activities. Three analytical categories emerged from the content analysis of the interviews: communication between the health team and the patient; patient's perception of their health/disease; concerns vs prospects.

CONCLUSION

The evaluation of quality of life for people who are at the end of life needs to balance technological and therapeutic advances, alongside aspects such as the perspective of these patients and the context of their lives.

Delivery Models and Health Economics of Supportive Care Services in England: A Multicentre Analysis

AIMS

Improvements in cancer treatment have led to more people living with and beyond cancer. These patients have symptom and support needs unmet by current services. The development of enhanced supportive care (ESC) services may meet the longitudinal care needs of these patients, including at the end of life. This study aimed to determine the impact and health economic benefits of ESC for patients living with treatable but not curable cancer.

MATERIALS AND METHODS

A prospective observational evaluation was undertaken over 12 months across eight cancer centres in England. ESC service design and costs were recorded. Data relating to patients' symptom burden were collected using the Integrated Palliative Care Outcome Scale (IPOS). For patients in the last year of life, secondary care use was compared against an NHS England published benchmark.

RESULTS

In total, 4594 patients were seen by ESC services, of whom 1061 died during follow-up. Mean IPOS scores improved across all tumour groups. In total, £1,676,044 was spent delivering ESC across the eight centres. Reductions in secondary care usage for the 1061 patients who died saved a total of £8,490,581.

CONCLUSIONS

People living with cancer suffer with complex and unmet needs. ESC services appear to be effective at supporting these vulnerable people and significantly reduce the costs of their 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".

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.

GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer

Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making.

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.

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.

The accuracy of clinician predictions of survival in the Prognosis in Palliative care Study II (PiPS2): A prospective observational study

BACKGROUND

Prognostic information is important for patients with cancer, their families, and clinicians. In practice, survival predictions are made by clinicians based on their experience, judgement, and intuition. Previous studies have reported that clinicians' survival predictions are often inaccurate. This study reports a secondary analysis of data from the Prognosis in Palliative care Study II (PiPS2) to assess the accuracy of survival estimates made by doctors and nurses.

METHODS AND FINDINGS

Adult patients (n = 1833) with incurable, locally advanced or metastatic cancer, recently referred to palliative care services (community teams, hospital teams, and inpatient palliative care units) were recruited. Doctors (n = 431) and nurses (n = 777) provided independent prognostic predictions and an agreed multi-professional prediction for each patient. Clinicians provided prognostic estimates in several formats including predictions about length of survival and probability of surviving to certain time points. There was a minimum follow up of three months or until death (whichever was sooner; maximum follow-up 783 days). Agreed multi-professional predictions about whether patients would survive for days, weeks or months+ were accurate on 61.9% of occasions. The positive predictive value of clinicians' predictions about imminent death (within one week) was 77% for doctors and 79% for nurses. The sensitivity of these predictions was low (37% and 35% respectively). Specific predictions about how many weeks patients would survive were not very accurate but showed good discrimination (patients estimated to survive for shorted periods had worse outcomes). The accuracy of clinicians' probabilistic predictions (assessed using Brier's scores) was consistently better than chance, improved with proximity to death and showed good discrimination between groups of patients with different survival outcomes.

CONCLUSIONS

Using a variety of different approaches, this study found that clinicians predictions of survival show good discrimination and accuracy, regardless of whether the predictions are about how long or how likely patients are to survive. Accuracy improves with proximity to death. Although the positive predictive value of estimates of imminent death are relatively high, the sensitivity of such predictions is relatively low. Despite limitations, the clinical prediction of survival should remain the benchmark against which any innovations in prognostication are judged.

STUDY REGISTRATION

ISRCTN13688211. http://www.isrctn.com/ISRCTN13688211.

Clinician estimates of prognosis: accuracy and impact-a retrospective inpatient hospice study

OBJECTIVE

To evaluate the accuracy and impact of clinicians' estimates of prognosis (CEP) in patients referred for hospice inpatient care.

METHODS

Retrospective review of 12 months' referrals to a London hospice unit. Data extracted included date of referral, admission and death and CEP.

RESULTS

N=383. Mean age 72 years (range 24-101). CEP accuracy: Median survival where CEP was 'days' (n=141) was 7 days (0-164); CEP 'weeks' (n=167) was 14 days (1-538); CEP 'months' (n=75) was 32 days (2-507). Kaplan-Meier survival curves showed significant difference between CEP of 'months' and 'weeks' (p<0.0001); 'months' and 'days' (p<0.0001); but not 'days' and 'weeks' (p=0.1). CEP impact: admission waiting time increased with increasing CEP: CEP 'days' (n=105) median 1 day (0-14); CEP 'weeks' (n=154) median 2 days (0-46); CEP 'months' (n=69) median 3 days (0-46). No significant difference was demonstrated in the number of discharge planning conversations between groups (0.9/patient).

CONCLUSIONS

CEP was accurate in over half of the cases but did not adequately discriminate between those with prognoses of days or weeks. CEP may affect the prioritisation given to patients by hospices. Inaccurate CEP on referral forms may influence other aspects of care; however, further research is needed.

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths

BACKGROUND

Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.

OBJECTIVES

To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.

METHODS

In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM).

RESULTS

Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.

CONCLUSION

In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.

Study recruitment factors in advanced cancer: the Prognosis in Palliative care Study II (PiPS2) - a multicentre, prospective, observational cohort project

OBJECTIVES

The Prognosis in Palliative care Study II (PiPS2) was a large multicentre observational study validating prognostic tools in patients with advanced cancer. Many palliative care studies fail to reach their recruitment target. To inform future studies, PiPS2 rigorously monitored and identified any potential recruitment barriers.

METHODS

Key recruitment stages (ie, whether patients were eligible for the study, approached by the researchers and whether consent was obtained for enrolment) were monitored via comprehensive screening logs at participating sites (inpatient hospices, hospitals and community palliative care teams). The reasons for patients' ineligibility, inaccessibility or decision not to consent were documented.

RESULTS

17 014 patients were screened across 27 participating sites over a 20-month recruitment period. Of those, 4642 (27%) were ineligible for participation in the study primarily due to non-cancer diagnoses. Of 12 372 eligible patients, 9073 (73%) were not approached, the most common reason being a clinical decision not to do so. Other reasons included patients' death or discharge before they were approached by the researchers. Of the 3299 approached patients, 1458 (44%) declined participation mainly because of feeling too unwell, experiencing severe distress or having other competing priorities. 11% (n=1841/17 014) of patients screened were enrolled in the study, representing 15% (n=1841/12 372) of eligible patients. Different recruitment patterns were observed across inpatient hospice, hospital and community palliative care teams.

CONCLUSIONS

The main barrier to recruitment was 'accessing' potentially eligible patients. Monitoring key recruitment stages may help to identify barriers and facilitators to enrolment and allow results to be put into better context.

TRIAL REGISTRATION NUMBER

ISRCTN13688211.

Prognostic models of survival in patients with advanced incurable cancer: the PiPS2 observational study

BACKGROUND

The Prognosis in Palliative care Study (PiPS) prognostic survival models predict survival in patients with incurable cancer. PiPS-A (Prognosis in Palliative care Study - All), which involved clinical observations only, and PiPS-B (Prognosis in Palliative care Study - Blood), which additionally required blood test results, consist of 14- and 56-day models that combine to create survival risk categories: 'days', 'weeks' and 'months+'.

OBJECTIVES

The primary objectives were to compare PIPS-B risk categories against agreed multiprofessional estimates of survival and to validate PiPS-A and PiPS-B. The secondary objectives were to validate other prognostic models, to assess the acceptability of the models to patients, carers and health-care professionals and to identify barriers to and facilitators of clinical use.

DESIGN

This was a national, multicentre, prospective, observational, cohort study with a nested qualitative substudy using interviews with patients, carers and health-care professionals.

SETTING

Community, hospital and hospice palliative care services across England and Wales.

PARTICIPANTS

For the validation study, the participants were adults with incurable cancer, with or without capacity to consent, who had been recently referred to palliative care services and had sufficient English language. For the qualitative substudy, a subset of participants in the validation study took part, along with informal carers, patients who declined to participate in the main study and health-care professionals.

MAIN OUTCOME MEASURES

For the validation study, the primary outcomes were survival, clinical prediction of survival and PiPS-B risk category predictions. The secondary outcomes were predictions of PiPS-A and other prognostic models. For the qualitative substudy, the main outcomes were participants' views about prognostication and the use of prognostic models.

RESULTS

For the validation study, 1833 participants were recruited. PiPS-B risk categories were as accurate as agreed multiprofessional estimates of survival (61%; p = 0.851). Discrimination of the PiPS-B 14-day model (c-statistic 0.837, 95% confidence interval 0.810 to 0.863) and the PiPS-B 56-day model (c-statistic 0.810, 95% confidence interval 0.788 to 0.832) was excellent. The PiPS-B 14-day model showed some overfitting (calibration in the large -0.202, 95% confidence interval -0.364 to -0.039; calibration slope 0.840, 95% confidence interval 0.730 to 0.950). The PiPS-B 56-day model was well-calibrated (calibration in the large 0.152, 95% confidence interval 0.030 to 0.273; calibration slope 0.914, 95% confidence interval 0.808 to 1.02). PiPS-A risk categories were less accurate than agreed multiprofessional estimates of survival (p < 0.001). The PiPS-A 14-day model (c-statistic 0.825, 95% confidence interval 0.803 to 0.848; calibration in the large -0.037, 95% confidence interval -0.168 to 0.095; calibration slope 0.981, 95% confidence interval 0.872 to 1.09) and the PiPS-A 56-day model (c-statistic 0.776, 95% confidence interval 0.755 to 0.797; calibration in the large 0.109, 95% confidence interval 0.002 to 0.215; calibration slope 0.946, 95% confidence interval 0.842 to 1.05) had excellent or reasonably good discrimination and calibration. Other prognostic models were also validated. Where comparisons were possible, the other prognostic models performed less well than PiPS-B. For the qualitative substudy, 32 health-care professionals, 29 patients and 20 carers were interviewed. The majority of patients and carers expressed a desire for prognostic information and said that PiPS could be helpful. Health-care professionals said that PiPS was user friendly and may be helpful for decision-making and care-planning. The need for a blood test for PiPS-B was considered a limitation.

LIMITATIONS

The results may not be generalisable to other populations.

CONCLUSIONS

PiPS-B risk categories are as accurate as agreed multiprofessional estimates of survival. PiPS-A categories are less accurate. Patients, carers and health-care professionals regard PiPS as potentially helpful in clinical practice.

FUTURE WORK

A study to evaluate the impact of introducing PiPS into routine clinical practice is needed.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN13688211.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 28. See the NIHR Journals Library website for further project information.