Estimating Length of Survival in End-stage Cancer a Review of the Literature

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Inquiry Article

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

• P. C. Stone,
• A. Kalpakidou,
• C. Todd,
• J. Griffiths,
• V. Keeley,
• M. Spencer,
• P. Buckle,
• D. Finlay,
• V. Vickerstaff,
• R. Z. Omar

x

• Published: April 28, 2021
• https://doi.org/ten.1371/periodical.pone.0249297

Figures

Abstruse

Background

Prognosis in Palliative care Written report (PiPS) models predict survival probabilities in avant-garde cancer. PiPS-A (clinical observations but) and PiPS-B (additionally requiring blood results) consist of 14- and 56-day models (PiPS-A14; PiPS-A56; PiPS-B14; PiPS-B56) to create survival risk categories: days, weeks, months. The primary aim was to compare PIPS-B gamble categories against agreed multi-professional person estimates of survival (AMPES) and to validate PiPS-A and PiPS-B. Secondary aims were to assess acceptability of PiPS to patients, caregivers and health professionals (HPs).

Methods and findings

A national, multi-centre, prospective, observational, cohort study with nested qualitative sub-written report using interviews with patients, caregivers and HPs. Validation study participants were adults with incurable cancer; with or without capacity; recently referred to customs, hospital and hospice palliative care services across England and Wales. Sub-study participants were patients, caregivers and HPs. 1833 participants were recruited. PiPS-B adventure categories were equally accurate equally AMPES [PiPS-B accuracy (910/1484; 61%); AMPES (914/1484; 61%); p = 0.851]. PiPS-B14 discrimination (C-statistic 0.837) and PiPS-B56 (0.810) were excellent. PiPS-B14 predictions were besides high in the 57–74% run a risk group (Calibration-in-the-large [CiL] -0.202; Scale slope [CS] 0.840). PiPS-B56 was well-calibrated (CiL 0.152; CS 0.914). PiPS-A risk categories were less accurate than AMPES (p<0.001). PiPS-A14 (C-statistic 0.825; CiL -0.037; CS 0.981) and PiPS-A56 (C-statistic 0.776; CiL 0.109; CS 0.946) had excellent or reasonably good discrimination and scale. Interviewed patients (due north = 29) and caregivers (due north = 20) wanted prognostic information and considered that PiPS may help communication. HPs (north = 32) plant PiPS user-friendly and considered risk categories potentially helpful for controlling. The demand for a claret test for PiPS-B was considered a limitation.

Conclusions

PiPS-B risk categories are as accurate every bit AMPES fabricated by experienced doctors and nurses. PiPS-A categories are less accurate. Patients, carers and HPs regard PiPS as potentially helpful in clinical exercise.

Report registration

ISRCTN13688211.

Citation: Rock PC, Kalpakidou A, Todd C, Griffiths J, Keeley V, Spencer K, et al. (2021) The Prognosis in Palliative care Study II (PiPS2): A prospective observational validation study of a prognostic tool with an embedded qualitative evaluation. PLoS ONE xvi(4): e0249297. https://doi.org/10.1371/journal.pone.0249297

Editor: Tim Luckett, Academy of Engineering Sydney, AUSTRALIA

Received: Baronial xviii, 2020; Accepted: March 15, 2021; Published: April 28, 2021

Copyright: © 2021 Stone et al. This is an open access article distributed nether the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are bachelor in the UCL Data Repository at DOI https://doi.org/x.5522/04/13117589.

Funding: This paper presents independent research funded by the National Constitute for Wellness Research, Wellness Technology Assessment (HTA) programme (NIHR-HTA 13/twenty/01). Awarded to PS, CT, JG, VK and RO. The Written report Sponsor was the Joint Enquiry Office at UCL (https://www.ucl.air-conditioning.uk/joint-research-office/) The funders and sponsor had no part in study blueprint, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: Competing interests - Prof Stone, Prof Todd, Prof Omar, Prof Keeley and Dr Griffiths received grants from National Establish for Wellness Inquiry, during the conduct of the report; Members of the PiPS2 investigators group, in non-NHS units, received funds from NIHR (via UCL) for recruitment of participants. No authors declare whatever fiscal relationships with any organisations that might have an interest in the submitted work in the previous three years, and no other relationships or activities that could appear to have influenced the submitted work. This does not alter our adherence to PLOS 1 policies on sharing data and materials.

Introduction

Patients with avant-garde incurable cancer, their relatives and clinical teams frequently want to know how long patients will survive. Prognostic information can allow patients and families adequate fourth dimension to prepare for the stop of life [1] and tin help with admission to services, challenge benefits and identifying patients for inclusion in clinical trials [ii]. Unlike prognoses made at diagnosis, or prior to starting systemic anti-cancer therapies (SACT) [three], those made in a palliative intendance context unremarkably rely on subjective judgments of clinicians, which show a wide variation in reported accuracy [4]. The Palliative Prognostic (PaP) score, widely used in palliative cancer care, classifies patients into take chances groups based on thirty-day survival probabilities [v]. I limitation of PaP is that scores are heavily influenced by the weighting given to clinical predictions of survival (CPS). This can brand PaP challenging to use when clinicians are unsure near survival times. The Prognosis in Palliative care Written report (PiPS) predictor models were adult by members of our own group to provide prognostic estimates that do not rely on clinicians' intuition [vi]. PiPS-A14 and PiPS-A56 predict 14-24-hour interval and 56-day survival in patients when no claret results are bachelor and PiPS-B14 and PiPS-B56 predict 14-twenty-four hour period and 56-day survival in patients when claret results are available. The outputs from each PiPS-A and PiPS-B model can be combined to produce take a chance categories to predict death within "days" (fewer than fourteen days); "weeks" (14 to 56 days); or "months+" (greater than 56 days). The regression equations for each model and a clarification of the decision rules for creating risk categories are provided in on-line (S1 File). An on-line estimator is available (www.ucl.air-conditioning.uk/psychiatry/pips).

In the original development study, PiPS-A and PiPS-B models showed adept discrimination. PiPS-B adventure categories were more accurate than doctors' or nurses' survival estimates, only were not statistically significantly better than agreed multi-professional person estimates of survival (AMPES) [vi]. The principal objectives of the new report (PiPS2), were: to externally validate the original PiPS models [6], in a different cohort of patients, including comparing of PiPS-B take a chance categories against AMPES. Secondary objectives of PiPS2 were to: explore clinicians' views most usefulness; identify barriers and facilitators to clinical use; and sympathize how clinicians discuss prognostic data with patients and relatives or caregivers. Further secondary objectives included evaluation of other prognostic tools, the results of which volition exist published separately. Only data relating to validation of PiPS-A and PiPS-B are presented here.

Methods

The PiPS2 written report was a multi-centre, prospective, validation study of the previously published PiPS prognostic models [vi] in a new cohort of patients with a nested qualitative sub-study using face-to-face interviews with patients, caregivers and wellness professionals (HPs). The protocol has been published (ISRCTN 13688211) [vii] and was approved by Yorkshire and Humber-Leeds East Research Ideals Committee (16/YH/0132).

Sample

Validation written report.

PiPS2 involved patients from 27 United kingdom palliative care services (S1 Table). Patients were recruited from customs and hospital palliative intendance teams, and inpatient palliative intendance units. Different the original development study [6], the sample for PiPS2 included participants who were receiving palliative, non-hormonal SACT.

Patients who lacked capacity were included so that the sample resembled patients in clinical do, many of whom are confused, semi-conscious, or comatose, which are all poor prognostic features. Capacity to participate was assessed past the Primary Investigator (or delegate) at each site [8]. Eligible patients with capacity were approached past a member of the clinical squad, handed a Patient Information Canvas, and invited to provide written informed consent to participate. For patients without chapters a personal consultee was sought for advice. For patients with no personal consultee, the advice of a nominated consultee was sought.

Inclusion criteria.

1. Incurable cancer
2. xviii+ years
3. Recent referral to palliative care
4. For patients with capacity, ability to read and understand Patient Information Sail

Exclusion criterion. Treatment with curative intent, as determined by attending clinician.

Embedded qualitative report.

The patient and caregiver sample comprised patients with capacity and caregivers of patients, who had been invited to participate in the PiPS2 validation study. We purposively sampled patients and caregivers so that our sample was as varied as possible and represented a wide range of views and experiences. The clinician sample comprised HPs who routinely made prognostic predictions.

Information collection

Validation study.

Predictor information were obtained from review of medical notes, discussion with HPs and/or directly from patients. Data required for calculation of PiPS scores are shown in Table 1.

Information were nerveless on the site of chief tumor and metastases, and the nature of on-going cancer treatment. Pulse rate and presence or absenteeism of those symptoms required for calculation of PiPS scores were recorded: anorexia, dysphagia, dyspnoea, fatigue and weight loss. Abbreviated Mental Test Score (AMTS) was used to appraise cognitive function [9]. To summate PiPS scores in patients with capacity, information technology was only necessary to continue with AMTS until four items had been answered correctly. Patients who lacked capacity were not required to consummate AMTS and were attributed scores of zero. Performance status was assessed using the Eastern Branch Oncology Group (ECOG) calibration [10]. Global health status was rated using a 7-bespeak clinician-rated scale with scores ranging from very poor (= ane) to excellent (= 7). For patients with capacity, blood specimens were obtained. For patients without chapters, if relevant results were available within ±72 hours of study enrolment, then they were included in analyses.

The attention doctor and nurse independently estimated survival. When they agreed, this was deemed as the AMPES. When estimates were initially discordant, the doctor and nurse discussed, and the consensus prediction was regarded as the AMPES. Clinicians were asked: to provide estimates of survival in terms of "days" (0–xiii days); "weeks" (14–55 days); or "months+" (56+ days). Clinicians were also asked to provide seniority and feel.

Dates of decease were obtained from NHS Digital (https://digital.nhs.great britain/). If data were missing, sites were contacted to confirm survival condition. Information were obtained at least five months after the final participant was recruited.

Embedded qualitative report.

Qualitative interviews explored PiPS acceptability with patients, caregivers and HPs. Interviews used topic guides (S2 File) based on literature reviews, previous consultations with service users and recommendations for end-of-life inquiry [11]. Topic guides were iterative to allow new themes to be explored with future participants. Interviews were conducted by the Manchester based researcher (KS) who had experience in communicating with palliative patients/discussing sensitive topics. Interviews were kept brief (< one hour), took place at a venue of participant'due south choice and were audio-recorded for transcription.

Outcomes

Validation report.

Primary outcomes were survival (from date of enrolment), predictions of survival made by clinicians, PiPS-A and PiPS-B chance categories.

Assay and sample size calculation

Validation study.

Sample size. To detect 5% deviation (McNemar'south test) in correct predictions between PiPS-B take a chance categories and AMPES [6], 1267 patients with complete PiPS-B data were required (80% ability; five% significance). Assuming 25% of participants would lack capacity (thereby unable to provide PiPS-B data), and assuming 5% missing data, we estimated a sample of 1778 would be required.

It has been recommended that validation data for risk models should have at least 100 events [12]. There is no guidance on sample size calculation for multi-centre prognostic validation studies where there is potential of clustering. To be bourgeois, we inflated number of events to validate prognostic models to 150. Bold an result rate of 17.eight%, based on the original study, we estimated 843 patients would be required to validate PiPS-B gamble categories. Therefore, the proposed sample size for the primary issue was considered to exist adequate to also validate PiPS-A and PiPS-B.

Statistical analyses. Model discrimination was assessed using the C-statistic which measures a adventure model's ability to discriminate between those who experience the outcome of interest (survive a given number of days) and those who die. The C-statistic is calculated past considering all possible pairs of patients in the report and estimating the proportion of pairs in which the probability predicted by the model for survival is higher for the patient who actually survived compared to the patient who died. A value of i indicates the model has perfect discrimination, while a value of 0.five indicates the model discriminates no better than gamble [13].

Model calibration was assessed using calibration slope (CS) and calibration in the large (CiL) [14] based on a logistic model. The calibration gradient is a measure of understanding between the observed and predicted risk of the outcome across the whole range of predicted values obtained from the model and values close to one indicate proficient calibration. A slope <1 indicates that some predictions are also extreme (that low risks are underestimated, and loftier risks are overestimated) and a slope >1 indicates the range of predicted probabilities is likewise narrow. Calibration-in-the-large measures the extent that predictions are systematically too depression or too high. It compares the mean of all predicted risks with the hateful observed adventure and should ideally be 0 [13].

Calibration of PiPS-B14 and PiPS-B56 was besides assessed by comparing observed and predicted proportions of events graphically for each decile of predicted adventure. Overall proportion of deaths (calculated combining days, weeks and months+ run a risk category predictions) predicted correctly past PiPS-B run a risk categories was compared with overall proportion of deaths predicted correctly by clinicians using McNemar's test. For secondary analyses, significance level for McNemar'southward tests was amended (0·05/3 = 0·0167) using a Bonferroni adjustment to account for multiple comparisons. Bias due to missing data was investigated and multiple imputation using chained equations was used to impute missing predictor values. Statistical analyses were performed using Stata v14 [xv]. The original PiPS study did not include patients receiving illness-modifying treatments expected to prolong survival, whereas non all such patients were excluded from PiPS2. Nosotros therefore chose to validate PiPS both in all eligible participants and in the sub-grouping who were no longer receiving non-hormonal SACT.

Embedded qualitative study.

Sample size was determined by data saturation. Interview transcripts were analysed using the five stages of Framework Assay facilitated by NVivo x (https://world wide web.qsrinternational.com/nvivo) [16]. Kickoff, the research team became immersed in the information. Second, a thematic framework was developed based on the topic guide. Thirdly, transcripts were indexed (coded) line-by-line using the thematic framework, but remaining open up to emerging themes. Fourthly, data were entered into a chart so that coded extracts could exist attributed to private participants. Finally, participants' views were compared and contrasted, and data were presented schematically (mapping). Rival explanations were explored. An iterative and inductive arroyo to analysis was followed with data analysis occurring alongside information collection. The qualitative enquiry team met regularly to talk over the evolution of codes, themes, categories and theories about phenomena beingness studied.

Results

Validation written report

A total of 17014 patients were screened at 27 sites (August 2016-April 2018); 3299 were eligible and invited to participate; 1833 (1610 with; 223 without chapters) were enrolled. There were no significant differences in age or gender between patients who agreed or did not agree to participate. Patients who declined consent were not obliged to provide reasons. The about common explanations volunteered were: fatigue; distress; malaise; or competing priorities. Median survival of participants from enrolment was 45 days (IQ Range 16 to 140). Proportion of participants not receiving non-hormonal SACT was 1603/1833 (87%). There were consummate data on 89% (1484/1671) of participants, who were potentially available to have PiPS-B risk categories calculated (i.east. those with capacity and those without chapters with a recent blood examination). Only minor differences were found between results obtained from analyzing complete and imputed data (S3 File), and and then only consummate information results are presented here.

Participant characteristics are shown in Table 2.

PiPS.

Discrimination and scale of PiPS-A and PiPS-B, 14-day and 56-twenty-four hour period models including the sub-grouping of participants no longer receiving non-hormonal SACT are shown in Tabular array 3. All of the PiPS models showed good or excellent discrimination (C-Index ranging from 0.772 to 0.837).

Figs 1–4 propose that PiPS-A14, PiPS-A56 and PiPS-B56 models were well-calibrated. PiPS-B14 showed some degree of over fitting, with predictions slightly higher for 57%-74% risk grouping (CiL -0.202: -0.364 to -0.039; CS 0.840: 0.730 to 0.950).

Fig one. PiPS-A all patients.

Observed and predicted proportion of events using PiPS-A14 and PiPS-A56 in all patients. Vertical bars represent observed (dark grayness) and model-based predicted (light grey) probabilities of surviving either days (left) or months (right). The chance groups were created using the model-based predicted probabilities with an equal number of participants being allocated into each risk group. The predicted probabilities used for each risk group are shown. These groups are selected for the purpose of validation rather than clinical decision making. PiPS-A14: due north = 1802; Proportion of events = 1407/1802 (78.ane%). PiPS-A56: due north = 1803; Proportion of events = 815/1803 (45.two%).

https://doi.org/10.1371/journal.pone.0249297.g001

Fig 2. PiPS-B all patients.

Observed and predicted proportion of events using PiPS-B14 and PiPS-B56 in all patients. Vertical confined represent observed (dark grey) and model-based predicted (calorie-free grey) probabilities of surviving either days (left) or months (right). The risk groups were created using the model-based predicted probabilities with an equal number of participants beingness allocated into each take chances group. The predicted probabilities used for each risk grouping are shown. These groups are selected for the purpose of validation rather than clinical decision making. PiPS-B14: northward = 1497; Proportion of events = 1238/1497 (82·7%). One participant was removed from this analysis every bit their PiPS-B14 value was an outlier. PiPS-B56: n = 1498; Proportion of events = 727/1498 (48·5%).

https://doi.org/10.1371/periodical.pone.0249297.g002

Fig 3. PiPS-A patients receiving non-hormonal SACT.

Observed and predicted proportion of events using PiPS-A14 and PiPS-A56 in patients receiving non-hormonal SACT. Vertical bars represent observed (dark grayness) and model-based predicted (light greyness) probabilities of surviving either days (left) or months (right). The take a chance groups were created using the model-based predicted probabilities with an equal number of participants being allocated into each risk group. The predicted probabilities used for each risk group are shown. These groups are selected for the purpose of validation rather than clinical decision making. PiPS-A14: n = 1573; Proportion of events = 1206/1573 (76.7%). PiPS-A56: n = 1574; Proportion of events = 655/1574 (41.vi%).

https://doi.org/10.1371/journal.pone.0249297.g003

Fig 4. PiPS-B patients receiving non-hormonal SACT.

Observed and predicted proportion of events using PiPS-B14 and PiPS-B56 in patients receiving not-hormonal SACT. Vertical bars represent observed (nighttime grey) and model-based predicted (light gray) probabilities of surviving either days (left) or months (right). The risk groups were created using the model-based predicted probabilities with an equal number of participants existence allocated into each risk group. The predicted probabilities used for each risk group are shown. These groups are selected for the purpose of validation rather than clinical decision making. PiPS-B14: n = 1300; Proportion of events = 1063/1300 (81.eight%). PiPS-B56: n = 1299; Proportion of events = 586/1299 (45.1).

https://doi.org/ten.1371/periodical.pone.0249297.g004

PiPS-A and PiPS-B fourteen-mean solar day and 56-24-hour interval model predictions were combined to create risk categories representing whether patients would survive for "days", "weeks" or "months" (S2). The accurateness of predictions based on PiPS-A and PiPS-B risk categories, compared against accurateness of AMPES is shown in Table 4.

The majority of AMPES were made by palliative care doctors (360/431 = 85.5%) and nurses (755/771 = 98.3%) with a median (IQ range) of 9 (5–20) and 19 (nine–30) years' of professional experience respectively. In that location were no statistically meaning differences between percentage of correct AMPES and percentage of correct predictions based on PiPS-B run a risk categories when compared to all observed deaths, in either the whole sample or in the sub-group no longer receiving non-hormonal SACT. In contrast, a statistically significantly higher percentage of AMPES were right compared to PiPS-A risk categories, in both samples.

Qualitative study

Interviews were held with 29 patients, 20 caregivers and 32 clinicians. The majority of patients (25/29; 86%) and caregivers (17/20; 85%) were recruited from two hospices in one city. Details about the analysis are available as S4 File. Illustrative quotes are shown in Table v.

The majority of patient and caregivers conspicuously expressed a want for detailed prognostic information, simply often reported that clinicians were vague, over-optimistic and unwilling to deliver accurate information about length of survival. The principal reason for wanting detailed information was to put finances in order and make funeral plans. All patients and caregivers considered PiPS was: acceptable for employ in clinical practice; a potentially useful aid for predicting life expectancy; and helpful for initiating sensitive conversations with patients and caregivers. Participants confirmed that life expectancy expressed in terms of days, weeks or months was nigh meaningful.

Clinicians reported finding estimating length of survival complex and oft challenging, and the process of carrying prognostic information to patients and caregivers to exist difficult and uncomfortable. Clinicians explained they avoided giving specific timeframes in discussions because they did not know or did not want the discussion to have a negative affect on patient or caregiver. They admitted being vague with patients and caregivers, and considered that PiPS might be a useful communication aid for conveying prognostic information.

Clinicians considered PiPS might act as an educational training tool, especially for less experienced staff. They further commented on how PiPS might help inform determination-making, in relation to treatment options, belch planning and admission to hospices, or when commissioning care. Clinicians said that, even if PiPS run a risk categories were no more accurate than their own estimates, they would even so regard them equally potentially benign tools that could help meliorate conviction in making survival predictions.

Clinicians identified a number of barriers to using PiPS in clinical exercise. The need for a claret exam was a potential barrier to using PiPS-B. Two of the doctors considered introducing PiPS into clinical practice could be fourth dimension-consuming, both in completing the tool and finding fourth dimension to communicate results to patients and families. Other barriers related to clinicians preferring to rely on their own clinical judgement, or wishing to avert prognostic discussions with patients and caregivers.

Word

In the PiPS2 written report, the previously published PiPS-A and PiPS-B models for predicting 14-day and 56-day survival [six] showed good or first-class discrimination. The PiPS-A risk categories ("days", "weeks" and "months+") were significantly less accurate than AMPES, and should not exist used in clinical practise in their current form except in a research setting. Withal, the PiPS-B risk categories were as authentic as AMPES at identifying patients who were expected to live for "days", "weeks" or "months+". Our qualitative work confirms that, even though PiPS-B take a chance categories were no more accurate than AMPES, they may still be a valuable addition to clinical practice because they could provide some objectivity and reproducibility into an area that is currently dominated past intuition.

PiPS2 is one of the largest prospective palliative care studies undertaken in the UK. The study was powered to demonstrate a difference between the accurateness of PiPS-B risk categories and AMPES. Previous prognostic studies have simply validated various prognostic tools statistically and have reported their discrimination, calibration and accuracy. However, in clinical practice "usual care" relies on clinician predictions. Therefore, it is important that newly proposed prognostic tools should exist at least as accurate every bit this before being considered for adoption into clinical practice. Our qualitative sub-study was a swell forcefulness because it allowed a greater understanding of the perceived value of these tools to patients, their families and the health care professionals looking subsequently them. One potential limitation of this study is that PiPS is just designed to exist used in patients with avant-garde cancer. There is an increasing recognition of the need to widen the access to palliative care services to more patients with non-malignant disease. Nonetheless, it remains the case that cancer patients currently make up the majority of palliative intendance referrals and would benefit from improved prognostication. Our qualitative research was limited by the relatively few views that were represented from patients who did not want to participate, in the PiPS2 quantitative study (and then who may accept had less positive opinions about PiPS) and from customs patients (whose views may accept differed from infirmary or hospice-based participants). Some other potential limitation was that the same research boyfriend recruited patients to both the quantitative and qualitative studies, and conducted the qualitative interviews (in the Greater Manchester expanse). There was therefore a adventure of respondents reporting overly positive experiences. However, methodologically (and ethically) it was appropriate for the same researcher to recruit to the nested study because of the need to purposively sample according to certain characteristics. Participants were gravely ill and recruitment needed to be as sensitive as possible. Also, while KS was function of the enquiry team for PiPS2, she was not involved in the original evolution of PiPS, and had no vested interest in a positive or negative response from patients.

In the last five years, two farther groups accept validated PiPS. Baba and colleagues [17] studied 2426 Japanese palliative cancer patients, some of whom were receiving palliative chemotherapy. They reported PiPS performed too as in the original written report [half dozen], only they did not compare its accuracy to that of clinicians. The only previous study to have done and so [18], involving 202 Korean cancer patients, reported PiPS risk categories were more accurate than doctors' estimates of survival. Nonetheless, this study was limited past beingness relatively small (n = 202) and because it used doctors' uni-professional person survival estimates rather than AMPES as the comparator.

There were some differences between participants in the original PiPS development study and in PiPS2. In the original study, median survival of participants was 34 days and none were receiving illness-modifying treatments, in PiPS2 it was 45 days and 12.v% were receiving non-hormonal SACT. This may explain the small degree of model over-fitting that we found and suggests that some recalibration may be required to use these models in palliative patients who are still receiving non-hormonal SACT. Baba and colleagues [17] previously reported that PiPS performed as well in patients who were or were not receiving palliative cancer treatments. In the sub-group analysis for this written report we constitute that excluding participants receiving palliative treatments did not make whatsoever substantial differences to our results, although calibration of PiPS-A56 and PiPS-B56 both improved somewhat. The use of PiPS-B adventure categories in this sample resulted in a lower proportion of wrong prognoses than when applied to the whole sample (and fewer incorrect prognoses compared to AMPES). However, the divergence in overall accuracy betwixt PiPS-B risk categories and AMPES remained non-significant (p = 0.582).

In that location is evidence that AMPES are more accurate than predictions made by staff acting alone [19]. All the same, it is not always convenient or practical to obtain a second opinion when making a prognosis. It may also exist more demanding in terms of time and resources to do then. PiPS-B may provide clinicians with added conviction in their prognostic predictions, and could human activity every bit a "second opinion" in situations when one is not readily available. In this written report, AMPES were unremarkably estimated past experienced palliative care staff who may have been more than accurate than less experienced individuals. Therefore, PiPS-B could be of particular value in less specialist health care settings. PiPS-B could also provide more than objective criteria past which to make up one's mind entry to clinical trials for palliative care patients. Scores may aid to describe the example-mix of patients and facilitate comparison between clinical services. PiPS may also assist to standardise communication betwixt professionals and foster greater trust in the objectiveness of prognostic estimates between referrers to, and providers of, palliative care services. Certain benefits and services are influenced past clinical predictions of survival only clinician confidence in their own predictions is low and this may be a barrier to admission. Routine apply of validated prognostic tools like PiPS may improve admission to such services.

The PiPS prognostic tools are freely available to use equally an on-line reckoner (www.ucl.ac.united kingdom of great britain and northern ireland/psychiatry/pips). However, information technology is of import to note that, since the tools are nonetheless being evaluated and refined, the figurer should simply be used and interpreted past palliative care physicians and other suitably qualified health professionals. The estimator should not be used as a replacement for clinical judgement and nor should it be used by patients solitary.

Although PiPS-B risk categories are every bit accurate as AMPES, further research is needed to determine whether their routine utilize could better outcomes for palliative care patients. This will probably crave a large multi-centre randomised controlled trial comparing usual practice (using clinician predictions) against enhanced care (additionally incorporating PiPS-B predictions). I of the difficulties with the pattern of such a study will be identifying and measuring those clinical outcomes which are most likely to be afflicted past better prognostication. Until a prognostic tool has been shown to ameliorate clinically relevant outcomes information technology is unlikely to exist adopted into practise, this is 1 of the reasons why many palliative prognostic tools exist, merely few are routinely used. It is possible that in other clinical settings (e.yard. primary care or acute oncology), or amidst other practitioners (e.1000. junior doctors or nurses), the clinician predictions of survival may be less accurate. In those circumstances, PiPS-B may have a greater role equally an aid to prognostication. Further research could also attempt to optimise the performance of the PiPS tools, either by aligning of the "conclusion rules", recalibration or a combination of the 2.

Supporting data

Acknowledgments

PS, CT, VK, JG and RO designed the study. DAF and Pb provided a service users' perspective and contributed to the blueprint of patient data sheets and the oversight of the study. AK was the written report director and was responsible for solar day-to-day running of the report and data quality control. VV and RO performed the statistical analyses. KS contributed to the blueprint and assay of the qualitative study. All authors contributed to assay and interpretation of the results and reviewed and approved the manuscript. The PiPS2 Investigator Grouping authors are: A Ahamed (St Ann's Hospice); Thousand Bennett (St Gemma's Hospice); JW Boland (St Andrew's Hospice); A Chauhan (John Eastwood Hospice); S Cox (Pilgrims Hospice); A Davies (Royal Surrey County Infirmary); C Faull (LOROS Hospice); C Ferguson (Marie Curie West Midlands); A Gregory (St Catherine's Hospice); Northward Heron (Worcestershire Majestic Hospital); C Hookey (Douglas Macmillan Hospice); One thousand Lingesan (Bronglais General Hospital); M Maddocks (King's Higher Infirmary); O Minton (St George'due south Healthcare NHS Trust); S Onions (St Richard's Hospice); P Perkins (Sue Ryder Leckhampton Hospice); C Radcliffe (Birmingham St Mary's Hospice); G Burbridge (St Giles Hospice); J Todd (Princess Alice Hospice); J Vriens (Phyllis Tuckwell Hospice); A Wilcock (Nottingham University Hospital) and Southward Yardley (Central and N Westward London NHS Foundation Trust). PS is the guarantor of the study.

We would similar to thank the UCL PRIMENT Clinical Trials Unit for their support, Karolina Christodoulides and Jane Harrington for their help with authoritative tasks and data monitoring, and Florence Todd-Fordham for contributing to data quality command procedures. Nosotros would finally like to thank all the patients, caregivers and clinicians who participated in this report and our collaborators beyond participating sites. Thanks are besides due to our Report Steering Commission members: Professor Miriam Johnson (Chair of the committee); Dr Susan Charman (statistician); and Angela McCullagh (PPI representative).

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