Research agenda for prognostication: what´s next?

Paul Glare Memorial Sloan Kettering Cancer Center

New York, NY, USA June 2014

Aims

• Review ‘state of the science’ of prognostication in palliative care

• Describe a broader conceptualization of prognostication in advanced disease

• What can we learn about prognostication from other academic disciplines?

I want to die at

home, Paul

Newman tells his

family as he's given

'weeks to live' By Andy Dolan Last updated at 6:43 PM on 08th August 2008

Died 27th September 2008

Frail: Paul Newman is wheeled

out of hospital

I want to die at

home, Paul

Newman tells his

family as he's given

'weeks to live' By Andy Dolan Last updated at 6:43 PM on 08th August 2008

Died 27th September 2008

Frail: Paul Newman is wheeled

out of hospital

Using prognostic information

Communicating the prognosis

Formulating the prognosis

1. State of the science of

prognosis

1. State of the science of

prognosis

Hypothesis

PS and weight loss = ‘final pathway’

Translational research

Predictive factors

e.g. CRP

Clinical trials

Prognostic models

e.g. PIPS

Systematic Reviews:

Pred factors, CPS

Guidelines/

Implementation

Gold Std. Framework

The Research Cycle & Prognosis

New Hypothesis

Disease related factors more relevant in the clinic

Translational research

-basic science

-qualitative, observational

Clinical trials

Systematic reviews

Guideline

Implementation

RESEARCH METHODS

& REPORTING

Prognosis and prognostic research: what, why, and how? Karel G M Moons,1 Patrick Royston,2 Yvonne Vergouwe,1 Diederick E Grobbee,1 Douglas G Altman3

Doctors have little specific research to draw on when predicting outcome. In this first article in a series Karel Moons and colleagues explain why research into prognosis is important and how to design such research Cite this as: BMJ 2009;338:b375 doi: 10.1136/bmj.b375 This article is the first in a series of four aiming to provide an accessible overview of the principles and methods of prognostic research

Setting a research agenda

on prognosis

A. Research on formulating the prognosis

B. Research on communicating the prognosis

C. Research on using prognostic information optimally

• What we already know

• Opportunities

• Challenges

A. Formulating the prognosis

What we know • Clinical predictions are inaccurate

– Poorly calibrated but good discriminative power

– Dr-Pt relationship is a factor

– IDT better than solo practitioners

– Probabilistic superior to temporal

• Predictive Factors ought to be superior – PS, symptoms, biomarkers

• QOL, psychosocial

• Combined into models

• Models (slightly) superior to subjective

Opportunities, Challenges

• Methodology: inception cohorts, hard vs soft factors, missing data

• Still a lot of unexplained variance

• mood, QOL, SES, spiritual

• Novel biomarkers

• “Nose & tail” of KM curve (M. Downing)

• Validating existing models

• Different populations e.g. hospital, ICU, clinic, Peds

• Changes in prognosis over time

• Other diseases

Prognosisresearchstrategy(PROGRESS)1:A frameworkforresearchingclinicaloutcomes OPENACCESS Understanding and improving the prognosis of a disease or health condition is a priority in clinical research and practice. In this article, the authors introduce a framework of four interrelated themes in prognosis research, describe the importance of the first of these themes (understanding future outcomes in relation to current diagnostic and treatment practices), and introduce recommendations for the field of prognosis research HarryHemingwayprofessorofclinicalepidemiology1,PeterCroftprofessorofepidemiology2,Pablo Perelclinicalseniorlecturer3,JillAHaydenassistantprofessor4,KeithAbramsprofessorofmedical statistics5,AdamTimmisprofessorofclinicalcardiology6,AndrewBriggsLindsaychairinhealth policy&economicevaluation7,RuzanUdumyanresearchassistant1,KarelGMMoonsprofessor ofclinicalepidemiology8,EwoutWSteyerbergprofessorofmedicaldecisionmaking9,IanRoberts professorofepidemiologyandpublichealth3,SaraSchroterseniorresearcher10,DouglasGAltman professorofstatisticsinmedicine11,RichardDRileyseniorlecturerinmedicalstatistics12,forthe PROGRESSGroup

BMJ BMJ 2013; 346: e5595doi:10.1136/bmj.e5595

RESEARCH METHODS & REPORTING

Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes

Understanding and improving the prognosis of a disease or health condition is a priority in clinical research and practice. In this article, the authors introduce a framework of four interrelated themes in prognosis research, describe the importance of the first of these themes (understanding future outcomes in relation to current diagnostic and treatment practices), and introduce recommendations for the field of prognosis research Harry Hemingway Professor of clinical epidemiology1, Peter Croft professor of epidemiology 2, Pablo ….

B. Communicating the prognosis

What we know • most cancer patients want

prognostic information , good or bad

• Information needs vary during illness

• “Clinicians underestimate what is wanted and overestimate what was said and understood”

• Information be individualized • Probabilities better than

temporal predictions – Survival curves are exponential and

quartiles and deciles are simple multiples of the median

• Question prompt lists may help promote discussion

Opportunities and challenges

• Presenting the data to diverse patients

• Training non-specialists

• Nomograms, calculators, apps

• Impact of communication on patient/family mood, QOL, satisfaction

C. Using the prognosis

Using the prognosis

• Clinicians do it all the time (conscious & subconscious)

• SUPPORT Study – Prognostic beliefs and

utilization of aggressive EOL care

• MGH Study of palliative care and lung cancer – Discussing prognosis and

utilization data

Opportunities, Challenges

• Prognostic models as clinical prediction tools e.g. Ottawa ankle rule

• Px as a referral trigger/admission criterion

• RCT’s of Subjective vs. Actuarial: utilization, outcomes, costs, satisfaction, survival

2. Prognostication beyond survival

A typical day on rounds…

• I’ll operate if you think she’ll be alive in 3 months

• There is a 20% chance of the hypercalcemia recurring

• Unfortunately, he’ll never walk again

• No, not every one with cancer develops pain

• Call me if the nausea doesn’t wear off in a few days

• Your insurance will probably want PA for this

• Your mother is also at an increased risk of dying in the next 18 months

The 5 D’s of prognosis

a. Death b. Disease progression/recurrence c. Discomfort/disability d. Drug toxicity e. Dollars (cost)

Fries & Ehrlich

1981 • Derivatives

Glare & Christakis 1999

The 5 D’s of prognosis

a. Death b. Disease progression/recurrence c. Discomfort/disability d. Drug toxicity e. Dollars (cost)

Fries & Ehrlich

1981 f. Derivatives

Glare & Christakis 2004

Clinical epidemiology and prognosis

the relative probabilities that a patient will develop each of the alternative outcomes of the natural history of his/her disease

Sackett 1991 the probability of the specified outcome (event or

quantity) with different combinations of predictors in a well defined population

Moons 2009

Clinical epidemiology and prognosis

the relative probabilities that a patient will develop each of the alternative outcomes of the natural history of his/her disease

Sackett 1991 the probability of the specified endpoint (event or

quantity) with different combinations of predictors in a well defined population

Moons 2009

b. Disease progression/recurrence

PHASE Stable Transitional End of Life

Dead

Stable 94% 4% <1% <1%

Transitional

11% 60% 5% 24%

Dead - - -

100%

Estimated One-Month Transition Probabilities (%)

Sutradhar et al. JPSM 2013

b. Discomfort/disability

Longer time to reach stable pain control: -young age (<60), - neuropathic pain -incidental pain

Higher Final Morphine dose: -neuropathic pain, -incidental pain, -psychological distress - addiction,

d. Drug toxicity

Predictive: lung cancer (OR 2.5) Protective : males (OR 0.4), steroids (OR 0.5)

e. Dollars (cost of care)

Type of pain management Effectiveness Cost/month

Guideline based care 80% $579

Oncologist based care 55% $466

Usual care 30% $315

f. Derivatives

Use of prognostic models • Inform individuals about the future course of their illness, or their risk of

developing illness

• Guide physicians and patients in joint decisions on further treatment, if any. – Nottingham prognostic index & long term risk of cancer recurrence or death in breast cancer

patients – APACHE score & hospital mortality in critically ill patients – models for predicting postoperative nausea and vomiting. – Framingham cardiovascular risk score & cholesterol lowering and antihypertensive drugs.

• Select relevant patients for therapeutic research. – RCT of tamoxifen to prevent breast cancer. – RCT of radiotherapy after breast conserving resection in patients with a low risk of cancer

recurrence.

• Compare differences in performance between hospitals. For example, the – clinical risk index for babies (CRIB) to compare performance and mortality among neonatal

intensive care units. – model to predict the hospital standardized mortality ratio to explain differences between

English hospitals.

Moons K, BMJ 2009

3. Crosswalk with other schools

e.g. Meteorology

Decision making to evacuate or stay put

• NWS statements/evacuation orders are important

• Residents evaluate own risk and make own protective decisions – Warnings of “certain death” often

produce negative attitudes

• Quantification of uncertainty has limited impact on decisions – make them more complicated

• Decision making on a binary outcome made under a tight deadline (e.g. to evacuate or not) is complex, non-linear, non-scientific

• “when the stakes are high and the uncertainties large, ‘soft values’ dominate ‘hard facts’ ”

Morss RE et al. Weather Climate & Society

2010;2(3):174-89

Conclusions

• In past 40 years, great progress has been made to improve prognostication

• Much remains to be done to improve the accuracy of survival predictions, especially validation of existing tools & ‘upstreaming’

• Communication of prognosis and use of prognosis are also priorities

• Prediction of other outcomes is under-studied

• Other disciplines can inform our research agenda on predictions and decision making