Princess Marga ret Hospital and Ontario Can Ontario Can ncer Institute ncer Institute 50th Annive 50th Annive ersary Event ersary Event 16 – 18 Oc ctober 2008 “The Unexpected Consequence es of Being a Research Fellow at PMH/OCI – Psychoso ocial and Proteomics” Peter Selby
Q Question ti Does measuring “patien nt-centred” variables formally, lead to improv ved outcomes?
Patient-centred, self-r reported information in clinical li i l practice ti A personal perspective A personal perspective • there is a NEED for PCSR information in practice • it can increase DISCOVER RY of the relevant issues • it is now LOGISTICALLY p possible to collect and manage the data th d t • its use MAY IMPROVE IMP PORTANT OUTCOMES in some settings settings • we are NOT YET VERY GO OOD at collecting or using the data
Patient-centred, self-r reported information in clinical li i l practice ti Challenges • clear conceptual frameworks and choice of topics • what to collect and when • how to train clinicians and alt ter clinical behaviour • how to adapt healthcare syst p y ems • how to change the culture in healthcare • • how to ENHANCE (not replac how to ENHANCE (not replac ce) the interactions between ce) the interactions between patients, carers, healthcare p professionals and healthcare systems
Patient-centred, self-r reported information in clinical clinical practice practice DISCOVERY DISCOVERY 114 Patients reporting on QLQ C30 moderate problems % in notes Fatigue 34 29 Pain 24 58 N & V 35 14 Dysnoea 18 56 Sleep 27 7 Appetite 18 39 Constipation Constipation 13 13 8 8 Diarrhoea 4 25 Finance 14 7 JCO 2002
LOGISTICS ● Touch-screen data collection ● Comparison TS vs paper (J Clin Onco C S ( C O ol, 1999) ) ● Patient compliance with regular QOL L collection (J Clin Oncol, 2003) 100 100 ent complete (%) 90 17 80 48 1264 110 70 339 60 242 120 tients 50 110 198 98 QL assessme Number of pat 40 147 100 114 90 30 83 8 80 20 70 60 10 50 0 40 1st visit 1st visit 2nd visit 2nd visit 3rd visit 3rd visit 4th visit 4th visit 5th visit 5 5th visit 5 30 30 20 visit 10 0 -100 -60 -40 -20 0 20 40
Randomised Tria al – Study Design Patients starting chem Patients starting chem mo /biological treatment mo-/biological treatment randomized I t Intervention 50% ti 50% Att Attention-cont ti t trol 25% t l 25% C Control 25% t l 25% EORTC QLQ-C30 EORTC QLQ-C3 30 No QL in clinic HADS on Touchscreen HADS on TS F Feedback db k N No feedback f db k Process outcomes: tape-recording of con nsultations – content analysis Patient outcomes @ baseline FACT G (QOL Questionnaire) FACT-G (QOL Questionnaire) post 3 interventions t 3 i t ti @ 4 months Continuity & Co-ordination of Care @ 6 months Satisfaction Satisfaction
Results – Patient well -being Proportion of patients Proportion of patients s with improvement s with improvement or deterioration in FAC CT-G scores NNT NNT T = 4.2 T = 4 2 Interv vs Attn-contr +Contr p=0.007, Interv + Attn-contr vs Control p=0.003 Improvement No o change Deterioration 100% 7% 14% 31% 80% 80% 46% 61% 60% 45% 45% 40% 20% 40% 32% 32% 24% 0% Intervention Attention-c control Control
Further analysis of commun ication and decision making Symptoms Functioning ● Providing QOL data lead to g ● Providing QOL data lead to ● Providing QOL data lead to more consistent discussion of more consistent discussion of - Insomnia (p=0.003) - Physical function (p=0.006) - Dyspnoea (p=0.03) Dyspnoea (p=0 03) - Emotional function (p=0.03) ● Symptoms more often raised by doctor ● Not initiated by doctor y ● Discussion of common symptoms depended mainly on ● No effect on social function whether the problem was raised at baseline - Pain, fatigue, nausea, appetite appetite
Health-Related Quality o f Life Assessments and Patient-Physician y Communication Detmar, Aaro onson et al, 2002 Randomised Trial: 214 patien nts / 10 physicians Use of QLQ-C30 led to: • more HRQL issues disc cussed • more health problems id dentified • support from patients a nd staff • NO CHANGE IN QL (o NO CHANGE IN QL (o on SF 36) on SF 36) • improvements in EMOT TIONAL FUNCTION (.04) and ROLE FUNCTION (.05) ( )
Health-Related Qua ality of Life (HRQL) and Satisfaction S Scores Over Time 100 140 140 90 80 120 70 70 100 100 on S a tis fa c tio F L IC 60 80 50 60 60 40 40 40 30 20 20 0 1 2 3 4 5 6 Mont th Control-FLIC Assessessment Control - FLIC Structured Intervention-FLIC Control-Satisfaction Assessment Control-Satisfaction Structured Interview-Satisfaction
The Social Difficulties Inventory (SDI)
A meaningful SD g DI scoring system g y Individual items Frequency of so ocial difficulties 700 600 of people 500 very much difficulty 400 quite a bit of difficulty number o a little difficulty 300 no difficulty 200 100 0 e y s s e e s s d e s e s s l t s r r e n n i s r t n k s c t e u e r m n v e n o e r i e e e g o r o y n a e f u i r c t h e e o l h c u t t a a i i a o l t e c d i t o t c t u d n n v W a a d t f o a f m h O d n r o l n e a r l e a e g m a a i c e o n e g l i y o e b n r n h n e p s s c n e e y g c p H i o o F t l I e p e e d e i i a v v n e l t a i t s g r e s d r t a u a o R i e d r a n a t i d d e e c c c c x x h h B B e e t h h r r f f i i c c e e e e n n m m f f o o l l n n n n i i P P n n W W o o e e i i o o G G n S S I f a n o u W u t e t n a D m r s m r i l o F P n a m p m a C p o l o P u C C S SDI items
A meaningful SDI scorin ng system-Rasch analysis Independence Domestic chores Personal care Care of dependents Measure of social distress (SD) Support for those close to you Welfare benefits Finances 16 items Financial services Unidimensional Work Planning the future Communication with close 72% of the variance Comm nication Communication with others ith others Differences in scores are equally Sexual matters Plans to have a family spaced Body image Interval scale Interval scale Isolation Isolation Getting around Where you live Age, gender, stage & site of Recreation disease, deprivation Holidays Holidays Other
Results: deriv ving a cut point g p Area under the Social Distress (SD) ( ) curve = .850 ROC Curve (16 Rasch items) 1.0 • Top 10% of researcher SD T 10% f h SD 0.8 ≥ 14 (gold standard for SD) 0.6 vity Sensitiv 0.4 • Using this gold standard the best cut-point was the best ‘cut-point’ was 0.2 patient SD ≥ 10 0.0 0.0 0.2 0.4 0.6 0.8 1.0 • sensitivity = .800 sensitivity = 800 1 - Specificity 1 S ifi it • specificity = .755 • positive predictive value =.29
Summ mary We have the technology Routine QL assessment does im mprove QL in some studies There is potential for making que estions asked more specific to i di id individual patients l ti t Future studies Future studies • Making assessments more individu ual • The roles undertaken by different • The roles undertaken by different members of the multidisciplinary te am • How people make decisions p p • Staff training & patient information needs • Widening access—web based systems
Galina Velikova Penny Wright
Question Question Can proteomic approach Can proteomic approach hes to biomarker discovery hes to biomarker discovery and evaluation improve our management of patients and their outco patients and their outco omes? omes?
Leed ds Proteomic Activities S Specific focussed Underlying Renal Cancer projects e.g. VHL, projects e.g. VHL, pathogenesis pathogenesis r response to TKIs… (“hypothesis driven” ) Marker C Clinical proteomic & Sample banking & profiling clinical data collection Target (“hypothesis driven and ( Dis Discovery h h h hypothesis generating”) h i i ”) Other diseases and clinical trials Underlying (area-specific clinical issues) pathogenesis p g
Why Proteomics? Why Proteomics? Functional entities in biological sys stems Post translational modifications Access in biological fluids A i bi l i l fl id Why not Proteomics alone? ? Absences of amplification steps (s Absences of amplification steps (s sensitivity) sensitivity) Highly multi-professional teams ne eeded and expensive equipment
TECHNOLOGY Clinical Proteomics – essentially profiling e.g comparison n of samples to find differences due to disease, drug trea tment etc – key in marker discovery but also pr y y p rovides biological information g “Cell-Mapping” Proteomics pp g – addressing the question of protein function from interactions e.g. isolating/characterising protein c g g g p complexes p Much of proteomics research is very p y technology-dependent: gy p 2D-PAGE ICAT T MALDI MS SELDI MS iTRA AQ LC/LC-MS/MS Nove el prefractionation Protein/antibody arrays
Novel Biomark kers: 2D PAGE mW Protein extract 3 pI 10 e.g. conditioned media 3 pI 10
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