Damian Fogarty is a Consultant Kidney Physician in the Regional Nephrology and Transplant Unit, Belfast Health and Social Care Trust. From 2010-14 was Chairman of the UK Renal Registry, an internationally recognised national audit body with many innovations and plaudits for its work. Damian has a particular interest in using routine data for quality improvement, better engagement with patient groups and the use of social media in all these areas. In this presentation at the Pathology Horizons 2015 conference of Cirdan,he discusses data analytics for pathology.

1. PATHOLOGY IN THE ERA OF CONNECTED
HEALTH: LINKING PATIENTS, OUTCOMES
AND DATA
Damian G Fogarty
Consultant Nephrologist
Belfast Health and Social Care Trust
Former Chairman, United Kingdom Renal Registry
E: damian.fogarty@belfasttrust.hscni.net
@DamianFog
Pathology Horizons 5-7th November 2015
‘Better Data, Better Health’

2. BELFAST CITY HOSPITAL WING A CLINIC
SOME TUESDAY ~2PM 2002
2

3. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
3

4.  DGF and patient Mary C age 72
 Mary C’s GP has written:
 “Dear Dr, We would appreciate your advice on this lady
with type 2 diabetes and an elevated serum creatinine.
Her creatinine is now 182umol/l (n <100). She has
angina, osteoporosis & SLE under steroid control”
 No nephrotoxic medications
 High Blood pressure 184/92
 Proteinuria
 Creatinine 190 umol/L
NEW MILLENNIUM
4

5. HER QUESTIONS & MY QUESTIONS
 How will my symptoms be (if I have any)?
 Will I have to travel to clinics a lot?
 Will I live to see my Grandkids born/grow up?
 Who will look after this…not my husband!
 What do you think doctor?
 What is her main disease? A renal biopsy?
 What are risks for progression, hosp admissions,
CVS events, dialysis, survival?
 What is the best form of dialysis for her?
 How long will she live?
 Which units manage them best?
5

6. CLASSICAL
DIABETIC NEPHROPATHY
6

7.  Main ‘record’ with General Practitioner
 EMIS, Vision etc
 Patient administration in BCH
 CSC managed PAS system…very old.
 Lab records in BCH and RVH are linked
 Notice that she is on the RVH diabetes system
 Diabetes systems in most units in the UK
 No systems for SLE, OP, IHD
 DGF enters her on Renal eMED system
 Renal systems in all units in the UK
MARY C DATA MANAGEMENT THEN
7

8. PROGRESSION OF CHRONIC KIDNEY DISEASE (CKD) TOWARDS
END STAGE RENAL DISEASE (ESRD)
8
From 2006 % kidney function now estimated with eGFR

9. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
9

10. DIALYSIS MID 60S- SELECTION, UNMET NEED, COMORBIDITY
10

11. 11

12. 12
www.renalreg.com

13. INFORMATION NEEDS OF THE MAIN STAKEHOLDERS
Manage
PatientClinical
EvidenceDATA
Safe Effective
Efficient
13

14. 14
14
UK Renal Registry
reports on all kidney
replacement therapy patients
across the 4 nations
www.renalreg.com
Transplant
Haemodialysis (HD)
Peritoneal dialysis
(PD)

15. – Patient has left white blood cells at another hospital
– She has no rigors or shaking chills, but her husband states she was very
hot in bed last night
– The patient has been depressed since she began seeing me in 1993
– When she fainted, her eyes rolled around the room
– Between you and me, we ought to be able to get this lady pregnant
In early 1990s UK Renal Registry set up as an
electronic return of data registry
15
Paper records in healthcare
multiple, unstructured, disorganised, illegible, duplicate, physical

16. First UK Renal Information System 1980s PROTON
16

17. RENAL UNIT IT SYSTEMS
Several suppliers and systems (6 main ones)
Proton, Vital Data, eMED Renal, CCL, Cybernius, Renal Plus, & 4-6
others in single units
Marked variation in functionality and support
Direct feeds from PAS and lab systems +/- others
Common UKRR extraction system
Role of the ‘minimum’ data-set
400 items for the registry yet 9-10,000 items on the electronic patient
record/Renal IT system 17

18.  1991 Renal association initiated UKRR
 1995 Standards and guidelines to define
what we will measure
 1998 1st report
 2015 18th report
 71 adult and 13 paediatric units
UKRR TIMELINE
18

19. MAIN AIMS OF REGISTRIES
 Enable clinicians to easily compile and review aggregate data
across units, regions, countries.
 Activity
Incidence and prevalence data
 Modalities of Renal Replacement Therapy
 Markers of dialysis care
Biochemistry
Anaemia
Lipids and diabetes care (vascular risks)
 Outcomes
Access to transplantation.
Infections
Mortality
 Research questions

20. UKRR ANNUAL REPORT
Core business
Evolving but largely unchanged
Quarterly returns & data cleaning streamlined
New web-site, twitter feed
Most frequently viewed chapters
Incidence & Prevalence
Biochemistry
Transplant
Survival
Anaemia

21. Extending Registry Data Collection
CKD stages 4 & 5
Conservative mgmt.
21
Acute Kidney Injury

22. TREATED AND UNTREATED ESRD IN AUSTRALIA
22

23. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
23

24. 24
www.renalreg.com
Figure 1.3. RRT incidence rates between 1980 and 2012

25. Growth in prevalent patients, by treatment modality
at the end of each year 1982-2010
0
10000
20000
30000
40000
50000
60000 1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Year
Numberofpatients
PD
Home HD
HD
Transplant
25

26. 26
Figure 1.6. Median age of incident RRT patients by centre in 2012
White points indicate transplant centres

27. LATE PRESENTATION
Late presentation (<90 days) ranged 7–32% 2011/12
27

28. KIDNEY TRANSPLANTATION IN THE UK
DATE PUBLISHED: 8 APRIL 2014
0
1000
2000
3000
4000
5000
6000
7000
8000
2009/10 2010/11 2011/12 2012/13 2013/14
Waiting list
Live donor Tx
Deceased donor Tx
Total Kidney Tx
http://www.organdonation.nhs.uk/statistics/downloads/united_kingdom_mar14.pdf
28

29. KIDNEY TRANSPLANTS (BELFAST PATIENTS)
0
20
40
60
80
100
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Deceased donor (Belfast) Live donor (Belfast)
Deceased donor (non-Belfast) Live donor (non-Belfast)
Median age: 41 45 46 4829

30. Older patients have multiple chronic problems=COMPLEX
Last century’s medicine
2000+ medicine
30

31. OLDER PATIENTS SURVIVING LONGER
31

32. UK Renal Registry 16th Annual Report
Figure 8.27. Serial 1 year survival for prevalent dialysis patients by UK country,
2000 to 2011 cohort years, adjusted to age 60
Note the confidence intervals…..no significant differences
32

33. ITALY SEEMS TO BE A GOOD PLACE TO DIALYSE!
33

34. SURVIVAL COMPLICATED
34
CONFOUNDING BY INDICATION

35. Figure 8.17. The effect on survival after sequential adjustment for age,
Primary renal disease (PRD) and comorbidity, 2007–2011 incident cohort
35

36. Linkage to Hospital Episodes
Systems (England only)
21,633 Incident RRT Patients
2002 – 2006
UKRR Data until Oct 2009
2.8 Million Episodes
1996-2011
290,000 Hospital Admissions
(~13 per patient)
2 Million Outpatient Appt.
11,547 Deaths up to
31/12/2010
14.4% At Home
HES
Hospital Associated Mortality
Renal Centre & Hospital Level
Length of Stay & Freq. of Admission
Start of RRT & End of Life
Comprehensively Adjusted Survival
Late presentation, Comorbidity & PRD
Fotheringham et al
Nephrol Dial Transplant.
2014 Feb;29(2):422-30.

37. 0 200 400 600 800 1000 1200
0.50.60.70.80.91.0
Total Patients Per Centre
ProportionSurviving3Years
Mean Centre-Specific Survival at three years adjusted to age 65 and male: 69.7%, range 60.2 – 78.7%.
Six centres with worse than expected survival highlighted in red.
CENTRE SPECIFIC THREE YEAR SURVIVAL ON RRT
ADJUSTED FOR AGE AND SEX
Fotheringham et al
Nephrol Dial Transplant.
2014 Feb;29(2):422-30.
37

38. CENTRE SPECIFIC SURVIVAL ADJUSTED FOR AGE, SEX, ETHNICITY,
SOCIOECONOMIC STATUS AND 16 COMORBID CONDITIONS
0 200 400 600 800 1000 1200
0.50.60.70.80.91.0
Total Patients Per Centre
ProportionSurviving3Years
Mean Centre-Specific Survival at three years adjusted to all characteristics
including demography and comorbidity: 78.8%, range 72.9 – 86.3%.
1 centre with worse than expected survival highlighted in red.
95% comorbidity
coverage with
HES data
Fotheringham et al
Nephrol Dial Transplant.
2014 Feb;29(2):422-30.
38

39. NEED DEEPER & WIDER DATA TO REALLY ADJUST
Age
Gender
Ethnicity
Other disease (comorbidity)
 Esp. diabetes, cancer
Late presentation for RRT
Social deprivation
Economic
Health service levels
39

40. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
40

41. 41
NORTHERN IRELAND ELECTRONIC CARE RECORD

42. INTEROPERABILITY CREATES OPPORTUNITIES FOR
NOVEL QUESTIONS
 Bowel Cancer Screening
 Existing & excellent NI Cancer Registry
 Cancer Patient Pathway System (CaPPS)
 NI Regional Accident and Emergency System (NIRAES)
 Enhanced Prescribing Database (EPD)
? Prescriptions for constipation before cancer diagnosis
? Palliative care input via NIECR
? Death at home/hospice/hospital
42

43. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
43

44. RENAL PATIENT VIEW-~40,000 REGISTERED PATIENTS
44
• 1,500 logins/day
• 14,000 /month.
• Look at ~6.6
pages per visit
• Login ~4.34 mins
Successful pilot of patients entering their own blood pressure data

45. UKRR STUDY GROUPS & CHAIRMEN
 Dialysis Study Group (Martin Wilkie)
 Transplant Study Group (Iain McPhee)
 Paediatric Research & Study Group (Manish Sinha)
 Polycystic Kidney Disease Group (Tess Harris)
 CKD Study Group (Nigel Brunskill)
 Rare Disease Groups ( n=13 and growing...)
45

46. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
46

47. ISSUES
 Data only as good as entered
 Avoid duplication
 IT investment alongside clinician and patient engagement
 More needed on
 Patient Reported Outcomes Measures PROMs
 Patient Reported Experience Measures PREMs
 Data governance important but too slow
 Common Law/Data Protection Act in NI;
 2006 & 2012 HSC bills in E&W
 1997 Fiona Caldicott Principles
 2013 Caldicott 2 added a 7th principle:
“duty to share information in the interests of the patients’ and
clients’ care”
47

48. NORTHERN
IRELAND
ONLY IN 53%
OF NATIONAL
AUDITS
Why?
What would
our patients say?
48

49. ESCALATION OF EVIDENCE DOMINATED BY
RANDOMISED CONTROLLED TRIALS
49

50. RCTS FAR FROM PERFECT
 Review of 74 nephrology RCTs
 Four CONSORT indicators of randomization quality
 26% provided no information at all on randomization.
 Randomization type not reported in 40%
 Method of allocation concealment not reported in 58%
 Very expensive & slow
 Data often not disclosed
 Selection biases in both Cohorts/RCTs
 Many clinical trials exclude high risk patients
 Age, Cognition, Language, Social class, Compliance
 Designed with many assumed apriori hypotheses
Steven Fishbane et al. Quality of reporting of randomization methodology in nephrology trials Kidney International (2012) 82, 1144–1146.
50

51. ANALYTICAL APPROACHES TO ACHIEVE QUASI-RANDOMISATION IN
RETROSPECTIVE DATABASE ANALYSES
 Instrument variable
 Inconsistent associations between pre-transplant dialysis modality and post-
transplant survival.
 Use centre level % of PD as predictor variable in models.
 No difference in outcomes between PD & HD patients
 Propensity matching
 Analysis of the Myocardial Ischaemia National Audit Project.
 35,881 patients diagnosed with Non ST elevation ACS
 eGFR of <60 ml/min: 15,680 (43.7%).
 If eGFR 45-59ml/min 33% less likely to undergo angiography
 If eGFR 15-29ml/min 64% less likely
 YET 30% reduction in death at 1 year for those accessing angiography (adjusted
OR 0.66, 95%CI 0.57-0.77),
 No evidence of modification by renal function/ CKD stage for this effect
(1) Kramer et al Nephrol. Dial. Transplant. (2012) 27 (12): 4473-4480
(2) Shaw C, Nitsch D, Junghans C, Shah S, O'Donoghue D, Fogarty D, Weston C, Sharpe CC. PLoS One. 2014
51

52. POPULATION ATTRIBUTABLE FACTORS/RISKS
52
90%
O’Donnell MJ et al Lancet 2010

53. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
53

54. VISION FOR 2020
 100% of health & care encounters on integrated flexible
communicative IT systems
 Involved patient in possession of e-notes
 Better use of Health Informatics, Machine Learning
 Better hardware & software
 New methods for capture (phones, speech, video)
 Faster database approaches (e.g. Hadoop, )
 Better use of business intelligence in health
 Analysts and Stats team in the hospital (Interpretation)
 Analyses close to & with the patient (Interpretation)
 Analysis by the patient of performance, safety etc
 ‘The Patient will see you now Doctor’ Eric Topol
54

55. WHAT HAPPENED TO MARY C?
EXPERIENCE IS OUR INTERNAL ‘REGISTRY’ 55

56. CONCLUSIONS
 Health & social care data is our new oil
 Mining it is only the first part
 Understanding variation key
 Improve quality
 Reduces inefficiency & costs
 Stimulates health service research questions
 Detailed interpretation requires local knowledge
 Patience required
 Patient engagement esp. in IG & PREMs/PROMs
 Registries, cohort studies and RCTs all needed as lots
of unknown unknowns left! 56

57. Special thanks to
UK Renal Registry
Ron Cullen
James Fotheringham
Charlie Tomson
Terry Feast
Peter Mathieson
Donal O’Donaghue
Centre for Public Health Queen’s University Belfast.
Collaborators: Students:
Aisling Courtney Elizabeth Reaney
Peter Maxwell Michael Quinn
Chris Cardwell Glynis Magee
Chris Patterson Sohel Badrul
Dermot O’Reilly Chris Hill
Frank Kee Andrea Rainey
Catriona Shaw
THANK YOU FOR YOUR ATTENTION