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PATHOLOGY IN THE ERA OF CONNECTED
HEALTH: LINKING PATIENTS, OUTCOMES
AND DATA
Damian G Fogarty
Consultant Nephrologist
Bel...
BELFAST CITY HOSPITAL WING A CLINIC
SOME TUESDAY ~2PM 2002
2
SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local ...
 DGF and patient Mary C age 72
 Mary C’s GP has written:
 “Dear Dr, We would appreciate your advice on this lady
with t...
HER QUESTIONS & MY QUESTIONS
 How will my symptoms be (if I have any)?
 Will I have to travel to clinics a lot?
 Will I...
CLASSICAL
DIABETIC NEPHROPATHY
6
 Main ‘record’ with General Practitioner
 EMIS, Vision etc
 Patient administration in BCH
 CSC managed PAS system…very...
PROGRESSION OF CHRONIC KIDNEY DISEASE (CKD) TOWARDS
END STAGE RENAL DISEASE (ESRD)
8
From 2006 % kidney function now estim...
SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local ...
DIALYSIS MID 60S- SELECTION, UNMET NEED, COMORBIDITY
10
11
12
www.renalreg.com
INFORMATION NEEDS OF THE MAIN STAKEHOLDERS
Manage
PatientClinical
EvidenceDATA
Safe Effective
Efficient
13
14
14
UK Renal Registry
reports on all kidney
replacement therapy patients
across the 4 nations
www.renalreg.com
Transplan...
– Patient has left white blood cells at another hospital
– She has no rigors or shaking chills, but her husband states she...
First UK Renal Information System 1980s PROTON
16
RENAL UNIT IT SYSTEMS
Several suppliers and systems (6 main ones)
Proton, Vital Data, eMED Renal, CCL, Cybernius, Renal Pl...
 1991 Renal association initiated UKRR
 1995 Standards and guidelines to define
what we will measure
 1998 1st report
...
MAIN AIMS OF REGISTRIES
 Enable clinicians to easily compile and review aggregate data
across units, regions, countries.
...
UKRR ANNUAL REPORT
Core business
Evolving but largely unchanged
Quarterly returns & data cleaning streamlined
New web-...
Extending Registry Data Collection
CKD stages 4 & 5
Conservative mgmt.
21
Acute Kidney Injury
TREATED AND UNTREATED ESRD IN AUSTRALIA
22
SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local ...
24
www.renalreg.com
Figure 1.3. RRT incidence rates between 1980 and 2012
Growth in prevalent patients, by treatment modality
at the end of each year 1982-2010
0
10000
20000
30000
40000
50000
6000...
26
Figure 1.6. Median age of incident RRT patients by centre in 2012
White points indicate transplant centres
LATE PRESENTATION
Late presentation (<90 days) ranged 7–32% 2011/12
27
KIDNEY TRANSPLANTATION IN THE UK
DATE PUBLISHED: 8 APRIL 2014
0
1000
2000
3000
4000
5000
6000
7000
8000
2009/10 2010/11 20...
KIDNEY TRANSPLANTS (BELFAST PATIENTS)
0
20
40
60
80
100
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2...
Older patients have multiple chronic problems=COMPLEX
Last century’s medicine
2000+ medicine
30
OLDER PATIENTS SURVIVING LONGER
31
UK Renal Registry 16th Annual Report
Figure 8.27. Serial 1 year survival for prevalent dialysis patients by UK country,
20...
ITALY SEEMS TO BE A GOOD PLACE TO DIALYSE!
33
SURVIVAL COMPLICATED
34
CONFOUNDING BY INDICATION
Figure 8.17. The effect on survival after sequential adjustment for age,
Primary renal disease (PRD) and comorbidity, 2007...
Linkage to Hospital Episodes
Systems (England only)
21,633 Incident RRT Patients
2002 – 2006
UKRR Data until Oct 2009
2.8 ...
0 200 400 600 800 1000 1200
0.50.60.70.80.91.0
Total Patients Per Centre
ProportionSurviving3Years
Mean Centre-Specific Su...
CENTRE SPECIFIC SURVIVAL ADJUSTED FOR AGE, SEX, ETHNICITY,
SOCIOECONOMIC STATUS AND 16 COMORBID CONDITIONS
0 200 400 600 8...
NEED DEEPER & WIDER DATA TO REALLY ADJUST
Age
Gender
Ethnicity
Other disease (comorbidity)
 Esp. diabetes, cancer
La...
SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local ...
41
NORTHERN IRELAND ELECTRONIC CARE RECORD
INTEROPERABILITY CREATES OPPORTUNITIES FOR
NOVEL QUESTIONS
 Bowel Cancer Screening
 Existing & excellent NI Cancer Regis...
SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local ...
RENAL PATIENT VIEW-~40,000 REGISTERED PATIENTS
44
• 1,500 logins/day
• 14,000 /month.
• Look at ~6.6
pages per visit
• Log...
UKRR STUDY GROUPS & CHAIRMEN
 Dialysis Study Group (Martin Wilkie)
 Transplant Study Group (Iain McPhee)
 Paediatric Re...
SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local ...
ISSUES
 Data only as good as entered
 Avoid duplication
 IT investment alongside clinician and patient engagement
 Mor...
NORTHERN
IRELAND
ONLY IN 53%
OF NATIONAL
AUDITS
Why?
What would
our patients say?
48
ESCALATION OF EVIDENCE DOMINATED BY
RANDOMISED CONTROLLED TRIALS
49
RCTS FAR FROM PERFECT
 Review of 74 nephrology RCTs
 Four CONSORT indicators of randomization quality
 26% provided no ...
ANALYTICAL APPROACHES TO ACHIEVE QUASI-RANDOMISATION IN
RETROSPECTIVE DATABASE ANALYSES
 Instrument variable
 Inconsiste...
POPULATION ATTRIBUTABLE FACTORS/RISKS
52
90%
O’Donnell MJ et al Lancet 2010
SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local ...
VISION FOR 2020
 100% of health & care encounters on integrated flexible
communicative IT systems
 Involved patient in p...
WHAT HAPPENED TO MARY C?
EXPERIENCE IS OUR INTERNAL ‘REGISTRY’ 55
CONCLUSIONS
 Health & social care data is our new oil
 Mining it is only the first part
 Understanding variation key
 ...
Special thanks to
UK Renal Registry
Ron Cullen
James Fotheringham
Charlie Tomson
Terry Feast
Peter Mathieson
Donal O’Donag...
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Damian Fogarty on Pathology in the era of connected health: Linking patients, outcomes and data

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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.

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Damian Fogarty on Pathology in the era of connected health: Linking patients, outcomes and data

  1. 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. 2. BELFAST CITY HOSPITAL WING A CLINIC SOME TUESDAY ~2PM 2002 2
  3. 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. 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. 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. 6. CLASSICAL DIABETIC NEPHROPATHY 6
  7. 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. 8. PROGRESSION OF CHRONIC KIDNEY DISEASE (CKD) TOWARDS END STAGE RENAL DISEASE (ESRD) 8 From 2006 % kidney function now estimated with eGFR
  9. 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. 10. DIALYSIS MID 60S- SELECTION, UNMET NEED, COMORBIDITY 10
  11. 11. 11
  12. 12. 12 www.renalreg.com
  13. 13. INFORMATION NEEDS OF THE MAIN STAKEHOLDERS Manage PatientClinical EvidenceDATA Safe Effective Efficient 13
  14. 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. 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. 16. First UK Renal Information System 1980s PROTON 16
  17. 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. 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. 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. 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. 21. Extending Registry Data Collection CKD stages 4 & 5 Conservative mgmt. 21 Acute Kidney Injury
  22. 22. TREATED AND UNTREATED ESRD IN AUSTRALIA 22
  23. 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. 24 www.renalreg.com Figure 1.3. RRT incidence rates between 1980 and 2012
  25. 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. 26 Figure 1.6. Median age of incident RRT patients by centre in 2012 White points indicate transplant centres
  27. 27. LATE PRESENTATION Late presentation (<90 days) ranged 7–32% 2011/12 27
  28. 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. 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. 30. Older patients have multiple chronic problems=COMPLEX Last century’s medicine 2000+ medicine 30
  31. 31. OLDER PATIENTS SURVIVING LONGER 31
  32. 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. 33. ITALY SEEMS TO BE A GOOD PLACE TO DIALYSE! 33
  34. 34. SURVIVAL COMPLICATED 34 CONFOUNDING BY INDICATION
  35. 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. 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. 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. 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. 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. 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. 41 NORTHERN IRELAND ELECTRONIC CARE RECORD
  42. 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. 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. 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. 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. 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. 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. 48. NORTHERN IRELAND ONLY IN 53% OF NATIONAL AUDITS Why? What would our patients say? 48
  49. 49. ESCALATION OF EVIDENCE DOMINATED BY RANDOMISED CONTROLLED TRIALS 49
  50. 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. 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. 52. POPULATION ATTRIBUTABLE FACTORS/RISKS 52 90% O’Donnell MJ et al Lancet 2010
  53. 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. 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. 55. WHAT HAPPENED TO MARY C? EXPERIENCE IS OUR INTERNAL ‘REGISTRY’ 55
  56. 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. 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

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