1. Julia Hippisley-Cox Sessional GP Epidemiologist Director QResearch Director ClinRisk Ltd EMIS NUG conference September 2010 Warwick University

2.  EMIS & EMIS Practices contributing data  Many GPs & nurses for suggestions, piloting  University of Nottingham  Academic colleagues  ClinRisk Ltd (software)  THIN (validation data)  Oxford University – independent validation

3.  Update on QSurveillance  QFeedback  Update on QScores ◦ QIntervention ◦ QFracture ◦ Qcancer  General discussion

5.  Real time infectious diseases surveillance system  Vaccine uptake reporting system  History ◦ 2004 - Pilot study on QResearch in 2004 ◦ 2005 - Upgraded to online QFLU ◦ 2006 – Separate Flu vaccine service ◦ 2007 – Separate Pneumo vaccine service ◦ 2007 – upgraded to QSurveillance Avon floods ◦ Included prospective consent for data extraction in emergency  Key part of HPA and DH emergency response

7.  Age/sex aggregated data  100-150 indicators  Infectious diseases  Vaccine uptake – flu, pneumo, MMR  Daily, weekly, monthly, quarterly, annual reports  No patients can be identified  Counts < 5 suppressed

8.  JHC custodian & responsible to practices, profession, ethics etc  No patients identifiable  Counts < 5 suppressed  Process for new indicators: ◦ Practice consent covers additional data extracted to support emergency response ◦ consult with relevant agency re need, ethics and advisory board (including NUG)

9.  Practice consent  Oversight board/review mechanism with NUG representation  Robust safeguards in place to protect patients and practices  Practices able to switch it on or off  Practice can access and benefit from data extracted

11.  Incredibly busy with flu pandemic  Daily reporting over 10 months  Unexpectedly high demand across NHS  Detailed coverage by media  Under resourced  Need to ensure its scalable, resilient, properly resourced.  Decision to industrialise it  Ensure practices can access and benefit from data

13. Qfeedback System

14. QScores

15.  Population level ◦ Risk stratification ◦ Identification of rank ordered list of patients for recall or reassurance  Individual assessment ◦ Who is most at risk of preventable disease? ◦ Who is likely to benefit from interventions? ◦ What is the balance of risks and benefits for my patient? ◦ Enable informed consent and shared decisions

16. Disease outcomesStatus  QRISK (CVD)  QDScore (diabetes)  QFracture  QKidney (CKD3b+)  Qcancer  Range of other significant outcomes  published  completed  In progress

17.  Different approach needed  Assess baseline risk of outcomes  Then how they change with interventions  Use RCTs and meta analyses for benefits  Use database analyses for unintended effects  Starting with commonly used drugs e.g ◦ Statins ◦ Antidepressants ◦ HRT ◦ Warfarin ◦ Antipsychotics ◦ NSAIDS

18.  Identify patients at high risk of vascular disease ◦ CVD ◦ Diabetes ◦ Stage 3b,4, 5 Kidney Disease  Assessment of individual’s risk profile  Risks and benefits of interventions ◦ Weight loss ◦ Smoking cessation ◦ BP control ◦ Statins

19.  Risk of CVD & “Heart age”  Extensively reviewed and externally validated  Now included in ◦ QOF ◦ DH Vascular Guidance ◦ NICE  Widespread use across NHS  Nearly all GP systems, many pharmacies, some hospitals, NHS Choices, Supermarkets, Occupational Health etc  Also free Open Source and Closed Software

21.  Predicts risk of type 2 diabetes  Published in BMJ (2009)  Independent external validation by Oxford University  Needed as epidemic of diabetes & obesity  Evidence diabetes can be prevented  Evidence that earlier diagnoses associated with better prognosis.

22.  Set of algorithms ◦ Identifies those at risk of  CKD3b+  End Stage Renal Failure ◦ Published BMC 2010  So we can then ◦ Identify high risk ◦ Modify risk factors ◦ Avoid nephrotoxic drugs ◦ Monitor more closely ◦ Prevent deterioration ◦ Improve outcomes

23.  Two recent papers: ◦ Unintended effects statins (BMJ, 2010) ◦ Individualising Risks & Benefits of Statins (Heart, 2010)  Conclusions: ◦ New tools to quantify likely benefit from statins ◦ New tools to identify patients who might get rare adverse effects eg myopathy for closer monitoring

24.  Many of the risk factors over overlap  Many of the interventions overlap  But different patients have different risk profiles ◦ Smoking biggest impact on CVD risk ◦ Obesity has biggest impact on diabetes risk ◦ Blood pressure biggest impact on CKD risk  Help set individual priorities  Development of personalised plans and achievable target

25. Offer information about: absolute risk of vascular disease absolute benefits/harms of an intervention Information should: present individualised risk/benefit scenarios present absolute risk of events numerically use appropriate diagrams and text

28.  Osteoporosis major cause preventable morbidity & mortality.  2 million women affected in E&W  180,000 osteoporosis fractures each year  30% women over 50 years will get vertebral fracture  20% hip fracture patients die within 6/12  50% hip fracture patients lose the ability to live independently  1.8 billion is cost of annual social and hospital care

29. 29

30. 30 Scane et al, Osteoporosis Int 1994; 4: 89-92.

31.  Effective interventions exist to reduce fracture risk  Challenge is better identification of high risk patients likely to benefit  Avoiding over treatment in those unlikley to benefit or who may be harmed  Some guidelines recommend BMD but high cost and low specificity  Other guidelines recommend using 10 year risk of fracture

32.  Cohort study using patient level QResearch database  Similar methodology to QRISK  Published in BMJ 2009  Algorithm includes established risk factors  Undertook validation against FRAX  Developed risk calculator which can  - identify high risk patients for assessment  - show risk of fracture to patients 

33. QFractureFRAX  Primary care  Works better in EMIS  Open Source  No funding  Includes extra risk factors eg ◦ Falls ◦ CVD ◦ Type 2 diabetes ◦ Asthma ◦ Antidepressants ◦ Detail smoking/Alcohol ◦ HRT  Selected cohorts  Over-predicts in EMIS  Not published  Industry sponsored  NOGG guidance

34.  64 year old women  Heavy smoker  Non drinker  BMI 20.6  Asthma  On steroids  Rheumatoid  H/O falls

37.  Need to quantify risks of interventions  Few large long term safety studies  Bisphosphonates may increase risk of ◦ Oesophageal cancer ◦ Atrial fibrillation ◦ Osteonecrosis of jaw ◦ Atypical fracture ◦ ? Other outcomes  Key thing for my patient is ◦ Baseline risk of fracture ◦ Likely benefit of intervention ◦ risk of adverse effects of intervention  What is the overall risk/benefit ratio?

39.  Tools to predict risk of range of common cancers  Risk stratification:  Identify those who need regular screening  Identify those who need ad hoc assessment  Patient communication  Background risk with family history – may be reassuring  Risk of cancer with “alarm” symptoms  Risks of cancer with smoking as decision aid for smoking cessation  Current  Ex smoker  Non smoker

40. CancersAlarm symptoms  Breast cancer  Prostate  Colorectal  Oesophageal  Renal/bladder  Lung  Ovary  Uterus  Breast lump  Prostatism  Rectal bleeding  Dysphagia  Haematuria  Haemoptysis  Abdo pain/distension  Post menopausal bleeding

41.  Information about QResearch database  Academic papers  Technical & statistical documents  Open source software  Patient information  Clinician information  Power points presentations  Information on how to contribute to the database (or email julia.hippisley- cox@nottingham.ac.uk )julia.hippisley- cox@nottingham.ac.uk

42.  Questions  Comments  Suggestions  Feedback