Upload
alyssa
View
42
Download
0
Tags:
Embed Size (px)
DESCRIPTION
New Risk Prediction Tools – generating clinical benefits from clinical data. Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk Ltd Primary Health Information 2012 24 April 2012. A cknowledgements. Co-author Dr Carol Coupland QResearch database University of Nottingham - PowerPoint PPT Presentation
Citation preview
+
New Risk Prediction Tools – generating clinical benefits from clinical dataJulia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk LtdPrimary Health Information 201224 April 2012
+Acknowledgements Co-author Dr Carol Coupland QResearch database University of Nottingham ClinRisk (software) EMIS & contributing practices & EMIS User Group BJGP and BMJ for publishing the work Oxford University (independent validation)
+About me
Inner city GP Clinical epidemiologist University Nottingham Director QResearch (NFP partnership UoN and EMIS) Director ClinRisk Ltd (Medical research & software) Member Ethics & Confidentility Committee NIGB
+QResearch Databasewww.qresearch.org
Over 700 general practices across the UK, 14 million patients
Joint not for profit venture University of Nottingham and EMIS (supplier > 55% GP practices)
Validated database – used to develop many risk tools Data linkage – deaths, deprivation, cancer, HES Available for peer reviewed academic research where
outputs made publically available Practices not paid for contribution but get integrated
QFeedback tool and utilities eg QRISK, QDiabetes.
+QFeedback – integrated into EMIS
+Clinical Research CycleClinical
practice & benefit
Clinical questions
Research +
innovation
Integration clinical system
+QScores – new family of Risk Prediction tools 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
Population level Risk stratification Identification of rank ordered list of patients for recall or
reassurance
GP systems integration Allow updates tool over time, audit of impact on services and
outcomes
+Current published & validated QScoresscores outcome Web linkQRISK CVD www.qrisk.org QDiabetes Type 2 diabetes www.qdiabetes.orgQKidney Moderate/severe renal
failurewww.qkidney.org
QThrombosis VTE www.qthrombosis.org QFracture Osteoporotic fracture www.qfracture.org
Qintervention Risks benefits interventions to lower CVD and diabetes risk
www.qintervention.org
QCancer Detection common cancers www.qcancer.org
+Today we will cover two types of tools Prognostic tool – QFracture Diagnostic tool - QCancer
+
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
QFracture: Background
11
+
Effective interventions exist to reduce fracture risk Challenge is better identification of high risk
patients likely to benefit Avoiding over treatment in those unlikely to
benefit or who may be harmed Draft NICE guideline (2012) recommend using 10
year risk of fracture either using QFracture or FRAX
QFracture also being piloted for QOF indicator
QFracture: challenge
+
Cohort study using patient level QResearch database
Similar methodology to QRISK Published in BMJ 2009 Algorithm includes established risk factors Developed risk calculator which can - identify high risk patients for assessment - show risk of fracture to patients
QFracture: development
+Advantages QFracture vs FRAX Published & validated More accurate in UK primary care Can be updated annually Independent of pharma industry Includes extra risk factors eg
Falls CVD Type 2 diabetes Asthma Antidepressants Detail smoking/Alcohol HRT
+
64 year old women Heavy smoker Non drinker BMI 20.6 Asthma On steroids Rheumatoid H/O falls
QFracture: Clinical example
+
+QFracture + other QScores on the app store
+QScores for systems integration
Possible to integrate QFracture (and the other QScores) into any clinical computer system Software libraries in Java or .NET Test harness Documentation Support For details see www.qfracture.org
+QCancer – the problem UK has poor track record in cancer diagnosis cf Europe Partly due to late diagnosis Late diagnosis might be late presentation or non-
recognition by GPs or both Earlier diagnosis may lead to more Rx options and
better prognosis Problem is that cancer symptoms can be diffuse and
non-specific so need better ways to quantify cancer risk to help prioritise investigation
+QCancer scores – what they need to do Accurately predict level of risk for individual based on
risk factors and symptoms Discriminate between patients with and without cancer Help guide decision on who to investigate or refer and
degree of urgency. Educational tool for sharing information with patient.
Sometimes will be reassurance. Symptom based approach rather than cancer based
approach
+Currently Qcancer predicts risk 6 cancers
PancreasLung Kindey
Ovary Colorectal Gastro-oesoph
+Methods – development
Huge sample from primary care aged 30-84 Identify
new alarm symptoms (eg rectal bleeding, haemoptysis, weight loss, appetite loss, abdominal pain, rectal bleeding) and
other risk factors (eg age, COPD, smoking, family history)
Identify patient with cancers Identify independent factors which predict cancers Measure of absolute risk of cancer. Eg 5% risk of
colorectal cancer
+Methods - validation
Once algorithms developed, tested performance separate sample of QResearch practices external dataset (Vision practices) at Oxford University
Measures of discrimination - identifying those who do and don’t have cancer
Measures of calibration - closeness of predicted risk to observed risk
Measure performance – PPV, sensitivity, ROC etc
+Results – the algorithms/predictorsOutcom
eRisk factors Symptoms
Lung Age, sex, smoking, deprivation, COPD, prior cancers
Haemoptysis, appetite loss, weight loss, cough, anaemia
Gastro-oeso
Age, sex, smoking status
Haematemsis, appetite loss, weight loss, abdo pain, dysphagia
Colorectal
Age, sex, alcohol, family history
Rectal bleeding, appetite loss, weight loss, abdo pain, change bowel habit, anaemia
Pancreas Age, sex, type 2, chronic pancreatitis
dysphagia, appetite loss, weight loss, abdo pain, abdo distension, constipation
Ovarian Age, family history Rectal bleeding, appetite loss, weight loss, abdo pain, abdo distension, PMB, anaemia
Renal Age, sex, smoking status, prior cancer
Haematuria, appetite loss, weight loss, abdo pain, anaemia
+Sensitivity for top 10% of predicted cancer risk
Cut point Threshold top 10%
Pick up rate for 10%
Colorectal 0.5 71Gastro-oesophageal
0.2 77
Ovary 0.2 63Pancreas 0.2 62Renal 0.1 87Lung 0.4 77
+Using QCancer in practice
Standalone tools a. Web calculator www.qcancer.org b. Windows desk top calculatorc. Iphone – simple calculator
Integrated into clinical systema. Within consultation: GP with patients with symptoms b. Batch: Run in batch mode to risk stratify entire
practice or PCT population
+GP system integration: Within consultation Uses data already recorded (eg age, family history) Stimulate better recording of positive and negative symptoms Automatic risk calculation in real time Display risk enables shared decision making between doctor
and patient Information stored in patients record and transmitted on
referral letter/request for investigation Allows automatic subsequent audit of process and clinical
outcomes Improves data quality leading to refined future algorithms.
+Iphone/iPad
+GP systems integrationBatch processing Similar to QRISK which is in 90% of GP practices– automatic
daily calculation of risk for all patients in practice based on existing data.
Identify patients with symptoms/adverse risk profile without follow up/diagnosis
Enables systematic recall or further investigation Systematic approach - prioritise by level of risk. Integration means software can be rigorously tested so ‘one
patient, one score, anywhere’ Cheaper to distribute updates
+Summary key points
Individualised level of risk - including age, FH, multiple symptoms
Electronic validated tool using proven methods which can be implemented into clinical systems
Standalone or integrated. If integrated into computer systems,
improve recording of symptoms and data quality ensure accuracy calculations help support decisions & shared decision making with patient enable future audit and assessment of impact on services and
outcomes
+Next steps - pilot work in clinical practice supported by DH
+
Thank you for listening
Any questions (if time)