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NHS Wales Predictive Model Review

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Presentation on theme: "NHS Wales Predictive Model Review"— Presentation transcript:

1 NHS Wales Predictive Model Review
24th February 2010 1

2 Agenda Model Development Methodology Model Performance
Potential Impact Discussion/Next Steps

3 Development Methodology
Modelling population of 298,077 patients 51 GP practices Split sample methodology Logistic regression to model unique relationship between independent variables in two years of patient history and dependent variable (outcome) in third year Inclusion of lag period Anonymised input data sets (IP, OP, GP Practice) Demographics Diagnosis Utilisation Procedures Drugs 50% Random ‘Development’ Sample 25% Random ‘Validation’ Sample TEST 25% Random ‘Test’ Sample

4 Identify diagnoses, procedures, drugs Predict any emergency admission
Operational Approach Identify diagnoses, procedures, drugs IP, OP, and GP data for prior 24 months Predict any emergency admission next 12 months Dependent Variable Independent Variables Historical Year 1 Historical Year 2 Lag period 3 month Year Following Prediction Prediction

5 Modelling Steps Quality Check (QC) the IP, OP and GP files
Identify patients to include in the model building – ‘membership’ Identify patients with emergency admission outcome – dependent variable Extract variables for inclusion in the model from IP, OP and GP files ~1300 variables created Randomly partition the data into Development (50%), Validation (25%) and Test (25%) samples Data mining to identify significant variables for entry into the model Logistic regression Variable stabilisation and optimisation of model Reporting of model performance on Test sample Data presented in the comparative slides following are for the PRISM model vs. the Combined Model run on the same Welsh patient test data set over the same time period to show benchmarked model performance

6 ‘Member’ Selection Creating the Model Development Population
Initial cleaning round registration date is prior to date of birth deceased date is prior to registration date removed date is prior to or equal to registration date missing pseudonymised NHS number duplicated records Continuous Membership Patients were only included if they had continuous membership in any of the 51 practices 8 day gap was allowed for practice-to-practice transfer Final membership total 298,077 from starting population of 534,955 (includes deceased) History Lag Outcome 1stMay thApril stAug stJul2007

7 Defining the Dependent Variable
An admission method with a HES value of (“21”, “22”,”23”,”24”,”25”,”27”,”28” or “29”) and a patient classification of ordinary admission (“1”) Admission Method codes 25, 27 and 29 are specific to Wales (i.e., not used in England) and contribute to the higher overall emergency admission rates than we are used to seeing in England. Code Description 21 A & E or dental casualty department of the health care provider 22 GP: after a request for immediate admission has been made direct to a hospital provider (i.e. not through a Bed Bureau) by a General Practitioner or deputy 23 Bed bureau 24 Consultant clinic of this or another health care provider 25 Domiciliary visit by Consultant 27 Via NHS Direct Services 28 Other means, including admitted from the A & E department of another provider where they had not been admitted 29 ’29’ is an internally derived code meaning Emergency Transfer from another hospital. Admission Method “81” and Intended Management of “8”

8 Variables in the Model - Demographic Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Other Clinical Rationale Baseline Intercept N/A Demographic DEM_Age Age Capped at 100 yrs Current Age Age is an important indicator of clinical risk DEM_Age_squ Age squared term 0.0005 DEM_Gender Gender ‘1’=male ‘0’=female 0.0906 Gender is an important indicator of clinical risk

9 Variables in the Model - Diagnoses Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Diagnoses GP_Neurosis_disord Neurotic, personality and other nonpsychotic disorders 0.1797 24 months Persons with mild to moderate depression and anxiety disorders are significantly higher users of health services, both for specific mental health issues and for issues related to physical health GP_Poisoning_disord Poisoning 0.7806 Intentional self poisoning is associated with mental health disorders, and thus with service usage. Unintentional poisoning is associated with poor socio-economic circumstance, in turn a predictor of overall clinical risk, as well as of poorer access to routine and preventative services and hence a greater emergency services utilization

10 Variables in the Model - Diagnoses Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Diagnoses GP_GI_Disorder GI disorders 0.1465 24 months Evidence from our own RCTs show significant (impactable) increase in overall services usage from GORD and IBS. Persons with IBD may also face acute exacerbations GP_Sprain_disord Sprains and strains of joints and adjacent muscles 0.2590 Sprains and strains may lead to mobility disorders or problems in self care; particularly among older people GP_Mental_disord Mental and behavioural disorders  0.2280 Persons with mild to moderate depression and anxiety disorders are significantly higher users of health services, both for specific mental health issues and for issues related to physical health

11 Variables in the Model - Drug Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Drug GP_Cephalosporins_rx_sqr Cephalosporins & Cephamycins 0.1428 Square root 24 months Association with chest and urinary infections which can be associated with debility or ltcs in older people GP_Corticosteroid_rx Corticosteroid Clinical Use 0.2132 Associated with asthma, COPD, Inflammatory bowel diseases etc. diabetogenic GP_Diuretics_rx Loop Diuretics 0.2085 CHF, CKD, Hypertension, diabetogenic GP_Macrolides_rx_sqr Macrolides 0.0933 Chest infection, penicillin allergy GP_Analgesics_rx Narcotic Analgesics 0.1696 End of life, lower back pain, arthropathies, chronic pain… all strong predictors of service usage

12 Variables in the Model - Drug Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Drug GP_Antidepressant_rx Other Antidepressant Drugs 0.1828 24 months Persons with mild to moderate depression and anxiety disorders are significantly higher users of health services, both for specific mental health issues and for issues related to physical health. Also used for chronic pain and neuropathies/neuralgias GP_Pencillin_rx Penicillinase Res Penicillins 0.0561 Immunosupression, nosocomial infection, chest infection GP_Sulphonamides_rx Sulphonamides & Trimethoprim 0.1460 UTI,HIV, Crohn’s, UC GP_Ulcer_rx Ulcer-Healing Drugs 0.1049 GORD, PUD. Significance independent of GI disease, possibly due to coding issues GP_VitaminB_rx Vitamin B Group 0.2976 24months Alcoholism, Pernicious Anaemia, malabsorption

13 Variables in the Model - Prescribing Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Prescribing GP_Polypharm Polypharmacy 0.1518 12 months Associated with increased adverse reactions and falls and fractures GP_Polypharm_squ Polypharmacy squared term Squared

14 Variables in the Model – Chronic Condition Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Chronic Condition GP_Num_cc Total number of chronic conditions 0.1235 All available GP Data Multiple comorbidities greatly increase unscheduled admission risk GP_Chf_id CHF (LTC) 0.1695 Heart failure is a strong predictor of clinical risk GP_Copd_id COPD (LTC) 0.1053 COPD is a strong predictor of clinical risk GP_Epilepsy_id Epilepsy (LTC) 0.3869 Epilepsy is a strong predictor of clinical risk

15 Variables in the Model – Clinical findings Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Clinical findings GP_Current_smoker Patient has stated that they are a current smoker 0.3043 All available GP Data Smoking is an important risk predictor IP_CerebralP_dx Inpatient admission with diagnosis Cerebral Palsy and other paralytic syndromes 0.6711 24 months Persons with CP have complex needs and may have challenges with regard to self care IP_Circulatory_dx Inpatient admission with diagnosis Symptoms and signs involving the circulatory and respiratory systems 0.2959 This includes acute MI, pneumonia, acute severe asthma, acute heart failure and many high care need conditions IP_Digestive_dx Inpatient admission with diagnosis Symptoms and signs involving the digestive system and abdomen 0.2978 Evidence from our own RCTs show significant (impactable) increase in overall services usage from GORD and IBS. IBD and PUD patients are also high users of unscheduled care IP_Urine_dx Inpatient admission with diagnosis Abnormal findings on examination of urine, without diagnosis 1.1327 UTI, haematuria IP_Alcohol_dx Inpatient admission with Alcohol related diagnosis 0.8691 Alcohol related head injury

16 Variables in the Model - Inpatient Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Inpatient Utilisation IP_Emerg_admit12 Emergency admissions in last 12 months of history period 0.4447 12 months Previous emergency admission is a strong predictor of future emergency admission IP_Non_emerg_admit Non-emergency admission 0.0989 Elective admissions are also a predictor of emergency admissions IP_Daynight Inpatient day & night cases 0.7238 Ambulatory admissions are also predictive of emergency admissions

17 Variables in the Model – OP and Deprivation Selected from ~1300 variables tested
Included Codes Variable Category Variable Name Description Beta Co-efficient Range Look back Read 2 ICD-10 Outpatient Utilisation OP_Referral_Emergency OP visit following an emergency admission 0.1959 24 months Follow-up activity is an additional indicator of emergency admission risk OP_Referral_GP OP visit with referral from a GP 0.1042 Persons with illnesses which require referral are at higher risk of urgent care need OP_Appointment OP visit with outcome 'Another appointment given' 0.1765 Deprivation Index DEM_Deprivation Deprivation 0.0055 Current Dep Index There is strong evidence for an association between deprivation, health need and urgent service usage

18 Agenda Model Development Methodology Model Performance
Potential Impact Discussion/Next Steps

19 Model Performance Comparison with the Combined Model (England) on Test sample
The PRISM model consistently outperforms the Combined Model (CM) when looking at specific cutpoints by ‘numbers’ of patients Test Sample N = 74,114

20 Model Performance Comparison with Combined Model on Test sample
Test Sample N = 74,114

21 PRISM Model vs. Combined Model 0.5% segment – 370 patients
The PRISM model identifies a slightly younger Very High Risk population than the CM; LTC prevalences are generally higher except hypertension Test Sample N = 74,114

22 Agenda Model Development Methodology Model Performance
Potential Impact Discussion/Next Steps

23 High Risk CHF Beta Blocker Gap ‘Campaign’
Using segmentation and evidence-based clinical quality indicators to target impactable patients Risk Segment Very High (Top 0.5%) High (1%-5%) Moderate (6%-20%) Low (Bottom 80%) Number of Patients 470 4,230 14,100 75,273 Total = 4,230 LTC = 2,582 CHF = Gap = High Risk CHF Beta Blocker Gap ‘Campaign’

24 Opportunity to Impact Admissions A segmentation approach, using multiple commissioning and intervention strategies aligned to risk, can significantly impact emergency admissions © 2008 Health Dialog UK Ltd – Commercial in Confidence

25 Agenda Model Development Methodology Model Performance
Potential Impact Discussion/Next Steps


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