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Tumour Matching N.Ireland Experience Colin Fox (IT Manager) Richard Middleton (Data Manager)

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Presentation on theme: "Tumour Matching N.Ireland Experience Colin Fox (IT Manager) Richard Middleton (Data Manager)"— Presentation transcript:

1 Tumour Matching N.Ireland Experience Colin Fox (IT Manager) Richard Middleton (Data Manager)

2 Background to Approach 1 New Registry with a need to produce incidence figures QUICKLY!! System implementation issues - problems with inherent code translations - switch to “Direct Mapping” approach PAS - ICD10 coding from April 1996 Pathology - SNOMED to ICD9/10 translation table Small catchment area, manageable numbers - manual review possible

3 Background to Approach 2 Bulk of incoming records from PAS and therefore already coded in ICD10 Typical composition of selected tumour sites on NICR system (1998 reg.): Site PAS (max) Path (max) PAS (mean) Path (mean) No. %Hi Lung 29 19 3.14 1.63 923 67 Breast 85 6 4.03 1.82 928 94 Skin 3810 0.53 1.28 2274 ~93

4 Philosophy behind Matching Algorithm Simple - use incoming raw data as much as possible to reduce complexity Repeatable - consistent results Fit for purpose - good enough to provide reasonable accuracy Never be perfect - an understanding of the limitations and any additional countermeasures needed

5 Tumour Matching Rules 1 NICR match tumours on ICD-10 Receive most data in ICD10 (PAS) Table coverts SNOMED into ICD10 First 3 digits from SOURCE record S are compared to the first 3 digits of tumour registration D(i), where i is the number of tumour registrations for a matched patient Match obtained when S=D(i) Applies to most tumours with some exceptions

6 Tumour Matching Rules 2 Exceptions Previous Rule applies except for Melanomas (C43), Colon (C18) and Skin (C44) Match based on complete ICD10 code Exception: skins (morphology considered) 1 BCC + 1SCC per patient Same site two tumour morphologies take highest morphology e.g. M80103, M81403 --> M81403 on database Exceptions Skins (as above) and Leukaemias & Lymphomas

7 Tumour Matching Rules 3 Matched tumours are consolidated as follows: If 4th digit in one of S or D(i) is 0-8 and the 4th digit of the other is “9” (NOS) the more specific sub-site is registered e.g. C50.4 & C50.9 –> C50.4 on database If 4th digits in both of S or D(i) differ and are between 0-8 merge as “8” e.g. C15.5 & C15.4 –> C15.8 on database

8 Updating Date of Diagnosis Preferred date is always “Date of first microscopic verification” – 80% cases in NICR have cytology or pathology If no microscopic verification Manual Resolution (PAS only)“Date of procedure which leads to diagnosis”, we use a Hierarchy e.g. CT better than XR This is different from UKACR & ENCR rules e.g. “Date treatment started” is before “Date of first microscopic verification”

9 Updating “Basis of Diagnosis” Hierarchy of “Basis of Diagnosis” Histopathology Cytology Clinical Investigation Clinical Opinion Death Certificate Rules same as UKACR & ENCR

10 Consequences of Approach 1 Loss of Multiple Primaries (currently <6% excluding NM skins) Not the same as UKACR rules ENCR Multiple Primary rules Time does not matter Laterality does not matter Same family of tumour Lose Clinical Statements e.g. “this is a new primary” Cross-checks with “Customers” e.g. Breast Screening

11 Consequences 2 Inaccurate Coding Pathology report generally “dumb down” text description Centroblastic centrocytic lymphoma gets coded as M95903 NHL “Carcinoma” used fairly freely TCC non invasive pTa coded as M81203 Secondary tumours coded as primary Adenocarcinoma of Liver M81403 should be M81406

12 Consequences 3 Misleading Information Site pathologist gives is not true site Difference in clinician term and cancer registration Cytology can give misleading information on site and behaviour of tumour Secondary Not very precise site “Sputum” could be lung or elsewhere in respiratory system Malignant cells from non-malignancy “in situ” tumour (Breast) “uncertain” tumour (Bladder)

13 How do you get round these problems? Make sure computer system can identify them - source history/audit Have access to full written pathology and cytology reports (may need hospital notes) Always check multiple tumours manually Checks on selected sites Compare with other algorithms

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