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Dr David Kennedy Dr Hugh Rayner Dr Jessie Raju Miss Kamaljit Chatha

Use of clinical laboratory databases to enable early identification of patients at highest risk of developing end-stage kidney disease. Dr David Kennedy Dr Hugh Rayner Dr Jessie Raju Miss Kamaljit Chatha. Chronic kidney disease (CKD). CKD is common – est. 9% adults in England.

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Dr David Kennedy Dr Hugh Rayner Dr Jessie Raju Miss Kamaljit Chatha

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  1. Use of clinical laboratory databases to enable early identification of patients at highest risk of developing end-stage kidney disease Dr David Kennedy Dr Hugh Rayner Dr Jessie Raju Miss Kamaljit Chatha

  2. Chronic kidney disease (CKD) • CKD is common – est. 9% adults in England. • Prevalence increased in older people, those with • diabetes and/or high blood pressure – upward trend. • Majority have mild to moderate disease – asymptomatic. • Minority progress to end-stage kidney disease (ESKD) • and require kidney replacement therapy (KRT) (dialysis). • KRT = poor quality of life & costs £25K per patient per year. • Early intervention can delay or halt progression to ESKD. • Some patients remain undetected until very late.

  3. Birmingham Heartlands Hospital Good Hope Hospital Solihull Hospital

  4. eGFR test • estimated Glomerular Filtration Rate (eGFR) • Calculated numerical result - marker of kidney function. • Based on serum creatinine conc. in blood. • Adjusted for age, gender and ethnicity. • From 2006 all UK biochemistry labs have reported eGFR for all creatinine tests requested for adults. • HEFT – approx. 9000 creatinine requests per week. • Results often looked at in isolation or compared to last 2-3.

  5. Objectives • Develop software capable of creating cumulative graphs • of eGFR (up to 5 years data). • Create a system for identifying patients at highest risk • of developing ESKD using data from the lab computer. • Build on previous HEFT diabetes renal system. • Monitor a large population (all clinics and community). • Clinical Scientists review eGFR graphs. • System must be capable of replication by other labs.

  6. HEFT Kidney Function Monitor • Oracle™ database updated daily with data from Heartlands and Good Hope lab computers • Generate lists of all patients from previous week • Aged 65 years or less with eGFR 50 or less • Aged > 65 years with eGFR 40 or less • Exclude renal patients and in-patients • Clinical Scientist reviews approx. 400 cumulative eGFR graphs identifying patients with significant declining trend or rapid deterioration. • High risk patients - report containing eGFR graph and information for further action sent to requesting doctor.

  7. Results – 1 • Testing using historical data • Estimated 410 eGFR graphs to review per week. • Time to review graphs & generate reports approx. 3 hrs. • 15-20% of graphs reviewed by clinical scientists are flagged high risk. • Compared to the renal consultant - clinical scientists flag more patients as high risk but successfully identify those at highest risk.

  8. Results – 2 • Testing using historical data • A random selection of patients were retrospectively flagged as high or low risk for one week in 2008. • Electronic data gathered in Jan / Feb 2012 (after 3.5 yrs). • All cause mortality was higher after 3.5 years in patients flagged as high risk compared to low risk. • The number of patients with a significantly declining eGFR over 3.5 years was higherfor patients flagged as high risk compared to low risk. • The number of patients flagged at high risk who showed a significant decline in eGFR but had no evidence of specialist referral is estimated at up to 3% (780 per year).

  9. Estimated cost savings • CKD progresses over years – showing early cost savings • is thus impossible. • A study at HEFT using cumulative eGFR graphs showed a significant fall in the number of diabetic patients requiring KRT after 5 year - estimated saving £390K • Our monitoring system includes many more patients than the initial study therefore estimated savings are even more. • Estimated cost of the new system at HEFT is £41K per year. • If 20 patients over the next 5 years are detected earlier and KRT is delayed by a year net savings = £500K.

  10. Diabetes patients starting dialysis or transplanted per year P<0.001 Rayner et al. BMJ Qual Saf 2011

  11. Future plans • New system was introduced routinely at HEFT in April. • Quality data is being gathered prospectively. • Qualitative feedback (by questionnaire) of primary and secondary care clinicians will be collected. • Once embedded at HEFT, we plan to promote our new system through the clinical biochemist community and the West Midlands Renal Network. • We plan to extend the concept of cumulative monitoring of biochemical tests to other chronic diseases • Preparing Health Innovation Challenge bid.

  12. Conclusions • We have developed a system for lab staff to review cumulative eGFR graphs for a large population and identify patients at highest risk of developing ESKD. • We have tested the system using historical data and now introduced it into routine practice. • Reports with eGFR graphs are sent to clinicians highlighting patients at an earlier stage so that appropriate interventions to delay or halt deteriorating kidney function can happen earlier. • An smaller study at HEFT suggests this system may significantly reduce the number of patients needing KRT possibly saving £500K net after 5 years.

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