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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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slide1

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

slide2

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.
slide3

Birmingham Heartlands Hospital

Good Hope Hospital

Solihull Hospital

slide4

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.
slide5

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.
slide6

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.
slide9

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.
slide10

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).
slide11

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.
slide13

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.
slide14

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.