The Risk Study. A prospective, multi-centre, evaluation of an AKI risk prediction tool for emergency hospital admissions

Transcription

1 The Risk Study A prospective, multi-centre, evaluation of an AKI risk prediction tool for emergency hospital admissions

2 Disclosures Research Funding Commercial Trials Honorarium/Travel Astute Medical SBRI/D4D s Renal Technologies BRS/BKPA Roche; Cheetah; La Jolla; AB Pharma Fresenius; Astute Medical; Eras Pharma; Ortho Clinical Diagnostics; Baxter/Gambro/Renal

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4 Thanks To: Risk Steering Committee: Nic Cole Chris Farmer Lui G Forni Andrew Lewington Marlies Ostermann Aled Phillips John Prowle Nick Selby Risk prediction for acute kidney Injury in acute medical admissions in the uk : The RISK study 4

5 Background Predictable and avoidable AKI should never occur 5

6 Background Murugan, R. & Kellum, J. A. (2011) Acute kidney injury: what s the prognosis? Nat. Rev. Nephrol. doi: /nrneph

7 Stage Based Management KDIGO Consensus Guideline for AKI But NO available method to reliably identify high risk to aid clinical judgment Actions recommended to start when patients are at high risk often resulting in failure to initiate kidney-sparing management strategies 7

8 Background Can We Predict Who Is At Risk of AKI?? 8

9 Do Prediction Scores Exist? 9

10 Do Prediction Scores Exist? They do : But Mostly Single Centre AKI Mortality 36-75% Where Externally validated : performance poor 10

11 Background 11

12 Background 12

13 Risk Scores 180 Cases H-AKI vs 360 Controls Demographic data from EPR Admission Data Risk Factors : Biological Plausibility / Known ICU Risk Factors 13

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15 Not Straightforward!!

16 Background 16

17 Risk Factors 17

18 Common Risk Factors Age, Gender Hypotension, Oliguria Liver Failure Hypoalbuminaemia Sepsis Mechanical Ventilation 18

19 Common Risk Factors Given the common risk factors. Can a UK wide RISK score for AKI prediction be developed? The RISK Study 19

20 Overview Prospective Multi-Centre Cohort Design Patients presenting to UK AMUs over a 24-hour period included 12 th September 4 th November

21 Overview Data generated as part of routine care Recorded on admission up until discharge or a maximum of 7 days Primary outcome: the development of h-aki (KDIGO stages 1-3) occurring after the first 24 hours of admission 21

22 Definitions COMMUNITY-ACQUIRED AKI (c-aki) Largely centre determined Verified using baseline creatinine (within 2 years) if available Detected also by a reduction in creatinine of 50% during admission 22

23 Definitions Hospital-ACQUIRED AKI (c-aki) AKI occurring after the first 24-hours of admission (after day 3) KDIGO staging using baseline creatinine (where available) or lowest creatinine within first 24-hours of admission 23

24 72 participating centres 2,446 admissions 1,843 admitted via A&E (75.3%) 24

25 The Cohort Selection: 25

26 : Results 26

27 Age/Sex Profile 27

28 Presenting Complaints 28

29 Co-Morbidities 29

30 Medication 30

31 Data : Compliance 31

32 Length of Stay 32

33 RISK : C-AKI 18 LOS Days C-AKI %age Patients >10 >20 33

34 RISK : H-AKI 18 %age H-AKI %age Patients >10 >20 34

35 Univariate Analysis 35

36 : Results 36

37 Univariate Analysis Variable P value Age 0.02 Diuretic <0.001 Proteinuria Antihypertensives 0.04 No CKD Stage <0.001 Physiology CVS Disease Hypertension Sodium Bilirubin 0.01 Haemoglobin

38 : Results H-AKI By Stage 38

39 : Results AKI : Time of Onset 39

40 : Results AKI : Reason For Admission 40

41 : Results Multivariate Analysis Backward selection etc.. 2 Models Generated.. 41

42 Sensitivity Multivariate Analysis Model 1 ROC Specificity Area under ROC curve =

43 Sensitivity Multivariate Analysis Model 2 ROC Specificity Area under ROC curve =

44 Multivariate Analysis Hosmer-Lemeshow 44

45 Multivariate Analysis So.. What s in the model? 45

46 Univariate Analysis 46

47 Applying Data to Other Models Original Paper Nephron Clin Pract 2013;123: DOI: / Received: January 16, 2013 Accepted: April 16, 2013 Published online: July 25, 2013 I dentifying the Patient at Risk of Acute Kidney Injury: A Predictive Scoring System for the Development of Acute Kidney Injury in Acute Medical Patients Lui G. Forni a Thomas Dawes a Hamish Sinclair a Elizabeth Cheek c Vivien Bewick c Mark Dennis b Richard Venn a a Department of Critical Care, Worthing Hospital, and b Department of Performance and Information, Western Sussex Hospitals Trust, Worthing, and c School of Computing, Mathematical and Information Sciences, University of Brighton, Brighton, UK The RISK Key Words Study 47 medical patients at greater risk of developing AKI. Conclu-

48 Applying Data to Other Models Table 4. APS acute kidney injury prediction score Score Age < RR <20 20 AVPU alert other CKD stage 3a 5 N Y CCF N Y DM N Y Liver disease N Y 48

49 Applying Data to Other Models 49

50 Sensitivity Applying Data to Other Models ROC Specificity Area under ROC curve =

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52 Where Next?? Simplify Prediction Tool??App for AKI probability? Role for Biomarkers? The RISK Study 52

53 THANK YOU : For Your Attention!