Dynamic clinical prediction models for cardiac surgery

Presented at the SCTS Annual Meeting 2013, Brighton, UK (17-19 March, 2013)

Transcript

1. Dynamic  clinical  predic.on   models  for  cardiac  surgery   Hickey  GL1,  Grant  SW2,  Caiado  C3,  Kendall  S4,     Dunning  J4,  Poullis  M5,  Buchan  I1,  Bridgewater  B1,2     1Northwest  Ins.tute  of  Bio-­‐Health  Informa.cs;  2University  Hospital  of  South   Manchester;  3University  of  Durham;  4The  James  Cook  University  Hospital;     5Liverpool  Heart  and  Chest  Hospital   This  research  was  generously  funded  by   Heart  Research  UK  [Grant  Number  RG2583]  
2. History  of  clinical  predic.on  models   for  cardiac  surgery   1989   • Parsonnet   1999   • Addi.ve  EuroSCORE   2003   • Logis.c  EuroSCORE   2008   • STS  Models   2012   • EuroSCORE  II   Future   • Where  next?   Procedure  specific   Mul.ple  outcomes   Dominant   European  model   for  ~10  years  
3. What’s  wrong  with  the  status  quo?   0.02 0.04 0.06 0.08 Mortality (proportion) Trend 2002 2004 2006 2008 2010 0.4 0.5 0.6 0.7 Time (annual quarter) O:E ratio O:E ratio LOESS 0.02 0.04 0.06 0.08 0.10 Mortality (proportion) Observed Expected Actual Overall average Trend 4 0.5 0.6 0.7 O:E ratio O:E ratio LOESS 0.02 0.04 0.06 0.08 0. Mortality (proportion) Observed Expected Actual Overall average Trend 2002 2004 2006 2008 2010 0.4 0.5 0.6 0.7 Time (annual quarter) O:E ratio O:E ratio LOESS In  April  2010,  predicted  mortality  was  2.7  x  observed  mortality  
4. Consequences   Logistic EuroSCORE Recalibrated EuroSCO 0% 5% 10% 15% 0 200 400 600 800 0 200 400 Number of procedures Mortality rate Number  of  procedures   MisrepresentaGon  
5. Op.ons  a   Approach   DescripGon   Do  nothing   Develop  a  model  (e.g.  on  1-­‐years  data)  and  leave  to   run  forever   Periodically  refit  model   Every,  e.g.  1-­‐year,  independently  refit  the  model   Rolling  window   Fit  model  to  a  fixed  window  (e.g.  2-­‐years)  of  data  and   then  rolling  the  window  incrementally  (e.g.  every  1-­‐ year)   Dynamic  logisGc  regression   Exploit  dynamic  sta.s.cal  models  that  can  update  in   ‘real  .me’  (1-­‐month)  online   a  not  an  exhaus.ve  list  
6. ‘Nuts  &  bolts’  of  dynamic  regression   •  Described  by  McCormick  et  al.  Biometrics   2012;  68:23-­‐30  (with  sogware)   •  Assumes  a  state-­‐space  equa.on:  βt  =  βt-­‐1  +  δ   for  risk  factors  (cf.  log  odds  ra.os)   •  As  each  batch  of  new  data  arrives,  model   updates  es.mate  of  βt  and  its  standard  error   using  Bayesian  sta.s.cs   •  Assump.ons  made  about  δ  and   approxima.ons  in  calcula.ons  
7. Strategy   •  Focus  on  EuroSCORE  risk  factors   •  Train  all  3  models  on  2001-­‐02  clinical  registry   data  for  all  adult  cardiac  surgery   •  ‘Update’  models  on  2002-­‐11  clinical  registry   data   •  Monitor  model  coefficients  
8. Results  
9. 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1500 2000 2500 3000 Time (months) Number of records 17 19 21 23 25 27 29 31 33 35 37 Number of contributing hospitals Procedures Contributing hospitals •  316,713  records   •  37  different  hospital   •  120  months  of  clinical  data  (10  years)  
10. Age (adjusted) Female Pulmonary disease Extracardiac arteriopathy Neurological dysfunction Previous cardiac surgery 0.05 0.06 0.07 0.2 0.4 0.6 0.0 0.2 0.4 0.2 0.4 0.6 0.8 −0.5 0.0 0.5 1.0 0.7 0.9 1.1 1.3 2002 2004 2006 2008 2010 2002 2004 2006 2008 2010 2002 2004 2006 2008 2010 Time Coefficient No update Rolling 2−year window Piecewise recalibration (1−year) Piecewise recalibration (2−year) Dynamic logistic regression Estimate 95% CI
11. Creatinine > 2.2mg/dl Active endocarditis Critical pre−op Unstable angina LV function: moderate LV function: poor 0.50 0.75 1.00 1.25 1.50 0.0 0.4 0.8 1.2 0.3 0.6 0.9 0.0 0.4 0.8 0.2 0.4 0.6 0.75 1.00 1.25 1.50 2002 2004 2006 2008 2010 2002 2004 2006 2008 2010 2002 2004 2006 2008 2010 Time Coefficient No update Rolling 2−year window Piecewise recalibration (1−year) Piecewise recalibration (2−year) Dynamic logistic regression Estimate 95% CI
12. Recent MI Pulmonary hypertension Emergency/salvage Other than isolated CABG Surgery on thoracic aorta VSD 0.0 0.2 0.4 0.6 0.8 0.00 0.25 0.50 0.75 0.50 0.75 1.00 1.25 1.50 0.6 0.8 1.0 0.50 0.75 1.00 1.25 −0.5 0.0 0.5 1.0 1.5 2002 2004 2006 2008 2010 2002 2004 2006 2008 2010 2002 2004 2006 2008 2010 Time Coefficient No update Rolling 2−year window Piecewise recalibration (1−year) Piecewise recalibration (2−year) Dynamic logistic regression Estimate 95% CI
13. Intercept −6.00 −5.75 −5.50 −5.25 2002 2004 2006 2008 2010 Time Coefficient Estimate 95% CI No update Rolling 2−year window Piecewise recalibration (1−year) Piecewise recalibration (2−year) Dynamic logistic regression
14. Conclusions   •  Doing  nothing  is  not  an  op.on   •  A  pa.ent  today  does  not  have  the  same  risk  as   10  years  ago   •  Is  it  sensible  to  wait  for  EuroSCORE  III?   •  Dynamic  regression  is  more  methodologically   complex  and  would  require  concerted  effort   to  implement