Your life in their hands

Mortality as a measure of clinical

performance

David Prytherch, Jeff Sirl, Paul Weaver, Paul Meredith, Michael Booth

………Hospitals and the NHS could tell you about throughput (number of patients treated), bed occupancy (the proportion of beds occupied in the hospital), and, latterly, the costs involved. But, generally speaking, quality of outcome was a closed book.

Chapter 27 para 2

“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001

Why look at Clinical Outcomes?

At national level, the indicators of performance should be comprehensible to the public as well as to healthcare professionals. They should be fewer and of high quality, rather than numerous but of questionable or variable quality.

Recommendations para 153

“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001

Why Mortality?

…… Variables such as case mix and where possible, in the case of surgery, operative risk must be allowed for, so that, wherever feasible, it is possible to compare like with like.

Chapter 27 para 49

“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001

Why Case-mix adjust?

For the future the multiple methods and systems for collecting data must be reduced. Data must be collected as the by-product of clinical care.

Summary para 96

“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001

How to collect the data?

What modelling provides:Stratified (=case mix adjusted model) enables: Comparison of expected and observed

outcomes

Comparison of outcomes / performance between hospitals and clinicians

Meaningful league tables based on clinical performance

Better understanding of the process of clinical care?

Provides a ruler

Previous / existing models (General Surgery) require data beyond that routinely stored in “core” systems.

Can useful models be constructed from the more limited data set stored in “core” systems?

An interesting question

General Surgery

results adapted from:

Towards a national clinical minimum datasetfor general surgery.

D. R. Prytherch, J. S. Sirl, P. C. Weaver,P. Schmidt, B. Higgins, G.L SuttonBritish Journal of Surgery, in print

5 year period, 1st August 1997 to 31st July 2002 28925 General Surgical in-patient episodeswith necessary dataModels constructed from years 1 and 2 andtested prospectively against years 3, 4 and 5

Data items used in BHOM modelsfor General Surgery:

•Urea•Na•K•Haemoglobin•White Cell Count•Age on admission•Sex•BUPA operative severity score•Mode of admission – elective or emergency•Mortality at discharge

General Surgery - total mortality

Ten 6- month periods 1st August 1997 31st J uly 2002

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10

Predicted Deaths

Reported Deaths

p-value (prospective)6 df = 0.96

Risk Bands (%)

Dis-charges

Mean Risk (%)

Predicted Deaths

Reported Deaths

2

0 to 5 2227 0.98 22 24 0.23

> 5 to 10 261 7.09 19 21 0.36

> 10 to 15

119 12.13 14 14 0.01

> 15 to 25

91 19.13 17 20 0.48

> 25 to 50

59 34.33 20 19 0.12

> 50 to 100 24 64.17 15 14 0.36

           

0 to 100 2751 3.91 108 112 1.56

General Surgery Study

Final 6 month period 1st February – 31st July 2002

2 = 1.56, 6 d.f, P = 0.96, no evidence of lack of fit

General Medicine

General Medicine Study

Data from HIS and Biochemistryand Haematology modules of pathology system

4 year period, 1st January 1998 - 31st December 2001

37283 discharges from GM with necessary data

Models constructed from 3 months (Jan – Mar 01) and tested prospectively against the other 45 months of the study.

Data items used in BHOM modelsfor General Medicine:•Urea

•Albumin•Creatinine•Na•K•Haemoglobin•White Cell Count•Age on admission•Sex•Mortality at discharge

General Medicine - Mortality at Discharge

Monthly, J anuary 1998 to December 2001

0

20

40

60

80

100

120

Predicteddeaths

Reporteddeaths

p-value

(prospective)

45 df = 0.11

Risk (%) Dis-charges

Mean Risk (%)

Predicted Deaths

Reported Deaths

2

0 to 5 1266 2.38 30 34 0.50

>5 to 7.5 281 6.52 18 22 0.79

>7.5 to 10 326 8.88 29 24 0.93

>10 to 12.5

160 11.47 18 21 0.43

>12.5 to 15

171 13.93 24 29 1.31

>15 to 20 158 17.72 28 30 0.18

>20 to 25 74 23.16 17 13 1.30

>25 to 33 67 28.58 19 19 0.00

>33 to 50 28 39.50 11 9 0.63

>50 to 100 13 64.46 8 7 0.64

           

0 to 100 2544 7.99 202 208 6.71

General Medicine Study

Final 3 month period 1st October – 31st December 2001

2 = 6.71

10 d.f

P = 0.75

no evidence of

lack of fit

How applicable are the models?

Discharges by speciality (2001: PHT + Elderly)

13.2% - General Medicine

8.7% - General Surgery

2.2% - Geriatric Medicine

0.3% - Ederly Acute

13.0% - Nephrology

6.6% - Gynaecology

56.0% - Others

Mortality at discharge by speciality (2001: PHT + Elderly)

45.4% - GeneralMedicine

9.7% - GeneralSurgery

15.3% - EderlyAcute

10.0% - GeriatricMedicine

19.6% -Others

Conclusions

Clinical data obtained from a single venesection

Clinical data are used operationally in care of individuals

No “extra” effort is required to collect data

Clinical data used are subject to extensive quality assurance

Available in all hospitals

Candidate National Clinical Minimum dataset

Sources of funding

Portsmouth NHS R&D Consortium

Portsmouth Hospitals NHS Trust

University of Portsmouth