Response
Sir,We thank Prytherch and Smith for their interest in our paper,1 in which we concentrated specifically on the relationship of abnormalities in selected laboratory variables with the mortality in hospital of all patients admitted acutely under any medical or surgical specialty. We did not explore the construction of comprehensive predictive models, and only mentioned two such examples, the APACHE II and MEWS scores, which include physiological variables. These and other composite scoring systems have been used in general and medical in-patient settings.2–5
Prytherch and Smith provide a list of references to complement our paper, and express surprise that we did not quote these, although most are restricted to surgical patients, who only constituted 25% of the admissions that we studied. The first two papers describe a highly selected subset of patients who had arterial blood gases measured in the emergency room.6, 7 Several other articles they cite focus on the quality of ICD discharge coding at a national level8, 9 or on models used to predict surgical outcomes, and which include surgery-specific data.10–12 Nevertheless, Prytherch and colleagues have recently shown that a simple model that combines routine admission biochemistry and haematology data with a small number of such items12 is better than more complex models at predicting surgical outcomes.13
We regret that our discussion omitted the useful contributions by Hucker14 and colleagues and Prytherch and colleagues,15 despite repeated literature searches, which failed to identify one of these.14 Both reports showed that relatively simple scoring systems can be constructed using a small number of data items collected in the panel of haematology and biochemistry tests performed on most patients on admission to hospital, and that these can be used to stratify the risk of adverse hospital outcome, which is different for elective and for emergency admissions. This has been confirmed in larger-scale models constructed for hospital billing purposes.16 We have come to a similar conclusion using prospectively collected datasets (manuscript in preparation) and look forward to discussing our findings with Prytherch, Smith and others.
Liverpool School of Tropical Medicine
Liverpool
UK
email: nicholas.beeching{at}rlbuht.nhs.uk
References
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2. Paterson R, Macleod DC, Thetford D, Beattie A, Graham C, Lam S, Bell D. Prediction of in-hospital mortality and length of stay using an early warning system: clinical audit. Clin Med (2006) 6:281–4.[Web of Science][Medline]
3. Kellett J, Beane B. The Simple Clinical Score predicts mortality for 30 days after admission to an acute medical unit. Q J Med (2006) 99:771–81.[Web of Science]
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5. Jones HJS, de Cossart L. Risk scoring in surgical patients. Br J Surg (1999) 86:146–57.
6. Vroonhof K, van Solinge W, Rovers MM, Huisman A. Differences in mortality on the basis of complete blood count in an unselected population at the emergency department. Lab Hematol (2006) 12:134–8.[CrossRef][Medline]
7. Vroonhof K, van Solinge WW, Rovers MM, Huisman A. Differences in mortality on the basis of complete blood count in an unselected population at the emergency department. Clin Chem Lab Med (2005) 43:536–41.[CrossRef][Web of Science][Medline]
8. Khuri SF, Daley J, Henderson W, et al. The Department of Veterans Affairs’ NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. National VA Surgical Quality Improvement Program. Ann Surg (1998) 228:491–507.[CrossRef][Web of Science][Medline]
9. Best WR, Khuri SF, Phelan M, et al. Identifying patient preoperative risk factors and postoperative adverse events in administrative databases: results from the Department of Veterans Affairs National Surgical Quality Improvement Program. J Am Coll Surg (2002) 194:257–66.[CrossRef][Web of Science][Medline]
10. Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit. Br J Surg (1991) 78:355–60.[Medline]
11. Prytherch DR, Whiteley MS, Higgins B, Weaver PC, Prout WG, Powell SJ. POSSUM and P-Possum for Predicting Mortality. Br J Surg (1998) 85:1217–20.[CrossRef][Web of Science][Medline]
12. Prytherch DR, Sirl JS, Weaver PC, Schmidt P, Higgins B, Sutton GL. Towards a national clinical minimum dataset for general surgery. Br J Surg (2003) 90:1300–5.[CrossRef][Web of Science][Medline]
13. Neary WD, Prytherch D, Foy C, Heather BP, Earnshaw JJ. Comparison of different methods of stratification in urgent and emergency surgery. Br J Surg (2007) 94:1300–5.[CrossRef][Web of Science][Medline]
14. Hucker TR, Mitchell GP, Blake LD, et al. Identifying the sick: can biochemical measurements be used to aid decision making on presentation to the accident and emergency department. Br J Anaesth (2005) 94:735–41.
15. Prytherch DR, Sirl JS, Schmidt P, Featherstone PI, Weaver PC, Smith GB. The use of routine laboratory data to predict in-hospital death in medical admissions. Resuscitation (2005) 66:203–7.[CrossRef][Web of Science][Medline]
16. Tabak YP, Johannes RS, Silber JH. Using automated clinical data for risk adjustment; development and validation of six disease-specific mortality predictive models for pay-for-performance. Med Care (2007) 45:789–805.[CrossRef][Web of Science][Medline]
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