Skip to main content
SpringerLink
Log in

Risk Stratification and Prediction Modelling in Surgery

  • Chapter
Key Topics in Surgical Research and Methodology

  • 2778 Accesses

Abstract

For more than three decades, multivariable risk factor analysis has been the main statistical technique for identifying and quantifying treatment outcome differences adjusted for patient characteristics, these differences being treated as associations with outcomes, and not causes. There is no guarantee that risk factor analysis is an effective strategy for discovery of a cause-and-effect mechanism. Multivariable logistic regression appears to be very suitable for epi-demiological surgical research, especially for dichoto-mous outcomes such as mortality, as disease occurrence has multiple risk factors, which can be mutually correlated. The mathematical model known as a multiple logistic regression assumes that the dependent variable (the outcome of interest, such as death in this case) is linearly and additively related to the independent variables (patient risk factors) on the logistic scale. The technique is useful primarily because it produces a direct estimate of the odds ratio of any risk factor, which reflects the dose—response relationship between these and the outcome. However, if this actual relationship is non-linear, then an adequate fit can usually be achieved by adding polynomial and product interaction terms to the model, although the meaning of the odds ratios in such an interaction model would be severely circumscribed. An essential step in risk stratification modeling (RSM) is to evaluate whether there is evidence of an interaction between the variables used in a model. This interaction would imply that the effect of one of the variables is not constant over levels of the other. An important drawback of logistic regression is that it can increase bias because of misclassification and measurement errors in confounding variables and differences between conditional and unconditional odds ratio estimates of treatment effects. In this chapter, we outline all the basic components that the surgeon needs to know about risk stratification and predictive modelling.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

AAA:

Abdominal aortic aneurysm

ANN:

Artificial neural network

APACHE:

Acute physiology and chronic health evaluation

APS:

Acute physiology score

ASA:

American Society of Anaesthesiology

ECG:

Electrocardiogram

ICU:

Intensive care unit

MPM:

Mortality prediction model

O:E:

Observed:expected

POSSUM:

Physiology and operative severity score for the enumeration of mortality and morbidity

ROC:

Receiver operating characteristic curve

RSM:

Risk stratification modelling

SAPS:

Simplified acute physiology score

SMR:

Standardised mortality ratio

SMS:

Surgical mortality score

VBHOM:

Vascular biochemistry and haematology outcome models

References

  1. Hadjianastassiou VG, Tekkis PP, Poloniecki JD et al (2004) Surgical mortality score: risk management tool for auditing surgical performance. World J Surg 28:193–200

    Article  PubMed  Google Scholar 

  2. Gunning K, Rowan K (1999) ABC of intensive care: outcome data and scoring systems. BMJ 319:241–244

    Article  CAS  PubMed  Google Scholar 

  3. Lemeshow S, Klar J, Teres D (1995) Outcome prediction for individual intensive care patients: useful, misused, or abused? Intensive Care Med 21:770–776

    Article  CAS  PubMed  Google Scholar 

  4. Marcin JP, Pollack MM, Patel KM et al (2000) Combining physician's subjective and physiology-based objective mortality risk predictions. Crit Care Med 28:2984–2990

    Article  CAS  PubMed  Google Scholar 

  5. Tekkis PP, Poloniecki JD, Thompson MR et al (2003) Operative mortality in colorectal cancer: prospective national study. BMJ 327:1196–1201

    Article  PubMed  Google Scholar 

  6. Cullen DJ, Chernow B (1994) Predicting outcome in critically ill patients. Crit Care Med 22:1345–1348

    Article  CAS  PubMed  Google Scholar 

  7. Hadjianastassiou VG, Franco L, Jerez JM et al (2006) Informed prognosis [corrected] after abdominal aortic aneu-rysm repair using predictive modeling techniques [corrected]. J Vasc Surg 43:467–473

    Article  PubMed  Google Scholar 

  8. Joint Commission Resources (1999) Florence nightingale: measuring health care outcomes. Joint Commission on Accreditation of Healthcare Organizations, Oakbrook, IL

    Google Scholar 

  9. Nightingale F, Jcaho (1999) Measuring Hospital Care Outcomes. Joint Commission on Accreditation of Healthcare

    Google Scholar 

  10. Mourouga P, Goldfrad C, Rowan KM (2000) Does it fit? Is it good? Assessment of scoring systems (severity scoring in the critically ill patient). Curr Opin Crit Care 6:176–180

    Article  Google Scholar 

  11. Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York

    Book  Google Scholar 

  12. Jones HJ, de Cossart L (1999) Risk scoring in surgical patients. Br J Surg 86:149–157

    Article  CAS  PubMed  Google Scholar 

  13. Vacanti CJ, VanHouten RJ, Hill RC (1970) A statistical analysis of the relationship of physical status to postoperative mortality in 68,388 cases. Anesth Analg 49:564–566

    Article  CAS  PubMed  Google Scholar 

  14. Schuster DP (1992) Predicting outcome after ICU admission. The art and science of assessing risk. Chest 102:1861–1870

    Article  CAS  PubMed  Google Scholar 

  15. Knaus WA, Zimmerman JE, Wagner DP et al (1981) APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med 9:591–597

    Article  CAS  PubMed  Google Scholar 

  16. Knaus WA, Draper EA, Wagner DP et al (1985) APACHE II: a severity of disease classification system. Crit Care Med 13:818–829

    Article  CAS  PubMed  Google Scholar 

  17. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    Article  PubMed  Google Scholar 

  18. Wisner DH (1992) History and current status of scoring systems for critical care. Arch Surg 127:352–356

    Article  CAS  PubMed  Google Scholar 

  19. Knaus WA, Wagner DP, Draper EA et al (1991) The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100:1619–1636

    Article  CAS  PubMed  Google Scholar 

  20. Lemeshow S, Le Gall JR (1994) Modeling the severity of illness of ICU patients. A systems update. JAMA 272:1049–1055

    Article  CAS  PubMed  Google Scholar 

  21. Berger MM, Marazzi A, Freeman J et al (1992) Evaluation of the consistency of Acute Physiology and Chronic Health Evaluation (APACHE II) scoring in a surgical intensive care unit. Crit Care Med 20:1681–1687

    Article  CAS  PubMed  Google Scholar 

  22. Beck DH, Taylor BL, Millar B et al (1997) Prediction of outcome from intensive care: a prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit. Crit Care Med 25:9–15

    Article  CAS  PubMed  Google Scholar 

  23. Barie PS, Hydo LJ, Fischer E (1995) Comparison of APACHE II and III scoring systems for mortality prediction in critical surgical illness. Arch Surg 130:77–82

    Article  CAS  PubMed  Google Scholar 

  24. Beck DH, Smith GB, Pappachan JV et al (2003) External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study. Intensive Care Med 29:249–256

    PubMed  Google Scholar 

  25. Rowan KM, Kerr JH, Major E et al (1993) Intensive Care Society's APACHE II study in Britain and Ireland-II: outcome comparisons of intensive care units after adjustment for case mix by the American APACHE II method. BMJ 307:977–981

    Article  CAS  PubMed  Google Scholar 

  26. Rowan KM, Kerr JH, Major E et al (1993) Intensive Care Society's APACHE II study in Britain and Ireland-I: variations in case mix of adult admissions to general intensive care units and impact on outcome. BMJ 307:972–977

    Article  CAS  PubMed  Google Scholar 

  27. Pappachan J V, Millar B, Bennett ED et al (1999) Comparison of outcome from intensive care admission after adjustment for case mix by the APACHE III prognostic system. Chest 115:802–810

    Article  CAS  PubMed  Google Scholar 

  28. Le Gall JR, Loirat P, Alperovitch A et al (1984) A simplified acute physiology score for ICU patients. Crit Care Med 12:975–977

    Article  PubMed  Google Scholar 

  29. Le Gall JR, Lemeshow S, Saulnier F (1993) A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 270:2957–2963

    Article  PubMed  Google Scholar 

  30. Beck DH, Smith GB, Taylor BL (2002) The impact of low-risk intensive care unit admissions on mortality probabilities by SAPS II, APACHE II and APACHE III. Anaesthesia 57:21–26

    Article  CAS  PubMed  Google Scholar 

  31. Nuttall P. The passionate statistician (Florence Nightingale). Nurs Times. 1983 Sep 28-Oct 4;79(39):25–7

    CAS  PubMed  Google Scholar 

  32. Lemeshow S, Teres D, Klar J et al (1993) Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA 270:2478–2486

    Article  CAS  PubMed  Google Scholar 

  33. Rowan KM, Kerr JH, Major E et al (1994) Intensive Care Society's acute physiology and chronic health evaluation (APACHE II) study in Britain and Ireland: a prospective, multicenter, cohort study comparing two methods for predicting outcome for adult intensive care patients. Crit Care Med 22:1392–1401

    Article  CAS  PubMed  Google Scholar 

  34. Livingston BM, MacKirdy FN, Howie JC et al (2000) Assessment of the performance of five intensive care scoring models within a large Scottish database. Crit Care Med 28:1820–1827

    Article  CAS  PubMed  Google Scholar 

  35. Boyd O, Grounds RM (1993) Physiological scoring systems and audit. Lancet 341:1573–1574

    Article  CAS  PubMed  Google Scholar 

  36. Iezzoni L (1997) Risk adjustment for measuring health care outcomes. Health Administration Press, Chicago

    Google Scholar 

  37. Khuri SF, Daley J, Henderson W et al (1997) Risk adjustment of the postoperative mortality rate for the comparative assessment of the quality of surgical care: results of the National Veterans Affairs Surgical Risk Study. J Am Coll Surg 185:315–327

    CAS  PubMed  Google Scholar 

  38. Copeland GP, Jones D, Walters M (1991) POSSUM: a scoring system for surgical audit. Br J Surg 78:355–360

    Article  CAS  PubMed  Google Scholar 

  39. Wijesinghe LD, Mahmood T, Scott DJ et al (1998) Comparison of POSSUM and the Portsmouth predictor equation for predicting death following vascular surgery. Br J Surg 85:209–212

    Article  CAS  PubMed  Google Scholar 

  40. Neary WD, Heather BP, Earnshaw JJ (2003) The physiological and operative severity Score for the enumeration of mortality and morbidity (POSSUM). Br J Surg 90:157–165

    Article  CAS  PubMed  Google Scholar 

  41. Whiteley MS, Prytherch DR, Higgins B et al (1996) An evaluation of the POSSUM surgical scoring system. Br J Surg 83:812–815

    Article  CAS  PubMed  Google Scholar 

  42. Prytherch DR, Whiteley MS, Higgins B et al (1998) POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and operative severity score for the enumeration of mortality and morbidity. Br J Surg 85:1217–1220

    Article  CAS  PubMed  Google Scholar 

  43. Prytherch DR, Ridler BM, Beard JD et al (2001) A model for national outcome audit in vascular surgery. Eur J Vasc Endovasc Surg 21:477–483

    Article  CAS  PubMed  Google Scholar 

  44. Prytherch DR, Sutton GL, Boyle JR (2001) Portsmouth POSSUM models for abdominal aortic aneurysm surgery. Br J Surg 88:958–963

    Article  CAS  PubMed  Google Scholar 

  45. Tekkis PP, Prytherch DR, Kocher HM et al (2004) Development of a dedicated risk-adjustment scoring system for colorectal surgery (colorectal POSSUM). Br J Surg 91:1174–1182

    Article  CAS  PubMed  Google Scholar 

  46. Tekkis PP, McCulloch P, Poloniecki JD et al (2004) Risk-adjusted prediction of operative mortality in oesophagogas-tric surgery with O-POSSUM. Br J Surg 91:288–295

    Article  CAS  PubMed  Google Scholar 

  47. Bann SD, Sarin S (2001) Comparative audit: the trouble with POSSUM. J R Soc Med 94:632–634

    CAS  PubMed  Google Scholar 

  48. Lloyd H, Ahmed I, Taylor S et al (2005) Index for predicting mortality in elderly surgical patients. Br J Surg 92:487–492

    Article  CAS  PubMed  Google Scholar 

  49. Prytherch DR, Ridler BM, Ashley S (2005) Risk-adjusted predictive models of mortality after index arterial operations using a minimal data set. Br J Surg 92:714–718

    Article  CAS  PubMed  Google Scholar 

  50. Prytherch DR, Sirl JS, Weaver PC et al (2003) Towards a national clinical minimum data set for general surgery. Br J Surg 90:1300–1305

    Article  CAS  PubMed  Google Scholar 

  51. Tang T, Walsh SR, Prytherch DR et al (2007) VBHOM, a data economic model for predicting the outcome after open abdominal aortic aneurysm surgery. Br J Surg 94:717–721

    Article  CAS  PubMed  Google Scholar 

  52. Hadjianastassiou V (2007) VBHOM, a data economic model for predicting the outcome after open abdominal aortic aneu-rysm surgery. Br J Surg 94:1308

    Article  CAS  PubMed  Google Scholar 

  53. Hadjianastassiou VG, Tekkis PP, Goldhill DR et al (2005) Quantification of mortality risk after abdominal aortic aneu-rysm repair. Br J Surg 92:1092–1098

    Article  CAS  PubMed  Google Scholar 

  54. Hadjianastassiou VG, Tekkis PP, Athanasiou T et al (2007) External validity of a mortality prediction model in patients after open abdominal aortic aneurysm repair using multilevel methodology. Eur J Vasc Endovasc Surg 34:514–521

    Article  CAS  PubMed  Google Scholar 

  55. Hadjianastassiou VG, Tekkis PP, Athanasiou T et al (2007) Comparison of mortality prediction models after open abdominal aortic aneurysm repair. Eur J Vasc Endovasc Surg 33:536–543

    Article  CAS  PubMed  Google Scholar 

  56. Daley J, Forbes MG, Young GJ et al (1997) Validating risk-adjusted surgical outcomes: site visit assessment of process and structure. National VA Surgical Risk Study. J Am Coll Surg 185:341–351

    CAS  PubMed  Google Scholar 

  57. Khuri SF, Daley J, Henderson W et al (1995) The National Veterans Administration Surgical Risk Study: risk adjustment for the comparative assessment of the quality of surgical care. J Am Coll Surg 180:519–531

    CAS  PubMed  Google Scholar 

  58. Khuri SF, Daley J, Henderson W et al (1998) 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 228:491–507

    Article  CAS  PubMed  Google Scholar 

  59. Samy AK, Murray G, MacBain G (1994) Glasgow aneurysm score. Cardiovasc Surg 2:41–44

    CAS  PubMed  Google Scholar 

  60. Samy AK, Murray G, MacBain G (1996) Prospective evaluation of the Glasgow Aneurysm Score. J R Coll Surg Edinb 41:105–107

    CAS  PubMed  Google Scholar 

  61. Prance SE, Wilson YG, Cosgrove CM et al (1999) Ruptured abdominal aortic aneurysms: selecting patients for surgery. Eur J Vasc Endovasc Surg 17:129–132

    Article  CAS  PubMed  Google Scholar 

  62. Tambyraja AL, Fraser SC, Murie JA et al (2005) Validity of the Glasgow Aneurysm Score and the Hardman Index in predicting outcome after ruptured abdominal aortic aneurysm repair. Br J Surg 92:570–573

    Article  CAS  PubMed  Google Scholar 

  63. De Ritis G, Giovannini C, Picardo S et al (1995) Multivariate prediction of in-hospital mortality associated with surgical procedures. Minerva Anestesiol 61:173–181

    PubMed  Google Scholar 

  64. Lee J (1986) An insight on the use of multiple logistic regression analysis to estimate association between risk factor and disease occurrence. Int J Epidemiol 15:22–29

    Article  CAS  PubMed  Google Scholar 

  65. Harrell FE Jr, Lee KL, Matchar DB et al (1985) Regression models for prognostic prediction: advantages, problems, and suggested solutions. Cancer Treat Rep 69:1071–1077

    PubMed  Google Scholar 

  66. Picard RR, Berk KN (1990) Data splitting. Am Stat 44:140–147

    Google Scholar 

  67. Hadorn DC, Draper D, Rogers WH et al (1992) Cross-validation performance of mortality prediction models. Stat Med 11:475–489

    Article  CAS  PubMed  Google Scholar 

  68. Efron B, Tibshirani R (1993) An introduction to the Bootstrap. Chapman & Hall, New York

    Google Scholar 

  69. Cox DR (1972) Regression models and life-tables. J R Stat Soc B 34:187–202

    Google Scholar 

  70. Bennett N (1976) Teaching styles and pupil progress. Open Books, London

    Google Scholar 

  71. Aitkin M, Anderson D, Hinde J (1981) Statistical modelling of data on teaching styles (with discussion). J Royal Statist Soc A 149:148–161

    Google Scholar 

  72. Spiegelhalter DJ, Aylin P, Best NG et al (2002) Commissioned analysis of surgical performance by using routine data: lessons from Bristol inquiry. J R Statist Soc A 165:1–31

    Article  Google Scholar 

  73. Marshall EC, Spiegelhalter DJ (1998) Reliability of league tables of in vitro fertilisation clinics: retrospective analysis of live birth rates. BMJ 316:1701–1704; discussion 1705

    Article  CAS  PubMed  Google Scholar 

  74. Kee F, Wilson RH, Harper C et al (1999) Influence of hospital and clinician workload on survival from colorectal cancer: cohort study. BMJ 318:1381–1385

    Article  CAS  PubMed  Google Scholar 

  75. McCulloch P, Ward J, Tekkis PP (2003) Mortality and morbidity in gastro-oesophageal cancer surgery: initial results of ASCOT multicentre prospective cohort study. BMJ 327:1192–1197

    Article  PubMed  Google Scholar 

  76. Beck DH, Smith GB, Pappachan JV (2002) The effects of two methods for customising the original SAPS II model for intensive care patients from South England. Anaesthesia 57:785–793

    CAS  PubMed  Google Scholar 

  77. Moreno R, Miranda DR, Fidler V et al (1998) Evaluation of two outcome prediction models on an independent database. Crit Care Med 26:50–61

    Article  CAS  PubMed  Google Scholar 

  78. Cologne JB, Shibata Y (1995) Optimal case-control matching in practice. Epidemiology 6:271–275

    Article  CAS  PubMed  Google Scholar 

  79. Rosenbaum PR (1989) Optimal matching in observational studies. J Am Stat Assoc 84:1024–1032

    Article  Google Scholar 

  80. D'Agostino RB Jr (1998) Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 17:2265–2281

    Article  PubMed  Google Scholar 

  81. Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55

    Article  Google Scholar 

  82. Rubin DB (1980) Bias reduction using mahalanobis-metric matching. Biometrics 36:293–298

    Article  Google Scholar 

  83. Drew PJ, Monson JR (2000) Artificial neural networks. Surgery 127:3–11

    Article  CAS  PubMed  Google Scholar 

  84. Cross SS, Harrison RF, Kennedy RL (1995) Introduction to neural networks. Lancet 346:1075–1079

    Article  CAS  PubMed  Google Scholar 

  85. Ramesh AN, Kambhampati C, Monson JR et al (2004) Artificial intelligence in medicine. Ann R Coll Surg Engl 86:334–338

    Article  CAS  PubMed  Google Scholar 

  86. Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. Harvard University Press, Cambridge

    Google Scholar 

  87. Burke H (1994) Artificial neural networks for cancer research: outcome prediction. Sem Surg Oncol 10:73–79

    Article  CAS  Google Scholar 

  88. Sawyer MD (2000) Invited commentary: artificial neural networks — an introduction. Surgery 127:1–2

    Article  CAS  PubMed  Google Scholar 

  89. Wei JT, Zhang Z, Barnhill SD et al (1998) Understanding arti-ficial neural networks and exploring their potential applications for the practicing urologist. Urology 52:161–172

    Article  CAS  PubMed  Google Scholar 

  90. De Laurentis M, Ravdin PM (1994) A technique for using neural network analysis to perform survival analysis of censored data. Cancer Lett 77:127–138

    Article  Google Scholar 

  91. Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346:1135–1138

    Article  CAS  PubMed  Google Scholar 

  92. Dybowski R, Gant V (1995) Artificial neural networks in pathology and medical laboratories. Lancet 346:1203–1207

    Article  CAS  PubMed  Google Scholar 

  93. Dybowski R, Weller P, Chang R et al (1996) Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet 347:1146–1150

    Article  CAS  PubMed  Google Scholar 

  94. Wong LS, Young JD (1999) A comparison of ICU mortality prediction using the APACHE II scoring system and artifi-cial neural networks. Anaesthesia 54:1048–1054

    Article  CAS  PubMed  Google Scholar 

  95. Bottaci L, Drew PJ, Hartley JE et al (1997) Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet 350:469–472

    Article  CAS  PubMed  Google Scholar 

  96. Wyatt J (1995) Nervous about artificial neural networks? Lancet 346:1175–1177

    Article  CAS  PubMed  Google Scholar 

  97. Moreno R, Apolone G, Miranda DR (1998) Evaluation of the uniformity of fit of general outcome prediction models. Intensive Care Med 24:40–47

    Article  CAS  PubMed  Google Scholar 

  98. Miller ME, Hui SL, Tierney WM (1991) Validation techniques for logistic regression models. Stat Med 10:1213–1226

    Article  CAS  PubMed  Google Scholar 

  99. Wasson JH, Sox HC, Neff RK et al (1985) Clinical prediction rules. Applications and methodological standards. N Engl J Med 313:793–799

    Article  CAS  PubMed  Google Scholar 

  100. Charlson ME, Ales KL, Simon R et al (1987) Why predictive indexes perform less well in validation studies. Is it magic or methods? Arch Intern Med 147:2155–2161

    Article  CAS  PubMed  Google Scholar 

  101. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    CAS  PubMed  Google Scholar 

  102. Harrell FE Jr, Califf RM, Pryor DB et al (1982) Evaluating the yield of medical tests. JAMA 247:2543–2546

    Article  PubMed  Google Scholar 

  103. Weijnen CF, Numans ME, de Wit NJ et al (2001) Testing for Helicobacter pylori in dyspeptic patients suspected of peptic ulcer disease in primary care: cross sectional study. BMJ 323:71–75

    Article  CAS  PubMed  Google Scholar 

  104. Schafer JH, Maurer A, Jochimsen F et al (1990) Outcome prediction models on admission in a medical intensive care unit: do they predict individual outcome? Crit Care Med 18:1111–1118

    Article  CAS  PubMed  Google Scholar 

  105. Apolone G, Bertolini G, D'Amico R et al (1996) The performance of SAPS II in a cohort of patients admitted to 99 Italian ICUs: results from GiViTI. Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva. Intensive Care Med 22:1368–1378

    Article  CAS  PubMed  Google Scholar 

  106. Poses RM, Cebul RD, Collins M et al (1986) The importance of disease prevalence in transporting clinical prediction rules. The case of streptococcal pharyngitis. Ann Intern Med 105:586–591

    Article  CAS  PubMed  Google Scholar 

  107. Ivanov J, Tu JV, Naylor CD (1999) Ready-made, recalibrated, or remodeled? Issues in the use of risk indexes for assessing mortality after coronary artery bypass graft surgery. Circulation 99:2098–2104

    Article  CAS  PubMed  Google Scholar 

  108. Lemeshow S, Klar J, Teres D et al (1994) Mortality probability models for patients in the intensive care unit for 48 or 72 hours: a prospective, multicenter study. Crit Care Med 22:1351–1358

    Article  CAS  PubMed  Google Scholar 

  109. Zhu BP, Lemeshow S, Hosmer DW et al (1996) Factors affecting the performance of the models in the Mortality Probability Model II system and strategies of customization: a simulation study. Crit Care Med 24:57–63

    Article  CAS  PubMed  Google Scholar 

  110. Altman DG, Royston P (2000) What do we mean by validating a prognostic model? Stat Med 19:453–473

    Article  CAS  PubMed  Google Scholar 

  111. Teres D, Lemeshow S (1999) When to customize a severity model. Intensive Care Med 25:140–142

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Transplantation, Directorate of Nephrology Transplantation and Urology, Guy's & St. Thomas' NHS Foundation Trust, Guy's Hospital, St. Thomas' Street, London, SE1 9RT, UK

    Vassilis G. Hadjianastassiou (Consultant Transplant Surgeon Clinician Lead in Pancreas Transplantation)

  2. The Department of Biosurgery and Surgical Technology, Imperial College London, 10th Floor, Queen Elizabeth the Queen Mother (QEQM) Building, Imperial College Healthcare NHS Trust, St Mary's Hospital Campus, Praed Street, London, W2 1NY, UK

    Thanos Athanasiou

  3. Nuffield Department of Surgery, University of Oxford, 6th Floor, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK

    Linda J. Hands

Authors
  1. Vassilis G. Hadjianastassiou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thanos Athanasiou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linda J. Hands
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. Biosurgery & Surgical Technology, Imperial College London St. Mary's Hospital London, 10th floor QEQM Bldg. Praed Street, London, W2 1NY, United Kingdom

    Thanos Athanasiou  & Ara Darzi  & 

  2. UCSF Global Health Sciences, 3333 California Street, Suite 285, San Francisco L, CA, 94143-0443, USA

    Haile Debas

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hadjianastassiou, V.G., Athanasiou, T., Hands, L.J. (2010). Risk Stratification and Prediction Modelling in Surgery. In: Athanasiou, T., Debas, H., Darzi, A. (eds) Key Topics in Surgical Research and Methodology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71915-1_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71915-1_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71914-4

  • Online ISBN: 978-3-540-71915-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation