Skip to main content

Combining various severity of illness scoring systems to improve outcome prediction: pilot experience in the critically ill obstetric population

Introduction

No perfect severity score exists to predict ICU mortality, thus the search for new systems is still a preoccupation.

Hypothesis

Use of many severity of illness scores simultaneously improves mortality prediction.

Patients and methods

An open prospective observational study as part of the APRiMo project [1]. The study period was January 1996–September 2004. Inclusion criteria were critically ill obstetric patients and ICU length of stay >24 hours. Exclusion criteria were those of the used scores. The main outcome of interest was the survival status at ICU discharge. The database was divided into two samples: development and validation datasets. Development database patients were chosen randomly (n = 414) and the remaining patients composed the validation dataset (n = 229). A multivariable logistic regression model was developed to predict mortality associating the Acute Physiology and Chronic Health Evaluation II score [2], Simplified Acute Physiology Score II [3], Admission Mortality Prediction Model (MPM-H0) and Day 1 Mortality Prediction Model (MPM-H24) [4]. Discrimination and calibration were assessed by goodness-of-fit C-hat statistics and area under the ROC curve. The developed model was then tested in the validation dataset. Good discrimination was retained if C-hat statistics P > 0.1 and good calibration if area under the ROC curve > 0.8.

Results

Six hundred and forty-three patients enrolled. The overall mortality rate was 11.51%. The new model predicted accurately 99% of survivors and more than 60% of nonsurvivors.

Conclusion

The 'multiscore' model seems to refine prognosis. This is partly due to mixing of new evaluated parameters. Testing the latest developed generations of scores and also organ dysfunction systems could be interesting.

Table 1

References

  1. 1.

    Haddad Z, et al.: Critically ill obstetric patients: outcome and predictability. Crit Care 2005,9(Suppl 1):S92-S93. 10.1186/cc3155

    Article  Google Scholar 

  2. 2.

    Knaus WA, et al.: APACHE II: a severity of disease classification system. Crit Care Med 1985, 13: 818-829. 10.1097/00003246-198510000-00009

    CAS  Article  Google Scholar 

  3. 3.

    Le Gall JR, et al.: A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 1993, 270: 2957-2963. 10.1001/jama.270.24.2957

    CAS  Article  Google Scholar 

  4. 4.

    Lemeshow S, et al.: Refining intensive care unit outcome prediction by using changing probabilities of mortality. Crit Care Med 1988, 16: 470-477. 10.1097/00003246-198805000-00002

    CAS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Haddad, Z., Kaddour, C., Skandrani, L. et al. Combining various severity of illness scoring systems to improve outcome prediction: pilot experience in the critically ill obstetric population. Crit Care 11, P458 (2007). https://doi.org/10.1186/cc5618

Download citation

Keywords

  • Validation Dataset
  • Chronic Health Evaluation
  • Multivariable Logistic Regression Model
  • Acute Physiology Score
  • Obstetric Patient