Disease-based Modeling to Predict Fluid Response in Intensive Care Units

Summary

Objective: To compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units.

Methods: Retrospective cohort study in -volving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid resuscitation. Within this cohort there were two disease-based groups, 802 patients with a diagnosis of pneumonia, and 143 patients with a diagnosis of pancreatitis. Fluid resuscitation either progressing to subsequent vasopressor administration or not was used as the primary outcome variable to compare general and disease-based modeling.

Results: Patients with pancreatitis, pneumonia and the general group all shared three common predictive features as core variables, arterial base excess, lactic acid and platelets. Patients with pneumonia also had non-invasive systolic blood pressure and white blood cells added to the core model, and pancreatitis patients additionally had temperature. Disease-based models had significantly higher values of AUC (p < 0.05) than the general group (0.82 f± 0.02 for pneumonia and 0.83 ± 0.03 for pancreatitis vs. 0.79 ± 0.02 for general patients).

Conclusions: Disease-based predictive mod -eling reveals a different set of predictive variables compared to general modeling and improved performance. Our findings add support to the growing body of evidence advantaging disease specific predictive modeling.

Keywords

Disease-based modeling - fluid resuscitation - intensive care units - decision modeling

• References

• 1 Celi LAG, Tang RJ, Villarroel MC. et al. A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury. Journal of Healthcare Engineering 2011; 2: 97-110.
• 2 Strand K, Flaaten H. Severity Scoring in the ICU: A Review. Acta Anaesthesioogical Scandinavica 2008; 52 (04) 467-478.
• 3 Bellazzi R, Diomidous M, Sarkar IN, Takabayashi K, Ziegler A, McCray AT. Data Analysis and Data Mining: Current Issues in Biomedical Informatics. Methods Inf Med 2011; 50 (06) 536-544.
• 4 Peek N, Combi C, Tucker A. Biomedical Data Mining. Methods Inf Med 2009; 48: 225-228.
• 5 Alam HB. Advances in resuscitation strategies. Int J Surg 2011; 9 (01) 5-12.
• 6 Alam HB, Rhee P. New developments in fluid resuscitation. Surg Clin North Am 2007; 87 (01) 55-72.
• 7 Boluyt N, Bollen CW, Bos AP, Kok JH, Offringa M. Fluid resuscitation in neonatal and pediatric hypovolemic shock: a Dutch Pediatric Society evidence-based clinical practice guideline. Intensive Care Med 2006; 32 (07) 995-1003.
• 8 Saeed M, Lieu C, Mark R. MIMIC II: A massive temporal ICU database to support research in intelligence patient monitoring. Computers in Cardiology 2002; 29: 641-644.
• 9 Clifford GD, Long WJ, Moody GB. et al. Robust parameter extraction for decision support using multimodal intensive care data. Philosophical transactions, Series A, Mathematical, physical, and engineering sciences 2009; 367: 411-429.
• 10 Allison PD. Missing Data. Sage University Papers Series on Quantitative Applications in the Social Sciences 2001.
• 11 Hoaglin D, Mosteller F, Tukey J. (editors). Understanding Robust and Exploratory Data Analysis. New York: John Wiley & Sons; 1983.
• 12 Cismondi F, Fialho A, Vieira S, Sousa J, Reti S, Howell M. et al. New methods to processing misaligned, unevenly sampled time series containing missing data. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Paris: 2011: 224-231.
• 13 Committee on Trauma. (1997). Shock. Advanced Trauma Life Support, Student Course Manual 6th Edition. Chicago: American College of Surgeons; 87-107.
• 14 EAST (2003) Clinical Practice Guidelines: Endpoints of Resuscitation (NGC:003293, p 28). Eastern Association for the Surgery of Trauma
• 15 Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001; 29: 1303-1310.
• 16 Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 2003; 348: 1546-1554.
• 17 Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived from ICD-9 — Critical care medicine administrative data. Med Care 2002; 48: 675-685.
• 18 Bates JHT, Young MP. Applying Fuzzy Logic to Medical Decision Making in the Intensive Care Unit. Am J Respir Crit Care Med 2003; 67 (07) 948-952.
• 19 Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics 1985; 15: 116-132.
• 20 Sousa JMC, Kaymak U. Fuzzy Decision Making in Modeling and Control. Singapore: World Scientific Pub.Co.; 2002.
• 21 Mendonça LF, Vieira SM, Sousa JMC. Decision tree search methods in fuzzy modeling and classification. Int J Approx Reason 2007; 44: 106-123.
• 22 Vieira SM, Sousa JMC, Runkler TA. Two cooperative ant colonies for feature selection using fuzzy models. Expert Systems with Applications 2010; 37 (04) 2714-2723.
• 23 Hadorn DC, Keeler E, Rogers W. et al. Assessing the Performance of Mortality Prediction Models. Santa Monica, CA: Rand; 1993.
• 24 Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29-36.
• 25 Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of International Joint Conference on AI. 1995: 1137-1145.