Use of dynamic microsimulation to predict disease progression in patients with pneumonia-related sepsis.

INTRODUCTION: Sepsis is the leading cause of death in critically ill patients and often affects individuals with community-acquired pneumonia. To overcome the limitations of earlier mathematical models used to describe sepsis and predict outcomes, we designed an empirically based Monte Carlo model that simulates the progression of sepsis in hospitalized patients over a 30-day period. METHODS: The model simulates changing health over time, as represented by the Sepsis-related Organ Failure Assessment (SOFA) score, as a function of a patient's previous health state and length of hospital stay. We used data from patients enrolled in the GenIMS (Genetic and Inflammatory Markers of Sepsis) study to calibrate the model, and tested the model's ability to predict deaths, discharges, and daily SOFA scores over time using different algorithms to estimate the natural history of sepsis. We evaluated the stability of the methods using bootstrap sampling techniques. RESULTS: Of the 1,888 patients originally enrolled, most were elderly (mean age 67.77 years) and white (80.72%). About half (47.98%) were female. Most were relatively ill, with a mean Acute Physiology and Chronic Health Evaluation III score of 56 and Pneumonia Severity Index score of 73.5. The model's estimates of the daily pattern of deaths, discharges, and SOFA scores over time were not statistically different from the actual pattern when information about how long patients had been ill was included in the model (P = 0.91 to 0.98 for discharges; P = 0.26 to 0.68 for deaths). However, model estimates of these patterns were different from the actual pattern when the model did not include data on the duration of illness (P < 0.001 for discharges; P = 0.001 to 0.040 for deaths). Model results were stable to bootstrap validation. CONCLUSION: An empiric simulation model of sepsis can predict complex longitudinal patterns in the progression of sepsis, most accurately by models that contain data representing both organ-system levels of and duration of illness. This work supports the incorporation into mathematical models of disease of the clinical intuition that the history of disease in an individual matters, and represents an advance over several prior simulation models that assume a constant rate of disease progression.

A clinically based discrete-event simulation of end-stage liver disease and the organ allocation process.

BACKGROUND: The optimal allocation of scarce donor livers is a contentious health care issue requiring careful analysis. The objective of this article was to design a biologically based discrete-event simulation to test proposed changes in allocation policies. METHODS: The authors used data from multiple sources to simulate end-stage liver disease and the complex allocation system. To validate the model, they compared simulation output with historical data. RESULTS: Simulation outcomes were within 1% to 2% of actual results for measures such as new candidates, donated livers, and transplants by year. The model overestimated the yearly size of the waiting list by 5% in the last year of the simulation and the total number of pretransplant deaths by 10%. CONCLUSION: The authors created a discrete-event simulation model that represents the biology of end-stage liver disease and the health care organization of transplantation in the United States.

Consequences of cold-ischemia time on primary nonfunction and patient and graft survival in liver transplantation: a meta-analysis.

INTRODUCTION: The ability to preserve organs prior to transplant is essential to the organ allocation process. OBJECTIVE: The purpose of this study is to describe the functional relationship between cold-ischemia time (CIT) and primary nonfunction (PNF), patient and graft survival in liver transplant. METHODS: To identify relevant articles Medline, EMBASE and the Cochrane database, including the non-English literature identified in these databases, was searched from 1966 to April 2008. Two independent reviewers screened and extracted the data. CIT was analyzed both as a continuous variable and stratified by clinically relevant intervals. Nondichotomous variables were weighted by sample size. Percent variables were weighted by the inverse of the binomial variance. RESULTS: Twenty-six studies met criteria. Functionally, PNF% = -6.678281+0.9134701*CIT Mean+0.1250879*(CIT Mean-9.89535)2-0.0067663*(CIT Mean-9.89535)3, r2 = .625, , p<.0001. Mean patient survival: 93% (1 month), 88% (3 months), 83% (6 months) and 83% (12 months). Mean graft survival: 85.9% (1 month), 80.5% (3 months), 78.1% (6 months) and 76.8% (12 months). Maximum patient and graft survival occurred with CITs between 7.5-12.5 hrs at each survival interval. PNF was also significantly correlated with ICU time, % first time grafts and % immunologic mismatches. CONCLUSION: The results of this work imply that CIT may be the most important pre-transplant information needed in the decision to accept an organ.

Simulation and critical care modeling.

PURPOSE OF REVIEW: Decisions made in critical care are often complicated, requiring an in-depth understanding of the relations between complex diseases, available interventions, and patients with a wide range of characteristics. Standard modeling techniques such as decision trees and statistical modeling have difficulty in capturing these interactions as the complexity of the problem increases. RECENT FINDINGS: Recent models in the literature suggest that simulation modeling techniques such as Markov modeling, Monte Carlo simulation, and discrete-event simulation are useful tools for analyzing complex systems in critical care. These simulation techniques are reviewed briefly, and examples from the literature are presented to demonstrate their usefulness in understanding real problems in critical care. SUMMARY: Simulation models provide useful tools for organizing and analyzing the interactions between therapies, tradeoffs, and outcomes.