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.

Impact of worsening renal function during hospital admission on resource utilization in patients with heart failure.

Renal impairment frequently accompanies heart failure (HF) and is a recognized independent risk factor for morbidity and mortality. Few data are available assessing the impact of worsening renal function (WRF) during hospitalization on health care resource use in patients with HF. Health Insurance Portability and Accountability Act-compliant, de-identified, clinical, laboratory, and economic data for patients admitted to a tertiary care medical center with a primary diagnosis of HF were extracted by MedMining and reviewed retrospectively by the authors. Patients were excluded if they had no previous HF or were admitted for acute coronary syndrome or coronary artery bypass grafting within 30 days of index hospitalization. WRF was defined as ≥ 0.3 mg/dl increase in serum creatinine from baseline at any time during hospitalization. Of 5,803 hospitalized patients with primary HF diagnosis, 827 patients (14%) fulfilled all prespecified inclusion and exclusion criteria (74 ± 14 years of age, 43% men, 98% white, admission serum creatinine 1.4 ± 0.9 mg/dl, estimated glomerular filtration rate < 90 ml/min/1.73 m(2) at admission in 83%). During index hospitalization, WRF was identified in nearly 33%. Compared to patients without WRF, those with WRF had greater prevalence of diabetes (54% vs 43%), lower estimated glomerular filtration rate (44 ± 30 vs 62 ± 35 ml/min/1.73 m(2)), higher serum potassium (4.3 ± 0.7 vs 4.2 ± 0.7 mEq/L), and higher B-type natriuretic peptide (845 ± 821 vs 795 ± 947 pg/ml) at baseline (all p values < 0.05). Patients developing WRF incurred higher total inpatient costs ($10,977, range 671 to 212,819, vs $7,820, range 697 to 269,797, p < 0.001) and longer hospital stay (8.2 ± 6.8 vs 5.7 ± 5.5 days, p < 0.001). In conclusion, occurrence of WRF during HF-related hospitalization is associated with higher hospitalization costs and longer hospital stay.