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

Saka, G; Kreke, Jennifer E.; Schaefer, Andrew J.; Chang, Chung‐Chou H.; Roberts, Mark S.; Angus, Derek C.

doi:10.1186/cc5942

Cited by 12 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our cohort consists of patients with community- and hospital-acquired infections, hospitalized in ICU or in general wards, whose mean age shows a young population, 33% of whose individuals have no comorbidities. In terms of the quantification of severity by SOFA and APACHE II scores, our data are comparatively low in comparison with those of other populations, even in the septic shock group [7-9,28-30]. …”

Section: Discussioncontrasting

confidence: 80%

“…The process has traditionally been understood as a linear sequence spanning different clinical stages [4], from infection to septic shock. However, with few exceptions such a clinical trial carried out almost two decades ago [5], some theoretical models with mathematical simulations [6,7], and cohort studies with highly selected patients, whether by ICU admission [8] or by the specific diagnosis of pneumonia [9]; a formal and adequate characterization of the potential progression from infection without systemic manifestations to septic shock or death has not been attempted. To understand this clinical behavior, and to know the timeline and the timing of relevant events within the sepsis syndrome, might be a cornerstone of the real dynamic behind infection and host’s response.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical course of sepsis, severe sepsis, and septic shock in a cohort of infected patients from ten Colombian hospitals

et al. 2013

View full text Add to dashboard Cite

BackgroundSepsis has several clinical stages, and mortality rates are different for each stage. Our goal was to establish the evolution and the determinants of the progression of clinical stages, from infection to septic shock, over the first week, as well as their relationship to 7-day and 28-day mortality.MethodsThis is a secondary analysis of a multicenter cohort of inpatients hospitalized in general wards or intensive care units (ICUs). The general estimating equations (GEE) model was used to estimate the risk of progression and the determinants of stages of infection over the first week. Cox regression with time-dependent covariates and fixed covariates was used to determine the factors related with 7-day and 28-day mortality, respectively.ResultsIn 2681 patients we show that progression to severe sepsis and septic shock increases with intraabdominal and respiratory sources of infection [OR = 1,32; 95%IC = 1,20-1,46 and OR = 1.21, 95%CI = 1,11-1,33 respectively], as well as according to Acute Physiology and Chronic Health Evaluation II (APACHE II) [OR = 1,03; 95%CI = 1,02-1,03] and Sequential Organ Failure Assessment (SOFA) [OR = 1,16; 95%CI = 1,14-1,17] scores. The variables related with first-week mortality were progression to severe sepsis [HR = 2,13; 95%CI = 1,13-4,03] and septic shock [HR = 3,00; 95%CI = 1,50-5.98], respiratory source of infection [HR = 1,76; 95%IC = 1,12-2,77], APACHE II [HR = 1,07; 95% CI = 1,04-1,10] and SOFA [HR = 1,09; 95%IC = 1,04-1,15] scores.ConclusionsIntraabdominal and respiratory sources of infection, independently of SOFA and APACHE II scores, increase the risk of clinical progression to more severe stages of sepsis; and these factors, together with progression of the infection itself, are the main determinants of 7-day and 28-day mortality.

show abstract

Section: Discussioncontrasting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Clinical course of sepsis, severe sepsis, and septic shock in a cohort of infected patients from ten Colombian hospitals

et al. 2013

View full text Add to dashboard Cite

show abstract

“…To overcome these limitations, investigators (123) have employed sophisticated data analysis, such as artificial neural networks, in an attempt to better define the cohort of septic patients who are at a high risk of death after admission to the emergency room. Other investigators (124) have developed mathematical models to better predict patient trajectories: An empirically based Monte Carlo microsimulation model was able to predict hospital discharges, in-hospital deaths, and serial SOFA scores of patients with sepsis, which demonstrates that the duration of disease is a critical factor in predicting the outcomes of sepsis.…”

Section: Biomarkersmentioning

confidence: 99%

The Pathogenesis of Sepsis

Stearns-Kurosawa

Osuchowski²,

Valentine³

et al. 2011

Annu. Rev. Pathol. Mech. Dis.

497

420

View full text Add to dashboard Cite

Sepsis is a serious clinical condition that represents a patient’s response to a severe infection and has a very high mortality rate. Normal immune and physiologic responses eradicate pathogens, and the pathophysiology of sepsis is due to the inappropriate regulation of these normal reactions. In an ideal scenario, the first pathogen contact with the inflammatory system should eliminate the microbe and quickly return the host to homeostasis. The septic response may accelerate due to continued activation of neutrophils and macrophages/monocytes. Upregulation of lymphocyte costimulatory molecules and rapid lymphocyte apoptosis, delayed apoptosis of neutrophils, and enhanced necrosis of cells/tissues also contribute to the pathogenesis of sepsis. The coagulation system is closely tied to the inflammatory response, with cross talk between the two systems driving the dysregulated response. Biomarkers may be used to help diagnose patients with sepsis, and they may also help to identify patients who would benefit from immunomodulatory therapies.

show abstract

“…This is the first study, to our knowledge, to consider pneumonia severity scores as dynamic tools that can be calculated at different time points in the hospital course. Our findings take into consideration the dynamic nature of clinical criteria and suggest that this approach might improve the performance of existing severity assessment tools 15. Moreover, further investigation is needed to explore potential contributors to initial inpatient therapy failure and subsequent need for ICU transfer.…”

Section: Discussionmentioning

confidence: 99%

CURB‐65 and SMRT‐CO in the prediction of early transfers to the intensive care unit among patients with community‐acquired pneumonia initially admitted to a general ward

Huamán

Diaz-Kuan

Hegab

et al. 2011

Journal of Hospital Medicine

View full text Add to dashboard Cite

show abstract

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

Cited by 12 publications

References 18 publications

Clinical course of sepsis, severe sepsis, and septic shock in a cohort of infected patients from ten Colombian hospitals

Clinical course of sepsis, severe sepsis, and septic shock in a cohort of infected patients from ten Colombian hospitals

The Pathogenesis of Sepsis

CURB‐65 and SMRT‐CO in the prediction of early transfers to the intensive care unit among patients with community‐acquired pneumonia initially admitted to a general ward

Contact Info

Product

Resources

About