The dynamics of disease progression in sepsis: Markov modeling describing the natural history and the likely impact of effective antisepsis agents

Optimizing the First Response to Sepsis: An Electronic Health Record-Based Markov Decision Process Model

Sepsis is considered a medical emergency where delays in initial treatment are associated with increased morbidity and mortality, yet there is no gold standard for identifying sepsis onset and thus treatment timing. We leverage electronic health record (EHR) data with clinical expertise to develop a continuous-time Markov decision process (MDP) optimal stopping model that identifies the optimal first intervention action (anti-infective, fluid, or wait). To study the impact of initial treatment of patients at risk for developing sepsis, we define the delayed treatment population who received delayed treatment upon admission or during hospitalization and serves as an approximation of the natural history of sepsis. We apply the optimal first treatment policy to sample patient visits from the nondelayed treatment population. This analysis indicates the average risk of death could be reduced by approximately 2.2%, the average time until treatment could be reduced by 106 minutes, and the average severity of the treatment state could be reduced by 15.5% compared with the treatment they received in the hospital. We study the properties of the optimal policy to define an easily interpretable initial treatment heuristic that considers a patient’s organ dysfunction, location, and septic shock status. This generalizable framework can inform personalized treatment of patients at risk for sepsis.

High Inter-Patient Variability in Sepsis Evolution: A Hidden Markov Model Analysis

BACKGROUND

Severe sepsis and septic shock are common in the intensive care unit (ICU) and contribute significantly to cost and mortality. Early treatment is critical but is confounded by the difficulty of real-time diagnosis. This study uses hidden Markov models (HMMs) to examine whether the time evolution of sepsis can add diagnostic accuracy or value using a proven set of bio-signals.

METHODS

Clinical data (N=36 patients; 6071 hours), including an hourly personalised insulin sensitivity metric. A two hidden state HMM is created to discriminate diagnosed cases (Severe Sepsis, Septic Shock) from controls (SIRS, Sepsis) states. Diagnostic performance is measured by ROC curves, likelihood ratios (LHRs), sensitivity/specificity, and diagnostic odds-ratios (DOR), for a best-case resubstitution estimate and a worst-case 80/20% repeated holdout analysis.

RESULTS

The HMM delivered near perfect results (95% Sensitivity; 96% Specificity) for best-case resubstitution estimates, but was comparatively poor (59% Sensitivity; 61% Specificity) for worst-case repeated holdout estimations. Adding the time evolution of sepsis did not add to the accuracy of diagnosis from using the signals alone without time history.

CONCLUSIONS

These potentially surprising results indicate significant inter-patient variability in the time evolution of sepsis, preventing effective diagnosis in the context of the bio-signals, data, and HMM topology used. Efforts for improved real-time, early sepsis diagnosis should concentrate on the robustness and efficacy of the bio-signals and data used, as well as the level of model complexity, to create more effective real-time classifiers.

Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia

Community-acquired pneumonia (CAP) is one of the most frequent infectious diseases worldwide, with high lethality. Risk evaluation is well established at hospital admission, and re-evaluation is advised for patients at higher risk. However, severe disease courses may develop from all levels of severity. We propose a stochastic continuous-time Markov model describing daily development of time courses of CAP severity. Disease states were defined based on the Sequential Organ Failure Assessment (SOFA) score. Model calibration was based on longitudinal data from 2838 patients with a primary diagnosis of CAP from four clinical studies (PROGRESS, MAXSEP, SISPCT, VISEP). We categorized CAP severity into five disease states and estimated transition probabilities for CAP progression between these states and corresponding sojourn times. Good agreement between model predictions and clinical data was observed. Time courses of mortality were correctly predicted for up to 28 days, including validation with patient data not used for model calibration. We conclude that CAP disease course follows a Markov process, suggesting the necessity of daily monitoring and re-evaluation of patient's risk. Our model can be used for regular updates of risk assessments of patients and could improve the design of clinical trials by estimating transition rates for different risk groups.

Peroxynitrite decomposition catalyst reduces vasopressin requirement in ovine MRSA sepsis

Background

Sepsis is one of the most frequent causes of death in the intensive care unit. Host vascular hypo-responsiveness to vasopressors during septic shock is one of the challenging problems. This study tested the hypothesis that adjunct therapy with peroxynitrite decomposition catalyst (WW-85) would reduce arginine vasopressin (AVP) requirements during sepsis resuscitation, using ovine sepsis model.

Methods

Thirteen adult female Merino sheep, previously instrumented with multiple vascular catheters, were subjected to “two-hit” (cotton smoke inhalation and intrapulmonary instillation of live methicillin-resistant Staphylococcus aureus; 3.5 × 10¹¹ colony-forming units) injury. Post injury, animals were awakened and randomly allocated to the following groups: (1) AVP: injured, fluid resuscitated, and titrated with AVP, n = 6 or (2) WW-85 + AVP: injured, fluid resuscitated, treated with WW-85, and titrated with AVP, n = 7. One-hour post injury, a bolus intravenous injection of WW-85 (0.1 mg/kg) was followed by a 23-h continuous infusion (0.02 mg/kg/h). Titration of AVP started at a dose of 0.01 unit/min, when mean arterial pressure (MAP) decreased by 10 mmHg from baseline, despite aggressive fluid resuscitation, and the rate was further adjusted to maintain MAP. After the injury, all animals were placed on a mechanical ventilator and monitored in the conscious state for 24 h.

Results

The injury induced severe hypotension refractory to aggressive fluid resuscitation. High doses of AVP were required to partially attenuate the sepsis-induced hypotension. However, the cumulative AVP requirement was significantly reduced by adjunct treatment with WW-85 at 17–24 h after the injury (p < 0.05). Total AVP dose and the highest AVP rate were significantly lower in the WW-85 + AVP group compared to the AVP group (p = 0.02 and 0.04, respectively). Treatment with WW-85 had no adverse effects. In addition, the in vitro effects of AVP on isolated artery diameter changes were abolished with peroxynitrite co-incubation.

Conclusions

The modulation of reactive nitrogen species, such as peroxynitrite, may be considered as a novel adjunct treatment option for septic shock associated with vascular hypo-responsiveness to vasopressors.

Estimating Lifetime Duration of Diabetes by Age and Gender in the Korean Population Using a Markov Model

BACKGROUND

Duration of type 2 diabetes is clinically important. Duration of morbidity is an independent and critical predictor of developing its complications. This study aims to explore an applicability of a Markov model to estimate the duration of diabetes in the Korean population.

METHODS

We constructed the Markov model with two Markov states, diabetes and death, for estimation of duration of diabetes. The cycle of the Markov model was 1 year. Each diabetes onset by 5 years was considered from 30 to 85 years old or above. The endpoint of the Markov was 100 years old. Type 2 diabetes was operationally defined using the 10th revision of International Statistical Classification of Diseases and prescriptions of anti-diabetic drugs from the National Health Insurance Services-National Sample cohort. In each incident and existing prevalence cases, survival probabilities were obtained. Durations of diabetes from the Markov model were compared with those from the DisMod II program. Reductions of life expectancy due to diabetes were defined as differences of life expectancies between diabetic patients and the general public. Sensitivity analyses were also conducted using a cure rate and 95% confidence interval of survival probability.

RESULTS

The duration of diabetes gradually decreased with incident age in both genders. In the early 30s, the duration was the largest at 48.9 and 41.9 years in women and men, respectively. In the average incident age group of type 2 diabetes, the late 50s, the reduction of life expectancy due to diabetes was estimated to be about two years in both genders. As annual cure probabilities increased, the durations of diabetes were reduced.

CONCLUSION

This study estimated the duration of diabetes using a Markov model. The model seems to work well and diabetes could reduce life expectancy by about 2 years on average. This approach could be useful to estimate the duration of illness, calculate disability-adjusted life years, and conduct economic evaluation studies on interventions for diabetic patients.

Early PREdiction of sepsis using leukocyte surface biomarkers: the ExPRES-sepsis cohort study

PURPOSE

Reliable biomarkers for predicting subsequent sepsis among patients with suspected acute infection are lacking. In patients presenting to emergency departments (EDs) with suspected acute infection, we aimed to evaluate the reliability and discriminant ability of 47 leukocyte biomarkers as predictors of sepsis (Sequential Organ Failure Assessment score ≥ 2 at 24 h and/or 72 h following ED presentation).

METHODS

In a multi-centre cohort study in four EDs and intensive care units (ICUs), we standardised flow-cytometric leukocyte biomarker measurement and compared patients with suspected acute infection (cohort-1) with two comparator cohorts: ICU patients with established sepsis (cohort-2), and ED patients without infection or systemic inflammation but requiring hospitalization (cohort-3).

RESULTS

Between January 2014 and February 2016, we recruited 272, 59 and 75 patients to cohorts 1, 2, and 3, respectively. Of 47 leukocyte biomarkers, 14 were non-reliable, and 17 did not discriminate between the three cohorts. Discriminant analyses for predicting sepsis within cohort-1 were undertaken for eight neutrophil (cluster of differentiation antigens (CD) CD15; CD24; CD35; CD64; CD312; CD11b; CD274; CD279), seven monocyte (CD35; CD64; CD312; CD11b; HLA-DR; CD274; CD279) and a CD8 T-lymphocyte biomarker (CD279). Individually, only higher neutrophil CD279 [OR 1.78 (95% CI 1.23-2.57); P = 0.002], higher monocyte CD279 [1.32 (1.03-1.70); P = 0.03], and lower monocyte HLA-DR [0.73 (0.55-0.97); P = 0.03] expression were associated with subsequent sepsis. With logistic regression the optimum biomarker combination was increased neutrophil CD24 and neutrophil CD279, and reduced monocyte HLA-DR expression, but no combination had clinically relevant predictive validity.

CONCLUSIONS

From a large panel of leukocyte biomarkers, immunosuppression biomarkers were associated with subsequent sepsis in ED patients with suspected acute infection.

CLINICAL TRIAL REGISTRATION

NCT02188992.

Mid regional pro-adrenomedullin for the prediction of organ failure in infection. Results from a single centre study

Biomarkers are widely used to confirm the presence of infection. However, it would be of the greatest importance to predict in advance the occurrence or worsening of organ dysfunction in infected patients allowing timely antibiotic escalation. This study investigates the ability of procalcitonin (PCT) and MR-proADM to predict the transition to sepsis in infected patients. The study was conducted in a neurointensive care unit over a three-month period. We included both patients with and without infection to investigate the specificity of organ dysfunction prediction in infected patients. Daily measurement of PCT and MR-proADM, SOFA, Pitt, and CPIS were performed. To measure the correlation between each biomarker and each severity score, linear mixed-effects models were developed. For each biomarker-score combination we tested the correlation of the score with the biomarker measured one and two days before, the same day, and the day after. Sixty-four critically ill patients, 31 with infection, were enrolled. The statistically significant biomarker-score combinations were PCT-SOFA, MR-proADM-SOFA, MR-proADM-Pitt, and MR-proADM-CPIS. The MR-proADM models predicting Pitt and CPIS variations with 24-hour anticipation showed the best fit. The scores increased by 0.6 ± 0.3 and 0.4 ± 0.2 for each unitary biomarker increase, respectively. The MR-proADM-SOFA combinations were equivalent when the biomarker was measured the day before or the same day (score increases were 1.5 ± 0.4 and 1.9 ± 0.4, respectively). The PCT-SOFA model had the best fit when PCT was measured the same day of the score. There was no difference in the predictive ability of the biomarker in infected and non-infected patients. This was a pivotal study conducted in a single neurointensive centre on a limited number of patients, and as such it does not provide definitive conclusions. PR-proADM predicted occurrence and worsening of organ failure in critically ill patients with and without infection. The combination with infection diagnostic biomarkers such as PCT would allow predicting evolution to sepsis in infected patients.

Natural Course of Clinically Isolated Syndrome: A Longitudinal Analysis Using a Markov Model

Clinically isolated syndrome (CIS) refers to the initial clinical episode with symptoms suggestive of multiple sclerosis (MS). Due to limited number of long-term follow-up studies, progression pattern from CIS to more advanced stages remains unclear. In the current study, we constructed a Markov model to simulate the natural course of CIS. The model estimated the probabilities of transition from CIS to more advanced disease stages and the duration needed for the progression. The analysis showed: (1) CIS is a solid disease identity: more than 85% of the subjects with a diagnosis of CIS progress to RRMS or more advanced stages within 20 years; (2) the reduction of life expectancy in subjects with CIS is marginal.

Matrix methods in health demography: a new approach to the stochastic analysis of healthy longevity and DALYs

BACKGROUND

Increases in human longevity have made it critical to distinguish healthy longevity from longevity without regard to health. Current methods focus on expectations of healthy longevity, and are often limited to binary health outcomes (e.g., disabled vs. not disabled). We present a new matrix formulation for the statistics of healthy longevity, based on health prevalence data and Markov chain theory, applicable to any kind of health outcome and which provides variances and higher moments as well as expectations of healthy life.

METHOD

The model is based on a Markov chain description of the life course coupled with the moments of health outcomes ("rewards") at each age or stage. As an example, we apply the method to nine European countries using the SHARE survey data on the binary outcome of disability as measured by activities of daily living, and the continuous health outcome of hand grip strength.

RESULTS

We provide analytical formulas for the mean, variance, coefficient of variation, skewness and other statistical properties of healthy longevity. The analysis is applicable to binary, categorical, ordinal, or interval scale health outcomes. The results are easily evaluated in any matrix-oriented software. The SHARE results reveal familiar patterns for the expectation of life and of healthy life: women live longer than men but spend less time in a healthy condition. New results on the variance shows that the standard deviation of remaining healthy life declines with age, but the coefficient of variation is nearly constant. Remaining grip strength years decrease with age more dramatically than healthy years but their variability pattern is similar to the pattern of healthy years. Patterns are similar across nine European countries.

CONCLUSIONS

The method extends, in several directions, current calculations of health expectancy (HE) and disability-adjusted life years (DALYs). It applies to both categorical and continuous health outcomes, to combinations of multiple outcomes (e.g., death and disability in the formulation of DALYs) and to age- or stage-classified models. It reveals previously unreported patterns of variation among individuals in the outcomes of healthy longevity.

Acute bag-valve breathing maneuvers plus manual chest compression is safe during stable septic shock: a randomized clinical trial

Objective

To evaluate the effects of bag-valve breathing maneuvers combined with standard manual chest compression techniques on safety, hemodynamics and oxygenation in stable septic shock patients.

Design

A parallel, assessor-blinded, randomized trial of two groups. A computer-generated list of random numbers was prepared by an independent researcher to allocate treatments.

Setting

The Intensive Care Unit at Hospital São Lucas, Pontifícia Universidade Católica do Rio Grande do Sul.

Participants

Fifty-two subjects were assessed for eligibility, and 32 were included. All included subjects (n = 32) received the allocated intervention (n = 19 for the Experimental Group and n = 13 for the Control Group).

Intervention

Twenty minutes of bag-valve breathing maneuvers combined with manual chest compression techniques (Experimental Group) or chest compression, as routinely used at our intensive care unit (Control Group). Follow-up was performed immediately after and at 30 minutes after the intervention.

Main outcome measure

Mean artery pressure.

Results

All included subjects completed the trial (N = 32). We found no relevant effects on mean artery pressure (p = 0.17), heart rate (p = 0.50) or mean pulmonary artery pressure (p = 0.89) after adjusting for subject age and weight. Both groups were identical regarding oxygen consumption after the data adjustment (p = 0.84). Peripheral oxygen saturation tended to increase over time in both groups (p = 0.05), and there was no significant association between cardiac output and venous oxygen saturation (p = 0.813). No clinical deterioration was observed.

Conclusion

A single session of bag-valve breathing maneuvers combined with manual chest compression is hemodynamically safe for stable septic-shocked subjects over the short-term.

Effect of common neuropathologies on progression of late life cognitive impairment

Brain pathologies of Alzheimer's (AD), cerebrovascular, and Lewy body diseases are common in old age, but the relationship of these pathologies with progression from normal cognitive function to the various stages of cognitive impairment is unknown. In this study, we fit latent Markov models from longitudinal cognitive data to empirically derive 3 latent stages corresponding to no impairment, mild impairment, and moderate impairment; then, we examined the associations of common neuropathologies with the rates of transition among these stages. Cognitive and neuropathological data were available from 653 autopsied participants in 2 ongoing cohort studies of aging who were cognitively healthy at baseline (mean baseline age 79.1 years) and had longitudinal cognitive data. On average, participants in these analyses developed mild impairment 5 years after enrollment, progressed to moderate impairment after an additional 3.4 years, and stayed impaired for 2.8 years until death. AD and chronic macroscopic infarcts were associated with a higher risk of progression to mild impairment and subsequently to moderate impairment. By contrast, Lewy bodies were associated only with progression from mild to moderate impairment. The 5-year probability of progression to mild or moderate impairment was 20% for persons without any of these 3 pathologies, 38% for AD only, 51% for AD and macroscopic infarcts, and 56% for AD, infarcts, and Lewy bodies. Thus, the presence of AD pathology alone nearly doubles the risk of developing cognitive impairment in late life, and the presence of multiple pathologies further increases this risk over multiple years before death.

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

Background

Sepsis 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.

Methods

This 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.

Results

In 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.

Conclusions

Intraabdominal 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.

Markov models in dentistry: application to resin-bonded bridges and review of the literature

Markov models are mathematical models that can be used to describe disease progression and evaluate the cost-effectiveness of medical interventions. Markov models allow projecting clinical and economic outcomes into the future and are therefore frequently used to estimate long-term outcomes of medical interventions. The purpose of this paper is to demonstrate its use in dentistry, using the example of resin-bonded bridges to replace missing teeth, and to review the literature. We used literature data and a four-state Markov model to project long-term outcomes of resin-bonded bridges over a time horizon of 60 years. In addition, the literature was searched in PubMed Medline for research articles on the application of Markov models in dentistry.

Impact of interleukin-10, soluble CD25 and interferon-γ on the prognosis and early diagnosis of bacteremic systemic inflammatory response syndrome: a prospective observational study

Introduction

The pathophysiology of sepsis consists of two phases. A first phase characterized by a substantial increase of pro-inflammatory mediators including cytokines and systemic inflammatory markers, and a second phase (immunoparalysis, immunodysregulation) associated with the rise of anti-inflammatory mediators. In this study we prospectively analyzed 52 consecutive patients with diagnosis of systemic inflammatory response syndrome (SIRS) at hospital admission to evaluate prognostic and early diagnostic performance of interleukin-10 (IL-10), soluble CD25 (sCD25) and interferon-γ (IFN-γ) and to confirm the prognostic accuracy of the sequential organ failure assessment (SOFA) score.

Methods

Patients were divided in two groups (group 1, n = 28 patients with bacteremic SIRS and group 2, n = 24 patients with non-bacteremic SIRS) and then stratified into survivors (n = 39) and nonsurvivors (n = 13). Serum markers were evaluated on the day of hospital admission (D-1) and on the 7^th day of hospital stay (D-7). Concentration of sCD25 was evaluated by a sandwich ELISA kit. Levels of IL-10 and IFN-γ were quantified by a cytokine biochip array by the evidence investigator analyzer. Differences between groups were established by the Mann-Whitney test. Accuracy, sensitivity and specificity of diagnostic markers were evaluated by the receiver-operating characteristic curve analysis. Multivariate analysis was carried out to evaluate whether studied biomarkers are independent predictors of poor outcome in prognosis, and of bacteremic SIRS in diagnosis.

Results

IL-10, sCD25 and SOFA scores of survivors and nonsurvivors were significantly different both at D-1 (P = 0.0014; P = 0.014 and P = 0.0311 respectively) and at D-7 (P = 0.0002, P = 0.014 and P = 0.0012 respectively). Between the above groups IFN-γ level was significantly different only at D-7 (P = 0.0013). Moreover IL-10 and sCD25 were significantly higher in bacteremic versus non-bacteremic SIRS patients at D-1 and at D-7 (P < 0.05). IFN-γ values showed a significant decrease (P < 0.05) in patients of group 1 only at D-7. The diagnostic accuracy of IL-10 and sCD25 was confirmed by the analysis of the AUROCC at D-1 and D-7 respectively. Multivariate analysis revealed that sCD25 and IL-10 are independent predictors of a poor outcome for our patients during the first day of hospital admission.

Conclusions

IL-10 and sCD25 gave a significant contribution to prognostic evaluation and early diagnosis of bacteremic SIRS. SOFA score appeared to be a reliable prognostic tool in this subset of patients.

A New Immunomodulatory Therapy for Severe Sepsis: Ulinastatin Plus Thymosin α 1

Objectives: To study the effect of immunomodulatory therapy with ulinastatin plus thymosin α 1 on septic patients. Method: A total of 56 sepsis patients were randomized into a treatment group, receiving immunomodulatory therapy, and a placebo group, a placebo. Acute Physiology and Chronic Health Evaluation II scores, clinical data, lymphocyte subsets, immunological indexes, and coagulation parameters were determined before admission and on the 3rd, 8th, and 28th day after admission to the Intensive Care Unit. Results: The treatment group experienced a 78% cumulative survival, the placebo group experienced a 60% cumulative survival; the survival difference was mirrored by Acute Physiology and Chronic Health Evaluation II scores and more quickly improved leukocyte counts, lymphocyte counts, coagulation parameters, and cytokine levels in the treatment. Conclusions: Combined immunomodulatory therapy with ulinastatin plus thymosin α1 appears to yield improved survival for patients with sepsis; this finding should be verified in larger clinical trials.

Magnitude of bacteraemia is a predictor of mortality during 1 year of follow-up

We evaluated magnitude of bacteraemia as a predictor of mortality, comprising all adult patients with a first-time mono-microbial bacteraemia. The number of positive bottles [1 (reference), 2, or 3] in the first positive blood culture (BC) was an index of magnitude of bacteraemia. We used Cox's regression analysis to determine age and comorbidity adjusted risk of mortality at days 0–7, 8–30, and 31–365. Of 6406 patients, 31·1% had BC index 1 (BCI 1), 18·3% BCI 2, and 50·6% BCI 3. BCI 3 patients had increased risk of mortality for days 0–7 (1·30, 95% CI 1·10–1·55) and days 8–30 (1·37, 95% CI 1·12–1·68), but not thereafter. However, in surgical patients mortality increased only beyond day 7 (8–30 days: 2·04, 95% CI 1·25–3·33; 31–365 days: 1·27, 95% CI 0·98–1·65). Thus, high magnitude of bacteraemia predicted mortality during the first month with a shift towards long-term mortality in surgical patients.

Between prediction, education, and quality control: simulation models in critical care

Today, computer-aided strategies in social sciences are an indispensable component of teaching programs. In recent years, microsimulation modeling has gained attention in its ability to represent predicted physiological developments visually, thus providing the user with a full understanding of the impacts of a proposed scheme. There are several microsimulation models in human medicine, and they can be either dynamic or static. If the model is dynamic the course of variables changes over time; in contrast, in the static case time constancy is assumed. In critical care there have been several approaches to implement microsimulation models to predict outcome. This commentary describes current approaches for predicting disease progression by using dynamic microsimulation in pneumonia-related sepsis.

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.

Sepsis incidence and outcome: contrasting the intensive care unit with the hospital ward

OBJECTIVE

To describe the outcome of patients with sepsis according to location on a ward or in an intensive care unit.

DESIGN

Prospective multicentered observational study.

SETTING

Three academic hospitals in Madrid, Spain.

PATIENTS

Consecutive patients with sepsis admitted to participating hospitals from March 1 to June 30, 2003.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

During the study period, 15,852 patients >18 yrs of age were admitted. Sepsis was identified in 702 patients, giving an estimated cumulative incidence rate of 367 cases per 100,000 adult area residents per year and a cumulative incidence rate among patients admitted to the hospital of 4.4%. Most septic patients had a community-acquired infection (71%). Severe sepsis developed in 199 patients (incidence rate, 104 cases per 100,000 adult area residents per year), and 59 patients developed septic shock (incidence rate, 31 cases per 100,000 adult area residents per year). Most of the patients met the criteria for severe sepsis or septic shock on the same day that they would have qualified for the septic status one step down the scale. In the other patients, the median time between sepsis and severe sepsis was 2 days (interquartile range, 2-5) and between severe sepsis and septic shock was 3 days (interquartile range, 1-4). Only 32% of severe sepsis patients received intensive care. The hospital mortality for all septic patients was 12.8%; for severe sepsis, 20.7%; and for septic shock, 45.7%.

CONCLUSIONS

This study shows the high incidence of sepsis in a general population of patients admitted to hospital. A significant proportion of patients with severe sepsis are not transferred to the intensive care unit.

Effectiveness of statins in reducing the rate of severe sepsis: a retrospective evaluation

STUDY OBJECTIVES

To determine whether use of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins) is associated with a reduced rate of severe sepsis, and to further characterize the effect of statins on the frequency of organ dysfunction in patients with severe sepsis.

DESIGN

Retrospective cohort study.

SETTING

University-associated teaching hospital.

PATIENTS

Fifty-three patients admitted with sepsis; 16 were receiving statins and 37 were not receiving statins (controls) before admission.

MEASUREMENTS AND MAIN RESULTS

Patients were identified by the International Classification of Diseases, Ninth Revision, Clinical Modification codes. Patient demographics, vital signs, and laboratory values were collected from their electronic medical records. The primary end point was rate of severe sepsis, defined in accordance with guidelines from the American College of Chest Physicians and the Society of Critical Care Medicine. Secondary end points were in-hospital mortality rate and rate of five categories of organ dysfunction (cardiovascular, renal, pulmonary, hematologic, and metabolic). Preadmission statin therapy, compared with no statin therapy, was associated with a 30% lower rate of severe sepsis (56% vs 86%, p<0.02). In-hospital mortality was not significantly different between groups (38% vs 49%, p=0.33); however, the rate of cardiovascular dysfunction, defined as hypotension requiring vasopressor therapy, was significantly lower in the statin group (38% vs 73%, p<0.02). No significant differences in the other organ dysfunction categories were noted between groups.

CONCLUSION

Statins appear to prevent sepsis from becoming severe, most notably through prevention of sepsis-induced hypotension. This potential role for statins in the prevention and treatment of severe sepsis should be further evaluated in a randomized controlled trial.

Application of nonhomogenous Markov models for analyzing longitudinal caries risk

OBJECTIVES

Markov modeling is a useful mathematical procedure for calculating probabilities of disease prognosis. Increasingly, Markov models are being applied in medical and health services research and also in social sciences research. The purpose of our study was to use the Markov process to determine time-dependent transition probabilities for caries-free children to convert to a caries-active state and to assess the impact of salivary mutans streptococci (MS) levels on caries status.

METHODS

Our analysis was based on data obtained from a 6-year longitudinal study of risk factors associated with caries onset in children.

RESULTS

Based on a two-state Markov model, the probability that a caries-free child would convert to a caries-active state during the study ranged between 0.0046 and 0.0471. The highest probability of converting from a caries-free state to a caries-active state was 0.0471 at age 8.5 years.

CONCLUSIONS

In addition to standard statistical methods of analyzing longitudinal caries data, Markov models show promise for use in the analysis of caries risk.

A Markov computer simulation model of the economics of neuromuscular blockade in patients with acute respiratory distress syndrome

Background

Management of acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU) is clinically challenging and costly. Neuromuscular blocking agents may facilitate mechanical ventilation and improve oxygenation, but may result in prolonged recovery of neuromuscular function and acute quadriplegic myopathy syndrome (AQMS). The goal of this study was to address a hypothetical question via computer modeling: Would a reduction in intubation time of 6 hours and/or a reduction in the incidence of AQMS from 25% to 21%, provide enough benefit to justify a drug with an additional expenditure of $267 (the difference in acquisition cost between a generic and brand name neuromuscular blocker)?

Methods

The base case was a 55 year-old man in the ICU with ARDS who receives neuromuscular blockade for 3.5 days. A Markov model was designed with hypothetical patients in 1 of 6 mutually exclusive health states: ICU-intubated, ICU-extubated, hospital ward, long-term care, home, or death, over a period of 6 months. The net monetary benefit was computed.

Results

Our computer simulation modeling predicted the mean cost for ARDS patients receiving standard care for 6 months to be $62,238 (5% – 95% percentiles $42,259 – $83,766), with an overall 6-month mortality of 39%. Assuming a ceiling ratio of $35,000, even if a drug (that cost $267 more) hypothetically reduced AQMS from 25% to 21% and decreased intubation time by 6 hours, the net monetary benefit would only equal $137.

Conclusion

ARDS patients receiving a neuromuscular blocker have a high mortality, and unpredictable outcome, which results in large variability in costs per case. If a patient dies, there is no benefit to any drug that reduces ventilation time or AQMS incidence. A prospective, randomized pharmacoeconomic study of neuromuscular blockers in the ICU to asses AQMS or intubation times is impractical because of the highly variable clinical course of patients with ARDS.

Macrophage migration inhibitory factor is associated with positive cultures in patients with sepsis after cardiac surgery

This prospective consecutive observational study describes the blood levels of macrophage migration inhibitory factor (MIF), other cytokines, and markers of acute-phase response in 49 consecutive patients who developed the clinical syndrome of sepsis after cardiac surgery. Before starting antimicrobial treatment, all patients underwent microbiologic screening, and blood samples were collected. These samples subsequently were assayed for MIF, macrophage chemoattractant protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-alpha), interleukin (IL)-6 and -10, procalcitonin (PCT), and C-reactive protein (CRP). Patients with positive cultures (n = 25) had a higher mortality (P = 0.046) and higher levels of MIF (P < 0.001) than those with negative cultures (n = 24). We could not detect significant difference between the groups concerning the levels of CRP, PCT, IL6, IL10, MCP-1, or TNF-alpha. MIF levels showed an area under receiver operator curve of 0.823 for the prediction of culture-proven bacterial infection, with the best cut-off value at 988.5 pg/mL. In conclusion, circulating levels of MIF could be indicated as a valuable marker of microbiologically documented sepsis in patients after cardiac surgery, which suggests that MIF may be prospectively explored as a useful diagnostic tool in this setting.

Mid-regional pro-adrenomedullin as a prognostic marker in sepsis: an observational study

Introduction

Measurement of biomarkers is a potential approach to early assessment and prediction of mortality in patients with sepsis. The aim of the present study was to evaluate the prognostic value of mid-regional pro-adrenomedullin (MR-proADM) levels in a cohort of medical intensive care patients and to compare it with other biomarkers and physiological scores.

Method

We evaluated blood samples from 101 consecutive critically ill patients admitted to the intensive care unit and from 160 age-matched healthy control individuals. The patients had initially been enrolled in a prospective observational study investigating the prognostic value of endocrine dysfunction in critically ill patients ("PEDCRIP" Study). The prognostic value of MR-proADM levels was compared with those of two physiological scores and of various biomarkers (for example C-reactive Protein, IL-6, procalcitonin). MR-proADM was measured in EDTA plasma from all patients using a new sandwich immunoassay.

Results

On admission, 53 patients had sepsis, severe sepsis, or septic shock, and 48 had systemic inflammatory response syndrome. Median MR-proADM levels on admission (nmol/l [range]) were 1.1 (0.3–3.7) in patients with systemic inflammatory response syndrome, 1.8 (0.4–5.8) in those with sepsis, 2.3 (1.0–17.6) in those with severe sepsis and 4.5 (0.9–21) in patients with septic shock. In healthy control individuals the median MR-proADM was 0.4 (0.21–0.97). On admission, circulating MR-proADM levels in patients with sepsis, severe sepsis, or septic shock were significantly higher in nonsurvivors (8.5 [0.8–21.0]; P < 0.001) than in survivors (1.7 [0.4–17.6]). In a receiver operating curve analysis of survival of patients with sepsis, the area under the curve (AUC) for MR-proADM was 0.81, which was similar to the AUCs for IL-6, Acute Physiology and Chronic Health Evaluation II score and Simplified Acute Physiology Score II. The prognostic value of MR-proADM was independent of the sepsis classification system used.

Conclusion

MR-proADM may be helpful in individual risk assessment in septic patients.

Systemic Inflammatory Response and Progression to Severe Sepsis in Critically Ill Infected Patients

RATIONALE

The systemic inflammatory response syndrome has low specificity to identify infected patients at risk of worsening to severe sepsis or shock.

OBJECTIVE

To examine the incidence of and risk factors for worsening sepsis in infected patients.

METHODS

A 1-year inception cohort study in 28 intensive care units of patients (n = 1,531) having a first episode of infection on admission or during the stay.

MEASUREMENTS AND MAIN RESULTS

The cumulative incidence of progression to severe sepsis or shock was 20% and 24% at Days 10 and 30, respectively. Variables independently associated (hazard ratio [HR]) with worsening sepsis included: temperature higher than 38.2 degrees C (1.6), heart rate greater than 120/minute (1.3), systolic blood pressure higher than 110 mm Hg (1.5), platelets higher than 150 x 109/L (1.5), serum sodium higher than 145 mmol/L (1.5), bilirubin higher than 30 mumol/L (1.3), mechanical ventilation (1.5), and five variables characterizing infection (pneumonia [HR 1.5], peritonitis [1.5], primary bacteremia [1.8], and infection with gram-positive cocci [1.3] or aerobic gram-negative bacilli [1.4]). The 12 weighted variables were included in a score (Risk of Infection to Severe Sepsis and Shock Score, range 0-49), summarized in four classes of "low" (score 0-8) and "moderate" (8.5-16) risk (9% and 17% probability of worsening, respectively), and of "high" (16.5-24) and "very high" (score > 24) risk (31% and 55% probability, respectively).

CONCLUSIONS

One of four patients presenting with infection/sepsis worsen to severe sepsis or shock. A score estimating this risk, using objectively defined criteria for systemic inflammatory response syndrome, could be used by physicians to stratify patients for clinical management and to test new interventions.

Septic shock

Septic shock, the most severe complication of sepsis, is a deadly disease. In recent years, exciting advances have been made in the understanding of its pathophysiology and treatment. Pathogens, via their microbial-associated molecular patterns, trigger sequential intracellular events in immune cells, epithelium, endothelium, and the neuroendocrine system. Proinflammatory mediators that contribute to eradication of invading microorganisms are produced, and anti-inflammatory mediators control this response. The inflammatory response leads to damage to host tissue, and the anti-inflammatory response causes leucocyte reprogramming and changes in immune status. The time-window for interventions is short, and treatment must promptly control the source of infection and restore haemodynamic homoeostasis. Further research is needed to establish which fluids and vasopressors are best. Some patients with septic shock might benefit from drugs such as corticosteroids or activated protein C. Other therapeutic strategies are under investigation, including those that target late proinflammatory mediators, endothelium, or the neuroendocrine system.

Dynamic microsimulation to model multiple outcomes in cohorts of critically ill patients

BACKGROUND

Existing intensive care unit (ICU) prediction tools forecast single outcomes, (e.g., risk of death) and do not provide information on timing.

OBJECTIVE

To build a model that predicts the temporal patterns of multiple outcomes, such as survival, organ dysfunction, and ICU length of stay, from the profile of organ dysfunction observed on admission.

DESIGN

Dynamic microsimulation of a cohort of ICU patients.

SETTING

49Forty-nine ICUs in 11 countries.

PATIENTS

One thousand four hundred and forty-nine patients admitted to the ICU in May 1995.

INTERVENTIONS

None. MODEL CONSTRUCTION: We developed the model on all patients (n=989) from 37 randomly-selected ICUs using daily Sequential Organ Function Assessment (SOFA) scores. We validated the model on all patients (n=460) from the remaining 12 ICUs, comparing predicted-to-actual ICU mortality, SOFA scores, and ICU length of stay (LOS).

MAIN RESULTS

In the validation cohort, the predicted and actual mortality were 20.1% (95%CI: 16.2%-24.0%) and 19.9% at 30 days. The predicted and actual mean ICU LOS were 7.7 (7.0-8.3) and 8.1 (7.4-8.8) days, leading to a 5.5% underestimation of total ICU bed-days. The predicted and actual cumulative SOFA scores per patient were 45.2 (39.8-50.6) and 48.2 (41.6-54.8). Predicted and actual mean daily SOFA scores were close (5.1 vs 5.5, P=0.32). Several organ-organ interactions were significant. Cardiovascular dysfunction was most, and neurological dysfunction was least, linked to scores in other organ systems.

CONCLUSIONS

Dynamic microsimulation can predict the time course of multiple short-term outcomes in cohorts of critical illness from the profile of organ dysfunction observed on admission. Such a technique may prove practical as a prediction tool that evaluates ICU performance on additional dimensions besides the risk of death.

Corticosteroids for severe sepsis and septic shock: a systematic review and meta-analysis

OBJECTIVE

To assess the effects of corticosteroids on mortality in patients with severe sepsis and septic shock.

DATA SOURCES

Randomised and quasi-randomised trials of corticosteroids versus placebo (or supportive treatment alone) retrieved from the Cochrane infectious diseases group's trials register, the Cochrane central register of controlled trials, Medline, Embase, and LILACS.

REVIEW METHOD

Two pairs of reviewers agreed on eligibility of trials. One reviewer entered data on to the computer and four reviewers checked them. We obtained some missing data from authors of trials and assessed methodological quality of trials.

RESULTS

16/23 trials (n = 2063) were selected. Corticosteroids did not change 28 day mortality (15 trials, n = 2022; relative risk 0.92, 95% confidence interval 0.75 to 1.14) or hospital mortality (13 trials, n = 1418; 0.89, 0.71 to 1.11). There was significant heterogeneity. Subgroup analysis on long courses (> or = 5 days) with low dose (< or = 300 mg hydrocortisone or equivalent) corticosteroids showed no more heterogeneity. The relative risk for mortality was 0.80 at 28 days (five trials, n = 465; 0.67 to 0.95) and 0.83 at hospital discharge (five trials, n = 465, 0.71 to 0.97). Use of corticosteroids reduced mortality in intensive care units (four trials, n = 425, 0.83, 0.70 to 0.97), increased shock reversal at 7 days (four trials, n = 425; 1.60, 1.27 to 2.03) and 28 days (four trials, n = 425, 1.26, 1.04 to 1.52) without inducing side effects.

CONCLUSIONS

For all trials, regardless of duration of treatment and dose, use of corticosteroids did not significantly affect mortality. With long courses of low doses of corticosteroids, however, mortality at 28 days and hospital morality was reduced.

Source of infection and other factors associated with case fatality in community-acquired bacteremia—a Danish population-based cohort study from 1992 to 1997

OBJECTIVE

To examine the association between the source of infection, other factors and the 30-day case-fatality rate (CFR) in patients with community-acquired bacteremia.

METHODS

We included in the study 1844 patients older than 15 years (median age 72 years) with a first episode of community-acquired bacteremia in the period 1992-97 from a population-based bacteremia database. Information on co-morbidity, antibiotic prescriptions and date of death was obtained from health registries through linkage with the patient's personal identification number. The outcome measure was the overall CFR.

RESULTS

The mean CFR was 18% (20% in 1992-95, 15% in 1996-97). The commonest sources of infection were the urinary tract (29%) and the respiratory tract (20%); patients with an undetermined source accounted for 21% in 1992-95 and 13% in 1996-97. The most frequent bacteria were Escherichia coli (33%) and Streptococcus pneumoniae (22%). Thirty-two per cent of patients did not receive appropriate empirical antibiotic therapy. The following factors were associated with CFR: source of infection other than the urinary tract, first four years of the study, age >/=75 years, and presence of co-morbidity. An undetermined source showed the strongest association with CFR during the period 1996-97.

CONCLUSIONS

As an undetermined source of infection was strongly associated with CFR, physicians should be aware of the significance of identifying and eliminating a source of infection, and more efforts should be directed at timely and appropriate empirical antibiotic therapy.

Multicenter, double-blind, placebo-controlled study of the use of filgrastim in patients hospitalized with pneumonia and severe sepsis

OBJECTIVE

To determine the safety and efficacy of filgrastim (r-metHuG-CSF) in combination with intravenous antibiotics to reduce the rate of mortality in patients with pneumonia and sepsis.

DESIGN

This study was multicenter, double-blind, and randomized.

SETTING

Intensive care units PATIENTS Adult patients with bacterial pneumonia, either acquired or nosocomial, as confirmed by chest radiograph and positive culture or Gram-negative stain, and severe sepsis, defined as sepsis-induced hypotension or organ dysfunction.

INTERVENTIONS

Standard antibiotic therapy with or without filgrastim (300 microg/day) or placebo administered as a 30-min intravenous infusion. The study drug was started within 24 hrs of enrollment and was continued for 5 days or until the white blood cell count reached >75.0 x 10(9) cells/L.

MEASUREMENTS AND MAIN RESULTS

The primary end point was the occurrence of mortality through day 29; secondary end points included occurrence of subsequent organ dysfunction, time to discharge from intensive care unit, number of days on mechanical ventilatory support, and time to death. Study-related observations were recorded through day 10 and included vital signs, onset of organ dysfunction, clinical laboratory variables, and adverse events. Filgrastim increased the white blood cell count to a median peak of 31.7 x 10(9) cells/L from a baseline of 12.3 x 10(9) cells/L. The two groups were well matched and did not differ significantly with regard to severe adverse events, time to death, occurrence of end-organ dysfunction, days of intensive care unit hospitalization, or days on mechanical ventilatory support. Mortality was low in both treatment groups; the mortality rate in patients with adult respiratory distress syndrome was similar between the two groups.

CONCLUSIONS

The addition of filgrastim to the antibiotic and supportive care treatment of patients with pneumonia complicated by severe sepsis appeared to be safe, but not efficacious in reducing mortality rates or complications from this infection.

A probabilistic transmission model of Salmonella in the primary broiler production chain

Annual data from the Finnish National Salmonella Control Programme were used to build up a probabilistic transmission model of salmonella in the primary broiler production chain. The data set consisted of information on grandparent, parent, and broiler flock populations. A probabilistic model was developed to describe the unknown true prevalences, vertical and horizontal transmissions, as well as the dynamical model of infections. By combining these with the observed data, the posterior probability distributions of the unknown parameters and variables could be derived. Predictive distributions were derived for the true number of infected broiler flocks under the adopted intervention scheme and these were compared with the predictions under no intervention. With the model, the effect of the intervention used in the programme, i.e., eliminating salmonella positive breeding flocks, could be quantitatively assessed. The 95% probability interval of the posterior predictive distribution for (broiler) flock prevalence under current (1999) situation was [1.3%-17.4%] (no intervention), and [0.9%-5.8%] (with intervention). In the scenario of one infected grandparent flock, these were [2.8%-43.1%] and [1.0%-5.9%], respectively. Computations were performed using WinBUGS and Matlab softwares.

New strategies for clinical trials in patients with sepsis and septic shock

OBJECTIVE

The difficulty in identifying new treatment modalities that significantly reduce the mortality and morbidity rates associated with sepsis has highlighted the need to reevaluate the approach to clinical trial design. The United Kingdom Medical Research Council convened an International Working Party to address these issues.

DATA SOURCES

The subject areas that were to be the focus of discussion were identified by the co-chairs, and group leaders were nominated. Preconference reading material was circulated to group members.

STUDY SELECTION AND DATA EXTRACTION

Small-group discussion fed into an iterative process of feedback from plenary sessions, followed by the formulation of recommendations. Finally, each working group prepared a summary of its recommendations and these are reported herein.

DATA SYNTHESIS

There were five key recommendations. First, investigators should no longer rely solely on the American College of Chest Physicians/Society of Critical Care Medicine definitions of sepsis or sepsis syndrome as the basis of trial entry. Entry criteria should be based on three principles: a) All patients should have infection; b) there should be evidence of a pathologic process that represents a biologically plausible target for the proposed intervention, for example, an abnormal circulating level of a biological marker pertinent to the study drug; and c) patients should fall into an appropriate category of severity (usually severe sepsis). Second, investigators should use a scoring system for organ dysfunctions that has been validated and that can be incorporated into all sepsis studies; agreement on the use of a single system would simplify comparisons between studies. Third, the primary outcome measure generally should be mortality rates, but under appropriate circumstances major morbidities could be considered as primary end points. Regardless of choice of the duration to primary end point, patients should be followed for > or =90 days. Fourth, sample size needs to be based on a realistic assessment of achievable effect size based on knowledge of the at-risk population. Fifth, subgroups should be few in number and should be defined a priori on the basis of variables present before randomization.

CONCLUSIONS

Important changes in several aspects of trial design may improve the quality of clinical studies in sepsis and maximize the chance of identifying effective therapeutic agents.

Functional protection by acute phase proteins alpha(1)-acid glycoprotein and alpha(1)-antitrypsin against ischemia/reperfusion injury by preventing apoptosis and inflammation

BACKGROUND

Ischemia followed by reperfusion (I/R) causes apoptosis, inflammation, and tissue damage leading to organ malfunction. Ischemic preconditioning can protect against such injury. This study investigates the contribution of the acute phase proteins alpha(1)-acid glycoprotein (AGP) and alpha(1)-antitrypsin (AAT) to the protective effect of ischemic preconditioning in the kidney.

METHODS AND RESULTS

Exogenous AGP and AAT inhibited apoptosis and inflammation after 45 minutes of renal I/R in a murine model. AGP and AAT administered at reperfusion prevented apoptosis at 2 hours and 24 hours, as evaluated by the presence of internucleosomal DNA cleavage, terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling, and the determination of renal caspase-1- and caspase-3-like activity. AGP and AAT exerted anti-inflammatory effects, as reflected by reduced renal tumor necrosis factor-alpha expression and neutrophil influx after 24 hours. In general, these agents improved renal function. Similar effects were observed when AGP and AAT were administered 2 hours after reperfusion but to a lesser extent and without functional improvement. Moreover, I/R elicited an acute phase response, as reflected by elevated serum AGP and serum amyloid P (SAP) levels after 24 hours, and increased hepatic acute phase protein mRNA levels after 18 hours of renal reperfusion.

CONCLUSIONS

We propose that the antiapoptotic and anti-inflammatory effects of AGP and AAT contribute to the delayed type of protection associated with ischemic preconditioning and other insults. This mechanism is potentially involved in the course of many clinical conditions associated with I/R injury. Moreover, exogenous administration of these proteins may provide new therapeutic means of treatment.

Decision analysis models and simulations in critical care medicine

Because clinical trials have frequently not been performed for many clinical problems in critical care and other clinical disciplines, models and simulations are often used to quantitatively assess important diagnostic, therapeutic, and cost-effectiveness issues. This article presents a brief overview of decision analysis models, one of the most common methodologies used to develop disease models and simulations, and their applications to problems in critical care medicine.

Markov cohort simulation study reveals evidence for sex-based risk difference in intensive care unit patients

BACKGROUND

Despite great advances in intensive care medicine, sepsis still is the leading cause of death. Different strategies have been developed to file the patient data into scoring systems, primarily to predict the outcome. The Markov simulation-predominantly used in economic science to describe chains of events depending on and influencing each other-seems to be an interesting and new approach in analyzing the course of disease of critically ill patients in an intensive care unit (ICU). Using such a Markov model, this study analyzes data from 660 surgical ICU patients, 44 of whom died of sepsis.

METHODS

A three-state Markov model (integrating sepsis, adult respiratory distress syndrome, and mortality) was constructed to describe the course of disease of critically ill patients in defined cycles and to develop the risk profile of different groups of patients. The model enables the comparison between age- and sex-related survival rates and shows the difference in life expectancy compared with an average untreated standard population.

RESULTS

Women aged up to 30 years (G1F) show the best prognosis (mortality after 19 cycles 8.3%). On the contrary, the corresponding male group (G1M) demonstrates the worst outcome (mortality after 19 cycles 57.7 %).

CONCLUSIONS

The findings of this study fit into the current discussion that female patients are better positioned to meet the challenge of sepsis.

Decision analysis in critical care medicine

Decision analysis, a quantitative approach to problem solving, has been applied to many clinical scenarios and has applications for the intensivist and for problems in the critically ill. This article provides a brief overview of the fundamental features of decision analysis along with an overview of its application to problems in critical care medicine and related fields.

The epidemiology of bacterial sepsis

As a result of better understanding of pathogenesis, new definitions of sepsis have been proposed, and the complexity of this syndrome is clearer. Population-based studies of bloodstream infections--what now is called sepsis--have helped us to understand the natural history of this very frequent problem. The mortality and morbidity of each of the systemic inflammatory response syndrome stages have been described; our ability to better understand and predict these stages will help us to make better therapeutic decisions.