C-Reactive Protein/Albumin Ratio Predicts 90-Day Mortality of Septic Patients

Introduction

Residual inflammation at ICU discharge may have impact upon long-term mortality. However, the significance of ongoing inflammation on mortality after ICU discharge is poorly described. C-reactive protein (CRP) and albumin are measured frequently in the ICU and exhibit opposing patterns during inflammation. Since infection is a potent trigger of inflammation, we hypothesized that CRP levels at discharge would correlate with long-term mortality in septic patients and that the CRP/albumin ratio would be a better marker of prognosis than CRP alone.

Methods

We evaluated 334 patients admitted to the ICU as a result of severe sepsis or septic shock who were discharged alive after a minimum of 72 hours in the ICU. We evaluated the performance of both CRP and CRP/albumin to predict mortality at 90 days after ICU discharge. Two multivariate logistic models were generated based on measurements at discharge: one model included CRP (Model-CRP), and the other included the CRP/albumin ratio (Model-CRP/albumin).

Results

There were 229 (67%) and 111 (33%) patients with severe sepsis and septic shock, respectively. During the 90 days of follow-up, 73 (22%) patients died. CRP/albumin ratios at admission and at discharge were associated with a poor outcome and showed greater accuracy than CRP alone at these time points (p = 0.0455 and p = 0.0438, respectively). CRP levels and the CRP/albumin ratio were independent predictors of mortality at 90 days (Model-CRP: adjusted OR 2.34, 95% CI 1.14–4.83, p = 0.021; Model-CRP/albumin: adjusted OR 2.18, 95% CI 1.10–4.67, p = 0.035). Both models showed similar accuracy (p = 0.2483). However, Model-CRP was not calibrated.

Conclusions

Residual inflammation at ICU discharge assessed using the CRP/albumin ratio is an independent risk factor for mortality at 90 days in septic patients. The use of the CRP/albumin ratio as a long-term marker of prognosis provides more consistent results than standard CRP values alone.     

The Incidence,Clinical Outcomes,and Risk Factors of Thrombocytopenia in Intra-Abdominal Infection Patients: A Retrospective Cohort Study

Background

Studies on the incidence and risk factors of thrombocytopenia among intra-abdominal infection patients remain absent, hindering efficacy assessments regarding thrombocytopenia prevention strategies.

Methods

We retrospectively studied 267 consecutively enrolled patients with intra-abdominal infections. Occurrence of thrombocytopenia was scanned for all patients. All-cause 28-day mortality was recorded. Variables from univariate analyses that were associated with occurrence of hospital-acquired thrombocytopenia were included in a multivariable logistic regression analysis to determine thrombocytopenia predictors.

Results

Median APACHE II score and SOFA score of the whole cohort was 12 and 3 respectively. The overall ICU mortality was 7.87% and the 28-day mortality was 8.98%. The incidence of thrombocytopenia among intra-abdominal infection patients was 21.73%. Regardless of preexisting or hospital-acquired one, thrombocytopenia is associated with an increased ICU mortality and 28-day mortality as well as length of ICU or hospital stay. A higher SOFA and ISTH score at admission were significant hospital-acquired thrombocytopenia risk factors.

Conclusions

This is the first study to identify a high incidence of thrombocytopenia in patients with intra-abdominal infections. Our findings suggest that the inflammatory milieu of intra-abdominal infections may uniquely predispose those patients to thrombocytopenia. More effective thrombocytopenia prevention strategies are necessary in intra-abdominal infection patients.     

Acute Kidney Injury Enhances Outcome Prediction Ability of Sequential Organ Failure Assessment Score in Critically Ill Patients

Introduction

Acute kidney injury (AKI) is a common and serious complication in intensive care unit (ICU) patients and also often part of a multiple organ failure syndrome. The sequential organ failure assessment (SOFA) score is an excellent tool for assessing the extent of organ dysfunction in critically ill patients. This study aimed to evaluate the outcome prediction ability of SOFA and Acute Physiology and Chronic Health Evaluation (APACHE) III score in ICU patients with AKI.

Methods

A total of 543 critically ill patients were admitted to the medical ICU of a tertiary-care hospital from July 2007 to June 2008. Demographic, clinical and laboratory variables were prospectively recorded for post hoc analysis as predictors of survival on the first day of ICU admission.

Results

One hundred and eighty-seven (34.4%) patients presented with AKI on the first day of ICU admission based on the risk of renal failure, injury to kidney, failure of kidney function, loss of kidney function, and end-stage renal failure (RIFLE) classification. Major causes of the ICU admissions involved respiratory failure (58%). Overall in-ICU mortality was 37.9% and the hospital mortality was 44.7%. The predictive accuracy for ICU mortality of SOFA (areas under the receiver operating characteristic curves: 0.815±0.032) was as good as APACHE III in the AKI group. However, cumulative survival rates at 6-month follow-up following hospital discharge differed significantly (p<0.001) for SOFA score ≤10 vs. ≥11 in these ICU patients with AKI.

Conclusions

For patients coexisting with AKI admitted to ICU, this work recommends application of SOFA by physicians to assess ICU mortality because of its practicality and low cost. A SOFA score of ≥ “11” on ICU day 1 should be considered an indicator of negative short-term outcome.     

Application of control measures for infections caused by multi-resistant gram-negative bacteria in intensive care unit patients

Multi-resistant gram-negative rods are important pathogens in intensive care units (ICU), cause high rates of mortality, and need infection control measures to avoid spread to another patients. This study was undertaken prospectively with all of the patients hospitalized at ICU, Anesthesiology of the Hospital S?o Paulo, using the ICU component of the National Nosocomial Infection Surveillance System (NNIS) methodology, between March 1, 1997 and June 30, 1998. Hospital infections occurring during the first three months after the establishment of prevention and control measures (3/1/97 to 5/31/97) were compared to those of the last three months (3/1/98 to 5/31/98). In this period, 933 NNIS patients were studied, with 139 during the first period and 211 in the second period. The overall rates of infection by multi-resistant microorganisms in the first and second periods were, respectively, urinary tract infection: 3.28/1000 patients/day; 2.5/1000 patients/day; pneumonia: 2.10/1000 patients/day; 5.0/1000 patients/day; bloodstream infection: 1.09/1000 patients/day; 2.5/1000 patients/day. A comparison between overall infection rates of both periods (Wilcoxon test) showed no statistical significance (p = 0.067). The use of intervention measures effectively decreased the hospital bloodstream infection rate (p < 0.001), which shows that control measures in ICU can contribute to preventing hospital infections.     

Readmissions and Death after ICU Discharge: Development and Validation of Two Predictive Models

Introduction

Early discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI) data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS) to assist ICU discharge planning.

Methods

Retrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011. Exclusion criteria: DNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2∶1) to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC) in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge.

Results

469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission) differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups.

Conclusions

Our models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although prospective validation is warranted, we speculate that these models may have value in assisting clinicians with ICU discharge planning.     

Serum Procalcitonin Level and SOFA Score at Discharge from the Intensive Care Unit Predict Post-Intensive Care Unit Mortality: A Prospective Study

Purpose

The final decision for discharge from the intensive care unit (ICU) is uncertain because it is made according to various patient parameters; however, it should be made on an objective evaluation. Previous reports have been inconsistent and unreliable in predicting post-ICU mortality. To identify predictive factors associated with post-ICU mortality, we analyzed physiological and laboratory data at ICU discharge.

Methods

Patients admitted to our ICU between September 2012 and August 2013 and staying for critical care>2 days were included. Sequential Organ Failure Assessment (SOFA) score; systemic inflammatory response syndrome score; white blood cell count; and serum C reactive protein, procalcitonin (PCT), interleukin-6 (IL-6), lactate, albumin, and hemoglobin levels were recorded. The primary end point was 90-day mortality after ICU discharge. Two hundred eighteen patients were enrolled (195 survivors, 23 non-survivors).

Results

Non-survivors presented a higher SOFA score and serum PCT, and IL-6 levels, as well as lower serum albumin and hemoglobin levels. Serum PCT, albumin, and SOFA score were associated with 90-day mortality in multiple logistic regression analysis. Hosmer-Lemeshow test showed chi-square value of 6.96, and P value of 0.54. The area under the curve (95% confidence interval) was 0.830 (0.771–0.890) for PCT, 0.688 (0.566–0.810) for albumin, 0.861 (0.796–0.927) for SOFA score, and increased to 0.913 (0.858–0.969) when these were combined. Serum PCT level at 0.57 ng/mL, serum albumin at 2.5 g/dL and SOFA score at 5.5 predict 90-day mortality, and high PCT, low albumin and high SOFA groups had significantly higher mortality. Serum PCT and SOFA score were significantly associated with survival days after ICU discharge in Cox regression analysis.

Conclusions

Serum PCT level and SOFA score at ICU discharge predict post-ICU mortality and survival days after ICU discharge. The combination of these two and albumin level might enable accurate prediction.     

Comparing mortality in renal patients on hemodialysis versus peritoneal dialysis using a marginal structural model

When comparing the causal effect of peritoneal dialysis (PD) and hemodialysis (HD) treatment on lowering mortality in renal patients, using observational data, it is necessary to adjust for different forms of confounding and informative censoring. Both the type of dialysis treatment that is started with and mortality are affected by baseline covariates. Longitudinal and baseline variables can affect both the probability of switching from one type of dialysis to the other, and mortality. Longitudinal and baseline variables can also affect the probability of receiving a kidney transplant, possibly causing informative censoring. Adjusting for longitudinal variables by including them as covariates in a regression model potentially causes bias, for instance by losing a possible indirect effect of dialysis on mortality via these longitudinal variables. Instead, we fitted a marginal structural model (MSM) to estimate the causal effect of dialysis type, adjusted for confounding and informative censoring. We used the MSM to compare the hazard of death as well as cumulative survival between the potential treatment trajectories "always PD" and "always HD" over time, conditional on age and diabetes mellitus status. We used inverse probability weighting (IPW) to fit the MSM.     

Cytomegalovirus and Herpes Simplex Virus Effect on the Prognosis of Mechanically Ventilated Patients Suspected to Have Ventilator-Associated Pneumonia

Objective

Cytomegalovirus (CMV) and herpes simplex virus (HSV) are common viruses that can affect critically ill patients who are not immunocompromised. The aim of this study was to determine whether the identification of CMV and/or HSV in mechanically ventilated critically ill patients suspected of having pneumonia was associated with an increased mortality.

Design

Prospective epidemiological study.

Setting

Medical intensive care unit of a tertiary medical center.

Patients

Ninety-three patients with suspected pneumonia.

Interventions

Patients with suspected pneumonia had bronchoalveolar lavage and blood samples taken to confirm the diagnosis. Antigenemia was used to detect CMV in the blood. Bronchoalveolar lavage samples were submitted to testing using quantitative real-time Polymerase Chain Reaction.

Measurements and Main Results

We identified 22 patients with a CMV infection, 26 patients with an HSV infection and 45 patients without CMV or HSV infection (control group). Mortality at day 60 was higher in patients with a CMV infection than in patients from the control group (55% vs. 20%, P<0.01). Mortality at day 60 was not significantly increased in the group with HSV infection. Duration of ICU stay and ICU mortality were significantly higher in patients with CMV infections when compared to patients from the control group, whereas ventilator free days were significantly lower in patients with CMV infections when compared to patients from the control group.

Conclusions

In critically ill patients, a CMV infection is associated with an increased mortality. Further interventional studies are needed to evaluate whether treatment could improve the prognosis.     

Principal stratification designs to estimate input data missing due to death

We consider studies of cohorts of individuals after a critical event, such as an injury, with the following characteristics. First, the studies are designed to measure "input" variables, which describe the period before the critical event, and to characterize the distribution of the input variables in the cohort. Second, the studies are designed to measure "output" variables, primarily mortality after the critical event, and to characterize the predictive (conditional) distribution of mortality given the input variables in the cohort. Such studies often possess the complication that the input data are missing for those who die shortly after the critical event because the data collection takes place after the event. Standard methods of dealing with the missing inputs, such as imputation or weighting methods based on an assumption of ignorable missingness, are known to be generally invalid when the missingness of inputs is nonignorable, that is, when the distribution of the inputs is different between those who die and those who live. To address this issue, we propose a novel design that obtains and uses information on an additional key variable-a treatment or externally controlled variable, which if set at its "effective" level, could have prevented the death of those who died. We show that the new design can be used to draw valid inferences for the marginal distribution of inputs in the entire cohort, and for the conditional distribution of mortality given the inputs, also in the entire cohort, even under nonignorable missingness. The crucial framework that we use is principal stratification based on the potential outcomes, here mortality under both levels of treatment. We also show using illustrative preliminary injury data that our approach can reveal results that are more reasonable than the results of standard methods, in relatively dramatic ways. Thus, our approach suggests that the routine collection of data on variables that could be used as possible treatments in such studies of inputs and mortality should become common.     

Vancomycin-resistant enterococci colonization-infection model: parameter impacts and outbreak risks

Vancomycin-resistant enterococci (VRE) infections have been linked to increased mortality and costs. A new model of a VRE-infested intensive care unit (ICU) is introduced. It incorporates critical features including the difference between colonization and infection, the role of special preventive care treatment cycles, fitness cost, and antibiotic use. Five patient stages are considered: susceptible, colonized with and without special preventive care, and infected with and without treatment. Parameter ranges are determined representing different ICUs and incorporated to numerically simulate the model. Basic reproductive number of the infection is derived and the impacts of the parameters are analysed. Strategies to minimize VRE infections and outbreak risk are explored with a focus on efficient and simultaneous control of critical parameters. In particular, threshold values of the level of special preventive care and ICU compliance rate are given to achieve desired goals under various constraints.     

Vancomycin-resistant enterococci colonization–infection model: parameter impacts and outbreak risks

Vancomycin-resistant enterococci (VRE) infections have been linked to increased mortality and costs. A new model of a VRE-infested intensive care unit (ICU) is introduced. It incorporates critical features including the difference between colonization and infection, the role of special preventive care treatment cycles, fitness cost, and antibiotic use. Five patient stages are considered: susceptible, colonized with and without special preventive care, and infected with and without treatment. Parameter ranges are determined representing different ICUs and incorporated to numerically simulate the model. Basic reproductive number of the infection is derived and the impacts of the parameters are analysed. Strategies to minimize VRE infections and outbreak risk are explored with a focus on efficient and simultaneous control of critical parameters. In particular, threshold values of the level of special preventive care and ICU compliance rate are given to achieve desired goals under various constraints.     

Adjunctive steroid in HIV-negative patients with severe Pneumocystis pneumonia

Background

High-dose steroid therapy has been proven effective in AIDS-related Pneumocystis pneumonia (PCP) but not in non-AIDS-related cases. We evaluated the effects on survival of steroids in HIV-negative patients with PCP.

Methods

Retrospective study patients admitted to the ICU with hypoxemic PCP. We compared patients receiving HDS (≥1 mg/Kg/day prednisone equivalent), low-dose steroids (LDS group, <1 mg/Kg/day prednisone equivalent), and no steroids (NS group). Variables independently associated with ICU mortality were identified.

Results

139 HIV-negative patients with PCP were included. Median age was 48 [40–60] years. The main underlying conditions were hematological malignancies (n=55, 39.6%), cancer (n=11, 7.9%), and solid organ transplantation (n=73, 52.2%). ICU mortality was 26% (36 deaths). The HDS group had 72 (51.8%) patients, the LDS group 35 (25%) patients, and the NS group 32 (23%) patients. Independent predictors of ICU mortality were SAPS II at ICU admission (odds ratio [OR], 1.04/point; [95%CI], 1.01-1.08, P=0.01), non-hematological disease (OR, 4.06; [95%CI], 1.19-13.09, P=0.03), vasopressor use (OR, 20.31; 95%CI, 6.45-63.9, P<0.001), and HDS (OR, 9.33; 95%CI, 1.97-44.3, P=0.02). HDS was not associated with the rate of ICU-acquired infections.

Conclusions

HDS were associated with increased mortality in HIV-negative patients with PCP via a mechanism independent from an increased risk of infection.     

Vitamin D Status and the Risk for Hospital-Acquired Infections in Critically Ill Adults: A Prospective Cohort Study

Introduction

To identify patient characteristics associated with low serum 25-hydroxyvitamin D (25(OH)D) concentrations in the medical intensive care unit (ICU) and examine the relationship between serum 25(OH)D and the risk for hospital-acquired infections.

Methods

This is a prospective observational cohort of adult patients admitted to the medical ICU at an urban safety net teaching hospital in Atlanta, Georgia from November 1, 2011 through October 31, 2012 with an anticipated ICU stay ≥ 1 day. Phlebotomy for serum 25(OH)D measurement was performed on all patients within 5 days of ICU admission. Patients were followed for 30 days or until death or hospital discharge, whichever came first. Hospital-acquired infections were determined using standardized criteria from review of electronic medical record.

Results

Among the 314 patients analyzed, 178 (57%) had a low vitamin D at a serum 25(OH)D concentration < 15 ng/mL. The patient characteristics associated with low vitamin D included admission during winter months (28% vs. 18%, P = 0.04), higher PaO2/FiO2 (275 vs. 226 torr, P = 0.03) and a longer time from ICU admission to study phlebotomy (1.8 vs. 1.5 days, P = 0.02). A total of 36 (11%) patients were adjudicated as having a hospital-acquired infection and in multivariable analysis adjusting for gender, alcohol use, APACHE II score, time to study phlebotomy, ICU length of stay and net fluid balance, serum 25(OH)D levels < 15 ng/mL were not associated with risk for hospital-acquired infections (HR 0.85, 95% CI 0.40-1.80, P = 0.7).

Conclusions

In this prospective, observational cohort of adults admitted to a single-center medical ICU, we did not find a significant association between low 25(OH)D and the risk for hospital-acquired infections.     

Can Interactions between Timing of Vaccine-Altered Influenza Pandemic Waves and Seasonality in Influenza Complications Lead to More Severe Outcomes?

Vaccination can delay the peak of a pandemic influenza wave by reducing the number of individuals initially susceptible to influenza infection. Emerging evidence indicates that susceptibility to severe secondary bacterial infections following a primary influenza infection may vary seasonally, with peak susceptibility occurring in winter. Taken together, these two observations suggest that vaccinating to prevent a fall pandemic wave might delay it long enough to inadvertently increase influenza infections in winter, when primary influenza infection is more likely to cause severe outcomes. This could potentially cause a net increase in severe outcomes. Most pandemic models implicitly assume that the probability of severe outcomes does not vary seasonally and hence cannot capture this effect. Here we show that the probability of intensive care unit (ICU) admission per influenza infection in the 2009 H1N1 pandemic followed a seasonal pattern. We combine this with an influenza transmission model to investigate conditions under which a vaccination program could inadvertently shift influenza susceptibility to months where the risk of ICU admission due to influenza is higher. We find that vaccination in advance of a fall pandemic wave can actually increase the number of ICU admissions in situations where antigenic drift is sufficiently rapid or where importation of a cross-reactive strain is possible. Moreover, this effect is stronger for vaccination programs that prevent more primary influenza infections. Sensitivity analysis indicates several mechanisms that may cause this effect. We also find that the predicted number of ICU admissions changes dramatically depending on whether the probability of ICU admission varies seasonally, or whether it is held constant. These results suggest that pandemic planning should explore the potential interactions between seasonally varying susceptibility to severe influenza outcomes and the timing of vaccine-altered pandemic influenza waves.     

Population biology of the parasitic phase of Ostertagia circumcincta

Four current models for the parasitic phase of the Ostertagia circumcincta life-cycle were evaluated with respect to their ability to represent the outcome of experimental infection studies and the population biology of the parasite in the field. Neither of the two discrete-time models was able to mimic the rise and fall in parasite numbers that characterize trickle infection experiments. When the infection rate is constant, both models predict that parasite numbers will eventually reach an asymptotic equilibrium value. This happens because both models are formulated such that parasite mortality is constant when the infection rate is constant. The two continuous-time models are able to mimic trickle infection experiments because both of them represent parasite mortality as an increasing function of the infection rate and the duration of infection. However, the continuous-time models do not adequately represent the demography of the parasitic phase in the field because neither of them takes any account of the effect of variations in infection rate from host to host on the overall mean parasite death rate.     

Modelling variability in lymphatic filariasis: macrofilarial dynamics in the Brugia pahangi--cat model.

A striking feature of lymphatic filariasis is the considerable heterogeneity in infection burden observed between hosts, which greatly complicates the analysis of the population dynamics of the disease. Here, we describe the first application of the moment closure equation approach to model the sources and the impact of this heterogeneity for macrofilarial population dynamics. The analysis is based on the closest laboratory equivalent of the life cycle and immunology of infection in humans--cats chronically infected with the filarial nematode Brugia pahangi. Two sets of long-term experiments are analysed: hosts given either single primary infections or given repeat infections. We begin by quantifying changes in the mean and aggregation of adult parasites (inversely measured by the negative binomial parameter, kappa in cohorts of hosts using generalized linear models. We then apply simple stochastic models to interpret observed patterns. The models and empirical data indicate that parasite aggregation tracks the decline in the mean burden with host age in primary infections. Conversely, in repeat infections, aggregation increases as the worm burden declines with experience of infection. The results show that the primary infection variability is consistent with heterogeneities in parasite survival between hosts. By contrast, the models indicate that the reduction in parasite variability with time in repeat infections is most likely due to the ''filtering'' effect of a strong, acquired immune response, which gradually acts to remove the initial variability generated by heterogeneities in larval mortality. We discuss this result in terms of the homogenizing effect of host immunity-driven density-dependence on macrofilarial burden in older hosts.     

Within-Host Models of High and Low Pathogenic Influenza Virus Infections: The Role of Macrophages

The World Health Organization identifies influenza as a major public health problem. While the strains commonly circulating in humans usually do not cause severe pathogenicity in healthy adults, some strains that have infected humans, such as H5N1, can cause high morbidity and mortality. Based on the severity of the disease, influenza viruses are sometimes categorized as either being highly pathogenic (HP) or having low pathogenicity (LP). The reasons why some strains are LP and others HP are not fully understood. While there are likely multiple mechanisms of interaction between the virus and the immune response that determine LP versus HP outcomes, we focus here on one component, namely macrophages (MP). There is some evidence that MP may both help fight the infection and become productively infected with HP influenza viruses. We developed mathematical models for influenza infections which explicitly included the dynamics and action of MP. We fit these models to viral load and macrophage count data from experimental infections of mice with LP and HP strains. Our results suggest that MP may not only help fight an influenza infection but may contribute to virus production in infections with HP viruses. We also explored the impact of combination therapies with antivirals and anti-inflammatory drugs on HP infections. Our study suggests a possible mechanism of MP in determining HP versus LP outcomes, and how different interventions might affect infection dynamics.     

Estimating causal treatment effects from longitudinal HIV natural history studies using marginal structural models

Several recently completed and ongoing studies of the natural history of HIV infection have generated a wealth of information about its clinical progression and how this progression is altered by therepeutic interventions and environmental factors. Natural history studies typically follow prospective cohort designs, and enroll large numbers of participants for long-term prospective follow-up (up to several years). Using data from the HIV Epidemiology Research Study (HERS), a six-year natural history study that enrolled 871 HIV-infected women starting in 1993, we investigate the therapeutic effect of highly active antiretroviral therapy regimens (HAART) on CD4 cell count using the marginal structural modeling framework and associated estimation procedures based on inverse-probability weighting (developed by Robins and colleagues). To evaluate treatment effects from a natural history study, specialized methods are needed because treatments are not randomly prescribed and, in particular, the treatment-response relationship can be confounded by variables that are time-varying. Our analysis uses CD4 data on all follow-up visits over a two-year period, and includes sensitivity analyses to investigate potential biases attributable to unmeasured confounding. Strategies for selecting ranges of a sensitivity parameter are given, as are intervals for treatment effect that reflect uncertainty attributable both to sampling and to lack of knowledge about the nature and existence of unmeasured confounding. To our knowledge, this is the first use in "real data" of Robins's sensitivity analysis for unmeasured confounding (Robins, 1999a, Synthese 121, 151-179). The findings from our analysis are consistent with recent treatment guidelines set by the U.S. Panel of the International AIDS Society (Carpenter et al., 2000, Journal of the American Medical Association 280, 381-391).     

Dermatomyositis and Polymyositis in the Intensive Care Unit: A Single-Center Retrospective Cohort Study of 102 Patients

IntroductionPatients with idiopathic inflammatory myopathies (IIMs) are sometimes complicated with life-threatening conditions requiring intensive care unit (ICU) admission. In the past, owing to the low incidence of IIM, little was known about such patients. Our aim was to investigate the clinical features and outcomes of these patients and identify their risk factors for mortality.MethodsA retrospective study was performed of IIM patients admitted over an 8-year period to the medical ICU of a tertiary referral center in China. We collected data regarding demographic features, IIM-related clinical characteristics, reasons for admission, organ dysfunction, and outcomes. Independent predictors of ICU mortality were identified through multivariate logistic regression analysis.ResultsOf the 102 patients in our cohort, polymyositis (PM), dermatomyositis (DM), and clinically amyopathic dermatomyositis (CADM) accounted for 23.5%, 64.7%, and 11.7% respectively. The median duration from the onset of IIM to ICU admission was 4.3 months (interquartile range [IQR], 2.6–9.4 months). Reasons for ICU admission were infection alone (39.2%), acute exacerbation of IIM alone (27.5%), the coexistence of both (27.5%), or other reasons (5.8%). Pneumonia accounted for 97% of the infections; 63.2% of infections with documented pathogens were caused by opportunistic agents. Rapid progressive interstitial lung disease (RP-ILD) was responsible for 87.5% of acute exacerbation of IIM. The median Acute Physiology and Chronic Health Evaluation II (APACHE II) score on ICU day 1 was 17 (IQR 14–20). On ICU admission, acute respiratory failure (ARF) was the most common type (80.4%) of organ failure. The mortality rate in the ICU was 79.4%. Factors associated with increased ICU mortality included a diagnosis of DM (including CADM), a high APACHE II score, the presence of ARF, a decreased PaO₂/FiO₂ ratio, and a low lymphocyte count at the time of ICU admission.ConclusionsThe outcome of IIM patients admitted to the ICU was extremely poor. A diagnosis of DM/CADM, the presence and severity of ARF, and the lymphocyte counts at ICU admission were shown to be valuable for predicting outcome. Opportunistic infections and rapidly progressive interstitial lung disease warrant concern in treating these patients.     

Get the News Out Loudly and Quickly: The Influence of the Media on Limiting Emerging Infectious Disease Outbreaks

During outbreaks of infectious diseases with high morbidity and mortality, individuals closely follow media reports of the outbreak. Many will attempt to minimize contacts with other individuals in order to protect themselves from infection and possibly death. This process is called social distancing. Social distancing strategies include restricting socializing and travel, and using barrier protections. We use modeling to show that for short-term outbreaks, social distancing can have a large influence on reducing outbreak morbidity and mortality. In particular, public health agencies working together with the media can significantly reduce the severity of an outbreak by providing timely accounts of new infections and deaths. Our models show that the most effective strategy to reduce infections is to provide this information as early as possible, though providing it well into the course of the outbreak can still have a significant effect. However, our models for long-term outbreaks indicate that reporting historic infection data can result in more infections than with no reporting at all. We examine three types of media influence and we illustrate the media influence with a simulated outbreak of a generic emerging infectious disease in a small city. Social distancing can never be complete; however, for a spectrum of outbreaks, we show that leaving isolation (stopping applying social distancing measures) for up to 4 hours each day has modest effect on the overall morbidity and mortality.