Validating a Proteomic Signature of Severe COVID-19.

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Using Bayesian Methods to Augment the Interpretation of Critical Care Trials. An Overview of Theory and Example Reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial.

Most randomized trials are designed and analyzed using frequentist statistical approaches such as null hypothesis testing and P values. Conceptually, P values are cumbersome to understand, as they provide evidence of data incompatibility with a null hypothesis (e.g., no clinical benefit) and not direct evidence of the alternative hypothesis (e.g., clinical benefit). This counterintuitive framework may contribute to the misinterpretation that the absence of evidence is equal to evidence of absence and may cause the discounting of potentially informative data. Bayesian methods provide an alternative, probabilistic interpretation of data. The reanalysis of completed trials using Bayesian methods is becoming increasingly common, particularly for trials with effect estimates that appear clinically significant despite P values above the traditional threshold of 0.05. Statistical inference using Bayesian methods produces a distribution of effect sizes that would be compatible with observed trial data, interpreted in the context of prior assumptions about an intervention (called "priors"). These priors are chosen by investigators to reflect existing beliefs and past empirical evidence regarding the effect of an intervention. By calculating the likelihood of clinical benefit, a Bayesian reanalysis can augment the interpretation of a trial. However, if priors are not defined a priori, there is a legitimate concern that priors could be constructed in a manner that produces biased results. Therefore, some standardization of priors for Bayesian reanalysis of clinical trials may be desirable for the critical care community. In this Critical Care Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduce a framework that researchers can use to select priors for a Bayesian reanalysis, and demonstrate how to apply our proposal by conducting a novel Bayesian trial reanalysis.

Society of Critical Care Medicine's International Consensus Conference on Prediction and Identification of Long-Term Impairments After Critical Illness.

BACKGROUND: After critical illness, new or worsening impairments in physical, cognitive, and/or mental health function are common among patients who have survived. Who should be screened for long-term impairments, what tools to use, and when remain unclear. OBJECTIVES: Provide pragmatic recommendations to clinicians caring for adult survivors of critical illness related to screening for postdischarge impairments. PARTICIPANTS: Thirty-one international experts in risk-stratification and assessment of survivors of critical illness, including practitioners involved in the Society of Critical Care Medicine's Thrive Post-ICU Collaboratives, survivors of critical illness, and clinical researchers. DESIGN: Society of Critical Care Medicine consensus conference on post-intensive care syndrome prediction and assessment, held in Dallas, in May 2019. A systematic search of PubMed and the Cochrane Library was conducted in 2018 and updated in 2019 to complete an original systematic review and to identify pre-existing systematic reviews. MEETING OUTCOMES: We concluded that existing tools are insufficient to reliably predict post-intensive care syndrome. We identified factors before (e.g., frailty, preexisting functional impairments), during (e.g., duration of delirium, sepsis, acute respiratory distress syndrome), and after (e.g., early symptoms of anxiety, depression, or post-traumatic stress disorder) critical illness that can be used to identify patients at high-risk for cognitive, mental health, and physical impairments after critical illness in whom screening is recommended. We recommend serial assessments, beginning within 2-4 weeks of hospital discharge, using the following screening tools: Montreal Cognitive Assessment test; Hospital Anxiety and Depression Scale; Impact of Event Scale-Revised (post-traumatic stress disorder); 6-minute walk; and/or the EuroQol-5D-5L, a health-related quality of life measure (physical function). CONCLUSIONS: Beginning with an assessment of a patient's pre-ICU functional abilities at ICU admission, clinicians have a care coordination strategy to identify and manage impairments across the continuum. As hospital discharge approaches, clinicians should use brief, standardized assessments and compare these results to patient's pre-ICU functional abilities ("functional reconciliation"). We recommend serial assessments for post-intensive care syndrome-related problems continue within 2-4 weeks of hospital discharge, be prioritized among high-risk patients, using the identified screening tools to prompt referrals for services and/or more detailed assessments.

Assessing the Course of Organ Dysfunction Using Joint Longitudinal and Time-to-Event Modeling in the Vasopressin and Septic Shock Trial.

Non-mortality septic shock outcomes (e.g., Sequential Organ Failure Assessment score) are important clinical endpoints in pivotal sepsis trials. However, comparisons of observed longitudinal non-mortality outcomes between study groups can be biased if death is unequal between study groups or is associated with an intervention (i.e., informative censoring). We compared the effects of vasopressin versus norepinephrine on the Sequential Organ Failure Assessment score in the Vasopressin and Septic Shock Trial to illustrate the use of joint modeling to help minimize potential bias from informative censoring. DESIGN: Secondary analysis of the Vasopressin and Septic Shock Trial data. SETTING: Twenty-seven ICUs in Canada, Australia, and United States. SUBJECTS: Seven hundred sixty-three participants with septic shock who received blinded vasopressin (n = 389) or norepinephrine infusions (n = 374). MEASUREMENTS AND MAIN RESULTS: Sequential Organ Failure Assessment scores were calculated daily until discharge, death, or day 28 after randomization. Mortality was numerically higher in the norepinephrine arm (28 d mortality of 39% vs 35%; p = 0.25), and there was a positive association between higher Sequential Organ Failure Assessment scores and patient mortality, characteristics that suggest a potential for bias from informative censoring of Sequential Organ Failure Assessment scores by death. The best-fitting joint longitudinal (i.e., linear mixed-effects model) and survival (i.e., Cox proportional hazards model for the time-to-death) model showed that norepinephrine was associated with a more rapid improvement in the total Sequential Organ Failure Assessment score through day 4, and then the daily Sequential Organ Failure Assessment scores converged and overlapped for the remainder of the study period. CONCLUSIONS: Short-term reversal of organ dysfunction occurred more rapidly with norepinephrine compared with vasopressin, although differences between study arms did not persist after day 4. Joint models are an accessible methodology that could be used in critical care trials to assess the effects of interventions on the longitudinal progression of key outcomes (e.g., organ dysfunction, biomarkers, or quality of life) that may be informatively truncated by death or other censoring events.

Predicting Mortality in Children With Pediatric Acute Respiratory Distress Syndrome: A Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology Study.

OBJECTIVES: Pediatric acute respiratory distress syndrome is heterogeneous, with a paucity of risk stratification tools to assist with trial design. We aimed to develop and validate mortality prediction models for patients with pediatric acute respiratory distress syndrome. DESIGN: Leveraging additional data collection from a preplanned ancillary study (Version 1) of the multinational Pediatric Acute Respiratory Distress syndrome Incidence and Epidemiology study, we identified predictors of mortality. Separate models were built for the entire Version 1 cohort, for the cohort excluding neurologic deaths, for intubated subjects, and for intubated subjects excluding neurologic deaths. Models were externally validated in a cohort of intubated pediatric acute respiratory distress syndrome patients from the Children's Hospital of Philadelphia. SETTING: The derivation cohort represented 100 centers worldwide; the validation cohort was from Children's Hospital of Philadelphia. PATIENTS: There were 624 and 640 subjects in the derivation and validation cohorts, respectively. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The model for the full cohort included immunocompromised status, Pediatric Logistic Organ Dysfunction 2 score, day 0 vasopressor-inotrope score and fluid balance, and PaO2/FIO2 6 hours after pediatric acute respiratory distress syndrome onset. This model had good discrimination (area under the receiver operating characteristic curve 0.82), calibration, and internal validation. Models excluding neurologic deaths, for intubated subjects, and for intubated subjects excluding neurologic deaths also demonstrated good discrimination (all area under the receiver operating characteristic curve ≥ 0.84) and calibration. In the validation cohort, models for intubated pediatric acute respiratory distress syndrome (including and excluding neurologic deaths) had excellent discrimination (both area under the receiver operating characteristic curve ≥ 0.85), but poor calibration. After revision, the model for all intubated subjects remained miscalibrated, whereas the model excluding neurologic deaths showed perfect calibration. Mortality models also stratified ventilator-free days at 28 days in both derivation and validation cohorts. CONCLUSIONS: We describe predictive models for mortality in pediatric acute respiratory distress syndrome using readily available variables from day 0 of pediatric acute respiratory distress syndrome which outperform severity of illness scores and which demonstrate utility for composite outcomes such as ventilator-free days. Models can assist with risk stratification for clinical trials.

Determining the Association Between End-of-Life Care Resources and Patient Outcomes in Pennsylvania ICUs.

OBJECTIVES: As ICUs are increasingly a site of end-of-life care, many have adopted end-of-life care resources. We sought to determine the association of such resources with outcomes of ICU patients. DESIGN: Retrospective cohort study. SETTING: Pennsylvania ICUs. PATIENTS: Medicare fee-for-service beneficiaries. INTERVENTIONS: Availability of any of one hospital-based resource (palliative care consultants) or four ICU-based resources (protocol for withdrawal of life-sustaining therapy, triggers for automated palliative care consultation, protocol for family meetings, and palliative care clinicians embedded in ICU rounds). MEASUREMENTS AND MAIN RESULTS: In mixed-effects regression analyses, admission to a hospital with end-of-life resources was not associated with mortality, length of stay, or treatment intensity (mechanical ventilation, hemodialysis, tracheostomy, gastrostomy, artificial nutrition, or cardiopulmonary resuscitation); however, it was associated with a higher likelihood of discharge to hospice (odds ratio, 1.58; 95% CI, 1.11-2.24), an effect that was driven by ICU-based resources (odds ratio, 1.37; 95% CI, 1.04-1.81) rather than hospital-based resources (odds ratio, 1.19; 95% CI, 0.83-1.71). Instrumental variable analysis using differential distance (defined as the additional travel distance beyond the hospital closest to a patient's home needed to reach a hospital with end-of-life resources) demonstrated that among those for whom differential distance would influence receipt of end-of-life resources, admission to a hospital with such resources was not associated with any outcome. CONCLUSIONS: ICU-based end-of-life care resources do not appear to change mortality but are associated with increased hospice utilization. Given that this finding was not confirmed by the instrumental variable analysis, future studies should attempt to verify this finding, and identify specific resources or processes of care that impact the care of ICU patients at the end of life.

Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, random forest, and elastic net regression models, with and without unstructured clinical text data, to predict a binary composite outcome of in-hospital death or ICU length of stay greater than or equal to 7 days using data from the first 48 hours of hospitalization. DESIGN: Retrospective cohort study with split sampling for model training and testing. SETTING: A single urban academic hospital. PATIENTS: All hospitalized patients who required ICU care at the Beth Israel Deaconess Medical Center in Boston, MA, from 2001 to 2012. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among eligible 25,947 hospital admissions, we observed 5,504 (21.2%) in which patients died or had ICU length of stay greater than or equal to 7 days. The gradient boosting machine model had the highest discrimination without (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.81-0.84) and with (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.88-0.90) text-derived variables. Both gradient boosting machines and random forests outperformed logistic regression without text data (p < 0.001), whereas all models outperformed logistic regression with text data (p < 0.02). The inclusion of text data increased the discrimination of all four model types (p < 0.001). Among those models using text data, the increasing presence of terms "intubated" and "poor prognosis" were positively associated with mortality and ICU length of stay, whereas the term "extubated" was inversely associated with them. CONCLUSIONS: Variables extracted from unstructured clinical text from the first 48 hours of hospital admission using natural language processing techniques significantly improved the abilities of logistic regression and other machine learning models to predict which patients died or had long ICU stays. Learning health systems may adapt such models using open-source approaches to capture local variation in care patterns.

Temporal Trends in Incidence, Sepsis-Related Mortality, and Hospital-Based Acute Care After Sepsis.

OBJECTIVES: A growing number of patients survive sepsis hospitalizations each year and are at high risk for readmission. However, little is known about temporal trends in hospital-based acute care (emergency department treat-and-release visits and hospital readmission) after sepsis. Our primary objective was to measure temporal trends in sepsis survivorship and hospital-based acute care use in sepsis survivors. In addition, because readmissions after pneumonia are subject to penalty under the national readmission reduction program, we examined whether readmission rates declined after sepsis hospitalizations related to pneumonia. DESIGN AND SETTING: Retrospective, observational cohort study conducted within an academic healthcare system from 2010 to 2015. PATIENTS: We used three validated, claims-based approaches to identify 17,256 sepsis or severe sepsis hospitalizations to examine trends in hospital-based acute care after sepsis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: From 2010 to 2015, sepsis as a proportion of medical and surgical admissions increased from 3.9% to 9.4%, whereas in-hospital mortality rate for sepsis hospitalizations declined from 24.1% to 14.8%. As a result, the proportion of medical and surgical discharges at-risk for hospital readmission after sepsis increased from 2.7% to 7.8%. Over 6 years, 30-day hospital readmission rates declined modestly, from 26.4% in 2010 to 23.1% in 2015, driven largely by a decline in readmission rates among survivors of nonsevere sepsis, and nonpneumonia sepsis specifically, as the readmission rate of severe sepsis survivors was stable. The modest decline in 30-day readmission rates was offset by an increase in emergency department treat-and-release visits, from 2.8% in 2010 to a peak of 5.4% in 2014. CONCLUSIONS: Owing to increasing incidence and declining mortality, the number of sepsis survivors at risk for hospital readmission rose significantly between 2010 and 2015. The 30-day hospital readmission rates for sepsis declined modestly but were offset by a rise in emergency department treat-and-release visits.

Altitude and regional gradients in chronic kidney disease prevalence in Costa Rica: Data from the Costa Rican Longevity and Healthy Aging Study.

OBJECTIVES: Recent studies in Central America indicate that mortality attributable to chronic kidney disease (CKD) is rising rapidly. We sought to determine the prevalence and regional variation of CKD and the relationship of biologic and socio-economic factors to CKD risk in the older-adult population of Costa Rica. METHODS: We used data from the Costa Rican Longevity and Health Aging Study (CRELES). The cohort was comprised of 2657 adults born before 1946 in Costa Rica, chosen through a sampling algorithm to represent the national population of Costa Ricans >60 years of age. Participants answered questionnaire data and completed laboratory testing. The primary outcome of this study was CKD, defined as an estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 . RESULTS: The estimated prevalence of CKD for older Costa Ricans was 20% (95% CI 18.5-21.9%). In multivariable logistic regression, older age (adjusted odds ratio [aOR] 1.08 per year, 95% CI 1.07-1.10, P < 0.001) was independently associated with CKD. For every 200 m above sea level of residence, subjects' odds of CKD increased 26% (aOR 1.26 95% CI 1.15-1.38, P < 0.001). There was large regional variation in adjusted CKD prevalence, highest in Limon (40%, 95% CI 30-50%) and Guanacaste (36%, 95% CI 26-46%) provinces. Regional and altitude effects remained robust after adjustment for socio-economic status. CONCLUSIONS: We observed large regional and altitude-related variations in CKD prevalence in Costa Rica, not explained by the distribution of traditional CKD risk factors. More studies are needed to explore the potential association of geographic and environmental exposures with the risk of CKD.

Impact of primary health care on mortality from heart and cerebrovascular diseases in Brazil: a nationwide analysis of longitudinal data.

OBJECTIVES: To evaluate the impact of Brazil's recently implemented Family Health Program (FHP), the largest primary health care programme in the world, on heart and cerebrovascular disease mortality across Brazil from 2000 to 2009. DESIGN: Ecological longitudinal design, evaluating the impact of FHP using negative binomial regression models for panel data with fixed effects specifications. SETTING: Nationwide analysis of data from Brazilian municipalities covering the period from 2000 to 2009. DATA SOURCES: 1622 Brazilian municipalities with vital statistics of adequate quality. MAIN OUTCOME MEASURES: The annual FHP coverage and the average FHP coverage in previous years were used as main independent variables and classified as none (0%), incipient (<30%), intermediate (30-69%), or consolidated (≥ 70%). Age standardised mortality rates from causes in the group of cerebrovascular (ICD-10 codes I60-69), ischaemic (ICD-10 I20-25), and other forms of heart diseases (ICD-10 I30-52), which were included in the national list of ambulatory care-sensitive conditions, were calculated for each municipality for each year. They accounted for 40% of all deaths from these groups during the study period. RESULTS: FHP coverage was negatively associated with mortality rates from cerebrovascular and heart diseases (ambulatory care-sensitive conditions) in both unadjusted and adjusted models for demographic, social, and economic confounders. The FHP had no effect on the mortality rate for accidents, used as a control. The rate ratio for the effect of consolidated annual FHP coverage on cerebrovascular disease mortality and on heart disease mortality was 0.82 (95% confidence interval 0.79 to 0.86) and 0.79 (0.75 to 0.80) respectively, reaching the value of 0.69 (0.66 to 0.73) and 0.64 (0.59 to 0.68) when the coverage was consolidated during all the previous eight years. Moreover, FHP coverage increased the number of health education activities, domiciliary visits, and medical consultations and reduced hospitalisation rates for cerebrovascular and heart disease. Several complementary analyses showed quantitatively similar results. CONCLUSIONS: Comprehensive and community based primary health care programmes, such as the FHP in Brazil, acting through cardiovascular disease prevention, care, and follow-up can contribute to decreased cardiovascular disease morbidity and mortality in a developing country such as Brazil.

Serum cytokines associated with severity and complications of kala-azar.

OBJECTIVES: Recent clinical data suggest that severe kala-azar (or visceral leishmaniasis) is an exaggerated innate immune response mediated by inflammatory cytokines, leading to a systemic inflammatory syndrome similar to what is observed in malaria, sepsis and other diseases. We tested this hypothesis by measuring serum cytokines in individuals with kala-azar. METHODS: We compared patients with severe kala-azar (i.e. hemorrhagic manifestations, n = 38) with patients without evidence of hemorrhage (n = 96). We conducted a detailed clinical and laboratory evaluation, measuring serum IL-1beta, IL-6, IL-8, IL-10, IL-12, interferon-gamma, and TNF-alpha, and markers of disseminated intravascular coagulation (DIC). RESULTS: Infants had higher levels of inflammatory cytokines, while HIV-infected patients had lower concentrations of IL-10 and interferon-gamma. Higher levels of IL-6, interferon-gamma, and IL-8 were found among deceased patients. IL-8 and interferon-gamma were independently associated with bleeding. Several cytokines were associated with different signs of severe clinical and laboratory manifestations, including DIC. IL-6 was highly positively and independently associated with IL-1beta, IL-8, IL-10, and negatively associated with TNF-alpha. IL-1beta and TNF-alpha were also highly independently associated with disease severity. CONCLUSION: In its severe form, kala-azar, a neglected tropical disease, initiates a systemic inflammatory response that leads to DIC and other manifestations. Children may have higher risk of death due to the more intense cytokine release. The data supports the notion that IL-6 is the central cytokine that is associated with lethal disease, but interferon-gamma, IL1beta, IL-8, and TNF-alpha are also involved with disease severity. Inhibition of IL-6 is a potential target of adjuvant therapy for severe or pediatric forms of this disease.

Urban parasitology: visceral leishmaniasis in Brazil.

Since the early 1980s, visceral leishmaniasis (VL) which is, in general, a rural zoonotic disease, has spread to the urban centers of the north, and now the south and west of Brazil. The principal drivers differ between cities, though human migration, large urban canid populations (animal reservoir), and a decidedly peripatetic and adaptable sand fly vector are the primary forces. The exact number of urban cases remains unclear as a result of challenges with surveillance. However, the number of urban cases registered continues to increase annually. Most control initiatives (e.g. culling infected dogs and household spraying to kill the sand fly) could be effective, but have proven hard to maintain at large scales due to logistical, financial and other reasons. In this article, the urbanization of VL in Brazil is reviewed, touching on these and other topics related to controlling VL within and outside Brazil.

Who is a typical patient with visceral leishmaniasis? Characterizing the demographic and nutritional profile of patients in Brazil, East Africa, and South Asia.

Drug-dosing recommendations for visceral leishmaniasis (VL) treatment are based on the patients' weight or age. A current lack of demographic and anthropometric data on patients hinders (1) the ability of health providers to properly prepare for patient management, (2) an informed drug procurement for disease control, and (3) the design of clinical trials and development of new drug therapies in the different endemic areas. We present information about the age, gender, weight, and height of 29,570 consecutive VL patients presenting to 20 locations in six geographic endemic regions of Brazil, East Africa, Nepal, and India between 1997 and 2009. Our compilation shows substantial heterogeneity in the types of patients seeking care for VL at the clinics within the different locations. This suggests that drug development, procurement, and perhaps even treatment protocols, such as the use of the potentially teratogenic drug miltefosine, may require distinct strategies in these geographic settings.