Ayaz Hyder

A small post-operative rise in serum creatinine predicts acute kidney injury in children undergoing cardiac surgery

To predict development of acute kidney injury and its outcome we retrospectively studied children having cardiac surgery. Acute kidney injury (AKI) was defined using the serum creatinine criteria of the pediatric Risk Injury Failure Loss End-Stage (pRIFLE) kidney disease definition. We tested whether a small rise (less than 50%) in creatinine on post-operative days 1 or 2 could predict a greater than 50% increase in serum creatinine within 48 h in 390 children. AKI occurred in 36% of patients, mostly in the first 4 post-operative days. Using logistic regression, significant independent risk factors for AKI were bypass time, longer vasopressor use, and a tendency for younger age. Using Cox regression, AKI was independently associated with longer intensive care unit stay and duration of ventilation. Patients whose serum creatinine did not increase on post-operative days 1 or 2 were unlikely to develop AKI (negative predictive values of 87 and 98%, respectively). Percentage serum creatinine rise on post-operative day 1 predicted AKI within 48 h (area under the curve=0.65). Our study shows that AKI after pediatric heart surgery is common and is a risk factor for poorer outcome. Small post-operative increases in serum creatinine may assist in the early prediction of AKI.

Acute kidney injury is an independent risk factor for pediatric intensive care unit mortality, longer length of stay and prolonged mechanical ventilation in critically ill children: a two-center retrospective cohort study

IntroductionIn adults, small (< 50%) serum creatinine (SCr) increases predict mortality. It is unclear whether different baseline serum creatinine (bSCr) estimation methods affect findings of acute kidney injury (AKI)-outcome associations. We characterized pediatric AKI, evaluated the effect of bSCr estimation approaches on AKI-outcome associations and evaluated the use of small SCr increases to predict AKI development.MethodsWe conducted a retrospective cohort database study of children (excluding postoperative cardiac or renal transplant patients) admitted to two pediatric intensive care units (PICUs) for at least one night in Montreal, QC, Canada. The AKI definition was based on the Acute Kidney Injury Network staging system, excluding the requirement of SCr increase within 48 hours, which was impossible to evaluate on the basis of our data set. We estimated bSCr two ways: (1) the lowest SCr level in the three months before admission or the average age- and gender-based norms (the standard method) or (2) by using average norms in all patients. Outcomes were PICU mortality and length of stay as well as required mechanical ventilation. We used multiple logistic regression analysis to evaluate AKI risk factors and the association between AKI and mortality. We used multiple linear regression analysis to evaluate the effect of AKI on other outcomes. We calculated diagnostic characteristics for early SCr increase (< 50%) to predict AKI development.ResultsOf 2,106 admissions (mean age ± SD = 5.0 ± 5.5 years; 47% female), 377 patients (17.9%) developed AKI (using the standard bSCr method) during PICU admission. Higher Pediatric Risk of Mortality score, required mechanical ventilation, documented infection and having a bSCr measurement were independent predictors of AKI development. AKI was associated with increased mortality (adjusted odds ratio (OR) = 3.7, 95% confidence interval (95% CI) = 2.1 to 6.4, using the standard bSCr method; OR = 4.5, 95% CI = 2.6 to 7.9, using normative bSCr values in all patients). AKI was independently associated with longer PICU stay and required mechanical ventilation. In children with no admission AKI, the initial percentage SCr increase predicted AKI development (area under the curve = 0.67, 95% CI = 0.60 to 0.74).ConclusionsAKI is associated with increased mortality and morbidity in critically ill children, regardless of the bSCr used. Paying attention to small early SCr increases may contribute to early AKI diagnosis in conjunction with other new AKI biomarkers.

Acute kidney injury in non-critically ill children treated with aminoglycoside antibiotics in a tertiary healthcare centre: a retrospective cohort study

BACKGROUND Aminoglycosides (AG) cause acute kidney injury (AKI), but the incidence and severity distribution are unclear, particularly in non-critically ill children. We determined the incidence, severity and risk factors of AG-associated AKI and assessed for associations with longer hospitalization and higher costs. METHODS At Texas Childrens Hospital, we conducted a retrospective cohort study of children treated with AG for ≥ 5 days in 2005, excluding children with admission primary renal diagnoses. AKI was defined by the paediatric Risk, Injury, Failure, Loss, End Stage Kidney Disease (pRIFLE) and Acute Kidney Injury Network (AKIN) definitions. Multiple logistic and linear regression analyses were used to assess independence of associations with outcomes. RESULTS Five hundred and fifty-seven children [mean ± SD age = 8.0 ± 5.9 years, 286 (51%) male, 489 (88%) gentamicin] were studied. The AKI rate was 33% and 20% by pRIFLE and AKIN definitions, respectively. Longer treatment, higher baseline estimated glomerular filtration rate, being on a medicine (versus surgical) treatment service and prior AG treatment were independent risk factors for AKI development. AKI by pRIFLE or AKIN was independently associated with longer hospital stay and higher total hospital costs. The pRIFLE definition was more sensitive for AKI detection, but the AKIN definition was more strongly related to outcomes. CONCLUSIONS AKI is common and associated with poorer outcomes in non-critically ill children treated with AG. Future research should attempt to understand how to best define AKI in the non-critical illness paediatric setting.

PM2.5 exposure and birth outcomes: Use of satellite- and monitor-based data

Background: Air pollution may be related to adverse birth outcomes. Exposure information from land-based monitoring stations often suffers from limited spatial coverage. Satellite data offer an alternative data source for exposure assessment. Methods: We used birth certificate data for births in Connecticut and Massachusetts, United States (2000–2006). Gestational exposure to PM2.5 was estimated from US Environmental Protection Agency monitoring data and from satellite data. Satellite data were processed and modeled by using two methods—denoted satellite (1) and satellite (2)—before exposure assessment. Regression models related PM2.5 exposure to birth outcomes while controlling for several confounders. Birth outcomes were mean birth weight at term birth, low birth weight at term (<2500 g), small for gestational age (SGA, <10th percentile for gestational age and sex), and preterm birth (<37 weeks). Results: Overall, the exposure assessment method modified the magnitude of the effect estimates of PM2.5 on birth outcomes. Change in birth weight per interquartile range (2.41 &mgr;g/m3) increase in PM2.5 was −6 g (95% confidence interval = −8 to −5), −16 g (−21 to −11), and −19 g (−23 to −15), using the monitor, satellite (1), and satellite (2) methods, respectively. Adjusted odds ratios, based on the same three exposure methods, for term low birth weight were 1.01 (0.98–1.04), 1.06 (0.97–1.16), and 1.08 (1.01–1.16); for SGA, 1.03 (1.01–1.04), 1.06 (1.03–1.10), and 1.08 (1.04–1.11); and for preterm birth, 1.00 (0.99–1.02), 0.98 (0.94–1.03), and 0.99 (0.95–1.03). Conclusions: Under exposure assessment methods, we found associations between PM2.5 exposure and adverse birth outcomes particularly for birth weight among term births and for SGA. These results add to the growing concerns that air pollution adversely affects infant health and suggest that analysis of health consequences based on satellite-based exposure assessment can provide additional useful information.

Integrating Data, Biology, and Decision Models for Invasive Species Management: Application to Leafy Spurge ( Euphorbia esula )

Invasive species are a major cause of environmental change and are often costly to control. Decision theory should offer managers guidance to formulate the optimal allocation of resources. Unfortunately, current decision theory models typically do not consider invasion dynamics and do not make full use of the best models of biological spread and best biological data from theoretical models. We developed a decision theory model that integrated population dynamics, spread, uncertainty, and changes in management policies. We applied this model to leafy spurge ( Euphorbia esula), a high-priority invasive weed in North America. We used field data to construct a biological model that included stochastic population dynamics and spatial spread and integrated it with decision theory using stochastic dynamic programming (SDP). The SDP model considered three control strategies: no control, biological control, and herbicide control. Solutions from the SDP model determined the optimal strategy to apply at a given state for any time horizon. The optimal strategy depended on the area and density of leafy spurge and varied with the time horizon; therefore, dynamic control is important in management programs. Biological control was consistently indicated as the optimal strategy for all time horizons. Herbicide control was the optimal strategy for small areas with high-density infestation for long time horizons. We conclude that dynamic control, forecasting, and the time horizon are important considerations for invasive species managers who are under financial, logistical, and time constraints.

Predictive Validation of an Influenza Spread Model

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability.

Surveillance in Patients With Barrett's Esophagus for Early Detection of Esophageal Adenocarcinoma: A Systematic Review and Meta-Analysis

Objectives:Although endoscopic surveillance of patients with Barrett’s esophagus (BE) has been widely implemented for early detection of esophageal adenocarcinoma (EAC), its justification has been debated. This systematic review aimed to evaluate benefits, safety, and cost effectiveness of surveillance for patients with BE.Methods:MEDLINE, EMBASE, EconLit, Scopus, Cochrane, and CINAHL were searched for published human studies that examined screening practices, benefits, safety, and cost effectiveness of surveillance among patients with BE. Reviewers independently reviewed eligible full-text study articles and conducted data extraction and quality assessment, with disagreements resolved by consensus. Random effects meta-analyses were performed to assess the incidence of EAC, EAC/high-grade dysplasia (HGD), and annual stage-specific transition probabilities detected among BE patients under surveillance, and relative risk of mortality among EAC patients detected during surveillance compared with those not under surveillance.Results:A total of 51 studies with 11,028 subjects were eligible; the majority were of high quality based on the Newcastle–Ottawa quality scale. Among BE patients undergoing endoscopic surveillance, pooled EAC incidence per 1,000 person-years of surveillance follow-up was 5.5 (95% confidence interval (CI): 4.2–6.8) and pooled EAC/HGD incidence was 7.7 (95% CI: 5.7–9.7). Pooled relative mortality risk among surveillance-detected EAC patients compared with nonsurveillance-detected EAC patients was 0.386 (95% CI: 0.242–0.617). Pooled annual stage-specific transition probabilities from nondysplastic BE to low-grade dysplasia, high-grade dysplasia, and EAC were 0.019, 0.003, and 0.004, respectively. There was, however, insufficient scientific evidence on safety and cost effectiveness of surveillance for BE patients.Conclusions:Our findings confirmed a low incidence rate of EAC among BE patients undergoing surveillance and a reduction in mortality by 61% among those who received regular surveillance and developed EAC. Because of knowledge gaps, it is important to assess safety of surveillance and health-care resource use and costs to supplement existing evidence and inform a future policy decision for surveillance programs.

Social deprivation and burden of influenza: Testing hypotheses and gaining insights from a simulation model for the spread of influenza.

Factors associated with the burden of influenza among vulnerable populations have mainly been identified using statistical methodologies. Complex simulation models provide mechanistic explanations, in terms of spatial heterogeneity and contact rates, while controlling other factors and may be used to better understand statistical patterns and, ultimately, design optimal population-level interventions. We extended a sophisticated simulation model, which was applied to forecast epidemics and validated for predictive ability, to identify mechanisms for the empirical relationship between social deprivation and the burden of influenza. Our modeled scenarios and associated epidemic metrics systematically assessed whether neighborhood composition and/or spatial arrangement could qualitatively replicate this empirical relationship. We further used the model to determine consequences of local-scale heterogeneities on larger scale disease spread. Our findings indicated that both neighborhood composition and spatial arrangement were critical to qualitatively match the empirical relationship of interest. Also, when social deprivation was fully included in the model, we observed lower age-based attack rates and greater delay in epidemic peak week in the most socially deprived neighborhoods. Insights from simulation models complement current understandings from statistical-based association studies. Additional insights from our study are: (1) heterogeneous spatial arrangement of neighborhoods is a necessary condition for simulating observed disparities in the burden of influenza and (2) unmeasured factors may lead to a better quantitative match between simulated and observed rate ratio in the burden of influenza between the most and least socially deprived populations.

Designing an Agent-Based Model Using Group Model Building: Application to Food Insecurity Patterns in a U.S. Midwestern Metropolitan City

Recent advances in computing resources have increased interest in systems modeling and population health. While group model building (GMB) has been effectively applied in developing system dynamics models (SD), few studies have used GMB for developing an agent-based model (ABM). This article explores the use of a GMB approach to develop an ABM focused on food insecurity. In our GMB workshops, we modified a set of the standard GMB scripts to develop and validate an ABM in collaboration with local experts and stakeholders. Based on this experience, we learned that GMB is a useful collaborative modeling platform for modelers and community experts to address local population health issues. We also provide suggestions for increasing the use of the GMB approach to develop rigorous, useful, and validated ABMs.