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Objective: Patients with hospital-acquired acute kidney injury (AKI) are at risk for increased mortality and further medical complications. Evaluating these patients with a prediction tool easily implemented within an electronic health record (EHR) would identify high-risk patients prior to the development of AKI and could prevent iatrogenically induced episodes of AKI and improve clinical management., Methods: The authors used structured clinical data acquired from an EHR to identify patients with normal kidney function for admissions from 1 August 1999 to 31 July 2003. Using administrative, computerized provider order entry and laboratory test data, they developed a 3-level risk stratification model to predict each of 2 severity levels of in-hospital AKI as defined by RIFLE criteria. The severity levels were defined as 150% or 200% of baseline serum creatinine. Model discrimination and calibration were evaluated using 10-fold cross-validation., Results: Cross-validation of the models resulted in area under the receiver operating characteristic (AUC) curves of 0.75 (150% elevation) and 0.78 (200% elevation). Both models were adequately calibrated as measured by the Hosmer-Lemeshow goodness-of-fit test chi-squared values of 9.7 (P = 0.29) and 12.7 (P = 0.12), respectively., Conclusions: The authors generated risk prediction models for hospital-acquired AKI using only commonly available electronic data. The models identify patients at high risk for AKI who might benefit from early intervention or increased monitoring.

Background: Frequently, prescribers fail to account for changing kidney function when prescribing medications. We evaluated the use of a computerized provider order entry intervention to improve medication management during acute kidney injury., Study Design: Quality improvement report with time series analyses., Setting & Participants: 1,598 adult inpatients with a minimum 0.5-mg/dL increase in serum creatinine level over 48 hours after an order for at least one of 122 nephrotoxic or renally cleared medications., Quality Improvement Plan: Passive noninteractive warnings about increasing serum creatinine level appeared within the computerized provider order entry interface and on printed rounding reports. For contraindicated or high-toxicity medications that should be avoided or adjusted, an interruptive alert within the system asked providers to modify or discontinue the targeted orders, mark the current dosing as correct and to remain unchanged, or defer the alert to reappear in the next session., Outcomes & Measurements: Intervention effect on drug modification or discontinuation, time to modification or discontinuation, and provider interactions with alerts., Results: The modification or discontinuation rate per 100 events for medications included in the interruptive alert within 24 hours of increasing creatinine level improved from 35.2 preintervention to 52.6 postintervention (P < 0.001); orders were modified or discontinued more quickly (P < 0.001). During the postintervention period, providers initially deferred 78.1% of interruptive alerts, although 54% of these eventually were modified or discontinued before patient death, discharge, or transfer. The response to passive alerts about medications requiring review did not significantly change compared with baseline., Limitations: Single tertiary-care academic medical center; provider actions were not independently adjudicated for appropriateness., Conclusions: A computerized provider order entry-based alerting system to support medication management after acute kidney injury significantly increased the rate and timeliness of modification or discontinuation of targeted medications., (Copyright © 2010 National Kidney Foundation, Inc. All rights reserved.)

Background: Risk factors for the development of acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU) include positive fluid balance, high tidal volumes (TVs), high airway pressures, and transfusion of blood products. However, research examining intraoperative factors such as fluid resuscitation, mechanical ventilation strategies, and blood administration on the postoperative development of ARDS is lacking., Methods: We assessed patients admitted to the ICU with postoperative hypoxemic respiratory failure requiring mechanical ventilation for the development of ARDS in the first 7 postoperative days using established clinical and radiological criteria. Data on risk factors for ARDS were obtained from the electronic anesthetic and medical records. Logistic regression was used to examine the independent association between fluid resuscitation, TV per ideal body weight, and number of blood products transfused during surgery and the postoperative development of ARDS, adjusting for important clinical covariates., Results: Of the 89 patients with postoperative respiratory failure, 25 developed ARDS. Compared with those who received <10 mL/kg/h fluid resuscitation in the operating room, patients receiving >20 mL/kg/h fluid resuscitation had a 3.8 times higher adjusted odds of developing ARDS (P = 0.04), and those receiving 10 to 20 mL/kg/h had a 2.4 times higher adjusted odds of developing ARDS (P = 0.14). TV per ideal body weight and the number of blood units transfused were not associated with ARDS development in this study., Conclusions: This cohort study provides evidence to suggest a relationship between intraoperative fluid resuscitation and the development of ARDS. Larger prospective trials are required to confirm these findings.

We discuss design and analysis of longitudinal studies after case-control sampling, wherein interest is in the relationship between a longitudinal binary response that is related to the sampling (case-control) variable, and a set of covariates. We propose a semiparametric modeling framework based on a marginal longitudinal binary response model and an ancillary model for subjects' case-control status. In this approach, the analyst must posit the population prevalence of being a case, which is then used to compute an offset term in the ancillary model. Parameter estimates from this model are used to compute offsets for the longitudinal response model. Examining the impact of population prevalence and ancillary model misspecification, we show that time-invariant covariate parameter estimates, other than the intercept, are reasonably robust, but intercept and time-varying covariate parameter estimates can be sensitive to such misspecification. We study design and analysis issues impacting study efficiency, namely: choice of sampling variable and the strength of its relationship to the response, sample stratification, choice of working covariance weighting, and degree of flexibility of the ancillary model. The research is motivated by a longitudinal study following case-control sampling of the time course of attention deficit hyperactivity disorder (ADHD) symptoms.

Background: In this study, we performed the infraclavicular block with combined ultrasound guidance and neurostimulation to selectively target cords to compare the success rates of placing a single injection of local anesthetic either in a central or peripheral location., Methods: Two hundred eighteen patients were enrolled in a consecutive, prospective study. Patients were randomized to injection of local anesthetic either centrally (posterior cord) or peripherally (medial or lateral cord) using ultrasound guidance and neurostimulation. Supervised senior anesthesiology residents or attending anesthesiologists performed the blocks. Both intent-to-treat and treatment-received analyses were used to compare central and peripheral placement efficacy., Results: The overall success rate was significantly higher for the central placements than peripheral placements (96% vs 85%, P = 0.004). Individual cord success rates were as follows: posterior 99%, lateral 92%, and medial 84% (P = 0.001). The central group required attending physician intervention more frequently (27% vs 6%, P < 0.001). Postoperative pain scores of < or =3 were more likely with central placement (100% vs 94%, P = 0.012)., Conclusion: Central placement of a single injection of local anesthetic targeted at the posterior cord resulted in a higher success rate for infraclavicular block.

Background: Critical to the development of molecular signatures from microarray and other high-throughput data is testing the statistical significance of the produced signature in order to ensure its statistical reproducibility. While current best practices emphasize sufficiently powered univariate tests of differential expression, little is known about the factors that affect the statistical power of complex multivariate analysis protocols for high-dimensional molecular signature development., Methodology/principal Findings: We show that choices of specific components of the analysis (i.e., error metric, classifier, error estimator and event balancing) have large and compounding effects on statistical power. The effects are demonstrated empirically by an analysis of 7 of the largest microarray cancer outcome prediction datasets and supplementary simulations, and by contrasting them to prior analyses of the same data., Conclusions/significance: THE FINDINGS OF THE PRESENT STUDY HAVE TWO IMPORTANT PRACTICAL IMPLICATIONS: First, high-throughput studies by avoiding under-powered data analysis protocols, can achieve substantial economies in sample required to demonstrate statistical significance of predictive signal. Factors that affect power are identified and studied. Much less sample than previously thought may be sufficient for exploratory studies as long as these factors are taken into consideration when designing and executing the analysis. Second, previous highly-cited claims that microarray assays may not be able to predict disease outcomes better than chance are shown by our experiments to be due to under-powered data analysis combined with inappropriate statistical tests.

Pharmaceutical safety has received substantial attention in the recent past; however, longitudinal clinical laboratory data routinely collected during clinical trials to derive safety profiles are often used ineffectively. For example, these data are frequently summarized by comparing proportions (between treatment arms) of participants who cross pre-specified threshold values at some time during follow-up. This research is intended, in part, to encourage more effective utilization of these data by avoiding unnecessary dichotomization of continuous data, acknowledging and making use of the longitudinal follow-up, and combining data from multiple clinical trials. However, appropriate analyses require careful consideration of a number of challenges (e.g. selection, comparability of study populations, etc.). We discuss estimation strategies based on estimating equations and maximum likelihood for analyses in the presence of three response history-dependent selection mechanisms: dropout, follow-up frequency, and treatment discontinuation. In addition, because clinical trials' participants usually represent non-random samples from target populations, we describe two sensitivity analysis approaches. All discussions are motivated by an analysis that aims to characterize the dynamic relationship between concentrations of a liver enzyme (alanine aminotransferase) and three distinct doses (no drug, low dose, and high dose) of an nk-1 antagonist across four Phase II clinical trials.

Marginalized models (Heagerty, 1999, Biometrics 55, 688-698) permit likelihood-based inference when interest lies in marginal regression models for longitudinal binary response data. Two such models are the marginalized transition and marginalized latent variable models. The former captures within-subject serial dependence among repeated measurements with transition model terms while the latter assumes exchangeable or nondiminishing response dependence using random intercepts. In this article, we extend the class of marginalized models by proposing a single unifying model that describes both serial and long-range dependence. This model will be particularly useful in longitudinal analyses with a moderate to large number of repeated measurements per subject, where both serial and exchangeable forms of response correlation can be identified. We describe maximum likelihood and Bayesian approaches toward parameter estimation and inference, and we study the large sample operating characteristics under two types of dependence model misspecification. Data from the Madras Longitudinal Schizophrenia Study (Thara et al., 1994, Acta Psychiatrica Scandinavica 90, 329-336) are analyzed.

The authors investigated the relation between ambient concentrations of five of the Environmental Protection Agency's criteria pollutants and asthma exacerbations (daily symptoms and use of rescue inhalers) among 990 children in eight North American cities during the 22-month prerandomization phase (November 1993-September 1995) of the Childhood Asthma Management Program. Short-term effects of carbon monoxide, nitrogen dioxide, particulate matter less than 10 mum in aerodynamic diameter (PM10), sulfur dioxide, and warm-season ozone were examined in both one-pollutant and two-pollutant models, using lags of up to 2 days. Lags in carbon monoxide and nitrogen dioxide were positively associated with both measures of asthma exacerbation, and the 3-day moving sum of sulfur dioxide levels was marginally related to asthma symptoms. PM10 and ozone were unrelated to exacerbations. The strongest effects tended to be seen with 2-day lags, where a 1-parts-per-million change in carbon monoxide and a 20-parts-per-billion change in nitrogen dioxide were associated with symptom odds ratios of 1.08 (95% confidence interval (CI): 1.02, 1.15) and 1.09 (95% CI: 1.03, 1.15), respectively, and with rate ratios for rescue inhaler use of 1.06 (95% CI: 1.01, 1.10) and 1.05 (95% CI: 1.01, 1.09), respectively. The authors believe that the observed carbon monoxide and nitrogen dioxide associations can probably be attributed to mobile-source emissions, though more research is required.

Study Objective: To determine whether increased exposure to particulate matter air pollution (PM), measured with personal, residential, or central site monitoring, was associated with pulmonary function decrements in either adults with COPD or children with asthma., Participants: We studied 57 adults with or without COPD and 17 children aged 6 to 13 years with physician-diagnosed asthma in Seattle during a 3-year panel study., Study Design and Measurements: Indoor and outdoor PM measurements were made at subjects' homes. The subjects wore personal exposure monitors for 10 consecutive 24-h periods, and PM was also measured at a central outdoor location. We assessed the within-subject effect of particulate exposure on FEV(1) and peak expiratory flow (PEF) in adults, and maximal midexpiratory flow (MMEF), PEF, FEV(1), and symptoms in children., Results: FEV(1) decrements were associated with 1-day lagged central site PM

Generalized estimating equations (Liang and Zeger, 1986) is a widely used, moment-based procedure to estimate marginal regression parameters. However, a subtle and often overlooked point is that valid inference requires the mean for the response at time t to be expressed properly as a function of the complete past, present, and future values of any time-varying covariate. For example, with environmental exposures it may be necessary to express the response as a function of multiple lagged values of the covariate series. Despite the fact that multiple lagged covariates may be predictive of outcomes, researchers often focus interest on parameters in a 'cross-sectional' model, where the response is expressed as a function of a single lag in the covariate series. Cross-sectional models yield parameters with simple interpretations and avoid issues of collinearity associated with multiple lagged values of a covariate. Pepe and Anderson (1994), showed that parameter estimates for time-varying covariates may be biased unless the mean, given all past, present, and future covariate values, is equal to the cross-sectional mean or unless independence estimating equations are used. Although working independence avoids potential bias, many authors have shown that a poor choice for the response correlation model can lead to highly inefficient parameter estimates. The purpose of this paper is to study the bias-efficiency trade-off associated with working correlation choices for application with binary response data. We investigate data characteristics or design features (e.g. cluster size, overall response association, functional form of the response association, covariate distribution, and others) that influence the small and large sample characteristics of parameter estimates obtained from several different weighting schemes or equivalently 'working' covariance models. We find that the impact of covariance model choice depends highly on the specific structure of the data features, and that key aspects should be examined before choosing a weighting scheme.

We evaluated the effects of abciximab treatment during early angioplasty after clinically failed thrombolysis for acute myocardial infarction. In the Global Use of Strategies To Open occluded coronary arteries (GUSTO-III) trial of reteplase versus alteplase for acute infarction (n = 15,059), 392 patients underwent angioplasty a median of 3.5 hours after thrombolysis and had complete procedural data. We compared 30-day mortality and in-hospital outcomes between patients who received abciximab (n = 83) and those who did not (n = 309), and (among patients given abciximab) between those randomized to alteplase versus reteplase. Patients given abciximab had anterior infarction less often, but were more often in Killip classes III or IV. The 30-day mortality rate tended to be lower with abciximab (3.6% vs 9.7%, p = 0.076), more so after adjustment for baseline differences (p = 0.042). The composite of death, stroke, or reinfarction did not differ significantly with abciximab treatment (12% vs 14%, p = 0.7), but it occurred less often among abciximab-treated patients who had been randomized to reteplase (n = 55) versus alteplase (n = 28) (7% vs 21%, p = 0.08). Severe bleeding was increased among abciximab-treated patients (3.6% vs 1.0%, p = 0.08), despite less heparin use. No intracranial hemorrhages occurred with abciximab. The use of abciximab for early angioplasty after clinically failed thrombolysis resulted in trends toward lower 30-day mortality and increased bleeding.

In vitro sister chromatid exchange (SCE) and chromosome aberration (ABS) tests have been extensively used to identify potential rodent carcinogens. A number of measures of potency were developed to describe in vitro SCE and ABS test results: the dose needed to induce a unit increase over the control; the lowest effective dose; the slope of the ordinary linear regression; the maximum observed slope; and the maximum fold increase over background. The ability of these potency measures to predict the qualitative and quantitative carcinogenicity of chemicals was compared to the predictivity of the qualitative in vitro responses. The results of the analyses showed that the quantitative measures of the SCE or ABS responses only minimally increased the predictivity of carcinogenesis when compared to the predictivity using the qualitative responses.

Many in vitro tests have been developed to identify chemicals that can damage cellular DNA or cause mutations, and secondarily to identify potential carcinogens. The test receiving by far the most use and attention has been the Salmonella (SAL) mutagenesis test developed by Ames and colleagues [(1973): Proc Natl Acad Sci USA 70:2281-2285; (1975): Mutat Res 31:347-364], because of its initial promise of high qualitative (YES/NO) predictivity for cancer in rodents and, by extension, in humans. In addition to the initial reports of high qualitative predictivity, there was also an early report by Meselson and Russell [in Hiatt HH et al (1977): "Origins of Human Cancer, Book C: Human Risk Assessment," pp 1473-1481] of a quantitative relationship between mutagenic potency measured in SAL and carcinogenic potency measured in rodents, for a small number of chemicals. However, other reports using larger numbers of chemicals have found only very weak correlations. The primary purpose of this study was to determine whether mutagenic potency, as measured in a number of different ways, could be used to improve predictivity of carcinogenicity, either qualitatively or quantitatively. To this end, eight measures of SAL mutagenic potency were used. This study firmly establishes that the predictive relationship between mutagenic potency in SAL and rodent carcinogenicity is, at best, weak. When predicting qualitative carcinogenicity, only qualitative mutagenicity is useful; none of the quantitative measures of potency considered improves the carcinogenicity prediction. In fact, when qualitative mutagenicity is forced out of the model, the quantitative measures are still not predictive of carcinogenicity. When predicting quantitative carcinogenicity, several possible methods were considered for summarizing potency over all experiments; however, in all cases, the relationship between mutagenic potency predictors and quantitative carcinogenicity is very weak.