Pre-hospital qSOFA as a predictor of sepsis and mortality.

BACKGROUND: The quick sequential organ failure assessment score (qSOFA) has been proposed as a simple tool to identify patients with sepsis who are at risk for poor outcomes. Its utility in the pre-hospital setting has not been fully elucidated. METHODS: This is a retrospective observational study of adult patients arriving by ambulance in September 2016 to an academic emergency department in Fresno, California. The qSOFA score was calculated from pre-hospital vital signs. We investigated its association with sepsis, ED diagnosis of infection, and mortality. RESULTS: Of 2292 adult medical patients transported by ambulance during the study period, the sensitivity of qSOFA for sepsis and in-hospital mortality were 42.9% and 40.6%, respectively. Specificity of qSOFA for sepsis and mortality were 93.8% and 91.9%, respectively. Of those with an ED diagnosis of infection compared to all patients, qSOFA was more specific but less sensitive for sepsis. Increasing qSOFA score was associated with a discharge diagnosis of sepsis (OR 4.21, 95% CI 3.41-5.21, p < 0.001), in-hospital mortality (OR 3.30, 95% CI 2.28-4.78, p < 0.001), and ED diagnosis of infection (OR 1.37, 95% CI 1.18-1.58, p < 0.001). Higher qSOFA score was associated with triage to a higher acuity zone and longer hospital and ICU length of stay, but not up-triage during ED stay. CONCLUSIONS: Pre-hospital qSOFA is specific, but poorly sensitive, for sepsis and sepsis outcomes, especially among patients with an ED diagnosis of infection. Higher qSOFA score was associated with worse outcomes.

Single-case experimental design yielded an effect estimate corresponding to a randomized controlled trial.

OBJECTIVES: We reanalyzed data from a previous randomized crossover design that administered high or low doses of intravenous immunoglobulin (IgG) to 12 patients with hypogammaglobulinaemia over 12 time points, with crossover after time 6. The objective was to see if results corresponded when analyzed as a set of single-case experimental designs vs. as a usual randomized controlled trial (RCT). STUDY DESIGN AND SETTINGS: Two blinded statisticians independently analyzed results. One analyzed the RCT comparing mean outcomes of group A (high dose IgG) to group B (low dose IgG) at the usual trial end point (time 6 in this case). The other analyzed all 12 time points for the group B patients as six single-case experimental designs analyzed together in a Bayesian nonlinear framework. RESULTS: In the randomized trial, group A [M = 794.93; standard deviation (SD) = 90.48] had significantly higher serum IgG levels at time six than group B (M = 283.89; SD = 71.10) (t = 10.88; df = 10; P < 0.001), yielding a mean difference of MD = 511.05 [standard error (SE) = 46.98]. For the single-case experimental designs, the effect from an intrinsically nonlinear regression was also significant and comparable in size with overlapping confidence intervals: MD = 495.00, SE = 54.41, and t = 495.00/54.41 = 9.10. Subsequent exploratory analyses indicated that how trend was modeled made a difference to these conclusions. CONCLUSIONS: The results of single-case experimental designs accurately approximated results from an RCT, although more work is needed to understand the conditions under which this holds.

A d-statistic for single-case designs that is equivalent to the usual between-groups d-statistic.

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.

Abstract: A Meta-Analysis of the Autocorrelation in Single Case Designs.

Single case design (SCD) experiments in the behavioral sciences utilize just one participant from whom data is collected over time. This design permits causal inferences to be made regarding various intervention effects, often in clinical or educational settings, and is especially valuable when between-participant designs are not feasible or when interest lies in the effects of an individualized treatment. Regression techniques are the most common quantitative practice for analyzing time series data and provide parameter estimates for both treatment and trend effects. However, the presence of serially correlated residuals, known as autocorrelation, can severely bias inferences made regarding these parameter estimates. Despite the severity of the issue, few researchers test or correct for the autocorrelation in their analyses. Shadish and Sullivan (in press) recently conducted a meta-analysis of over 100 studies in order to assess the prevalence of the autocorrelation in the SCD literature. Although they found that the meta-analytic weighted average of the autocorrelation was close to zero, the distribution of autocorrelations was found to be highly heterogeneous. Using the same set of SCDs, the current study investigates various factors that may be related to the variation in autocorrelation estimates (e.g., study and outcome characteristics). Multiple moderator variables were coded for each study and then used in a metaregression in order to estimate the impact these predictor variables have on the autocorrelation. This current study investigates the autocorrelation using a multilevel meta-analytic framework. Although meta-analyses involve nested data structures (e.g., effect sizes nested within studies nested within journals), there are few instances of meta-analysts utilizing multilevel frameworks with more than two levels. This is likely attributable to the fact that very few software packages allow for meta-analyses to be conducted with more than two levels and those that do allow this provide sparse documentation on how to implement these models. The proposed presentation discusses methods for carrying out a multilevel meta-analysis. The presentation also discusses the findings from the metaregression on the autocorrelation and the implications these findings have on SCDs.