Expanding the statistical toolbox: analytic approaches for cohort studies with healthcare-associated infectious outcomes

Stacked probability plots of the extended illness-death model using constant transition hazards - an easy to use shiny app

BACKGROUND

Extended illness-death models (a specific class of multistate models) are a useful tool to analyse situations like hospital-acquired infections, ventilation-associated pneumonia, and transfers between hospitals. The main components of these models are hazard rates and transition probabilities. Calculation of different measures and their interpretation can be challenging due to their complexity.

METHODS

By assuming time-constant hazards, the complexity of these models becomes manageable and closed mathematical forms for transition probabilities can be derived. Using these forms, we created a tool in R to visualize transition probabilities via stacked probability plots.

RESULTS

In this article, we present this tool and give some insights into its theoretical background. Using published examples, we give guidelines on how this tool can be used. Our goal is to provide an instrument that helps obtain a deeper understanding of a complex multistate setting.

CONCLUSION

While multistate models (in particular extended illness-death models), can be highly complex, this tool can be used in studies to both understand assumptions, which have been made during planning and as a first step in analysing complex data structures. An online version of this tool can be found at https://eidm.imbi.uni-freiburg.de/ .

Impact of the COVID-19 pandemic on the incidence and mortality of hospital-onset bloodstream infection: a cohort study

The COVID-19 pandemic burdens hospitals, but consequences for quality of care outcomes such as healthcare-associated infections are largely unknown. This cohort included all adult hospital episodes (n=186 945) at an academic centre between January 2018 and January 2021. Data were collected from the hospitals’ electronic health record data repository. Hospital-onset bloodstream infection (HOB) was defined as any positive blood culture obtained ≥48 hours after admission classified based on microbiological and hospital administrative data. Subgroup analyses were performed with exclusion of potential contaminant bacteria. The cohort was divided into three groups: controls (prepandemic period), non-COVID-19 (pandemic period) and COVID-19 (pandemic period) based on either PCR-confirmed SARS-CoV-2 infections from respiratory samples or International Classification of Diseases 10th Revision diagnoses U071 and U72 at discharge. Adjusted incidence rate ratios (aIRR) and risk of death in patients with HOB were compared between the prepandemic and pandemic periods using Poisson and logistic regression. The incidence of HOB was increased for the COVID-19 group compared with the prepandemic period (aIRR 3.34, 95% CI 2.97 to 3.75). In the non-COVID-19 group, the incidence was slightly increased compared with prepandemic levels (aIRR 1.20, 95% CI 1.08 to 1.32), but the difference decreased when excluding potential contaminant bacteria (aIRR 1.15, 95% CI 1.00 to 1.31, p=0.04). The risk of dying increased for both the COVID-19 group (adjusted odds ratio (aOR) 2.44, 95% CI 1.75 to 3.38) and the non-COVID-19 group (aOR 1.63, 95% CI 1.22 to 2.16) compared with the prepandemic controls. These findings were consistent also when excluding potential contaminants. In summary, we observed a higher incidence of HOB during the COVID-19 pandemic, and the mortality risk associated with HOB was greater, compared with the prepandemic period. Results call for specific attention to quality of care during the pandemic.

Spinal cord injury after open and endovascular repair of descending thoracic and thoracoabdominal aortic aneurysms: A meta-analysis

OBJECTIVE

An inclusive contemporary analysis of spinal cord injury (SCI) rates in patients undergoing aneurysm repair and the factors associated with complications has not been performed.

METHODS

Following a systematic literature search, studies from 2008 to 2018 on repair of descending thoracic aneurysm (DTA) and thoracoabdominal aortic aneurysm (TAAA) were pooled in a meta-analysis performed using the generic inverse variance method. The primary outcome was permanent SCI. Secondary outcomes were temporary SCI, operative mortality, long-term mortality, postoperative stroke, and cerebrospinal fluid (CSF) drain-related complications.

RESULTS

One-hundred sixty-nine studies (22,634 patients) were included. The pooled rate of permanent SCI was 4.5% (95% confidence interval [CI], 3.8-5.4); 3.5% (95% CI, 1.8-6.7) for DTA and 7.6% (96% CI, 6.2-9.3) for TAAA repair (P for subgroups = .02), 5.7% (95% CI, 4.3-7.5) for open repair and 3.9% (95% CI, 3.1-4.8) for endovascular repair (P for subgroups = .03). Rates for Crawford extents I, II, III, IV, and V aneurysms were 4.0% (95% CI, 3.0-5.0), 15.0% (95% CI, 10.0-22.0), 7.0% (95% CI, 6.0-9.0), 2.0% (95% CI, 2.0-4.0), and 7.0% (95% CI, 2.0-23.0) respectively (P for subgroups <.001). The pooled rates for operative mortality, late mortality at a mean follow-up of 5.0 years, stroke, and temporary SCI were 7.4% (95% CI, 6.1-9.4), 1.0% (95% CI, 0.0-1.0), 4.2% (95% CI, 3.6-4.8), and 3.7% (95% CI, 3.0-4.6), respectively. The pooled rates for severe, moderate, and minor CSF-drain related complications were 5.1% (95% CI, 2.23-11.1), 4.1% (95% CI, 0.6-22.0), and 3.6% (95% CI, 1.2-8.0) respectively.

CONCLUSIONS

Despite improvement, both open and endovascular aneurysm repair remain associated with a substantial risk of permanent SCI. The risk is greater for TAAA repair, especially extent II, III, and V.

Analysis of multisite intervention studies using generalized linear mixed models

Multisite intervention studies have become increasingly common in infection control, for example, looking for a change in hospital infection rates after a regional policy change. The design of these studies can take various forms, from pre–post observational studies to randomized trials, in which sites are randomly assigned to the intervention or in which the intervention is sequentially introduced to different sites over time. Data collected under these settings are clustered by hospital and/or ward, consist of repeated measurements and, in some cases, exhibit temporal and/or seasonal patterns. Failure to account for these features in data analysis could well result in biased estimates of intervention effectiveness and impact on the generalizability of model results.

Basic parametric analysis for a multi-state model in hospital epidemiology

Background

The extended illness-death model is a useful tool to study the risks and consequences of hospital-acquired infections (HAIs). The statistical quantities of interest are the transition-specific hazard rates and the transition probabilities as well as attributable mortality (AM) and the population-attributable fraction (PAF). In the most general case calculation of these expressions is mathematically complex.

Methods

When assuming time-constant hazards calculation of the quantities of interest is facilitated. In this situation the transition probabilities can be expressed in closed mathematical forms. The estimators for AM and PAF can be easily derived from these forms.

Results

In this paper, we show how to explicitly calculate all the transition probabilities of an extended-illness model with constant hazards. Using a parametric model to estimate the time-constant transition specific hazard rates of a data example, the transition probabilities, AM and PAF can be directly calculated. With a publicly available data example, we show how the approach provides first insights into principle time-dynamics and data structure.

Conclusion

Assuming constant hazards facilitates the understanding of multi-state processes. Even in a non-constant hazards setting, the approach is a helpful first step for a comprehensive investigation of complex data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-017-0379-4) contains supplementary material, which is available to authorized users.

Multistate Modeling to Analyze Nosocomial Infection Data: An Introduction and Demonstration

OBJECTIVE Multistate and competing risks models have become an established and adequate tool with which to quantify determinants and consequences of nosocomial infections. In this tutorial article, we explain and demonstrate the basics of these models to a broader audience of professionals in health care, infection control, and hospital epidemiology. METHODS Using a publicly available data set from a cohort study of intensive care unit patients, we show how hospital infection data can be displayed and explored graphically and how simple formulas are derived under some simplified assumptions for illustrating the basic ideas behind multistate models. Only a few simply accessible values (event counts and patient days) and a pocket calculator are needed to reveal basic insights into cumulative risk and clinical outcomes of nosocomial infection in terms of mortality and length of stay. RESULTS We show how to use these values to perform basic multistate analyses in own data or to correct biased estimates in published data, as these values are often reported. We also show relationships between multistate-based hazard ratios and odds ratios, which are derived from the popular logistic regression model. CONCLUSIONS No sophisticated statistical software is required to apply a basic multistate model and to avoid typical pitfalls such as time-dependent or competing-risks bias. Infect Control Hosp Epidemiol 2017;38:953-959.