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Paulina Staus,1 Maja von Cube,1,* Derek Hazard,1,* Sam Doerken,1 Ksenia Ershova,2 James Balmford1 ,† Martin Wolkewitz1 1Institute of Medical Biometry and Statistics, Division Methods in Clinical Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; 2Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA*These authors contributed equally to this work†James Balmford passed away in March 2020Correspondence: Paulina Staus, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany, Email staus@imbi.uni-freiburg.dePurpose: When studying nosocomial infections, resource-efficient sampling designs such as nested case-control, case-cohort, and point prevalence studies are preferred. However, standard analyses of these study designs can introduce selection bias, especially when interested in absolute rates and risks. Moreover, nosocomial infection studies are often subject to competing risks. We aim to demonstrate in this tutorial how to address these challenges for all three study designs using simple weighting techniques.Patients and Methods: We discuss the study designs and explain how inverse probability weights (IPW) are applied to obtain unbiased hazard ratios (HR), odds ratios and cumulative incidences. We illustrate these methods in a multi-state framework using a dataset from a nosocomial infections study (n = 2286) in Moscow, Russia.Results: Including IPW in the analysis corrects the unweighted naïve analyses and enables the estimation of absolute risks. Resulting estimates are close to the full cohort estimates using substantially smaller numbers of patients.Conclusion: IPW is a powerful tool to account for the unequal selection of controls in case-cohort, nested case-control and point prevalence studies. Findings can be generalized to the full population and absolute risks can be estimated. When applied to a multi-state model, competing risks are also taken into account.Graphical Abstract: Keywords: selection bias, hospital infection, intensive care units, proportional hazards models, risk assessment, cohort studies

Martin Wolkewitz,1 Jerome Lambert,1 Maja von Cube,1 Lars Bugiera,1 Marlon Grodd,1 Derek Hazard,1 Nicole White,2 Adrian Barnett,2 Klaus Kaier1 1Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; 2School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, AustraliaCorrespondence: Klaus KaierInstitute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyEmail kaier@imbi.uni-freiburg.deAbstract: By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing in-hospital COVID-19 data.Keywords: competing risk bias, immortal-time bias, competing events, time-dependent bias, time-varying exposure, time-to-event analysis

Background: Even though the population-attributable fraction (PAF) is a well-established metric, it is often incorrectly estimated or interpreted not only in clinical application, but also in statistical research articles. The risk of bias is especially high in more complex time-to-event data settings., Methods: We explain how the PAF can be defined, identified and estimated in time-to-event settings with competing risks and time-dependent exposures. By using multi-state methodology and inverse probability weighting, we demonstrate how to reduce or completely avoid severe types of biases including competing risks bias, immortal time bias and confounding due to both baseline and time-varying patient characteristics., Results: The method is exemplarily applied to a real data set. Moreover, we estimate the number of deaths that were attributable to ventilator-associated pneumonia in France in the year 2016. The example demonstrates how, under certain simplifying assumptions, PAF estimates can be extrapolated to a target population of interest., Conclusions: Defining and estimating the PAF in advanced time-to-event settings within a framework that unifies causal and multi-state modelling enables to tackle common sources of bias and allows straightforward implementation with standard software packages., (© The Author(s) 2022; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Osteopontin (OPN), synthesized in the thick ascending limb of Henle's loop and in the distal tubule, is involved in the pathogenesis of kidney fibrosis, a hallmark of kidney failure (KF). In a cohort of chronic kidney disease (CKD) patients, we evaluated OPN's association with kidney markers and KF., Methods: OPN was measured from baseline serum samples of German Chronic Kidney Disease study participants. Cross-sectional regression models for estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) as well as Cox regression models for all-cause mortality and KF were evaluated to estimate the OPN effect. Additionally, the predictive ability of OPN and time-dependent population-attributable fraction were evaluated., Results: Over a median follow-up of 6.5 years, 471 KF events and 629 deaths occurred among 4950 CKD patients. One-unit higher log(OPN) was associated with 5.5 mL/min/1.73 m2 lower eGFR [95% confidence interval (95% CI) -6.4 to -4.6] and 1% change in OPN with 0.7% higher UACR (estimated effect 0.7, 95% CI 0.6-0.8). Moreover, higher OPN levels were associated with a higher risk of KF [hazard ratio (HR) 1.4, 95% CI 1.2-1.7] and all-cause mortality (HR 1.5, 95% CI 1.3-1.8). After 6 years, 31% of the KF events could be attributed to higher OPN levels (95% CI 3%-56%)., Conclusions: In this study, higher OPN levels were associated with kidney function markers worsening and a higher risk for adverse outcomes. A larger proportion of KF could be attributed to higher OPN levels, warranting further research on OPN with regards to its role in CKD progression and possible treatment options., (© The Author(s) 2022. Published by Oxford University Press on behalf of the ERA.)

Objectives: To analyse the adherence and impact of quality-of-care indicators (QCIs) in the management of Staphylococcus aureus bloodstream infection in a prospective and multicentre cohort., Methods: Analysis of the prospective, multicentre international S. Aureus Collaboration cohort of S. Aureus bloodstream infection cases observed between January 2013 and April 2015. Multivariable analysis was performed to evaluate the impact of adherence to QCIs on 90-day mortality., Results: A total of 1784 cases were included. Overall, 90-day mortality was 29.9% and mean follow-up period was 118 days. Adherence was 67% (n = 1180/1762) for follow-up blood cultures, 31% (n = 416/1342) for early focus control, 77.6% (n = 546/704) for performance of echocardiography, 75.5% (n = 1348/1784) for adequacy of targeted antimicrobial therapy, 88.6% (n = 851/960) for adequacy of treatment duration in non-complicated bloodstream infections and 61.2% (n = 366/598) in complicated bloodstream infections. Full bundle adherence was 18.4% (n = 328/1784). After controlling for immortal time bias and potential confounders, focus control (adjusted hazard ratio = 0.76; 95% CI, 0.59-0.99; p 0.038) and adequate targeted antimicrobial therapy (adjusted hazard ratio = 0.75; 95% CI, 0.61-0.91; p 0.004) were associated with low 90-day mortality., Discussion: Adherence to QCIs in S. Aureus bloodstream infection did not reach expected rates. Apart from the benefits of application as a bundle, focus control and adequate targeted therapy were independently associated with low mortality., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: In patients with chronic heart failure, thirst can be perceived as an intensive and burdensome symptom, which may have a negative impact on patients' quality of life. To initiate thirst-relieving interventions, assessment of thirst and its related distress is essential. At the time of this study, no instrument was available to evaluate thirst distress in patients with heart failure in Germany., Objective: The aims of this study were to translate the "Thirst Distress Scale for patients with Heart Failure" (TDS-HF) from English into German and to test validity and reliability of the scale., Methods: The English version of the TDS-HF was translated into German. A linguistically and culturally sensitive forward-and-backward translation was performed. Psychometric evaluation included confirmatory factor analysis, reliability in terms of internal consistency, and concurrent validity., Results: Eighty-four hospitalized patients (mean age, 72 ± 10 years; 29% female; mean left ventricular ejection fraction, 36% ± 12%; 62% New York Heart Association functional classes III-IV, 45% on fluid restriction) from an acute care hospital were involved in the study. The item-total correlation ranged from 0.58 to 0.78. Interitem correlations varied between 0.37 and 0.79. Internal consistency was high, with a Cronbach α of 0.89. There was a high correlation between the total score of the TDS-HF and the visual analog scale to assess thirst intensity ( r = 0.72, P ≤ .001), and a low correlation with fluid restriction ( r = 0.35, P = .002)., Conclusions: The evaluation of the German TDS-HF showed satisfactory psychometric properties in this sample. The instrument is usable for further research and additional psychometric testing., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: An essential aspect of preventing further COVID-19 outbreaks and to learn for future pandemics is the evaluation of different political strategies, which aim at reducing transmission of and mortality due to COVID-19. One important aspect in this context is the comparison of attributable mortality., Methods: We give a comprehensive overview of six epidemiological measures that are used to quantify COVID-19 attributable mortality (p-score, standardized mortality ratio, absolute number of excess deaths, per capita rate, z-score and the population attributable fraction)., Results: By defining the six measures based on observed and expected deaths, we explain their relationship. Moreover, three publicly available data examples serve to illustrate the interpretational strengths and weaknesses of the various measures. Finally, we give recommendation which measures are suitable for an evaluation of public health strategies against COVID-19. The R code to reproduce the results is available as online supplementary material., Conclusion: The number of excess deaths should be always reported together with the population attributable fraction, the p-score or the standardized mortality ratio instead of a per capita rate. For a complete picture of COVID-19 attributable mortality, quantifying and communicating its relative burden also to a lay audience is of major importance., (© 2021. The Author(s).)

Background and Objective: Observational studies may provide valuable evidence on real-world causal effects of drug effectiveness in patients with coronavirus disease 2019 (COVID-19). As patients are usually observed from hospital admission to discharge and drug initiation starts during hospitalization, advanced statistical methods are needed to account for time-dependent drug exposure, confounding and competing events. Our objective is to evaluate the observational studies on the three common methodological pitfalls in time-to-event analyses: immortal time bias, confounding bias and competing risk bias., Methods: We performed a systematic literature search on 23 October 2020, in the PubMed database to identify observational cohort studies that evaluated drug effectiveness in hospitalized patients with COVID-19. We included articles published in four journals: British Medical Journal, New England Journal of Medicine, Journal of the American Medical Association and The Lancet as well as their sub-journals., Results: Overall, out of 255 articles screened, 11 observational cohort studies on treatment effectiveness with drug exposure-outcome associations were evaluated. All studies were susceptible to one or more types of bias in the primary study analysis. Eight studies had a time-dependent treatment. However, the hazard ratios were not adjusted for immortal time in the primary analysis. Even though confounders presented at baseline have been addressed in nine studies, time-varying confounding caused by time-varying treatment exposure and clinical variables was less recognized. Only one out of 11 studies addressed competing event bias by extending follow-up beyond patient discharge., Conclusions: In the observational cohort studies on drug effectiveness for treatment of COVID-19 published in four high-impact journals, the methodological biases were concerningly common. Appropriate statistical tools are essential to avoid misleading conclusions and to obtain a better understanding of potential treatment effects., (Copyright © 2021 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)

Objectives: Hospital-acquired infections (HAIs) place a substantial burden on health systems. Tools are required to quantify the change in this burden as a result of a preventive intervention. We aim to estimate how much a reduction in the rate of hospital-acquired infections translates into a change in hospital mortality and length of stay., Methods: Using multistate modelling and competing risks methodology, we created a tool to estimate the reduction in burden after the introduction of a preventive effect on the infection rate. The tool requires as inputs the patients' length of hospital stay, patients' infection information (status, time), patients' final outcome (discharged alive, dead), and a preventive effect. We demonstrated the methods on both simulated data and 3 published data sets from Germany, France, and Spain., Results: A hypothetical prevention that cuts the infection rate in half would result in 21 lives and 2212 patient-days saved in French ventilator-associated pneumonia data, 61 lives and 3125 patient-days saved in Spanish nosocomial infection data, and 20 lives and 1585 patient-days saved in German nosocomial pneumonia data., Conclusions: Our tool provides a quick and easy means of acquiring an impression of the impact a preventive measure would have on the burden of an infection. The tool requires quantities routinely collected and computation can be done with a calculator. R code is provided for researchers to determine the burden in various settings with various effects. Furthermore, cost data can be used to get the financial benefit of the reduction in burden., (Copyright © 2021 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Introduction: Self-management (SM) may facilitate patient participation and involvement to become active and knowledgeable partners in the care of complex chronic conditions such as ventricular assist device (VAD) therapy. The 'SM model for patients on VAD support' will serve to distinguish between SM components, and will guide the development, implementation and evaluation of an evidence-based curriculum., Methods and Analysis: This is a 3-phase, multicentre study. In phase 1, a prevalence study will be performed. Phase 2 aims to develop an evidence-based, interprofessional curriculum for SM support for VAD patients. In phase 3, a non-blinded block-randomised controlled trial (RCT), allocation ratio 1:1, intervention group superiority, with an unblinded multifacetted intervention with assessments before (T1) and after (T2) the intervention, and two follow-up assessments at three (T3), and 12 (T4) months after VAD implantation, will be performed. The curriculum guides the intervention in the RCT. Patient recruitment will consider centre-related volume: power analyses require 384 patients for phase 1, and 142 patients for phase 3., Ethics and Dissemination: Ethical considerations will be continuously taken into account and approved by the institutional review boards. Central ethical review board approval has been obtained by the Albert-Ludwigs University Freiburg. This study will be performed in concordance with the Declaration of Helsinki and the European data protection law. Publications will exclusively report aggregated data and will be distributed in the scientific community, and patient support groups. Report languages will be German and English., Trial Registration Numbers: NCT04234230 and NCT04526964; Pre-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Many trials investigate potential effects of treatments for coronavirus disease 2019. To provide sufficient information for all involveddecision-makers (clinicians, public health authorities, and drug regulatory agencies), a multiplicity of endpoints must be considered. The objectives are to provide hands-on statistical guidelines for harmonizing heterogeneous endpoints in coronavirus disease 2019 clinical trials., Design: Randomized controlled trials for patients infected with coronavirus disease 2019., Setting: General methods that apply to any randomized controlled trial for patients infected with coronavirus disease 2019., Patients: Coronavirus disease 2019 positive individuals., Interventions: None., Measurements and Main Results: We develop a multistate model that is based on hospitalization, mechanical ventilation, death, and discharge. These events are both categories of the ordinal endpoint recommended by the World Health Organization and also within the core outcome set of the Core Outcome Measures in Effectiveness Trials initiative for coronavirus disease 2019 trials. To support our choice of states in the multistate model, we also perform a brief review of registered coronavirus disease 2019 clinical trials. Based on the multistate model, we give recommendation for compact, informative illustration of time-dynamic treatment effects and explorative statistical analysis. A majority of coronavirus disease 2019 clinical trials collect information on mechanical ventilation, hospitalization, and death. Using reconstructed and real data of coronavirus disease 2019 trials, we show how a stacked probability plot provides a detailed understanding of treatment effects on the patients' course of hospital stay. It contributes to harmonizing multiple endpoints and differing lengths of follow-up both within and between trials., Conclusions: All ongoing clinical trials should include a stacked probability plot in their statistical analysis plan as descriptive analysis. While primary analysis should be on an early endpoint with appropriate capability to be a surrogate (parameter), our multistate model provides additional detailed descriptive information and links results within and between coronavirus disease 2019 trials., Competing Interests: Dr. von Cube has been supported by the Education and Qualification for Postdocs Medical Scientist Program of the University of Freiburg. Dr. Lambert was partly supported by the Edmond de Rothschild Foundation Medical Fellowship Program. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Attributable mortality is an important metric that mirrors the public health effect of a potentially harmful infection by accounting not only for the effect of infection on mortality, but also for its prevalence within the target population. We did a systematic literature review to identify how attributable deaths were quantified and interpreted in core clinical infectious diseases journals between Jan 1, 2013, and April 6, 2020. Of the 1591 abstracts screened, 234 entered the primary analysis. Our summary of the epidemiological measures used in these articles reveals fundamental shortcomings in the conception of attributable mortality. Because of its importance as a basis for decision making on public health matters, it is essential to correctly quantify and report on attributable mortality. Our recommendation for quantification and reporting of attributable deaths aids clinical researchers in the correct statistical assessment of the burden of infections. Fictional as well as real data is used to illustrate these issues., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Reported mortality of hospitalised Coronavirus Disease-2019 (COVID-19) patients varies substantially, particularly in critically ill patients. So far COVID-19 in-hospital mortality and modes of death under state of the art care have not been systematically studied., Methods: This retrospective observational monocenter cohort study was performed after implementation of a non-restricted, dynamic tertiary care model at the University Medical Center Freiburg, an experienced acute respiratory distress syndrome (ARDS) and extracorporeal membrane-oxygenation (ECMO) referral center. All hospitalised patients with PCR-confirmed SARS-CoV-2 infection were included. The primary endpoint was in-hospital mortality, secondary endpoints included major complications and modes of death. A multistate analysis and a Cox regression analysis for competing risk models were performed. Modes of death were determined by two independent reviewers., Results: Between February 25, and May 8, 213 patients were included in the analysis. The median age was 65 years, 129 patients (61%) were male. 70 patients (33%) were admitted to the intensive care unit (ICU), of which 57 patients (81%) received mechanical ventilation and 23 patients (33%) ECMO support. Using multistate methodology, the estimated probability to die within 90 days after COVID-19 onset was 24% in the whole cohort. If the levels of care at time of study entry were accounted for, the probabilities to die were 16% if the patient was initially on a regular ward, 47% if in the intensive care unit (ICU) and 57% if mechanical ventilation was required at study entry. Age ≥65 years and male sex were predictors for in-hospital death. Predominant complications-as judged by two independent reviewers-determining modes of death were multi-organ failure, septic shock and thromboembolic and hemorrhagic complications., Conclusion: In a dynamic care model COVID-19-related in-hospital mortality remained very high. In the absence of potent antiviral agents, strategies to alleviate or prevent the identified complications should be investigated. In this context, multistate analyses enable comparison of models-of-care and treatment strategies and allow estimation and allocation of health care resources., Competing Interests: The authors have declared that no competing interests exist.

Background: The clinical progress of patients hospitalized due to COVID-19 is often associated with severe pneumonia which may require intensive care, invasive ventilation, or extracorporeal membrane oxygenation (ECMO). The length of intensive care and the duration of these supportive therapies are clinically relevant outcomes. From the statistical perspective, these quantities are challenging to estimate due to episodes being time-dependent and potentially multiple, as well as being determined by the competing, terminal events of discharge alive and death., Methods: We used multistate models to study COVID-19 patients' time-dependent progress and provide a statistical framework to estimate hazard rates and transition probabilities. These estimates can then be used to quantify average sojourn times of clinically important states such as intensive care and invasive ventilation. We have made two real data sets of COVID-19 patients (n = 24* and n = 53**) and the corresponding statistical code publically available., Results: The expected lengths of intensive care unit (ICU) stay at day 28 for the two cohorts were 15.05* and 19.62** days, while expected durations of mechanical ventilation were 7.97* and 9.85** days. Predicted mortality stood at 51%* and 15%**. Patients mechanically ventilated at the start of the example studies had a longer expected duration of ventilation (12.25*, 14.57** days) compared to patients non-ventilated (4.34*, 1.41** days) after 28 days. Furthermore, initially ventilated patients had a higher risk of death (54%* and 20%** vs. 48%* and 6%**) after 4 weeks. These results are further illustrated in stacked probability plots for the two groups from time zero, as well as for the entire cohort which depicts the predicted proportions of the patients in each state over follow-up., Conclusions: The multistate approach gives important insights into the progress of COVID-19 patients in terms of ventilation duration, length of ICU stay, and mortality. In addition to avoiding frequent pitfalls in survival analysis, the methodology enables active cases to be analyzed by allowing for censoring. The stacked probability plots provide extensive information in a concise manner that can be easily conveyed to decision makers regarding healthcare capacities. Furthermore, clear comparisons can be made among different baseline characteristics.

Polymorphisms in the drug transporter gene ABCB1 predict the treatment response of selected antidepressants and limit anticonvulsive medication's effectiveness. The ABCB1 locus encodes the energy-dependent transporter P-glycoprotein (P-gp) of the blood brain barrier (BBB), which serves as an efflux pump of its substrates in the expressing tissues of vertebrates. One experimental setup to determine a posteriori the P-gp substrate status is the use of the double abcb1ab knock-out (KO) mice model. Since so far, P-gp substrate status of donepezil, a cholinesterase inhibitor wildly used in Alzheimer's disease therapy was inconclusive, we performed subcutanous (s.c.), continuous injections over 11 days in double abcb1ab KO and P-gp competent wildtype (WT) mice. Both in brain and in testis concentrations of donepezil were significantly higher in P-gp deficient mice compared to WT controls (2.39 and 2.24 times respectively). In conclusion, in mice donepezil's brain bioavailability depends on P-gp., Competing Interests: Declaration of competing interest None., (Copyright © 2020. Published by Elsevier Ltd.)

The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure was extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance, an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand that is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care., (© 2019 John Wiley & Sons, Ltd.)

Background: The impact of valve surgery on outcomes of Staphylococcus aureus infective endocarditis (SAIE) remains controversial. We tested the hypothesis that early valve surgery (EVS) improves survival by using a novel approach that allows for inclusion of major confounders in a time-dependent way., Methods: EVS was defined as valve surgery within 60 days. Univariable and multivariable Cox regression analyses were performed. To account for treatment selection bias, we additionally used a weighted Cox model (marginal structural model) that accounts for time-dynamic imbalances between treatment groups. To address survivor bias, EVS was included as a time-dependent variable. Follow-up of patients was 1 year., Results: Two hundred and three patients were included in the analysis; 50 underwent EVS. All-cause mortality at day 30 was 26%. In the conventional multivariable Cox regression model, the effect of EVS on the death hazard was 0.85 (95% confidence interval [CI], .47-1.52). Using the weighted Cox model, the death hazard rate (HR) of EVS was 0.71 (95% CI, .34-1.49). In subgroup analyses, no survival benefit was observed in patients with septic shock (HR, 0.80 [CI, .26-2.46]), in NVIE (HR, 0.76 [CI, .33-1.71]) or PVIE (HR, 1.02 [CI, .29-3.54]), or in patients with EVS within 14 days (HR, 0.97 [CI, .46-2.07])., Conclusions: Using both a conventional Cox regression model and a weighted Cox model, we did not find a survival benefit for patients who underwent EVS in our cohort. Until results of randomized controlled trials are available, EVS in SAIE should be based on individualized decisions of an experienced multidisciplinary team., Clinical Trials Registration: German Clinical Trials registry (DRKS00005045)., (© The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Objectives: We aim to examine the effect of early adequate treatment in comparison with inadequate or delayed treatment on being extubated or discharged alive over time, in patients with Pseudomonas aeruginosa-related ventilator-associated pneumonia., Design: Retrospective analyses of a prospective observational multicenter cohort study., Setting: ICU., Patients: Patients of the French prospective database (OUTCOMEREA) were included if they acquired a ventilator-associated pneumonia due to P. aeruginosa between 1997 and 2014 and were mechanically ventilated for more than 48 hours., Interventions: Early adequate treatment in comparison with inadequate or delayed adequate treatment., Measurements and Main Results: Multistate models were applied to estimate the time-dependent probability of being extubated or discharged alive, and separate Cox regression analyses were used to assess the treatment effect on all important events that influence the outcome of interest. A propensity score-adjusted innovative regression technique was used for a combined and comprehensive patient-relevant summary effect measure. No evidence was found for a difference between adequate and inadequate or delayed treatment on being extubated or discharged alive. However, for all patients, the probability of being extubated or discharged alive remains low and does not exceed 50% even 40 days after a P. aeruginosa-related ventilator-associated pneumonia., Conclusions: Early adequate treatment does not seem to be associated with an improved prognosis. Its potential benefit requires further investigation in larger observational studies.

Background: In many studies the information of patients who are dying in the hospital is censored when examining the change in length of hospital stay (cLOS) due to hospital-acquired infections (HIs). While appropriate estimators of cLOS are available in literature, the existence of the bias due to censoring of deaths was neither mentioned nor discussed by the according authors., Methods: Using multi-state models, we systematically evaluate the bias when estimating cLOS in such a way. We first evaluate the bias in a mathematically closed form assuming a setting with constant hazards. To estimate the cLOS due to HIs non-parametrically, we relax the assumption of constant hazards and consider a time-inhomogeneous Markov model., Results: In our analytical evaluation we are able to discuss challenging effects of the bias on cLOS. These are in regard to direct and indirect differential mortality. Moreover, we can make statements about the magnitude and direction of the bias. For real-world relevance, we illustrate the bias on a publicly available prospective cohort study on hospital-acquired pneumonia in intensive-care., Conclusion: Based on our findings, we can conclude that censoring the death cases in the hospital and considering only patients discharged alive should be avoided when estimating cLOS. Moreover, we found that the closed mathematical form can be used to describe the bias for settings with constant hazards.

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.

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.

Analysing the determinants and consequences of hospital-acquired infections involves the evaluation of large cohorts. Infected patients in the cohort are often rare for specific pathogens, because most of the patients admitted to the hospital are discharged or die without such an infection. Death and discharge are competing events to acquiring an infection, because these individuals are no longer at risk of getting a hospital-acquired infection. Therefore, the data is best analysed with an extended survival model - the extended illness-death model. A common problem in cohort studies is the costly collection of covariate values. In order to provide efficient use of data from infected as well as uninfected patients, we propose a tailored case-cohort approach for the extended illness-death model. The basic idea of the case-cohort design is to only use a random sample of the full cohort, referred to as subcohort, and all cases, namely the infected patients. Thus, covariate values are only obtained for a small part of the full cohort. The method is based on existing and established methods and is used to perform regression analysis in adapted Cox proportional hazards models. We propose estimation of all cause-specific cumulative hazards and transition probabilities in an extended illness-death model based on case-cohort sampling. As an example, we apply the methodology to infection with a specific pathogen using a large cohort from Spanish hospital data. The obtained results of the case-cohort design are compared with the results in the full cohort to investigate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)