Impact of adherence to individual quality-of-care indicators on the prognosis of bloodstream infection due to Staphylococcus aureus: a prospective observational multicentre cohort.

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.

The population-attributable fraction for time-to-event data.

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.

Association of osteopontin with kidney function and kidney failure in chronic kidney disease patients: the GCKD study.

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.

Inverse Probability Weighting Enhances Absolute Risk Estimation in Three Common Study Designs of Nosocomial Infections.

Purpose: 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.

Translation and Psychometric Evaluation of the German Version of the Thirst Distress Scale for Patients With Heart Failure.

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.

Quantifying and communicating the burden of COVID-19.

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.

Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study.

BACKGROUND: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. METHODS: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. RESULTS: Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835-0.910]). CONCLUSIONS: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

Predicting Potential Prevention Effects on Hospital Burden of Nosocomial Infections: A Multistate Modeling Approach.

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.

Self-management for patients on ventricular assist device support: a national, multicentre study: protocol for a 3-phase study.

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.

Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness.

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.

Harmonizing Heterogeneous Endpoints in Coronavirus Disease 2019 Trials Without Loss of Information.

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.

COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany.

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.

Quantification and interpretation of attributable mortality in core clinical infectious disease journals.

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.

Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them.

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.

Joint analysis of duration of ventilation, length of intensive care, and mortality of COVID-19 patients: a multistate approach.

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.

Donepezil, a cholinesterase inhibitor used in Alzheimer's disease therapy, is actively exported out of the brain by abcb1ab p-glycoproteins in mice.

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.