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/ .

Activated phosphoinositide 3-kinase δ syndrome: Update from the ESID Registry and comparison with other autoimmune-lymphoproliferative inborn errors of immunity.

BACKGROUND: Activated phosphoinositide-3-kinase δ syndrome (APDS) is an inborn error of immunity (IEI) with infection susceptibility and immune dysregulation, clinically overlapping with other conditions. Management depends on disease evolution, but predictors of severe disease are lacking. OBJECTIVES: This study sought to report the extended spectrum of disease manifestations in APDS1 versus APDS2; compare these to CTLA4 deficiency, NFKB1 deficiency, and STAT3 gain-of-function (GOF) disease; and identify predictors of severity in APDS. METHODS: Data was collected from the ESID (European Society for Immunodeficiencies)-APDS registry and was compared with published cohorts of the other IEIs. RESULTS: The analysis of 170 patients with APDS outlines high penetrance and early onset of APDS compared to the other IEIs. The large clinical heterogeneity even in individuals with the same PIK3CD variant E1021K illustrates how poorly the genotype predicts the disease phenotype and course. The high clinical overlap between APDS and the other investigated IEIs suggests relevant pathophysiological convergence of the affected pathways. Preferentially affected organ systems indicate specific pathophysiology: bronchiectasis is typical of APDS1; interstitial lung disease and enteropathy are more common in STAT3 GOF and CTLA4 deficiency. Endocrinopathies are most frequent in STAT3 GOF, but growth impairment is also common, particularly in APDS2. Early clinical presentation is a risk factor for severe disease in APDS. CONCLUSIONS: APDS illustrates how a single genetic variant can result in a diverse autoimmune-lymphoproliferative phenotype. Overlap with other IEIs is substantial. Some specific features distinguish APDS1 from APDS2. Early onset is a risk factor for severe disease course calling for specific treatment studies in younger patients.

Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data.

BACKGROUND: Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. METHODS: For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented. RESULTS: Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results. CONCLUSIONS: Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

SAPS2, APACHE2, SOFA, and Core-10-TISS upon admission as risk indicators for ICU-acquired infections: a retrospective cohort study.

PURPOSE: Early identification of high-risk patients is an important component in improving infection prevention. The SAPS2, APACHE2, Core-10-TISS, and SOFA scores are already widely used to estimate mortality, morbidity and nursing workload, but this study evaluated their usefulness in assessing a patient's risk of ICU-acquired infection. METHODS: We conducted a retrospective cohort study by analyzing all patient admissions to seven ICUs at Charité Berlin, Germany in 2017 and 2018. The four scores were documented by physicians on the day of admission. The infection control staff monitored daily whether the patients experienced lower respiratory tract infections (LRTIs), urinary tract infections (UTIs), or primary blood stream infections (PBSIs). For each combination of scoring system and infection type, an adjusted Fine and Gray model was fitted. RESULTS: We analyzed 5053 ICU admissions and observed at least one ICU-acquired infection in N = 253 patients (incidence density: 4.73 per 1000 days). 59.0% (N = 2983) of the patients were male, median age was 66 years (IQR 55-77) and median length of stay was 6 days (IQR 4-12). All models showed that patients with a higher score value were at higher risk for ICU-acquired first PBSI, LRTI, or UTI, except for the model of APACHE2 and PBSI. Patients with a SAPS2 score of > 50 points showed an increased risk of infection of sHR = 2.34 for PBSIs (CI 1.06-5.17, p < 0.05), sHR = 2.33 for LRTIs (1.53-2.55, p < 0.001) and sHR = 2.25 for UTIs (1.23-4.13, p < 0.01) when compared to the reference group with 0-30 points. CONCLUSIONS: The result of this study showed that admission scores of SAPS2, Core-10-TISS, APACHE2, and SOFA might be adequate indicators for assessing a patient's risk of ICU-acquired infection.

Limiting calcium overload after cardiac arrest: The role of human albumin in controlled automated reperfusion of the whole body.

BACKGROUND: Regarding the overall inadequate results after cardiopulmonary resuscitation, the development of new treatment concepts is urgently needed. Controlled Automated Reperfusion of the whoLe body (CARL) represents a therapy bundle to control the conditions of reperfusion and the composition of the reperfusate after cardiac arrest (CA). The aim of this study was to investigate the plasma expander's role in the CARL priming solution and examine its mechanism of action. METHODS: Viscosity, osmolality, colloid osmotic pressure (COP), pH and calcium binding of different priming solutions were measured in vitro and compared to in vivo data. N = 16 pigs were allocated to receive CARL following 20 min of untreated CA with either human albumin 20% (HA, N = 8) or gelatin polysuccinate 4% (GP, N = 8). Blood gas analyses were performed during the first hour of reperfusion and catecholamine and fluid requirements were recorded. Neurological outcome was assessed by neurological deficit scoring (NDS) on the seventh day. RESULTS: In vitro, addition of HA to the CARL priming solution resulted in higher COP and higher calcium-binding than GP. In vivo, treatment with HA led to greater reduction of ionized calcium and higher extracorporeal flows within the first 30 min of reperfusion with no difference in catecholamine support and fluid requirement. Seven-day survival of 75% with no difference in NDS was observed in both groups. CONCLUSIONS: Our data show that the plasma expander in the CARL priming solution has a significant effect on the initial reperfusate and can potentially influence the course of resuscitation. However, seven-day survival and NDS did not differ between groups.

An empirical evaluation of the impact scenario of pooling bodies of evidence from randomized controlled trials and cohort studies in medical research.

BACKGROUND: Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess treatment effects of medical interventions. We aimed to hypothetically pool bodies of evidence (BoE) from RCTs with matched BoE from cohort studies included in the same systematic review. METHODS: BoE derived from systematic reviews of RCTs and cohort studies published in the 13 medical journals with the highest impact factor were considered. We re-analyzed effect estimates of the included systematic reviews by pooling BoE from RCTs with BoE from cohort studies using random and common effects models. We evaluated statistical heterogeneity, 95% prediction intervals, weight of BoE from RCTs to the pooled estimate, and whether integration of BoE from cohort studies modified the conclusion from BoE of RCTs. RESULTS: Overall, 118 BoE-pairs based on 653 RCTs and 804 cohort studies were pooled. By pooling BoE from RCTs and cohort studies with a random effects model, for 61 (51.7%) out of 118 BoE-pairs, the 95% confidence interval (CI) excludes no effect. By pooling BoE from RCTs and cohort studies, the median I2 was 48%, and the median contributed percentage weight of RCTs to the pooled estimates was 40%. The direction of effect between BoE from RCTs and pooled effect estimates was mainly concordant (79.7%). The integration of BoE from cohort studies modified the conclusion (by examining the 95% CI) from BoE of RCTs in 32 (27%) of the 118 BoE-pairs, but the direction of effect was mainly concordant (88%). CONCLUSIONS: Our findings provide insights for the potential impact of pooling both BoE in systematic reviews. In medical research, it is often important to rely on both evidence of RCTs and cohort studies to get a whole picture of an investigated intervention-disease association. A decision for or against pooling different study designs should also always take into account, for example, PI/ECO similarity, risk of bias, coherence of effect estimates, and also the trustworthiness of the evidence. Overall, there is a need for more research on the influence of those issues on potential pooling.

Evaluating agreement between bodies of evidence from randomized controlled trials and cohort studies in medical research: a meta-epidemiological study.

BACKGROUND: Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess the treatment effects of medical interventions. To evaluate the agreement of effect estimates between bodies of evidence (BoE) from randomized controlled trials (RCTs) and cohort studies and to identify factors associated with disagreement. METHODS: Systematic reviews were published in the 13 medical journals with the highest impact factor identified through a MEDLINE search. BoE-pairs from RCTs and cohort studies with the same medical research question were included. We rated the similarity of PI/ECO (Population, Intervention/Exposure, Comparison, Outcome) between BoE from RCTs and cohort studies. The agreement of effect estimates across BoE was analyzed by pooling ratio of ratios (RoR) for binary outcomes and difference of mean differences for continuous outcomes. We performed subgroup analyses to explore factors associated with disagreements. RESULTS: One hundred twenty-nine BoE pairs from 64 systematic reviews were included. PI/ECO-similarity degree was moderate: two BoE pairs were rated as "more or less identical"; 90 were rated as "similar but not identical" and 37 as only "broadly similar". For binary outcomes, the pooled RoR was 1.04 (95% CI 0.97-1.11) with considerable statistical heterogeneity. For continuous outcomes, differences were small. In subgroup analyses, degree of PI/ECO-similarity, type of intervention, and type of outcome, the pooled RoR indicated that on average, differences between both BoE were small. Subgroup analysis by degree of PI/ECO-similarity revealed high statistical heterogeneity and wide prediction intervals across PI/ECO-dissimilar BoE pairs. CONCLUSIONS: On average, the pooled effect estimates between RCTs and cohort studies did not differ. Statistical heterogeneity and wide prediction intervals were mainly driven by PI/ECO-dissimilarities (i.e., clinical heterogeneity) and cohort studies. The potential influence of risk of bias and certainty of the evidence on differences of effect estimates between RCTs and cohort studies needs to be explored in upcoming meta-epidemiological studies.

Data-driven prediction of COVID-19 cases in Germany for decision making.

BACKGROUND: The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. METHODS: We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. RESULTS: The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. CONCLUSIONS: We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

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.

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.

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.

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.

Carotid geometry is an independent predictor of wall thickness - a 3D cardiovascular magnetic resonance study in patients with high cardiovascular risk.

BACKGROUND: The posterior wall of the proximal internal carotid artery (ICA) is the predilection site for the development of stenosis. To optimally prevent stroke, identification of new risk factors for plaque progression is of high interest. Therefore, we studied the impact of carotid geometry and wall shear stress on cardiovascular magnetic resonance (CMR)-depicted wall thickness in the ICA of patients with high cardiovascular disease risk. METHODS: One hundred twenty-one consecutive patients ≥50 years with hypertension, ≥1 additional cardiovascular risk factor and ICA plaque ≥1.5 mm thickness and < 50% stenosis were prospectively included. High-resolution 3D-multi-contrast (time of flight, T1, T2, proton density) and 4D flow CMR were performed for the assessment of morphological (bifurcation angle, ICA/common carotid artery (CCA) diameter ratio, tortuosity, and wall thickness) and hemodynamic parameters (absolute/systolic wall shear stress (WSS), oscillatory shear index (OSI)) in 242 carotid bifurcations. RESULTS: We found lower absolute/systolic WSS, higher OSI and increased wall thickness in the posterior compared to the anterior wall of the ICA bulb (p < 0.001), whereas this correlation disappeared in ≥10% stenosis. Higher carotid tortuosity (regression coefficient = 0.764; p < 0.001) and lower ICA/CCA diameter ratio (regression coefficient = - 0.302; p < 0.001) were independent predictors of increased wall thickness even after adjustment for cardiovascular risk factors. This association was not found for bifurcation angle, WSS or OSI in multivariate regression analysis. CONCLUSIONS: High carotid tortuosity and low ICA diameter were independent predictors for wall thickness of the ICA bulb in this cross-sectional study, whereas this association was not present for WSS or OSI. Thus, consideration of geometric parameters of the carotid bifurcation could be helpful to identify patients at increased risk of carotid plaque generation. However, this association and the potential benefit of WSS measurement need to be further explored in a longitudinal study.

Mechanical ventilation and the daily cost of ICU care.

BACKGROUND: Intensive care units represent one of the largest clinical cost centers in hospitals. Mechanical ventilation accounts for a significant share of this cost. There is a relative dearth of information quantifying the impact of ventilation on daily ICU cost. We thus determine daily costs of ICU care, incremental cost of mechanical ventilation per ICU day, and further differentiate cost by underlying diseases. METHODS: Total ICU costs, length of ICU stay, and duration of mechanical ventilation of all 10,637 adult patients treated in ICUs at a German hospital in 2013 were analyzed for never-ventilated patients (N = 9181), patients ventilated at least 1 day, (N = 1455) and all patients (N = 10,637). Total ICU costs were regressed on the number of ICU days. Finally, costs were analyzed separately by ICD-10 chapter of main diagnosis. RESULTS: Daily non-ventilated costs were €999 (95%CI €924 - €1074), and ventilated costs were €1590 (95%CI €1524 - €1657), a 59% increase. Costs per non-ventilated ICU day differed substantially and were lowest for endocrine, nutritional or metabolic diseases (€844), and highest for musculoskeletal diseases (€1357). Costs per ventilated ICU day were lowest for diseases of the circulatory system (€1439) and highest for cancer patients (€1594). The relative cost increase due to ventilation was highest for diseases of the respiratory system (94%) and even non-systematic for patients with musculoskeletal diseases (13%, p = 0.634). CONCLUSIONS: Results show substantial variability of ICU costs for different underlying diseases and underline mechanical ventilation as an important driver of ICU costs.

The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking.

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.

Investigating the Impact of Early Valve Surgery on Survival in Staphylococcus aureus Infective Endocarditis Using a Marginal Structural Model Approach: Results of a Large, Prospectively Evaluated Cohort.

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 population-attributable fraction for time-dependent exposures and competing risks-A discussion on estimands.

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.

Estimands to quantify prolonged hospital stay associated with nosocomial infections.

BACKGROUND: Length of stay evaluations are very common to determine the burden of nosocomial infections. However, there exist fundamentally different methods to quantify the prolonged length of stay associated with nosocomial infections. Previous methodological studies emphasized the need to account for the timing of infection in order to differentiate the length of stay before and after the infection. METHODS: We derive four different approaches in a simple multi-state framework, display their mathematical relationships in a multiplicative as well as additive way and apply them to a real cohort study (n=756 German intensive-care unit patients of whom 124 patients acquired a nosocomial infection). RESULTS: The first approach ignores the timing of infection and quantifies the difference of eventually infected and eventually uninfected; it is 12.31 days in the real data. The second approach compares the average sojourn time with infection with the average sojourn time of being hypothetically uninfected; it is 2.12 days. The third one compares the average length of stay of a population in a world with nosocomial infections with a population in a hypothetical world without nosocomial infections; it is 0.35 days. Finally, approach four compares the mean residual length of stay between currently infected and uninfected patients on a daily basis; the difference is 1.77 days per infected patient. CONCLUSIONS: The first approach should be avoided because it compares the eventually infected with the eventually uninfected, but has no prospective interpretation. The other approaches differ in their interpretation but are suitable because they explicitly distinguish between the pre- and post-time of the nosocomial infection.

Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital.

BACKGROUND: Pseudomonas aeruginosa-related pneumonia is an ongoing healthcare challenge. Estimating its financial burden is complicated by the time-dependent nature of the disease. METHODS: Two hundred thirty-six cases of Pseudomonas aeruginosa-related pneumonia were recorded at a 2000 bed German teaching hospital between 2011 and 2014. Thirty-five cases (15%) were multidrug-resistant (MDR) Pseudomonas aeruginosa. Hospital- and community-acquired cases were distinguished by main diagnoses and exposure time. The impact of Pseudomonas aeruginosa-related pneumonia on the three endpoints cost, reimbursement, and length of stay was analyzed, taking into account (1) the time-dependent nature of exposure, (2) clustering of costs within diagnostic groups, and (3) additional confounders. RESULTS: Pseudomonas aeruginosa pneumonia is associated with substantial additional costs that are not fully reimbursed. Costs are highest for hospital-acquired cases (€19,000 increase over uninfected controls). However, community-acquired cases are also associated with a substantial burden (€8400 when Pseudomonas aeruginosa pneumonia is the main reason for hospitalization, and €6700 when not). Sensitivity analyses for hospital-acquired cases showed that ignoring or incorrectly adjusting for time-dependency substantially biases results. Furthermore, multidrug-resistance was rare and only showed a measurable impact on the cost of community-acquired cases. CONCLUSIONS: Pseudomonas aeruginosa pneumonia creates a substantial financial burden for hospitals. This is particularly the case for nosocomial infections. Infection control interventions could yield significant cost reductions. However, to evaluate the potential effectiveness of different interventions, the time-dependent aspects of incremental costs must be considered to avoid introduction of bias.

Impact of mechanical ventilation on the daily costs of ICU care: a systematic review and meta regression.

The impact of mechanical ventilation on the daily costs of intensive care unit (ICU) care is largely unknown. We thus conducted a systematic search for studies measuring the daily costs of ICU stays for general populations of adults (age ≥18 years) and the added costs of mechanical ventilation. The relative increase in the daily costs was estimated using random effects meta regression. The results of the analyses were applied to a recent study calculating the excess length-of-stay associated with ICU-acquired (ventilator-associated) pneumonia, a major complication of mechanical ventilation. The search identified five eligible studies including a total of 54 766 patients and ~238 037 patient days in the ICU. Overall, mechanical ventilation was associated with a 25.8% (95% CI 4.7%-51.2%) increase in the daily costs of ICU care. A combination of these estimates with standardised unit costs results in approximate daily costs of a single ventilated ICU day of €1654 and €1580 in France and Germany, respectively. Mechanical ventilation is a major driver of ICU costs and should be taken into account when measuring the financial burden of adverse events in ICU settings.