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/ .

Methodological biases in observational hospital studies of COVID-19 treatment effectiveness: pitfalls and potential.

Introduction: This study aims to discuss and assess the impact of three prevalent methodological biases: competing risks, immortal-time bias, and confounding bias in real-world observational studies evaluating treatment effectiveness. We use a demonstrative observational data example of COVID-19 patients to assess the impact of these biases and propose potential solutions. Methods: We describe competing risks, immortal-time bias, and time-fixed confounding bias by evaluating treatment effectiveness in hospitalized patients with COVID-19. For our demonstrative analysis, we use observational data from the registry of patients with COVID-19 who were admitted to the Bellvitge University Hospital in Spain from March 2020 to February 2021 and met our predefined inclusion criteria. We compare estimates of a single-dose, time-dependent treatment with the standard of care. We analyze the treatment effectiveness using common statistical approaches, either by ignoring or only partially accounting for the methodological biases. To address these challenges, we emulate a target trial through the clone-censor-weight approach. Results: Overlooking competing risk bias and employing the naïve Kaplan-Meier estimator led to increased in-hospital death probabilities in patients with COVID-19. Specifically, in the treatment effectiveness analysis, the Kaplan-Meier estimator resulted in an in-hospital mortality of 45.6% for treated patients and 59.0% for untreated patients. In contrast, employing an emulated trial framework with the weighted Aalen-Johansen estimator, we observed that in-hospital death probabilities were reduced to 27.9% in the "X"-treated arm and 40.1% in the non-"X"-treated arm. Immortal-time bias led to an underestimated hazard ratio of treatment. Conclusion: Overlooking competing risks, immortal-time bias, and confounding bias leads to shifted estimates of treatment effects. Applying the naïve Kaplan-Meier method resulted in the most biased results and overestimated probabilities for the primary outcome in analyses of hospital data from COVID-19 patients. This overestimation could mislead clinical decision-making. Both immortal-time bias and confounding bias must be addressed in assessments of treatment effectiveness. The trial emulation framework offers a potential solution to address all three methodological biases.

Diagnostic evaluation of paediatric autoimmune lymphoproliferative immunodeficiencies (ALPID): a prospective cohort study.

BACKGROUND: Lymphoproliferation and autoimmune cytopenias characterise autoimmune lymphoproliferative syndrome. Other conditions sharing these manifestations have been termed autoimmune lymphoproliferative syndrome-like diseases, although they are frequently more severe. The aim of this study was to define the genetic, clinical, and immunological features of these disorders to improve their diagnostic classification. METHODS: In this prospective cohort study, patients were referred to the Center for Chronic Immunodeficiency in Freiburg, Germany, between Jan 1, 2008 and March 5, 2022. We enrolled patients younger than 18 years with lymphoproliferation and autoimmune cytopenia, lymphoproliferation and at least one additional sign of an inborn error of immunity (SoIEI), bilineage autoimmune cytopenia, or autoimmune cytopenia and at least one additional SoIEI. Autoimmune lymphoproliferative syndrome biomarkers were determined in all patients. Sanger sequencing followed by in-depth genetic studies were recommended for patients with biomarkers indicative of autoimmune lymphoproliferative syndrome, while IEI panels, exome sequencing, or genome sequencing were recommended for patients without such biomarkers. Genetic analyses were done as decided by the treating physician. The study was registered on the German Clinical Trials Register, DRKS00011383, and is ongoing. FINDINGS: We recruited 431 children referred for autoimmune lymphoproliferative syndrome evaluation, of whom 236 (55%) were included on the basis of lymphoproliferation and autoimmune cytopenia, 148 (34%) on the basis of lymphoproliferation and another SoIEI, 33 (8%) on the basis of autoimmune bicytopenia, and 14 (3%) on the basis of autoimmune cytopenia and another SoIEI. Median age at diagnostic evaluation was 9·8 years (IQR 5·5-13·8), and the cohort comprised 279 (65%) boys and 152 (35%) girls. After biomarker and genetic assessments, autoimmune lymphoproliferative syndrome was diagnosed in 71 (16%) patients. Among the remaining 360 patients, 54 (15%) had mostly autosomal-dominant autoimmune lymphoproliferative immunodeficiencies (AD-ALPID), most commonly affecting JAK-STAT (26 patients), CTLA4-LRBA (14), PI3K (six), RAS (five), or NFκB (three) signalling. 19 (5%) patients had other IEIs, 17 (5%) had non-IEI diagnoses, 79 (22%) were unresolved despite extended genetics (ALPID-U), and 191 (53%) had insufficient genetic workup for diagnosis. 16 (10%) of 161 patients with a final diagnosis had somatic mutations. Alternative classification of patients fulfilling common variable immunodeficiency or Evans syndrome criteria did not increase the proportion of genetic diagnoses. INTERPRETATION: The ALPID phenotype defined in this study is enriched for patients with genetic diseases treatable with targeted therapies. The term ALPID might be useful to focus diagnostic and therapeutic efforts by triggering extended genetic analysis and consideration of targeted therapies, including in some children currently classified as having common variable immunodeficiency or Evans syndrome. FUNDING: Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy. TRANSLATION: For the German translation of the abstract see Supplementary Materials section.

The Impact of Head Position on Neurological and Histopathological Outcome Following Controlled Automated Reperfusion of the Whole Body (CARL) in a Pig Model.

Introduction: Based on extracorporeal circulation, targeted reperfusion strategies have been developed to improve survival and neurologic recovery in refractory cardiac arrest: Controlled Automated Reperfusion of the whoLe Body (CARL). Furthermore, animal and human cadaver studies have shown beneficial effects on cerebral pressure due to head elevation during conventional cardiopulmonary resuscitation. Our aim was to evaluate the impact of head elevation on survival, neurologic recovery and histopathologic outcome in addition to CARL in an animal model. Methods: After 20 min of ventricular fibrillation, 46 domestic pigs underwent CARL, including high, pulsatile extracorporeal blood flow, pH-stat acid-base management, priming with a colloid, mannitol and citrate, targeted oxygen, carbon dioxide and blood pressure management, rapid cooling and slow rewarming. N = 25 were head-up (HUP) during CARL, and N = 21 were supine (SUP). After weaning from ECC, the pigs were extubated and followed up in the animal care facility for up to seven days. Neuronal density was evaluated in neurohistopathology. Results: More animals in the HUP group survived and achieved a favorable neurological recovery, 21/25 (84%) versus 6/21 (29%) in the SUP group. Head positioning was an independent factor in neurologically favorable survival (p < 0.00012). Neurohistopathology showed no significant structural differences between HUP and SUP. Distinct, partly transient clinical neurologic deficits were blindness and ataxia. Conclusions: Head elevation during CARL after 20 min of cardiac arrest independently improved survival and neurologic outcome in pigs. Clinical follow-up revealed transient neurologic deficits potentially attributable to functions localized in the posterior perfusion area, whereas histopathologic findings did not show corresponding differences between the groups. A possible explanation of our findings may be venous congestion and edema as modifiable contributing factors of neurologic injury following prolonged cardiac arrest.

Postoperative Staphylococcus aureus Infections in Patients With and Without Preoperative Colonization.

Importance: Staphylococcus aureus surgical site infections (SSIs) and bloodstream infections (BSIs) are important complications of surgical procedures for which prevention remains suboptimal. Contemporary data on the incidence of and etiologic factors for these infections are needed to support the development of improved preventive strategies. Objectives: To assess the occurrence of postoperative S aureus SSIs and BSIs and quantify its association with patient-related and contextual factors. Design, Setting, and Participants: This multicenter cohort study assessed surgical patients at 33 hospitals in 10 European countries who were recruited between December 16, 2016, and September 30, 2019 (follow-up through December 30, 2019). Enrolled patients were actively followed up for up to 90 days after surgery to assess the occurrence of S aureus SSIs and BSIs. Data analysis was performed between November 20, 2020, and April 21, 2022. All patients were 18 years or older and had undergone 11 different types of surgical procedures. They were screened for S aureus colonization in the nose, throat, and perineum within 30 days before surgery (source population). Both S aureus carriers and noncarriers were subsequently enrolled in a 2:1 ratio. Exposure: Preoperative S aureus colonization. Main Outcomes and Measures: The main outcome was cumulative incidence of S aureus SSIs and BSIs estimated for the source population, using weighted incidence calculation. The independent association of candidate variables was estimated using multivariable Cox proportional hazards regression models. Results: In total, 5004 patients (median [IQR] age, 66 [56-72] years; 2510 [50.2%] female) were enrolled in the study cohort; 3369 (67.3%) were S aureus carriers. One hundred patients developed S aureus SSIs or BSIs within 90 days after surgery. The weighted cumulative incidence of S aureus SSIs or BSIs was 2.55% (95% CI, 2.05%-3.12%) for carriers and 0.52% (95% CI, 0.22%-0.91%) for noncarriers. Preoperative S aureus colonization (adjusted hazard ratio [AHR], 4.38; 95% CI, 2.19-8.76), having nonremovable implants (AHR, 2.00; 95% CI, 1.15-3.49), undergoing mastectomy (AHR, 5.13; 95% CI, 1.87-14.08) or neurosurgery (AHR, 2.47; 95% CI, 1.09-5.61) (compared with orthopedic surgery), and body mass index (AHR, 1.05; 95% CI, 1.01-1.08 per unit increase) were independently associated with S aureus SSIs and BSIs. Conclusions and Relevance: In this cohort study of surgical patients, S aureus carriage was associated with an increased risk of developing S aureus SSIs and BSIs. Both modifiable and nonmodifiable etiologic factors were associated with this risk and should be addressed in those at increased S aureus SSI and BSI risk.

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.

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.

Concurrent analysis of hospital stay durations and mortality of emerging severe acute respiratory coronavirus virus 2 (SARS-CoV-2) variants using real-time electronic health record data at a large German university hospital.

Multistate methodology proves effective in analyzing hospitalized coronavirus disease 2019 (COVID-19) patients with emerging variants in real time. An analysis of 2,548 admissions in Freiburg, Germany, showed reduced severity over time in terms of shorter hospital stays and higher discharge rates when comparing more recent phases with earlier phases of the pandemic.

Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study.

Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine's effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach.

Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units.

Background: The Efficacy and effectiveness of vaccination against SARS-CoV-2 have clearly been shown by randomized trials and observational studies. Despite these successes on the individual level, vaccination of the population is essential to relieving hospitals and intensive care units. In this context, understanding the effects of vaccination and its lag-time on the population-level dynamics becomes necessary to adapt the vaccination campaigns and prepare for future pandemics. Methods: This work applied a quasi-Poisson regression with a distributed lag linear model on German data from a scientific data platform to quantify the effects of vaccination and its lag times on the number of hospital and intensive care patients, adjusting for the influences of non-pharmaceutical interventions and their time trends. We separately evaluated the effects of the first, second and third doses administered in Germany. Results: The results revealed a decrease in the number of hospital and intensive care patients for high vaccine coverage. The vaccination provides a significant protective effect when at least approximately 40% of people are vaccinated, whatever the dose considered. We also found a time-delayed effect of the vaccination. Indeed, the effect on the number of hospital patients is immediate for the first and second doses while for the third dose about 15 days are necessary to have a strong protective effect. Concerning the effect on the number of intensive care patients, a significant protective response was obtained after a lag time of about 15-20 days for the three doses. However, complex time trends, e.g. due to new variants, which are independent of vaccination make the detection of these findings challenging. Conclusion: Our results provide additional information about the protective effects of vaccines against SARS-CoV-2; they are in line with previous findings and complement the individual-level evidence of clinical trials. Findings from this work could help public health authorities efficiently direct their actions against SARS-CoV-2 and be well-prepared for future pandemics.

Target Trial Emulation Using Hospital-Based Observational Data: Demonstration and Application in COVID-19.

Methodological biases are common in observational studies evaluating treatment effectiveness. The objective of this study is to emulate a target trial in a competing risks setting using hospital-based observational data. We extend established methodology accounting for immortal time bias and time-fixed confounding biases to a setting where no survival information beyond hospital discharge is available: a condition common to coronavirus disease 2019 (COVID-19) research data. This exemplary study includes a cohort of 618 hospitalized patients with COVID-19. We describe methodological opportunities and challenges that cannot be overcome applying traditional statistical methods. We demonstrate the practical implementation of this trial emulation approach via clone-censor-weight techniques. We undertake a competing risk analysis, reporting the cause-specific cumulative hazards and cumulative incidence probabilities. Our analysis demonstrates that a target trial emulation framework can be extended to account for competing risks in COVID-19 hospital studies. In our analysis, we avoid immortal time bias, time-fixed confounding bias, and competing risks bias simultaneously. Choosing the length of the grace period is justified from a clinical perspective and has an important advantage in ensuring reliable results. This extended trial emulation with the competing risk analysis enables an unbiased estimation of treatment effects, along with the ability to interpret the effectiveness of treatment on all clinically important outcomes.

Intensity and lag-time of non-pharmaceutical interventions on COVID-19 dynamics in German hospitals.

Introduction: Evaluating the potential effects of non-pharmaceutical interventions on COVID-19 dynamics is challenging and controversially discussed in the literature. The reasons are manifold, and some of them are as follows. First, interventions are strongly correlated, making a specific contribution difficult to disentangle; second, time trends (including SARS-CoV-2 variants, vaccination coverage and seasonality) influence the potential effects; third, interventions influence the different populations and dynamics with a time delay. Methods: In this article, we apply a distributed lag linear model on COVID-19 data from Germany from January 2020 to June 2022 to study intensity and lag time effects on the number of hospital patients and the number of prevalent intensive care patients diagnosed with polymerase chain reaction tests. We further discuss how the findings depend on the complexity of accounting for the seasonal trends. Results and discussion: Our findings show that the first reducing effect of non-pharmaceutical interventions on the number of prevalent intensive care patients before vaccination can be expected not before a time lag of 5 days; the main effect is after a time lag of 10-15 days. In general, we denote that the number of hospital and prevalent intensive care patients decrease with an increase in the overall non-pharmaceutical interventions intensity with a time lag of 9 and 10 days. Finally, we emphasize a clear interpretation of the findings noting that a causal conclusion is challenging due to the lack of a suitable experimental study design.

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.

[Forecasting models to guide intensive care COVID-19 capacities in Germany]. / Prognosemodelle zur Steuerung von intensivmedizinischen COVID-19-Kapazitäten in Deutschland.

BACKGROUND: Time-series forecasting models play a central role in guiding intensive care coronavirus disease 2019 (COVID-19) bed capacity in a pandemic. A key predictor of future intensive care unit (ICU) COVID-19 bed occupancy is the number of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general population, which in turn is highly associated with week-to-week variability, reporting delays, regional differences, number of unknown cases, time-dependent infection rates, vaccinations, SARS-CoV­2 virus variants, and nonpharmaceutical containment measures. Furthermore, current and also future COVID ICU occupancy is significantly influenced by ICU discharge and mortality rates. METHODS: Both the number of new SARS-CoV­2 infections in the general population and intensive care COVID-19 bed occupancy rates are recorded in Germany. These data are statistically analyzed on a daily basis using epidemic SEIR (susceptible, exposed, infection, recovered) models using ordinary differential equations and multiple regression models. RESULTS: Forecast results of the immediate trend (20-day forecast) of ICU occupancy by COVID-19 patients are made available to decision makers at various levels throughout the country. CONCLUSION: The forecasts are compared with the development of available ICU bed capacities in order to identify capacity limitations at an early stage and to enable short-term solutions to be made, such as supraregional transfers.

Excess length of stay and readmission following hospital-acquired bacteraemia: a population-based cohort study applying a multi-state model approach.

OBJECTIVES: Population-based estimates of excess length of stay after hospital-acquired bacteraemia (HAB) are few and prone to time-dependent bias. We investigated the excess length of stay and readmission after HAB. METHODS: This population-based cohort study included the North Denmark Region adult population hospitalized for ≥48 hours, from 2006 to 2018. Using a multi-state model with 45 days of follow-up, we estimated adjusted hazard ratios (aHRs) for end of stay and discharge alive. The excess length of stay was defined as the difference in residual length of stay between infected and uninfected patients, estimated using a non-parametric approach with HAB as time-dependent exposure. Confounder effects were estimated using pseudo-value regression. Readmission after HAB was investigated using the Cox regression. RESULTS: We identified 3457 episodes of HAB in 484 291 admissions in 205 962 unique patients. Following HAB, excess length of stay was 6.6 days (95% CI, 6.2-7.1 days) compared with patients at risk. HAB was associated with decreased probability of end of hospital stay (aHR, 0.60; 95% CI, 0.57-0.62) driven by the decreased hazard for discharge alive; the aHRs ranged from 0.30 (95% CI, 0.23-0.40) for bacteraemia stemming from 'heart and vascular' source to 0.72 (95% CI, 0.69-0.82) for the 'urinary tract'. Despite increased post-discharge mortality (aHR, 2.76; 95% CI, 2.38-3.21), HAB was associated with readmission (aHR, 1.42; 95% CI, 1.31-1.53). CONCLUSION: HAB was associated with considerably excess length of hospital stay compared with hospitalized patients without bacteraemia. Among patients discharged alive, HAB was associated with increased readmission rates.

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.

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.

Association between women's authorship and women's editorship in infectious diseases journals: a cross-sectional study.

BACKGROUND: Gender inequity is still pervasive in academic medicine, including journal publishing. We aimed to ascertain the proportion of women among first and last authors and editors in infectious diseases journals and assess the association between women's editorship and women's authorship while controlling for a journal's impact factor. METHODS: In this cross-sectional study, we randomly selected 40 infectious diseases journals (ten from each 2020 impact factor quartile), 20 obstetrics and gynaecology journals (five from each 2020 impact factor quartile), and 20 cardiology journals (five from each 2020 impact factor quartile) that were indexed in Journal Citation Reports, had an impact factor, had retrievable first and last author names, and had the name of more than one editor listed. We retrieved the names of the first and last authors of all citable articles published by the journals in 2018 and 2019 that counted towards their 2020 impact factor and collected the names of all the journals' editors-in-chief, deputy editors, section editors, and associate editors for the years 2018 and 2019. We used genderize.io to predict the gender of each first author, last author, and editor. The outcomes of interest were the proportions of women first authors and women last authors. We assessed the association between women's editorship and women's authorship by fitting quasi-Poisson regression models comprising the variables: the proportion of women last authors or women first authors; the proportion of women editors; the presence of a woman editor-in-chief; and journal 2020 impact factor. FINDINGS: We found 11 027 citable infectious diseases articles, of which 167 (1·5%) had an indeterminable first author gender, 155 (1·4%) had an indeterminable last author gender, and seven (0·1%) had no authors indexed. 5350 (49·3%) of 10 853 first authors whose gender could be determined were predicted to be women and 5503 (50·7%) were predicted to be men. Women accounted for 3788 (34·9%) of 10 865 last authors whose gender could be determined and men accounted for 7077 (65·1%). Of 577 infectious diseases journal editors, 190 (32·9%) were predicted to be women and 387 (67·1%) were predicted to be men. Of the 40 infectious diseases journals, 13 (32·5%) had a woman as editor-in-chief. For infectious diseases journals, the proportion of women editors had a significant effect on women's first authorship (incidence rate ratio 1·32, 95% CI 1·06-1·63; p=0·012) and women's last authorship (1·92, 1·45-2·55; p<0·0001). The presence of a woman editor-in-chief, the proportion of women last or first authors, and the journal's impact factor exerted no effect in these analyses. INTERPRETATION: The proportion of women editors appears to influence the proportion of women last and first authors in the analysed infectious diseases journals. These findings might help to explain gender disparities observed in publishing in academic medicine and suggest a need for revised policies towards increasing women's representation among editors. FUNDING: The European Society of Clinical Microbiology and Infectious Diseases.

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.