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/ .

Rheuma-VOR study: optimising healthcare of rheumatic diseases by multiprofessional coordinating centres.

OBJECTIVES: Early diagnosis of inflammatory arthritis is critical to prevent joint damage and functional incapacities. However, the discrepancy between recommendations of early diagnosis and reality is remarkable. The Rheuma-VOR study aimed to improve the time to diagnosis of patients with early arthritis by coordinating cooperation between primary care physicians, specialists and patients in Germany. METHODS: This prospective non-randomised multicentre study involved 2340 primary care physicians, 72 rheumatologists, 4 university hospitals and 4 rheumatology centres in 4 German Federal States. The two coprimary endpoints (time to diagnosis and screening performance of primary care physicians) were evaluated for early versus late implementation phase. Additionally, time to diagnosis and secondary endpoints (decrease of disease activity, increase in quality of life and overall well-being, improvement of fatigue, depression, functional ability, and work ability, reduction in drug and medical costs and hospitalisation) were compared with a reference cohort of the German Rheumatism Research Centre (DRFZ) reflecting standard care. RESULTS: A total of 7049 patients were enrolled in the coordination centres and 1537 patients were diagnosed with a rheumatic disease and consented to further participation. A follow-up consultation after 1 year was realised in 592 patients. The time to diagnosis endpoint and the secondary endpoints were met. In addition, the calculation of cost-effectiveness shows that Rheuma-VOR has a dominant cost-benefit ratio compared with standard care. DISCUSSION: Rheuma-VOR has shown an improvement in rheumatological care, patient-reported outcome parameters and cost savings by coordinating the cooperation of primary care physicians, rheumatologists and patients, in a nationwide approach.

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.

[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.

An online program with individualized vs automated support for significant others of depressed individuals - study protocol of a randomized controlled trial.

BACKGROUND: Due to budget restrictions in mental health care, non-professional caregivers are increasingly burdened with the emotional and practical care for their depressed relatives. However, informal family caregiving is mostly a stressful role with negative consequences on the physical and mental health of the caretakers to the extent that they have an elevated risk of experiencing psychiatric disorders themselves. While psychoeducation for relatives of depressed individuals showed positive results both in terms of the caretakers' strain and the depressive symptoms of the affected person, there are major barriers to participate in presence in those programs. Digital programs might be a viable alternative. We found no empirically evaluated digital program available for informal caregivers of depressed patients. METHODS: An online program for relatives of depressed individuals has been developed including four interactive modules on 1) psychoeducation, 2) how to strengthen the relationship with the depressed person, 3) how to deal with the depressive symptoms of the patient, and 4) find the right balance between caring for the depressed person and self-care. We investigate if this self-help program is more effective when used with individualized versus automated e-mail support, and if both supported conditions are more effective than treatment-as-usual (TAU in form of written information material) in terms of the risk of mental diseases in caregivers. The primary outcome is the reduction of the caregiver's nonspecific mental distress as measured by the change of the Kessler Psychological Distress Scale score from baseline to four weeks after randomization. Caregivers (n = 500:500:250) will be randomized to one of the three conditions. DISCUSSION: Psychological support for caregivers of individuals with mental disorders such as depression should be offered as part of integrated services. There is a huge potential to develop and implement interactive online approaches to support informal caregivers of patients with depression to function in their multiple roles and to help them to remain healthy. TRIAL REGISTRATION: DRKS, DRKS00025241 . Registered 5 Mai 2021.

Coronary revascularization in acute coronary syndrome: does the choice of the conduit matter?

BACKGROUND: Our retrospective study evaluates the impact on short - and long-term outcome according to the graft selection during emergency coronary revascularization surgery. METHODS: Three hundred thirty-nine Patients with acute coronary syndrome undergoing emergency coronary bypass surgery at our institution from 2008 until 2018 were analyzed using propensity score analysis. The primary endpoint was in-hospital survival. Secondary endpoints were postoperative bleeding, contractile dysfunction, postoperative percutaneous coronary intervention, myocardial infarction and wound infection. Patients were divided in two groups according to the bypass grafts received (group 0 [N.=222]: combined arterial and venous grafts for the and group 1 [N.=117]: complete arterial revascularization). RESULTS: There is significant improvement of left ventricular function postoperatively in patients undergoing emergency coronary revascularization for acute myocardial infarction where arterial grafts were used. Furthermore, there is significant difference between postoperative cardiac enzymes with lower values in group 1 (creatine kinase myocardial band P=0.0001; Troponin T P=0.010). There was no significant difference in short and long-term survival between two groups. Five-years survival analysis between both groups did not show significant difference with Log-Rank test adjusted P value=0.49 (unadjusted P value=0.005). There was no significant difference in perioperative myocardial infarction, postoperative bleeding, and the need for postoperative coronary angiography. CONCLUSIONS: Emergency coronary artery bypass grafting with arterial grafts shows significant improvement of left ventricular function postoperatively. However, utilization of bilateral internal mammary artery grafts in emergency coronary revascularization has no significant impact on short term or 5-year survival.

A simulation-based pilot study of crisis checklists in the emergency department.

Checklists can improve adherence to standardized procedures and minimize human error. We aimed to test if implementation of a checklist was feasible and effective in enhancing patient care in an emergency department handling internal medicine cases. We developed four critical event checklists and confronted volunteer teams with a series of four simulated emergency scenarios. In two scenarios, the teams were provided access to the crisis checklists in a randomized cross-over design. Simulated patient outcome plus statement of the underlying diagnosis defined the primary endpoint and adherence to key processes such as time to commence CPR represented the secondary endpoints. A questionnaire was used to capture participants' perception of clinical relevance and manageability of the checklists. Six teams of four volunteers completed a total of 24 crisis sequences. The primary endpoint was reached in 8 out of 12 sequences with and in 2 out of 12 sequences without a checklist (Odds ratio, 10; CI 1.11, 123.43; p = 0.03607, Fisher's exact test). Adherence to critical steps was significantly higher in all scenarios for which a checklist was available (performance score of 56.3% without checklist, 81.9% with checklist, p = 0.00284, linear regression model). All participants rated the checklist as useful and 22 of 24 participants would use the checklist in real life. Checklist use had no influence on CPR quality. The use of context-specific checklists showed a statistically significant influence on team performance and simulated patient outcome and contributed to adherence to standard clinical practices in emergency situations.

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.

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.