Overcoming denominator problems in refugee settings with fragmented electronic records for health and immigration data: a prediction-based approach.

BACKGROUND: Epidemiological studies in refugee settings are often challenged by the denominator problem, i.e. lack of population at risk data. We develop an empirical approach to address this problem by assessing relationships between occupancy data in refugee centres, number of refugee patients in walk-in clinics, and diseases of the digestive system. METHODS: Individual-level patient data from a primary care surveillance system (PriCarenet) was matched with occupancy data retrieved from immigration authorities. The three relationships were analysed using regression models, considering age, sex, and type of centre. Then predictions for the respective data category not available in each of the relationships were made. Twenty-one German on-site health care facilities in state-level registration and reception centres participated in the study, covering the time period from November 2017 to July 2021. RESULTS: 445 observations ("centre-months") for patient data from electronic health records (EHR, 230 mean walk-in clinics visiting refugee patients per month and centre; standard deviation sd: 202) of a total of 47.617 refugee patients were available, 215 for occupancy data (OCC, mean occupancy of 348 residents, sd: 287), 147 for both (matched), leaving 270 observations without occupancy (EHR-unmatched) and 40 without patient data (OCC-unmatched). The incidence of diseases of the digestive system, using patients as denominators in the different sub-data sets were 9.2% (sd: 5.9) in EHR, 8.8% (sd: 5.1) when matched, 9.6% (sd: 6.4) in EHR- and 12% (sd 2.9) in OCC-unmatched. Using the available or predicted occupancy as denominator yielded average incidence estimates (per centre and month) of 4.7% (sd: 3.2) in matched data, 4.8% (sd: 3.3) in EHR- and 7.4% (sd: 2.7) in OCC-unmatched. CONCLUSIONS: By modelling the ratio between patient and occupancy numbers in refugee centres depending on sex and age, as well as on the total number of patients or occupancy, the denominator problem in health monitoring systems could be mitigated. The approach helped to estimate the missing component of the denominator, and to compare disease frequency across time and refugee centres more accurately using an empirically grounded prediction of disease frequency based on demographic and centre typology. This avoided over-estimation of disease frequency as opposed to the use of patients as denominators.

Prevention of invasive ventilation (PRiVENT)-a prospective, mixed-methods interventional, multicentre study with a parallel comparison group: study protocol.

BACKGROUND: Invasive mechanical ventilation (IMV) is a standard therapy for intensive care patients with respiratory failure. With increasing population age and multimorbidity, the number of patients who cannot be weaned from IMV increases, resulting in impaired quality of life and high costs. In addition, human resources are tied up in the care of these patients. METHODS: The PRiVENT intervention is a prospective, mixed-methods interventional, multicentre study with a parallel comparison group selected from insurance claims data of the health insurer Allgemeine Ortskrankenkasse Baden-Württemberg (AOK-BW) conducted in Baden-Württemberg, Germany, over 24 months. Four weaning centres supervise 40 intensive care units (ICUs), that are responsible for patient recruitment. The primary outcome, successful weaning from IMV, will be evaluated using a mixed logistic regression model. Secondary outcomes will be evaluated using mixed regression models. DISCUSSION: The overall objective of the PRiVENT project is the evaluation of strategies to prevent long-term IMV. Additional objectives aim to improve weaning expertise in and cooperation with the adjacent Intensive Care Units. TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov (NCT05260853).

Using real-world data to predict health outcomes-The prediction design: Application and sample size planning.

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants' fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design-the prediction design-may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/) facilitates the application of the proposed methods, while a real-world application example illustrates them.

Dynamic contrast enhanced MRI of pulmonary adenocarcinomas for early risk stratification: higher contrast uptake associated with response and better prognosis.

BACKGROUND: To explore the prognostic value of serial dynamic contrast-enhanced (DCE) MRI in patients with advanced pulmonary adenocarcinoma undergoing first-line therapy with either tyrosine-kinase inhibitors (TKI) or platinum-based chemotherapy (PBC). METHODS: Patients underwent baseline (day 0, n = 98), and post-therapeutic DCE MRI (PBC: day + 1, n = 52); TKI: day + 7, n = 46) at 1.5T. Perfusion curves were acquired at 10, 40, and 70 s after contrast application and analysed semiquantitatively. Treatment response was evaluated at 6 weeks by CT (RECIST 1.1); progression-free survival (PFS) and overall survival  were analysed with respect to clinical and perfusion parameters. Relative uptake was defined as signal difference between contrast and non-contrast images, divided by the non-contrast signal. Predictors of survival were selected using Cox regression analysis. Median follow-up was 825 days. RESULTS: In pre-therapeutic and early post-therapeutic MRI, treatment responders (n = 27) showed significantly higher relative contrast uptake within the tumor at 70 s after application as compared to non-responders (n = 71, p ≤ 0.02), response defined as PR by RECIST 1.1 at 6 weeks. There was no significant change of perfusion at early MRI after treatment. In multivariate regression analysis of selected parameters, the strongest association with PFS were relative uptake at 40 s in the early post-treatment MRI and pre-treatment clinical data (presence of liver metastases, ECOG performance status). CONCLUSION: Higher contrast uptake within the tumor at pre-treatment and early post-treatment MRI was associated with treatment response and better prognosis. DCE MRI of pulmonary adenocarcinoma may provide important prognostic information.

Using independent cross-sectional survey data to predict post-migration health trajectories among refugees by estimating transition probabilities and their variances.

Health research is often concerned with the transition of health conditions and their relation with given exposures, therefore requiring longitudinal data. However, such data is not always available and resource-intensive to collect. Our aim is to use a pseudo-panel of independent cross-sectional data (e.g., data of T0$T_0$ and T1$T_1$ ) to extrapolate and approximate longitudinal health trajectories ( T0$T_0$ - T1$T_1$ ). Methods will be illustrated by examples of studying contextual effects on health among refugees by calculating transition probabilities with associated variances. The data consist of two cross-sectional health surveys among randomly selected refugee samples in reception ( T0$T_0$ ) and accommodation centers ( T1$T_1$ ) located in Germany's third-largest federal state. Self-reported measures of physical and mental health, health-related quality of life, health care access, and unmet medical needs of 560 refugees were collected. Missing data were imputed by multiple imputation. For each imputed data set, transition probabilities were calculated based on (i) probabilistic discrete event systems with Moore-Penrose generalized inverse matrix method (PDES-MP) and (ii) propensity score matching (PSM). By application of sampling approaches, exploiting the fact that status membership is multinomially distributed, results of both methods were pooled by Rubin's Rule, accounting for within and between-imputation variance. Most of the analyzed estimates of the transition probabilities and their variances are comparable between both methods. However, it seems that they handle sparse cells differently: either assigning an average value for the transition probability for all states with high certainty (i) or assigning a more extreme value for the transition probability with large variance estimate (ii).

Optimal designs for phase II/III drug development programs including methods for discounting of phase II results.

BACKGROUND: Go/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%. METHODS: We integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the "right amount of adjustment", the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies. RESULTS: In general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the "naïve" program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design. CONCLUSION: Individual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).

Long-Term Efficacy and Safety of Rituximab Versus Tacrolimus in Children With Steroid Dependent Nephrotic Syndrome.

Introduction: In the Rituximab for Relapse Prevention in Nephrotic Syndrome (RITURNS) trial, we demonstrated superior efficacy of single-course rituximab over maintenance tacrolimus in preventing relapses in children with steroid dependent nephrotic syndrome (SDNS) during a 1-year observation. Here we present the long-term outcomes of all 117 trial completers, who were followed up for another 2 years. Methods: Relapsing patients in the rituximab arm received a second course of rituximab, either with (n = 44) or without mycophenolate mofetil (MMF) cotreatment (n = 15). In the tacrolimus arm, second line rituximab monotherapy was initiated after relapses (n = 32) or electively (n = 24). Results: All 12-month relapse-free patients in the rituximab arm relapsed in the second postexposure year, resulting in similar median relapse-free survival times in the 2 trial arms (62 vs. 59 weeks). Second line rituximab in the tacrolimus arm was less effective than first-line therapy in patients switched to rituximab following a relapse (relapse-free survival 55 vs. 63 weeks, P < 0.01). B-cell counts 6 months post-rituximab predicted relapse risk both for first and second line therapy. MMF cotreatment yielded much improved 2-year relapse-free survival as compared to rituximab monotherapy (67% vs. 9%, P < 0.0001). Higher grade 2 adverse event rates were observed post-rituximab versus on tacrolimus (0.87 vs. 0.53 per year). Conclusion: The superior therapeutic effect of rituximab in SDNS vanishes during the second year post-exposure. Rituximab appears to yield longer remission when applied as first line as compared to second line therapy. Maintenance MMF following rituximab induces long-term disease remission.

From Research into Practice: Converting Epidemiological Data into Relevant Information for Planning of Regional Health Services for Refugees in Germany.

Health data of refugees and asylum seekers (ASR) is not routinely collected in Germany. Based on health data of ASR collected in 2018 in regional accommodation centres, we developed a dashboard to estimate regional burden of disease in Baden-Wuerttemberg, Germany. We aimed to find out how scientific data can support actors involved in healthcare planning for ASR in Germany and, within this scope, to explore how healthcare planning is conducted in this context. We conducted 12 qualitative semi-structured interviews including a usability test for a health data dashboard with regional decision-makers. Results showed that healthcare planning processes for ASR in Germany involve a complex set of actors in both long- and short-term decision-making. Data gained from representative surveys can support long-term decision-making and thus support the resilience of the health system, but it must balance the need for simple data presentation with transparent communication of potentially complex methods.

Health monitoring of refugees in reception centres for asylum seekers: Decentralized surveillance network for the analysis of routine medical data.

Refugees and asylum seekers living in reception centres tend to be not adequately included in population-based studies, routine medical data and official statistics. As part of the research project 'Health and primary-care sentinel surveillance in reception- and accommodation-centres for asylum-seekers in Germany' (PriCare), a health-monitoring approach was developed for the secondary use of routine medical data from on-site outpatient clinics in reception centres. To this end, a software application (Refugee Care Manager, RefCare©) for the digitisation and harmonisation of medical records was designed and implemented in reception centres in three German federal states. The approach of distributed computing in a surveillance network allows for the decentralised, harmonised analysis of the routine medical data stored in RefCare© in a manner that fully complies with data protection regulations and circumvents the need for centralised data storage. RefCare© provides an integrated surveillance feature that enables analyses of 64 indicators on population, morbidity, healthcare processes and quality of care to be undertaken across multiple facilities. This article describes the conceptual and practical approach and the technical procedures put in place to do so, and provides examples of the results that have been gained so far.

Enzymatic Debridement for Burn Wound Care: Interrater Reliability and Impact of Experience in Post-intervention Therapy Decision.

Enzymatic debridement (ED) has become a reliable tool for eschar removal. Although ED application is simple, wound bed evaluation and therapy decision post-intervention are prone to subjectivity and failure. Experience in ED might be the key, but this has not been proven yet. The aim of this study was to assess interrater reliability (IR) in post-intervention wound bed evaluation and therapy decision as well as the impact of experience. In addition, the authors introduce video assessment as a valuable tool for post-ED decision-making and education. A video-based survey was conducted among physicians with various experiences in ED. The survey involved multiple-choice and 5-point Likert scale questions about professional status, experience in ED, confidence in post-ED wound bed evaluation, and therapy decision. Subsequently, videos of 15 mixed pattern to full-thickness burns immediately after removal of the enzyme complex were demonstrated. Participants were asked for evaluation of each burn wound, including bleeding pattern and consequent therapy decision. IR ≥ 80% was considered as a consensus. Responses were stratified according to participants' experience in applying ED (<10, 10-19, 20-49, and ≥50 applications). IR was assessed by chi-square test (raw agreement [RA]; ≥80% was considered as a consensus) and by calculation of Krippendorff's alpha. In addition, expert consensus for therapy decision was compared with the actual clinical course of each shown patient. Last, participants were asked for their opinion on video as an assessment tool for post-ED wound bed evaluation, decision-making, and training. Thirty-one physicians from 11 burn centers participated in the survey. The overall consensus (raw agreement [RA] ≥ 80%) in post-ED wound bed evaluation and therapy decision was achieved in 20 and 40%, respectively. Krippendorff's alpha is given by 0.32 (95% confidence interval: 0.15, 0.49) and 0.31 (95% confidence interval: 0.16, 0.47), respectively. Subgroup analysis revealed that physicians with high experience in ED achieved significantly more consensus in post-intervention wound bed evaluation and therapy decision compared with physicians with moderate experience (60 vs 13.3%; P = .02 and 86.7 vs 33.3%; P = .04, respectively). Video analysis was considered a feasible (90.3%) and beneficial (93.5%) tool for post-intervention wound bed evaluation and therapy decision as well as useful for training purposes (100%). Reliability of wound bed evaluation and therapy decision after ED depends on the experience of the rating physician. Video analysis is deemed to be a valuable tool for ED evaluation, decision-making, and user training.

Validation of the T Descriptor (TNM-8) in T3N0 Non-Small-Cell Lung Cancer Patients; a Bicentric Cohort Analysis with Arguments for Redefinition.

The current pT3N0 category represents a heterogeneous subgroup involving tumor size, separate tumor nodes in one lobe, and locoregional growth pattern. We aim to validate outcomes according to the eighth edition of the TNM staging classification. A total of 281 patients who had undergone curative lung cancer surgery staged with TNM-7 in two German centers were retrospectively analyzed. The subtypes tumor size >7 cm and multiple nodules were grouped as T3a, and the subtypes parietal pleura invasion and mixed were grouped as T3b. We stratified survival by subtype and investigated the relative benefit of adjuvant chemotherapy according to subtype. The 5-year overall survival (OS) rates differed between the different subtypes tumor diameter >7 cm (71.5%), multiple nodules in one lobe (71.0%) (grouped as T3a), parietal pleura invasion (59.%), and mixed subtype (5-year OS 50.3%) (grouped as T3b), respectively. The cohort as a whole did not gain significant OS benefit from adjuvant chemotherapy. In contrast, adjuvant chemotherapy significantly improved OS in the T3b subgroup (logrank p = 0.03). This multicenter cohort analysis of pT3N0 patients identifies a new prognostic mixed subtype. Tumors >7 cm should not be moved to pT4. Patients with T3b tumors have significantly worse survival than patients with T3a tumors.

COVID-19 pneumonia and its lookalikes: How radiologists perform in differentiating atypical pneumonias.

PURPOSE: To examine the performance of radiologists in differentiating COVID-19 from non-COVID-19 atypical pneumonia and to perform an analysis of CT patterns in a study cohort including viral, fungal and atypical bacterial pathogens. METHODS: Patients with positive RT-PCR tests for COVID-19 pneumonia (n = 90) and non-COVID-19 atypical pneumonia (n = 294) were retrospectively included. Five radiologists, blinded to the pathogen test results, assessed the CT scans and classified them as COVID-19 or non-COVID-19 pneumonia. For both groups specific CT features were recorded and a multivariate logistic regression model was used to calculate their ability to predict COVID-19 pneumonia. RESULTS: The radiologists differentiated between COVID-19 and non-COVID-19 pneumonia with an overall accuracy, sensitivity, and specificity of 88% ± 4 (SD), 79% ± 6 (SD), and 90% ± 6 (SD), respectively. The percentage of correct ratings was lower in the early and late stage of COVID-19 pneumonia compared to the progressive and peak stage (68 and 71% vs 85 and 89%). The variables associated with the most increased risk of COVID-19 pneumonia were band like subpleural opacities (OR 5.55, p < 0.001), vascular enlargement (OR 2.63, p = 0.071), and subpleural curvilinear lines (OR 2.52, p = 0.021). Bronchial wall thickening and centrilobular nodules were associated with decreased risk of COVID-19 pneumonia with OR of 0.30 (p = 0.013) and 0.10 (p < 0.001), respectively. CONCLUSIONS: Radiologists can differentiate between COVID-19 and non-COVID-19 atypical pneumonias at chest CT with high overall accuracy, although a lower performance was observed in the early and late stage of COVID 19 pneumonia. Specific CT features might help to make the correct diagnosis.