The analysis of survey data is a frequently arising issue in clinical trials, particularly when capturing quantities which are difficult to measure. Typical examples are questionnaires about patient's well‐being, pain, or consent to an intervention. In these, data is captured on a discrete scale containing only a limited number of possible answers, from which the respondent has to pick the answer which fits best his/her personal opinion. This data is generally located on an ordinal scale as answers can usually be arranged in an ascending order, for example, “bad”, “neutral”, “good” for well‐being. Since responses are usually stored numerically for data processing purposes, analysis of survey data using ordinary linear regression models are commonly applied. However, assumptions of these models are often not met as linear regression requires a constant variability of the response variable and can yield predictions out of the range of response categories. By using linear models, one only gains insights about the mean response which may affect representativeness. In contrast, ordinal regression models can provide probability estimates for all response categories and yield information about the full response scale beyond the mean. In this work, we provide a concise overview of the fundamentals of latent variable based ordinal models, applications to a real data set, and outline the use of state‐of‐the‐art‐software for this purpose. Moreover, we discuss strengths, limitations and typical pitfalls. This is a companion work to a current vignette‐based structured interview study in pediatric anesthesia.

(1) Background: Automated blood culture headspace analysis for the detection of volatile organic compounds of microbial origin (mVOC) could be a non-invasive method for bedside rapid pathogen identification. We investigated whether analyzing the gaseous headspace of blood culture (BC) bottles through gas chromatography-ion mobility spectrometry (GC-IMS) enables differentiation of infected and non-infected; (2) Methods: BC were gained out of a rabbit model, with sepsis induced by intravenous administration of E. coli (EC group; n = 6) and control group (n = 6) receiving sterile LB medium intravenously. After 10 h, a pair of blood cultures was obtained and incubated for 36 h. The headspace from aerobic and anaerobic BC was sampled every two hours using an autosampler and analyzed using a GC-IMS device. MALDI-TOF MS was performed to confirm or exclude microbial growth in BCs; (3) Results: Signal intensities (SI) of 113 mVOC peak regions were statistically analyzed. In 24 regions, the SI trends differed between the groups and were considered to be useful for differentiation. The principal component analysis showed differentiation between EC and control group after 6 h, with 62.2% of the data variance described by the principal components 1 and 2. Single peak regions, for example peak region P_15, show significant SI differences after 6 h in the anaerobic environment (p < 0.001) and after 8 h in the aerobic environment (p < 0.001); (4) Conclusions: The results are promising and warrant further evaluation in studies with an extended microbial panel and indications concerning its transferability to human samples.

Objective: Previous symptom prevalence studies show a diverse spectrum of symptoms and a large diversity in symptom intensities in patients being just diagnosed as having incurable cancer. It is unclear, how physical symptoms and psychosocial burden should be recorded in order to determine the variable need for palliative care and further support. Therefore, we compared two different strategies for detecting physical symptoms and psychosocial burden of patients with newly diagnosed incurable cancer and their effects on the further course of the disease.Methods: SCREBEL is a controlled, randomized, non-blinded, longitudinal study of the research network of the Palliative Medicine Working Group (APM) of the German Cancer Society (DKG). We compared: a less complex repeated brief screening for symptoms and burden in patients using the NCCN Distress Thermometer and IPOS questionnaire versus a multidimensional comprehensive assessment using the FACT-G and their entity-specific questionnaires, the PHQ4 scales, SCNS-34-SF, IPOS and NCCN Distress Thermometer. The primary study endpoint was quality of life (QoL), measured using FACT-G, after six months. Secondary study endpoints were QoL by using evaluation of secondary scores (NCCN DT, IPOS, PHQ4, SCNS-SF-34G) at time 6 months, the number of hospital days, the utilization of palliative care, emergency services, and psychosocial care structures. To assess effects and differences, multiple linear regression models were fitted and survival analyses were conducted.Results: 504 patients were included in the study. 262 patients were lost to follow-up, including 155 fatalities. There were no significant differences between the low-threshold screening approach and a comprehensive assessment with respect to symptoms and other aspects of QoL. Using the IPOS, we were able to measure an improvement in the quality of life in the low-threshold screening arm by a decrease of 0.67 points (95%-CI: 0.34 to 0.99) every 30 days. (p<0.001). Data on the involvement of emergency facilities and on supportive services were insufficient for analysis.Conclusion: A comprehensive, multidimensional assessment did not significantly differ from brief screening in preserving several dimensions of quality of life. These findings may positively influence the implementation of structured low-threshold screening programs for supportive and palliative needs in DKG certified cancer centers.DRKS -No. DRKS00017774 https://drks.de/search/de/trial/DRKS00017774.

Background: Maintenance of normothermia is an important quality metric in pediatric anesthesia. While inadvertent hypothermia is effectively prevented by forced‐air warming, this therapeutic approach can lead to iatrogenic hyperthermia in young children.Aims: To estimate the influence of external warming by forced air on the development of intraoperative hyperthermia in anesthetized children aged 6 years or younger.Methods: We pooled data from two previous clinical studies. Primary outcome was the course of core temperature over time analyzed by a quadratic regression model. Secondary outcomes were the incidence of hyperthermia (body core temperature >38°C), the probability of hyperthermia over the duration of warming in relation to age and surface‐area‐to‐weight ratio, respectively, analyzed by multiple logistic regression models. The influence of baseline temperature on hyperthermia was estimated using a Cox proportional hazards model.Results: Two hundred children (55 female) with a median age of 2.1 [1^st–3^rd quartile 1–4.2] years were analyzed. Mean temperature increased by 0.43°C after 1 h, 0.64°C after 2 h, and reached a peak of 0.66°C at 147 min. Overall, 33 children were hyperthermic at at least one measurement point. The odds ratios of hyperthermia were 1.14 (95%‐CI: 1.07–1.22) or 1.13 (95%‐CI: 1.06–1.21) for every 10 min of warming therapy in a model with age or surface‐area‐to weight ratio (ceteris paribus), respectively. Odds ratio was 1.33 (95%‐CI: 1.07–1.71) for a decrease of 1 year in age and 1.63 (95%‐CI: 0.93–2.83) for an increase of 0.01 in the surface‐to‐weight‐area ratio (ceteris paribus). An increase of 0.1°C in baseline temperature increased the hazard of becoming hyperthermic by a factor of 1.33 (95%‐CI: 1.23–1.43).Conclusions: In children, external warming by forced‐air needs to be closely monitored and adjusted in a timely manner to avoid iatrogenic hyperthermia especially during long procedures, in young age, higher surface‐area‐to‐weight ratio, and higher baseline temperature.

BACKGROUND: There is a growing consensus that screening for subclinical preheart failure (HF) may reduce the burden of symptomatic HF by allowing for timely preventive interventions. However, validated screening algorithms are currently lacking. The aim of this study was to evaluate a fully mobile telemedically supervised cardiac magnetic resonance, as a simple standardized, and ubiquitously applicable screening algorithm for subclinical pre-HF in rural and underresourced regions. METHODS: HERZCHECK was a cross-sectional cohort study conducted at 12 sites across rural and underresourced regions of Germany. Asymptomatic participants (40–69 years) with ≥1 cardiovascular risk factor—obesity, smoking, arterial hypertension, diabetes, hypercholesterolemia, or chronic kidney disease—were enrolled and underwent telemedically supervised contrast-free short cardiac magnetic resonance in mobile screening units. Subclinical pre-HF was diagnosed using a predefined cutoff of global longitudinal strain ≥−15%. A matched and entropy-balanced control cohort constructed from claims data was used to determine the time difference between detection of subclinical pre-HF in HERZCHECK and the first symptom-based diagnosis of HF within the standard of care. RESULTS: Between June 2021 and April 2023, 4666 participants were enrolled in the study, of which 4509 participants were included in the final analysis. The prevalence of subclinical pre-HF in the studied at-risk population was 22.7% (95% CI, 21.5%–23.9%). Global longitudinal strain-based screening identified subclinical pre-HF 6.7 years before the average onset of symptomatic HF within the standard of care (n=8420). CONCLUSIONS: Subclinical pre-HF affects approximately one-fourth of the at-risk population in rural and underresourced regions. The HERZCHECK approach identifies patients suitable for targeted preventive interventions ≈7 years earlier than the standard of care. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05122793.