NT-proBNP or Self-Reported Functional Capacity in Estimating Risk of Cardiovascular Events After Noncardiac Surgery

ImportanceNearly 16 million surgical procedures are conducted in North America yearly, and postoperative cardiovascular events are frequent. Guidelines suggest functional capacity or B-type natriuretic peptides (BNP) to guide perioperative management. Data comparing the performance of these approaches are scarce.ObjectiveTo compare the addition of either N-terminal pro-BNP (NT-proBNP) or self-reported functional capacity to clinical scores to estimate the risk of major adverse cardiac events (MACE).Design, Setting, and ParticipantsThis cohort study included patients undergoing inpatient, elective, noncardiac surgery at 25 tertiary care hospitals in Europe between June 2017 and April 2020. Analysis was conducted in January 2023. Eligible patients were either aged 45 years or older with a Revised Cardiac Risk Index (RCRI) of 2 or higher or a National Surgical Quality Improvement Program, Risk Calculator for Myocardial Infarction and Cardiac (NSQIP MICA) above 1%, or they were aged 65 years or older and underwent intermediate or high-risk procedures.ExposuresPreoperative NT-proBNP and the following self-reported measures of functional capacity were the exposures: (1) questionnaire-estimated metabolic equivalents (METs), (2) ability to climb 1 floor, and (3) level of regular physical activity.Main Outcome and MeasuresMACE was defined as a composite end point of in-hospital cardiovascular mortality, cardiac arrest, myocardial infarction, stroke, and congestive heart failure requiring transfer to a higher unit of care.ResultsA total of 3731 eligible patients undergoing noncardiac surgery were analyzed; 3597 patients had complete data (1258 women [35.0%]; 1463 (40.7%) aged 75 years or older; 86 [2.4%] experienced a MACE). Discrimination of NT-proBNP or functional capacity measures added to clinical scores did not significantly differ (Area under the receiver operating curve: RCRI, age, and 4MET, 0.704; 95% CI, 0.646-0.763; RCRI, age, and 4MET plus floor climbing, 0.702; 95% CI, 0.645-0.760; RCRI, age, and 4MET plus physical activity, 0.724; 95% CI, 0.672-0.775; RCRI, age, and 4MET plus NT-proBNP, 0.736; 95% CI, 0.682-0.790). Benefit analysis favored NT-proBNP at a threshold of 5% or below, ie, if true positives were valued 20 times or more compared with false positives. The findings were similar for NSQIP MICA as baseline clinical scores.Conclusions and relevanceIn this cohort study of nearly 3600 patients with elevated cardiovascular risk undergoing noncardiac surgery, there was no conclusive evidence of a difference between a NT-proBNP–based and a self-reported functional capacity–based estimate of MACE risk.Trial RegistrationClinicalTrials.gov Identifier: NCT0301693

Fiskalische Kosten einer steuerlichen Förderung von Forschung und Entwicklung in Deutschland - Eine empirische Analyse verschiedener Gestaltungsoptionen

Der Beitrag berechnet die Aufkommensausfälle verschiedener Gestaltungsmodelle für eine steuerliche Forschungsförderung in Deutschland auf Basis eines Mikrosimulationsmodells. Die fiskalischen Kosten betragen zwischen 464 Mio. € und 5.701 Mio. €. Eine Erstattungsoption der Steuergutschrift über die Gewerbe- und Körperschaftsteuerschuld hinaus ist unerlässlich, da sonst etwa ein Drittel der Unternehmen nicht oder nur teilweise in den Genuss der Förderung kommen würde und sich dadurch starke Verzerrungen zwischen ertragsstarken und ertragsschwachen Unternehmen ergeben. Eine Differenzierung der Fördersätze für KMU und große Unternehmen kann die Aufkommensausfälle wirksam begrenzen. Eine Kappungsgrenze in Höhe eines absoluten Betrages ist wegen der Verzerrungen innerhalb der Gruppe großer Unternehmen ungünstig. Als besonders pragmatisch erscheint eine Verrechnung der Steuergutschrift mit der abzuführenden Lohnsteuer

A randomized trial of effects of health risk appraisal combined with group sessions or home visits on preventive behaviors in older adults

Background. To explore effects of a health risk appraisal for older people (HRA-O) program with reinforcement, we conducted a randomized controlled trial in 21 general practices in Hamburg, Germany. Methods. Overall, 2,580 older patients of 14 general practitioners trained in reinforcing recommendations related to HRA-O-identified risk factors were randomized into intervention (n = 878) and control (n = 1,702) groups. Patients (n = 746) of seven additional matched general practitioners who did not receive this training served as a comparison group. Patients allocated to the intervention group, and their general practitioners, received computer-tailored written recommendations, and patients were offered the choice between interdisciplinary group sessions (geriatrician, physiotherapist, social worker, and nutritionist) and home visits (nurse). Results. Among the intervention group, 580 (66%) persons made use of personal reinforcement (group sessions: 503 [87%], home visits: 77 [13%]). At 1-year follow-up, persons in the intervention group had higher use of preventive services (eg, influenza vaccinations, adjusted odds ratio 1.7; 95% confidence interval 1.4-2.1) and more favorable health behavior (eg, high fruit/fiber intake, odds ratio 2.0; 95% confidence interval 1.6-2.6), as compared with controls. Comparisons between intervention and comparison group data revealed similar effects, suggesting that physician training alone had no effect. Subgroup analyses indicated favorable effects for HRA-O with personal reinforcement, but not for HRA-O without reinforcement. Conclusions. HRA-O combined with physician training and personal reinforcement had favorable effects on preventive care use and health behavio

Teaching Image Processing and Visualization Principles to Medicine Students

Although image processing becomes increasingly important in most applications such as medicine, image processing and visualization is usually not a part of the medical education and therefore not widely spread in clinical daily routine. Contrary to students from computer science, medical students are usually not familiar to computational models or algorithms and require a different view of the algorithms instead of knowing each computational detail. To solve this problem this paper presents the concept of a lecture that aims to impart image processing and visualization principals for students in medicine in order to pioneer a higher acceptance and propagation of image processing techniques in clinical daily routine

Uncertainty-aware Brain Lesion Visualization

A brain lesion is an area of tissue that has been damaged through injury or disease. Its analysis is an essential task for medical researchers to understand diseases and find proper treatments. In this context, visualization approaches became an important tool to locate, quantify, and analyze brain lesions. Unfortunately, image uncertainty highly effects the accuracy of the visualization output. These effects are not covered well in existing approaches, leading to miss-interpretation or a lack of trust in the analysis result. In this work, we present an uncertainty-aware visualization pipeline especially designed forbrain lesions. Our method is based on an uncertainty measure for image data that forms the input of an uncertainty-aware segmentation approach. Here, medical doctors can determine the lesion in the patient’s brain and the result can be visualize dby an uncertainty-aware geometry rendering. We applied our approach to two patient datasets to review the lesions. Our results indicate increased knowledge discovery in brain lesion analysis that provides a quantification of trust in the generated results

Teaching Image Processing and Visualization Principles to Medicine Students

Although image processing becomes increasingly important in most applications such as medicine, image processing and visualization is usually not a part of the medical education and therefore not widely spread in clinical daily routine. Contrary to students from computer science, medical students are usually not familiar to computational models or algorithms and require a different view of the algorithms instead of knowing each computational detail. To solve this problem this paper presents the concept of a lecture that aims to impart image processing and visualization principals for students in medicine in order to pioneer a higher acceptance and propagation of image processing techniques in clinical daily routine