Institut für Klinische Epidemiologie und Biometrie
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- Rudolf-Virchow-Zentrum (1)
Sonstige beteiligte Institutionen
- Clinical Trial Center (CTC) / Zentrale für Klinische Studien Würzburg (ZKSW) (5)
- Klinische Studienzentrale (Universitätsklinikum) (2)
- Comprehensive Cancer Center Mainfranken, University Hospital Würzburg, Würzburg, Germany (1)
- Deutsches Zentrum für Herzinsuffizienz (1)
- Interdisziplinäre Zentrum für Klinische Forschung (IZKF) (1)
- Medizinische Klinik und Poliklinik 1, Abteilung Kardiologie (1)
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- Servicezentrum Medizin-Informatik (1)
- Servicezentrum Medizin-Informatik (Universitätsklinikum) (1)
- Universitätsklinikum Würzburg (UKW) (1)
Summary
Blood oxygen saturation is an important clinical parameter, especially in postoperative hospitalized patients, monitored in clinical practice by arterial blood gas (ABG) and/or pulse oximetry that both are not suitable for a long-term continuous monitoring of patients during the entire hospital stay, or beyond. Technological advances developed recently for consumer-grade fitness trackers could—at least in theory—help to fill in this gap, but benchmarks on the applicability and accuracy of these technologies in hospitalized patients are currently lacking. We therefore conducted at the postanaesthesia care unit under controlled settings a prospective clinical trial with 201 patients, comparing in total >1,000 oxygen blood saturation measurements by fitness trackers of three brands with the ABG gold standard and with pulse oximetry. Our results suggest that, despite of an overall still tolerable measuring accuracy, comparatively high dropout rates severely limit the possibilities of employing fitness trackers, particularly during the immediate postoperative period of hospitalized patients.
Highlights
•The accuracy of O2 measurements by fitness trackers is tolerable (RMSE ≲4%)
•Correlation with arterial blood gas measurements is fair to moderate (PCC = [0.46; 0.64])
•Dropout rates of fitness trackers during O2 monitoring are high (∼1/3 values missing)
•Fitness trackers cannot be recommended for O2 measuring during critical monitoring
Objectives
To evaluate whether a multimodal intervention in general practice reduces the proportion of second line antibiotic prescriptions and the overall proportion of antibiotic prescriptions for uncomplicated urinary tract infections in women.
Design
Parallel, cluster randomised, controlled trial.
Setting
General practices in five regions in Germany. Data were collected between 1 April 2021 and 31 March 2022.
Participants
General practitioners from 128 randomly assigned practices.
Interventions
Multimodal intervention consisting of guideline recommendations for general practitioners and patients, provision of regional data for antibiotic resistance, and quarterly feedback, which included individual first line and second line proportions of antibiotic prescribing, benchmarking with regional or supra-regional practices, and telephone counselling. Participants in the control group received no information on the intervention.
Main outcome measures
Primary outcome was the proportion of second line antibiotics prescribed by general practices, in relation to all antibiotics prescribed, for uncomplicated urinary tract infections after one year between the intervention and control group. General practices were randomly assigned in blocks (1:1), with a block size of four, into the intervention or control group using SAS version 9.4; randomisation was stratified by region. The secondary outcome was the prescription proportion of all antibiotics, relative within all cases (instances of UTI diagnosis), for the treatment of urinary tract infections after one year between the groups. Adverse events were assessed as exploratory outcomes.
Results
110 practices with full datasets identified 10 323 cases during five quarters (ie, 15 months). The mean proportion of second line antibiotics prescribed was 0.19 (standard deviation 0.20) in the intervention group and 0.35 (0.25) in the control group after 12 months. After adjustment for preintervention proportions, the mean difference was −0.13 (95% confidence interval −0.21 to −0.06, P<0.001). The overall proportion of all antibiotic prescriptions for urinary tract infections over 12 months was 0.74 (standard deviation 0.22) in the intervention and 0.80 (0.15) in the control group with a mean difference of −0.08 (95% confidence interval −0.15 to −0.02, P<0.029). No differences were noted in the number of complications (ie, pyelonephritis, admission to hospital, or fever) between the groups.
Conclusions
The multimodal intervention in general practice significantly reduced the proportion of second line antibiotics and all antibiotic prescriptions for uncomplicated urinary tract infections in women.
Trial registration
German Clinical Trials Register (DRKS), DRKS00020389
Psychosocial factors affect mental health and health-related quality of life (HRQL) in a complex manner, yet gender differences in these interactions remain poorly understood. We investigated whether psychosocial factors such as social support and personal and work-related concerns impact mental health and HRQL differentially in women and men during the first year of the COVID-19 pandemic. Between June and October 2020, the first part of a COVID-19-specific program was conducted within the “Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression (STAAB)” cohort study, a representative age- and gender-stratified sample of the general population of Würzburg, Germany. Using psychometric networks, we first established the complex relations between personal social support, personal and work-related concerns, and their interactions with anxiety, depression, and HRQL. Second, we tested for gender differences by comparing expected influence, edge weight differences, and stability of the networks. The network comparison revealed a significant difference in the overall network structure. The male (N = 1370) but not the female network (N = 1520) showed a positive link between work-related concern and anxiety. In both networks, anxiety was the most central variable. These findings provide further evidence that the complex interplay of psychosocial factors with mental health and HRQL decisively depends on gender. Our results are relevant for the development of gender-specific interventions to increase resilience in times of pandemic crisis.
Long-term sequelae in hospitalized Coronavirus Disease 2019 (COVID-19) patients may result in limited quality of life. The current study aimed to determine health-related quality of life (HRQoL) after COVID-19 hospitalization in non-intensive care unit (ICU) and ICU patients. This is a single-center study at the University Hospital of Wuerzburg, Germany. Patients eligible were hospitalized with COVID-19 between March 2020 and December 2020. Patients were interviewed 3 and 12 months after hospital discharge. Questionnaires included the European Quality of Life 5 Dimensions 5 Level (EQ-5D-5L), patient health questionnaire-9 (PHQ-9), the generalized anxiety disorder 7 scale (GAD-7), FACIT fatigue scale, perceived stress scale (PSS-10) and posttraumatic symptom scale 10 (PTSS-10). 85 patients were included in the study. The EQ5D-5L-Index significantly differed between non-ICU (0.78 ± 0.33 and 0.84 ± 0.23) and ICU (0.71 ± 0.27; 0.74 ± 0.2) patients after 3- and 12-months. Of non-ICU 87% and 80% of ICU survivors lived at home without support after 12 months. One-third of ICU and half of the non-ICU patients returned to work. A higher percentage of ICU patients was limited in their activities of daily living compared to non-ICU patients. Depression and fatigue were present in one fifth of the ICU patients. Stress levels remained high with only 24% of non-ICU and 3% of ICU patients (p = 0.0186) having low perceived stress. Posttraumatic symptoms were present in 5% of non-ICU and 10% of ICU patients. HRQoL is limited in COVID-19 ICU patients 3- and 12-months post COVID-19 hospitalization, with significantly less improvement at 12-months compared to non-ICU patients. Mental disorders were common highlighting the complexity of post-COVID-19 symptoms as well as the necessity to educate patients and primary care providers about monitoring mental well-being post COVID-19.
Stress experiences of healthcare assistants in family practice at the onset of the COVID-19 pandemic
(2023)
Background: At the beginning of the pandemic in 2020, healthcare assistants in general practices were confronted with numerous new challenges. The aim of the study was to investigate the stress factors of healthcare assistants in March/April 2020 as well as in the further course of the pandemic in 2020.
Methods: From August to December 2020, 6,300 randomly selected healthcare assistants in four German states were invited to participate in the study. We performed a mixed methods design using semi-structured telephone interviews and a cross-sectional survey with quantitative and open questions. The feeling of psychological burden was assessed on a 6-point likert-scale. We defined stress factors and categorized them in patient, non-patient and organizational stress factors. The results of the three data sets were compared within a triangulation protocol.
Results: One thousand two hundred seventy-four surveys were analyzed and 28 interviews with 34 healthcare assistants were conducted. Of the participants, 29.5% reported experiences of a very high or high feeling of psychological burden in March/April 2020. Worries about the patients’ health and an uncertainty around the new disease were among the patient-related stress factors. Non-patient-related stress factors were problems with the compatibility of work and family, and the fear of infecting relatives with COVID-19. Organizational efforts and dissatisfaction with governmental pandemic management were reported as organizational stress factors. Support from the employer and team cohesion were considered as important resources.
Discussion: It is necessary to reduce stress among healthcare assistants by improving their working conditions and to strengthen their resilience to ensure primary healthcare delivery in future health crises.
During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.
Introduction.
Mobile health (mHealth) integrates mobile devices into healthcare, enabling remote monitoring, data collection, and personalized interventions. Machine Learning (ML), a subfield of Artificial Intelligence (AI), can use mHealth data to confirm or extend domain knowledge by finding associations within the data, i.e., with the goal of improving healthcare decisions. In this work, two data collection techniques were used for mHealth data fed into ML systems: Mobile Crowdsensing (MCS), which is a collaborative data gathering approach, and Ecological Momentary Assessments (EMA), which capture real-time individual experiences within the individual’s common environments using questionnaires and sensors. We collected EMA and MCS data on tinnitus and COVID-19. About 15 % of the world’s population suffers from tinnitus.
Materials & Methods.
This thesis investigates the challenges of ML systems when using MCS and EMA data. It asks: How can ML confirm or broad domain knowledge? Domain knowledge refers to expertise and understanding in a specific field, gained through experience and education. Are ML systems always superior to simple heuristics and if yes, how can one reach explainable AI (XAI) in the presence of mHealth data? An XAI method enables a human to understand why a model makes certain predictions. Finally, which guidelines can be beneficial for the use of ML within the mHealth domain? In tinnitus research, ML discerns gender, temperature, and season-related variations among patients. In the realm of COVID-19, we collaboratively designed a COVID-19 check app for public education, incorporating EMA data to offer informative feedback on COVID-19-related matters. This thesis uses seven EMA datasets with more than 250,000 assessments. Our analyses revealed a set of challenges: App user over-representation, time gaps, identity ambiguity, and operating system specific rounding errors, among others. Our systematic review of 450 medical studies assessed prior utilization of XAI methods.
Results.
ML models predict gender and tinnitus perception, validating gender-linked tinnitus disparities. Using season and temperature to predict tinnitus shows the association of these variables with tinnitus. Multiple assessments of one app user can constitute a group. Neglecting these groups in data sets leads to model overfitting. In select instances, heuristics outperform ML models, highlighting the need for domain expert consultation to unveil hidden groups or find simple heuristics.
Conclusion.
This thesis suggests guidelines for mHealth related data analyses and improves estimates for ML performance. Close communication with medical domain experts to identify latent user subsets and incremental benefits of ML is essential.