[FG] Leuppi Jörg

Unspezifische untere Rückenschmerzen stellen ein komplexes Krankheitsbild dar, von welchen Patienten jeglicher Populationen weltweit betroffen sind. Etwa 5% aller Notfallkonsultationen sind mit Rückenschmerzen assoziiert.

Die Ätiologie sowie die optimale Behandlung dieser Erkrankung ist weiterhin unklar.

Die frühzeitige bildgebende Diagnostik wird aufgrund der hohen finanziellen Kosten und der Strahlenbelastung der Patienten in den ersten 6 Wochen nicht empfohlen, sofern keine Red Flags vorliegen. Ebenso sollte auf eine analgetische Therapie mit Opioiden bei akuter Symptomatik verzichtet werden. Hierbei sind insbesondere die unerwünschten Nebenwirkungen sowie gefährliche Überdosierungen von Bedeutung. Zusätzliche Konsequenzen können längere Arbeitsplatzabsenzen sowie Mehrfachkonsultationen sein. Im Spitalalltag bleibt es jedoch eine Herausforderung, die Diskrepanz zwischen Patientenanforderungen und den Empfehlungen der Guidelines zu meistern.

Wir planen eine retrospektive Single-Center-Beobachtungsstudie. Ziel der Studie ist die Analyse des Rückenschmerzenmanagements anhand von Fallserie-Analysen von Patientinnen und Patienten, welche in den Jahren zwischen 2018 und 2023 auf die Notfallstation mit Rückenschmerzen eingetreten sind. Aus den Erkenntnissen der Studie sollen weitere Massnahmen wie Patientenedukation und Mitarbeiterschulungen entstehen, um so die Versorgung und Sicherheit der Patienten zu verbessern. 

Enhancing Personalized Healthcare and Clinical Research through Interoperable Data Management: A Focus on Symptoms Management

BACKGROUND

In healthcare organizations and hospitals in particular, patient medical data are stored in a multitude of heterogeneous systems and formats. These systems are often not interconnected, and information is not transferred across interfaces. The lack of flexibility and the difficulties involved in current data systems not only impede the quality of health care providers’ work and ability to deliver personalized health care but also limit research opportunities with large amounts of data.

Existing research emphasizes the critical need for interoperability between patient information systems to enhance personalized healthcare and facilitate clinical research (1–3).

To help solve this problem at the Cantonal Hospital of Baselland (KSBL) we have developed a new concept for data selection and extraction based on an innovative approach to data management. We will use the DAWIMED software developed by ID-Berlin (main company) and ID-Suisse (Swiss subsidiary) to enable interoperability between patient information systems and make routine clinical data accessible for clinical research and quality management.

This project aims to use this innovative approach to improve personalized healthcare and clinical research, with a specific focus on symptom management.

The project includes two clinically driven research studies:

1.    Assessment of the Management of Patients Admitted with Dyspnea as Their Chief Complaint

2.    Machine Learning to Predict the Cause of Dyspnea Based on Parameters Available at Admission

RESEARCH STUDIES

1.    Assessment of the Management of Patients Admitted with Dyspnea as Their Chief Complaint

Dyspnea, or shortness of breath, is a prevalent symptom leading to numerous emergency department (ED) visits and hospital admissions. It can result from various conditions, including cardiac and pulmonary diseases, as well as other less common causes. Dyspnea's diverse causes necessitate a comprehensive and prompt assessment using medical history, physical exams, biomarkers, and radiological evaluations. Despite its prevalence, there is limited data on its frequency and causes in Switzerland, and patient data is often fragmented across unstructured paper documents and digital systems. Identifying relevant patient data from mixed formats (paper and digital) is complex and often requires time-consuming manual searches.

The study aims to:

·        Describe the management of patients presenting with dyspnea at the KSBL ED.

·        Assess adherence to the medStandards algorithm for dyspnea management.

Significance: Understanding and improving the management of dyspnea can enhance patient outcomes, reduce in-hospital mortality, and streamline ED processes.

2.    Machine Learning to Predict the Cause of Dyspnea Based on Parameters Available at Admission

Dyspnea has multifactorial causes, ranging from common conditions like respiratory infections and heart failure to rarer causes like anaphylaxis or metabolic disorders. The wide variability in patient demographics, comorbidities, and clinical presentations complicates the diagnostic process. Distinguishing between cardiac and pulmonary causes is particularly difficult due to overlapping symptoms. Accurate and rapid diagnosis is crucial for effective treatment but is often challenging due to the symptom's urgency and diverse etiologies.

The study aims to:

• Identify parameters collected during the initial ED assessment (e.g., symptoms, vital signs, medical history) that predict the underlying cause of dyspnea.
• Develop predictive scores or digital tools to assist physicians in making prompt and accurate diagnoses.

Significance: By leveraging machine learning, the study seeks to enhance diagnostic accuracy, reduce the need for extensive testing, and improve the timeliness and effectiveness of dyspnea management in the ED. This approach can lead to better patient outcomes and more efficient use of resources.

Ziel dieser Studie ist es, ein besseres Verständnis von Risikofaktoren, protektiven Faktoren, Komplikationen, therapeutischem Nutzen verschiedener Behandlungsstrategien, sowie dem generellen Verlauf von COVID-19 von am KSBL hospitalisierten Patienten zu erlangen. Zu diesem Zweck möchten wir eine umfassende Datenbank mit Daten von Patienten, die am Kantonsspital Baselland (KSBL) während der Pandemie aufgrund von COVID-19 hospitalisiert waren anlegen. Diese Datenbank soll als Grundlage für die explorative retrospektive Untersuchung der o.g. Fragestellungen dienen.

Diese Studie ist eine retrospektive single-center Kohortenstudie. Die Studienpopulation wird definiert durch alle aufgrund von COVID-19 am KSBL hospitalisierten Patienten zwischen Januar 2020 und August 2022. Es sollen keine genetischen Daten oder zusätzliches biologisches Material untersucht werden. Diese Daten sollen zunächst deskriptiv zusammengefasst und dann explorativ analysiert werden.

Background: The recent increasing complexity in internal medicine and the concurrent demand for improving quality while cutting down resources represent a major challenge for healthcare providers. Disease-specific clinical guidelines for diagnostic and therapeutic management provide support in this demanding situation. However, according to previous studies, guideline adherence is often poor in clinical practice. Clinical audits are a powerful instrument in the evaluation of guideline adherence and can ultimately improve quality of hospital care.

Objectives: With the QUA-DIT project we seek to identify disease-specific areas of in-hospital management where diagnostic and/or therapeutic guideline-adherence is poor. Furthermore, we plan to investigate the effect of guideline-adherence on patient outcome related quality indicators. By establishing a sustainable quality control program addressing these areas, we intend to lay the foundation for the overall improvement of hospital care.

Methods: The QUA-DIT project consists of seven clinically driven audits in typical acute diagnoses in the department of internal medicine at the Cantonal Hospital Baselland. Recent clinical routine data concerning diagnostic workup, treatment, and other measures of care will be collected and descriptively compared to the established national and international disease-specific guidelines. Ultimately, association of guideline-adherence with quality indicators mortality, length of hospital stay, and rehospitalization will be assessed by multivariable regression models. We expect to include data of 2410 patients into our analyses.

Relevance: Establishing a series of clinical audits with the same methodology and the aim to improve quality in different fields of internal medicine is an innovative approach. Based on the audit findings, we plan to implement several measures to achieve improvement in the evidence-based management of our patients. Being transparent about the results with the intention of publication is a way of creating awareness in the internal medicine community and may serve as a motivation for other hospitals to follow the example. The QUA-DIT project represents a significant step for improvement of the overall healthcare system, and ultimately for the health of the population.

Background

Public health faces the dual challenge of providing optimal care while managing costs, particularly with an aging population and increasing prevalence of chronic conditions. Public hospitals, which often serve older patients with lower socioeconomic status and higher comorbidities, are under pressure to improve quality while reducing costs. Key quality indicators, such as length of hospital stay (LOHS), rehospitalization, and mortality rates, are essential for monitoring and comparing hospital performance.

Aim and Relevance

This research aims to identify predictors of LOHS, rehospitalization, and mortality in patients with pulmonary embolism (PE), chronic obstructive pulmonary disease (COPD), and community-acquired pneumonia (CAP). By understanding these predictors, the project seeks to:

1. Address modifiable factors to develop strategies for improving patient outcomes.
2. Raise awareness among clinicians about non-modifiable risk profiles.
3. Inform the development of better prediction models.
4. Optimize hospital bed management strategies.
5. Provide evidence-based recommendations for policymakers and healthcare providers.

General Conclusions

• PE Cohort Study: The PESI score, diabetes, serum troponin, and NT-proBNP levels are significant predictors for LOHS, mortality, and rehospitalization. These findings highlight the importance of comprehensive risk assessment for PE patients.
• CAP Cohort Study: Factors such as sex, age, qSOFA score, and presence of cancer significantly influence LOHS, rehospitalization, and mortality. Rehabilitation post-discharge plays a crucial role in rehospitalization rates.
• AECOPD Cohort Study: Oxygen supplementation at admission, age, COPD GOLD III/IV, active cancer, and rehabilitation are significant predictors of LOHS and mortality. The study emphasizes the complexity of predicting rehospitalization in AECOPD patients and suggests incorporating disease course variables for better accuracy.

Overall, the findings underscore the significance of disease-specific and broader health system factors in predicting hospital outcomes. The research aims to enhance patient management, discharge planning, and hospital performance, ultimately contributing to improved public healthcare systems for an aging population with complex chronic conditions.

Cough is one of the most common causes for seeking medical advice in general practices. Post-infectious cough is defined as lasting 3 to 8 weeks after an upper respiratory tract infection (URTI). It can be very bothersome, disabling in daily activities with substantial impact on physical and psychosocial health, leading to impaired quality of life (QoL). Recommendations for the management of post-infectious cough in primary care are scarce and incoherent. To provide a wide overview of treatment options for patients with post-infectious cough, we conducted and recently published a systematic review and meta-analysis of randomised clinical trials (RCTs) assessing the patient-relevant benefits and potential harms of available treatments. Only six eligible RCTs assessing different treatment regimens (i.e. inhaled fluticasone propionate, inhaled budesonide, salbutamol plus ipratropium-bromide, montelukast, nociception-opioid-1-receptor agonist, codeine, and gelatine) were identified. None of these RCTs found clear patient-relevant benefits and most had an unclear or high risk of bias.Post-infectious cough is thought to be mediated by inflammatory processes that are also present in exacerbations of asthma or chronic obstructive pulmonary diseases (COPD). There is strong evidence that oral corticosteroids for 5 days provide patient-relevant benefit without relevant harm for these conditions. Our systematic search identified no RCTs evaluating oral corticosteroids for post-infectious cough. Thus, a high quality RCT assessing the benefits and harms of orally administered corticosteroids for post-infectious cough is needed.Aim: To assess whether a 5-day treatment with oral corticosteroids provides patient-relevant benefits by improving the cough-related QoL and reducing the duration and intensity of post-infectious cough in adult primary care patients.Methods: The proposed study is a multicentre, 1:1 randomised, parallel-group, placebo-controlled, superiority trial with blinded patients, general practitioners (GP), and outcome assessors in a primary health care setting. We plan to include 204 patients aged 18 or older seeing their GP for a cough lasting 3 to 8 weeks following an URTI. Any concurrent disease possibly explaining the symptoms, such as pneumonia, asthma, COPD, gastroesophageal reflux disease, allergic rhinitis, or cancer will be ruled out before trial inclusion. Patients will receive oral corticosteroid treatment (equivalent of 40 mg prednisone once daily) for 5 days or placebo for 5 days.The primary outcome is cough-related QoL assessed by the Leicester Cough Questionnaire (LCQ) score 14 days after randomisation. Secondary outcomes are i) cough-related QoL (at day 7, 28, and 3 months), ii) cough-related QoL sub-domains physical, psychological, and social, iii) overall cessation of cough, iv) re-consultations at GP and/or hospitalisations, v) total adverse events (AE), and vi) serious adverse events (SAE). All outcomes will be assessed through follow-up phone calls by study nurses 7, 14, 28 days, and 3 months after randomisation. Clinical follow-up visits with the GP are at the discretion of the patient and the GP.Assumptions for sample size estimation are informed by our systematic review. 204 patients are required to detect a statistically significant minimal clinically important difference of 1.3 of the LCQ score at 14 days (primary outcome; power 80%, two-sided significance level 5%, drop-out-rate 10%). Primary analysis will be done with ANCOVA (analysis of covariance) according to the intention to treat principle. Baseline LCQ scores, duration of cough, age, sex, and smoking status will be covariates.Importance: This will be the first RCT investigating whether oral corticosteroids are beneficial and safe in patients with post-infectious cough, which would have a substantial impact on the well-being and management of these patients in Switzerland and beyond.

Asthma is a chronic respiratory airway disease that affects more than 300 million people worldwide. Severe Asthma (SA) affects around 5-10% of all asthma patients yet accounts for half of the asthma related costs and carries a high burden of disease . Since the introduction of highly targeted treatments (monoclonal antibodies) in 2005, the treatment of severe asthma has been revolutionized. Nowadays, most of the patients with a severe asthma are treated with monoclonal antibodies. However, in Switzerland as in other countries, we have had only a little knowledge about the real-life situation of patients with SA, regarding long-term clinical management and therapeutic outcomes. Recognizing that knowledge gap, multiple countries, including Switzerland, have established national registries observing severe asthma and build up an international network of clinical registries.

The overall aim of this cohort is to gain comprehensive information about severe asthma in Switzerland, with the focus on the following:

• Describing SA Patient characteristics in Switzerland
• Treatment regimens over time
• Clinical course of SA patients over time including changes and influencing factors of asthma related outcomes