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Hintergrund und Fragestellung
Die Entwöhnung von Beatmungsgeräten wird nicht immer auf der primär behandelnden Intensivstation abgeschlossen. Die Weiterverlegung in andere Behandlungseinrichtungen stellt einen sensiblen Abschnitt in der Behandlung und Rehabilitation des Weaningpatienten dar. Ziel der vorliegenden Studie war die Untersuchung des Überleitungsmanagements und des Interhospitaltransfers von Weaningpatienten unter besonderer Berücksichtigung der Dokumentationsqualität.
Methodik
Es erfolge eine retrospektive Datenanalyse eines Jahrs (2018) auf 2 Intensivstationen eines Universitätsklinikums. Eingeschlossen wurden alle beatmeten Patienten mit folgenden Tracerdiagnosen: COPD, Asthma, Polytrauma, Pneumonie, Sepsis, ARDS und Reanimation (Beatmung > 24 h).
Ergebnisse
Insgesamt konnten 750 Patienten in die Untersuchung eingeschlossen werden (Alter 64 [52, 8–76; Median, IQR]; 32 % weiblich). Davon waren 48 (6,4 %) Patienten zum Zeitpunkt der Verlegung nicht entwöhnt (v. a. Sepsis und ARDS). Die Routinedokumentation war bei den Abschnitten „Spontaneous Breathing Trial“, „Bewertung der Entwöhungsbereitschaft“ und „vermutete Entwöhnbarkeit“ ausreichend, um die Erfüllung der Parameter der S2k-Leitlinie „Prolongiertes Weaning“ adäquat zu beurteilen. Vorwiegend wurden diese Patienten mit Tracheostoma (76 %) in Rehabilitationskliniken (44 %) mittels spezialisierten Rettungsmitteln des arztbegleiteten Patiententransports verlegt (75 %).
Diskussion
Die Verlegung nicht entwöhnter Patienten nach initialem Intensivaufenthalt ist ein relevantes Thema für den Interhospitaltransfer. Die Routinedokumentation eines strukturierten Weaningprozesses ist in Kernelementen ausreichend, um den Weaningprozess lückenlos zu beschreiben. Dies ist für die Kontinuität in der Weiterbehandlung dieser Patienten von großer Bedeutung.
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
Background: Proportions of patients dying from the coronavirus disease-19 (COVID-19) vary between different countries. We report the characteristics; clinical course and outcome of patients requiring intensive care due to COVID-19 induced acute respiratory distress syndrome (ARDS).
Methods: This is a retrospective, observational multicentre study in five German secondary or tertiary care hospitals. All patients consecutively admitted to the intensive care unit (ICU) in any of the participating hospitals between March 12 and May 4, 2020 with a COVID-19 induced ARDS were included.
Results: A total of 106 ICU patients were treated for COVID-19 induced ARDS, whereas severe ARDS was present in the majority of cases. Survival of ICU treatment was 65.0%. Median duration of ICU treatment was 11 days; median duration of mechanical ventilation was 9 days. The majority of ICU treated patients (75.5%) did not receive any antiviral or anti-inflammatory therapies. Venovenous (vv) ECMO was utilized in 16.3%. ICU triage with population-level decision making was not necessary at any time. Univariate analysis associated older age, diabetes mellitus or a higher SOFA score on admission with non-survival during ICU stay.
Conclusions: A high level of care adhering to standard ARDS treatments lead to a good outcome in critically ill COVID-19 patients.
Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.