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      Simulation der Letalität nach verschiedenen Ex-ante- und Ex-post-Triage-Verfahren bei Menschen mit Behinderungen und Vorerkrankungen Translated title: Simulation of mortality after different ex-ante and ex-post-triage methods in people with disabilities and comorbidities

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          Abstract

          Der stetige Anstieg an zu behandelnden Patienten während der COVID-19-Pandemie hat das Gesundheitssystem vor eine Vielzahl an Herausforderungen gestellt. Die Intensivstation ist einer der in diesem Zusammenhang besonders stark betroffenen Bereiche. Nur durch umfangreiche Infektionsschutzmaßnahmen sowie einen enormen logistischen Aufwand konnten in Deutschland selbst in Hochphasen der Pandemie die Behandlung aller Intensivpatienten ermöglicht und eine Triage auch in Regionen mit hohem Patientendruck bei gleichzeitig geringen Kapazitäten verhindert werden. Im Hinblick auf die Pandemievorsorge hat der Deutsche Bundestag ein Gesetz zur Triage verabschiedet, das eine Ex-post-Triage explizit untersagt. Bei einer Ex-post-Triage werden auch Patienten, die bereits auf der Intensivstation behandelt werden, in die Triage-Entscheidung einbezogen und Behandlungskapazitäten nach individueller Erfolgsaussicht verteilt. In der Literatur finden sich rechtliche, ethische und soziale Überlegungen zur Triage bei Pandemien, eine quantitative Bewertung im Hinblick auf verschiedene Patientengruppen auf der Intensivstation gibt es hingegen nicht. Der Fokus der Arbeit liegt auf dieser Forschungslücke, und es wird eine quantitative simulationsbasierte Evaluation von Ex-ante- und Ex-post-Triage-Politiken unter Berücksichtigung von Überlebenswahrscheinlichkeiten, Beeinträchtigungen und Vorerkrankungen durchgeführt. Die Ergebnisse zeigen, dass eine Anwendung von Ex-post-Triage, basierend auf Überlebenswahrscheinlichkeiten in allen Patientengruppen, zu einer Reduktion der Mortalität auf der Intensivstation führt. In dem Szenario, das der realen Situation wohl am nächsten kommt, wird eine Reduktion der Mortalität auf der Intensivstation um ca. 15 % schon bei einer einmaligen Anwendung der Ex-post-Triage erreicht. Dieser mortalitätsreduzierende Effekt ist umso größer, je mehr Patienten auf eine intensivmedizinische Behandlung warten.

          Zusatzmaterial online

          Die Online-Version dieses Beitrags (10.1007/s00101-023-01302-3) enthält weitere Tabellen.

          Translated abstract

          The significant increase in patients during the COVID-19 pandemic presented the healthcare system with a variety of challenges. The intensive care unit is one of the areas particularly affected in this context. Only through extensive infection control measures as well as an enormous logistical effort was it possible to treat all patients requiring intensive care in Germany even during peak phases of the pandemic, and to prevent triage even in regions with high patient pressure and simultaneously low capacities. Regarding pandemic preparedness, the German Parliament passed a law on triage that explicitly prohibits ex post (tertiary) triage. In ex post triage, patients who are already being treated are included in the triage decision and treatment capacities are allocated according to the individual likelihood of success. Legal, ethical, and social considerations for triage in pandemics can be found in the literature, but there is no quantitative assessment with respect to different patient groups in the intensive care unit. This study addressed this gap and applied a simulation-based evaluation of ex ante (primary) and ex post triage policies in consideration of survival probabilities, impairments, and pre-existing conditions. The results show that application of ex post triage based on survival probabilities leads to a reduction in mortality in the intensive care unit for all patient groups. In the scenario close to a real-world situation, considering different impaired and prediseased patient groups, a reduction in mortality of approximately 15% was already achieved by applying ex post triage on the first day. This mortality-reducing effect of ex post triage is further enhanced as the number of patients requiring intensive care increases.

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          Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study

          Summary Background Although diabetes has been associated with COVID-19-related mortality, the absolute and relative risks for type 1 and type 2 diabetes are unknown. We assessed the independent effects of diabetes status, by type, on in-hospital death in England in patients with COVID-19 during the period from March 1 to May 11, 2020. Methods We did a whole-population study assessing risks of in-hospital death with COVID-19 between March 1 and May 11, 2020. We included all individuals registered with a general practice in England who were alive on Feb 16, 2020. We used multivariable logistic regression to examine the effect of diabetes status, by type, on in-hospital death with COVID-19, adjusting for demographic factors and cardiovascular comorbidities. Because of the absence of data on total numbers of people infected with COVID-19 during the observation period, we calculated mortality rates for the population as a whole, rather than the population who were infected. Findings Of the 61 414 470 individuals who were alive and registered with a general practice on Feb 16, 2020, 263 830 (0·4%) had a recorded diagnosis of type 1 diabetes, 2 864 670 (4·7%) had a diagnosis of type 2 diabetes, 41 750 (0·1%) had other types of diabetes, and 58 244 220 (94·8%) had no diabetes. 23 698 in-hospital COVID-19-related deaths occurred during the study period. A third occurred in people with diabetes: 7434 (31·4%) in people with type 2 diabetes, 364 (1·5%) in those with type 1 diabetes, and 69 (0·3%) in people with other types of diabetes. Unadjusted mortality rates per 100 000 people over the 72-day period were 27 (95% CI 27–28) for those without diabetes, 138 (124–153) for those with type 1 diabetes, and 260 (254–265) for those with type 2 diabetes. Adjusted for age, sex, deprivation, ethnicity, and geographical region, compared with people without diabetes, the odds ratios (ORs) for in-hospital COVID-19-related death were 3·51 (95% CI 3·16–3·90) in people with type 1 diabetes and 2·03 (1·97–2·09) in people with type 2 diabetes. These effects were attenuated to ORs of 2·86 (2·58–3·18) for type 1 diabetes and 1·80 (1·75–1·86) for type 2 diabetes when also adjusted for previous hospital admissions with coronary heart disease, cerebrovascular disease, or heart failure. Interpretation The results of this nationwide analysis in England show that type 1 and type 2 diabetes were both independently associated with a significant increased odds of in-hospital death with COVID-19. Funding None.
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            Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study

            Summary Background Nationwide, unbiased, and unselected data of hospitalised patients with COVID-19 are scarce. Our aim was to provide a detailed account of case characteristics, resource use, and outcomes of hospitalised patients with COVID-19 in Germany, where the health-care system has not been overwhelmed by the pandemic. Methods In this observational study, adult patients with a confirmed COVID-19 diagnosis, who were admitted to hospital in Germany between Feb 26 and April 19, 2020, and for whom a complete hospital course was available (ie, the patient was discharged or died in hospital) were included in the study cohort. Claims data from the German Local Health Care Funds were analysed. The data set included detailed information on patient characteristics, duration of hospital stay, type and duration of ventilation, and survival status. Patients with adjacent completed hospital stays were grouped into one case. Patients were grouped according to whether or not they had received any form of mechanical ventilation. To account for comorbidities, we used the Charlson comorbidity index. Findings Of 10 021 hospitalised patients being treated in 920 different hospitals, 1727 (17%) received mechanical ventilation (of whom 422 [24%] were aged 18–59 years, 382 [22%] were aged 60–69 years, 535 [31%] were aged 70–79 years, and 388 [23%] were aged ≥80 years). The median age was 72 years (IQR 57–82). Men and women were equally represented in the non-ventilated group, whereas twice as many men than women were in the ventilated group. The likelihood of being ventilated was 12% for women (580 of 4822) and 22% for men (1147 of 5199). The most common comorbidities were hypertension (5575 [56%] of 10 021), diabetes (2791 [28%]), cardiac arrhythmia (2699 [27%]), renal failure (2287 [23%]), heart failure (1963 [20%]), and chronic pulmonary disease (1358 [14%]). Dialysis was required in 599 (6%) of all patients and in 469 (27%) of 1727 ventilated patients. The Charlson comorbidity index was 0 for 3237 (39%) of 8294 patients without ventilation, but only 374 (22%) of 1727 ventilated patients. The mean duration of ventilation was 13·5 days (SD 12·1). In-hospital mortality was 22% overall (2229 of 10 021), with wide variation between patients without ventilation (1323 [16%] of 8294) and with ventilation (906 [53%] of 1727; 65 [45%] of 145 for non-invasive ventilation only, 70 [50%] of 141 for non-invasive ventilation failure, and 696 [53%] of 1318 for invasive mechanical ventilation). In-hospital mortality in ventilated patients requiring dialysis was 73% (342 of 469). In-hospital mortality for patients with ventilation by age ranged from 28% (117 of 422) in patients aged 18–59 years to 72% (280 of 388) in patients aged 80 years or older. Interpretation In the German health-care system, in which hospital capacities have not been overwhelmed by the COVID-19 pandemic, mortality has been high for patients receiving mechanical ventilation, particularly for patients aged 80 years or older and those requiring dialysis, and has been considerably lower for patients younger than 60 years. Funding None.
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              Association of hypertension and antihypertensive treatment with COVID-19 mortality: a retrospective observational study

              Abstract Aims It remains unknown whether the treatment of hypertension influences the mortality of patients diagnosed with coronavirus disease 2019 (COVID-19). Methods and results This is a retrospective observational study of all patients admitted with COVID-19 to Huo Shen Shan Hospital. The hospital was dedicated solely to the treatment of COVID-19 in Wuhan, China. Hypertension and the treatments were stratified according to the medical history or medications administrated prior to the infection. Among 2877 hospitalized patients, 29.5% (850/2877) had a history of hypertension. After adjustment for confounders, patients with hypertension had a two-fold increase in the relative risk of mortality as compared with patients without hypertension [4.0% vs. 1.1%, adjusted hazard ratio (HR) 2.12, 95% confidence interval (CI) 1.17–3.82, P = 0.013]. Patients with a history of hypertension but without antihypertensive treatment (n = 140) were associated with a significantly higher risk of mortality compared with those with antihypertensive treatments (n = 730) (7.9% vs. 3.2%, adjusted HR 2.17, 95% CI 1.03–4.57, P = 0.041). The mortality rates were similar between the renin–angiotensin–aldosterone system (RAAS) inhibitor (4/183) and non-RAAS inhibitor (19/527) cohorts (2.2% vs. 3.6%, adjusted HR 0.85, 95% CI 0.28–2.58, P = 0.774). However, in a study-level meta-analysis of four studies, the result showed that patients with RAAS inhibitor use tend to have a lower risk of mortality (relative risk 0.65, 95% CI 0.45–0.94, P = 0.20). Conclusion While hypertension and the discontinuation of antihypertensive treatment are suspected to be related to increased risk of mortality, in this retrospective observational analysis, we did not detect any harm of RAAS inhibitors in patients infected with COVID-19. However, the results should be considered as exploratory and interpreted cautiously.
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                Author and article information

                Contributors
                axel.heller@uni-a.de
                Journal
                Anaesthesiologie
                Anaesthesiologie
                Die Anaesthesiologie
                Springer Medizin (Heidelberg )
                2731-6858
                2731-6866
                26 June 2023
                26 June 2023
                2023
                : 72
                : 8
                : 555-564
                Affiliations
                [1 ]GRID grid.7307.3, ISNI 0000 0001 2108 9006, Lehrstuhl für Health Care Operations/Health Information Management, Wirtschaftswissenschaftliche und Medizinische Fakultät, , Universität Augsburg, ; Universitätsstr. 16, 86159 Augsburg, Deutschland
                [2 ]GRID grid.5170.3, ISNI 0000 0001 2181 8870, Professor of Decision Science in Healthcare, Department of Technology, Management, and Economics, , Technical University of Denmark, ; Lyngby, Dänemark
                [3 ]GRID grid.7307.3, ISNI 0000 0001 2108 9006, Klinik für Anästhesiologie und Operative Intensivmedizin, Medizinische Fakultät, Universitätsklinikum Augsburg, , Universität Augsburg, ; Stenglinstr. 2, 86156 Augsburg, Deutschland
                [4 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Institut für Ethik, Geschichte und Theorie der Medizin, , Ludwig-Maximilians-Universität München, ; Lessingstr. 2, 80336 München, Deutschland
                [5 ]Professur für Angewandte Datenwissenschaften im Gesundheitswesen, Nürnberg School of Health, Technische Hochschule Nürnberg Georg Simon Ohm, Klinikum Nürnberg, Nürnberg, Deutschland
                Article
                1302
                10.1007/s00101-023-01302-3
                10400691
                37358616
                72db4d54-0b8f-4c78-8768-06e0af245aff
                © The Author(s) 2023

                Open Access Dieser Artikel wird unter der Creative Commons Namensnennung 4.0 International Lizenz veröffentlicht, welche die Nutzung, Vervielfältigung, Bearbeitung, Verbreitung und Wiedergabe in jeglichem Medium und Format erlaubt, sofern Sie den/die ursprünglichen Autor(en) und die Quelle ordnungsgemäß nennen, einen Link zur Creative Commons Lizenz beifügen und angeben, ob Änderungen vorgenommen wurden.

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                History
                : 15 February 2023
                : 24 April 2023
                : 10 May 2023
                Funding
                Funded by: Universität Augsburg (3144)
                Categories
                Originalien
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                © Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2023

                ethik,lebenswertgleichheit,alter,überlebenswahrscheinlichkeit,scores,ethics,life value equality,age,probability of survival

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