Benjamin Hotter1, Sarah Hoffmann1, Lena Ulm1,2, Christian Meisel3, Alejandro Bustamante4, Joan Montaner5, Mira Katan6, Craig J Smith7, Andreas Meisel1. 1. Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health; Center for Stroke Research Berlin, NeuroCure Clinical Research Center and Department of Neurology, Charité University Hospital Berlin, Germany (B.H., S.H., L.U., A.M.). 2. Friedrich Loeffler Institute of Medical Microbiology, University Medicine Greifswald, Germany (L.U.). 3. Department of Medical Immunology, Charité University Medicine & Labor Berlin-Charité Vivantes, Germany (C.M.). 4. Neurovascular Research Laboratory, Vall d'Hebron Institut de Recerca, Spain (A.B.). 5. Stroke Research Program, Institute of Biomedicine of Seville, IBiS/Hospital Universitario Virgen del Rocio/CSIC/University of Seville & Department of Neurology, Hospital Universitario Virgen Macarenca, Spain (J.M.). 6. Department of Neurology, UniversitätsSpital Zürich, Switzerland (M.K.). 7. Division of Cardiovascular Sciences, University of Manchester, Lydia Becker Institute of Immunology and Inflammation, Manchester Centre for Clinical Neurosciences, Salford, United Kingdom (C.J.S.).
Abstract
BACKGROUND AND PURPOSE: Several clinical scoring systems as well as biomarkers have been proposed to predict stroke-associated pneumonia (SAP). We aimed to externally and competitively validate SAP scores and hypothesized that 5 selected biomarkers would improve performance of these scores. METHODS: We pooled the clinical data of 2 acute stroke studies with identical data assessment: STRAWINSKI and PREDICT. Biomarkers (ultrasensitive procalcitonin; mid-regional proadrenomedullin; mid-regional proatrionatriuretic peptide; ultrasensitive copeptin; C-terminal proendothelin) were measured from hospital admission serum samples. A literature search was performed to identify SAP prediction scores. We then calculated multivariate regression models with the individual scores and the biomarkers. Areas under receiver operating characteristic curves were used to compare discrimination of these scores and models. RESULTS: The combined cohort consisted of 683 cases, of which 573 had available backup samples to perform the biomarker analysis. Literature search identified 9 SAP prediction scores. Our data set enabled us to calculate 5 of these scores. The scores had area under receiver operating characteristic curve of 0.543 to 0.651 for physician determined SAP, 0.574 to 0.685 for probable and 0.689 to 0.811 for definite SAP according to Pneumonia in Stroke Consensus group criteria. Multivariate models of the scores with biomarkers improved virtually all predictions, but mostly in the range of an area under receiver operating characteristic curve delta of 0.05. CONCLUSIONS: All SAP prediction scores identified patients who would develop SAP with fair to strong capabilities, with better discrimination when stricter criteria for SAP diagnosis were applied. The selected biomarkers provided only limited added predictive value, currently not warranting addition of these markers to prediction models. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01264549 and NCT01079728.
BACKGROUND AND PURPOSE: Several clinical scoring systems as well as biomarkers have been proposed to predict stroke-associated pneumonia (SAP). We aimed to externally and competitively validate SAP scores and hypothesized that 5 selected biomarkers would improve performance of these scores. METHODS: We pooled the clinical data of 2 acute stroke studies with identical data assessment: STRAWINSKI and PREDICT. Biomarkers (ultrasensitive procalcitonin; mid-regional proadrenomedullin; mid-regional proatrionatriuretic peptide; ultrasensitive copeptin; C-terminal proendothelin) were measured from hospital admission serum samples. A literature search was performed to identify SAP prediction scores. We then calculated multivariate regression models with the individual scores and the biomarkers. Areas under receiver operating characteristic curves were used to compare discrimination of these scores and models. RESULTS: The combined cohort consisted of 683 cases, of which 573 had available backup samples to perform the biomarker analysis. Literature search identified 9 SAP prediction scores. Our data set enabled us to calculate 5 of these scores. The scores had area under receiver operating characteristic curve of 0.543 to 0.651 for physician determined SAP, 0.574 to 0.685 for probable and 0.689 to 0.811 for definite SAP according to Pneumonia in Stroke Consensus group criteria. Multivariate models of the scores with biomarkers improved virtually all predictions, but mostly in the range of an area under receiver operating characteristic curve delta of 0.05. CONCLUSIONS: All SAP prediction scores identified patients who would develop SAP with fair to strong capabilities, with better discrimination when stricter criteria for SAP diagnosis were applied. The selected biomarkers provided only limited added predictive value, currently not warranting addition of these markers to prediction models. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01264549 and NCT01079728.
Authors: Johann Otto Pelz; Katharina Kubitz; Manja Kamprad-Lachmann; Kristian Harms; Martin Federbusch; Carsten Hobohm; Dominik Michalski Journal: Front Neurol Date: 2021-11-08 Impact factor: 4.003