Modellierung von nosokomialen Infektionen unter Verwendung von individuellen Patientendaten
Zusammenfassung der Projektergebnisse
In this project, several methodological challenges in clinical epidemiology, in particular related to hospital-acquired infections, have been explored and investigated using mathematics, simulations and analysis of real and complex data sets. Especially the time-dependent complexity of clinical exposures (such as invasive devices, hospital-acquired infections or treatments) as well as clinical outcomes (such as survival or length of hospital stay) is challenging for statisticians and epidemiologists. With real data, we demonstrated that inappropriate statistics can easily lead to flawed conclusions regarding the risks and burden of hospital-acquired infections or the effect of treatments. Therefore, special emphasize was given to make more appropriate models understandable to a broader audience of professionals in health care, infection control, and hospital epidemiology. The project lead to several publications in highly-ranked journals and to deep collaborations with international researchers and the pharmaceutical industry aiming to find new drugs for antibioticresistant pathogens, one of the leading world-wide challenge in medicine.
Projektbezogene Publikationen (Auswahl)
- A full competing risk analysis of hospital-acquired infections can easily be performed by a case-cohort approach: J Clin Epidemiol, 2016; 74: 187- 193
Wolkewitz M, Palomar-Martinez M, Olaechea-Astigarraga P, Alvarez-Lerma F, Schumacher M
(Siehe online unter https://doi.org/10.1016/j.jclinepi.2015.11.011) - Multiple time scales in modeling the incidence of infections acquired in intensive care units. BMC Med Res Methodol, 2016; 16 (online): 116
Wolkewitz M, Cooper B, Palomar-Martinez M, Alvarez-Lerma F, Olaechea-Astigarraga P Barnett AG, Schumacher M
(Siehe online unter https://doi.org/10.1186/s12874-016-0199-y) - Neuraminidase Inhibitors and Hospital Mortality in British Patients with H1N1 Influenza A: A Re-Analysis of Observational Data. Plos One, 2016; 11 (online): e0160430
Wolkewitz M, Schumacher M
(Siehe online unter https://doi.org/10.1371/journal.pone.0160430) - Multistate Modeling to Analyze Nosocomial Infection Data: An Introduction and Demonstration. Infect Cont Hosp Ep, 2017; 38: 953-959
Wolkewitz M, von Cube M, Schumacher M
(Siehe online unter https://doi.org/10.1017/ice.2017.107) - Survival biases lead to flawed conclusions in observational treatment studies of influenza patients. J Clin Epidemiol, 2017; 84: 121-129
Wolkewitz M, Schumacher M
(Siehe online unter https://doi.org/10.1016/j.jclinepi.2017.01.008) - (2018). Methodological challenges in using pointprevalence versus cohort data in risk factor analyses of nosocomial infections. Ann Epidemiol
Wolkewitz, M, Mandel, M, Palomar-Martinez, M, Alvarez-Lerma, F, Olaechea-Astigarraga, P, Schumacher, M
(Siehe online unter https://doi.org/10.1016/j.annepidem.2018.03.017)
