Early mortality in German patients with lung cancer: risk factors associated with 30-and 60-day mortality.

Despite therapeutic advances, early mortality in lung cancer is still prevalent. In this study, we aimed to assess risk factors for 30- and 60-day mortality in German lung cancer patients. In this retrospective cross-sectional analysis, we used data of lung cancer patients treated at LMU Hospital Munich between 2015 and 2019. We categorized patients into 30-day mortality, 60 day-mortality, and longer survival. We used Student's t-test and ANOVA to compare means and Chi2-test to compare frequencies. We used logistic regression analysis to identify factors associated with a risk for early mortality. Of the 2454 lung cancer patients, 2.0% (n = 50) died within 30 and 1.7% (n = 41) within 30 to 60 days of diagnosis. Older age and advanced stage at diagnosis were significantly associated with early mortality in the univariate and the multivariate analysis. Patients in the 30-day mortality group significantly more often did not receive tumor-directed therapy. They were also more likely to die in an acute care setting compared to the 60-day mortality group. The group of patients who died unexpectedly (12.0%) was dominantly female, with a high proportion of patients with unintentional weight loss at the time of diagnosis. Our results suggest that in the treatment of patients with lung cancer there is a need for a greater focus on older patients. Moreover, physicians should pay special attention to females with recent weight loss and patients with a comorbidity of diabetes mellitus or renal impairment. Engaging a case manager focused on detecting patients with the above characteristics could help improve overall care.

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform.

BACKGROUND: Many comprehensive cancer centers incorporate tumor documentation software supplying structured information from the associated centers' oncology patients for internal and external audit purposes. However, much of the documentation data included in these systems often remain unused and unknown by most of the clinicians at the sites. OBJECTIVE: To improve access to such data for analytical purposes, a prerollout of an analysis layer based on the business intelligence software QlikView was implemented. This software allows for the real-time analysis and inspection of oncology-related data. The system is meant to increase access to the data while simultaneously providing tools for user-friendly real-time analytics. METHODS: The system combines in-memory capabilities (based on QlikView software) with innovative techniques that compress the complexity of the data, consequently improving its readability as well as its accessibility for designated end users. Aside from the technical and conceptual components, the software's implementation necessitated a complex system of permission and governance. RESULTS: A continuously running system including daily updates with a user-friendly Web interface and real-time usage was established. This paper introduces its main components and major design ideas. A commented video summarizing and presenting the work can be found within the Multimedia Appendix. CONCLUSIONS: The system has been well-received by a focus group of physicians within an initial prerollout. Aside from improving data transparency, the system's main benefits are its quality and process control capabilities, knowledge discovery, and hypothesis generation. Limitations such as run time, governance, or misinterpretation of data are considered.

Dynamic Survival Analysis Using In-Memory Technology.

In the field of oncology, survival analysis is one of the most important tools in terms of measuring therapy success, evaluating risk or prognostic factors. Within this work, a variety of common survival analyses were embedded into a real-time analysis platform based on a comprehensive oncological dataset. The analysis platform utilizes an in-memory database, therefore allowing spontaneous adjustments of the survival curves to selections and stratifications. In contrast to classical statistics, where individual scripts have to be formulated for every possible request, the running system allows the deduction of instantaneous results.

Inhibitory gradient along the dorsoventral axis in the medial entorhinal cortex.

Local inhibitory microcircuits in the medial entorhinal cortex (MEC) and their role in network activity are little investigated. Using a combination of electrophysiological, optical, and morphological circuit analysis tools, we find that layer II stellate cells are embedded in a dense local inhibitory microcircuit. Specifically, we report a gradient of inhibitory inputs along the dorsoventral axis of the MEC, with the majority of this local inhibition arising from parvalbumin positive (PV+) interneurons. Finally, the gradient of PV+ fibers is accompanied by a gradient in the power of extracellular network oscillations in the gamma range, measured both in vitro and in vivo. The reported differences in the inhibitory microcircuitry in layer II of the MEC may therefore have a profound functional impact on the computational working principles at different locations of the entorhinal network and influence the input pathways to the hippocampus.