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OBJECTIVEVarious quality indicators are currently under investigation, aiming at measuring the quality of care in neurosurgery; however, the discipline currently lacks practical scoring systems for accurately assessing risk. The aim of this study was to develop three accurate, easy-to-use risk scoring systems for nosocomial infections, reoperations, and adverse events for patients with cerebral and spinal tumors.METHODSThe authors developed a semiautomatic registry with administrative and clinical data and included all patients with spinal or cerebral tumors treated between September 2017 and May 2019. Patients were further divided into development and validation cohorts. Multivariable logistic regression models were used to develop risk scores by assigning points based on β coefficients, and internal validation of the scores was performed.RESULTSIn total, 1000 patients were included. An unplanned 30-day reoperation was observed in 6.8% of patients. Nosocomial infections were documented in 7.4% of cases and any adverse event in 14.5%. The risk scores comprise variables such as emergency admission, nursing care level, ECOG performance status, and inflammatory markers on admission. Three scoring systems, NoInfECT for predicting the incidence of nosocomial infections (low risk, 1.8%; intermediate risk, 8.1%; and high risk, 26.0% [p < 0.001]), LEUCut for 30-day unplanned reoperations (low risk, 2.2%; intermediate risk, 6.8%; and high risk, 13.5% [p < 0.001]), and LINC for any adverse events (low risk, 7.6%; intermediate risk, 15.7%; and high risk, 49.5% [p < 0.001]), showed satisfactory discrimination between the different outcome groups in receiver operating characteristic curve analysis (AUC ≥ 0.7).CONCLUSIONSThe proposed risk scores allow efficient prediction of the likelihood of adverse events, to compare quality of care between different providers, and further provide guidance to surgeons on how to allocate preoperative care.

Introduction: Risk stratification based on standardized quality measures has become crucial in neurosurgery. Contemporary quality indicators have often been developed for a wide range of neurosurgical procedures collectively. Their accuracy for tumor inherent characteristics of patients diagnosed with meningioma remain questionable. The objective of this study was the analysis of currently applied quality indicators in meningioma surgery and the identification of potential new measures. Methods: Data of 133 patients that were operated due to intracranial meningiomas were subject to a retrospective analysis. The primary outcomes of interest were classical quality indicators such as the 30-days readmission-, 30-day reoperation-, 30-day mortality-, 30-day nosocomial infection- and the 30-day surgical site infection rate. Uni- and multivariate analyses were performed. The occurrence of a new postoperative neurological deficit was analyzed as a potential new quality indicator. Results: The overall unplanned readmission rate was 3.8%, 13 patients were reoperated within 30 days (9.8%). The 30-day nosocomial infection- and surgical site infection rates were 6.8% and 1.5% respectively. A postoperative new neurological deficit or neurological deterioration as a currently assessed quality feature was observed in 12 patients (9.2%). The edema volume on preoperative scans has proved to have a significant impact on the occurrence of a new postoperative neurological deficit (p= 0.023). Conclusions: Classical quality indicators in neurosurgery have proved to correlate with considerable deterioration of the patient´s health in meningioma surgery and thus should be taken into consideration for application in meningioma patients. The occurrence of a new postoperative neurological deficit is common and procedure specific. Thus, this should be elucidated for application as a complementary quality indicator in meningioma surgery.

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Abstract. Development processes in engineering disciplines are inherently complex. Throughout the development process, different kinds of inter-dependent design documents are created which have to be kept consistent with each other. Graph transformations are well suited for modeling the operations provided for maintaining inter-document consistency. In this paper, we describe a novel approach to rule execution for graph-based integration tools operating interactively and incrementally. Rather than executing a rule in atomic way, we break rule execution up into multiple phases. In this way, the user of an integration tool may be informed about all potential rule applications and their mutual conflicts so that he may take a judicious decision how to proceed.

Development processes in engineering disciplines are inherently complex. Throughout the development process, the system to be built is modeled from different perspectives, on different levels of abstraction, and with different intents. Since state-ofthe-art development processes are highly incremental and iterative, models of the system are not constructed in one shot; rather, they are extended and improved repeatedly. Furthermore, models are related by manifold dependencies and need to be maintained mutually consistent with respect to these dependencies. Thus, tools are urgently needed which assist developers in maintaining consistency between inter-dependent and evolving models. These tools have to operate incrementally, i.e., they have to propagate changes performed on one model into related models which are affected by these changes. In addition, they need to support user interactions in settings where the effects of changes cannot be determined automatically and deterministically. We present an algorithm for incremental and interactive consistency maintenance which meets these requirements. The algorithm is based on graphs, which are used as the data model for representing the models to be integrated, and graph transformation rules, which describe the modifications of the graphs to be performed on a high level of abstraction.

The pathologic diagnosis of neoplasia requires localization and classification of lesional tissue, a process that depends on the recognition of an abnormal spatial distribution of neoplastic elements relative to admixed normal background tissue. In endometrial intraepithelial neoplasia (EIN), a pre‐cancer usually managed by hysterectomy, a clonally mutated proliferation of cytologically altered glands (‘neoplastic‐EIN’) aggregates in clusters that also contain background non‐neoplastic glands (‘background‐NL’). Here, we used image analysis to classify individual glands within endometrial tissue fragments as neoplastic‐EIN or background‐NL, and we used the distribution of predictions to localize foci diagnostic of EIN. Nuclear coordinates were automatically assigned and were used as vertices to generate Delaunay triangulations for each gland. Graph statistical variables were used to develop random forest algorithms to classify glands as neoplastic‐EIN or background‐NL. Individual glands in an independent validation set were scored by a ‘ground truth’ biomarker (PAX2 immunohistochemistry). We found that exclusion of small glands led to improvement in classification accuracy. Using an inclusion threshold of 200 nuclei per gland, our final model classification accuracy was 77.5% in the validation set, with a positive predictive value of 0.81. We leveraged this high positive predictive value in a point cloud overlay display to assist end‐user identification of EIN foci. This study demonstrates that graph theory approaches applied to small‐scale anatomic elements in the endometrium allow biologic classification by machine learning, and that spatial superimposition over large‐scale tissue expanses can have practical diagnostic utility. We expect this augmented diagnostic approach to be generalizable to commonly encountered problems in other organ systems. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.