Abstract. This paper presents a study that compares the three space partitioning and spatial indexing techniques, KD Tree, Quad KD Tree, and PR Tree. KD Tree is a data structure proposed by Bentley (Bentley and Friedman, 1979) that aims to cluster objects according to their spatial location. Quad KD Tree is a data structure proposed by Berezcky (Bereczky et al., 2014) that aims to partition objects using heuristic methods. Unlike Bereczky’s partitioning technique, a new partitioning technique is presented based on dividing objects according to space-driven, in the context of this study. PR Tree is a data structure proposed by Arge (Arge et al., 2008) that is an asymptotically optimal R-Tree variant, enables data-driven segmentation. This study mainly aimed to search and render big spatial data in real-time safety-critical avionics navigation map application. Such a real-time system needs to efficiently reach the required records inside a specific boundary. Performing range query during the runtime (such as finding the closest neighbors) is extremely important in performance. The most crucial purpose of these data structures is to reduce the number of comparisons to solve the range searching problem. With this study, the algorithms’ data structures are created and indexed, and worst-case analyses are made to cover the whole area to measure the range search performance. Also, these techniques’ performance is benchmarked according to elapsed time and memory usage. As a result of these experimental studies, Quad KD Tree outperformed in range search analysis over the other techniques, especially when the data set is massive and consists of different geometry types.

BackgroundAnastomotic leakage is a major complication in colorectal surgery, resulting in significant morbidity and mortality rates. Despite substantial progress in surgical technique, anastomotic leakage rates remain stable. An early diagnosis of anastomotic leaks was proven to reduce adverse outcomes and improve survival.ObjectiveThis study aims to find a novel scoring system for detecting anastomotic leaks using inflammatory and nutritional indicators after colorectal surgery. Our purpose was to analyze the diagnostic accuracy of leak scores ((CRPPOD3)(CRPPOD1)∗preoperativealbuminlevel) in predicting postoperative complications.DesignThe study included colorectal cancer patients who underwent curative surgery at Koc University Hospital between 2014 and 2018. Patients were categorized into two groups depending on the presence of anastomotic leaks and compared in terms of preoperative albumin levels, CRP levels in postoperative days 1 and 3, anastomotic leakage rates, length of hospital stay, and CRP quotient, which was calculated by dividing POD 3 CRP level to POD 1 CRP level. The bedside leak score is calculated by dividing the CRP quotient by the preoperative albumin level. The predictive value of bedside leak score, CRP quotient, and preoperative albumin levels in estimating anastomotic leakage was analyzed, and a cutoff value for the leak score was calculated.ResultsA total of 183 patients were included in the study. The leak score, CRP POD 3–1 ratio, and preoperative albumin levels were found to successfully detect anastomotic leakage. The area under the curve for the leak score was calculated as 0.78. The optimal cutoff value was found to be 50.3 for the bedside leak score, which shows 90.9% sensitivity and 59.3% specificity.ConclusionThe leak score may represent a valuable diagnostic tool for detecting patients at risk for anastomotic leakage after colorectal surgery and planning a better strategy to reduce morbidity and mortality rates and associated costs. However, further multicenter studies with large cohorts are necessary to confirm these results.

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