scholarly journals Near-Optimal Distributed Band-Joins through Recursive Partitioning

Author(s):  
Rundong Li ◽  
Wolfgang Gatterbauer ◽  
Mirek Riedewald
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Achiraya Teyateeti ◽  
Paul D Brown ◽  
Anita Mahajan ◽  
Nadia N Laack ◽  
Bruce E Pollock

Abstract Background To compare the outcomes between patients with leptomeningeal disease (LMD) and distant brain recurrence (DBR) after stereotactic radiosurgery (SRS) brain metastases (BM) resection cavity. Methods Twenty-nine patients having single-fraction SRS after BM resection who developed either LMD (n = 11) or DBR (n = 18) as their initial and only site of intracranial progression were retrospectively reviewed. Results Patients developing LMD more commonly had a metachronous presentation (91% vs 50%, P = .04) and recursive partitioning class 1 status (45% vs 6%, P = .02). There was no difference in the median time from SRS to the development of LMD or DBR (5.0 vs 3.8 months, P = .68). The majority of patients with LMD (10/11, 91%) developed the nodular variant (nLMD). Treatment for LMD was repeat SRS (n = 4), whole-brain radiation therapy (WBRT; n = 5), resection + WBRT (n = 1), and no treatment (n = 1). Treatment for DBR was repeat SRS (n = 9), WBRT (n = 3), resection + resection cavity SRS (n = 1), and no treatment (n = 5). Median overall survival (OS) from time of resection cavity SRS was 15.7 months in the LMD group and 12.7 months in the DBR group (P = .60), respectively. Median OS in salvage SRS and salvage WBRT were 25.4 and 5.0 months in the nLMD group (P = .004) while 18.7 and 16.2 months in the DBR group (P = .30), respectively. Conclusions Following BM resection cavity SRS, nLMD recurrence is much more frequent than classical LMD. Salvage SRS may be considered for selected patients with nLMD, reserving salvage WBRT for patients with extensive intracranial disease without compromising survival. Further study with larger numbers of patients is needed.


BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e037341
Author(s):  
Timothy E Dribin ◽  
Kenneth A Michelson ◽  
David Vyles ◽  
Mark I Neuman ◽  
David C Brousseau ◽  
...  

IntroductionThere remain significant knowledge gaps about the management and outcomes of children with anaphylaxis. These gaps have led to practice variation regarding decisions to hospitalise children and length of observation periods following treatment with epinephrine. The objectives of this multicentre study are to (1) determine the prevalence of and risk factors for severe, persistent, refractory and biphasic anaphylaxis, as well as persistent and biphasic non-anaphylactic reactions; (2) derive and validate prediction models for emergency department (ED) discharge; and (3) determine data-driven lengths of ED and inpatient observation prior to discharge to home based on initial reaction severity.Methods and analysisThe study is being conducted through the Pediatric Emergency Medicine Collaborative Research Committee (PEMCRC). Children 6 months to less than 18 years of age presenting to 30 participating EDs for anaphylaxis from October 2015 to December 2019 will be eligible. The primary outcomes for each objective are (1) severe, persistent, refractory or biphasic anaphylaxis, as well as persistent or biphasic non-anaphylactic reactions; (2) safe ED discharge, defined as no receipt of acute anaphylaxis medications or hypotension beyond 4 hours from first administered dose of epinephrine; and (3) time from first to last administered dose of epinephrine and vasopressor cessation. Analyses for each objective include (1) descriptive statistics to estimate prevalence and generalised estimating equations that will be used to investigate risk factors for anaphylaxis outcomes, (2) least absolute shrinkage and selection operator regression and binary recursive partitioning to derive and validate prediction models of children who may be candidates for safe ED discharge, and (3) Kaplan-Meier analyses to assess timing from first to last epinephrine doses and vasopressor cessation based on initial reaction severity.Ethics and disseminationAll sites will obtain institutional review board approval; results will be published in peer-reviewed journals and disseminated via traditional and social media, blogs and online education platforms.


2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S690-S691
Author(s):  
Joshua C Herigon ◽  
Amir Kimia ◽  
Marvin Harper

Abstract Background Antibiotics are the most commonly prescribed drugs for children and frequently inappropriately prescribed. Outpatient antimicrobial stewardship interventions aim to reduce inappropriate antibiotic use. Previous work has relied on diagnosis coding for case identification which may be inaccurate. In this study, we sought to develop automated methods for analyzing note text to identify cases of acute otitis media (AOM) based on clinical documentation. Methods We conducted a cross-sectional retrospective chart review and sampled encounters from 7/1/2018 – 6/30/2019 for patients < 5 years old presenting for a problem-focused visit. Complete note text and limited structured data were extracted for 12 randomly selected weekdays (one from each month during the study period). An additional weekday was randomly selected for validation. The primary outcome was correctly identifying encounters where AOM was present. Human review was considered the “gold standard” and was compared to ICD codes, a natural language processing (NLP) model, and a recursive partitioning (RP) model. Results A total of 2,724 encounters were included in the training cohort and 793 in the validation cohort. ICD codes and NLP had good performance overall with sensitivity 91.2% and 93.1% respectively in the training cohort. However, NLP had a significant drop-off in performance in the validation cohort (sensitivity: 83.4%). The RP model had the highest sensitivity (97.2% training cohort; 94.1% validation cohort) out of the 3 methods. Figure 1. Details of encounters included in the training and validation cohorts. Table 1. Performance of ICD coding, a natural language processing (NLP) model, and a recursive partitioning (RP) model for identifying cases of acute otitis media (AOM) Conclusion Natural language processing of outpatient pediatric visit documentation can be used successfully to create models accurately identifying cases of AOM based on clinical documentation. Combining NLP and structured data can improve automated case detection, leading to more accurate assessment of antibiotic prescribing practices. These techniques may be valuable in optimizing outpatient antimicrobial stewardship efforts. Disclosures All Authors: No reported disclosures


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