Dataset Information

Overnight glucose control with an automated, unified safety system in children and adolescents with type 1 diabetes at diabetes camp.

ABSTRACT: To determine the safety and efficacy of an automated unified safety system (USS) in providing overnight closed-loop (OCL) control in children and adolescents with type 1 diabetes attending diabetes summer camps.The Diabetes Assistant (DIAS) USS used the Dexcom G4 Platinum glucose sensor (Dexcom) and t:slim insulin pump (Tandem Diabetes Care). An initial inpatient study was completed for 12 participants to evaluate safety. For the main camp study, 20 participants with type 1 diabetes were randomized to either OCL or sensor-augmented therapy (control conditions) per night over the course of a 5- to 6-day diabetes camp.Subjects completed 54 OCL nights and 52 control nights. On an intention-to-treat basis, with glucose data analyzed regardless of system status, the median percent time in range, from 70-150 mg/dL, was 62% (29, 87) for OCL nights versus 55% (25, 80) for sensor-augmented pump therapy (P = 0.233). A per-protocol analysis allowed for assessment of algorithm performance. The median percent time in range, from 70-150 mg/dL, was 73% (50, 89) for OCL nights (n = 41) versus 52% (24, 83) for control conditions (n = 39) (P = 0.037). There was less time spent in the hypoglycemic range <50, <60, and <70 mg/dL during OCL compared with the control period (P = 0.019, P = 0.009, and P = 0.023, respectively).The DIAS USS algorithm is effective in improving time spent in range as well as reducing nocturnal hypoglycemia during the overnight period in children and adolescents with type 1 diabetes in a diabetes camp setting.

SUBMITTER: Ly TT

PROVIDER: S-EPMC4179507 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Overnight glucose control with an automated, unified safety system in children and adolescents with type 1 diabetes at diabetes camp.

Ly Trang T TT Breton Marc D MD Keith-Hynes Patrick P De Salvo Daniel D Clinton Paula P Benassi Kari K Mize Benton B Chernavvsky Daniel D Place Jéróme J Wilson Darrell M DM Kovatchev Boris P BP Buckingham Bruce A BA

Diabetes care 20140530 8

<h4>Objective</h4>To determine the safety and efficacy of an automated unified safety system (USS) in providing overnight closed-loop (OCL) control in children and adolescents with type 1 diabetes attending diabetes summer camps.<h4>Research design and methods</h4>The Diabetes Assistant (DIAS) USS used the Dexcom G4 Platinum glucose sensor (Dexcom) and t:slim insulin pump (Tandem Diabetes Care). An initial inpatient study was completed for 12 participants to evaluate safety. For the main camp st ...[more]

PMID: 24879841

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Project description:ObjectiveThe study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity.Materials and methodsBinary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected from a state-wide reporting system. Generalizability was evaluated on different and independent hospital-level reporting system. Concepts were extracted from report narratives using the UMLS Metathesaurus, and their relevance and frequency were used as semantic features. Performance was evaluated by F-score, Hamming loss, and exact match score and was compared with SVM ensembles using bag-of-words (BOW) features on 3 testing datasets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent =6000/5950).ResultsSVMs using semantic features met or outperformed those based on BOW features to identify 10 different incident types (F-score [semantics/BOW]: benchmark = 82.6%/69.4%; original = 77.9%/68.8%; independent = 78.0%/67.4%) and extreme-risk events (F-score [semantics/BOW]: benchmark = 87.3%/87.3%; original = 25.5%/19.8%; independent = 49.6%/52.7%). For incident type, the exact match score for semantic classifiers was consistently higher than BOW across all test datasets (exact match [semantics/BOW]: benchmark = 48.9%/39.9%; original = 57.9%/44.4%; independent = 59.5%/34.9%).DiscussionBOW representations are not ideal for the automated identification of incident reports because they do not account for text semantics. UMLS semantic representations are likely to better capture information in report narratives, and thus may explain their superior performance.ConclusionsUMLS-based semantic classifiers were effective in identifying incidents by type and extreme-risk events, providing better generalizability than classifiers using BOW.

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Dataset Information

Overnight glucose control with an automated, unified safety system in children and adolescents with type 1 diabetes at diabetes camp.

Publications

Overnight glucose control with an automated, unified safety system in children and adolescents with type 1 diabetes at diabetes camp.

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