Hospitalised traumatic brain injury victims in France: An analysis of the French hospital discharge database for 2011–2016

2021 ◽  
Vol 64 (6) ◽  
pp. 101437
Author(s):  
Louis-Marie Paget ◽  
Francis Chin ◽  
Nathalie Beltzer
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Hospital Discharge ◽  
Hospital Discharge Database ◽  
French Hospital ◽  
Discharge Database

scholarly journals Challenges of a French Hospital Discharge algorithm to detect non-accidental paediatric burns

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
C Hermetet ◽  
E Laurent ◽  
Y El Allali ◽  
C Gaborit ◽  
A-I Lecuyer ◽  
...  
Keyword(s):  
Child Maltreatment ◽  
Hospital Discharge ◽  
Healthcare Professionals ◽  
Qualitative Studies ◽  
Hospital Discharge Database ◽  
Paediatric Burns ◽  
Icd 10 ◽  
Judicial Authority ◽  
French Hospital ◽  
Discharge Database

Abstract Background Child maltreatment includes physical, psychological, sexual abuse and acts of neglect. Among the resulting non-accidental injuries, burns are responsible for an important morbi-mortality. The main objective was to build a detection algorithm of non-accidental paediatric burns (NAB), using ICD-10 codes in the hospital resumes from the French Hospital Discharge Database (HDD). Methods Children aged 0 to 16 years old hospitalised at the University Hospital of Tours from 2012 to 2017 with a coded burn were included. “Probable” or “possible” HDD cases of NAB were defined based on specific ICD-10 codes during the inclusion stay or the previous year. A chart review was performed on all the HDD cases and HDD non cases matched on sex and age with a 1:2 ratio. Performance parameters were estimated for three clinical definitions of suspected child maltreatment: excluding neglect, including neglect with restriction then broad definition. For clinical cases, report to the judicial authority (RJA) or worrying information (WI) was searched. Results Among the 253 included children, 83 “probable” cases and 153 non cases were analysed. Sensitivity varied from 48% (95%CI [36-60]) to 90% [55-100] when excluding neglect, specificity from 70% [63;77] to 68% [61;74]. The positive and negative likelihood ratios varied respectively from 1,6 [1,2;2,3] to 2,8 [2,1;3,7] and from 0,7 [0,6;0,9] to 0,1 [0,0;0,9]. The proportion of clinical cases with no RJA/WI without reason varied from 0 (when excluding neglect) to > 85% (with broadest definition); all corresponded to a possible isolated neglect. Conclusions The performances of the algorithm varied tremendously according to the clinical definition level of child maltreatment. Neglect is obviously difficult to clinically detect. Training for healthcare professionals and qualitative studies on obstacles to RJA/WI should be added to this work. Key messages The performances of an algorithm to detect non-accidental pediatric burns (maltreatment) using the French hospital discharge database dropped when including neglect, difficult to diagnose clinically. Training for healthcare professionals and qualitative studies on obstacles to the judicial authority (RJA) or worrying information (WI) should be added to this diagnostic study.

scholarly journals Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach

2021 ◽  
Author(s):  
Jessica Pinaire ◽  
Etienne Chabert ◽  
Jérôme Azé ◽  
Sandra Bringay ◽  
Pascal Poncelet ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Hospital Discharge ◽  
Pattern Mining ◽  
Model Complexity ◽  
Sequential Patterns ◽  
Machine Model ◽  
Hospital Discharge Database ◽  
String Distance ◽  
French Hospital ◽  
Discharge Database

Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity. In this respect, we explored methods for medical events’ prediction based on the extraction of sets of relevant event sequences of a national hospital discharge database. It is illustrated to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). We mined sequential patterns from the French Hospital Discharge Database. We compared several predictive models using a text string distance to measure the similarity between patients’ patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. Indeed discrimination ranged from 0.71 to 0.99, together with a good overall accuracy. Thus, sequential patterns mining appear motivating for event prediction in medical settings as described here for ACS.

Management of immune thrombocytopenia in adults: a population-based analysis of the French hospital discharge database from 2009 to 2012

2015 ◽  
Vol 170 (2) ◽  
pp. 218-222 ◽  
Author(s):  
Marc Michel ◽  
Florence Suzan ◽  
Daniel Adoue ◽  
Dominique Bordessoule ◽  
Jean-Pierre Marolleau ◽  
...  
Keyword(s):  
Hospital Discharge ◽  
Immune Thrombocytopenia ◽  
Population Based ◽  
Hospital Discharge Database ◽  
French Hospital ◽  
Discharge Database

Abstract O.02: Validation of Kawasaki Disease Incidence Assessment as Derived from Health System Administrative Databases vs. Active Retrospective Surveillance in Ontario, Canada

Circulation ◽  
2015 ◽  
Vol 131 (suppl_2) ◽  
Author(s):  
Cedric Manlhiot ◽  
Sunita O’Shea ◽  
Bailey Bernknopf ◽  
Michael Labelle ◽  
Mathew Mathew ◽  
...  
Keyword(s):  
Kawasaki Disease ◽  
Hospital Discharge ◽  
Hospital Admissions ◽  
Disease Incidence ◽  
Administrative Databases ◽  
Canadian Hospital ◽  
Hospital Discharge Database ◽  
Incident Cases ◽  
Discharge Database

Introduction: Historically, 2 methods have been used to determine the incidence of Kawasaki disease (KD): active or passive surveillance, or the use of administrative databases. Given the increasing regulatory requirements, mainly around patient privacy, periodic retrospective surveillances have become increasingly challenging. Administrative databases are not curated datasets and doubts have been cast on their accuracy. Methods: The Hospital for Sick Children has been conducting retrospective triennial surveillances of KD since 1995 by contacting all hospitals in Ontario and manually reviewing all cases through chart review, reconciling inter-hospital transfers and multiple readmissions. We queried the Canadian hospital discharge database (Canadian Institute for Health Information) for hospitalizations associated with a diagnosis of KD between 2004-9. The administrative dataset was manually reviewed; patient national health number, institution and dates of admission/discharge were used to identify inter-hospital transfers, readmission and follow-up episodes. Results: The Canadian hospital discharge database reported 1,685 admissions during the study period (281±44 per year) for Ontario. Manual review of the dataset identified 219 (13%) as inter-hospital transfers (56, 26%), readmissions (122, 56%), admissions for follow-up of coronary artery aneurysms (14, 6%) or hospital admissions not related to KD (27, 12%). When these admissions were removed, the total number of incident cases for the study period was 1,466 (244±45 per year). The retrospective triennial surveillance identified 1,373 KD cases during the same period (229±33 per year). The Canadian hospital discharge database overestimated the number of cases in all 6 years by an average of 6.7±5.9%. The overestimation likely comes from patients who were originally diagnosed with KD but in whom the diagnosis of KD was subsequently excluded (historically ~5-6%). Conclusions: Reliance on administrative data to determine incidence of KD is possible and accurate; data should be manually reviewed to remove non-incident cases and estimates should be adjusted to reflect the expected proportion of patients in whom the diagnosis of KD will be subsequently excluded.

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