Definition of a Practical Taxonomy for Referencing Data Quality Problems in Health Care Databases.

INTRODUCTION: Health care information systems can generate and/or record huge volumes of data, some of which may be reused for research, clinical trials, or teaching. However, these databases can be affected by data quality problems; hence, an important step in the data reuse process consists in detecting and rectifying these issues. With a view to facilitating the assessment of data quality, we developed a taxonomy of data quality problems in operational databases. MATERIAL: We searched the literature for publications that mentioned "data quality problems," "data quality taxonomy," "data quality assessment," or "dirty data." The publications were then reviewed, compared, summarized, and structured using a bottom-up approach, to provide an operational taxonomy of data quality problems. The latter were illustrated with fictional examples (though based on reality) from clinical databases. RESULTS: Twelve publications were selected, and 286 instances of data quality problems were identified and were classified according to six distinct levels of granularity. We used the classification defined by Oliveira et al to structure our taxonomy. The extracted items were grouped into 53 data quality problems. DISCUSSION: This taxonomy facilitated the systematic assessment of data quality in databases by presenting the data's quality according to their granularity. The definition of this taxonomy is the first step in the data cleaning process. The subsequent steps include the definition of associated quality assessment methods and data cleaning methods. CONCLUSION: Our new taxonomy enabled the classification and illustration of 53 data quality problems found in hospital databases.

Mid-term pulmonary sequelae after hospitalisation for COVID-19: The French SISCOVID cohort.

BACKGROUND: Even though COVID-19 clinical features, pathogenesis, complications, and therapeutic options have been largely described in the literature, long-term consequences in patients remain poorly known. METHODS: The French, multicentre, non-interventional SISCOVID study evaluated lung impairment three (M3) and six months (M6) after hospital discharge in patients recovered from COVID-19. Evaluation was based on clinical examination, pulmonary function tests, and chest computed tomography (CT-scan). RESULTS: Of the 320 included patients (mean age: 61 years; men: 64.1%), 205 had had a severe form of COVID-19, being hospitalised in an intensive care unit (ICU), and requiring high flow nasal cannula, non-invasive ventilation, or invasive mechanical ventilation. At M6, 54.1% of included patients had persistent dyspnoea (mMRC score ≥1), 20.1% severe impairment in gas diffusing capacity (DLCO <60% pred.), 21.6% restrictive ventilatory pattern (total lung capacity <80% pred.), and 40% a fibrotic-like pattern at CT-scan. Fibrotic-like pattern and restrictive ventilatory pattern were significantly more frequent in patients recovered from severe than non-severe COVID-19. Improved functional and radiological outcomes were observed between M3 and M6. At M6, age was an independent risk factor for severe DLco impairment and fibrotic-like pattern and severe COVID-19 form was independent risk factor for restrictive ventilatory profile and fibrotic-like pattern. CONCLUSION: Six months after discharge, patients hospitalised for COVID-19, especially those recovered from a severe form of COVID-19, frequently presented persistent dyspnoea, lung function impairment, and persistent fibrotic-like pattern, confirming the need for long-term post-discharge follow-up in these patients and for further studies to better understand long-term COVID-19 lung impairment.

Withdrawal Notice: Is There a Link between Lung Parenchyma Lesions, BMI, CRP and D-dimer in COVID-19 Patients Who Had Acute Pulmonary Embolism?

The article has been withdrawn at the request of the editor of the journal Cardiovascular & Hematological Disorders-Drug Targets due to incoherent content.Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

lntraoperative administration of 6% hydroxyethyl starch 130/0.4 is not associated with acute kidney injury in elective non-cardiac surgery: A sequential and propensity-matched analysis.

BACKGROUND: Intraoperative use of hydroxyethyl starch (HES) may increase the risk of postoperative acute kidney injury (AKI). Data from large populations are lacking. We aimed to assess whether intraoperative administration of 6% HES 130/0.4 is associated with AKI in non-cardiac surgery. METHODS: This retrospective study used the electronic records concerning elective abdominal, urologic, thoracic and peripheral vascular surgeries from 2010 to 2015. HES and non-HES patients were compared using a propensity score matching. Postoperative AKI, defined by stage 3 of the Kidney Disease Improving Global Outcomes (KDIGO) score, was the primary outcome. Because the use of HES markedly decreased in 2013, additional analyses, restricted to the 2010-2012 period, were also performed. RESULTS: In total, 23,045, and 11,691 patients were included in the whole, and restricted periods, respectively. The reduction in HES use was not accompanied by any change in the incidence of AKI. Unadjusted association between HES and KDIGO 3 AKI was significant (OR [95% CI] of 2.13 [1.67, 2.71]). For the whole period, 6460 patients were matched. Odd ratios for KDIGO 3 and all-stage AKI when using HES (10.3±4.7mL.kg-1) were 1.20 (95% CI [0.74, 1.95]), and 1.21 (95% CI [0.95, 1.54]), respectively. There was no association with the initiation of renal replacement therapy or in-hospital mortality either. Similar results were found for the restricted period. CONCLUSION: The intraoperative use of moderate doses of 6% HES 130/0.4 was not associated with increased risk of AKI. No conclusion can be drawn for higher doses of HES.

Direct transport vs secondary transfer to level I trauma centers in a French exclusive trauma system: Impact on mortality and determinants of triage on road-traffic victims.

BACKGROUND: Transporting a severely injured patient directly to a trauma center (TC) is consensually considered optimal. Nevertheless, disagreement persists regarding the association between secondary transfer status and outcome. The aim of the study was to compare adjusted mortality between road traffic trauma patients directly or secondarily transported to a level 1 trauma center (TC) in an exclusive French trauma system with a physician staffed prehospital emergency medical system (EMS). METHODS: A retrospective cohort study was performed using 2015-2017 data from a regional trauma registry (Traumabase®), an administrative database on road-traffic accidents and prehospital-EMS records. Multivariate logistic regression models were computed to determine the role of the modality of admission on mortality and to identify factors associated with secondary transfer. The primary outcome was day-30 mortality. Results: During the study period, 121.955 victims of road-traffic accident were recorded among which 4412 trauma patients were admitted in the level 1 regional TCs, 4031 directly and 381 secondarily transferred from lower levels facilities. No significant association between all-cause 30-day mortality and the type of transport was observed (Odds ratio 0.80, 95% confidence interval (CI) [0.3-1.9]) when adjusted for potential confounders. Patients secondarily transferred were older, with low-energy mechanism and presented higher head and abdominal injury scores. Among all 947 death, 43 (4.5%) occurred in lower-level facilities. The population-based undertriage leading to death was 0.15%, 95%CI [0.12-0.19]. CONCLUSION: In an exclusive trauma system with physician staffed prehospital care, road-traffic victims secondarily transferred to a TC do not have an increased mortality when compared to directly transported patients.

Toward a Literature-Driven Definition of Big Data in Healthcare.

OBJECTIVE: The aim of this study was to provide a definition of big data in healthcare. METHODS: A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. RESULTS: A total of 196 papers were included. Big data can be defined as datasets with Log(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. CONCLUSION: Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

Pediatric emergency department crowding: survival tree clustering for length of patient stay.

The objective of this study is to analyse the length of patient stay in Pediatric emergency department according to diagnosis and the number of patients over a 3 year-period. A survival tree was used, to explore the underlying construct of overcrowding depending of the length of patient stay. The tree was used to cluster 55.183 patients with respect to length of stay where partitioning is based on covariates such as the number of patients, the diagnosis and existence of complementary exams. The hazard ratio test was used to determine optimal partition. The approach is illustrated using Electronic Medical Record Software database available at the Pediatric Emergency Department of Lille University Hospital.