Cecilia Scarinzi

Traumatic peripheral nerve injuries: epidemiological findings, neuropathic pain and quality of life in 158 patients

The objectives of this study were (1) epidemiological analysis of traumatic peripheral nerve injuries; (2) assessment of neuropathic pain and quality of life in patients affected by traumatic neuropathies. All consecutive patients with a diagnosis of traumatic neuropathies from four Italian centres were enrolled. Electromyography confirmed clinical level and site diagnosis of peripheral nerve injury. All patients were evaluated by disability scales, pain screening tools, and quality of life tests. 158 consecutive patients for a total of 211 traumatic neuropathies were analysed. The brachial plexus was a frequent site of traumatic injury (36%) and the radial, ulnar, and peroneal were the most commonly involved nerves with 15% of iatrogenic injuries. Seventy‐two percent of the traumatic neuropathies were painful. Pain was present in 66% and neuropathic pain in 50% of all patients. Patients had worse quality of life scores than did the healthy Italian population. Moreover, there was a strong correlation between the quality of life and the severity of the pain, particularly neuropathic pain (Short Form‐36 [SF‐36] p < 0.005; Beck Depression Inventory [BDI] p < 0.0001). Traumatic neuropathies were more frequent in young males after road accidents, mainly in the upper limbs. Severe neuropathic pain and not only disability contributed to worsening the quality of life in patients with traumatic neuropathies.

Ingested foreign bodies causing complications and requiring hospitalization in European children: Results from the ESFBI study

Background:  In young children, particularly those aged 1–3 years, aerodigestive tract foreign bodies (FB) are a common pediatric problem. The aim of the present study was therefore to characterize the risk of complications and prolonged hospitalization due to FB in the upper digestive tract in terms of the characteristics of the injured patients (age, gender), typology and features of the FB, the circumstances of the accident and hospitalization details.

Information Extraction Approaches to Unconventional Data Sources for Injury Surveillance System: the Case of Newspapers Clippings

Injury Surveillance Systems based on traditional hospital records or clinical data have the advantage of being a well established, highly reliable source of information for making an active surveillance on specific injuries, like choking in children. However, they suffer the drawback of delays in making data available to the analysis, due to inefficiencies in data collection procedures. In this sense, the integration of clinical based registries with unconventional data sources like newspaper articles has the advantage of making the system more useful for early alerting. Usage of such sources is difficult since information is only available in the form of free natural-language documents rather than structured databases as required by traditional data mining techniques. Information Extraction (IE) addresses the problem of transforming a corpus of textual documents into a more structured database. In this paper, on a corpora of Italian newspapers articles related to choking in children due to ingestion/inhalation of foreign body we compared the performance of three IE algorithms- (a) a classical rule based system which requires a manual annotation of the rules; (ii) a rule based system which allows for the automatic building of rules; (b) a machine learning method based on Support Vector Machine. Although some useful indications are extracted from the newspaper clippings, this approach is at the time far from being routinely implemented for injury surveillance purposes.

Teleconsulting for Minor Head Injury: The Piedmont Experience:

We evaluated the benefits of teleconsulting for patients hospitalised with minor head injuries in centres without neurosurgery. In the Piedmont region, 1462 consultation requests were received at specialist centres in 2009, relating to 519 patients with a minor head injury diagnosis (ICD 850–854). These were compared with the details of 1895 patients admitted with the same diagnosis during 2009, but for whom no consultations were requested. The mortality risk in the two groups was estimated using logistic regression, after adjusting for the principal confounding factors (sex, age, seriousness of the patients injury at diagnosis, referral centre). The estimated risk of death for patients for whom no consultation was requested was an odds ratio of 1.32 (95% CI 1.08 to 1.74) compared to those who received a teleconsultation. However, after adjusting for the confounding factors, the risk was not significant (odds ratio = 1.25, 95% CI 0.83 to 1.91). A stratified analysis identified a significant effect for elderly people, aged over 70 years, in whom the odds ratio was 1.14 (95% CI 1.04 to 1.82). The results confirm the benefits of telemedicine, in particular for elderly patients, when teleconsultation is requested in the case of minor head injury.

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children.

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well.

Understanding the epidemiology of foreign body injuries in children using a data-driven Bayesian network

Bayesian networks (BNs) are probabilistic expert systems which have emerged over the last few decades as a powerful data mining technique. Also, BNs have become especially popular in biomedical applications where they have been used for diagnosing diseases and studying complex cellular networks, among many other applications. In this study, we built a BN in a fully automated way in order to analyse data regarding injuries due to the inhalation, ingestion and aspiration of foreign bodies (FBs) in children. Then, a sensitivity analysis was carried out to characterize the uncertainty associated with the model. While other studies focused on characteristics such as shape, consistency and dimensions of the FBs which caused injuries, we propose an integrated environment which makes the relationships among the factors underlying the problem clear. The advantage of this approach is that it gives a picture of the influence of critical factors on the injury severity and allows for the comparison of the effect of different FB characteristics (volume, FB type, shape and consistency) and childrens features (age and gender) on the risk of experiencing a hospitalization. The rates it consents to calculate provide a more rational basis for promoting care-givers’ education of the most influential risk factors regarding the adverse outcomes.

Economic burden of injuries in children: Cohort study based on administrative data in a northwestern Italian region

Background:  The aims of the present study were to identify which types of injuries are responsible for the major component of the health burden and to estimate the relative costs in a cohort of Italian children.

Adaptive Bayesian Networks for quantitative risk assessment of foreign body injuries in children

Injuries due to foreign body (FB) aspiration/ingestion/insertion represent a common public health issue in paediatric patients, which causes significant morbidity and mortality. The aim of this study is to present a Bayesian Network (BN) model for the identification of risk factors for FB injuries in children and provide their quantitative assessment. Combining a priori knowledge and observed data, a BN learning algorithm was used to generate the pattern of the relationships between possible causal factors of FB injuries. Finally, the BN was used for making inference on scenarios of interest, providing, for instance, the risk that an accident caused by a spherical object swallowed by a male child aged five while playing leads to hospitalization. BNs as a tool for quantitative risk assessment may assist in determining the hazard of consumer products giving an insight into their most influential specific features on the risk of experiencing severe injuries.