Methodological issues on analysis of prediction tools in evaluating febrile young infants at risk for serious infections

2020 ◽  
Vol 37 (3) ◽  
pp. 175.1-175 ◽  
Author(s):  
Mehdi Naderi ◽  
Siamak Sabour
Keyword(s):  
At Risk ◽  
Methodological Issues ◽  
Prediction Tools ◽  
Young Infants ◽  
Serious Infections

Analysis of emergency department prediction tools in evaluating febrile young infants at risk for serious infections

2019 ◽  
pp. emermed-2018-208210 ◽  
Author(s):  
Sarah Hui Wen Yao ◽  
Gene Yong-Kwang Ong ◽  
Ian K Maconochie ◽  
Khai Pin Lee ◽  
Shu-Ling Chong
Keyword(s):  
At Risk ◽  
Emergency Department ◽  
Clinical Assessment ◽  
Low Risk ◽  
Assessment Tools ◽  
Predictive Values ◽  
Nice Guideline ◽  
Risk Criteria ◽  
Young Infants ◽  
Serious Infections

ObjectiveFebrile infants≤3 months old constitute a vulnerable group at risk of serious infections (SI). We aimed to (1) study the test performance of two clinical assessment tools—the National Institute for Health and Care Excellence (NICE) Traffic Light System and Severity Index Score (SIS) in predicting SI among all febrile young infants and (2) evaluate the performance of three low-risk criteria—the Rochester Criteria (RC), Philadelphia Criteria (PC) and Boston Criteria (BC) among well-looking febrile infants.MethodsA retrospective validation study was conducted. Serious illness included both bacterial and serious viral illness such as meningitis and encephalitis. We included febrile infants≤3 months old presenting to a paediatric emergency department in Singapore between March 2015 and February 2016. Infants were assigned to high-risk and low-risk groups for SI according to each of the five tools. We compared the performance of the NICE guideline and SIS at initial clinical assessment for all infants and the low-risk criteria—RC, PC and BC—among well-looking infants. We presented their performance using sensitivity, specificity, positive, negative predictive values and likelihood ratios.ResultsOf 1057 infants analysed, 326 (30.8%) were diagnosed with SI. The NICE guideline had an overall sensitivity of 93.3% (95% CI 90.0 to 95.7), while the SIS had a sensitivity of 79.1% (95% CI 74.3 to 83.4). The incidence of SI was similar among infants who were well-looking and those who were not. Among the low-risk criteria, the RC performed with the highest sensitivity in infants aged 0–28 days (98.2%, 95% CI 90.3% to 100.0%) and 29–60 days (92.4%, 95% CI 86.0% to 96.5%), while the PC performed best in infants aged 61–90 days (100.0%, 95% CI 95.4% to 100.0%).ConclusionsThe NICE guideline achieved high sensitivity in our study population, and the RC had the highest sensitivity in predicting for SI among well-appearing febrile infants. Prospective validation is required.

Severe Reactions to Cow's Milk in Very Young Infants At Risk of Atopy

1996 ◽  
Vol 17 (4) ◽  
pp. 205-208 ◽  
Author(s):  
Arnaldo Cantani ◽  
Donatella Gagliesi
Keyword(s):  
At Risk ◽  
Cow’S Milk ◽  
Cow's Milk ◽  
Young Infants

scholarly journals Characteristics of revisits of children at risk for serious infections in pediatric emergency care

2018 ◽  
Vol 177 (4) ◽  
pp. 617-624 ◽  
Author(s):  
Evelien de Vos-Kerkhof ◽  
Dorien H. F. Geurts ◽  
Ewout W. Steyerberg ◽  
Monica Lakhanpaul ◽  
Henriette A. Moll ◽  
...  
Keyword(s):  
At Risk ◽  
Emergency Care ◽  
Pediatric Emergency ◽  
Children At Risk ◽  
Serious Infections ◽  
Pediatric Emergency Care

Monitoring of early humoral immunity to identify lung recipients at risk for development of serious infections: A multicenter prospective study

2018 ◽  
Vol 37 (8) ◽  
pp. 1001-1012 ◽  
Author(s):  
Elizabeth Sarmiento ◽  
Jose Cifrian ◽  
Leticia Calahorra ◽  
Carles Bravo ◽  
Sonia Lopez ◽  
...  
Keyword(s):  
At Risk ◽  
Prospective Study ◽  
Humoral Immunity ◽  
Serious Infections ◽  
Multicenter Prospective Study

Love, Logic, and Other Approaches to Prevention

PEDIATRICS ◽  
1979 ◽  
Vol 64 (6) ◽  
pp. 968-969
Author(s):  
Barbara Starfield
Keyword(s):  
At Risk ◽  
Motor Vehicles ◽  
Older Children ◽  
Risk Of Death ◽  
Young Infants ◽  
Knowing That

Baker's1 on accidents to passengers in motor vehicles inform us that infants, particularly young infants, are at greatest risk of death. Most pediatricians would have guessed otherwise, knowing that toddlers' newly acquired locomotion skills, independence, and inquisitiveness generally make them the most vulnerable target. Why, in this case, is the youngest most likely to suffer? It is possible that young infants are at greatest risk because they are passengers in cars more often than older children, but Baker1 cites evidence that they are actually less likely to travel in cars than older children. This means that the youngest, exposure for exposure, are even more at risk, probably because they are more vulnerable anatomically and because they are held in arms rather than in proper infant carriers.

scholarly journals AI enabled suicide prediction tools: a qualitative narrative review

2020 ◽  
Vol 27 (3) ◽  
pp. e100175
Author(s):  
Daniel D’Hotman ◽  
Erwin Loh
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Narrative Review ◽  
Online Data ◽  
Prediction Tools ◽  
Traditional Health ◽  
People With Mental Illness ◽  
Suicide Prediction ◽  
Exciting Opportunity ◽  
Independent Review

Background: Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services. Advances in artificial intelligence (AI) present opportunities for the development of novel tools for predicting suicide.Method: We searched Google Scholar and PubMed for articles relating to suicide prediction using artificial intelligence from 2017 onwards.Conclusions: This paper presents a qualitative narrative review of research focusing on two categories of suicide prediction tools: medical suicide prediction and social suicide prediction. Initial evidence is promising: AI-driven suicide prediction could improve our capacity to identify those at risk of suicide, and, potentially, save lives. Medical suicide prediction may be relatively uncontroversial when it pays respect to ethical and legal principles; however, further research is required to determine the validity of these tools in different contexts. Social suicide prediction offers an exciting opportunity to help identify suicide risk among those who do not engage with traditional health services. Yet, efforts by private companies such as Facebook to use online data for suicide prediction should be the subject of independent review and oversight to confirm safety, effectiveness and ethical permissibility.

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