Rotterdam

Two-state solution’ proposed for renaming PCOS.

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New terminology is warranted for improved diagnosis and treatment of polycystic ovary syndrome phenotypes, according to researchers.

 “We would like to propose a nosological ‘two-state solution’ to the conflict. The endocrine syndrome of hyperandrogenism and chronic anovulation, eg, the National Institutes of Health (NIH) phenotype, should have a new name that acknowledges both its reproductive features as well as its long-term metabolic risks. The phenotypes diagnosed by ovarian morphology, eg, the remaining Rotterdam phenotypes, should continue to be known as PCOS,” wroteAndrea Dunaif, MD, vice chair for research in the department of medicine at Northwestern University Feinberg School of Medicine, and Bart Fauser, MD, of the department of reproductive medicine and gynecology at the University Medical Center in Utrecht, the Netherlands.

 

The researchers cited recommendations from the NIH Office for Disease Prevention’s Evidence-based Methodology Workshop on PCOS held last year, which suggested clarifying benefits and drawbacks from diagnostic criteria; causes, predictors and long-term consequences; and treatment and prevention strategies. They added that the syndrome is often overlooked outside of obstetrics and gynecology visits.

Currently, the diagnostic criteria for PCOS by the NIH include hyperandrogenism and chronic anovulation; Rotterdam includes two of the following: hyperandrogenism, chronic anovulation and polycystic ovaries. Finally, the Androgen Excess Society criteria state that PCOS is marked by hyperandrogenism plus ovarian dysfunction indicated by oligo/amenorrhea and/or polycystic ovaries, according to the researchers.

“Specifically, we want to ensure that this recommendation does not lead to Balkanization of the field, which will clearly undermine the broad interdisciplinary efforts required for meaningful scientific advances in our understanding of PCOS,” they wrote.

Source: Endocrine Today

Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study.

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Abstract

Objective To derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department.

Design Prospective observational diagnostic study.

Setting Three paediatric emergency care units: two in the Netherlands and one in the United Kingdom.

Participants Children with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation <94%, and C reactive protein.

Main outcome measures Pneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs.

Results Oxygen saturation <94% and presence of tachypnoea were important predictors of pneumonia. A raised C reactive protein level predicted the presence of both pneumonia and other SBIs, whereas chest wall retractions and oxygen saturation <94% were useful to rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86).

Conclusion A validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections.

 

Source: BMJ