Prevalence rates of UTI varied by age, gender, race, and circumcision status. Uncircumcised male infants less than 3 months of age and females less than 12 months of age had the highest baseline prevalence of UTI. Prevalence estimates can help clinicians make informed decisions regarding diagnostic testing in children presenting with signs and symptoms of urinary tract infection.

SummaryBackground and objectives Autosomal dominant polycystic kidney disease (ADPKD) is characterized by increased total kidney volume (TKV) and renal failure. This study aimed to determine if height-adjusted TKV (htTKV) predicts the onset of renal insufficiency.Design, setting, participants, & measurements This prospective, observational, longitudinal, multicenter study included 241 adults with ADPKD and preserved renal function. Magnetic resonance imaging and iothalamate clearance were used to measure htTKV and GFR, respectively. The association between baseline htTKV and the attainment of stage 3 CKD (GFR ,60 ml/min per 1.73 m 2 ) during follow-up was determined.Results After a mean follow-up of 7.9 years, stage 3 CKD was attained in 30.7% of the enrollees. Using baseline htTKV, negative correlations with GFR increased from 20.22 at baseline to 20.65 at year 8. In multivariable analysis, a baseline htTKV increase of 100 cc/m significantly predicted the development of CKD within 8 years with an odds ratio of 1.48 (95% confidence interval: 1.29, 1.70). In receiver operator characteristic curve analysis, baseline htTKV of 600 cc/m most accurately defined the risk of developing stage 3 CKD within 8 years with an area under the curve of 0.84 (95% confidence interval: 0.79, 0.90). htTKV was a better predictor than baseline age, serum creatinine, BUN, urinary albumin, or monocyte chemotactic protein-1 excretion (P,0.05).Conclusions Baseline htTKV $600 cc/m predicted the risk of developing renal insufficiency in ADPKD patients at high risk for renal disease progression within 8 years of follow-up, qualifying htTKV as a prognostic biomarker in ADPKD.

Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics. However, in Electronic Health Records (EHR) the visit sequences of patients include multiple concepts (diagnosis, procedure, and medication codes) per visit. This structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within each visit. In this work, we propose Med2Vec, which not only learns distributed representations for both medical codes and visits from a large EHR dataset with over 3 million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec displays significant improvement in key medical applications compared to popular baselines such as Skipgram, GloVe and stacked autoencoder, while providing clinically meaningful interpretation.

The QoR-40 is a widely used and extensively validated measure of quality of recovery. The QoR-40 is a suitable measure of postoperative quality of recovery in a range of clinical and research situations.

The RRP decreases the duration of antibiotic use, the length of inpatient stay, and the time in isolation.