ABSTRACT. Objective. Computerized medical decision support tools have been shown to improve the quality of care and have been cited by the Institute of Medicine as one method to reduce pharmaceutical errors. We evaluated the impact of an antiinfective decision support tool in a pediatric intensive care unit (PICU).Methods. We enhanced an existing adult antiinfective management tool by adding and changing medical logic to make it appropriate for pediatric patients. Process and outcomes measures were monitored prospectively during a 6-month control and a 6-month intervention period. Mandatory use of the decision support tool was initiated for all antiinfective orders in a 26-bed PICU during the intervention period. Clinician opinions of the decision support tool were surveyed via questionnaire.Results. The rate of pharmacy interventions for erroneous drug doses declined by 59%. The rate of antiinfective subtherapeutic patient days decreased by 36%, and the rate of excessive-dose days declined by 28%. The number of orders placed per antiinfective course decreased 11.5%, and the robust estimate of the antiinfective costs per patient decreased 9%. The type of antiinfectives ordered and the number of antiinfective doses per patient remained similar, as did the rates of adverse drug events and antibiotic-bacterial susceptibility mismatches. The surveyed clinicians reported that use of the program improved their antiinfective agent choices as well as their awareness of impairments in renal function and reduced the likelihood of adverse drug events.Conclusions. Use of the pediatric antiinfective decision support tool in a PICU was considered beneficial to patient care by the clinicians and reduced the rates of erroneous drug orders, improved therapeutic dosage targets, and was associated with a decreased robust estimate of antiinfective costs per patient. Pediatrics 2001;108(4). URL: http://www.pediatrics.org/cgi/content/full/108/4/ e75; antiinfective agents, decision support systems, drug therapy, medication errors, child, infant.

Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.

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In contrast to previously published reports, we find octreotide therapy for postoperative chylothoraces to be successful in only a minority of cases.

Congenital hemidysplasia with ichthyosiform nevus and limb defects (CHILD) syndrome is a rare X-linked dominant malformation syndrome characterized by unilaterally distributed ichthyosiform nevi, often sharply delimited at the midline, and ipsilateral limb defects. At least two-thirds of cases demonstrate involvement of the right side. Mutations in an essential enzyme of cholesterol biosynthesis, NAD(P)H steroid dehydrogenase-like [NSDHL], have been reported in five unrelated patients with right-sided CHILD syndrome and in a sixth patient with bilaterally, symmetric nevi and mild skeletal anomalies, but not with CHILD syndrome as originally defined. Although all of the molecularly diagnosed cases with the CHILD phenotype to date have had right-sided disease, we report here a novel nonsense mutation (E151X) of NSDHL in an infant with left-sided CHILD syndrome. This result demonstrates that both right- and left-sided CHILD syndrome can be caused by mutations in the same gene.