Vancomycin dosing required to achieve a therapeutic level in children post-surgical correction of congenital heart disease.

The vancomycin dosing range for safe and effective treatment remains uncertain for children who had corrective surgery for a congenital heart disease (CHD). We aimed to determine the vancomycin dosing requirements for this subgroup of patients. This prospective cohort study included children younger than 14 years old with CHD who received intravenous vancomycin for at least 3 days at the Pediatric Cardiology section of King Abdulaziz Medical City, Riyadh. In total, 140 pediatric patients with CHD were included with a median age of 0.57 years (interquartile range 0.21-2.2). The mean vancomycin total daily dose (TDD), 37.71 ±â€…6.8 mg/kg/day, was required to achieve a therapeutic trough concentration of 7-20 mg/L. The patient's age group and the care setting were significant predictors of the vancomycin dosing needs. Neonates required significantly lower doses of 34 ±â€…6.03 mg/kg/day (P = .002), and young children higher doses of 43.97 ±â€…9.4 mg/kg/day (P = .003). The dosage requirements were independent of the type of cardiac lesion, cardiopulmonary surgery exposure, sex, and BMI percentile. However, the patients in the pediatric cardiac ward required higher doses of vancomycin 41.08 ±â€…7.06 mg/kg/day (P = .039). After the treatment, 11 (8.5%) patients had an elevated Scr, and 3 (2.3%) patients developed AKI; however, none of the patients' sociodemographic factors or clinical variables, or vancomycin therapy characteristics was significantly associated with the renal dysfunction. Overall, the vancomycin TDD requirements are lower in pediatric post-cardiac surgery compared to non-cardiac patients and are modulated by several factors.

A systematic map of medical data preprocessing in knowledge discovery.

BACKGROUND AND OBJECTIVE: Datamining (DM) has, over the last decade, received increased attention in the medical domain and has been widely used to analyze medical datasets in order to extract useful knowledge and previously unknown patterns. However, historical medical data can often comprise inconsistent, noisy, imbalanced, missing and high dimensional data. These challenges lead to a serious bias in predictive modeling and reduce the performance of DM techniques. Data preprocessing is, therefore, an essential step in knowledge discovery as regards improving the quality of data and making it appropriate and suitable for DM techniques. The objective of this paper is to review the use of preprocessing techniques in clinical datasets. METHODS: We performed a systematic map of studies regarding the application of data preprocessing to healthcare and published between January 2000 and December 2017. A search string was determined on the basis of the mapping questions and the PICO categories. The search string was then applied in digital databases covering the fields of computer science and medical informatics in order to identify relevant studies. The studies were initially selected by reading their titles, abstracts and keywords. Those that were selected at that stage were then reviewed using a set of inclusion and exclusion criteria in order to eliminate any that were not relevant. This process resulted in 126 primary studies. RESULTS: Selected studies were analyzed and classified according to their publication years and channels, research type, empirical type and contribution type. The findings of this mapping study revealed that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade. A significant number of the selected studies used data reduction and cleaning preprocessing tasks. Moreover, the disciplines in which preprocessing have received most attention are: cardiology, endocrinology and oncology. CONCLUSIONS: Researchers should develop and implement standards for an effective integration of multiple medical data types. Moreover, we identified the need to perform literature reviews.

Knowledge discovery in cardiology: A systematic literature review.

CONTEXT: Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses. OBJECTIVE: The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology. METHOD: We performed a systematic literature review of empirical studies on the application of DM techniques in cardiology published in the period between 1 January 2000 and 31 December 2015. RESULTS: A total of 149 articles published between 2000 and 2015 were selected, studied and analyzed according to the following criteria: DM techniques and performance of the approaches developed. The results obtained showed that a significant number of the studies selected used classification and prediction techniques when developing DM models. Neural networks, decision trees and support vector machines were identified as being the techniques most frequently employed when developing DM models in cardiology. Moreover, neural networks and support vector machines achieved the highest accuracy rates and were proved to be more efficient than other techniques.

Hydroxylation of progesterone by some Trichoderma species.

Thirty-three isolates belonging to six species of the genus Trichoderma were tested for the ability to hydroxylate progesterone to 11alpha-, 11beta-, 11alpha,17alpha- and 6beta, 17alpha-derivatives, and epicortisol. T. aureoviride, T. harzianum, T. polysporum and T. pseudokoningii produced 11alpha-hydroxyprogesterone. T. harzianum and T. hamatum can form only the 11beta-isomer. T. koningii and T. hamatum produced 11alpha-, 11beta-, 11alpha,17alpha- and 6beta,11alpha-hydroxy derivatives. 11alpha, 11beta, 6beta,11alpha- and 11alpha,17alpha-hydroxyprogesterones and epicortisol are produced by T. aureoviride and T. pseudokoningii. Cortisol was produced only when the medium was fortified by 10 g/L peptone. This is the first record of conversion of progesterone to mono-, di- and trihydroxyprogesterones by these Trichoderma species.