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Drug drug interaction is defined as

Drug–drug interaction is defined as a pharmacological or clinical response to the administration of two or more drugs that is different from the response they initiate when individually administered (David and Tatro, 2012). The knowledge of the pharmacological characteristics of the drug interactions assists in their clinical management. The access to databases with detailed information on the pDDIs involved risks, their mechanism of action and management orientation largely collaborate with the prevention of adverse events (Blix et al., 2008; Duan et al., 2011; Papadopoulos and Smithburger, 2010).
Currently there are many evidences about the existence of an important relationship between the adverse events and the presence of drug interactions. A study developed by Plaza et al. (2010) in Chile pointed out in its results that 23% of clinically significant adverse events observed in the studied ICU during the research were related to drug interactions.
It was also demonstrated the need for continuous education actions linked to the presence of interactions and the use of computerized systems for their detection, which can result in satisfactory diminishing of prescription orders with potential interactions (Paterno et al., 2009; Smithburger et al., 2011; Wright et al., 2012).
Here is accentuated the necessary collaboration among the interactions alert systems and their critical evaluation by the intensivist team. The achievement of ideal results concerning the prevention of interactions combines alert systems with the pharmacist’s professional evaluation, avoiding the exposure of the clinical team to the “alert fatigue”, buy EAI045 that represents the great number of interactions signaled by the systems while not all being clinically relevant. Even though the whole clinical decision is individualized and requires a judicious evaluation on a case by case basis, it is evident the need for the critical evaluation of the clinical relevancy of the prevalent pDDIs in ICU outlining their risk profile and collecting information about their management and frequency in ICU prescription orders (Smithburger et al., 2010a,b, 2011, 2012).

Materials and methods
The study group is composed of patients admitted to the studied ICU during the data collection period. This is a general ICU, tending for potentially critical patients or patients with an unbalance of one or more organic systems due to high-complexity surgeries, grave infections and other clinical situations that demand intensive life support. The inclusion criteria were admission in ICU for more than 24h, be 18 or older and have valid prescription orders with 2 or more drugs.

From January to December of 2011 were analyzed prescription orders of 369 patients (1 prescription per patient), mean age of 57.03±14.62, admitted for at least 24h in the adult Intensive Care Unit of HC – UNICAMP (average of hospitalization in adult ICU=13.34±16.49days). The study group (205 men and 164 women) represents approximately 37% of the population admitted in the ICU during this period, which has 24 beds and receives about a thousand patients per year. In the assessed period 205 different types of drugs were prescribed, (13.04±4.26 per prescription order). Table 1 shows the distribution of the drugs observed in this study according to the Anatomical Therapeutic Chemical (ATC) Classification.
During the study there were 1844 pDDIs identified, quantified, classified and distributed in 405 combinations among the prescribed drugs. In the analyzed prescription 89% presented at least one pDDI, the emphasis being on the prevalence of moderate and important interactions, present in 74% and 67% of prescription orders, respectively. Table 2 shows interactions distribution by ATC classification, their total frequency in prescription orders and the frequency of the considered clinically relevant interactions.
It was observed a number of potential interactions classified as contraindicated representing 7% of pDDIs found in the analyzed prescription orders. Metoclopramide is the drug most involved in this severity class of pDDIs, being present in 6 out of 12 listed types. By analyzing the risks associated with the observed pDDIs it was possible to determine the frequency for each physiological system, as presented by Table 3.

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Materials and Methods

Study Population

This cross-sectional study was approved by the institutional review board, and all the participants provided written informed consent. From May 2013 to February 2014, consecutive 220 individuals who visited the health screening of our hospital and met the following inclusion criteria for the study were recruited: age greater than 20 years, scheduled for conventional chest radiography, and underwent pulmonary function test. Patients with any of the following criteria were excluded: pregnant (n  =  0), potentially pregnant or lactating (n  =  0), refused to provide informed consent (n  =  22), had incomplete datasets of dynamic chest radiography (n  =  3), had incomplete datasets of pulmonary function tests (n  =  1), could not follow tidal breathing instructions (eg, holding breath or taking a deep breath) (n  =  18), or their diaphragmatic motion could not be analyzed by the software described next (n  =  4). Thus, a total of 172 participants (103 men, 69 women; mean age 56.3 ± 9.8 years; age range 36–85 years) were finally included in the analysis ( Fig 1). The data from 47 participants of this study buy EAI045 were analyzed in a different study (under review). The heights and weights of the participants were measured, and the body mass index (BMI, weight in kilograms divided by height squared in meters) was calculated.

Figure 1. Flow diagram of the study population.Figure optionsDownload full-size imageDownload high-quality image (83 K)Download as PowerPoint slide

Imaging Protocol of Dynamic Chest Radiology (“Dynamic X-Ray Phrenicography”)

Posteroanterior dynamic chest radiography (“dynamic X-ray phrenicography”) was performed using a prototype system (Konica Minolta, Inc., Tokyo, Japan) composed of an FPD (PaxScan 4030CB, Varian Medical Systems, Inc., Salt Lake City, UT, USA) and a pulsed X-ray generator (DHF-155HII with Cineradiography option, Hitachi Medical Corporation, Tokyo, Japan). All participants were scanned in the standing position and instructed to breathe normally in a relaxed way without deep inspiration or expiration (tidal breathing). The exposure conditions were as follows: tube voltage, 100 kV; tube current, 50 mA; pulse duration of pulsed X-ray, 1.6 ms; source-to-image distance, 2 m; additional filter, 0.5 mm Al + 0.1 mm Cu. The additional filter was used to filter out soft X-rays. The exposure time was approximately 10–15 seconds. The pixel size was 388 × 388 µm, the matrix size was 1024  × 768, and the overall image area was 40 × 30 cm. The gray-level range of the images was 16,384 (14 bits), and the signal intensity was proportional to the incident exposure of the X-ray detector. The dynamic image data, captured at 15 frames/s, were synchronized with the pulsed X-ray. The pulsed X-ray prevented excessive radiation exposure to the subjects. The entrance surface dose was approximately 0.3–0.5 mGy.

Image Analysis

The diaphragmatic motions on sequential chest radiographs (dynamic image data) during tidal breathing were analyzed using prototype software (Konica Minolta, Inc.) installed in an independent workstation (Operating system: Windows 7 Pro SP1; Microsoft, Redmond WA; CPU: Intel Core i5-5200U, 2.20 GHz; memory 16 GB). The edges of the diaphragms on each dynamic chest radiograph were automatically determined by means of edge detection using a Prewitt Filter 18 ;  19. A board-certified radiologist with 14 years of experience in interpreting chest radiography selected the highest point of each diaphragm as the point of interest on the radiograph of the resting end-expiratory position (Fig 2a). These points were automatically traced by the template-matching technique throughout the respiratory phase (Fig 2b, Supplementary Video S1), and the vertical excursions of the bilateral diaphragm were calculated (Fig 2c): the null point was set at the end of the expiratory phase, that is, the lowest point (0 mm) of the excursion on the graph is the highest point of each diaphragm at the resting end-expiratory position. Then the peak motion speed of each diaphragm was calculated during inspiration and expiration by the differential method (Fig 2c). If several respiratory cycles were involved in the 10 to 15-second examination time, the averages of the measurements were calculated.