Potential Drug-Drug Interaction Detection Using the Uptodate Mobİle Application in Intensive Care: A Retrospective, Observational Study

Potential Drug-Drug Interaction Detection Using the Uptodate Mobİle Application in Intensive Care: A Retrospective, Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Potential Drug-Drug Interaction Detection Using the Uptodate Mobİle Application in Intensive Care: A Retrospective, Observational Study Munevver Kayhan, Oguzhan Kayhan, Yalim Dikmen, Mehmet Aykut Ozturk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3511386/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: It was aimed to investigate the frequency of potential drug-drug interactions (pDDI) and the effect of the number of drugs used on pDDI with the Uptodate drug interactions application. Methods: Patients older than 12 years of age who were treated in the intensive care unit for 3 days or more in 2016 were included in the study. pDDIs were detected by entering the drugs used for more than 24 hours into the Uptodate application. The total number of mild, moderate and severe pDDIs and the number of medications used, length of stay, age, number of chronic diseases, mechanical ventilation (MV) support, hospitalization diagnoses, and APACHE II score were compared statistically. Results: While PIE was found to increase with the number of medications administered, it was found that it did not show an exact association with the number of days of hospitalization. However, it was higher in patients who received MV support, had a high APACHE II score, and died. pDDI was seen least in the postoperative follow-up diagnosis group. Conclusion: It was determined that pDDI increased as the number of medications used in critically ill patients increased. Critically ill patient Intensive care unit Drug-drug interaction Adverse drug reactions Uptodate Figures Figure 1 INTRODUCTION Drug-drug interactions are often unpredictable and undesirable, regardless of their positive or negative effects. Decreased absorption, decreased metabolism, kidney problems, and polypharmacy are among the reasons that increase drug-drug interactions in critically ill patients [1]. Many and different types of medications are used in intensive care patients due to systemic diseases and organ failures [2]. Drug-related adverse events are seen twice as frequently as in normal services [3]. It has been reported that 23% of clinically important adverse events in intensive care unit (ICU) are related to drug-drug interactions [4]. Excessive number of drugs increases the possibility of interaction [5, 6]. As a result, morbidity and mortality increase [7]. Potential drug-drug interaction (pDDI) is the possibility of drugs changing each other's effects and it is possible to detect it with computer programs. 40–80% of patients are exposed to at least one pDDI during their stay in the ICU [8]. It has been observed that the number of pDDI is related to the number of medications taken daily [1]. pDDI can be detected with programs such as Stockley's Drug Interactions, Micromedex Drug Interactions, and Epocrates [1]. In addition, mobile applications such as Uptodate (Lexicomp Drug Interactions) and MedScape, which can be accessed via smartphones and computers, are used to detect pDDI [9, 10]. This study aimed to investigate the frequency of pDDI detected with the Uptodate Drug Interactions mobile application, the effect of the number of drugs used on pDDI, and its relationship with some factors affecting intensive care mortality. MATERIALS AND METHODS Design of the Study Approval for the research was received from the Ethics Committee of Istanbul University - Cerrahpasa, Cerrahpasa Medical Faculty (Date: 08.11.2017 Number: 419987). The study was planned as a retrospective cross-sectional study. Clinical Trials registration was not conducted because it was not a prospective clinical trial. It was performed in a single center in the 12-bed tertiary ICU of a university hospital. Patients who were admitted to intensive care in 2016 were included in the study. Patient treatment plans were scanned and the names of the drugs used for each patient were recorded one by one on the Excel file. Criteria for inclusion in the study were determined as being admitted to intensive care, being over 12 years of age, and receiving treatment for 3 days or more. Patients whose files could not be accessed or whose treatment plans were missing were excluded from the study. Patient length of stay, age, number of chronic diseases, mechanical ventilation (MV) support, hospitalization diagnosis, outcome, names and number of medications used daily, acute physiology and chronic health evaluation II (APACHE II) score were recorded. Drugs used for more than 24 hours were considered as data. Medications prescribed in single doses were not recorded as data. During the analysis phase, hospitalization diagnoses were brought together under certain diagnostic groups. All medications were obtained from paper treatment plans. The names, routes of administration, and doses of the drugs were recorded in an Excel file, with a separate column for each patient each day. The treatment plan applied for each day was entered into the Uptodate mobile application and the pDDIs that occurred for the relevant day were recorded. The same procedure was performed repeatedly for each patient's each hospitalization day and pDDIs were recorded. pDDI detection with Uptodate The generic names or active ingredients and routes of administration of the drugs administered to a patient within a day were entered into the Uptodate (Lexicomp Drug Interactions) mobile application. The result was obtained with each pair of drugs with potential interactions grouped as A, B, C, D and X. Group A was not recorded as data. B-C interactions were recorded as mild, D interaction as moderate, and X interaction as severe. This process was repeated for each patient's treatment plan for each day of their stay. Interaction types and numbers were recorded on separate days. The Uptodate screening tool uses different databases to detect the presence or absence of significant interactions for a given drug pair. If conflicting evidence is presented between these databases, it uses scientific literature and prospectus information to provide clinical practice recommendations to clinicians [10]. In the program, pDDI is grouped as A, B, C, D and X, and the interaction group of each drug pair is taken as the result output. The meanings of groups A, B, C, D and X: In group A, there are no known drug-drug interactions. In group B, data show that the mentioned drugs may interact, but there is no clinical evidence from their concomitant use. In group C, data suggest that the indicated agents may interact with each other in a clinically significant manner. The benefits of using these two drugs together usually outweigh the risks. An appropriate monitoring plan should be established to prevent possible adverse effects. Dosage adjustment of agents may be necessary in some patient groups. In group D, the data suggest that the two drugs may interact with each other in a clinically meaningful way. Patient-specific evaluation should be made to determine whether the benefits of concomitant treatment outweigh the risks. Precautions should be taken to determine the benefits and/or minimal toxicity that occurs with the use of active substances. These actions include close monitoring, empirical dosage changes, and selection of alternative agents. In group X, data indicate that the indicated agents may interact with each other in a clinically significant manner. The risks associated with the combined use of these agents generally outweigh the benefits. These agents are generally contraindicated. Statistical Analysis Each patient's total mild, moderate and severe pDDIs, length of stay, age, number of chronic diseases, MV support, hospitalization diagnosis groups, total and average number of drug use were statistically compared with the APACHE II score. Patients are listed in order from least to most according to the total and daily number of medications they used during their hospitalization. It was then divided into 5 consecutive groups. The mean of each group was calculated. The groups were compared statistically in terms of mild, moderate and severe pDDI. The suitability of the variables to normal distribution was examined using visual (histogram and probability graphs) and analytical methods (Kolmogorov Smirnov Test). It was determined that not all of the data showed normal distribution. Descriptive analyzes are shown as percentages, and mean ± standard deviation (SD) and median (minimum-maximum) values are given for continuous variables. In data that did not comply with normal distribution, Mann-Whitney U test was used for comparison analyzes between two groups. Comparisons of more than two groups were analyzed using the Kruskal-Wallis Test. Pairwise comparisons of groups with significant Kruskal-Wallis test results were evaluated with the Mann-Whitney U test with Bonferroni correction. Friedman test was used to evaluate more than two repeated measurements of dependent groups. The results are within the 95% confidence interval, and the margin of statistical error is accepted as 0.05. Statistical evaluation was made using the Statistical Package for Social Sciences (SPSS) for Windows 25.0 (IBM SPSS Inc., Chicago, IL) program. RESULTS The files of 276 of 835 patients hospitalized in intensive care who met the inclusion criteria were examined (Figure 1). Patient Characteristics 55.4% of the patients included in the study were male and 44.6% were female. The average age is 60.98±17.11 (age range 14-88). 62% of the patients were 60 years or older (Table 1). Table 1. Patient Characteristics Male Age (Mean±SD) BMI (Mean±SD) n(%) 153(55,4) 60,98±17,11 24,60±5,89 Disease History Cardiovascular System Respiratory system Gastrointestinal System Neurological System Renal System %54,7 %37 %31,2 %22,8 %19,9 Hospitalization Diagnostic Group Respiratory System Disease Shock Postoperative follow-up Neurological System Disease Urinary System Disease 118(42,8) 89(32,2) 35(12,7) 25(9,1) 9(3,3) MV Support There is None 183(66,3) 93(33,7) Intensive Care Result Transfer to service Ex 180(65,2) 96(34,8) Total Length of stay Mean±SD Median (IQR) 10,76±11,63 7(5-12) Length of stay 3-10 11-20 21-30 31 and over 192(69,6) 50(18,1) 21(7,6) 13(4,7) Total Number of Medicines Ordered Mean±SD Median (IQR) 111,61±130,85 69,00(38,25-130,75) Daily Number of Medicines Ordered Mean±SD Median (IQR) 9,77±3,18 9,59(7,67-12,00) SD: Standard Deviation, IQR: Inter Quartile Range The average APACHE II score was 22.22±7.35. The patients were divided into 5 groups according to their hospitalization diagnoses (Appendix 1). While the total mild pDDI per patient was 28.5, the daily mild pDDI was 3.32. Moderate pDDI was 3 and 0.36, respectively, while severe interaction was zero in both (Table 2). Table 2. Potential Drug-Drug Interaction - Median (IQR) Total Daily Mild Interaction 28,50(7,00-69,00) 3,32(1,23-6,62) Moderate Interaction 3(0-9,00) 0,36(0-1,00) Severe Interaction 0(0-0) 0(0-0) IQR: Inter Quartile Range 3.1.1. Total pDDI according to total number of drugs The patients were divided into 5 groups of 20% each according to the total number of medications ordered in the intensive care unit. It was determined that mild pDDI increased as the number of drug use increased (p<0.001). In pairwise group comparisons, except for the first-second and second-third group comparisons, it was observed that the moderate pDDI increased as the number of drugs used increased (p<0.001). Severe pDDI was found to be higher in the group using the most medication than in the two groups using the least amount of medication (p<0.001). It was found that pDDI decreased in all groups from mild to severe (Table 3). Table 3. Total pDDI According to Total Number of Drugs – Median (IQR) Total Number of Drugs Mean (SD) Mild Moderate Severe p 2 1) 22,75 ± 5,91 2) 44,69 ± 7,91 3) 71,09 ± 8,97 4)113,15 ± 18,22 5) 302,91 ± 183,94 3(0-6) 9(5-19,5) 32(18,5-49) 53(34,5-74,5) 138(67,5-189,5) 0(0-1) 0(0-4) 3(0-7,5) 6(1-10,5) 19,5(6,5-35) 0(0-0) 0(0-0) 0(0-2,5) 0(0-2) 0(0-9) <0,001 <0,001 <0,001 <0,001 <0,001 p 1 <0,001 <0,001 <0,001 1 Tests used in comparison between groups: Kruskal-Wallis Test, p<0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed. 2 Tests used for intra-group comparison: Friedman Test, p<0.05 was considered significant. pDDI; Potential Drug-Drug Interaction, IQR; Inter Quartile Range, SD, Standard Deviation 3.1.2. Daily pDDI according to daily drug numbers The patients were divided into 5 groups of 20% each according to the daily number of medications ordered in the intensive care unit. It was determined that mild pDDI was least in the first group, then in the second group, and there was no significant difference between the next three groups. It was found that moderate pDDI was lowest in the first group and the difference between the first and fifth groups was significant (p<0.001). It was determined that severe pDDI was higher in the fifth group than in the first and second groups (p = 0.001). It was found that pDDI decreased from mild to severe in all groups (Table 4). Table 4. Daily pDDI According to Mean Number of Medications per Day– Median (IQR) Mean Number of Drugs Mild Moderate Severe p 2 1) 5,51 ±1,19 2) 8,01 ±0,48 3) 9,53 ±0,44 4) 11,34 ±0,67 5) 14,37 ±1,75 1(0-1,83) 2,25(0,82-3,66) 4,57(2,53-7,06) 4,87(3,20-9,54) 5,93(3,20-8,40) 0(0-0,33) 0(0-0,78) 0,50(0-1,04) 0,75(0,10-1,11) 0,90(0,21-1,44) 0(0-0) 0(0-0) 0(0-0,42) 0(0-0,23) 0(0-0,49) <0,001 <0,001 <0,001 <0,001 <0,001 p 1 <0,001 <0,001 0,001 1 Tests used in comparison between groups: Kruskal-Wallis Test, p<0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed. 2 Tests used for intra-group comparison: Friedman Test, p<0.05 was considered significant. pDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range 3.1.3. Total pDDI according to length of stay The patients were divided into 4 groups according to their length of stay (Table 5). Moderate pDDI was found less in patients hospitalized for 3-10 days (p<0.001). Severe pDDI was more common in patients hospitalized for 21-30 days than in patients hospitalized for 3-10 days (p=0.010). pDDI was found to decrease from mild to severe interaction in all groups (p<0.001). Table 5. Total pDDI According to Length of Stay – Median (IQR) Length of Stay (Days) Mild Moderate Severe p 2 3-10 (n=192) 11-20 (n=50) 21-30 (n=21) 31 or more (n=13) 14,00(4-36) 62,50(29-107) 143,00(54-171) 279,00(147-370) 1,00(0-5) 8,00(2-19) 20,00(7-34) 32,00(11-61) 0(0-0) 0(0-3) 0(0-11) 0(0-8) <0,001 <0,001 <0,001 <0,001 p 1 <0,001 <0,001 0,010 1 Tests used in comparison between groups: Kruskal-Wallis Test, p<0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed. 2 Tests used for intra-group comparison: Friedman Test, p<0.05 was considered significant. pDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range 3.1.4. Total pDDI according to hospitalization diagnosis groups Patients divided into 5 groups according to their hospitalization diagnosis (Appendix 1) were compared according to the degree of pDDI (Table 6). Mild and moderate pDDI was observed less frequently in the postoperative follow-up group than in the neurological system, respiratory system and shock groups (p<0.001). Severe pDDI was also observed to be less common in the postoperative follow-up group than in the respiratory system and shock groups (p = 0.017). It was determined that pDDI decreased from mild to severe in all groups. Table 6. Total pDDI According to Hospitalization Diagnosis –Median (IQR) Hospitalization Diagnosis Group Mild Moderate Severe p 2 Neurological System (n=25) Postoperative follow-up (n=35) Respiratory System (n=118) Shock (n=89) Urinary System (n=9) 41(26-73) 7(3-19,5) 36(9-76) 27(7-73) 11(7-36) 8(2-16) 0(0-2) 3(0-9) 3(0-9) 3(1-4) 0(0-0) 0(0-0) 0(0-2) 0(0-1) 0(0-0) <0,001 <0,001 <0,001 <0,001 0,001 p 1 <0,001 <0,001 0,017 1 Tests used in comparison between groups: Kruskal-Wallis Test, p<0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed. 2 Tests used for intra-group comparison: Friedman Test, p<0.05 was considered significant. pDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range 3.1.5. Total pDDI according to survival and mechanical ventilation Mild and moderate pDDI was found to be higher in patients who died (Table 7). Mild and moderate pDDI was found to be higher in patients receiving MV support (Table 7). Table 7. Total pDDI According to Survival and Mechanical Ventilation – Median (IQR) ICU Result Mild Moderate Severe p 2 Alive (n=180) Exitus (n=96) 16(5-42) 57,5(29,5-107) 1,5(0-7,5) 4,5(2-15,5) 0(0-0) 0(0-2) <0,001 <0,001 p 1 <0,001 <0,001 0,107 MV Support Yes (n=183) No (n=93) 37(12,5-84) 9(4-36) 4(1-12,5) 0(0-4) 0(0-1) 0(0-0) <0,001 <0,001 p 1 <0,001 <0,001 0,243 1 Tests used in comparison between groups: Kruskal-Wallis Test, p<0.05 was considered significant. 2 Tests used for intra-group comparison: Friedman Test, p<0.05 was considered significant. pDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range, ICU: Intensive Care Unit, MV: Mechanical Ventilation 3.1.6. Total pDDI according to the number of systemic diseases The patients were divided into 3 groups according to the number of systemic diseases they had before admission. Mild pDDI was found to be more common in groups with more systemic disease (p<0.001). There was no difference between the groups in terms of moderate pDDI (p=0.285). It was found that severe pDDI was more common in patients with 3 or more systemic diseases than in those with no systemic disease (p = 0.022). It was found that pDDI decreased in all groups from mild to severe (Table 8). Table 8. Total pDDI According to Number of Systemic Diseases – Median (IQR) Number of Systemic Diseases Mild Moderate Severe p 2 No (n=23) 1-2 (n=138) ³3 (n=115) 6(1,5-25,5) 24,5(5-57) 39(14,5-92) 2(0-9) 3(0-8) 3(0-12) 0(0-0) 0(0-0) 0(0-2,5) <0,001 <0,001 <0,001 p 1 <0,001 0,285 0,022 1 Tests used in comparison between groups: Kruskal-Wallis Test, p<0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed. 2 Tests used for intra-group comparison: Friedman Test, p<0.05 was considered significant. pDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range 3.1.7. Total pDDI according to APACHE II score Patients were divided into 3 groups in terms of APACHE II score and pDDI values were compared. Mild and moderate pDDI was found to be higher in the 2 groups with an APACHE II score of 20 or more (p<0.001 and p<0.006). There was no difference in terms of severe pDDI (p=0.713). It was found that pDDI decreased in all groups from mild to severe (Table 9). Table 9. Total pDDI According to APACHE II Score – Median (IQR) APACHE II Score Mild Moderate Severe p 2 0-19 (n=101) 20-29 (n=136) 30 or more (n=39) 14(4-41) 30(9-73) 45(29,5-108,5) 2(0-6) 3(0-10) 5(1-18) 0(0-0) 0(0-0) 0(0-2) <0,001 <0,001 <0,001 p 1 <0,001 <0,006 0,713 1 Tests used in comparison between groups: Kruskal-Wallis Test, p<0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed. 2 Tests used for intra-group comparison: Friedman Test, p<0.05 was considered significant. pDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range, APACHE II: Acute Physiology and Chronic Health Evaluation II DISCUSSION In this study, it was determined that as the number of medications administered during intensive care stay increased, the potential mild, moderate and severe drug-drug interactions detected by Uptodate increased. Additionally, it was determined that an increase in the length of stay in the intensive care unit was associated with an increase in pDDI. Mild pDDI detected are approximately 10 times more common than moderate pDDI. Mild pDDI has minimal impact on the patient's clinic or the use of medication is more likely to benefit the patient. Moderate and severe pDDI is more clinically significant and was found to be highest in the patient group who used the most medication. According to the results of our study, mild and moderate interactions increased with the duration of ICU stay, while no clear results were obtained for severe interactions. This situation suggests that even though the patients' stay is long, physicians are careful not to use drugs together that may cause serious interactions. However, the possible reasons why pDDIs are less common in the postoperative patient group may be the shorter length of stay and the lower number of medications used. Similarly, the fact that severe pDDI, but not moderate pDDI, was found more frequently in patients with more diseases before intensive care unit admission can be explained by the possibility of using multiple medications in those with multiple diseases. Another explanation may be that intensive care physicians' level of knowledge about drugs used outside intensive care is low. In the study conducted by Uijtendaal et al. [11], who reached similar results to those obtained in the current study, it was determined that 54% of the patients were exposed to at least one pDDI on 27% of the hospitalization days. In the same study, it was determined that the number of days and number of patients exposed to ≥1 pDDI increased in patients with long stay in the intensive care unit, high expected mortality rate according to APACHE IV, chronic diseases and high number of medications used, in patients who received MV support and who died in intensive care. In addition, similar to our results on the effect of length of stay, Gutiérrez-Valencia et al. [12] found in their study that the number of medications used temporarily during ICU stay increased significantly and PIIE increased due to this increase. In the meta-analysis conducted by Fitzmaurice et al. [13] to investigate pDDI in the intensive care unit, it was emphasized that 58% of patients admitted to the intensive care unit may be exposed to at least one pDDI. It has been stated that higher risk drugs are typically given to critically ill patients compared to other patient populations, and therefore pDDI may occur between 1 and 10 times per patient. In another meta-analysis, it was stated that 67% of patients in intensive care units were exposed to at least one pDDI [14]. In our study, the daily and total values of mild pDDI were found to be significantly higher than those of moderate and severe pDDI. However, our findings of moderate and severe interactions, which are more clinically significant, were found to be similar to the study by Fitzmaurice et al. Many studies [2, 16, 17], such as Jain et al. [15], who emphasized that as age increases, systemic diseases increase and the number of drugs used may increase accordingly, it has been observed that pDDI increases with the number of drugs used. Similar results were obtained in our study. Rodrigues et al. showed that the duration of stay in intensive care was longer in patients with more PDDI and more severe pDDI. In our study, it was determined that mild, moderate and severe pDDI increased as the duration of stay in intensive care increased. Depending on the severity of the critical illness, the length of stay of patients in intensive care units may vary and patients may receive quite complex treatments during this period. The cause and effect relationship between patients with a high number of pDDIs and the prolonged stay of these patients in intensive care is not clear. Although long periods of stay in the intensive care unit increase the risk of pDDI, we think that the negative clinical responses caused by pDDI may also prolong the stay of patients in the intensive care unit. It has been shown that prolonged mechanical ventilation due to sedation, fluid overload, and exceeding therapeutic drug concentrations are less common in intensive care units where clinical pharmacologists take an active role [20]. Although pharmaceutical care is practiced across many disciplines, critically ill patients require additional evaluation due to the complexity of medication regimens and disease states. We are aware of the importance of clinical pharmacologists as a part of the multidisciplinary team, and we think that in addition to all these positive aspects, they can also contribute to the training of the intensive care team. Medication errors may be more common in patients with multiple drug use, long hospital stays and multiple organ failure. In this respect, we think that intensive care units are very important in terms of treatment planning, administration, monitoring and evaluation of results. Electronic order systems warn healthcare professionals about appropriate dosage, correct drug selection, and drug-drug interactions while creating a treatment plan. For this reason, we think that it is useful to use an electronic order system that warns the physician who decides on the treatment and the nurses who apply it, in order to minimize the margin of error in intensive care units where a patient-based treatment plan is made. Although drug combinations causing interactions were not recorded in our study, combivent and quetiapine, combivent and carvedilol stand out as the most common combinations that cause severe pDDI. In our study, severe pDDI was detected much less frequently than moderate and mild pDDI. We think that this situation may have arisen because physicians are better aware of drug combinations that are contraindicated to be used together. Some enteral nutrition products and blood products are not available in the database we use to detect pDDI. The interaction of the ingredients in these treatments with other drugs has not been evaluated. CONCLUSION As a result, in the current study, in the screening performed on critically ill patients with the Uptodate mobile application, it was determined that PDDI increased as the number of drugs used in the intensive care unit increased, and that there was a relationship between length of stay and pDDI rates. Abbreviations pDDI, potential drug-drug interaction; ICU, intensive care unit; MV, mechanical ventilation; APACHE II, acute physiology and chronic health evaluation II; SD, standard deviation; Declarations Author statement The authors declare no conflicts of interest in writing this manuscript. This manuscript has received no funding. The authors did not use a Generative AI and AI-assisted technologies. The dataset used for the completion of this study are available from the corresponding author under reasonable request. Funding Sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest The authors declare that they have no conflicts of interest that relate to the research described in this paper. Study Ethics The study was in accordance with the Declaration of Helsinki and later revisions of ethical principles for medical research. Informed Consent Ethical commitee waived the informed consent due to retrospective nature of the study. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Munevver Kayhan, Oguzhan Kayhan, Yalim Dikmen and Mehmet Aykut Ozturk. The first draft of the manuscript was written by Munevver Kayhan and Oguzhan Kayhan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Vanham D, Spinewine A, Hantson P, Wittebole X, Wouters D, Sneyers B. Drug-drug interactions in the intensive care unit: Do they really matter? J Crit Care. 2017;38:97-103. Smithburger PL, Kane-Gill SL, Seybert AL. Drug-drug interactions in the medical intensive care unit: an assessment of frequency, severity and the medications involved. Int J Pharm Pract. 2012;20(6):402-8. Cullen DJ, Sweitzer BJ, Bates DW, Burdick E, Edmondson A, Leape LL. Preventable adverse drug events in hospitalized patients: a comparative study of intensive care and general care units. Crit Care Med. 1997;25(8):1289-97. Plaza J, Alamo M, Torres P, Fuentes A, López F. Drug interactions and adverse events induced by drugs used in an intensive care unit. Revista medica de Chile. 2010;138(4):452-60. Alvim MM, Silva LA, Leite IC, Silvério MS. Adverse events caused by potential drug-drug interactions in an intensive care unit of a teaching hospital. Rev Bras Ter Intensiva. 2015;27(4):353-9. Ismail M, Khan F, Noor S, Haider I, Haq IU, Ali Z, et al. Potential drug-drug interactions in medical intensive care unit of a tertiary care hospital in Pakistan. Int J Clin Pharm. 2016;38(5):1052-6. Murtaza G, Khan MY, Azhar S, Khan SA, Khan TM. Assessment of potential drug-drug interactions and its associated factors in the hospitalized cardiac patients. Saudi Pharm J. 2016;24(2):220-5. Moura C, Prado N, Acurcio F. Potential drug-drug interactions associated with prolonged stays in the intensive care unit: a retrospective cohort study. Clin Drug Investig. 2011;31(5):309-16. Monteiro CRA, Schoueri JHM, Cardial DT, Linhares LC, Turke KC, Steuer LV, et al. Evaluation of the systemic and therapeutic repercussions caused by drug interactions in oncology patients. Rev Assoc Med Bras (1992). 2019;65(5):611-7. Patel D, Bertz R, Ren S, Boulton DW, Någård M. A Systematic Review of Gastric Acid-Reducing Agent-Mediated Drug-Drug Interactions with Orally Administered Medications. Clin Pharmacokinet. 2020;59(4):447-62. Uijtendaal EV, van Harssel LL, Hugenholtz GW, Kuck EM, Zwart-van Rijkom JE, Cremer OL, et al. Analysis of potential drug-drug interactions in medical intensive care unit patients. Pharmacotherapy. 2014;34(3):213-9. Gutiérrez‐Valencia M, Izquierdo M, Malafarina V, Alonso‐Renedo J, González‐Glaría B, Larrayoz‐Sola B, et al. Impact of hospitalization in an acute geriatric unit on polypharmacy and potentially inappropriate prescriptions: A retrospective study. Geriatrics & gerontology international. 2017;17(12):2354-60. Fitzmaurice MG, Wong A, Akerberg H, Avramovska S, Smithburger PL, Buckley MS, et al. Evaluation of Potential Drug–Drug Interactions in Adults in the Intensive Care Unit: A Systematic Review and Meta-Analysis. Drug safety. 2019:1-10. Zheng WY, Richardson L, Li L, Day R, Westbrook J, Baysari M. Drug-drug interactions and their harmful effects in hospitalised patients: a systematic review and meta-analysis. European journal of clinical pharmacology. 2018;74(1):15-27. Jain S, Jain P, Sharma K, Saraswat P. A prospective analysis of drug interactions in patients of intensive cardiac care unit. Journal of clinical and diagnostic research: JCDR. 2017;11(3):FC01. Cruciol-Souza JM, Thomson JC. Prevalence of potential drug-drug interactions and its associated factors in a Brazilian teaching hospital. J Pharm Pharm Sci. 2006;9(3):427-33. Grenouillet-Delacre M, Verdoux H, Moore N, Haramburu F, Miremont-Salamé G, Etienne G, et al. Life-threatening adverse drug reactions at admission to medical intensive care: a prospective study in a teaching hospital. Intensive Care Med. 2007;33(12):2150-7. Rodrigues AT, Stahlschmidt R, Granja S, Pilger D, Falcão ALE, Mazzola PG. Prevalence of potential drug-drug interactions in the intensive care unit of a Brazilian teaching hospital. Brazilian Journal of Pharmaceutical Sciences. 2017;53(1). Smithburger PL, Gill SLK, Benedict NJ, Falcione BA, Seybert AL. Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Annals of Pharmacotherapy. 2010;44(11):1718-24. Askari M, Eslami S, Louws M, Wierenga PC, Dongelmans DA, Kuiper RA, et al. Frequency and nature of drug-drug interactions in the intensive care unit. Pharmacoepidemiol Drug Saf. 2013;22(4):430-7. Cvikl M, Sinkovič A. Interventions of a clinical pharmacist in a medical intensive care unit–A retrospective analysis. Bosnian Journal of Basic Medical Sciences. 2020. Colpaert K, Claus B, Somers A, Vandewoude K, Robays H, Decruyenaere J. Impact of computerized physician order entry on medication prescription errors in the intensive care unit: a controlled cross-sectional trial. Critical Care. 2006;10(1):R21. Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3511386","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":244795337,"identity":"381b4b30-84b2-4623-b5d8-adfc90a3688e","order_by":0,"name":"Munevver Kayhan","email":"","orcid":"","institution":"Istanbul University- Cerrahpasa, Cerrahpasa Medical Faculty, Anesthesiology and Intensive Care, Istanbul, Turkey","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Munevver","middleName":"","lastName":"Kayhan","suffix":""},{"id":244795338,"identity":"224a98c6-1256-4254-adc5-cf0103d65237","order_by":1,"name":"Oguzhan Kayhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYJACZhAhwcDABqRseMBCPCRoSYNrkSBWy2EGglr4+dcYfy6oucMg2X7G7MHPHedlDI4fYHzwto2hzrwBuxbJGW8MjGcce8YgzZNjbth75jaPwZkEZsO5bQwSMgewazG4ccYgmYftMIMcQ46ZBG8bUMsNBjZpXqAWXC4DaTnM8w+ohf+NmeTftnMgLey/8Wo532PYzNt2mEFaIscMaPgBsC3M+LRIzmArZubtO8wjOeNZmbRsWzKP5JnEZsk55yQkZ+AMscObP/N8OywncT55m+TbNjt7vuOHD354U2bDjzOUJRLAFDAiOAygQowNDHhjkv8AjMX+ALeqUTAKRsEoGNEAAA2tUDs2rMS3AAAAAElFTkSuQmCC","orcid":"","institution":"Istanbul University- Cerrahpasa, Cerrahpasa Medical Faculty, Anesthesiology and Intensive Care, Istanbul, Turkey","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Oguzhan","middleName":"","lastName":"Kayhan","suffix":""},{"id":244795339,"identity":"a7c1bd0c-d801-472a-852e-5d13b01376cb","order_by":2,"name":"Yalim Dikmen","email":"","orcid":"","institution":"Istanbul University- Cerrahpasa, Cerrahpasa Medical Faculty, Anesthesiology and Intensive Care, Istanbul, Turkey","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yalim","middleName":"","lastName":"Dikmen","suffix":""},{"id":244795340,"identity":"c18b201a-a29b-4ba1-8943-9777c8f52698","order_by":3,"name":"Mehmet Aykut Ozturk","email":"","orcid":"","institution":"Istanbul University- Cerrahpasa, Cerrahpasa Medical Faculty, Pharmacology, Istanbul, Turkey","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"Aykut","lastName":"Ozturk","suffix":""}],"badges":[],"createdAt":"2023-10-30 11:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3511386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3511386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45771109,"identity":"a61ef3f7-e474-462e-83bc-a250f09dd691","added_by":"auto","created_at":"2023-11-02 20:40:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16564,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Diagram\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3511386/v1/0ec14bb51650734ddbf9bd9c.jpg"},{"id":46107485,"identity":"5e4bd5c8-6eb7-4b7e-9389-83f3e9fec405","added_by":"auto","created_at":"2023-11-08 17:14:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":480348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3511386/v1/cb8336bc-8926-428e-8fc2-0db345247088.pdf"},{"id":45771110,"identity":"592f8696-3858-4ac2-a81a-e516d9ea0938","added_by":"auto","created_at":"2023-11-02 20:40:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16317,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3511386/v1/9ca3a886296076f79c83f7d7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePotential Drug-Drug Interaction Detection Using the Uptodate Mobİle Application in Intensive Care: A Retrospective, Observational Study\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDrug-drug interactions are often unpredictable and undesirable, regardless of their positive or negative effects. Decreased absorption, decreased metabolism, kidney problems, and polypharmacy are among the reasons that increase drug-drug interactions in critically ill patients [1].\u003c/p\u003e \u003cp\u003eMany and different types of medications are used in intensive care patients due to systemic diseases and organ failures [2]. Drug-related adverse events are seen twice as frequently as in normal services [3]. It has been reported that 23% of clinically important adverse events in intensive care unit (ICU) are related to drug-drug interactions [4]. Excessive number of drugs increases the possibility of interaction [5, 6]. As a result, morbidity and mortality increase [7].\u003c/p\u003e \u003cp\u003ePotential drug-drug interaction (pDDI) is the possibility of drugs changing each other's effects and it is possible to detect it with computer programs. 40\u0026ndash;80% of patients are exposed to at least one pDDI during their stay in the ICU [8]. It has been observed that the number of pDDI is related to the number of medications taken daily [1].\u003c/p\u003e \u003cp\u003epDDI can be detected with programs such as Stockley's Drug Interactions, Micromedex Drug Interactions, and Epocrates [1]. In addition, mobile applications such as Uptodate (Lexicomp Drug Interactions) and MedScape, which can be accessed via smartphones and computers, are used to detect pDDI [9, 10].\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the frequency of pDDI detected with the Uptodate Drug Interactions mobile application, the effect of the number of drugs used on pDDI, and its relationship with some factors affecting intensive care mortality.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign of the Study\u003c/h2\u003e \u003cp\u003e Approval for the research was received from the Ethics Committee of Istanbul University - Cerrahpasa, Cerrahpasa Medical Faculty (Date: 08.11.2017 Number: 419987). The study was planned as a retrospective cross-sectional study. Clinical Trials registration was not conducted because it was not a prospective clinical trial.\u003c/p\u003e \u003cp\u003eIt was performed in a single center in the 12-bed tertiary ICU of a university hospital. Patients who were admitted to intensive care in 2016 were included in the study. Patient treatment plans were scanned and the names of the drugs used for each patient were recorded one by one on the Excel file.\u003c/p\u003e \u003cp\u003eCriteria for inclusion in the study were determined as being admitted to intensive care, being over 12 years of age, and receiving treatment for 3 days or more. Patients whose files could not be accessed or whose treatment plans were missing were excluded from the study.\u003c/p\u003e \u003cp\u003ePatient length of stay, age, number of chronic diseases, mechanical ventilation (MV) support, hospitalization diagnosis, outcome, names and number of medications used daily, acute physiology and chronic health evaluation II (APACHE II) score were recorded. Drugs used for more than 24 hours were considered as data. Medications prescribed in single doses were not recorded as data. During the analysis phase, hospitalization diagnoses were brought together under certain diagnostic groups.\u003c/p\u003e \u003cp\u003eAll medications were obtained from paper treatment plans. The names, routes of administration, and doses of the drugs were recorded in an Excel file, with a separate column for each patient each day. The treatment plan applied for each day was entered into the Uptodate mobile application and the pDDIs that occurred for the relevant day were recorded. The same procedure was performed repeatedly for each patient's each hospitalization day and pDDIs were recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003epDDI detection with Uptodate\u003c/h2\u003e \u003cp\u003eThe generic names or active ingredients and routes of administration of the drugs administered to a patient within a day were entered into the Uptodate (Lexicomp Drug Interactions) mobile application. The result was obtained with each pair of drugs with potential interactions grouped as A, B, C, D and X. Group A was not recorded as data. B-C interactions were recorded as mild, D interaction as moderate, and X interaction as severe. This process was repeated for each patient's treatment plan for each day of their stay. Interaction types and numbers were recorded on separate days.\u003c/p\u003e \u003cp\u003eThe Uptodate screening tool uses different databases to detect the presence or absence of significant interactions for a given drug pair. If conflicting evidence is presented between these databases, it uses scientific literature and prospectus information to provide clinical practice recommendations to clinicians [10]. In the program, pDDI is grouped as A, B, C, D and X, and the interaction group of each drug pair is taken as the result output.\u003c/p\u003e \u003cp\u003eThe meanings of groups A, B, C, D and X:\u003c/p\u003e \u003cp\u003eIn group A, there are no known drug-drug interactions.\u003c/p\u003e \u003cp\u003eIn group B, data show that the mentioned drugs may interact, but there is no clinical evidence from their concomitant use.\u003c/p\u003e \u003cp\u003eIn group C, data suggest that the indicated agents may interact with each other in a clinically significant manner. The benefits of using these two drugs together usually outweigh the risks. An appropriate monitoring plan should be established to prevent possible adverse effects. Dosage adjustment of agents may be necessary in some patient groups.\u003c/p\u003e \u003cp\u003eIn group D, the data suggest that the two drugs may interact with each other in a clinically meaningful way. Patient-specific evaluation should be made to determine whether the benefits of concomitant treatment outweigh the risks. Precautions should be taken to determine the benefits and/or minimal toxicity that occurs with the use of active substances. These actions include close monitoring, empirical dosage changes, and selection of alternative agents.\u003c/p\u003e \u003cp\u003eIn group X, data indicate that the indicated agents may interact with each other in a clinically significant manner. The risks associated with the combined use of these agents generally outweigh the benefits. These agents are generally contraindicated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eEach patient's total mild, moderate and severe pDDIs, length of stay, age, number of chronic diseases, MV support, hospitalization diagnosis groups, total and average number of drug use were statistically compared with the APACHE II score. Patients are listed in order from least to most according to the total and daily number of medications they used during their hospitalization. It was then divided into 5 consecutive groups. The mean of each group was calculated. The groups were compared statistically in terms of mild, moderate and severe pDDI.\u003c/p\u003e \u003cp\u003eThe suitability of the variables to normal distribution was examined using visual (histogram and probability graphs) and analytical methods (Kolmogorov Smirnov Test). It was determined that not all of the data showed normal distribution. Descriptive analyzes are shown as percentages, and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and median (minimum-maximum) values are given for continuous variables. In data that did not comply with normal distribution, Mann-Whitney U test was used for comparison analyzes between two groups. Comparisons of more than two groups were analyzed using the Kruskal-Wallis Test. Pairwise comparisons of groups with significant Kruskal-Wallis test results were evaluated with the Mann-Whitney U test with Bonferroni correction. Friedman test was used to evaluate more than two repeated measurements of dependent groups. The results are within the 95% confidence interval, and the margin of statistical error is accepted as 0.05. Statistical evaluation was made using the Statistical Package for Social Sciences (SPSS) for Windows 25.0 (IBM SPSS Inc., Chicago, IL) program.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe files of 276 of 835 patients hospitalized in intensive care who met the inclusion criteria were examined (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e55.4% of the patients included in the study were male and 44.6% were female. The average age is 60.98\u0026plusmn;17.11 (age range 14-88). 62% of the patients were 60 years or older (Table 1).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\" style=\"width: 15.6453%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Patient Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.37956204379562%\" valign=\"top\" style=\"width: 40.6997%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eAge (Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003cp\u003eBMI (Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.62043795620438%\" valign=\"top\" style=\"width: 33.8096%;\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003cp\u003e153(55,4)\u003c/p\u003e\n \u003cp\u003e60,98\u0026plusmn;17,11\u003c/p\u003e\n \u003cp\u003e24,60\u0026plusmn;5,89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.10948905109489%\" valign=\"top\" style=\"width: 53.839%;\"\u003e\n \u003cp\u003eDisease History\u003c/p\u003e\n \u003cp\u003eCardiovascular System\u003c/p\u003e\n \u003cp\u003eRespiratory system\u003c/p\u003e\n \u003cp\u003eGastrointestinal System\u003c/p\u003e\n \u003cp\u003eNeurological System\u003c/p\u003e\n \u003cp\u003eRenal System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\" style=\"width: 33.8096%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e%54,7\u003c/p\u003e\n \u003cp\u003e%37\u003c/p\u003e\n \u003cp\u003e%31,2\u003c/p\u003e\n \u003cp\u003e%22,8\u003c/p\u003e\n \u003cp\u003e%19,9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.10948905109489%\" valign=\"top\" style=\"width: 53.839%;\"\u003e\n \u003cp\u003eHospitalization Diagnostic Group\u003c/p\u003e\n \u003cp\u003eRespiratory System Disease\u003c/p\u003e\n \u003cp\u003eShock\u003c/p\u003e\n \u003cp\u003ePostoperative follow-up\u003c/p\u003e\n \u003cp\u003eNeurological System Disease\u003c/p\u003e\n \u003cp\u003eUrinary System Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\" style=\"width: 33.8096%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e118(42,8)\u003c/p\u003e\n \u003cp\u003e89(32,2)\u003c/p\u003e\n \u003cp\u003e35(12,7)\u003c/p\u003e\n \u003cp\u003e25(9,1)\u003c/p\u003e\n \u003cp\u003e9(3,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.10948905109489%\" valign=\"top\" style=\"width: 53.839%;\"\u003e\n \u003cp\u003eMV Support\u003c/p\u003e\n \u003cp\u003eThere is\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\" style=\"width: 33.8096%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e183(66,3)\u003c/p\u003e\n \u003cp\u003e93(33,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.37956204379562%\" valign=\"top\" style=\"width: 40.6997%;\"\u003e\n \u003cp\u003eIntensive Care Result\u003c/p\u003e\n \u003cp\u003eTransfer to service\u003c/p\u003e\n \u003cp\u003eEx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.62043795620438%\" valign=\"top\" style=\"width: 46.7886%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e180(65,2)\u003c/p\u003e\n \u003cp\u003e96(34,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.37956204379562%\" valign=\"top\" style=\"width: 40.6997%;\"\u003e\n \u003cp\u003eTotal Length of stay\u003c/p\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.62043795620438%\" valign=\"top\" style=\"width: 46.7886%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10,76\u0026plusmn;11,63\u003c/p\u003e\n \u003cp\u003e7(5-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.37956204379562%\" valign=\"top\" style=\"width: 40.6997%;\"\u003e\n \u003cp\u003eLength of stay\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3-10\u003c/p\u003e\n \u003cp\u003e11-20\u003c/p\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003cp\u003e31 and over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.62043795620438%\" valign=\"top\" style=\"width: 46.7886%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e192(69,6)\u003c/p\u003e\n \u003cp\u003e50(18,1)\u003c/p\u003e\n \u003cp\u003e21(7,6)\u003c/p\u003e\n \u003cp\u003e13(4,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.37956204379562%\" valign=\"top\" style=\"width: 40.6997%;\"\u003e\n \u003cp\u003eTotal Number of Medicines Ordered\u003c/p\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.62043795620438%\" valign=\"top\" style=\"width: 46.7886%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e111,61\u0026plusmn;130,85\u003c/p\u003e\n \u003cp\u003e69,00(38,25-130,75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.37956204379562%\" valign=\"top\" style=\"width: 40.6997%;\"\u003e\n \u003cp\u003eDaily Number of Medicines Ordered\u003c/p\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.62043795620438%\" valign=\"top\" style=\"width: 46.7886%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9,77\u0026plusmn;3,18\u003c/p\u003e\n \u003cp\u003e9,59(7,67-12,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" style=\"width: 87.6486%;\"\u003e\n \u003cp\u003e\u003csup\u003eSD: Standard Deviation, IQR: Inter Quartile Range\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average APACHE II score was 22.22\u0026plusmn;7.35. The patients were divided into 5 groups according to their hospitalization diagnoses (Appendix 1).\u003c/p\u003e\n\u003cp\u003eWhile the total mild pDDI per patient was 28.5, the daily mild pDDI was 3.32. Moderate pDDI was 3 and 0.36, respectively, while severe interaction was zero in both (Table 2).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Potential Drug-Drug Interaction - Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.021897810218977%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.5985401459854%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.37956204379562%\" valign=\"top\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.021897810218977%\" valign=\"top\"\u003e\n \u003cp\u003eMild Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.5985401459854%\" valign=\"top\"\u003e\n \u003cp\u003e28,50(7,00-69,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.37956204379562%\" valign=\"top\"\u003e\n \u003cp\u003e3,32(1,23-6,62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.021897810218977%\" valign=\"top\"\u003e\n \u003cp\u003eModerate Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.5985401459854%\" valign=\"top\"\u003e\n \u003cp\u003e3(0-9,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.37956204379562%\" valign=\"top\"\u003e\n \u003cp\u003e0,36(0-1,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.021897810218977%\" valign=\"top\"\u003e\n \u003cp\u003eSevere Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.5985401459854%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.37956204379562%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003eIQR: Inter Quartile Range\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1. Total pDDI according to total number of drugs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients were divided into 5 groups of 20% each according to the total number of medications ordered in the intensive care unit. It was determined that mild pDDI increased as the number of drug use increased (p\u0026lt;0.001). In pairwise group comparisons, except for the first-second and second-third group comparisons, it was observed that the moderate pDDI increased as the number of drugs used increased (p\u0026lt;0.001). Severe pDDI was found to be higher in the group using the most medication than in the two groups using the least amount of medication (p\u0026lt;0.001). It was found that pDDI decreased in all groups from mild to severe (Table 3).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Total pDDI According to Total Number of Drugs \u0026ndash; Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eTotal Number of Drugs Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e1) 22,75 \u0026plusmn; 5,91\u003c/p\u003e\n \u003cp\u003e2) 44,69 \u0026plusmn; 7,91\u003c/p\u003e\n \u003cp\u003e3) 71,09 \u0026plusmn; 8,97\u003c/p\u003e\n \u003cp\u003e4)113,15 \u0026plusmn; 18,22\u003c/p\u003e\n \u003cp\u003e5) 302,91 \u0026plusmn; 183,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\" valign=\"top\"\u003e\n \u003cp\u003e3(0-6)\u003c/p\u003e\n \u003cp\u003e9(5-19,5)\u003c/p\u003e\n \u003cp\u003e32(18,5-49)\u003c/p\u003e\n \u003cp\u003e53(34,5-74,5)\u003c/p\u003e\n \u003cp\u003e138(67,5-189,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-1)\u003c/p\u003e\n \u003cp\u003e0(0-4)\u003c/p\u003e\n \u003cp\u003e3(0-7,5)\u003c/p\u003e\n \u003cp\u003e6(1-10,5)\u003c/p\u003e\n \u003cp\u003e19,5(6,5-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-2,5)\u003c/p\u003e\n \u003cp\u003e0(0-2)\u003c/p\u003e\n \u003cp\u003e0(0-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1 Tests used in comparison between groups: Kruskal-Wallis Test, p\u0026lt;0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed.\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2 Tests used for intra-group comparison: Friedman Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003epDDI; Potential Drug-Drug Interaction, IQR; Inter Quartile Range, SD, Standard Deviation\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.2. Daily pDDI according to daily drug numbers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients were divided into 5 groups of 20% each according to the daily number of medications ordered in the intensive care unit. It was determined that mild pDDI was least in the first group, then in the second group, and there was no significant difference between the next three groups. It was found that moderate pDDI was lowest in the first group and the difference between the first and fifth groups was significant (p\u0026lt;0.001). It was determined that severe pDDI was higher in the fifth group than in the first and second groups (p = 0.001). It was found that pDDI decreased from mild to severe in all groups (Table 4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. Daily pDDI According to Mean Number of Medications per Day\u0026ndash; Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eMean Number of Drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\" valign=\"top\"\u003e\n \u003cp\u003e1) 5,51 \u0026plusmn;1,19\u003c/p\u003e\n \u003cp\u003e2) 8,01 \u0026plusmn;0,48\u003c/p\u003e\n \u003cp\u003e3) 9,53 \u0026plusmn;0,44\u003c/p\u003e\n \u003cp\u003e4) 11,34 \u0026plusmn;0,67\u003c/p\u003e\n \u003cp\u003e5) 14,37 \u0026plusmn;1,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e1(0-1,83)\u003c/p\u003e\n \u003cp\u003e2,25(0,82-3,66)\u003c/p\u003e\n \u003cp\u003e4,57(2,53-7,06)\u003c/p\u003e\n \u003cp\u003e4,87(3,20-9,54)\u003c/p\u003e\n \u003cp\u003e5,93(3,20-8,40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0,33)\u003c/p\u003e\n \u003cp\u003e0(0-0,78)\u003c/p\u003e\n \u003cp\u003e0,50(0-1,04)\u003c/p\u003e\n \u003cp\u003e0,75(0,10-1,11)\u003c/p\u003e\n \u003cp\u003e0,90(0,21-1,44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-0,42)\u003c/p\u003e\n \u003cp\u003e0(0-0,23)\u003c/p\u003e\n \u003cp\u003e0(0-0,49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1 Tests used in comparison between groups: Kruskal-Wallis Test, p\u0026lt;0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed.\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2 Tests used for intra-group comparison: Friedman Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003epDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.3. Total pDDI according to length of stay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients were divided into 4 groups according to their length of stay (Table 5). Moderate pDDI was found less in patients hospitalized for 3-10 days (p\u0026lt;0.001). Severe pDDI was more common in patients hospitalized for 21-30 days than in patients hospitalized for 3-10 days (p=0.010). pDDI was found to decrease from mild to severe interaction in all groups (p\u0026lt;0.001).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5. Total pDDI According to Length of Stay \u0026ndash; Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eLength of Stay (Days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\"\u003e\n \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3-10 (n=192)\u003c/p\u003e\n \u003cp\u003e11-20 (n=50)\u003c/p\u003e\n \u003cp\u003e21-30 (n=21)\u003c/p\u003e\n \u003cp\u003e31 or more (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e14,00(4-36)\u003c/p\u003e\n \u003cp\u003e62,50(29-107)\u003c/p\u003e\n \u003cp\u003e143,00(54-171)\u003c/p\u003e\n \u003cp\u003e279,00(147-370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1,00(0-5)\u003c/p\u003e\n \u003cp\u003e8,00(2-19)\u003c/p\u003e\n \u003cp\u003e20,00(7-34)\u003c/p\u003e\n \u003cp\u003e32,00(11-61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-3)\u003c/p\u003e\n \u003cp\u003e0(0-11)\u003c/p\u003e\n \u003cp\u003e0(0-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0,010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1 Tests used in comparison between groups: Kruskal-Wallis Test, p\u0026lt;0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed.\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2 Tests used for intra-group comparison: Friedman Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003epDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.4. Total pDDI according to hospitalization diagnosis groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients divided into 5 groups according to their hospitalization diagnosis (Appendix 1) were compared according to the degree of pDDI (Table 6). Mild and moderate pDDI was observed less frequently in the postoperative follow-up group than in the neurological system, respiratory system and shock groups (p\u0026lt;0.001). Severe pDDI was also observed to be less common in the postoperative follow-up group than in the respiratory system and shock groups (p = 0.017). It was determined that pDDI decreased from mild to severe in all groups.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6. Total pDDI According to Hospitalization Diagnosis \u0026ndash;Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.673469387755105%\"\u003e\n \u003cp\u003eHospitalization Diagnosis Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003eNeurological System (n=25)\u003c/p\u003e\n \u003cp\u003ePostoperative follow-up (n=35)\u003c/p\u003e\n \u003cp\u003eRespiratory System (n=118)\u003c/p\u003e\n \u003cp\u003eShock (n=89)\u003c/p\u003e\n \u003cp\u003eUrinary System (n=9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e41(26-73)\u003c/p\u003e\n \u003cp\u003e7(3-19,5)\u003c/p\u003e\n \u003cp\u003e36(9-76)\u003c/p\u003e\n \u003cp\u003e27(7-73)\u003c/p\u003e\n \u003cp\u003e11(7-36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e8(2-16)\u003c/p\u003e\n \u003cp\u003e0(0-2)\u003c/p\u003e\n \u003cp\u003e3(0-9)\u003c/p\u003e\n \u003cp\u003e3(0-9)\u003c/p\u003e\n \u003cp\u003e3(1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-2)\u003c/p\u003e\n \u003cp\u003e0(0-1)\u003c/p\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.673469387755105%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1 Tests used in comparison between groups: Kruskal-Wallis Test, p\u0026lt;0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed.\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2 Tests used for intra-group comparison: Friedman Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003epDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.5. Total pDDI according to survival and mechanical ventilation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMild and moderate pDDI was found to be higher in patients who died (Table 7). Mild and moderate pDDI was found to be higher in patients receiving MV support (Table 7).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 7. Total pDDI According to Survival and Mechanical Ventilation \u0026ndash; Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003eICU Result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" colspan=\"2\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" colspan=\"2\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003eAlive (n=180)\u003c/p\u003e\n \u003cp\u003eExitus (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\" valign=\"top\"\u003e\n \u003cp\u003e16(5-42)\u003c/p\u003e\n \u003cp\u003e57,5(29,5-107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1,5(0-7,5)\u003c/p\u003e\n \u003cp\u003e4,5(2-15,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" colspan=\"2\"\u003e\n \u003cp\u003e0,107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003eMV Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.77777777777777%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003eYes (n=183)\u003c/p\u003e\n \u003cp\u003eNo (n=93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e37(12,5-84)\u003c/p\u003e\n \u003cp\u003e9(4-36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4(1-12,5)\u003c/p\u003e\n \u003cp\u003e0(0-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-1)\u003c/p\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e0,243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1 Tests used in comparison between groups: Kruskal-Wallis Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2 Tests used for intra-group comparison: Friedman Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003epDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range, ICU: Intensive Care Unit, MV: Mechanical Ventilation\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.6. Total pDDI according to the number of systemic diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients were divided into 3 groups according to the number of systemic diseases they had before admission. Mild pDDI was found to be more common in groups with more systemic disease (p\u0026lt;0.001). There was no difference between the groups in terms of moderate pDDI (p=0.285). It was found that severe pDDI was more common in patients with 3 or more systemic diseases than in those with no systemic disease (p = 0.022). It was found that pDDI decreased in all groups from mild to severe (Table 8).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 8. Total pDDI According to Number of Systemic Diseases \u0026ndash; Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eNumber of Systemic Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" valign=\"top\"\u003e\n \u003cp\u003eNo (n=23)\u003c/p\u003e\n \u003cp\u003e1-2 (n=138)\u003c/p\u003e\n \u003cp\u003e\u0026sup3;3 (n=115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e6(1,5-25,5)\u003c/p\u003e\n \u003cp\u003e24,5(5-57)\u003c/p\u003e\n \u003cp\u003e39(14,5-92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e2(0-9)\u003c/p\u003e\n \u003cp\u003e3(0-8)\u003c/p\u003e\n \u003cp\u003e3(0-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-2,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0,285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0,022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1 Tests used in comparison between groups: Kruskal-Wallis Test, p\u0026lt;0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed.\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2 Tests used for intra-group comparison: Friedman Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003epDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.7. Total pDDI according to APACHE II score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were divided into 3 groups in terms of APACHE II score and pDDI values were compared. Mild and moderate pDDI was found to be higher in the 2 groups with an APACHE II score of 20 or more (p\u0026lt;0.001 and p\u0026lt;0.006). There was no difference in terms of severe pDDI (p=0.713). It was found that pDDI decreased in all groups from mild to severe (Table 9).\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 9. Total pDDI According to APACHE II Score \u0026ndash; Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.77319587628866%\"\u003e\n \u003cp\u003eAPACHE II Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.77319587628866%\" valign=\"top\"\u003e\n \u003cp\u003e0-19 (n=101)\u003c/p\u003e\n \u003cp\u003e20-29 (n=136)\u003c/p\u003e\n \u003cp\u003e30 or more (n=39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e14(4-41)\u003c/p\u003e\n \u003cp\u003e30(9-73)\u003c/p\u003e\n \u003cp\u003e45(29,5-108,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" valign=\"top\"\u003e\n \u003cp\u003e2(0-6)\u003c/p\u003e\n \u003cp\u003e3(0-10)\u003c/p\u003e\n \u003cp\u003e5(1-18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-0)\u003c/p\u003e\n \u003cp\u003e0(0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.77319587628866%\"\u003e\n \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026lt;0,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e0,713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1 Tests used in comparison between groups: Kruskal-Wallis Test, p\u0026lt;0.05 was considered significant. In pairwise group comparisons, Bonferroni correction was applied and Mann Whitney U Test was performed.\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e2 Tests used for intra-group comparison: Friedman Test, p\u0026lt;0.05 was considered significant.\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003epDDI: Potential Drug-Drug Interaction, IQR: Inter Quartile Range, APACHE II: Acute Physiology and Chronic Health Evaluation II\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, it was determined that as the number of medications administered during intensive care stay increased, the potential mild, moderate and severe drug-drug interactions detected by Uptodate increased. Additionally, it was determined that an increase in the length of stay in the intensive care unit was associated with an increase in pDDI.\u003c/p\u003e \u003cp\u003eMild pDDI detected are approximately 10 times more common than moderate pDDI. Mild pDDI has minimal impact on the patient's clinic or the use of medication is more likely to benefit the patient. Moderate and severe pDDI is more clinically significant and was found to be highest in the patient group who used the most medication.\u003c/p\u003e \u003cp\u003eAccording to the results of our study, mild and moderate interactions increased with the duration of ICU stay, while no clear results were obtained for severe interactions. This situation suggests that even though the patients' stay is long, physicians are careful not to use drugs together that may cause serious interactions. However, the possible reasons why pDDIs are less common in the postoperative patient group may be the shorter length of stay and the lower number of medications used. Similarly, the fact that severe pDDI, but not moderate pDDI, was found more frequently in patients with more diseases before intensive care unit admission can be explained by the possibility of using multiple medications in those with multiple diseases. Another explanation may be that intensive care physicians' level of knowledge about drugs used outside intensive care is low.\u003c/p\u003e \u003cp\u003eIn the study conducted by Uijtendaal et al. [11], who reached similar results to those obtained in the current study, it was determined that 54% of the patients were exposed to at least one pDDI on 27% of the hospitalization days. In the same study, it was determined that the number of days and number of patients exposed to \u0026ge;1 pDDI increased in patients with long stay in the intensive care unit, high expected mortality rate according to APACHE IV, chronic diseases and high number of medications used, in patients who received MV support and who died in intensive care. In addition, similar to our results on the effect of length of stay, Guti\u0026eacute;rrez-Valencia et al. [12] found in their study that the number of medications used temporarily during ICU stay increased significantly and PIIE increased due to this increase.\u003c/p\u003e \u003cp\u003eIn the meta-analysis conducted by Fitzmaurice et al. [13] to investigate pDDI in the intensive care unit, it was emphasized that 58% of patients admitted to the intensive care unit may be exposed to at least one pDDI. It has been stated that higher risk drugs are typically given to critically ill patients compared to other patient populations, and therefore pDDI may occur between 1 and 10 times per patient. In another meta-analysis, it was stated that 67% of patients in intensive care units were exposed to at least one pDDI [14]. In our study, the daily and total values of mild pDDI were found to be significantly higher than those of moderate and severe pDDI. However, our findings of moderate and severe interactions, which are more clinically significant, were found to be similar to the study by Fitzmaurice et al.\u003c/p\u003e \u003cp\u003eMany studies [2, 16, 17], such as Jain et al. [15], who emphasized that as age increases, systemic diseases increase and the number of drugs used may increase accordingly, it has been observed that pDDI increases with the number of drugs used. Similar results were obtained in our study.\u003c/p\u003e \u003cp\u003eRodrigues et al. showed that the duration of stay in intensive care was longer in patients with more PDDI and more severe pDDI. In our study, it was determined that mild, moderate and severe pDDI increased as the duration of stay in intensive care increased. Depending on the severity of the critical illness, the length of stay of patients in intensive care units may vary and patients may receive quite complex treatments during this period. The cause and effect relationship between patients with a high number of pDDIs and the prolonged stay of these patients in intensive care is not clear. Although long periods of stay in the intensive care unit increase the risk of pDDI, we think that the negative clinical responses caused by pDDI may also prolong the stay of patients in the intensive care unit.\u003c/p\u003e \u003cp\u003eIt has been shown that prolonged mechanical ventilation due to sedation, fluid overload, and exceeding therapeutic drug concentrations are less common in intensive care units where clinical pharmacologists take an active role [20]. Although pharmaceutical care is practiced across many disciplines, critically ill patients require additional evaluation due to the complexity of medication regimens and disease states. We are aware of the importance of clinical pharmacologists as a part of the multidisciplinary team, and we think that in addition to all these positive aspects, they can also contribute to the training of the intensive care team.\u003c/p\u003e \u003cp\u003eMedication errors may be more common in patients with multiple drug use, long hospital stays and multiple organ failure. In this respect, we think that intensive care units are very important in terms of treatment planning, administration, monitoring and evaluation of results. Electronic order systems warn healthcare professionals about appropriate dosage, correct drug selection, and drug-drug interactions while creating a treatment plan. For this reason, we think that it is useful to use an electronic order system that warns the physician who decides on the treatment and the nurses who apply it, in order to minimize the margin of error in intensive care units where a patient-based treatment plan is made.\u003c/p\u003e \u003cp\u003eAlthough drug combinations causing interactions were not recorded in our study, combivent and quetiapine, combivent and carvedilol stand out as the most common combinations that cause severe pDDI. In our study, severe pDDI was detected much less frequently than moderate and mild pDDI. We think that this situation may have arisen because physicians are better aware of drug combinations that are contraindicated to be used together.\u003c/p\u003e \u003cp\u003eSome enteral nutrition products and blood products are not available in the database we use to detect pDDI. The interaction of the ingredients in these treatments with other drugs has not been evaluated.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAs a result, in the current study, in the screening performed on critically ill patients with the Uptodate mobile application, it was determined that PDDI increased as the number of drugs used in the intensive care unit increased, and that there was a relationship between length of stay and pDDI rates.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003epDDI, potential drug-drug interaction; ICU, intensive care unit; MV, mechanical ventilation; APACHE II, acute physiology and chronic health evaluation II; SD, standard deviation;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest in writing this manuscript.\u003c/p\u003e\n\u003cp\u003eThis manuscript has received no funding.\u003c/p\u003e\n\u003cp\u003eThe authors did not use a Generative AI and AI-assisted technologies.\u003c/p\u003e\n\u003cp\u003eThe dataset used for the completion of this study are available from the corresponding author under reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest that relate to the research described in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was in accordance with the Declaration of Helsinki and later revisions of ethical principles for medical research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical commitee waived the informed consent due to retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Munevver Kayhan, Oguzhan Kayhan, Yalim Dikmen and Mehmet Aykut Ozturk. The first draft of the manuscript was written by Munevver Kayhan and Oguzhan Kayhan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVanham D, Spinewine A, Hantson P, Wittebole X, Wouters D, Sneyers B. Drug-drug interactions in the intensive care unit: Do they really matter? J Crit Care. 2017;38:97-103.\u003c/li\u003e\n\u003cli\u003eSmithburger PL, Kane-Gill SL, Seybert AL. Drug-drug interactions in the medical intensive care unit: an assessment of frequency, severity and the medications involved. Int J Pharm Pract. 2012;20(6):402-8.\u003c/li\u003e\n\u003cli\u003eCullen DJ, Sweitzer BJ, Bates DW, Burdick E, Edmondson A, Leape LL. Preventable adverse drug events in hospitalized patients: a comparative study of intensive care and general care units. Crit Care Med. 1997;25(8):1289-97.\u003c/li\u003e\n\u003cli\u003ePlaza J, Alamo M, Torres P, Fuentes A, L\u0026oacute;pez F. Drug interactions and adverse events induced by drugs used in an intensive care unit. Revista medica de Chile. 2010;138(4):452-60.\u003c/li\u003e\n\u003cli\u003eAlvim MM, Silva LA, Leite IC, Silv\u0026eacute;rio MS. Adverse events caused by potential drug-drug interactions in an intensive care unit of a teaching hospital. Rev Bras Ter Intensiva. 2015;27(4):353-9.\u003c/li\u003e\n\u003cli\u003eIsmail M, Khan F, Noor S, Haider I, Haq IU, Ali Z, et al. Potential drug-drug interactions in medical intensive care unit of a tertiary care hospital in Pakistan. Int J Clin Pharm. 2016;38(5):1052-6.\u003c/li\u003e\n\u003cli\u003eMurtaza G, Khan MY, Azhar S, Khan SA, Khan TM. Assessment of potential drug-drug interactions and its associated factors in the hospitalized cardiac patients. Saudi Pharm J. 2016;24(2):220-5.\u003c/li\u003e\n\u003cli\u003eMoura C, Prado N, Acurcio F. Potential drug-drug interactions associated with prolonged stays in the intensive care unit: a retrospective cohort study. Clin Drug Investig. 2011;31(5):309-16.\u003c/li\u003e\n\u003cli\u003eMonteiro CRA, Schoueri JHM, Cardial DT, Linhares LC, Turke KC, Steuer LV, et al. Evaluation of the systemic and therapeutic repercussions caused by drug interactions in oncology patients. Rev Assoc Med Bras (1992). 2019;65(5):611-7.\u003c/li\u003e\n\u003cli\u003ePatel D, Bertz R, Ren S, Boulton DW, N\u0026aring;g\u0026aring;rd M. A Systematic Review of Gastric Acid-Reducing Agent-Mediated Drug-Drug Interactions with Orally Administered Medications. Clin Pharmacokinet. 2020;59(4):447-62.\u003c/li\u003e\n\u003cli\u003eUijtendaal EV, van Harssel LL, Hugenholtz GW, Kuck EM, Zwart-van Rijkom JE, Cremer OL, et al. Analysis of potential drug-drug interactions in medical intensive care unit patients. Pharmacotherapy. 2014;34(3):213-9.\u003c/li\u003e\n\u003cli\u003eGuti\u0026eacute;rrez‐Valencia M, Izquierdo M, Malafarina V, Alonso‐Renedo J, Gonz\u0026aacute;lez‐Glar\u0026iacute;a B, Larrayoz‐Sola B, et al. Impact of hospitalization in an acute geriatric unit on polypharmacy and potentially inappropriate prescriptions: A retrospective study. Geriatrics \u0026amp; gerontology international. 2017;17(12):2354-60.\u003c/li\u003e\n\u003cli\u003eFitzmaurice MG, Wong A, Akerberg H, Avramovska S, Smithburger PL, Buckley MS, et al. Evaluation of Potential Drug\u0026ndash;Drug Interactions in Adults in the Intensive Care Unit: A Systematic Review and Meta-Analysis. Drug safety. 2019:1-10.\u003c/li\u003e\n\u003cli\u003eZheng WY, Richardson L, Li L, Day R, Westbrook J, Baysari M. Drug-drug interactions and their harmful effects in hospitalised patients: a systematic review and meta-analysis. European journal of clinical pharmacology. 2018;74(1):15-27.\u003c/li\u003e\n\u003cli\u003eJain S, Jain P, Sharma K, Saraswat P. A prospective analysis of drug interactions in patients of intensive cardiac care unit. Journal of clinical and diagnostic research: JCDR. 2017;11(3):FC01.\u003c/li\u003e\n\u003cli\u003eCruciol-Souza JM, Thomson JC. Prevalence of potential drug-drug interactions and its associated factors in a Brazilian teaching hospital. J Pharm Pharm Sci. 2006;9(3):427-33.\u003c/li\u003e\n\u003cli\u003eGrenouillet-Delacre M, Verdoux H, Moore N, Haramburu F, Miremont-Salam\u0026eacute; G, Etienne G, et al. Life-threatening adverse drug reactions at admission to medical intensive care: a prospective study in a teaching hospital. Intensive Care Med. 2007;33(12):2150-7.\u003c/li\u003e\n\u003cli\u003eRodrigues AT, Stahlschmidt R, Granja S, Pilger D, Falc\u0026atilde;o ALE, Mazzola PG. Prevalence of potential drug-drug interactions in the intensive care unit of a Brazilian teaching hospital. Brazilian Journal of Pharmaceutical Sciences. 2017;53(1).\u003c/li\u003e\n\u003cli\u003eSmithburger PL, Gill SLK, Benedict NJ, Falcione BA, Seybert AL. Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Annals of Pharmacotherapy. 2010;44(11):1718-24.\u003c/li\u003e\n\u003cli\u003eAskari M, Eslami S, Louws M, Wierenga PC, Dongelmans DA, Kuiper RA, et al. Frequency and nature of drug-drug interactions in the intensive care unit. Pharmacoepidemiol Drug Saf. 2013;22(4):430-7.\u003c/li\u003e\n\u003cli\u003eCvikl M, Sinkovič A. Interventions of a clinical pharmacist in a medical intensive care unit\u0026ndash;A retrospective analysis. Bosnian Journal of Basic Medical Sciences. 2020.\u003c/li\u003e\n\u003cli\u003eColpaert K, Claus B, Somers A, Vandewoude K, Robays H, Decruyenaere J. Impact of computerized physician order entry on medication prescription errors in the intensive care unit: a controlled cross-sectional trial. Critical Care. 2006;10(1):R21.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Critically ill patient, Intensive care unit, Drug-drug interaction, Adverse drug reactions, Uptodate","lastPublishedDoi":"10.21203/rs.3.rs-3511386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3511386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e It was aimed to investigate the frequency of potential drug-drug interactions (pDDI) and the effect of the number of drugs used on pDDI with the Uptodate drug interactions application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Patients older than 12 years of age who were treated in the intensive care unit for 3 days or more in 2016 were included in the study. pDDIs were detected by entering the drugs used for more than 24 hours into the Uptodate application. The total number of mild, moderate and severe pDDIs and the number of medications used, length of stay, age, number of chronic diseases, mechanical ventilation (MV) support, hospitalization diagnoses, and APACHE II score were compared statistically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e While PIE was found to increase with the number of medications administered, it was found that it did not show an exact association with the number of days of hospitalization. However, it was higher in patients who received MV support, had a high APACHE II score, and died. pDDI was seen least in the postoperative follow-up diagnosis group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e It was determined that pDDI increased as the number of medications used in critically ill patients increased.\u003c/p\u003e","manuscriptTitle":"Potential Drug-Drug Interaction Detection Using the Uptodate Mobİle Application in Intensive Care: A Retrospective, Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-02 20:40:50","doi":"10.21203/rs.3.rs-3511386/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9736321a-5232-4b98-9742-ce6b8d7a973d","owner":[],"postedDate":"November 2nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-08T17:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-02 20:40:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3511386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3511386","identity":"rs-3511386","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}