A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey

[EN] The paper aims to compare the results of the selection/choice of cream separators by using multi-criteria decision-making methods in an integrated manner for an enterprise with a dairy processing capacity of 80 to 100 tons per day operating in the Turkish food sector. A total of 7 alternative products and 7 criteria for milk processing were determined. Criterion weights were calculated using entropy method and then integrated into TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions), GRA (Grey Relational Analysis) and COPRAS (Complex Proportional Assessment) methods. Sensitivity analyses were carried out on the results obtained from the three methods to check for their reliability. At the end of the study, similar alternative and appropriate results were found from the TOPSIS and COPRAS methods. However, different alternative but appropriate or suitable results were obtained from the GRA method. Sensitivity analysis of the three methods showed that all the methods used were valid. In the review of available and related literature, very few studies on machine selection in the dairy and food sector in general were found. For this reason, it is thought that the study will contribute to the decision-making process of companies in the dairy sector in their choice of machinery selections. As far as is known, this paper is the first attempt in extant literature to compare in an integrated manner the results of TOPSIS, COPRAS and GRA methods considered in the study.Özcan, S.; Çelik, AK. (2021). A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey. International Journal of Production Management and Engineering. 9(2):81-92. https://doi.org/10.4995/ijpme.2021.14734OJS819292Ahmed, M., Qureshi, M.N., Mallick, J., Kahla, N.B. (2019). Selection of sustainable supplementary concrete materials using OSM-AHP-TOPSIS approach. Advances in Materials Science and Engineering, 2019, 1-12. https://doi.org/10.1155/2019/2850480Aloini, D., Dulmin, R., Mininno, V. (2014). A peer IF-TOPSIS based decision support system for packaging machine selection. Expert Systems with Applications, 41(5), 2157-2165https://doi.org/10.1016/j.eswa.2013.09.014Alpay, S., Ihpar, M. (2018). Equipment selection based on two different fuzzy multi criteria decision making methods: Fuzzy TOPSIS and fuzzy VIKOR. Open Geosciences, 10(1), 661-677. https://doi.org/10.1515/geo-2018-0053Antucheviciene, J., Zavadskas, E.K., Zakarevičius, A. (2012). Ranking redevelopment decisions of derelict buildings and analysis of ranking results. Economic Computation and Economic Cybernetics Studies and Research, 46(2), 37-63. Retrieved June 08, 2020 from http://www.ecocyb.ase.ro/22012/Edmundas%20ZAVADSKAS%20_DA_.pdfAyağ, Z., Özdemir, R.G. (2006). A fuzzy AHP approach to evaluating machine tool alternatives. Journal of Intelligent Manufacturing, 17(2), 179-190. https://doi.org/10.1007/s10845-005-6635-1Belton, V., Stewart, T.J. (2002). Multiple criteria decision analysis: An integrated approach. Berlin: Kluwer Academic Publishers.https://doi.org/10.1007/978-1-4615-1495-4Camcı, A., Temur, G.T., Beşkese, A. (2018). CNC router selection for SMEs in woodwork manufacturing using hesitant fuzzy AHP method. Journal of Enterprise Information Management, 31(4), 529-549. https://doi.org/10.1108/JEIM-01-2018-0017Çakır, S. (2018). An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design. Journal of Intelligent Manufacturing, 29(7), 1433-1445. https://doi.org/10.1007/s10845-015-1189-3Çelen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: With an application to Turkish deposit banking market. Informatica, 25(2), 185-208. https://doi.org/10.15388/Informatica.2014.10Chandan, R.C. (2008). Dairy Processing and Quality Assurance: An Overview. Ramesh C. Chandan, Arun Kilara, Nagendra Shah (Eds.), In Dairy Processing and Quality Assurance (pp. 1-40). New Jersey: Wiley-Blackwell. https://doi.org/10.1002/9780813804033Chatterjee, P., Chakraborty, S. (2014). Investigating the effect of normalization norms in flexible manfacturing sytem selection using Multi-Criteria Decision-Making methods. Journal of Engineering Science and Technology Review, 7(3), 141-150. https://doi.org/10.25103/jestr.073.23Clarke, M.P., Denby, B., Schofield, D. (1990). Decision making tools for surface mine equipment selection. Mining Science and Technology, 10(3), 323-335. https://doi.org/10.1016/0167-9031(90)90530-6Datta, S., Sahu, N., Mahapatra, S. (2013). Robot selection based on grey-MULTIMOORA approach. Grey Systems: Theory and Application, 3(2), 201-232. https://doi.org/10.1108/GS-05-2013-0008Deng, H., Yeh, C.H., Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers and Operations Research, 27(10), 963-973. https://doi.org/10.1016/S0305-0548(99)00069-6Doğan, M., Aslan, D., Aktar, T., Sarac, M.G. (2016). A methodology to evaluate the sensory properties of instant hot chocolate beverage with different fat contents: multi-criteria decision-making techniques approach. European Food Research and Technology, 242(6), 953-966. https://doi.org/10.1007/s00217-015-2602-zErtuğrul, İ., Güneş, M. (2007). Fuzzy multi-criteria decision making method for machine selection. P. Melin, O. Castillo, E.G. Ramirez, J. Kacprzyk and W. Pedrycz (Eds.), In Analysis and Design of Intelligent Systems Using Soft Computing Techniques (pp. 638-648). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-540-72432-2_65Ertuğrul, İ., Öztaş, T. (2015). The application of sewing machine selection with the multi-objective optimization on the basis of ratio analysis method (MOORA) in apparel sector. Textile and Apparel, 25(1), 80-85. Retrieved May 17, 2020 from https://dergipark.org.tr/tr/pub/tekstilvekonfeksiyon/issue/23647/251887FAO. (2019a). Dairy Market Review. FAO Publishing, Rome.FAO. (2019b). Food Outlook - Biannual Report on Global Food Markets. FAO Publishing, Rome.Feizabadi, A., Doolabi, M.S., Sadrnezhaad, S.K., Zafarani, H.R., Doolabi, D.S. (2017). MCDM selection of pulse parameters for best tribological performance of Cr-Al2O3 nano-composite co-deposited from trivalent chromium bath. Journal of Alloys and Compounds, 727, 286-296. https://doi.org/10.1016/j.jallcom.2017.08.098Feng, C.M., Wang, R.T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142. https://doi.org/10.1016/S0969-6997(00)00003-XGuo, X., Sun, Z. (2016). A novel evaluation approach for tourist choice of destination based on grey relation analysis. Scientific Programming, 2016, 1-10. https://doi.org/10.1155/2016/1812094Gurmeric, V.E., Dogan, M., Toker, O.S., Senyigit, E., Ersoz, N.B. (2013). Application of different multi-criteria decision techniques to determine optimum flavour of prebiotic pudding based on sensory analyses. Food and Bioprocess Technology, 6(10), 2844-2859. https://doi.org/10.1007/s11947-012-0972-9Hwang, C.L., Yoon, K. (1980). Multiple attribute decision making methods and applications: A state-of-the-art survey. New York: Springer-Verlag.Jahan, A., Yazdani, M., Edwards, K.L. (2021). TOPSIS-RTCID for range target-based criteria and interval data. International Journal of Production Management and Engineering, 9(1), 1-14. https://doi.org/10.4995/ijpme.2021.13323Kabak, M., Dağdeviren, M. (2017). A hybrid approach based on ANP and Grey Relational Analysis for machine selection. Technical Gazette, 24(Supplement 1), 109-118. https://doi.org/10.17559/TV-20141123105333Kang, H.Y., Lee, A.H.I., Yang, C.Y. (2012). A fuzzy ANP model for supplier selection as applied to IC packaging. Journal of Intelligent Manufacturing, 23(5), 1477-1488.https://doi.org/10.1007/s10845-010-0448-6Karaman, S.,Toker, Ö.S., Yüksel, F., Çam, M., Kayacier, A., Dogan, M. (2014). Physicochemical, bioactive, and sensory properties of persimmon-based ice cream: Technique for order preference by similarity to ideal solution to determine optimum concentration. Journal of Dairy Science, 97(1), 97-110. https://doi.org/10.3168/jds.2013-7111Karim, R., Karmaker, C.L. (2016). Machine selection by AHP and TOPSIS methods. American Journal of Industrial Engineering, 4(1), 7-13. https://doi.org/10.12691/ajie-4-1-2Kumru, M., Kumru, P.Y. (2015). A fuzzy ANP model for the selection of 3D coordinate-measuring machine. Journal of Intelligent Manufacturing, 26(5), 999-1010. https://doi.org/10.1007/s10845-014-0882-yNguyen, H.T., Dawal, S. Z. Md., Nukman, Y., Aoyama, H. (2014). A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes. Expert Systems with Applications, 41(6), 3078-3090. https://doi.org/10.1016/j.eswa.2013.10.039OECD/FAO. (2019). OECD-FAO Agricultural Outlook 2019-2028. OECD Publishing, Paris.Önüt, S., Kara, S.S., Işik, E. (2009). Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company. Expert Systems with Applications, 36(2), 3887-3895. https://doi.org/10.1016/j.eswa.2008.02.045Özceylan, E., Kabak, M., Dağdeviren, M. (2016). A fuzzy-based decision making procedure for machine selection problem. Journal of Intelligent and Fuzzy Systems, 30(3), 1841-1856. https://doi.org/10.3233/IFS-151895Özdağoğlu, A., Yakut, E., Bahar, S. (2017). Machine selection in a dairy product company with Entropy and SAW methods integration. Faculty of Economics and Administrative Sciences Journal, 32(1), 341-359. https://doi.org/10.24988/deuiibf.2017321605Özgen, A., Tuzkaya, G., Tuzkaya, U.R., Özgen, D. (2011). A multi-criteria decision making approach for machine tool selection problem in a fuzzy environment. International Journal of Computational Intelligence Systems, 4(4), 431-445. https://doi.org/10.1080/18756891.2011.9727802Ozturk, G., Dogan, M., Toker, O.S. (2014). Physicochemical, functional and sensory properties of mellorine enriched with different vegetable juices and TOPSIS approach to determine optimum juice concentration. Food Bioscience, 7, 45-55. https://doi.org/10.1016/j.fbio.2014.05.001Pang, B., Bai, S. (2013). An integrated fuzzy synthetic evaluation approach for supplier selection based on analytic network process. Journal of Intelligent Manufacturing, 23(5), 163-174. https://doi.org/10.1007/s10845-011-0551-3Paramasivam, V., Senthil, V., Ramasamy, N.R. (2011). Decision making in equipment selection: an integrated approach with digraph and matrix approach, AHP and ANP. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1233-1244. https://doi.org/10.1007/s00170-010-2997-4Pavličić, D.M. (2001). Normalisation affects the results of MADM methods. Yugoslav Journal of Operations Research, 11(2), 251-265. Retrieved May 6, 2020 from http://scindeks.ceon.rs/article.aspx?artid=0354-02430102251PSamanta, B., Sarkar, B., Mukherjee, S.K. (2002). Selection of opencast mining equipment by a multi-criteria decision-making process. Mining Technology, 111(2), 136-142. https://doi.org/10.1179/mnt.2002.111.2.136Seçme, N.Y., Bayrakdaroğlu, A., Kahraman, C. (2009). Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications, 36(9), 11699-11709. https://doi.org/10.1016/j.eswa.2009.03.013Sharma, A., Yadava, V. (2011). Optimization of cut quality characteristics during nd:yag laser straight cutting of ni-based superalloy thin sheet using grey relational analysis with entropy measurement. Materials and Manufacturing Processes, 26(12), 1522-1529. https://doi.org/10.1080/10426914.2011.551910Shih, H. S., Shyur, H.J., Lee, E.S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8), 801-813. https://doi.org/10.1016/j.mcm.2006.03.023Stanujkic, D., Đorđević, B., Đorđević, M. (2013). Comparative analysis of some prominent MCDM methods: A case of ranking Serbian Banks. Serbian Journal of Management, 8(2), 213-241. https://doi.org/10.5937/sjm8-3774Štirbanović, Z., Stanujkić, D., Miljanović, I., Milanović, D. (2019). Application of MCDM methods for flotation machine selection. Minerals Engineering, 137, 140-146. https://doi.org/10.1016/j.mineng.2019.04.014Sun, C.C. (2014). Combining grey relation analysis and entropy model for evaluating the operational performance: An empirical study. Quality and Quantity, 48(3), 1589-1600. https://doi.org/10.1007/s11135-013-9854-0Taha, Z., Rostam, S. (2011). A fuzzy AHP-ANN-based decision support system for machine tool selection in a flexible manufacturing cell. International Journal of Advanced Manufacturing Technology, 57(5-8), 719-733. https://doi.org/10.1007/s00170-011-3323-5Temiz, I., Çalış, G. (2017). Selection of construction equipment by using multi-criteria decision making methods. Procedia Engineering, 196, 286-293. https://doi.org/10.1016/j.proeng.2017.07.201Tosun, N. (2006). Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. The International Journal of Advanced Manufacturing Technology, 28(5-6), 450-455. https://doi.org/10.1007/s00170-004-2386-yUğur, L.O. (2017). Application of the VIKOR multi-criteria decision method for construction machine buying. Journal of Polytechnic, 20(4), 879-885. https://doi.org/10.2339/politeknik.369058Ulubeyli, S., Kazaz, A. (2009). A multiple criteria decision-making approach to the selection of concrete pumps. Journal of Civil Engineering and Management, 15(4), 369-376. https://doi.org/10.3846/1392-3730.2009.15.369-376Vafaei, N., Ribeiro, R.A., Camarinha-Matos, L.M. (2018). Data normalisation techniques in decision making: Case study with TOPSIS method. International Journal of Information and Decision Sciences, 10(1), 19-38. https://doi.org/10.1504/IJIDS.2018.090667Vatansever, K., Kazançoğlu, Y. (2014). Integrated usage of fuzzy multi criteria decision making techniques for machine selection problems and an application. International Journal of Business and Social Science, 5(9), 12-24. https://doi.org/10.1504/IJIDS.2018.090667https://doi.org/10.1504/IJIDS.2018.090667Wang, T.C., Lee, H.D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, 36(5), 8980-8985. https://doi.org/10.1016/j.eswa.2008.11.035Wu, J., Sun, J., Liang, L., Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165. https://doi.org/10.1016/j.eswa.2010.10.046Wu, W., Peng, Y. (2016). Extension of grey relational analysis for facilitating group consensus to oil spill emergency management. Annals of Operations Research, 238(1-2), 615-635. https://doi.org/10.1007/s10479-015-2067-2Wu, Z., Ahmad, J., Xu, J. (2016). A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information. Applied Soft Computing, 42, 314-324. https://doi.org/10.1016/j.asoc.2016.02.007Yazdani-Chamzini, A., Yakhchali, S.H. (2012). Tunnel Boring Machine (TBM) selection using fuzzy multicriteria decision making methods. Tunnelling and Underground Space Technology, 30, 194-204. https://doi.org/10.1016/j.tust.2012.02.021Yılmaz, B., Dağdeviren, M. (2010). Comparative analysis of PROMETHEE and fuzzy PROMETHEE methods in equipment selection problem. Journal of the Faculty of Engineering and Architecture of Gazi University, 25(4), 811-826. Retrieved May 6, 2020 from https://avesis.gazi.edu.tr/yayin/989e528e-9184-4d8e-8970-fccfabbbed73/comparative-analysis-of-promethee-and-fuzzy-promethee-methods-in-equipment-selection-problemYılmaz, B., Dağdeviren, M. (2011). A combined approach for equipment selection: F-PROMETHEE method and zero-one goal programming. Expert Systems with Applications, 38(9), 11641-11650. https://doi.org/10.1016/j.eswa.2011.03.043Zavadskas, E.K., Kaklauskas, A., Banaitis, A., Kvederyte, N. (2004). Housing credit access model: The case for Lithuania. European Journal of Operational Research, 155(2), 335-352. https://doi.org/10.1016/S0377-2217(03)00091-2Zhang, H., Gu, C.L., Gu, L. W., Zhang, Y. (2011). The evaluation of tourism destination competitiveness by TOPSIS and information entropy: A case in the Yangtze River Delta of China. Tourism Management, 32(2), 443-451. https://doi.org/10.1016/j.tourman.2010.02.00

A Comparison of Ordered and Unordered Response Models for Analyzing Road Traffic Injury Severities in the North-Eastern Turkey

Road traffic injuries are estimated to be one of the major causes of death worldwide and a majority of them occur in low- and middle income countries. In that respect, further studies that address to determine risk factors that may influence road traffic injury severities in the corresponding countries may contribute the existing road safety literature. This paper determines possible risk factors influencing road traffic injury severity in north-eastern Turkey. For this purpose, a retrospective cross-sectional study is conducted analysing 11,771 traffic accidents reported by the police during the sample period of 2008-2013. As the accident severity is inherently ordered, the data are analysed using both ordered and unordered response models. The estimation results reveal that several driver (age and education level), accident (speeding violation, avoiding manoeuvre and right-of-way rule), vehicle (bus/minivan, single-unit truck/heavy truck, private and single vehicles), temporal (time of day, morning peak, evening peak), environmental (summer and cloudy or rainy weather), geometry (asphalt road and road class type), and control characteristics (presence of crosswalk and traffic lights) were found to have an impact on injury severity. This paper is most probably the first attempt to analyse possible risk factors of road traffic injury severities in Turkey using both ordered and unordered response models. The evidence of this study may be valuable for future road safety policies in emerging countries

Examining the Relationship Among Economic Growth, Exports and Total Productivity for OECD Countries Using Data Envelopment Analysis and Panel Data Analyses

The main objective of this paper is to explore the relationship between Total Factor Productivity (TFP) and economic growth and exports for OECD countries for the sample period 1990-2013. For this purpose, firstly, TFP values were calculated using data envelopment analysis (DEA) for the corresponding countries within the availability of their labor force and fixed capital formation data for the relevant sample period. Secondly, several panel data analyses were performed to determine the impact of TFP values on economic growth and exports of OECD countries. Consequently, results reveal a statistically significant positive impact of TFP on both economic growth and exports for OECD countries

An investigation of export–import ratios in Turkey using spline regression models

This paper examines the use of spline functions in linear, squared, and cubic spline regression models and exhibits the estimation of spline parameters from data by ordinary least squares. Determination of the number and the location of knots is central to spline regression. In this paper, we initially propose a method based on the coefficient of determination R2 related to the estimation of knots in spline regression. This proposed method as applied to export–import ratio distributions in Turkey for the years 1923–2010 determines the knots, and linear, quadratic, and cubic spline regression models are established accordingly. Results reveal that spline regression models offer better results than polynomial regression models, and that the quadratic spline regression model is the best explanatory model for export–import ratio distributions in the smoothest spline regression models

Inferior vena cava and pulmonary artery diameters for prognosis of Coronavirus disease

Aim: In this study, we aimed to analyze the relationship between pulmonary artery (PA) and inferior vena cava (IVC) diameters in non-contrast chest computerized tomography (CT) images of patients with coronavirus disease 2019 (COVID-19) and overall survival. Material and Methods: This retrospective study consisted of 404 consecutive patients who underwent chest CT after admission to the emergency department between May 1 and June 31. 2021. CT measurements were performed by two radiologists. The prognostic value of PA and IVC diameters, the computerized tomography severity score (CT-55), quick sequential organ failure assessment (qSOFA), and confusion, urea, respiratory rate, blood pressure, and age >= years (CURB-65) score on overall survival were examined. Results: The median age of the participants was 62 years (49-72), and 196 (48.5%) were male. Of the 404 patients, 61 died after admission. While main-PA, left-PA, right-PA (p < 0.001) and NC-transverse (IVC-Tr) (p = 0.045) diameters were larger and statistically significant in the patients who died (AUC; 0.686, 0.722, 0.746, and 0.581, respectively), a statistically significant difference was not detected in terms of IVC anteroposterior diameter (IVC-AP) (p = 0.053) and the IVC-Tr/AP (p = 0.754) ratio. There was a statistical difference in mortality in ciSOFA, CURB-65, and CT-SS values (AUC; 0.727, 0.798, and 0.708 p < 0.001, respectively). Discussion: PA diameters measured from chest CT images at admission (main-PA >= 26.5 mm, right-PA >= 22.9 mm, and left-PA >= 21.6 mm) and the IVC-Tr diameter (>= 34.5 mm) can be used as mortality predictors for COVID-19, along with other prognostic scores

Assessing Postgraduate Students’ Satisfaction with Quality of Services at a Turkish University Using Alternate Ordered Response Models

The aim of this study is to determine postgraduate students’ general satisfaction with the quality of academic services. For this purpose, a written-questionnaire was conducted to 400 graduate students at Atatürk University, Turkey. The dependent variable of the study was the satisfaction level of graduate students which has a natural order. Hence, four different ordered logit models were performed to determine factors that may influence satisfaction levels of graduate students with the quality of academic services. Along with standard ordered logit model, other alternative ordered response models were also performed including generalized ordered logit model, partial constrained generalized ordered logit model, and heterogeneous choice model. Results reveal that a variety of factors are associated with quality of higher education services including age group, tuition fee, undergraduate education, monthly individual income, monthly household income, type of graduate school, current status of postgraduate education, advisor’s academic degree, and time elapsed for postgraduate education. The outcome of this study may give a valuable information for decision-makers of higher education institutions and may provide a benchmarking option in terms of past, present and future higher education policies

Birinci Trimester MPV/Trombosit ve PDW/Trombosit Oranlarının Abortus İmminens ve Abortusu Öngörmede Etkinliği

Amaç: Bu çalışmanın amacı, gebelerde birinci trimester döneminde yapılan rutin tam kan sayımı ile elde edilen trombosit değeri, ortalama trombosit hacmi (MPV) değeri, platelet dağılım genişliği (PDW) değeri, MPV/trombosit oranı ve PDW/trombosit oranının gebelik süresince oluşabilecek abortus imminens ve abortus durumlarını öngörmedeki rolünü incelemektir. Gereç ve Yöntem: Çalışmaya gebe polikliniklerinde 2018-2020 yılları arasında gebeliğin 6.-12. haftaları arasında tam kan sayımı örneği veren 300 hasta alındı. Hasta dosyalarından hastaların yaşları, abortus sayıları, gebelik haftaları, trombosit değerleri, MPV değerleri, PDW değerleri, MPV/trombosit oranları ve PDW/trombosit oranları kaydedildi. Hastalar düşük, düşük tehdidi ve kontrol olmak üzere 3 gruba ayrıldı. Tüm veriler karşılaştırmalı olarak analiz edildi. Bulgular: Abortus grubu, abortus imminens grubu ve kontrol grubu arasında trombosit değerleri, PDW değerleri, MPV değerleri, PDW/trombosit oranları ve MPV/trombosit oranları açısından anlamlı (p>0,05) bir farklılık gösterilmemiştir. Laboratuvar parametreleri açısından değerlendirildiğinde ise ortalama trombosit değeri 256,7±65,6, PDW değeri 12,2±1,8, MPV değeri 10,2±0,8, MPV/trombosit oranı 0,042±0,011 ve PDW/trombosit oranı 0,051±0,016 olarak saptanmıştır. Sonuç: Birinci trimesterde sağlıklı gebelerin tam kan sayımı testinden elde edilen MPV, PDW ile MPV/trombosit ve PDW/trombosit oranları ileri dönem abortus ve abortus imminens riskini öngörmede etkin değildir

Clopidogrel responsiveness in chronic kidney disease patients with acute coronary syndrome

Objective: Cardiovascular diseases are the leading cause of death in patients with chronic kidney disease (CKD). There is conflicting evidence about effect of CKD on clopidogrel responsiveness. We aimed to evaluate the clopidogrel responsiveness in CKD patients with acute coronary syndrome (ACS). Methods: A total of 101 patients; 55 with moderate to severe CKD and 46 with normal renal function or mild CKD, hospitalized with ACS were included in our study. Multiplate test was used to determine clopidogrel responsiveness. Platelet aggregation results were presented as aggregation unit (AU)*min and values over 470 AU*min were accepted as clopidogrel low responders. Results: The 101 patients (mean age 64.76±8.67 years; 61 [60.4%] male) were grouped into the two study groups as follows: group 1; 55 patients with eGFR<60 ml/min/1.73 m2 and group 2; 46 patients with eGFR>60 ml/min/1.73 m2. 35 patients (34.7%) of the study population were found to have low response to clopidogrel (16 [34.8%] patients in group 1 and 18 [33.3%] patients in group 2, p=0.879) . There was no significant difference between group 1 and 2 for Multiplate test results (414.67±281.21 vs 421.56±316.19 AU*min, p=0.909). Clopidogrel low responsiveness were independently related to Multiplate test results of aspirin responsiveness (OR=1.004, CI 1.002–1.007, p=0.001) and hemoglobin (OR=0.727, CI 0.571–0.925, p=0.010). Multiplate results were also independently related to Multiplate test results of aspirin responsiveness (β=0.402, p<0.0001) and hemoglobin (β=-0.251, p=0.007). Conclusion: Platelet response to clopidogrel does not differ between patients with eGFR < 60 ml/min/1.73 m2 and eGFR>60 ml/min/1.73 m2

Middle Ear Pressure and Factors Affecting It in Intubated Patients Hospitalized in Intensive Care

Objective:To assess the probable agents affecting middle ear pressure in intubated patients hospitalized in intensive care units with various diagnoses.Methods:Middle ear pressure of 38 patients hospitalized in intensive care units within our faculty hospital was measured using portable tympanograms and acoustic reflectometry. The mode of the device to which each patient was attached and patients’ blood pressure, Glasgow Coma Score, and additional disease parameters other than admission diagnosis were recorded. All data collected were subjected to statistical analysis to determine whether or not they affected middle ear pressure.Results:Septal deviation, survey, and mode of automatic respiratory device emerged as factors affecting middle ear pressure (odds coefficient 4.796, 3.745, 2.557, respectively, with 95% CI). Although aged over 60, additional disease and nasogastric tube also compromised middle ear pressure; the levels involved were not statistically significant.Conclusion:Middle ear pressure in patients hospitalized in intensive care units may change, particularly after the seventh day. This may particularly involve septal deviation, survey, and mode of automatic respiratory device, and tympanograms and reflectometry may be added to the patient-monitoring protocol in terms of changes in middle ear pressure