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Can we predict patients that will not benefit from invasive mechanical

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dc.contributor.author Ozlu, T
dc.contributor.author Pehlivanlar Kucuk, M
dc.contributor.author Kaya, A
dc.contributor.author Yarar, E
dc.contributor.author Kirakli, C
dc.contributor.author Sengoren Dikis, O
dc.contributor.author Kefeli Celik, H
dc.contributor.author Ozkan, S
dc.contributor.author Bektas Aksoy, H
dc.contributor.author Kucuk, AO
dc.contributor.author Palabiyik, O
dc.contributor.author Cortuk, M
dc.contributor.author Ergun, R
dc.contributor.author Kozanhan, B
dc.contributor.author Diken, OE
dc.contributor.author Bacakoglu, F
dc.contributor.author Uzun, S
dc.contributor.author Aksoy, I
dc.contributor.author Cinemre, H
dc.contributor.author Zerman, A
dc.contributor.author Usalan, A
dc.contributor.author Turan, IO
dc.contributor.author Ozdemir, E
dc.contributor.author Fazlioglu, N
dc.contributor.author Yildirim, F
dc.contributor.author Gunay, E
dc.contributor.author Yilmaz, N
dc.contributor.author Acar, BA
dc.contributor.author Akan, B
dc.contributor.author Arpag, H
dc.contributor.author Sezgi, C
dc.contributor.author Can, A
dc.contributor.author Yalcinsoy, M
dc.contributor.author Karaoglanoglu, S
dc.contributor.author Sehitogullari, A
dc.contributor.author Arslan, S
dc.contributor.author Aydemir, Y
dc.contributor.author Ozturk, A
dc.contributor.author Hocanli, I
dc.contributor.author Tutluoglu, B
dc.contributor.author Capraz, F
dc.contributor.author Salmanoglu, M
dc.contributor.author Ekici, A
dc.contributor.author Murat, N
dc.contributor.author Sahin, H
dc.contributor.author Ataman, S
dc.contributor.author Edipoglu, O
dc.contributor.author Yildiz, T
dc.contributor.author Doganay, Z
dc.contributor.author Dagli, C
dc.contributor.author Aksu, EA
dc.contributor.author Zitouni, B
dc.contributor.author Egilmez, AI
dc.contributor.author Sahiner, Y
dc.contributor.author Ekren, PK
dc.contributor.author Durmus, ZG
dc.contributor.author Koseoglu, HI
dc.contributor.author Baydin, A
dc.contributor.author Nalbant, A
dc.contributor.author Aydin, D
dc.contributor.author Bindal, A
dc.contributor.author Balas, S
dc.contributor.author Karamise, SE
dc.contributor.author Araz, O
dc.contributor.author Acar, T
dc.contributor.author Kahraman, H
dc.contributor.author Demir, M
dc.contributor.author Burnik, C
dc.contributor.author Canakci, E
dc.contributor.author Bilgin, C
dc.contributor.author Yagan, O
dc.contributor.author Aydemir, S
dc.contributor.author Gucyetmez, B
dc.contributor.author Benli, MO
dc.contributor.author Onem, Y
dc.date.accessioned 2022-10-11T13:33:24Z
dc.date.available 2022-10-11T13:33:24Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/11616/76401
dc.description.abstract Background/aim: The present study aimed to define the clinical and laboratory criteria for predicting patients that will not benefit from invasive mechanical ventilation (IMV) treatment and determine the prediction of mortality and prognosis of these critical ill patients.
dc.description.abstract Materials and methods: The study was designed as an observational, multicenter, prospective, and cross-sectional clinical study. It was conducted by 75 researchers at 41 centers in intensive care units (ICUs) located in various geographical areas of Turkey. It included a total of 1463 ICU patients who were receiving invasive mechanical ventilation (IMV) treatment. A total of 158 parameters were examined via logistic regression analysis to identify independent risk factors for mortality; using these data, the IMV Mortality Prediction Score (IMPRES) scoring system was developed.
dc.description.abstract Results: The following cut-off scores were used to indicate mortality risk: <2, low risk; 2-5, moderate risk; 5.1-8, high risk; >8, very high risk. There was a 26.8% mortality rate among the 254 patients who had a total IMPRES score of lower than 2. The mortality rate was 93.3% for patients with total 1M PRES scores of greater than 8 (P < 0.001).
dc.description.abstract Conclusion: The present study included a large number of patients from various geographical areas of the country who were admitted to various types of ICUs, had diverse diagnoses and comorbidities, were intubated with various indications in either urgent or elective settings, and were followed by physicians from various specialties. Therefore, our data are more general and can be applied to a broader population. This study devised a new scoring system for decision-making for critically ill patients as to whether they need to be intubated or not and presents a rapid and accurate prediction of mortality and prognosis prior to ICU admission using simple clinical data.
dc.source TURKISH JOURNAL OF MEDICAL SCIENCES
dc.title Can we predict patients that will not benefit from invasive mechanical
dc.title ventilation? A novel scoring system in intensive care: the IMV Mortality
dc.title Prediction Score (IMPRES)


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