Eczacılık Fakültesi / Faculty of Pharmacy

Permanent URI for this collectionhttps://hdl.handle.net/11727/5700

Browse

Filters

2021 - 20242

Settings

Subject: search.filters.subject.COVID-19

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    An Essential Component Of Antimicrobial Stewardship During The COVID-19 Pandemic In The Intensive Care Unit: De-Escalation
    (Başkent Üniversitesi Eczacılık Fakültesi, 2024-05-24) Pehlivanli, Aysel; Ozgun, Cigdem; Sasal-Solmaz, Firdevs Gonca; Yuksel, Didem; Basgut, Bilgen; Ozcelikay, Arif Tanju; Unal, Mustafa Necmettin
    Background The antimicrobial de-escalation strategy (ADE) plays a crucial role in antimicrobial stewardship, reducing the likelihood of bacterial resistance. This study aims to evaluate how often the intensive care unit (ICU) used ADE for empirical treatment during COVID-19.Materials Adult ICU patients receiving empirical antimicrobial therapy for bacterial infections were retrospectively studied from September 2020 to December 2021. ADE was defined as (1) discontinuation of an antimicrobial in case of empirical combination therapy or (2) replacement of the antimicrobial to narrow the antimicrobial spectrum within the first 3 days of therapy, according to the test results and clinical picture.Results A total of 99 patients were included in the study. The number of patients who received empirical combined therapy (38.4%) was lower than those who received monotherapy (61.6%). The most preferred monotherapy (45.9%) was piperacillin-tazobactam, while the most preferred in combination treatment (22.7%) was meropenem. Within the first 3 days of admittance to the ICU, 3% of patients underwent ADE for their empirical antimicrobial therapy, 61.6% underwent no change, and 35.4% underwent change other than ADE. Procalcitonin levels were below 2 mu g/L on the third day of treatment in 69.7% of the patients. Culture or culture-antibiogram results of 50.5% of the patients were obtained within the first 3 days of empirical therapy. There was no growth in the culture results of 21 patients (21.2%) during their ICU stay.Conclusion In this study, ADE practice was much lower than expected. In order to reduce the significant differences between theory and reality, clinical, laboratory, and organisational conditions must be objectively assessed along with patient characteristics.
  • No Thumbnail Available
    Item
    Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests
    (2021) Cubukcu, Hikmet Can; Topcu, Deniz Ilhan; Bayraktar, Nilufer; Gulsen, Murat; Sari, Nuran; Arslan, Ayse Hande; 0000-0002-1219-6368; 0000-0002-7886-3688; 34791032; E-3717-2019; Y-8758-2018
    Objectives The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. Methods We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. Results The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). Conclusions ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.