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020 ▼a 9780438239302
035 ▼a (MiAaPQ)AAI10954441
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 615
1001 ▼a Rutter, W. Cliff.
24510 ▼a Using Machine Learning to Predict Acute Kidney Injuries Among Patients Treated with Empiric Antibiotics.
260 ▼a [S.l.] : ▼b University of Kentucky., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 242 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: David S. Burgess
5021 ▼a Thesis (Ph.D.)--University of Kentucky, 2018.
520 ▼a Acute kidney injury (AKI) is a significant adverse effect of many medications that leads to increased morbidity, cost, and mortality among hospitalized patients. Recent literature supports a strong link between empiric combination antimicrobial
520 ▼a Chapter 1 presents and summarizes the published literature connecting combination antimicrobial therapy with increased AKI incidence. This chapter sets the specific aims I aim to achieve during my dissertation project.
520 ▼a Chapter 2 describes a study in which patients receiving vancomycin (VAN) in combination with piperacillin-tazobactam (TZP) or cefepime (CFP). I matched over 1,600 patients receiving both combinations and found a significantly lower incidence of
520 ▼a Chapter 3 presents a study of patients receiving VAN in combination with meropenem (MEM) or TZP. This study included over 10,000 patients and used inverse probability of treatment weighting to conserve data for this population. After controlling
520 ▼a Chapter 4 describes a study in which patients receiving TZP or ampicillinsulbactam (SAM) with or without VAN were analyzed for AKI incidence. The purpose of this study was to identify whether the addition of a beta-lactamase inhibitor to a betal
520 ▼a Chapter 5 presents a study of almost 30,000 patients who received combination antimicrobial therapy over an 8-year period. This study demonstrates similar AKI incidence to previous literature and the studies presented in the previous chapters. A
520 ▼a The studies conducted present a clear message that patients receiving VAN+TZP are at significantly greater risk of AKI than alternative regimens for empiric coverage of infection.
590 ▼a School code: 0102.
650 4 ▼a Pharmaceutical sciences.
690 ▼a 0572
71020 ▼a University of Kentucky.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0102
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15001223 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자