The impact of low hemoglobin levels and transfusion on critical care patients with severe ischemic stroke

2014 ◽  
Vol 29 (2) ◽  
pp. 236-240 ◽  
Author(s):  
L. Kellert ◽  
F. Schrader ◽  
P. Ringleb ◽  
T. Steiner ◽  
J. Bösel
Resuscitation ◽  
2013 ◽  
Vol 84 (7) ◽  
pp. 878-882 ◽  
Author(s):  
Kelby Cleverley ◽  
Negareh Mousavi ◽  
Lyle Stronger ◽  
Kimberly Ann-Bordun ◽  
Lillian Hall ◽  
...  

2017 ◽  
Vol 36 ◽  
pp. S65
Author(s):  
G.D. Ceniccola ◽  
R.S.F. Pequeno ◽  
A.B.M. De Oliveira ◽  
T.P. Holanda ◽  
V.S. Mendonça ◽  
...  

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Alejandra Molano-Triviño ◽  
Eduardo Zúñiga ◽  
José Garcia-Habeych ◽  
Juan Camilo Castellanos De la Hoz ◽  
Noelia Niño Caro ◽  
...  

Abstract Background and Aims Clinical outcomes of Acute Kidney Injury (AKI) in ICU mainly depend on opportune preventive strategies. Thus, early identification of AKI is mandatory, and alternative diagnostic strategies become plausible: one of them, Renal Angina Index (RAI), described by Matsuura1, predicts the development of AKI KDIGO 2-3, at 7th day after admission to the intensive care unit according to a cut-off point >6 on a scale with a “creatinine score” (determined by the difference in serum creatinine between that at ICU admission and the first 24 hours in the ICU) and the impact of the patients medical history. 1Kidney Int Rep (2018) 3, 677-683. Our aim is to describe predictive capacity of the Renal Angina Index (RAI) in adult critical care patients in our population. Method We retrospectively selected from our Critical Care Nephrology database adult patients admitted in any of our hospital`s ICU between February to August 2020, excluding those at admission with diagnosis of AKI, serum creatinine > 2.5 mg/dl, or those receiving dialysis (acute or chronic) or kidney transplantation. We defined AKI according to KDIGO criteria. The RAI score was defined as the worst condition score multiplied by the creatinine score. The performance of the RAI score was assessed by Receiver Operating Characteristic (ROC) analysis power to detect a difference of 0.2 between the area under the curve (AUC), under the null hypothesis of AUC = 0.5 (no diagnostic accuracy). The optimal cut point was estimated with the Youden method. Results From 1204 new ICU patients, we included 372 patients (women 40.3%), with mean age 60.9 (18-98) (table 1). Main indication for ICU admission was medical conditions. Mean APACHE II was 22.9, hemodinamic support was required in 41,1% patients, mechanical ventilation in 58.6% patients and diabetes mellitus was present in 21.5% patients. AKI KDIGO 2-3 developed in 26.8% of patients. Mean creatinine at admission was statistically different in patients with AKI (CI 0.95 –0.51 - --0.15 mg/dl, p=0.0004). The requirement of hemodynamic (p = 0.003) and ventilatory support (p = 0.009), sepsis (p = 0.003), and COVID-19 (p = 0.03) were more frequent in patients who developed AKI. Renal replacement therapy was required in 39 (60%) of patients with severe AKI (incidence 10,5%). RAI cutt-off point determined by Youden method in the overall sample was 24, being significantly higher in patients who developed AKI (16.54 Vs 7.47, CI 0.95 –13.5--4.99, p <0.001). A cut-off point of 24 was required for the Best predictive capacity for severe AKI, with sensitivity, specificity, positive and negative likelihood ratio of 34%, 94%, 5.5 and 0.7 respectively. Conclusion In our population, RAI score requires a cutoff point much higher than that originally described to predict the development of severe AKI. Losing its discriminatory capacity.


Author(s):  
Shinya Hasegawa ◽  
Yasuaki Tagashira ◽  
Shutaro Murakami ◽  
Yasunori Urayama ◽  
Akane Takamatsu ◽  
...  

Abstract Background The present study assessed the impact of time-out on vancomycin use and compared the strategy’s efficacy when led by pharmacists versus infectious disease (ID) physicians at a tertiary care center. Methods Time-out consisting of a telephone call to inpatient providers and documentation of vancomycin use > 72 hours was performed by ID physicians and clinical pharmacists in the Departments of Medicine and Surgery/Critical Care. Patients in the Department of Medicine were assigned to the ID physician-led arm, and patients in the Department of Surgery/Critical Care were assigned to the clinical pharmacist-led arm in the initial, six-month phase and were switched in the second, six-month phase. The primary outcome was the change in weekly days of therapy (DOT) per 1,000 patient-days (PD), and vancomycin use was compared using interrupted time-series analysis. Results Of 587 patients receiving vancomycin, 132 participated, with 79 and 53 enrolled in the first and second phases, respectively. Overall vancomycin use decreased although the difference was statistically non-significant (change in slope, −0.25 weekly DOT per 1,000 PD; 95% confidence interval, −0.68 to 0.18, p = 0.24). The weekly vancomycin DOT per 1,000 PD remained unchanged during phase 1 but decreased significantly in phase 2 (change in slope, −0.49; −0.84 to −0.14, p = 0.007). Antimicrobial use decreased significantly in the surgery/critical care patients in the pharmacist-led arm (change in slope, −0.77; −1.33 to −0.22, p = 0.007). Conclusions Vancomycin time-out was moderately effective, and clinical pharmacist-led time-out with surgery/critical care patients substantially reduced vancomycin use.


Author(s):  
Megan A. Rech ◽  
Elisabeth Donahey ◽  
Joshua M. DeMott ◽  
Laura L. Coles ◽  
Gary D. Peksa

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