scholarly journals External Validation of Model-Based Dosing Guidelines for Vancomycin, Gentamicin, and Tobramycin in Critically Ill Neonates and Children: A Pragmatic Two-Center Study

2020 ◽  
Vol 22 (4) ◽  
pp. 433-444 ◽  
Author(s):  
Stan J. F. Hartman ◽  
Lynn B. Orriëns ◽  
Samanta M. Zwaag ◽  
Tim Poel ◽  
Marika de Hoop ◽  
...  
Keyword(s):  
Critically Ill ◽  
External Validation ◽  
Model Based ◽  
Critically Ill Neonates ◽  
Center Study

scholarly journals P42 External validation of model-based dosing guidelines for vancomycin, gentamicin and tobramycin in critically ill children

2019 ◽  
Vol 104 (6) ◽  
pp. e34.2-e34
Author(s):  
S Hartman ◽  
S Zwaag ◽  
L Orriëns ◽  
T Poel ◽  
M de Hoop - Sommen ◽  
...  
Keyword(s):  
Critically Ill ◽  
Clinical Laboratory ◽  
External Validation ◽  
Interindividual Variation ◽  
Critically Ill Children ◽  
Target Range ◽  
Future Research ◽  
Target Attainment ◽  
Model Based ◽  
Trough Concentrations

BackgroundPharmacokinetic models are frequently used to simulate dosing strategies for special populations, including critically ill children. The Dutch Pediatric Formulary (DPF) partially bases its guidelines on these models. However, prospective validation of updated dosing regimens is rare. We aimed to identify target attainment and safety of vancomycin, gentamicin and tobramycin after a dose update in the DPF for critically ill neonates and children.MethodsRetropsective cohort study in PICU and NICU patients receiving vancomycin, gentamicin or tobramycin between January 2015 and March 2017 in 2 university hospitals. Demographic clinical laboratory and TDM-data were collected. Target (steady state) trough concentrations for vancomycin, gentamicin and tobramycin used were 10–15, ≤1 and ≤1 mg/l, respectively. Target gentamicin peak concentrations used were 8–12 mg/l.Results486 patients were included in total (165 vancomycin, 97 gentamicin and 224 tobramycin). Trough concentrations of vancomycin, gentamicin and tobramycin were within the target range in 37.5%, 85.3% and 77.2% of patients, respectively. Target attainment of gentamicin peak concentrations in NICU patients was 31%. Non-target trough concentrations were most prevalent in term NICU patients (vancomycin 70%, gentamicin 26% and tobramycin 36.8%). Gentamicin peak concentrations were subtherapeutic in 91% and 45.5% for term and preterm NICU patients, respectively. Creatinine concentrations correlated positively with antibiotic concentrations (correlation coefficient range 0.46–0.54, p≤0.01 in all cohorts).ConclusionDespite recent model-based dosing alterations, sub- and supratherapeutic concentrations of vancomycin, gentamicin and tobramycin are still frequent in critically ill children. Linear dose alterations did offer improvements in target attainment, but did not fully address all relevant covariates that contribute to the large interindividual variation in clearance and/or volume of distribution in these patients. Creatinine clearance was consistently correlated with concentrations of all 3 drugs, but future research is needed to identify whether including this parameter in dosing can improve target attainment and safety.Disclosure(s)Nothing to disclose

A Risk Score for Predicting the Incidence of Hemorrhage in Critically Ill Neonates: Development and Validation Study

2020 ◽  
Author(s):  
Rozeta Sokou ◽  
Daniele Piovani ◽  
Aikaterini Konstantinidi ◽  
Andreas G. Tsantes ◽  
Stavroula Parastatidou ◽  
...  
Keyword(s):  
Prediction Model ◽  
Critically Ill ◽  
Risk Score ◽  
External Validation ◽  
Prognostic Index ◽  
Bleeding Risk ◽  
Outcome Variable ◽  
Calibration Plot ◽  
Characteristic Analysis ◽  
Critically Ill Neonates

AbstractThe aim of the study was to develop and validate a prediction model for hemorrhage in critically ill neonates which combines rotational thromboelastometry (ROTEM) parameters and clinical variables. This cohort study included 332 consecutive full-term and preterm critically ill neonates. We performed ROTEM and used the neonatal bleeding assessment tool (NeoBAT) to record bleeding events. We fitted double selection least absolute shrinkage and selection operator logit regression to build our prediction model. Bleeding within 24 hours of the ROTEM testing was the outcome variable, while patient characteristics, biochemical, hematological, and thromboelastometry parameters were the candidate predictors of bleeding. We used both cross-validation and bootstrap as internal validation techniques. Then, we built a prognostic index of bleeding by converting the coefficients from the final multivariable model of relevant prognostic variables into a risk score. A receiver operating characteristic analysis was used to calculate the area under curve (AUC) of our prediction index. EXTEM A10 and LI60, platelet counts, and creatinine levels were identified as the most robust predictors of bleeding and included them into a Neonatal Bleeding Risk (NeoBRis) index. The NeoBRis index demonstrated excellent model performance with an AUC of 0.908 (95% confidence interval [CI]: 0.870–0.946). Calibration plot displayed optimal calibration and discrimination of the index, while bootstrap resampling ensured internal validity by showing an AUC of 0.907 (95% CI: 0.868–0.947). We developed and internally validated an easy-to-apply prediction model of hemorrhage in critically ill neonates. After external validation, this model will enable clinicians to quantify the 24-hour bleeding risk.

scholarly journals COVID-19: Up to 82% critically ill patients had low Vitamin C values

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Teresa Maria Tomasa-Irriguible ◽  
Lara Bielsa-Berrocal
Keyword(s):  
Vitamin C ◽  
Critically Ill ◽  
Potential Role ◽  
Plasma Vitamin ◽  
Single Center ◽  
Berlin Definition ◽  
Single Center Study ◽  
Prevention And Treatment ◽  
Center Study

AbstractThere are limited proven therapeutic options for the prevention and treatment of COVID-19. We underwent an observational study with the aim of measure plasma vitamin C levels in a population of critically ill COVID-19 adult patients who met ARDS criteria according to the Berlin definition. This epidemiological study brings to light that up to 82% had low Vitamin C values. Notwithstanding the limitation that this is a single-center study, it nevertheless shows an important issue. Given the potential role of vitamin C in sepsis and ARDS, there is gathering interest of whether supplementation could be beneficial in COVID-19.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

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