scholarly journals Diabetes and Hemoglobin A1c as Risk Factors for Nosocomial Infections in Critically Ill Patients

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Eirini Tsakiridou ◽  
Demosthenes Makris ◽  
Vasiliki Chatzipantazi ◽  
Odysseas Vlachos ◽  
Grigorios Xidopoulos ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Prospective Observational Study ◽  
Hemoglobin A1c ◽  
District Hospital ◽  
Bloodstream Infections ◽  
Inclusion Criteria ◽  
Icu Mortality ◽  
Independent Risk Factors

Objective. To evaluate whether diabetes mellitus (DM) and hemoglobin A1c (HbA1c) are risk factors for ventilator-associated pneumonia (VAP) and bloodstream infections (BSI) in critically ill patients.Methods. Prospective observational study; patients were recruited from the intensive care unit (ICU) of a general district hospital between 2010 and 2012. Inclusion criteria: ICU hospitalization >72 hours and mechanical ventilation >48 hours. HbA1c was calculated for all participants. DM, HbA1c, and other clinical and laboratory parameters were assessed as risk factors for VAP or BSI in ICU.Results. The overall ICU incidence of VAP and BSI was 26% and 30%, respectively. Enteral feeding OR (95%CI) 6.20 (1.91–20.17;P=0.002) and blood transfusion 3.33 (1.23–9.02;P=0.018) were independent risk factors for VAP. BSI in ICU (P=0.044) and ICU mortality (P=0.038) were significantly increased in diabetics. Independent risk factors for BSI in ICU included BSI on admission 2.45 (1.14–5.29;P=0.022) and stroke on admission2.77 (1.12–6.88;P=0.029). Sepsis 3.34 (1.47–7.58;P=0.004) and parenteral feeding 6.29 (1.59–24.83;P=0.009) were independently associated with ICU mortality. HbA1c ≥ 8.1% presented a significant diagnostic performance in diagnosing repeated BSI in ICU.Conclusion. DM and HbA1c were not associated with increased VAP or BSI frequency. HbA1c was associated with repeated BSI episodes in the ICU.

scholarly journals Augmented Renal Clearance in Critically Ill Patients with Sepsis: Prevalence, Risk Factors and Outcome

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Shahir Asraf bin Abdul Rahim ◽  
Azrina Md Ralib ◽  
Abdul Hadi Mohamad ◽  
Ariff Osman ◽  
Mohd Basri Mat Nor
Keyword(s):  
Risk Factors ◽  
Cardiac Index ◽  
Creatinine Clearance ◽  
Renal Clearance ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Prospective Observational Study ◽  
Estimated Glomerular Filtration Rate ◽  
Inclusion Criteria ◽  
Augmented Renal Clearance

Introduction: Augmented renal clearance (ARC) is a phenomenon where there is elevated renal clearance and defined by creatinine clearance more than 130ml/min. ARC results in changes of the pharmacokinetic and pharmacodynamic of antimicrobial therapy being administered, which may result in its subtherapeutic dose. We evaluated the prevalence, risk factors and outcome of ARC in critically ill patients with sepsis. Materials and method: This is an interim analysis of single centre, prospective observational study of critically ill patients. Inclusion criteria were patients older than 18 years old with sepsis with plasma creatinine less than 130 µmol/l. Urinary creatinine and flow rate were measured and creatinine clearance (CrCl) calculated.ARC is defined as CrCl of more 130 ml/min. Ultrasonic cardiac output montoring (USCOM) was used to measure cardiac index. Results: Nineteen patients were analysed so far, of which 11 (57.9%) had ARC. There were no differences age, gender, or category of patients between patients with and without ARC. Baseline APACHE II and SOFA score were similar in the two groups (p=0.47 and 0.06, respectively).There was no difference in the hospital mortality (p=0.86). However, duration of ICU admission amongst survivors was longer in patients with ARC (10 (5-12) vs 4 (3-5) days, p=0.04). Of the 11 with ARC, 7 persisted to day 2. Measured creatinine clearance correlated well with the estimated glomerular filtration rate (r=0.68, p<0.0001), however it did not correlate with cardiac index (r=0.40, p=0.14). Conclusion: ARC occurs in almost half of critically ill patients with sepsis, and is associated with longer duration of ICU stay. However, there was no difference in the outcome in this small study. Future larger study may be important to investigate this.

Disease Severity Is an Independent Risk Factor of Mortality and Outcomes in Critically Ill Patients With Carbapenem-resistant Klebsiella Pneumoniae Bloodstream Infections: a 5-year Retrospective Analysis

2021 ◽  
Author(s):  
Yuzhen Qiu ◽  
Wen Xu ◽  
Yunqi Dai ◽  
Ruoming Tan ◽  
Jialin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Septic Shock ◽  
Klebsiella Pneumoniae ◽  
Critically Ill Patients ◽  
Bloodstream Infections ◽  
Polymyxin B ◽  
Mortality Rates ◽  
Carbapenem Resistant ◽  
All Cause Mortality ◽  
Independent Risk Factors

Abstract Background: Carbapenem-resistant Klebsiella pneumoniae bloodstream infections (CRKP-BSIs) are associated with high morbidity and mortality rates, especially in critically ill patients. Comprehensive mortality risk analyses and therapeutic assessment in real-world practice are beneficial to guide individual treatment.Methods: We retrospectively analyzed 87 patients with CRKP-BSIs (between July 2016 and June 2020) to identify the independent risk factors for 28-day all-cause mortality. The therapeutic efficacies of tigecycline-and polymyxin B-based therapies were analyzed.Results: The 28-day all-cause mortality and in-hospital mortality rates were 52.87% and 67.82%, respectively, arising predominantly from intra-abdominal (56.32%) and respiratory tract infections (21.84%). A multivariate analysis showed that 28-day all-cause mortality was independently associated with the patient’s APACHE II score (p = 0.002) and presence of septic shock at BSI onset (p = 0.006). All-cause mortality was not significantly different between patients receiving tigecycline- or polymyxin B-based therapy (55.81% vs. 53.85%, p = 0.873), and between subgroups mortality rates were also similar. Conclusions: Critical illness indicators (APACHE II scores and presence of septic shock at BSI onset) were independent risk factors for 28-day all-cause mortality. There was no significant difference between tigecycline- and polymyxin B-based therapy outcomes. Prompt and appropriate infection control should be implemented to prevent CRKP infections.

scholarly journals Risk Factors For Kidney Damage In COVID-19 Patients Admitted To The Intensive Care Unit

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Diana K. Sarkisian ◽  
Natalia V. Chebotareva ◽  
Valerie McDonnell ◽  
Armen V. Oganesyan ◽  
Tatyana N. Krasnova ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Kidney Damage ◽  
Elderly Age ◽  
Total Blood ◽  
Independent Risk Factors

Background — Acute kidney injury (AKI) reaches 29% in the intensive care unit (ICU). Our study aimed to determine the prevalence, features, and the main AKI factors in critically ill patients with coronavirus disease 2019 (COVID-19). Material and Methods — The study included 37 patients with COVID-19. We analyzed the total blood count test results, biochemical profile panel, coagulation tests, and urine samples. We finally estimated the markers of kidney damage and mortality. Result — All patients in ICU had proteinuria, and 80.5% of patients had hematuria. AKI was observed in 45.9% of patients. Independent risk factors were age more than 60 years, increased C-reactive protein (CRP) level, and decreased platelet count. Conclusion — Kidney damage was observed in most critically ill patients with COVID-19. The independent risk factors for AKI in critically ill patients were elderly age, a cytokine response with a high CRP level.

scholarly journals Accuracy of a Bedside Swallow Test After Extubation in ICU: A Prospective Validation of a Clinical Test Compared to a Fiberoptic Endoscopy.

2020 ◽  
Author(s):  
Adel Maamar ◽  
Valentine Parent ◽  
Emmanuel Guérot ◽  
Pauline Berneau ◽  
Aurélien Frérou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Validation Cohort ◽  
Clinical Test ◽  
Exclusion Criteria ◽  
Predictive Values ◽  
Swallowing Evaluation ◽  
Independent Risk Factors ◽  
Preliminary Study

Abstract Background: Swallowing disorders (SDs) are frequent after extubation in intensive care unit (ICU) exposing patients to aspiration pneumonia. There is no validated bedside swallowing evaluation (BSE) after extubation. We aimed to evaluate the accuracy of our BSE in comparison with fiberoptic endoscopic evaluation of swallowing (FEES) in critically ill patients after extubation, and to identify the incidence and risk factors of SD.Methods: After a preliminary study in a first center, we conducted a 1-year prospective study as a validation cohort in a second center. Patients intubated for longer than 48 hours were included. Exclusion criteria were a known laryngeal pathology, a preexisting SD and an admission for stroke. FEES of the larynx and BSE were assessed within 24 hours after extubation to compare the accuracy of the BSE to the FEES procedure.Results: One hundred and twenty eight patients were included, respectively 69 and 79 in the preliminary study and the validation cohort. Thirteen of 69 (19%) and 33/79 (42%) had SD assessed by FEES. The area under curve (AUC) reached respectively 0.86 (95% CI 0.73-0.98) and 0.83 (95% CI 0.74-0.92). Sensitivities were 77% (95% CI 0.54-0.99) and 85% (95% CI 0.73-0.94), specificities 94% (95% CI 0.87-0.98) and 80% (95% CI 0.7-0.91), and negative predictive values (NPV) were 95% and 90% in respectively preliminary study and validation cohort. Independent risk factors for SD were duration of intubation (OR=1.08; 95% CI 1.02-1.17, p=0.03) and hemodynamic failure (OR=4.46; 95% CI 1.27-21, p=0.03).Conclusion: Our BSE is accurate to detect SDs after extubation in critically ill patients and can easily be implemented in an ICU setting.

scholarly journals Plasma tryptophan and tyrosine levels are independent risk factors for delirium in critically ill patients

2009 ◽  
Vol 35 (11) ◽  
pp. 1886-1892 ◽  
Author(s):  
P. P. Pandharipande ◽  
A. Morandi ◽  
J. R. Adams ◽  
T. D. Girard ◽  
J. L. Thompson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Plasma Tryptophan ◽  
Independent Risk Factors

scholarly journals Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Alejandro Rodríguez ◽  
◽  
Manuel Ruiz-Botella ◽  
Ignacio Martín-Loeches ◽  
María Jimenez Herrera ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Clustering Analysis ◽  
Unsupervised Clustering ◽  
Older Age ◽  
Icu Mortality ◽  
Factors Associated ◽  
Clinical Phenotypes

Abstract Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

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