Scoring System for the One-Year Mortality Prediction of Sepsis Patients in Intensive Care Units

2018 ◽  
pp. 367-370 ◽  
Author(s):  
Javier E. García-Gallo ◽  
Nelson J. Fonseca-Ruiz ◽  
John F. Duitama-Muñoz
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Scoring System ◽  
Mortality Prediction ◽  
One Year ◽  
The One

scholarly journals Resistance Rates of Acinetobacter baumannii Against Some Antibiotics: Comparison of 2018 and 2006 Results

ANKEM Dergisi ◽  
2020 ◽  
Author(s):  
Nurullah Uzuner ◽  
Selahhattin Atmaca ◽  
Muhammet Çelik ◽  
Handan Kangül
Keyword(s):  
Intensive Care ◽  
Acinetobacter Baumannii ◽  
Intensive Care Units ◽  
Treatment Options ◽  
Resistance Rate ◽  
Serious Infection ◽  
One Year ◽  
The One ◽  
Comparison Of The Results ◽  
Resistance Rates

Acinetobacter baumannii is a type of bacteria that causes serious hospital infections in intensive care units (ICUs) and immunocompromised patients. In this study, the one-year cumulative antibiogram results of A.baumannii strains, which are serious infection factors especially in intensive care patients, were retrospectively analyzed, at the same time, the results of sensitivity in a similar study conducted in our hospital in 2006 were compared with our results. Of the 388 isolates included in the study, 208 were isolated from male (53.6 %), 180 from female (46.4 %) patients, 87 % of the strains were from adults, 13 % from children (including newborns). 46.4 % of the factors were produced by the respiratory tract, 26.80 % from blood culture, 11.85 % from urine, 9.53 % from the wound 85 % of the samples were sent from intensive care units, 15 % from services (9.4 % internal service, 5.6 % surgical service), 6.95 % from the burn unit. The vast majority of the isolated A.baumannii strains were found to be adults. As a result of the antibiogram, the highest resistance rate to imipenem with 94.84 %; the lowest resistance rate was determined against colistin with 20 %. In the comparison of the results obtained in our hospital with the results of similar studies conducted in 2006, a significant increase in resistance was found for amikacin, ciprofloxacin, imipenem and meropenem (p<0.005), For trimethoprim / sulfamethoxazole, the resistance rate decreased (p>0.005). In this study, we showed that the resistance rates against A.baumannii strains increased over time, and the treatment options related to this are now very limited. Determining the resistance rates of common infectious agents at certain intervals by each hospital will be a guide in the effective treatment of infections that develop due to strains with limited antibiotic options.

scholarly journals Efficacy of chest X-ray in the diagnosis of COVID-19 pneumonia: comparison with computed tomography through a simplified scoring system designed for triage

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Akın Çinkooğlu ◽  
Selen Bayraktaroğlu ◽  
Naim Ceylan ◽  
Recep Savaş
Keyword(s):  
Computed Tomography ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Scoring System ◽  
Imaging Modality ◽  
Ground Glass Opacity ◽  
Initial Presentation ◽  
X Ray ◽  
Chest X Ray

Abstract Background There is no consensus on the imaging modality to be used in the diagnosis and management of Coronavirus disease 2019 (COVID-19) pneumonia. The purpose of this study was to make a comparison between computed tomography (CT) and chest X-ray (CXR) through a scoring system that can be beneficial to the clinicians in making the triage of patients diagnosed with COVID-19 pneumonia at their initial presentation to the hospital. Results Patients with a negative CXR (30.1%) had significantly lower computed tomography score (CTS) (p < 0.001). Among the lung zones where the only infiltration pattern was ground glass opacity (GGO) on CT images, the ratio of abnormality seen on CXRs was 21.6%. The cut-off value of X-ray score (XRS) to distinguish the patients who needed intensive care at follow-up (n = 12) was 6 (AUC = 0.933, 95% CI = 0.886–0.979, 100% sensitivity, 81% specificity). Conclusions Computed tomography is more effective in the diagnosis of COVID-19 pneumonia at the initial presentation due to the ease detection of GGOs. However, a baseline CXR taken after admission to the hospital can be valuable in predicting patients to be monitored in the intensive care units.

scholarly journals The national rate of intensive care units-acquired infections, one-year retrospective study in Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Neda Izadi ◽  
Babak Eshrati ◽  
Yadollah Mehrabi ◽  
Korosh Etemad ◽  
Seyed-Saeed Hashemi-Nazari
Keyword(s):  
Intensive Care ◽  
Tract Infection ◽  
Intensive Care Units ◽  
Bloodstream Infections ◽  
Surgical Site Infections ◽  
Increased Risk ◽  
Neonatal Icu ◽  
Mortality Causes ◽  
One Year ◽  
Multi Center Study

Abstract Background Hospital-acquired infections (HAIs) in intensive care units (ICUs) are among the avoidable morbidity and mortality causes. This study aimed at investigating the rate of ICU-acquired infections (ICU-AIs) in Iran. Methods For the purpose of this multi-center study, the rate of ICU-AIs calculated based on the data collected through Iranian nosocomial infections surveillance system and hospital information system. The data expanded based on 12 months of the year (13,632 records in terms of “hospital-ward-month”), and then, the last observation carried forward method was used to replace the missing data. Results The mean (standard deviation) age of 52,276 patients with HAIs in the ICUs was 47.37 (30.78) years. The overall rate of ICU-AIs was 96.61 per 1000 patients and 16.82 per 1000 patient-days in Iran’s hospitals. The three main HAIs in the general ICUs were ventilator-associated events (VAE), urinary tract infection (UTI), and pneumonia events & lower respiratory tract infection (PNEU & LRI) infections. The three main HAIs in the internal and surgical ICUs were VAE, UTI, and bloodstream infections/surgical site infections (BSI/SSI). The most prevalent HAIs were BSI, PNEU & LRI and eye, ear, nose, throat, or mouth (EENT) infections in the neonatal ICU and PNEU & LRI, VAE, and BSI in the PICU. Device, catheter, and ventilator-associated infections accounted for 60.96, 18.56, and 39.83% of ICU-AIs, respectively. The ventilator-associated infection rate was 26.29 per 1000 ventilator-days. Based on the Pabon Lasso model, the lowest rates of ICU-AIs (66.95 per 1000 patients and 15.19 patient-days) observed in zone III, the efficient area. Conclusions HAIs are common in the internal ICU wards. In fact, VAE and ventilator-related infections are more prevalent in Iran. HAIs in the ICUs leads to an increased risk of ICU-related mortality. Therefore, to reduce ICU-AIs, the specific and trained personnel must be responsible for the use of the devices (catheter use and ventilators), avoid over use of catheterization when possible, and remove catheters earlier.

scholarly journals Bacteraemia in Intensive Care Unit: Clinical, Bacteriological, and Prognostic Prospective Study

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zineb Lachhab ◽  
Mohammed Frikh ◽  
Adil Maleb ◽  
Jalal Kasouati ◽  
Nouafal Doghmi ◽  
...  
Keyword(s):  
Intensive Care ◽  
Acinetobacter Baumannii ◽  
Intensive Care Units ◽  
Bloodstream Infections ◽  
Blood Cultures ◽  
Common Source ◽  
Gram Negative ◽  
Extended Spectrum Beta Lactamases ◽  
Gram Negative Organisms ◽  
One Year

Objectives.We conducted a one-year observational study from December 2012 to November 2013 to describe the epidemiology of bacteraemia in intensive care units (ICU) of Mohammed V Military Teaching Hospital of Rabat (Morocco).Methods.The study consisted of monitoring all blood cultures coming from intensive care units and studying the bacteriological profile of positive blood cultures as well as their clinical significance.Results.During this period, a total of 46 episodes of bacteraemia occurred, which corresponds to a rate of 15,4/1000 patients. The rate of nosocomial infections was 97% versus 3% for community infections. The most common source of bacteraemia was the lungs in 33%, but no source was identified in 52% of the episodes. Gram negative organisms were isolated in 83,6% of the cases withAcinetobacter baumanniibeing the most frequent. Antibiotic resistance was very high with 42,5% of extended-spectrum beta-lactamases (ESBLs) in Enterobacteriaceae and 100% of carbapenemase inAcinetobacter baumannii. The antibiotherapy introduced in the first 24 hours was adequate in 72% of the cases.Conclusions.Bloodstream infections in ICU occur most often in patients over 55 years, with hypertension and diabetes. The bacteria involved are mainly Gram negative bacteria multiresistant to antibiotics. Early administration of antibiotics significantly reduces patients mortality.

scholarly journals Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study

2015 ◽  
Vol 3 (1) ◽  
pp. 42-52 ◽  
Author(s):  
Romain Pirracchio ◽  
Maya L Petersen ◽  
Marco Carone ◽  
Matthieu Resche Rigon ◽  
Sylvie Chevret ◽  
...  
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Population Based ◽  
Mortality Prediction ◽  
Population Based Study

scholarly journals Early warning score using electronically-held data for patients discharged from intensive care units

2020 ◽  
Author(s):  
Marco Pimentel ◽  
Alistair Johnson ◽  
Julie Darbyshire ◽  
Lionel Tarassenko ◽  
David Clifton ◽  
...  
Keyword(s):  
Adverse Event ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Early Warning ◽  
Vital Sign ◽  
Scoring System ◽  
Vital Signs ◽  
Early Warning Score ◽  
Hospital Death ◽  
Icu Discharge

Abstract Rationale. Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with early warning score (EWS) systems being used to identify those at risk of deterioration. Objectives. We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWSs) with a risk score of a future adverse event calculated on discharge from ICU.Methods. A modified Delphi process identified common, and candidate variables frequently collected and stored in electronic records as the basis for a ‘static’ score of the patient’s condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital-sign data from the day of hospital discharge, which is combined with the static score and used continuously to quantify and update the patient’s risk of deterioration throughout their hospital stay. Data from two NHS Foundation Trusts (UK) were used to develop and (externally) validate the model.Measurements and Main Results. A total of 12,394 vital-sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4,831 from 136 patients in the validation cohort. Outcome validation of our model yielded an area under the receiver operating characteristic curve (AUROC) of 0.724 for predicting ICU re-admission or in-hospital death within 24h. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (NEWS, 0.653). Conclusion. We showed that a scoring system incorporating data from a patient’s stay in ICU has better performance than commonly-used EWS systems based on vital signs alone.

scholarly journals Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units

2021 ◽  
Vol 11 ◽  
Author(s):  
Ximing Nie ◽  
Yuan Cai ◽  
Jingyi Liu ◽  
Xiran Liu ◽  
Jiahui Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Random Forest ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Mortality Prediction ◽  
Machine Learning Algorithms

Objectives: This study aims to investigate whether the machine learning algorithms could provide an optimal early mortality prediction method compared with other scoring systems for patients with cerebral hemorrhage in intensive care units in clinical practice.Methods: Between 2008 and 2012, from Intensive Care III (MIMIC-III) database, all cerebral hemorrhage patients monitored with the MetaVision system and admitted to intensive care units were enrolled in this study. The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. The primary outcome was hospital mortality. Model performance was assessed with accuracy and receiver operating characteristic curve analysis.Results: Of 760 cerebral hemorrhage patients enrolled from MIMIC database [mean age, 68.2 years (SD, ±15.5)], 383 (50.4%) patients died in hospital, and 377 (49.6%) patients survived. The area under the receiver operating characteristic curve (AUC) of six machine learning algorithms was 0.600 (nearest neighbors), 0.617 (decision tree), 0.655 (neural net), 0.671(AdaBoost), 0.819 (random forest), and 0.725 (gcForest). The AUC was 0.423 for Acute Physiology and Chronic Health Evaluation II score. The random forest had the highest specificity and accuracy, as well as the greatest AUC, showing the best ability to predict in-hospital mortality.Conclusions: Compared with conventional scoring system and the other five machine learning algorithms in this study, random forest algorithm had better performance in predicting in-hospital mortality for cerebral hemorrhage patients in intensive care units, and thus further research should be conducted on random forest algorithm.

scholarly journals Candida tropicalis: insight into the characteristics and outcome of adult patients admitted in medical and surgical intensive care units

2019 ◽  
Vol 7 (4) ◽  
pp. 1184
Author(s):  
Raminder Sandhu ◽  
Ramnika Aggarwal ◽  
Surinder Kumar ◽  
Diksha Budhani
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Candida Tropicalis ◽  
Clinical Samples ◽  
Surgical Intensive Care ◽  
Prolonged Duration ◽  
One Year ◽  
Systemic Infections ◽  
Culture Positive

Background: Non albicans species are emerging increasingly as significant ICU pathogens.  The increasing incidence of C. tropicalis infections is a significant problem because of its ability to develop rapid resistance to fluconazole.Methods: The study was designed to isolate, evaluate the risk factors and outcome of C. tropicalis infection from intensive care units. Identification was done by the biochemical methods. A total of 89 patients culture positive for C. tropicalis were selected for retrospective analysis over a period of one year. We collected various data about risk factors and outcome from the medical records.Results: A total of 89 patients culture positive for Candida tropicalis were analysed. Majority of these culture isolates were obtained from their blood (59.55%) followed by urine samples (31.46%). The indwelling devices (93.2%) remained a highest risk followed by prolonged administration of antibiotic therapy (92.1%) and admission in ICU for more than a week (88.8%). Overall mortality rate was 31.5%. Mortality was higher in patients with longer total length of stay in hospital (89.3%; p 1.000), indwelling devices (85.7%; p 0.5663) and in whom the antimicrobial therapy was administered for prolonged duration (82.1%; p 0.7581), although these factors remained statistically insignificant. 92.1% of isolates were sensitive to amphotericin B and showed 52.8%; 9.0% sensitivity to itraconazole and fluconazole respectively.Conclusions: C. tropicalis is now classified as the third or fourth NAC species being commonly isolated from clinical samples and associated with persistent systemic infections leading to a longer stay in the hospital. Several virulence factors seem to be responsible for high dissemination and mortality.

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