scholarly journals Early Prediction of Patient Mortality Based on Routine Laboratory Tests and Predictive Models in Critically Ill Patients

Data Mining ◽  
2018 ◽  
Author(s):  
Sven Van Poucke ◽  
Ana Kovacevic ◽  
Milan Vukicevic
Keyword(s):  
Critically Ill ◽  
Predictive Models ◽  
Critically Ill Patients ◽  
Laboratory Tests ◽  
Early Prediction ◽  
Routine Laboratory

scholarly journals BEDSIDE LAPAROSCOPY AS DIAGNOSTIC TOOL REVIEW OF ABDOMINAL PATHOLOGIES IN CRITICALLY ILL PATIENTS: REVIEW

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Danilo Coco ◽  
Silvana Leanza
Keyword(s):  
Retrospective Study ◽  
Diagnostic Accuracy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Laboratory Tests ◽  
Diagnostic Laparoscopy ◽  
Delayed Diagnosis ◽  
Definite Diagnosis ◽  
Imaging Results ◽  
Increase Rate

Introduction: The diagnosis of abdominal pathologies in critically ill patients is often difficult because of inconclusive laboratory tests or imaging results, or the inability to safely transfer a patient to the radiology room. These causes give a delayed diagnosis of abdominal pathology in the intensive care unit (ICU) and increase rate of morbidity and mortality. The aim of this retrospective study is to evaluate the safety and diagnostic accuracy of bedside diagnostic laparoscopy in the identification of intra-abdominal pathology in critically ill patients. Aim: The aim of this retrospective study is to evaluate the safety and diagnostic accuracy of bedside diagnostic laparoscopy in the identification of intra-abdominal pathology in critically ill patients. Materials and Methods: A  literature research was carried out including PubMed, Medline, Embase, Cochrane and Google Scholar databases to identify articles reporting on importance of diagnostic accuracy of bedside diagnostic laparoscopy in the identification of intra-abdominal pathology in critically ill patients. Conclusions: Bedside diagnostic laparoscopy represents a safe and accurate technique for diagnosing intraabdominal pathology in an ICU setting and should be taken into consideration when patient transfer to radiology or the operating room is considered unsafe or when routine radiological examinations are not conclusive enough to reach a definite diagnosis. Keywords: Bedside laparoscopy, critically ill patients, ultrasonography (US), computed tomography (CT) , emergency surgery

Early Prediction of Sepsis Incidence in Critically Ill Patients Using Specific Genetic Polymorphisms

2016 ◽  
Vol 55 (3) ◽  
pp. 193-203 ◽  
Author(s):  
Vlad Laurentiu David ◽  
Muhammed Furkan Ercisli ◽  
Alexandru Florin Rogobete ◽  
Eugen S. Boia ◽  
Razvan Horhat ◽  
...  
Keyword(s):  
Genetic Polymorphisms ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Early Prediction

scholarly journals Procalcitonin for early prediction of survival outcome in postoperative critically ill patients with severe sepsis

2006 ◽  
Vol 97 (4) ◽  
pp. 503-508 ◽  
Author(s):  
A.A. Dahaba ◽  
B. Hagara ◽  
A. Fall ◽  
P.H. Rehak ◽  
W.F. List ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Survival Outcome ◽  
Early Prediction ◽  
Prediction Of Survival

Early prediction of Candida glabrata fungemia in nonneutropenic critically ill patients*

2010 ◽  
Vol 38 (3) ◽  
pp. 826-830 ◽  
Author(s):  
Yves Cohen ◽  
Philippe Karoubi ◽  
Christophe Adrie ◽  
Rémy Gauzit ◽  
Thierry Marsepoil ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Candida Glabrata ◽  
Early Prediction

scholarly journals A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e32286 ◽  
Author(s):  
Kyle B. Enfield ◽  
Katherine Schafer ◽  
Mike Zlupko ◽  
Vitaly Herasevich ◽  
Wendy M. Novicoff ◽  
...  
Keyword(s):  
Critically Ill ◽  
Predictive Models ◽  
Critically Ill Patients ◽  
Mortality Rates

AKI Prediction Models in ICU: A Comparative Study (Preprint)

2020 ◽  
Author(s):  
Qing Qian ◽  
Haixia Sun ◽  
Jinming Wu ◽  
Jiayang Wang ◽  
Lei Yang
Keyword(s):  
Kidney Disease ◽  
Comparative Study ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Gradient Boosting ◽  
Support Vector ◽  
Early Prediction ◽  
Icu Admission ◽  
Extreme Gradient Boosting ◽  
Mimic Iii

BACKGROUND Acute kidney injury (AKI) is highly prevalent in critically ill patients and associated with significant morbidity and mortality as well as high financial costs. Early prediction of AKI provides an opportunity to develop strategies for early diagnosis, effective prevention, and timely treatment. Machine learning models have been developed for early prediction of AKI on critically ill patients by different researchers under different scenario. OBJECTIVE This comparative study aims to assess the performances of existing models for early prediction of AKI in the Intensive Care Unit (ICU) setting. METHODS The data was collected from the MIMIC-III database for all patients above 18 years old who had valid creatinine measured for 72 hours following ICU admission. Those with existing condition of kidney disease on admission were excluded. 17 predictor variables including patient demographics and physiological indicators were selected on the basis of the Kidney Disease Improving Global Outcomes (KDIGO) and medical literatures. Six models from three different types of methods were tested including Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Decision (LightGBM), and Convolutional Neural Network (CNN). The area under ROC curve (AUC), accuracy, precision, recall and F1 value were calculated for each model to evaluate the performance. RESULTS We extracted 17205patient ICU records from MIMIC-III dataset. LightGBM had the best performance, with all the evaluation indicators achieved the highest (with average AUC 0.905, F1 0.897, Recall 0.836, P<.001). XGBoost had the second best performance (P<.001) and LR, RF, SVM performed similarly (P=0.082, 0.158, 0.710) on AUC. CNN got the lowest score on accuracy, precision, F1 and AUC. SVM and LR had relatively low recall than others. Creatinine were found to have the most significant effect on the early prediction of AKI onset in LR, RF, SVM and LightGBM. CONCLUSIONS LightGBM demonstrated the best predictive capability in predicting AKI present at the first 72 hours of ICU admission. LightGBM and XGBoost showed great potential for clinical application owing to their high recall. This study can provide references for AI-powered clinical decision support system for early AKI prediction in ICU setting.

scholarly journals Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit. A retrospective study

2014 ◽  
Vol 133 (3) ◽  
pp. 199-205 ◽  
Author(s):  
Ary Serpa Neto ◽  
Murillo Santucci Cesar de Assunção ◽  
Andréia Pardini ◽  
Eliézer Silva
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Predictive Models ◽  
Critically Ill Patients ◽  
Goodness Of Fit ◽  
Apache Ii ◽  
Goodness Of Fit Test

CONTEXT AND OBJECTIVE: Prognostic models reflect the population characteristics of the countries from which they originate. Predictive models should be customized to fit the general population where they will be used. The aim here was to perform external validation on two predictive models and compare their performance in a mixed population of critically ill patients in Brazil.DESIGN AND SETTING: Retrospective study in a Brazilian general intensive care unit (ICU).METHODS: This was a retrospective review of all patients admitted to a 41-bed mixed ICU from August 2011 to September 2012. Calibration (assessed using the Hosmer-Lemeshow goodness-of-fit test) and discrimination (assessed using area under the curve) of APACHE II and SAPS III were compared. The standardized mortality ratio (SMR) was calculated by dividing the number of observed deaths by the number of expected deaths.RESULTS: A total of 3,333 ICU patients were enrolled. The Hosmer-Lemeshow goodness-of-fit test showed good calibration for all models in relation to hospital mortality. For in-hospital mortality there was a worse fit for APACHE II in clinical patients. Discrimination was better for SAPS III for in-ICU and in-hospital mortality (P = 0.042). The SMRs for the whole population were 0.27 (confidence interval [CI]: 0.23 - 0.33) for APACHE II and 0.28 (CI: 0.22 - 0.36) for SAPS III.CONCLUSIONS: In this group of critically ill patients, SAPS III was a better prognostic score, with higher discrimination and calibration power.

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