scholarly journals A Novel Nomogram for Predicting Survival in Patients with Severe Acute Pancreatitis: An Analysis Based on the Large MIMIC-III Clinical Database

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Didi Han ◽  
Fengshuo Xu ◽  
Chengzhuo Li ◽  
Luming Zhang ◽  
Rui Yang ◽  
...  
Keyword(s):  
Acute Pancreatitis ◽  
Intensive Care ◽  
Severe Acute Pancreatitis ◽  
Medical Information ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Mortality Rates ◽  
Training Cohort ◽  
Net Reclassification Improvement ◽  
Mimic Iii

Background. Severe acute pancreatitis (SAP) can cause various complications. Septic shock is a relatively common and serious complication that causes uncontrolled systemic inflammatory response syndrome, which is one of the main causes of death. This study aimed to develop a nomogram for predicting the overall survival of SAP patients during the initial 24 hours following admission. Materials and Methods. All the data utilized in this study were obtained from the MIMIC-III (Medical Information Mart for Intensive Care III) database. The data were analyzed using multivariate Cox regression, and the performance of the proposed nomogram was evaluated based on Harrell’s concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). The clinical value of the prediction model was tested using decision-curve analysis (DCA). The primary outcomes were 28-day, 60-day, and 90-day mortality rates. Results. The 850 patients included in the analysis comprised 595 in the training cohort and 255 in the validation cohort. The training cohort consisted of 353 (59.3%) males and 242 (40.7%) females with SAP. Multivariate Cox regression showed that weight, sex, insurance status, explicit sepsis, SAPSII score, Elixhauser score, bilirubin, anion gap, creatinine, hematocrit, hemoglobin, RDW, SPO2, and respiratory rate were independent prognostic factors for the survival of SAP patients admitted to an intensive care unit. The predicted values were compared using C-indexes, calibration plots, integrated discrimination improvement, net reclassification improvement, and DCA. Conclusions. We have identified some important demographic and laboratory parameters related to the prognosis of patients with SAP and have used them to establish a more accurate and convenient nomogram for evaluating their 28-day, 60-day, and 90-day mortality rates.

scholarly journals A novel nomogram for predicting survival in patients with severe acute pancreatitis: an analysis based on the large MIMIC-III clinical database

2020 ◽  
Author(s):  
Di di Han ◽  
Shuo Feng Xu ◽  
Zhuo Cheng Li ◽  
Ming Lu Zhang ◽  
Rui Yang ◽  
...  
Keyword(s):  
Acute Pancreatitis ◽  
Intensive Care ◽  
Severe Acute Pancreatitis ◽  
Medical Information ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Mortality Rates ◽  
Training Cohort ◽  
Net Reclassification Improvement ◽  
Mimic Iii

Abstract Background Severe acute pancreatitis (SAP) can cause various complications. Septic shock is a relatively common and serious complication that causes uncontrolled systemic inflammatory response syndrome, which is one of the main causes of death. This study aimed to develop a nomogram for predicting the overall survival of SAP patients during the initial 24 hours following admission. Materials and Methods All data utilized in this study were obtained from the MIMIC-III (Medical Information Mart for Intensive Care III) database. The data were analyzed using multivariate Cox regression, and the performance of the proposed nomogram was evaluated based on Harrell’s concordance index (C-index) and the area under the receiver operating characteristic curve(AUC). The clinical value of the prediction model was tested using decision-curve analysis (DCA). The primary outcomes were 28-day,60-day, and 90-day mortality rates. Results The 850 patients included in the analysis comprised 595 in the training cohort and 255 in the validation cohort. The training cohort consisted of 353 (59.3%) males and 242 (40.7%) females with SAP. Multivariate Cox regression showed that weight, sex, insurance status, explicit sepsis, SAPSII score, Elixhauser score, bilirubin, anion gap, creatinine, hematocrit, hemoglobin, RDW, SpO 2 , and respiratory rate were independent prognostic factors for the survival of SAP patients admitted to an intensive care unit. The predicted values were compared using C-indexes, calibration plots, integrated discrimination improvement, net reclassification improvement, and DCA. Conclusions We have identified some important demographic and laboratory parameters related to the prognosis of patients with SAP, and have used them to establish a more accurate and convenient nomogram for evaluating their 28-day, 60-day, and 90-day all-cause mortality rates. The prognostic value of the novel nomogram is superior to that of the traditional SAPSII scoring system alone.

scholarly journals Pancreatic Perfusion CT in Early Stage of Severe Acute Pancreatitis

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Yoshihisa Tsuji ◽  
Naoki Takahashi ◽  
Chiba Tsutomu
Keyword(s):  
Acute Pancreatitis ◽  
Intensive Care ◽  
Severe Acute Pancreatitis ◽  
Early Stage ◽  
Mortality Rates ◽  
Perfusion Ct ◽  
Clinical Usefulness ◽  
Systemic Complications ◽  
Pancreatic Perfusion ◽  
Local And Systemic Complications

Early intensive care for severe acute pancreatitis is essential for improving SAP mortality rates. However, intensive therapies for SAP are often delayed because there is no ideal way to accurately evaluate severity in the early stages. Currently, perfusion CT has been shown useful to predict prognosis of SAP in the early stage. In this presented paper, we would like to review the clinical usefulness and limitations of perfusion CT for evaluation of local and systemic complications in early stage of SAP.

scholarly journals Association between body mass index and short-term mortality in patients with intra-abdominal infections: a retrospective, single-centre cohort study using the Medical Information Mart for Intensive Care database

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e046623
Author(s):  
Qinglin Li ◽  
Yingmu Tong ◽  
Sinan Liu ◽  
Kaibo Yang ◽  
Chang Liu ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Cohort Study ◽  
Intensive Care ◽  
Body Mass ◽  
Medical Information ◽  
Cox Regression ◽  
Obese Patients ◽  
Short Term ◽  
Short Term Mortality ◽  
Mimic Iii

ObjectivesThis study aimed to determine the relationship between the body mass index (BMI) and short-term mortality of patients with intra-abdominal infection (IAI) using the Medical Information Mart for Intensive Care (MIMIC-III) database.DesignRetrospective cohort study.SettingAdult intensive care units (ICUs) at a tertiary hospital in the USA .ParticipantsAdult IAI ICU patients from 2001 to 2012 in the MIMIC-III database.InterventionsIn univariate analysis, we compared the differences in the characteristics of patients in each BMI group. Cox regression models were used to evaluate the relationships between BMI and short-term prognosis.Primary and secondary outcome measures90-day survival.ResultsIn total, 1161 patients with IAI were included. There were 399 (34.4%) patients with a normal BMI (<25 kg/m2), 357 (30.8%) overweight patients (25–30 kg/m2) and 405 (34.9%) obese patients (>30 kg/m2) who tended to be younger (p<0.001) and had higher Sequential Organ Failure Assessment scores (p<0.05). The mortality of obese patients at 90 days was lower than that of patients with a normal BMI (20.74% vs 23.25%, p<0.05), but their length of stay in the ICU was higher (4.9 days vs 3.6 days, p<0.001); however, their rate of mechanical ventilation utilisation was higher (61.48% vs 56.86%, p<0.05). In the Cox regression model, we also confirmed that BMI was a protective factor in patients with IAIs, and the adjusted mortality rate of patients with a higher BMI was 0.97 times lower than that of patients with a lower BMI (p<0.001, HR=0.97, 95% CI 0.96 to 0.99).ConclusionsIAI patients with an overweight or obese status might have lower 90-day mortality than patients with a normal BMI.

scholarly journals THE PANC 3 SCORE PREDICTING SEVERITY OF ACUTE PANCREATITIS

2016 ◽  
Vol 29 (1) ◽  
pp. 5-8 ◽  
Author(s):  
Murilo Gamba BEDUSCHI ◽  
André Luiz Parizi MELLO ◽  
Bruno VON-MÜHLEN ◽  
Orli FRANZON
Keyword(s):  
Acute Pancreatitis ◽  
Intensive Care ◽  
Length Of Stay ◽  
Severe Acute Pancreatitis ◽  
Predictive Value ◽  
High Specificity ◽  
Daily Practice ◽  
Severe Form ◽  
Inclusion Criteria ◽  
Positive Score

Background: About 20% of cases of acute pancreatitis progress to a severe form, leading to high mortality rates. Several studies suggested methods to identify patients that will progress more severely. However, most studies present problems when used on daily practice. Objective: To assess the efficacy of the PANC 3 score to predict acute pancreatitis severity and its relation to clinical outcome. Methods: Acute pancreatitis patients were assessed as to sex, age, body mass index (BMI), etiology of pancreatitis, intensive care need, length of stay, length of stay in intensive care unit and mortality. The PANC 3 score was determined within the first 24 hours after diagnosis and compared to acute pancreatitis grade of the Revised Atlanta classification. Results: Out of 64 patients diagnosed with acute pancreatitis, 58 met the inclusion criteria. The PANC 3 score was positive in five cases (8.6%), pancreatitis progressed to a severe form in 10 cases (17.2%) and five patients (8.6%) died. Patients with a positive score and severe pancreatitis required intensive care more often, and stayed for a longer period in intensive care units. The PANC 3 score showed sensitivity of 50%, specificity of 100%, accuracy of 91.4%, positive predictive value of 100% and negative predictive value of 90.6% in prediction of severe acute pancreatitis. Conclusion: The PANC 3 score is useful to assess acute pancreatitis because it is easy and quick to use, has high specificity, high accuracy and high predictive value in prediction of severe acute pancreatitis.

scholarly journals Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e025925 ◽  
Author(s):  
Christopher J McWilliams ◽  
Daniel J Lawson ◽  
Raul Santos-Rodriguez ◽  
Iain D Gilchrist ◽  
Alan Champneys ◽  
...  
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Medical Information ◽  
Learning Algorithm ◽  
Adverse Outcomes ◽  
Royal Infirmary ◽  
Automated Method ◽  
Improved Performance ◽  
Discharge Criteria ◽  
Mimic Iii

ObjectiveThe primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.DesignWe used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.SettingBristol Royal Infirmary general intensive care unit (GICU).PatientsTwo cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.ResultsIn both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.ConclusionsOur findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

scholarly journals Neutrophil-to-lymphocyte ratio as a predictor of mortality in intensive care unit patients: a retrospective analysis of the Medical Information Mart for Intensive Care III Database

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e053548
Author(s):  
Xie Wu ◽  
Qipeng Luo ◽  
Zhanhao Su ◽  
Yinan Li ◽  
Hongbai Wang ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Inflammatory Markers ◽  
Predictive Power ◽  
Medical Information ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Mortality Rates ◽  
Simplified Acute Physiology Score ◽  
Icu Patients

ObjectivesIdentifying high-risk patients in the intensive care unit (ICU) is important given the high mortality rate. However, existing scoring systems lack easily accessible, low-cost and effective inflammatory markers. We aimed to identify inflammatory markers in routine blood tests to predict mortality in ICU patients and evaluate their predictive power.DesignRetrospective case–control study.SettingSingle secondary care centre.ParticipantsWe analysed data from the Medical Information Mart for Intensive Care III database. A total of 21 822 ICU patients were enrolled and divided into survival and death groups based on in-hospital mortality.Primary and secondary outcome measuresThe predictive values of potential inflammatory markers were evaluated and compared using receiver operating characteristic curve analysis. After identifying the neutrophil-to-lymphocyte ratio (NLR) as having the best predictive ability, patients were redivided into low (≤1), medium (1–6) and high (>6) NLR groups. Univariate and multivariate logistic regression analyses were performed to evaluate the association between the NLR and mortality. The area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to assess whether incorporating the NLR could improve the predictive power of existing scoring systems.ResultsThe NLR had the best predictive ability (AUC: 0.609; p<0.001). In-hospital mortality rates were significantly higher in the low (OR (OR): 2.09; 95% CI 1.64 to 2.66) and high (OR 1.64; 95% CI 1.50 to 1.80) NLR groups than in the medium NLR group. Adding the NLR to the Simplified Acute Physiology Score II improved the AUC from 0.789 to 0.798, with an NRI and IDI of 16.64% and 0.27%, respectively.ConclusionsThe NLR predicted mortality in ICU patients well. Both low and high NLRs were associated with elevated mortality rates, including the NLR may improve the predictive power of the Simplified Acute Physiology Score II.

scholarly journals Association between lactate/albumin ratio and all-cause mortality in patients with acute respiratory failure: A retrospective analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255744
Author(s):  
Yan Lu ◽  
Haoyang Guo ◽  
Xuya Chen ◽  
Qiaohong Zhang
Keyword(s):  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Medical Information ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Severe Heart Failure ◽  
Albumin Ratio ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
All Cause Mortality

Previous studies have shown that lactate/albumin ratio (LAR) can be used as a prognostic biomarker to independently predict the mortality of sepsis and severe heart failure. However, the role of LAR as an independent prognostic factor in all-cause mortality in patients with acute respiratory failure (ARF) remains to be clarified. Therefore, we retrospectively analyzed 2170 patients with ARF in Medical Information Mart for Intensive Care Database III from 2001 to 2012. By drawing the receiver operating characteristic curve, LAR shows a better predictive value in predicting the 30-day mortality of ARF patients (AUC: 0.646), which is higher than that of albumin (AUC: 0.631) or lactate (AUC: 0.616) alone, and even higher than SOFA score(AUC: 0.642). COX regression analysis and Kaplan-Meier curve objectively and intuitively show that high LAR is a risk factor for patients with ARF, which is positively correlated with all-cause mortality. As an easy-to-obtain and objective biomarker, LAR deserves further verification by multi-center prospective studies.

scholarly journals Monitoring Approach of Fatality Risk Factors for Patients with Severe Acute Pancreatitis Admitted to the Intensive Care Unit. A Retrospective, Monocentric Study

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2013
Author(s):  
Tudorel Mihoc ◽  
Cristi Tarta ◽  
Ciprian Duta ◽  
Raluca Lupusoru ◽  
Greta Dancu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Acute Pancreatitis ◽  
Intensive Care ◽  
Severe Acute Pancreatitis ◽  
Fatal Outcome ◽  
Factors Associated ◽  
Reactive Protein ◽  
Wide Range ◽  
Secondary Objective

Acute pancreatitis is an unpredictable disease affecting the pancreas and it is characterized by a wide range of symptoms and modified lab tests, thus there is a continuing struggle to classify this disease and to find risk factors associated with a worse outcome. The main objective of this study was to identify the risk factors associated with the fatal outcome of the intensive care unit’s patients diagnosed and admitted for severe acute pancreatitis, the secondary objective was to investigate the prediction value for the death of different inflammatory markers at the time of their admission to the hospital. This retrospective study included all the patients with a diagnosis of acute pancreatitis admitted to the Intensive Care Unit of the Emergency County Hospital Timisoara between 1 January 2016 and 31 May 2021. The study included 53 patients diagnosed with severe acute pancreatitis, out of which 21 (39.6%) survived and 32 (60.4%) died. For the neutrophils/lymphocytes ratio, a cut-off value of 12.4 was found. When analyzing age, we found out that age above 52 years old can predict mortality, and for the platelets/lymphocytes ratio, a cut-off value of 127 was found. Combining the three factors we get a new model for predicting mortality, with an increased performance, AUROC = 0.95, p < 0.001. Multiple persistent organ failure, age over 50, higher values of C reactive protein, and surgery were risk factors for death in the patients with severe acute pancreatitis admitted to the intensive care unit. The model design from the neutrophils/lymphocytes ratio, platelets/lymphocytes ratio, and age proved to be the best in predicting mortality in severe acute pancreatitis.

scholarly journals Federated Learning on Clinical Benchmark Data: Performance Assessment

10.2196/20891 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e20891
Author(s):  
Geun Hyeong Lee ◽  
Soo-Yong Shin
Keyword(s):  
Hospital Mortality ◽  
High Performance ◽  
Medical Information ◽  
Characteristic Curve ◽  
Real Life ◽  
Personal Privacy ◽  
Comparative Performance ◽  
Client Server ◽  
Benchmark Datasets ◽  
Mimic Iii

Background Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas. Objective The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset. Methods To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Medical Information Mart for Intensive Care-III (MIMIC-III), and electrocardiogram (ECG) datasets were used to evaluate the performance of FL. To test FL in a realistic setting, the MNIST dataset was split into 10 different clients, with one digit for each client. In addition, we conducted four different experiments according to basic, imbalanced, skewed, and a combination of imbalanced and skewed data distributions. We also compared the performance of FL to that of the state-of-the-art method with respect to in-hospital mortality using the MIMIC-III dataset. Likewise, we conducted experiments comparing basic and imbalanced data distributions using MIMIC-III and ECG data. Results FL on the basic MNIST dataset with 10 clients achieved an area under the receiver operating characteristic curve (AUROC) of 0.997 and an F1-score of 0.946. The experiment with the imbalanced MNIST dataset achieved an AUROC of 0.995 and an F1-score of 0.921. The experiment with the skewed MNIST dataset achieved an AUROC of 0.992 and an F1-score of 0.905. Finally, the combined imbalanced and skewed experiment achieved an AUROC of 0.990 and an F1-score of 0.891. The basic FL on in-hospital mortality using MIMIC-III data achieved an AUROC of 0.850 and an F1-score of 0.944, while the experiment with the imbalanced MIMIC-III dataset achieved an AUROC of 0.850 and an F1-score of 0.943. For ECG classification, the basic FL achieved an AUROC of 0.938 and an F1-score of 0.807, and the imbalanced ECG dataset achieved an AUROC of 0.943 and an F1-score of 0.807. Conclusions FL demonstrated comparative performance on different benchmark datasets. In addition, FL demonstrated reliable performance in cases where the distribution was imbalanced, skewed, and extreme, reflecting the real-life scenario in which data distributions from various hospitals are different. FL can achieve high performance while maintaining privacy protection because there is no requirement to centralize the data.

scholarly journals Comprehensive Analysis of the Functions, Prognostic and Diagnostic Values of RNA Binding Proteins in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Xueping Ke ◽  
Zhen Fu ◽  
Jingjing Yang ◽  
Shijin Yu ◽  
Tingyuan Yan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Training Cohort

Abstract Background: Increasing evidence has suggested that RNA binding protein (RBP) dysregulation plays an important part in tumorigenesis. Here, we sought to explore the potential molecular functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The Limma package was applied to identify the differently expressed RBPs between HNSCC and normal samples with |log2 fold change (FC)|≥1 and false discovery rate (FDR)<0.05. the immunohistochemistry images from the Human Protein Atlas database The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest and the prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in training cohort and validated in validation cohort. All these analyses were performed using the R software.Results: A total of 84 aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic related hub gene and were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in LASSO-logistic regression analysis and shown high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24 and NR0B1) were identified as prognosis associated hub genes by least absolute shrinkage and selection operator (LASSO) and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The area under the curve of the time-dependent receiver operator characteristic curve of the prognostic model were 0.664 at 3 years and 0.635 at 5 years in training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive effificacy for prognosis in HNSCC.Conclusions: Our results demonstrate the values of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insights to understand potential role of dysregulated RBP in HNSCC.

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