Sentiment Analysis for Necessary Preview of 30-Day Mortality in Sepsis Patients and the Control Strategies

This study was to preview the risk of 30-day mortality in sepsis patients using sentiment analysis. The clinical data of patients and nursing notes were collected from the Medical Information Mart for Intensive Care (MIMIC-III) database. The factors influencing 30-day mortality were analyzed using the Cox regression model. And, the prognostic index (PI) was estimated. The receiver operating characteristic (ROC) curve was used to determine the PI cut-off point and assess the prediction ability of the model. In total, 1844 of 3560 patients were eligible for the study, with a 30-day mortality of 37.58%. Multivariate Cox analysis showed that sentiment polarity scores, sentiment subjectivity scores, simplified acute physiology score (SAPS)-II, age, and intensive care unit (ICU) types were all associated with the risk of 30-day mortality ( P < 0.05 ). In the preview of 30-day mortality, the area under the curve (AUC) of ROC was 0.78 (95%CI: 0.74–0.81, P < 0.001 ) when the cut-off point of PI was 0.467. The documented notes from nurses were described for the first time. Sentiment scores measured in nursing notes are associated with the risk of 30-day mortality in sepsis patients and may improve the preview of 30-day mortality.

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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 ◽

10.1136/bmjopen-2021-053548 ◽

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.

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Utility of a metabolic-associated nomogram to predict the recurrence-free survival of stage I cervical cancer

Future Oncology ◽

10.2217/fon-2020-1024 ◽

2021 ◽

Vol 17 (11) ◽

pp. 1325-1337

Author(s):

Yan Zhang ◽

Huan Lu ◽

Jinjin Zhang ◽

Shixuan Wang

Keyword(s):

Cervical Cancer ◽

Risk Score ◽

Cox Regression ◽

Area Under The Curve ◽

Gene Expression Omnibus ◽

Stage I ◽

Recurrence Free Survival ◽

Free Survival ◽

Cox Regression Analysis ◽

Cox Analysis

Aims: To identify metabolism-associated genes (MAGs) that serve as biomarkers to predict prognosis associated with recurrence-free survival (RFS) for stage I cervical cancer (CC). Patients & methods: By analyzing the Gene Expression Omnibus (GEO) database for 258 cases of stage I CC via univariate Cox analysis, LASSO and multivariate Cox regression analysis, we unveiled 11 MAGs as a signature that was also validated using Kaplan–Meier and receiver operating characteristic analyses. In addition, a metabolism-related nomogram was developed. Results: High accuracy of this signature for prediction was observed (area under the curve at 1, 3 and 5 years was 0.964, 0.929 and 0.852 for the internal dataset and 0.759, 0.719 and 0.757 for the external dataset). The high-risk score group displayed markedly worse RFS than did the low-risk score group. The indicators performed well in our nomogram. Conclusions: We identified a novel signature as a biomarker for predicting prognosis and a nomogram to facilitate the individual management of stage I CC patients.

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The effect of ventilator-associated pneumonia on the prognosis of intensive care unit patients within 90 days and 180 days

BMC Infectious Diseases ◽

10.1186/s12879-021-06383-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wenjuan Luo ◽

Rui Xing ◽

Canmin Wang

Keyword(s):

Intensive Care Unit ◽

Mechanical Ventilation ◽

Intensive Care ◽

Medical Information ◽

Length Of Hospital Stay ◽

Simplified Acute Physiology Score ◽

Serum Lactate ◽

Binary Logistic Regression Analysis ◽

Ventilator Associated Pneumonia ◽

Icu Patients

Abstract Background Mechanical ventilation (MV) is often applied in critically ill patients in intensive care unit (ICU) to protect the airway from aspiration, and supplement more oxygen. MV may result in ventilator-associated pneumonia (VAP) in ICU patients. This study was to estimate the 90-day and 180-day mortalities of ICU patients with VAP, and to explore the influence of VAP on the outcomes of ICU patients. Methods Totally, 8182 patients who aged ≥18 years and received mechanical ventilation (MV) in ICU from Medical Information Mart for Intensive Care III (MIMIC III) database were involved in this study. All subjects were divided into the VAP group (n = 537) and the non-VAP group (n = 7626) based on the occurrence of VAP. Clinical data of all participants were collected. The effect of VAP on the prognosis of ICU patients was explored by binary logistic regression analysis. Results The results delineated that the 90-day mortality of VAP patients in ICU was 33.33% and 180-day mortality was 37.62%. The 90-day and 180-day mortality rates were higher in the VAP group than in the non-VAP group. After adjusting the confounders including age, ethnicity, heart failure, septicemia, simplified acute physiology score II (SAPSII) score, sequential organ failure assessment (SOFA) score, serum lactate, white blood cell (WBC), length of ICU stay, length of hospital stay, length of ventilation, antibiotic treatment, Pseudomonas aeruginosa (P.aeruginosa), methicillin-resistant Staphylococcus aureus (MRSA), other pathogens, the risk of 90-day and 180-day mortalities in VAP patients were 1.465 times (OR = 1.465, 95%CI: 1.188–1.807, P < 0.001) and 1.635 times (OR = 1.635, 95%CI: 1.333–2.005, P < 0.001) higher than those in non-VAP patients, respectively. Conclusions Our study revealed that ICU patients with VAP had poorer prognosis than those without VAP. The results of this study might offer a deeper insight into preventing the occurrence of VAP.

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Mid-regional pro-adrenomedullin predicts poor outcome in non-selected patients admitted to an intensive care unit

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2018-0645 ◽

2019 ◽

Vol 57 (4) ◽

pp. 549-555 ◽

Cited By ~ 7

Author(s):

Chiara Bellia ◽

Luisa Agnello ◽

Bruna Lo Sasso ◽

Giulia Bivona ◽

Maurizio Santi Raineri ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Critically Ill ◽

Critically Ill Patients ◽

Area Under The Curve ◽

Scoring Systems ◽

Simplified Acute Physiology Score ◽

Icu Admission ◽

Open Issue ◽

The One

Abstract Background Mortality risk and outcome in critically ill patients can be predicted by scoring systems, such as APACHE and SAPS. The identification of prognostic biomarkers, simple to measure upon admission to an intensive care unit (ICU) is an open issue. The aim of this observational study was to assess the prognostic value of plasma mid-regional pro-adrenomedullin (MR-proADM) at ICU admission in non-selected patients in comparison to Acute Physiology and Chronic Health Evaluation II (APACHEII) and Simplified Acute Physiology Score II (SAPSII) scores. Methods APACHEII and SAPSII scores were calculated after 24 h from ICU admission. Plasma MR-proADM levels were measured by TRACE-Kryptor on admission (T0) and after 24 h (T24). The primary endpoint was intra-hospital mortality; secondary endpoint was length of stay (LOS). Results One hundred and twenty-six consecutive non-selected patients admitted to an ICU were enrolled. Plasma MR-proADM levels were correlated with LOS (r=0.28; p=0.0014 at T0; r=0.26; p=0.005 at T24). Multivariate analysis showed that T0 MR-proADM was a significant predictor of mortality (odds ratio [OR]: 1.27; 95% confidence interval [95%CI]: 1.03–1.55; p=0.022). Receiver operating characteristic curves analysis revealed that MR-proADM on ICU admission identified non-survivors with high accuracy, not inferior to the one of APACHEII and SAPSII scores (area under the curve [AUC]: 0.71; 95%CI: 0.62–0.78; p=0.0002 for MR-proADM; AUC: 0.71; 95%CI: 0.62–0.79; p<0.0001 for APACHEII; AUC: 0.8; 95%CI: 0.71–0.87; p<0.0001 for SAPSII). Conclusions Our findings point out a role of MR-proADM as a prognostic tool in non-selected patients in ICUs being a reliable predictor of mortality and LOS and support its use on admission to an ICU to help the management of critically ill patients.

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Identification of prognostic immune genes in laryngeal cancer

Journal of International Medical Research ◽

10.1177/0300060520964662 ◽

2020 ◽

Vol 48 (11) ◽

pp. 030006052096466

Author(s):

Huan Xiao ◽

Qi-sheng Su ◽

Chao-qian Li

Keyword(s):

Laryngeal Cancer ◽

Prognostic Model ◽

Cox Regression ◽

Area Under The Curve ◽

Predictive Biomarkers ◽

The Cancer Genome Atlas ◽

Bioinformatics Analyses ◽

Prediction Ability ◽

Immune Genes ◽

Immune Related Genes

Objective In this study, we aimed to identify prognostic immune-related genes and establish a prognostic model for laryngeal cancer based on these genes. Methods Transcriptome profiles and clinical data of patients with laryngeal cancer were downloaded from The Cancer Genome Atlas database. Integrated bioinformatics analyses were performed to identify genes associated with prognosis. Results Thirty prognostic immune-related genes for laryngeal cancer were identified. We constructed a regulatory network of prognosis comprising transcription factors and immune-related genes. Multivariate Cox regression analyses identified 15 immune-related genes in the network that were used to establish the prognostic model. The model exhibited excellent prognostic prediction ability with a high area under the curve value (0.916). The calculated risk score based on expression of the 15 immune-related genes was shown to be an independent prognostic factor for laryngeal cancer. Conclusion We identified prognostic immune-related genes and established a prognostic model for laryngeal cancer, which might help identify novel predictive biomarkers and therapeutic targets of laryngeal cancer.

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Association Between Preadmission Metformin Use and Outcomes in Intensive Care Unit Patients With Sepsis and Type 2 Diabetes: A Cohort Study

Frontiers in Medicine ◽

10.3389/fmed.2021.640785 ◽

2021 ◽

Vol 8 ◽

Author(s):

Qilin Yang ◽

Jiezhao Zheng ◽

Weiyan Chen ◽

Xiaohua Chen ◽

Deliang Wen ◽

...

Keyword(s):

Type 2 Diabetes ◽

Intensive Care ◽

Medical Information ◽

Cox Regression ◽

Sensitivity Analyses ◽

Regression Analyses ◽

Patients With Diabetes ◽

Reduced Risk ◽

Related Mortality

Background: Sepsis is a deadly disease worldwide. Effective treatment strategy of sepsis remains limited. There still was a controversial about association between preadmission metformin use and mortality in sepsis patients with diabetes. We aimed to assess sepsis-related mortality in patients with type 2 diabetes (T2DM) who were preadmission metformin and non-metformin users.Methods: The patients with sepsis and T2DM were included from Medical Information Mart for Intensive Care -III database. Outcome was 30-day mortality. We used multivariable Cox regression analyses to calculate adjusted hazard ratio (HR) with 95% CI.Results: We included 2,383 sepsis patients with T2DM (476 and 1,907 patients were preadmission metformin and non-metformin uses) between 2001 and 2012. The overall 30-day mortality was 20.1% (480/2,383); it was 21.9% (418/1,907), and 13.0% (62/476) for non-metformin and metformin users, respectively. After adjusted for potential confounders, we found that preadmission metformin use was associated with 39% lower of 30-day mortality (HR = 0.61, 95% CI: 0.46–0.81, p = 0.007). In sensitivity analyses, subgroups analyses, and propensity score matching, the results remain stable.Conclusions: Preadmission metformin use may be associated with reduced risk-adjusted mortality in patients with sepsis and T2DM. It is worthy to further investigate this association.

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A Novel Prediction Prognosis Model of sepsis with acute respiratory failure: A cohort study from the MIMIC-IV database

10.21203/rs.3.rs-449660/v1 ◽

2021 ◽

Author(s):

Lina Zhao ◽

Jing Yang ◽

Yunying Wang ◽

Zheng Ge. Zeng ◽

Tao Liu ◽

...

Keyword(s):

Respiratory Failure ◽

Acute Respiratory Failure ◽

Medical Information ◽

Cox Regression ◽

Abdominal Cavity ◽

Simplified Acute Physiology Score ◽

Hospital Death ◽

Short Term ◽

Short Term Mortality ◽

Prognosis Model

Abstract Objectives: Acute respiratory failure is significantly related to increased short-term mortality in sepsis patients. We aimed to develop a novel prognosis model for predicting the risk for hospital mortality in sepsis patients with acute respiratory failure.Methods: We researched the Medical Information Mart for Intensive Care (MIMIC)-IV database, and developed a matched cohort of adult sepsis with acute respiratory failure. After applying multivariate Cox regression, a nomogram was developed based on identified risk factors of the mortality in the cohort. Besides, the discrimination of the nomogram in predicting individual hospital death was evaluated by the area under o the characteristic operating curve (ROC).Results: A total of 663 sepsis patients with acute respiratory failure were included in this study. Systolic blood pressure, white blood cell count, neutrophils, mechanical ventilation, PaO2 < 60mmHg, abdominal cavity infection, Klebsiella pneumoniae, Acinetobacter baumannii, and immunosuppressive disease were the independent risk predictors of the mortality in sepsis patients with acute respiratory failure. The area under curve of the nomogram in the ROC was 0.880 (95% CI: 0.851-0.908) that provided significantly higher discrimination compared with simplified acute physiology score II [0.656 (95% CI: 0.612-0.701)].Conclusion: The model has good performance in predicting the mortality risk of sepsis patients with acute respiratory failure, and it can be clinically useful to evaluate the short-term prognosis in critically ill patients with sepsis and acute respiratory failure.

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

Emergency Medicine International ◽

10.1155/2021/9190908 ◽

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.

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

10.21203/rs.3.rs-135817/v1 ◽

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.

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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 ◽

10.1136/bmjopen-2020-046623 ◽

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.

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