scholarly journals Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yinlong Ren ◽  
Luming Zhang ◽  
Fengshuo Xu ◽  
Didi Han ◽  
Shuai Zheng ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Model ◽  
Sofa Score ◽  
Medical Information ◽  
Troponin T ◽  
Characteristic Curve ◽  
Oxygenation Index ◽  
Lung Infection ◽  
Calibration Plot ◽  
Icu Patients

Abstract Background Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection. Methods Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis. Results The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713–0.773) and 0.746 (95% C.I.: 0.699–0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. Conclusion Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

scholarly journals Risk Factor Analysis and Nomogram for Predicting In-Hospital Mortality in Patients with Sepsis and Lung Infection

2021 ◽  
Author(s):  
Yinlong Ren ◽  
Luming Zhang ◽  
Fengshuo Xu ◽  
Didi Han ◽  
Shuai Zheng ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Model ◽  
Sofa Score ◽  
Medical Information ◽  
Troponin T ◽  
Characteristic Curve ◽  
Oxygenation Index ◽  
Lung Infection ◽  
Calibration Plot ◽  
Discrimination Ability

Abstract BackgroundLung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection.MethodsData were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0[10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥2.The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis.ResultsThe risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score(GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio(NLR), and CRRT, MV, and vasopressor use were included in the nomogram. Compared with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% confidence interval [95% CI]=0.713–0.773, p < 0.001) and 0.746 (95% CI=0.699–0.790, p < 0.001) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis(DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. ConclusionOur new nomogram is a convenient tool for accurate predictions of in-hospital mortality among patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

scholarly journals The Value of Admission Serological Indicators for Predicting 28-Day Mortality in Intensive Care Patients With Acute Heart Failure: Construction and Validation of a Nomogram

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaoyuan Wei ◽  
Yu Min ◽  
Jiangchuan Yu ◽  
Qianli Wang ◽  
Han Wang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Intensive Care ◽  
Acute Heart Failure ◽  
Sofa Score ◽  
Medical Information ◽  
Troponin T ◽  
Adequate Treatment ◽  
Intensive Care Patients ◽  
Creatine Kinase Mb ◽  
Death Group

Background: Acute heart failure (AHF) is a severe clinical syndrome characterized as rapid onset or worsening of symptoms of chronic heart failure (CHF). Risk stratification for patients with AHF in the intensive care unit (ICU) may help clinicians to predict the 28-day mortality risk in this subpopulation and further raise the quality of care.Methods: We retrospectively reviewed and analyzed the demographic characteristics and serological indicators of patients with AHF in the Medical Information Mart for Intensive Care III (MIMIC III) (version 1.4) between June 2001 and October 2012 and our medical center between January 2019 and April 2021. The chi-squared test and the Fisher's exact test were used for comparison of qualitative variables among the AHF death group and non-death group. The clinical variables were selected by using the least absolute shrinkage and selection operator (LASSO) regression. A clinical nomogram for predicting the 28-day mortality was constructed based on the multivariate Cox proportional hazard regression analysis and further validated by the internal and external cohorts.Results: Age &gt; 65 years [hazard ratio (HR) = 2.47], the high Sequential Organ Failure Assessment (SOFA) score (≥3 and ≤8, HR = 2.21; ≥9 and ≤20, HR = 3.29), lactic acid (Lac) (&gt;2 mmol/l, HR = 1.40), bicarbonate (HCO3-) (&gt;28 mmol/l, HR = 1.59), blood urea nitrogen (BUN) (&gt;21 mg/dl, HR = 1.75), albumin (&lt;3.5 g/dl, HR = 2.02), troponin T (TnT) (&gt;0.04 ng/ml, HR = 4.02), and creatine kinase-MB (CK-MB) (&gt;5 ng/ml, HR = 1.64) were the independent risk factors for predicting 28-day mortality of intensive care patients with AHF (p &lt; 0.05). The novel nomogram was developed and validated with a promising C-index of 0.814 (95% CI: 0.754–0.882), 0.820 (95% CI: 0.721–0.897), and 0.828 (95% CI: 0.743–0.917), respectively.Conclusion: This study provides a new insight in early predicting the risk of 28-day mortality in intensive care patients with AHF. The age, the SOFA score, and serum TnT level are the leading three predictors in evaluating the short-term outcome of intensive care patients with AHF. Based on the nomogram, clinicians could better stratify patients with AHF at high risk and make adequate treatment plans.

scholarly journals A Nomogram for Predicting In-Hospital Mortality in Patients with Takotsubo Syndrome

2021 ◽  
Author(s):  
Jun Chen ◽  
Yimin Wang ◽  
Xinyang Shou ◽  
Qiang Liu ◽  
Ziwei Mei
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Risk Prediction ◽  
Clinical Utility ◽  
Risk Prediction Model ◽  
Multivariate Logistic Regression Model ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Takotsubo Syndrome

Abstract BACKGROUND Despite the large number of studies focus on the prognosis and in-hospital outcomes risk factors of patients with takotsubo syndrome, there was still lack of utility and visual risk prediction model for predicting the in-hospital mortality of patients with takotsubo syndrome. OBJECTIVES Our study aimed to establish a utility risk prediction model for the prognosis of in-hospital patients with takotsubo syndrome (TTS). METHODS The study is a retrospective cohort study. Model of in-hospital mortality of TTS patients was developed by multivariable logistic regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. The clinical utility of the model was evaluated by decision curve analysis (DCA). RESULTS Overall, 368 TTS patients (320 Survivals and 48 deaths) were included in our research from MIMIC-IV database. The incidence of in-hospital mortality with TTS is 13.04%. Lasso regression and multivariate logistic regression model verified that potassium, pt, age, myocardial infarction, WBC, hematocrit, anion gap and SOFA score were significantly associated with in-hospital mortality of TTS patients. The nomogram demonstrated a good discrimination with a AUC of ROC 0.811(95%Cl: 0.746-0.876) in training set and 0.793(95%Cl: 0.724-0.862) in test set. The calibration plot of risk prediction model showed predicted probabilities against observed death rates indicated excellent concordance. DCA showed that the nomogram has good clinical benefits. Conclusion We developed a nomogram that predict hospital mortality in patients with TTS according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

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 Effects of high-flow oxygen therapy on patients with hypoxemia after extubation and predictors of reintubation: a retrospective study based on the MIMIC-IV database

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Taotao Liu ◽  
Qinyu Zhao ◽  
Bin Du
Keyword(s):  
Mechanical Ventilation ◽  
Treatment Group ◽  
Medical Information ◽  
Characteristic Curve ◽  
Oxygenation Index ◽  
High Flow ◽  
Control Group ◽  
Severe Hypoxemia ◽  
High Flow Oxygen ◽  
Weaning Failure

Abstract Background To investigate the indications for high-flow nasal cannula oxygen (HFNC) therapy in patients with hypoxemia during ventilator weaning and to explore the predictors of reintubation when treatment fails. Methods Adult patients with hypoxemia weaning from mechanical ventilation were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The patients were assigned to the treatment group or control group according to whether they were receiving HFNC or non-invasive ventilation (NIV) after extubation. The 28-day mortality and 28-day reintubation rates were compared between the two groups after Propensity score matching (PSM). The predictor for reintubation was formulated according to the risk factors with the XGBoost algorithm. The areas under the receiver operating characteristic curve (AUC) was calculated for reintubation prediction according to values at 4 h after extubation, which was compared with the ratio of SpO2/FiO2 to respiratory rate (ROX index). Results A total of 524,520 medical records were screened, and 801 patients with moderate or severe hypoxemia when undergoing mechanical ventilation weaning were included (100 < PaO2/FiO2 ≤ 300 mmHg), including 358 patients who received HFNC therapy after extubation in the treatment group. There were 315 patients with severe hypoxemia (100 < PaO2/FiO2 ≤ 200 mmHg) before extubation, and 190 patients remained in the treatment group with median oxygenation index 166[157,180] mmHg after PSM. There were no significant differences in the 28-day reintubation rate or 28-day mortality between the two groups with moderate or severe hypoxemia (all P > 0.05). Then HR/SpO2 was formulated as a predictor for 48-h reintubation according to the important features predicting weaning failure. According to values at 4 h after extubation, the AUC of HR/SpO2 was 0.657, which was larger than that of ROX index (0.583). When the HR/SpO2 reached 1.2 at 4 h after extubation, the specificity for 48-h reintubation prediction was 93%. Conclusions The treatment effect of HFNC therapy is not inferior to that of NIV, even on patients with oxygenation index from 160 to 180 mmHg when weaning from ventilator. HR/SpO2 is more early and accurate in predicting HFNC failure than ROX index.

scholarly journals Federated Learning on Clinical Benchmark Data: Performance Assessment (Preprint)

2020 ◽  
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 Development and Validation of Risk Prediction Model for In-hospital Mortality Among Patients Hospitalized With Acute Exacerbation Chronic Obstructive Pulmonary Disease Between 2015 and 2019

2021 ◽  
Vol 8 ◽  
Author(s):  
Fen Dong ◽  
Xiaoxia Ren ◽  
Ke Huang ◽  
Yanyan Wang ◽  
Jianjun Jiao ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Respiratory Failure ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Pulmonary Disease ◽  
Calibration Plot ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
C Statistic ◽  
Acute Exacerbations

Background: In patients with chronic obstructive pulmonary disease (COPD), acute exacerbations affect patients' health and can lead to death. This study was aimed to develop a prediction model for in-hospital mortality in patients with acute exacerbations of COPD (AECOPD).Method: A retrospective study was performed in patients hospitalized for AECOPD between 2015 and 2019. Patients admitted between 2015 and 2017 were included to develop model and individuals admitted in the following 2 years were included for external validation. We analyzed variables that were readily available in clinical practice. Given that death was a rare outcome in this study, we fitted Firth penalized logistic regression. C statistic and calibration plot quantified the model performance. Optimism-corrected C statistic and slope were estimated by bootstrapping. Accordingly, the prediction model was adjusted and then transformed into risk score.Result: Between 2015 and 2017, 1,096 eligible patients were analyzed, with a mean age of 73 years and 67.8% male. The in-hospital mortality was 2.6%. Compared to survivors, non-survivors were older, more admitted from emergency, more frequently concomitant with respiratory failure, pneumothorax, hypoxic-hypercarbic encephalopathy, and had longer length of stay (LOS). Four variables were included into the final model: age, respiratory failure, pneumothorax, and LOS. In internal validation, C statistic was 0.9147, and the calibration slope was 1.0254. Their optimism-corrected values were 0.90887 and 0.9282, respectively, indicating satisfactory discrimination and calibration. When externally validated in 700 AECOPD patients during 2018 and 2019, the model demonstrated good discrimination with a C statistic of 0.8176. Calibration plot illustrated a varying discordance between predicted and observed mortality. It demonstrated good calibration in low-risk patients with predicted mortality rate ≤10% (P = 0.3253) but overestimated mortality in patients with predicted rate &gt;10% (P &lt; 0.0001). The risk score of 20 was regarded as a threshold with an optimal Youden index of 0.7154.Conclusion: A simple prediction model for AECOPD in-hospital mortality has been developed and externally validated. Based on available data in clinical setting, the model could serve as an easily used instrument for clinical decision-making. Complications emerged as strong predictors, underscoring an important role of disease management in improving patients' prognoses during exacerbation episodes.

scholarly journals Incidence and prognosis of myocardial injury in patients with severe trauma

2021 ◽  
Author(s):  
Alexandra Stroda ◽  
Simon Thelen ◽  
René M’Pembele ◽  
Antony Adelowo ◽  
Carina Jaekel ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Hospital Mortality ◽  
Myocardial Injury ◽  
Injury Severity ◽  
Troponin T ◽  
Characteristic Curve ◽  
Severe Trauma ◽  
University Hospital ◽  
Resuscitation Room ◽  
The Impact

Abstract Purpose Severe trauma can lead to end organ damages of varying severity, including myocardial injury. In the non-cardiac surgery setting, there is extensive evidence that perioperative myocardial injury is associated with increased morbidity and mortality. The impact of myocardial injury on outcome after severe trauma has not been investigated adequately yet. We hypothesized that myocardial injury is associated with increased in-hospital mortality in patients with severe trauma. Materials/methods This retrospective cohort study included patients ≥ 18 years with severe trauma [defined as injury severity score (ISS) ≥ 16] that were admitted to the resuscitation room of the Emergency Department of the University Hospital Duesseldorf, Germany, between 2016 and 2019. The main endpoint was in-hospital mortality. Main exposure was myocardial injury at arrival [defined as high-sensitive troponin T (hsTnT) > 14 ng/l]. For statistical analysis, receiver operating characteristic curve (ROC) and multivariate binary logistic regression were performed. Results Out of 368 patients, 353 were included into statistical analysis (72.5% male, age: 55 ± 21, ISS: 28 ± 12). Overall in-hospital mortality was 26.1%. Myocardial injury at presentation was detected in 149 (42.2%) patients. In-hospital mortality of patients with and without myocardial injury at presentation was 45% versus 12.3%, respectively. The area under the curve (AUC) for hsTnT and mortality was 0.76 [95% confidence interval (CI) 0.71–0.82]. The adjusted odds ratio of myocardial injury for in-hospital mortality was 2.27 ([95%CI 1.16–4.45]; p = 0.017). Conclusion Myocardial injury after severe trauma is common and independently associated with in-hospital mortality. Thus, hsTnT might serve as a new prognostic marker in this cohort.

scholarly journals Effects of high-flow oxygen therapy on patients with hypoxemia after extubation and predictors of reintubation: a retrospective study based on the MIMIC-IV database

2020 ◽  
Author(s):  
Taotao Liu ◽  
Qinyu Zhao ◽  
Bin Du
Keyword(s):  
Mechanical Ventilation ◽  
Treatment Group ◽  
Medical Information ◽  
Characteristic Curve ◽  
Oxygenation Index ◽  
High Flow ◽  
Control Group ◽  
Severe Hypoxemia ◽  
High Flow Oxygen ◽  
Weaning Failure

Abstract Purpose To investigate the indications for high-flow nasal cannula oxygen (HFNC) therapy in patients with hypoxemia during ventilator weaning and to explore the predictors of reintubation when treatment fails. Methods Adult patients with hypoxemia weaning from mechanical ventilation were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The patients were assigned to the treatment group or control group according to whether they were receiving HFNC or non-invasive ventilation (NIV) after extubation. The 28-day mortality and 28-day reintubation rates were compared between the two groups after Propensity score matching (PSM). The predictor for reintubation was formulated according to the risk factors with the XGBoost algorithm. The areas under the receiver operating characteristic curve (AUC) was calculated for reintubation prediction according to values at 4 hours after extubation, which was compared with the ratio of SpO2/FiO2 to respiratory rate (ROX index). Results A total of 524520 medical records were screened, and 801 patients with moderate or severe hypoxemia when undergoing mechanical ventilation weaning were included (100 < PaO2/FiO2 ≤ 300 mmHg), including 358 patients who received HFNC therapy after extubation in the treatment group. There were 315 patients with severe hypoxemia (100 < PaO2/FiO2 ≤ 200 mmHg) before extubation, and 190 patients remained in the treatment group with median oxygenation index 166[157,180] mmHg after PSM. There were no significant differences in the 28-day reintubation rate or 28-day mortality between the two groups with moderate or severe hypoxemia (all P > 0.05). Then HR/SpO2 was formulated as a predictor for 48-hour reintubation according to the important features predicting weaning failure. According to values at 4 hours after extubation, the AUC of HR/SpO2 was 0.657, which was larger than that of ROX index (0.583). When the HR/SpO2 reached 1.2 at 4 hours after extubation, the specificity for 48-hour reintubation prediction was 93%. Conclusions The treatment effect of HFNC therapy is not inferior to that of NIV, even on patients with oxygenation index from 160 to 180 mmHg when weaning from ventilator. HR/SpO2 is more early and accurate in predicting HFNC failure than ROX index.

scholarly journals A nomogram to predict in-hospital mortality of neonates admitted to the intensive care unit

2021 ◽  
Author(s):  
Xihua Huang ◽  
Zhenyu Liang ◽  
Tang Li ◽  
Yu Lingna ◽  
Wei Zhu ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Predictive Model ◽  
Sofa Score ◽  
Medical Information ◽  
Hospital Death ◽  
Operating Characteristics ◽  
Distribution Width ◽  
Mimic Iii

Abstract Background To explore the influencing factors for in-hospital mortality in the neonatal intensive care unit (NICU) and to establish a predictive nomogram. Methods Neonatal data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Both univariate and multivariate logit binomial general linear models were used to analyse the factors influencing neonatal death. The area under the receiver operating characteristics (ROC) curve was used to assess the predictive model, which was visualized by a nomogram. Results A total of 1258 neonates from the NICU in the MIMIC-III database were eligible for the study, including 1194 surviving patients and 64 deaths. Multivariate analysis showed that red cell distribution width (RDW) (odds ratio [OR] 0.813, p=0.003) and total bilirubin (TBIL; OR 0.644, p&lt;0.001) had protective effects on neonatal in-hospital death, while lymphocytes (OR 1.205, p=0.025), arterial partial pressure of carbon dioxide (PaCO2; OR 1.294, p=0.016) and sequential organ failure assessment (SOFA) score (OR 1.483, p&lt;0.001) were its independent risk factors. Based on this, the area under the curve of this predictive model was up to 0.865 (95% confidence interval 0.813 to 0.917), which was also confirmed by a nomogram. Conclusions The nomogram constructed suggests that RDW, TBIL, lymphocytes, PaCO2 and SOFA score are all significant predictors for in-hospital mortality in the NICU.

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