Development of a novel score for the prediction of hospital mortality in patients with severe sepsis: the use of electronic healthcare records with LASSO regression

Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review

Background. Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. Methods. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database. Eligible studies investigated the development of ML models incorporating commonly available laboratory and clinical data for predicting survival outcomes in adult ICU patients with sepsis. Study selection followed the PRISMA guidelines and relied on predefined inclusion criteria. All records were independently assessed by two reviewers, with conflicts resolved by a third senior reviewer. Data related to study design, methodology, results, and interpretation of the results were extracted in a predefined grid. Results. Overall, 19 studies were identified, encompassing primarily logistic regression, random forests, and neural networks. Most used datasets were US-based (MIMIC-III, MIMIC-IV, and eICU-CRD). The most common variables used in model development were age, albumin levels, lactate levels, and ventilator. ML models demonstrated superior performance metrics compared to conventional methods and traditional scoring systems. The best-performing model was a gradient boosting decision tree, with an area under curve of 0.992, an accuracy of 0.954, and a sensitivity of 0.917. However, several critical limitations should be carefully considered when interpreting the results, such as population selection bias (i.e., single center studies), small sample sizes, limited external validation, and model interpretability. Conclusions. Through real-time integration of routine laboratory and clinical data, ML-based tools can assist clinical decision-making and enhance the consistency and quality of sepsis management across various healthcare contexts, including ICUs with limited resources.

Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review

INTRODUCTION

Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis.

METHODS

Following a pre-established protocol registered at the International Prospective Register of Systematic Reviews, we performed a comprehensive search of databases from inception to February 2024. We included peer-reviewed articles reporting predicting mortality in critically ill adult patients with sepsis.

RESULTS

Among the 1822 articles, 31 were included, involving 1,477,200 adult patients with sepsis. Nineteen studies had a high risk of bias. Among the diverse ML models, Logistic regression and eXtreme Gradient Boosting were the most frequently used, in 22 and 16 studies, respectively. Nine studies performed internal and external validation. Compared with conventional scoring systems such as SOFA, the ML models showed slightly higher performance in predicting mortality (AUROC ranges: 0.62-0.90 vs. 0.47-0.86).

CONCLUSIONS

ML models demonstrate a modest improvement in predicting sepsis-associated mortality. The certainty of these findings remains low due to the high risk of bias and significant heterogeneity. Studies should include comprehensive methodological details on calibration and hyperparameter selection, adopt a standardized definition of sepsis, and conduct multicenter prospective designs along with external validations.

Prognostic prediction of sepsis patient using transformer with skip connected token for tabular data

Sepsis is known as a common syndrome in intensive care units (ICU), and severe sepsis and septic shock are among the leading causes of death worldwide. The purpose of this study is to develop a deep learning model that supports clinicians in efficiently managing sepsis patients in the ICU by predicting mortality, ICU length of stay (>14 days), and hospital length of stay (>30 days). The proposed model was developed using 591 retrospective data with 16 tabular data related to a sequential organ failure assessment (SOFA) score. To analyze tabular data, we designed the modified architecture of the transformer that has achieved extraordinary success in the field of languages and computer vision tasks in recent years. The main idea of the proposed model is to use a skip-connected token, which combines both local (feature-wise token) and global (classification token) information as the output of a transformer encoder. The proposed model was compared with four machine learning models (ElasticNet, Extreme Gradient Boosting [XGBoost]), and Random Forest) and three deep learning models (Multi-Layer Perceptron [MLP], transformer, and Feature-Tokenizer transformer [FT-Transformer]) and achieved the best performance (mortality, area under the receiver operating characteristic (AUROC) 0.8047; ICU length of stay, AUROC 0.8314; hospital length of stay, AUROC 0.7342). We anticipate that the proposed model architecture will provide a promising approach to predict the various clinical endpoints using tabular data such as electronic health and medical records.

Construction and evaluation of shortterm and longterm mortality risk prediction model for patients with sepsis based on MIMICIV database

OBJECTIVES

Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk.

METHODS

Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model.

RESULTS

A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value.

CONCLUSIONS

The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study

BACKGROUND

Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department.

METHODS

This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP).

RESULTS

Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results.

CONCLUSION

Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Sarcopenia prediction using shear-wave elastography, grayscale ultrasonography, and clinical information with machine learning fusion techniques: feature-level fusion vs. score-level fusion

This study aimed to develop and evaluate a sarcopenia prediction model by fusing numerical features from shear-wave elastography (SWE) and gray-scale ultrasonography (GSU) examinations, using the rectus femoris muscle (RF) and categorical/numerical features related to clinical information. Both cohorts (development, 70 healthy subjects; evaluation, 81 patients) underwent ultrasonography (SWE and GSU) and computed tomography. Sarcopenia was determined using skeletal muscle index calculated from the computed tomography. Clinical and ultrasonography measurements were used to predict sarcopenia based on a linear regression model with the least absolute shrinkage and selection operator (LASSO) regularization. Furthermore, clinical and ultrasonography features were combined at the feature and score levels to improve sarcopenia prediction performance. The accuracies of LASSO were 70.57 ± 5.00-81.54 ± 4.83 (clinical) and 69.00 ± 4.52-69.73 ± 5.47 (ultrasonography). Feature-level fusion of clinical and ultrasonography (accuracy, 70.29 ± 6.63 and 83.55 ± 4.32) showed similar performance with clinical features. Score-level fusion by AdaBoost showed the best performance (accuracy, 73.43 ± 6.57-83.17 ± 5.51) in the development and evaluation cohorts, respectively. This study might suggest the potential of machine learning fusion techniques to enhance the accuracy of sarcopenia prediction models and improve clinical decision-making in patients with sarcopenia.

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges

The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligence and ML (AI) in critical care.

Derivation and validation of a risk prediction score for nonsteroidal anti-inflammatory drug-related serious gastrointestinal complications in the elderly

AIMS

Few studies have quantified the impact of risk factors on GI complications in elderly nonsteroidal anti-inflammatory drug (NSAID) users. This study aimed to develop and validate a risk prediction score for severe GI complications to identify high-risk elderly patients using NSAID.

METHODS

We used the following two Korean claims datasets: customized data with an enrolment period 2016-2017 for model development, and the sample data in 2019 for external validation. We conducted a nested case-control study for model development and validation. NSAID users were identified as the elderly (≥65 years) who received NSAIDs for more than 30 days. Serious GI complications were defined as hospitalizations or emergency department visits, with a main diagnosis of GI bleeding or perforation. We applied the logistic least absolute shrinkage and selection operator (LASSO) regression model for variable selection and model fitting.

RESULTS

We identified 8176 cases and 81 760 controls with a 1:10 matched follow-up period in the derivation cohort. In the external validation cohort, we identified 372 cases from 254 551 patients. The risk predictors were high-dose NSAIDs, nonselective NSAID, complicated GI ulcer history, male sex, concomitant gastroprotective agents, relevant co-medications, severe renal disease and cirrhosis. Area under the receiver operating characteristic curve was 0.79 (95% confidence interval, 0.77-0.81) in the external validation dataset.

CONCLUSIONS

The prediction model may be a useful tool for reducing the risk of serious GI complications by identifying high-risk elderly patients.

Identifying Predictors Associated with Risk of Death or Admission to Intensive Care Unit in Internal Medicine Patients with Sepsis: A Comparison of Statistical Models and Machine Learning Algorithms

Background: Sepsis is a time-dependent disease: the early recognition of patients at risk for poor outcome is mandatory. Aim: To identify prognostic predictors of the risk of death or admission to intensive care units in a consecutive sample of septic patients, comparing different statistical models and machine learning algorithms. Methods: Retrospective study including 148 patients discharged from an Italian internal medicine unit with a diagnosis of sepsis/septic shock and microbiological identification. Results: Of the total, 37 (25.0%) patients reached the composite outcome. The sequential organ failure assessment (SOFA) score at admission (odds ratio (OR): 1.83; 95% confidence interval (CI): 1.41-2.39; p < 0.001), delta SOFA (OR: 1.64; 95% CI: 1.28-2.10; p < 0.001), and the alert, verbal, pain, unresponsive (AVPU) status (OR: 5.96; 95% CI: 2.13-16.67; p < 0.001) were identified through the multivariable logistic model as independent predictors of the composite outcome. The area under the receiver operating characteristic curve (AUC) was 0.894; 95% CI: 0.840-0.948. In addition, different statistical models and machine learning algorithms identified further predictive variables: delta quick-SOFA, delta-procalcitonin, mortality in emergency department sepsis, mean arterial pressure, and the Glasgow Coma Scale. The cross-validated multivariable logistic model with the least absolute shrinkage and selection operator (LASSO) penalty identified 5 predictors; and recursive partitioning and regression tree (RPART) identified 4 predictors with higher AUC (0.915 and 0.917, respectively); the random forest (RF) approach, including all evaluated variables, obtained the highest AUC (0.978). All models' results were well calibrated. Conclusions: Although structurally different, each model identified similar predictive covariates. The classical multivariable logistic regression model was the most parsimonious and calibrated one, while RPART was the easiest to interpret clinically. Finally, LASSO and RF were the costliest in terms of number of variables identified.

Using exterior housing conditions to predict elevated pediatric blood lead levels

Housing-based lead paint dust is the most common source of lead exposure for US-born children. Although year of housing construction is a critical indicator of the lead hazard to US children, not all housing of the same age poses the same risk to children. Additional information about housing condition is required to differentiate the housing-based lead risk at the parcel level. This study aimed to identify and assess a method for gathering and using observations of exterior housing conditions to identify active housing-based lead hazards at the parcel level. We used a dataset of pediatric blood lead observations (sample years 2000-2013, ages 6-72 months, n = 6,589) to assess associations between observations of exterior housing conditions and housing-based lead risk. We used graphical and Lasso regression methods to estimate the likelihood of an elevated blood lead observation (≥3.5 μg/dL). Our methods estimate a monotonic increase in the likelihood of an elevated blood lead observation as housing conditions deteriorate with the largest changes associated with homes in the greatest disrepair. Additionally we estimate that age of home construction works in consort with housing conditions to amplify risks among those houses built before 1952. Our analysis indicates that a survey of external housing conditions can be used in combination with age of housing in the identification process, at the parcel level, of homes that pose a housing-based lead hazard to children.

Diagnostic performance of machine learning models using cell population data for the detection of sepsis: a comparative study

OBJECTIVES

To compare the artificial intelligence algorithms as powerful machine learning methods for evaluating patients with suspected sepsis using data from routinely available blood tests performed on arrival at the hospital. Results were compared with those obtained from the classical logistic regression method.

METHODS

The study group consisted of consecutive patients with fever and suspected infection admitted to the Emergency Department. The complete blood counts (CBC) were acquired using the Mindray BC-6800 Plus analyser (Mindray Diagnostics, Shenzhen, China). Cell Population Data (CPD) were also recorded. The ML and artificial intelligence (AI) models were developed; their performance was evaluated using several indicators, such as the area under the receiver operating curve (AUC), calibration plots and decision curve analysis (DCA).

RESULTS

Overall, all the tested approaches obtained an AUC>0.90. The logistic regression (LR) performed well compared to the ML/AI models. The naïve Bayes and the K-nearest neighbour (KNN) methods did not show good calibration properties. The multi-layer perceptron (MLP) model was the best in terms of discrimination, calibration and clinical usefulness.

CONCLUSIONS

The best performance in the early detection of sepsis was achieved using the ML and AI models. However, external validation studies are needed to strengthen model derivation and procedure updating.

Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model

OBJECTIVE

Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis.

METHODS

This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach.

RESULTS

A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively.

CONCLUSION

An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.

Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics

Purpose

To build machine learning models for predicting the risk of in-hospital death in patients with sepsis within 48 h, using only dynamic changes in the patient's vital signs.

Methods

This retrospective observational cohort study enrolled septic patients from five emergency departments (ED) in Taiwan. We adopted seven variables, i.e., age, sex, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature.

Results

Among all 353,253 visits, after excluding 159,607 visits (45%), the study group consisted of 193,646 ED visits. With a leading time of 6 h, the convolutional neural networks (CNNs), long short-term memory (LSTM), and random forest (RF) had accuracy rates of 0.905, 0.817, and 0.835, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.840, 0.761, and 0.770, respectively. With a leading time of 48 h, the CNN, LSTM, and RF achieved accuracy rates of 0.828, 0759, and 0.805, respectively, and an AUC of 0.811, 0.734, and 0.776, respectively.

Conclusion

By analyzing dynamic vital sign data, machine learning models can predict mortality in septic patients within 6 to 48 h of admission. The performance of the testing models is more accurate if the lead time is closer to the event.

Melatonin use and the risk of 30-day mortality among US veterans with sepsis: A retrospective study

Prior research suggests melatonin has beneficial effects that could improve survival among sepsis patients. This exploratory analysis sought to compare 30-day survival among melatonin treated and untreated patients with sepsis. A retrospective cohort study was conducted among patients with a primary inpatient admission diagnosis for sepsis utilizing the International Classification of Diseases, versions 9 and 10, Clinical Modification (ICD-9-CM and ICD-10-CM) diagnosis codes between 2000 and 2021. Propensity score weighting was utilized, accounting for demographic, clinical, and laboratory factors. Weighted Cox models were estimated for 30-day in-hospital and 30-day overall survival. A total of 9386 patients were included in the study with 593 exposed to melatonin within the first day of hospitalization. Propensity score weighted Cox models reveal melatonin was associated with a 37.9% decreased risk of 30-day in-hospital mortality (HR = 0.621; 95% CI = [0.415-0.931]) and a 33.5% decreased risk of 30-day overall mortality (HR = 0.665; 95% CI = [0.493-0.897]). Factors associated with higher risk of both in-hospital and overall mortality include male sex, white race, age, higher Charlson comorbidity burden, sodium and potassium levels, intensive care unit stay, invasive ventilation, and vasopressor use. Higher serum albumin levels are associated with lower mortality risks. Among patients diagnosed with sepsis, exposure to melatonin was associated with a lower in-hospital and 30-day mortality. Additional research is warranted to fully understand the role of melatonin in sepsis.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Background

Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.

Methods

ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.

Conclusion

This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

Understanding no-show behaviour for cervical cancer screening appointments among hard-to-reach women in Bogotá, Colombia: A mixed-methods approach

The global burden of cervical cancer remains a concern and higher early mortality rates are associated with poverty and limited health education. However, screening programs continue to face implementation challenges, especially in developing country contexts. In this study, we use a mixed-methods approach to understand the reasons for no-show behaviour for cervical cancer screening appointments among hard-to-reach low-income women in Bogotá, Colombia. In the quantitative phase, individual attendance probabilities are predicted using administrative records from an outreach program (N = 23384) using both LASSO regression and Random Forest methods. In the qualitative phase, semi-structured interviews are analysed to understand patient perspectives (N = 60). Both inductive and deductive coding are used to identify first-order categories and content analysis is facilitated using the Framework method. Quantitative analysis shows that younger patients and those living in zones of poverty are more likely to miss their appointments. Likewise, appointments scheduled on Saturdays, during the school vacation periods or with lead times longer than 10 days have higher no-show risk. Qualitative data shows that patients find it hard to navigate the service delivery process, face barriers accessing the health system and hold negative beliefs about cervical cytology.

Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma

Objective

We aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC).

Methods

We divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models.

Results

After ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692–0.970) and 0.797(95% CI 0.632–0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834–0.999), 0.714(95% CI 0.535–0.848), and 0.843(95% CI 0.657–0.928) in training set 1 and 0.750(95% CI 0.500–0.938), 0.786(95% CI 0.571–1.000), and 0.667(95% CI 0.467–0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients.

Conclusion

We developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.

Diagnosis of sepsis with inflammatory biomarkers, cytokines, endothelial functional markers from SIRS patients

BACKGROUND

Sepsis is a life-threatening illness with a challenging diagnosis. Rapid detection is the key to successful treatment of sepsis. To investigate diagnostic value, the plasma protein profiles of inflammatory biomarkers, cytokines, and endothelial functional markers were compared between healthy controls, SIRS, and septic patients.

METHODS

The plasma protein profiles were performed by Luminex Assay in a cohort of 50 SIRS patients, 82 septic patients and 25 healthy controls. Fourteen plasma proteins were analyzed in the same cohort: IL-1β, IL-6, IL-8, IL-10, CCL-2, VEGF, VEGF-C, VEGFR2, CD62E, CD62P, MFG-E8, ICAM-1, TFPI, Urokinase.

RESULT

IL-2R, IL-6, IL-8, IL-10, CCL-2, ICAM-1, and Urokinase were significantly higher in sepsis patients than SIRS patients. VEGF, IL-1β, CD62E, CD62P, MFG-E8, and TFPI have no statistical difference. VEGF-C, VEGFR2 were significantly different in SIRS patients than sepsis patients. Urokinase, ICAM-1, and VEGFR2 were significantly different between sepsis group and SIRS group. The AUCs of Urokinase, ICAM-1, and VEGFR2 and the combination for the diagnosis of sepsis were 0.650, 0.688, 0.643, and 0.741, respectively.

CONCLUSIONS

Most patients have the higher level of several cytokines and developed endothelial cell injury in the initial phase of sepsis, Urokinase, ICAM-1, and VEGFR2 may be useful to evaluate severity and prognosis of sepsis patients.

The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury

Objective

To compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients.

Methods

Data were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden’s index.

Results

Among the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients.

Conclusion

OASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute.

Trial Registration

This study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011.

Survival Prediction in Home Hospice Care Patients with Lung Cancer Based on LASSO Algorithm

PURPOSE

The aim of the present study was to develop a nomogram for prognostic prediction of patients with lung cancer in hospice.

METHODS

The data was collected from 1106 lung cancer patients in hospice between January 2008 and December 2018. The data were split into a training set, which was used to identify the most important prognostic factors by the least absolute shrinkage and selection operator (LASSO) and to build the nomogram, while the testing set was used to validate the nomogram. The performance of the nomogram was assessed by c-index, calibration curve and the decision curve analysis (DCA).

RESULTS

A total of 1106 patients, including 835 (75%) from the training set and 271 (25%) from testing set, were retrospectively analyzed in this study. Using the LASSO regression, 5 most important prognostic predictors that included sex, Karnofsky Performance Scale (KPS), quality-of-life (QOL), edema and anorexia, were selected out of 28 variables. Validated c-indexes of training set at 15, 30, and 90 days were .778 [.737-.818], .776 [.743-.809], and .751 [.713-.790], respectively. Similarly, the validated c-indexes of testing set at 15, 30, and 90 days were .789 [.714-.864], .748 [.685-.811], and .757 [.691-.823], respectively. The nomogram-predicted survival was well calibrated, as the predicted probabilities were close to the expected probabilities. Moreover, the DCA curve showed that nomogram received superior standardized net benefit at a broad threshold.

CONCLUSIONS

The study built a non-lab nomogram with important predictor to analyze the clinical parameters using LASSO. It may be a useful tool to allow clinicians to easily estimate the prognosis of the patients with lung cancer in hospice.

Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information

OBJECTIVE

Quantify tradeoffs in performance, reproducibility, and resource demands across several strategies for developing clinically relevant word embeddings.

MATERIALS AND METHODS

We trained separate embeddings on all full-text manuscripts in the Pubmed Central (PMC) Open Access subset, case reports therein, the English Wikipedia corpus, the Medical Information Mart for Intensive Care (MIMIC) III dataset, and all notes in the University of Pennsylvania Health System (UPHS) electronic health record. We tested embeddings in six clinically relevant tasks including mortality prediction and de-identification, and assessed performance using the scaled Brier score (SBS) and the proportion of notes successfully de-identified, respectively.

RESULTS

Embeddings from UPHS notes best predicted mortality (SBS 0.30, 95% CI 0.15 to 0.45) while Wikipedia embeddings performed worst (SBS 0.12, 95% CI -0.05 to 0.28). Wikipedia embeddings most consistently (78% of notes) and the full PMC corpus embeddings least consistently (48%) de-identified notes. Across all six tasks, the full PMC corpus demonstrated the most consistent performance, and the Wikipedia corpus the least. Corpus size ranged from 49 million tokens (PMC case reports) to 10 billion (UPHS).

DISCUSSION

Embeddings trained on published case reports performed as least as well as embeddings trained on other corpora in most tasks, and clinical corpora consistently outperformed non-clinical corpora. No single corpus produced a strictly dominant set of embeddings across all tasks and so the optimal training corpus depends on intended use.

CONCLUSION

Embeddings trained on published case reports performed comparably on most clinical tasks to embeddings trained on larger corpora. Open access corpora allow training of clinically relevant, effective, and reproducible embeddings.

A New Scoring System for Predicting In-hospital Death in Patients Having Liver Cirrhosis With Esophageal Varices

Introduction: Liver cirrhosis is caused by the development of various acute and chronic liver diseases. Esophageal varices is a common and serious complication of liver cirrhosis during decompensation. Despite the development of various treatments, the prognosis for liver cirrhosis with esophageal varices (LCEV) remains poor. We aimed to establish and validate a nomogram for predicting in-hospital death in LCEV patients. Methods: Data on LCEV patients were extracted from the Medical Information Mart for Intensive Care III and IV (MIMIC-III and MIMIC-IV) database. The patients from MIMIC-III were randomly divided into training and validation cohorts. Training cohort was used for establishing the model, validation and MIMIC-IV cohorts were used for validation. The independent prognostic factors for LCEV patients were determined using the least absolute shrinkage and selection operator (LASSO) method and forward stepwise logistic regression. We then constructed a nomogram to predict the in-hospital death of LCEV patients. Multiple indicators were used to validate the nomogram, including the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA). Results: Nine independent prognostic factors were identified by using LASSO and stepwise regressions: age, Elixhauser score, anion gap, sodium, albumin, bilirubin, international normalized ratio, vasopressor use, and bleeding. The nomogram was then constructed and validated. The AUC value of the nomogram was 0.867 (95% CI = 0.832-0.904) in the training cohort, 0.846 (95% CI = 0.790-0.896) in the validation cohort and 0.840 (95% CI = 0.807-0.872) in the MIMIC-IV cohort. High AUC values indicated the good discriminative ability of the nomogram, while the calibration curves and the Hosmer-Lemeshow test results demonstrated that the nomogram was well-calibrated. Improvements in NRI and IDI values suggested that our nomogram was superior to MELD-Na, CAGIB, and OASIS scoring system. DCA curves indicated that the nomogram had good value in clinical applications. Conclusion: We have established the first prognostic nomogram for predicting the in-hospital death of LCEV patients. The nomogram is easy to use, performs well, and can be used to guide clinical practice, but further external prospective validation is still required.

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

BACKGROUND

Sepsis is associated with high mortality and predictive models can help in clinical decision-making. The objective of this study was to carry out a systematic review of these models.

METHODS

In 2019, we conducted a systematic review in MEDLINE and EMBASE (CDR42018111121:PROSPERO) of articles that developed predictive models for mortality in septic patients (inclusion criteria). We followed the CHARMS recommendations (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), extracting the information from its 11 domains (Source of data, Participants, etc). We determined the risk of bias and applicability (participants, outcome, predictors and analysis) through PROBAST (Prediction model Risk Of Bias ASsessment Tool).

RESULTS

A total of 14 studies were included. In the CHARMS extraction, the models found showed great variability in its 11 domains. Regarding the PROBAST checklist, only one article had an unclear risk of bias as it did not indicate how missing data were handled while the others all had a high risk of bias. This was mainly due to the statistical analysis (inadequate sample size, handling of continuous predictors, missing data and selection of predictors), since 13 studies had a high risk of bias. Applicability was satisfactory in six articles. Most of the models integrate predictors from routine clinical practice. Discrimination and calibration were assessed for almost all the models, with the area under the ROC curve ranging from 0.59 to 0.955 and no lack of calibration. Only three models were externally validated and their maximum discrimination values in the derivation were from 0.712 and 0.84. One of them (Osborn) had undergone multiple validation studies.

DISCUSSION

Despite most of the studies showing a high risk of bias, we very cautiously recommend applying the Osborn model, as this has been externally validated various times.

Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients. Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores. Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit. Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Background

Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs.

Methods

The source database used for model development and validation is the medical information mart for intensive care (MIMIC) III. We identified adult sepsis patients using the new sepsis definition Sepsis-3. A total of 86 predictor variables consisting of demographics, laboratory tests and comorbidities were used. We employed the least absolute shrinkage and selection operator (LASSO), random forest (RF), gradient boosting machine (GBM) and the traditional logistic regression (LR) method to develop prediction models. In addition, the prediction performance of the four developed models was evaluated and compared with that of an existent scoring tool – simplified acute physiology score (SAPS) II – using five different performance measures: the area under the receiver operating characteristic curve (AUROC), Brier score, sensitivity, specificity and calibration plot.

Results

The records of 16,688 sepsis patients in MIMIC III were used for model training and test. Amongst them, 2949 (17.7%) patients had in-hospital death. The average AUROCs of the LASSO, RF, GBM, LR and SAPS II models were 0.829, 0.829, 0.845, 0.833 and 0.77, respectively. The Brier scores of the LASSO, RF, GBM, LR and SAPS II models were 0.108, 0.109, 0.104, 0.107 and 0.146, respectively. The calibration plots showed that the GBM, LASSO and LR models had good calibration; the RF model underestimated high-risk patients; and SAPS II had the poorest calibration.

Conclusion

The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.

A Novel Predictive Method Incorporating Parameters of Main Pulmonary Artery Bifurcation for Short-Term Prognosis in Non-high-risk Acute Pulmonary Embolism Patients

The aim of this study was to build a formula to predict short-term prognosis using main pulmonary artery (MPA) parameters reconstructed from computed tomographic pulmonary angiography in non-high-risk acute pulmonary embolism (PE) patients. After reconstructing the MPA and its centerline, the MPA, the right and left pulmonary artery inlet, and the MPA outlet plane were differentiated to measure the cross-sectional area (CSA), the maximal diameter and the hydraulic diameter. The MPA bifurcation area, volume and angle were measured. MPA dilation was defined as >29 mm at the transverse section plane. The patients were randomly divided into a training set and a validation set. A least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was used to build a predictive formula. The performances of the predictive formula from LASSO were tested by the area under the receiver operating characteristic curve (AUC) and precision-recall (PR) curve with 10-fold cross-validation. The clinical utility was assessed by decision curve analysis (DCA). In total, 296 patients were enrolled and randomly divided (50:50) into a training set and a validation set. The LASSO predictive formula (lambda.1SE) was as follows: 0.92 × MPA bifurcation area + 0.50 × MPA outlet hydraulic diameter + 0.10 × MPA outlet CSA. The AUCs of the predictive formula were 0.860 (95% CI: 0.795-0.912) and 0.943 (95% CI: 0.892-0.975) in the training set and validation set, respectively. The LASSO predictive formula had a higher average area under the PR curve than MPA dilation (0.71 vs. 0.23 in the training set and 0.55 vs. 0.23 in the validation set) and added a net benefit in clinical utility by DCA. Integration of MPA outlet CSA, hydraulic diameter, and bifurcation area with the LASSO predictive formula as a novel weighting method facilitated the prediction of poor short-term prognosis within 30 days after hospital admission in non-high-risk acute PE patients.

Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images

Background

Acute xerostomia is the most common side effect of radiation therapy (RT) for head and neck (H&N) malignancies. Investigating radiation-induced changes of computed tomography (CT) radiomics in parotid glands (PGs) and saliva amount (SA) can predict acute xerostomia during the RT for nasopharyngeal cancer (NPC).

Methods

CT and SA data from 35 patients with stages I-IVB were randomly collected from an NPC clinical trial registered on the clinicaltrials.gov (ID: NCT01762514). All patients received radical treatment based on intensity-modulated RT (IMRT) with a prescription dose of 68.1 Gy in 30 fractions. The patients' ages ranged 24-72 years, and each patient had five CT sets acquired at treatment position: at the 0^th, 10^th, 20^th, 30^th fractions during the RT, and at 3-month later after the RT. The PGs for each CT set were delineated by a radiation oncologist and verified independently by another. Patients' saliva was collected every other 10 days during the RT. Acute xerostomia was evaluated based on the RTOG acute toxicity scoring and the SA. In total, 1,703 radiomics features were calculated for PGs from each CT set, including feature value at 0^th fraction (FV_0F), FV_10F, and delta FV (ΔFV_10F-0F), respectively. Extensive experiments were conducted to achieve the optimal results. RidgeCV and Recursive Feature Elimination (RFE) were used for feature selection, while linear regression was used for predicting SA_30F. Four more patients were added for independent testing.

Results

Substantial changes in various radiomics metrics of PGs were observed during the RT. Eight normalized feature value (NFV), selected from NFV_0F, predicted SA_10F with a mean square error (MSE) of 0.9042 and a R² score of 0.7406. Fourteen NFV, selected from ΔNFV_10F-0F, NFV_0F, and NFV_10F to predict SA_30F, showed the best predictive ability with an MSE of 0.0569. The model predicted the level of acute xerostomia with a precision of 0.9220 and a sensitivity of 100%, compared to the clinical observed SA. For the independent test, the MSE of PSA_30F was 0.0233.

Conclusions

This study demonstrated that radiation-induced acute xerostomia level could be early predicted based on the SA and radiomics changes of the PGs during IMRT delivery. SA, NFV_0F, NFV_10F, and especially ΔNFV_10F-0F provided the best performance on acute xerostomia prediction for individual patient based on RidgeCV_RFE_LinearRegression method of delta radiomics.

Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome

Heart disease remains the leading cause of death in the United States. Compared with risk assessment guidelines that require manual calculation of scores, machine learning-based prediction for disease outcomes such as mortality can be utilized to save time and improve prediction accuracy. This study built and evaluated various machine learning models to predict one-year mortality in patients diagnosed with acute myocardial infarction or post myocardial infarction syndrome in the MIMIC-III database. The results of the best performing shallow prediction models were compared to a deep feedforward neural network (Deep FNN) with back propagation. We included a cohort of 5436 admissions. Six datasets were developed and compared. The models applying Logistic Model Trees (LMT) and Simple Logistic algorithms to the combined dataset resulted in the highest prediction accuracy at 85.12% and the highest AUC at .901. In addition, other factors were observed to have an impact on outcomes as well.

Use of ulinastatin was associated with reduced mortality in critically ill patients with sepsis

Background

Ulinastatin has anti-inflammatory properties and could potentially benefit critically ill septic patients. Nevertheless, clinical studies have yielded conflicting results. The present study examined the efficacy of ulinastatin in intensive care unit (ICU) patients with sepsis and/or septic shock.

Methods

All septic patients admitted to the ICU of Wuhu No. 2 People's Hospital between 2014 and 2017 were screened for potential eligibility for this retrospective study. The primary outcome was 28-day mortality, and its correlation with ulinastatin was assessed using multiple logistic regression models.

Results

The study included 263 patients, with an overall 28-day mortality of 38%. Patients receiving ulinastatin showed significantly lower mortality than the control patients (31% vs. 55%; P<0.001). Ulinastatin use was associated with significantly reduced risk of death (OR: 0.317, 95% CI: 0.158-0.621; P=0.001) after adjustment for age, Sequential Organ Failure Assessment score, vasopressor use, and patient type as determined with a multivariable regression model.

Conclusions

Treatment with ulinastatin was associated with a decrease in 28-day mortality in critically ill septic patients.

Predictors of 90-Day Readmission in Children Undergoing Spinal Cord Tumor Surgery: A Nationwide Readmissions Database Analysis

OBJECTIVE

A fair number of hospital admissions occur after 30 days; thus, the true readmission rate could have been underestimated. Therefore, we hypothesized that the 90-day readmission rate might better characterize the factors contributing to readmission for pediatric patients undergoing spinal tumor resection.

METHODS

The Nationwide Readmissions Database was used to study the patient demographic data, comorbidities, admissions, hospital course, spinal tumor behavior (malignant vs. benign), complications, revisions, and 30- and 90-day readmissions.

RESULTS

Of the 397 patients included in the 30-day cohort, 43 (10.8%) had been readmitted. In comparison, the 90-day readmission rate was significantly greater; 52 of 325 patients were readmitted (16.0%; P < 0.04). Patients aged 16-20 constituted the largest subgroup. However, the highest readmission rate was observed for patients aged <5 years (30-day, 21.7%; 90-day, 26.4%). Medicaid patients were more likely to be readmitted than were private insurance patients (30-day odds ratio [OR], 3.3 [P < 0.001]; 90-day OR, 2.29 [P < 0.02]). In both cohorts, patients with malignant tumors required readmission more often than did those with benign tumors (30-day OR, 2.78 [P < 0.02]; 90-day OR, 1.92 [P = 0.08]). In the 90-day cohort, the patients had been readmitted 26.4 days after discharge versus 10.6 days in the 30-day cohort. Within the 90-day cohort, 18.6% of the readmissions were for spinal reoperation, 28.3% for chemotherapy or hematologic complications, and 25.6% for other central nervous system disorders. The median charges for each readmission were ∼$50,000 and ∼$40,000 for the 30- and 90-day cohorts, respectively. Medicaid insurance, malignant tumors, and younger age were significant predictors of readmission in the 90-day cohort.

CONCLUSIONS

The prevalence and charges associated with unplanned hospital readmissions after spinal tumor resection were remarkably high. Younger age, Medicaid insurance, malignant tumors, and complications during the initial admission were significant predictors of 90-day readmission.

Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis

BACKGROUND AND OBJECTIVE

Sepsis is a heterogeneous disease and identification of its subclasses may facilitate and optimize clinical management. This study aimed to identify subclasses of sepsis and its responses to different amounts of fluid resuscitation.

METHODS

This was a retrospective study conducted in an intensive care unit at a large tertiary care hospital. The patients fulfilling the diagnostic criteria of sepsis from June 1, 2001 to October 31, 2012 were included. Clinical and laboratory variables were used to perform the latent profile analysis (LPA). A multivariable logistic regression model was used to explore the independent association of fluid input and mortality outcome.

RESULTS

In total, 14,993 patients were included in the study. The LPA identified four subclasses of sepsis: profile 1 was characterized by the lowest mortality rate and having the largest proportion and was considered the baseline type; profile 2 was characterized by respiratory dysfunction; profile 3 was characterized by multiple organ dysfunction (kidney, coagulation, liver, and shock), and profile 4 was characterized by neurological dysfunction. Profile 3 showed the highest mortality rate (45.4%), followed by profile 4 (27.4%), 2 (18.2%), and 1 (16.9%). Overall, the amount of fluid needed for resuscitation was the largest on day 1 (median 5115 mL, interquartile range (IQR) 2662 to 8800 mL) and decreased rapidly on day 2 (median 2140 mL, IQR 900 to 3872 mL). Higher cumulative fluid input in the first 48 h was associated with reduced risk of hospital mortality for profile 3 (odds ratio (OR) 0.89, 95% CI 0.83 to 0.95 for each 1000 mL increase in fluid input) and with increased risk of death for profile 4 (OR 1.20, 95% CI 1.11 to 1.30).

CONCLUSION

The study identified four subphenotypes of sepsis, which showed different mortality outcomes and responses to fluid resuscitation. Prospective trials are needed to validate our findings.

Diagnostic and predictive performance of biomarkers in patients with sepsis in an intensive care unit

Objective

This study was performed to compare the predictive performance of serum procalcitonin (PCT), N-terminal brain natriuretic propeptide (NT-proBNP), interleukin-6 (IL-6), prothrombin time (PT), thrombin time (TT), and Sequential Organ Failure Assessment (SOFA) score in the intensive care unit (ICU).

Methods

This retrospective cohort study enrolled 150 patients with sepsis and septic shock and 30 control patients without sepsis. Each patient was followed until death or 28 days. Correlations between variables were assessed with Spearman’s rho test. The Kruskal–Wallis and Mann–Whitney U tests were used for between-group comparisons.

Results

Receiver operating characteristic curve analysis of the SOFA score, PCT, NT-proBNP, IL-6, PT, and TT showed an area under the curve of 0.872, 0.732, 0.711, 0.706, 0.806, and 0.691, respectively, for diagnosing sepsis. Binary logistic regression demonstrated that the SOFA score was an independent predictor of 28-day mortality and septic shock. The correlation coefficient (r) between SOFA and PCT, NT-proBNP and SOFA, IL-6 and SOFA, PT and SOFA, and TT and SOFA was 0.79, 0.52, 0.57, 0.56, and 0.58, respectively.

Conclusion

While the SOFA score is the gold standard, analysis of multiple biomarkers could increase the performance capacity for diagnosis and prognosis in patients with sepsis in the ICU.

Prompt admission to intensive care is associated with improved survival in patients with severe sepsis and/or septic shock

Objective To investigate the association between time from hospital admission to intensive care unit (ICU) admission (door to ICU time) and hospital mortality in patients with sepsis. Methods This retrospective observational study included routinely collected healthcare data from patients with sepsis. The primary endpoint was hospital mortality, defined as the survival status at hospital discharge. Door to ICU time was calculated and included in a multivariable model to investigate its association with mortality. Results Data from 13 115 patients were included for analyses, comprising 10 309 survivors and 2 806 non-survivors. Door to ICU time was significantly longer for non-survivors than survivors (median, 43.0 h [interquartile range, 12.4, 91.3] versus 26.7 h [7.0, 74.2]). In the multivariable regression model, door to ICU time remained significantly associated with mortality (odds ratio [OR] 1.11, 95% confidence interval [CI] 1.006, 1.017) and there was a significant interaction between age and door to ICU time (OR 0.99, 95% CI 0.99, 1.00). Conclusion A shorter time from hospital door to ICU admission was shown to be independently associated with reduced hospital mortality in patients with severe sepsis and/or septic shock.

Incorporation of point-of-care ultrasound into morning round is associated with improvement in clinical outcomes in critically ill patients with sepsis

OBJECTIVES

Point-of-care ultrasound (POCUS) has been widely used in the intensive care unit (ICU). However, it is largely unknown whether the use of POCUS is associated with improved patient-important outcomes. The study aimed to investigate whether incorporation of POCUS during morning round on a routine basis was able to improve clinical outcomes in critically ill patients with sepsis.

DESIGN

It was a prospective observational study.

SETTING

A tertiary care emergency intensive care unit.

PATIENTS

All patients admitted to the emergency ICU from January 2016 to December 2017 were screened for potential eligibility. Sepsis was defined as infection plus signs of organ dysfunction.

INTERVENTION

The intervention group incorporated POCUS during morning round on a routine basis, and a checklist was developed to improve the compliance. The control group did not have the mandates to perform POCUS during morning round, but could use POCUS when necessary.

MEASUREMENTS

Clinical outcomes of mortality, length of stay in ICU, durations of vasopressors and mechanical ventilation were compared between the intervention and control groups. Multivariable regression model was employed to adjust for confounding factors.

MAIN RESULTS

A total of 129 subjects, including 88 in the control group and 41 in the intervention group, were included for analysis. Univariate analysis showed that the intervention group had shorter durations of mechanical ventilation (MV) (4.5 ± 1.2 vs. 5.7 ± 1.0 days; p = 0.034) and more negative fluid balance (-143 vs. 48 ml/24 h; p = 0.003) on day 3. In multivariable model, routine incorporation of POCUS was associated with lower risk of prolonged (>7 days) ICU stay (OR: 0.39, 95% CI: 0.29-0.88; p = 0.029).

CONCLUSIONS

The study showed that incorporation of POCUS during morning round on a routine basis was associated with shortened duration of MV and length of stay in ICU. The possible mechanism underlying the relationship may be via reduced fluid administration. Future randomized controlled trials are needed to validate current findings.