Predicting Prolonged Length of ICU Stay through Machine Learning

Machine Learning-Based Mortality Risk Prediction Model in Patients with Sepsis

Objective

The aim of our study was to establish and validate a machine learning-based predictive model for mortality risk in elderly patients with sepsis. By integrating traditional biomarkers, novel biomarkers, clinical data, and established scoring systems, the model seeks to enhance predictive accuracy and thereby improve clinical outcomes in high-risk patient population.

Methods

Conducted at Beijing Chao-Yang Hospital from August 2021 to August 2023, our study included 180 emergency department patients meeting Sepsis 3.0 diagnostic criteria. Data collected included patient demographics, vital signs, laboratory parameters, disease-related scores, major comorbidities, and the 28-day mortality. Variables were analyzed using univariate analysis and LASSO regression, and the machine learning model was constructed using R statistical software and validated internally via bootstrap resampling and calibration curves.

Results

The model identified seven significant variables: SOFA, APACHE II, MAP, ALB, PCT, LTB, and VEGF. These variables constituted our final prediction model, which achieved an AUC of 0.845 (95% CI: 0.786, 0.905), with a sensitivity of 75.9% and a specificity of 85.0%. Internal validation yielded a bootstrap-corrected AUC of 0.857 (95% CI: 0.799, 0.912), confirming the model's statistical robustness. The nomogram provided a visual tool for predicting 28-day mortality risk, and decision curve analysis demonstrated strong potential for clinical utility.

Conclusion

The predictive model, which incorporates SOFA, APACHE II, MAP, ALB, PCT, LTB, and VEGF, shows significant potential in predicting the 28-day mortality risk for elderly sepsis patients. It provides a convenient and rapid tool for clinical use. Further research with larger sample sizes and external validation is warranted to confirm these findings and enhance the model's applicability.

Development of an emergency department length-of-stay prediction model based on machine learning

BACKGROUND

The problem of prolonged emergency department length of stay (EDLOS) is becoming increasingly crucial. This study aims to develop a machine learning (ML) model to predict EDLOS, with EDLOS as the outcome variable and demographic characteristics, triage level, and medical resource utilization as predictive factors.

METHODS

A retrospective analysis was performed on the patients who visited the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to September 2021, and a total of 321,012 cases were identified. According to the inclusion and exclusion criteria, 187,028 cases were finally included in the analysis. ML analysis was performed using R-squared (R2), and the predictive factors and the EDLOS were used as independent variables and dependent variables, respectively, to establish models. The performance evaluation of the ML models was conducted through the utilization of the mean absolute error (MAE), root mean square error (RMSE), and R2, enabling an objective comparative analysis.

RESULTS

In the comparative analysis of the six ML models, light gradient boosting machine (LightGBM) model demonstrated the lowest MAE (443.519) and RMSE (826.783), and the highest R² value (0.48), indicating better model fit and predictive performance. Among the top 10 predictive factors associated with EDLOS according to the LightGBM model, the emergency waiting time, age, and emergency arrival time had the most significant impact on the EDLOS.

CONCLUSION

The LightGBM model suggests that the emergency waiting time, age, and emergency arrival time may be used to predict the EDLOS.

Predictors of Prolonged Intensive Care Unit Stay and In-Hospital Mortality Following Cardiac Surgery: An Integrated Analysis from the PROCARD-ATI Study

Background: Despite advances in surgical techniques and perioperative management, reliable intraoperative predictors of adverse postoperative outcomes in cardiac surgery remain elusive. This study aimed to identify perioperative factors associated with prolonged intensive care unit (ICU) stay and in-hospital mortality while defining actionable thresholds. Methods: A retrospective analysis was conducted on 130 adult cardiac surgery patients (with a median age of 61 years, 66.2% men) from October 2022 to November 2024. Data on preoperative risk factors, intraoperative variables (cardiopulmonary bypass time-CPBT, aortic cross-clamp time-AXCT), and postoperative outcomes (ICU length of stay, in-hospital mortality) were extracted from electronic medical records. Results: Prolonged ICU stay (≥7 days) occurred in 38.5% of patients, and in-hospital mortality was 10%. AXCT was the sole independent predictor of prolonged ICU stay (OR = 1.046, 95% CI = 1.014-1.080, p = 0.005), with a 110-min cut-off (sensitivity 71%, specificity 61%, AUC = 0.729). A Kaplan-Meier analysis showed significantly longer ICU stays above this threshold (p = 0.006). For in-hospital mortality, prolonged CPBT (OR = 1.030, 95% CI = 1.003-1.057, p = 0.030), emergency surgery (OR = 0.043, 95% CI = 0.002-0.863, p = 0.040), and higher AXCT (OR = 0.965, 95% CI = 0.934-0.997, p = 0.034) were the independent predictors. A receiver operating characteristic analysis identified 140 min for AXCT (sensitivity 67%, specificity 70%, AUC = 0.707) and 227 min for CPBT (sensitivity 83%, specificity 69%, AUC = 0.824) as the optimal cut-offs. A combined model (emergency surgery yes/no, AXCT > 140 min, CPBT > 227 min) yielded excellent discrimination (AUC = 0.846). Conclusions: These findings suggest perioperative benchmarks that may guide surgical teams in refining operative strategies, reducing ICU resource utilization, and improving survival following cardiac surgery.

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital

BACKGROUND

The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefore, this study aimed to predict the LOS for patients hospitalized in various clinics using different artificial intelligence (AI) models.

METHODS

The study analyzed 162,140 hospitalized patients aged 18 and older at various clinics of a university hospital in northern Türkiye from 2012 to 2020. Three soft computing methods-Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Multiple Linear Regression Analysis (MLR)-were employed to estimate LOS using inputs such as medical and imaging services (number of CT, USG, ECG, hemogram tests, medical biochemistry, and number of direct x-rays), demographic, and diagnostic data (patients' age, sex, season of hospitalization, type of hospitalization, diagnosis, and second diagnosis). The LOS predictions utilized single and double-hidden layer ANNs with various training algorithms (Levenberg-Marquardt-LM, Bayesian Regularization-BR and Scaled Conjugate Gradient-SCG) and activation functions (tangent-sigmoid, purelin), ANFIS with Grid Partitioning (ANFIS-GP), and MLR. Model performance was evaluated using the Coefficient of Determination (R²), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

RESULTS

Of the patients, 54% were male and 43.5% were treated in surgical clinics. The mean age was 55.1 years, with 32.9% of participants aged 65 years or older. Hospital stays were 2-7 days for 39.7% of patients, over 7 days for 30.9%, and 1 day for 29.4%. Neoplasm-related diagnoses (ICD codes) accounted for 25.1% of admissions. Variables influencing LOS were identified through feature selection from patients in various hospital wards. The most significant factors affecting LOS include second diagnosis, the number of hemogram tests, computerized tomography scans (CT), ultrasonography (USG), and direct X-rays. Utilizing these factors, 12 models with varied input variables were developed and analyzed. The double hidden layer ANN model with the Levenberg-Marquardt (LM) training algorithm outperformed the others, achieving R² values of 0.854 for training and 0.807 for the test dataset, with RMSE values of 2.397 days and 2.774 days and MAE values of 1.787 days and 1.994 days, respectively. Following ANN-LM, the best results were obtained with ANFIS-GP, while MLR exhibited the lowest performance.

CONCLUSIONS

Various AI models can effectively predict LOS for patients in different hospital units. Accurate LOS predictions can help health managers allocate resources more equitably across units.

Transformer-based model for predicting length of stay in intensive care unit in sepsis patients

Introduction

Sepsis, a life-threatening condition with a high mortality rate, requires intensive care unit (ICU) admission. The increasing hospitalization rate for patients with sepsis has escalated medical costs due to the strain on ICU resources. Efficient management of ICU resources is critical to addressing this challenge.

Methods

This study utilized the dataset collected from 521 patients with sepsis at Chungbuk National University Hospital between July 2020 and August 2023. A transformer-based deep learning model was developed to predict ICU length of stay (LOS). The model incorporated global and local input data analysis through classification and feature-wise tokens, based on sequential organ failure assessment (SOFA) criteria. Model performance was evaluated using four-fold cross-validation.

Results

The proposed model achieved a mean absolute error (MAE) of 2.05 days for predicting ICU LOS. The result demonstrates the ability of the proposed model to provide accurate and reliable predictions.

Discussion

The proposed model offers valuable insights for healthcare resource management by optimizing ICU resource allocation and potentially reducing medical expenses. These findings highlight the applicability of the proposed model to efficient healthcare cost management.

Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study

BACKGROUND

Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general population.

OBJECTIVE

In this study, we developed and validated a machine learning (ML)-based LoS prediction model for planned admissions using the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM).

METHODS

Retrospective patient-level prediction models used electronic health record (EHR) data converted to the OMOP CDM (version 5.3) from Seoul National University Bundang Hospital (SNUBH) in South Korea. The study included 137,437 hospital admission episodes between January 2016 and December 2020. Covariates from the patient, condition occurrence, medication, observation, measurement, procedure, and visit occurrence tables were included in the analysis. To perform feature selection, we applied Lasso regularization in the logistic regression. The primary outcome was an LoS of 7 days or longer, while the secondary outcome was an LoS of 3 days or longer. The prediction models were developed using 6 ML algorithms, with the training and test set split in a 7:3 ratio. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Shapley Additive Explanations (SHAP) analysis measured feature importance, while calibration plots assessed the reliability of the prediction models. External validation of the developed models occurred at an independent institution, the Seoul National University Hospital.

RESULTS

The final sample included 129,938 patient entry events in the planned admissions. The Extreme Gradient Boosting (XGB) model achieved the best performance in binary classification for predicting an LoS of 7 days or longer, with an AUROC of 0.891 (95% CI 0.887-0.894) and an AUPRC of 0.819 (95% CI 0.813-0.826) on the internal test set. The Light Gradient Boosting (LGB) model performed the best in the multiclassification for predicting an LoS of 3 days or more, with an AUROC of 0.901 (95% CI 0.898-0.904) and an AUPRC of 0.770 (95% CI 0.762-0.779). The most important features contributing to the models were the operation performed, frequency of previous outpatient visits, patient admission department, age, and day of admission. The RF model showed robust performance in the external validation set, achieving an AUROC of 0.804 (95% CI 0.802-0.807).

CONCLUSIONS

The use of the OMOP CDM in predicting hospital LoS for planned admissions demonstrates promising predictive capabilities for stays of varying durations. It underscores the advantage of standardized data in achieving reproducible results. This approach should serve as a model for enhancing operational efficiency and patient care coordination across health care settings.

Urea to creatinine ratio as a predictor of persistent critical illness

INTRODUCTION

Persistent critical illness (PCI) is a syndrome in which the acute presenting problem has been stabilized, but the patient's clinical state does not allow ICU discharge. The burden associated with PCI is substantial. The most obvious marker of PCI is prolonged ICU length of stay (LOS), usually greater than 10 days. Urea to Creatinine ratio (UCr) has been suggested as an early marker of PCI development.

METHODS

A single-center retrospective study. Data of patients admitted to a general mixed medical-surgical ICU during Jan 1st 2018 till Dec 31st 2022 was extracted, including demographic data, baseline characteristics, daily urea and creatinine results, renal replacement therapy (RRT) provided, and outcome measures - length of stay, and mortality (ICU, and 90 days). Patients were defined as PCI patients if their LOS was >10 days. We used Fisher exact test or Chi-square to compare PCI and non-PCI patients. The association between UCr with PCI development was assessed by repeated measures linear model. Multivariate Cox regression was used for 1 year mortality assessment.

RESULTS

2098 patients were included in the analysis. Patients who suffered from PCI were older, with higher admission prognostic scores. Their 90-day mortality was significantly higher than non-PCI patients (34.58% vs 12.18%, p < 0.0001). A significant difference in UCr was found only on the first admission day among all patients. This was not found when examining separately surgical, trauma, or transplantation patients. We did not find a difference in UCr in different KDIGO (Kidney Disease Improving Global Outcomes) stages. Elevated UCr and PCI were found to be significantly associated with 1 year mortality.

CONCLUSION

In this single center retrospective cohort study, UCr was not found to be associated with PCI development.

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Objective

The study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources.

Methods

Regression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation.

Results

In regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation.

Conclusion

This study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.

Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU

BACKGROUND

Timely ventilator liberation can prevent morbidities associated with invasive mechanical ventilation in the pediatric ICU (PICU). There currently exists no standard benchmark for duration of invasive mechanical ventilation in the PICU. This study sought to develop and validate a multi-center prediction model of invasive mechanical ventilation duration to determine a standardized duration of invasive mechanical ventilation ratio.

METHODS

This was a retrospective cohort study using registry data from 157 institutions in the Virtual Pediatric Systems database. The study population included encounters in the PICU between 2012-2021 involving endotracheal intubation and invasive mechanical ventilation in the first day of PICU admission who received invasive mechanical ventilation for > 24 h. Subjects were stratified into a training cohort (2012-2017) and 2 validation cohorts (2018-2019/2020-2021). Four models to predict the duration of invasive mechanical ventilation were trained using data from the first 24 h, validated, and compared.

RESULTS

The study included 112,353 unique encounters. All models had observed-to-expected (O/E) ratios close to one but low mean squared error and R2 values. The random forest model was the best performing model and achieved an O/E ratio of 1.043 (95% CI 1.030-1.056) and 1.004 (95% CI 0.990-1.019) in the validation cohorts and 1.009 (95% CI 1.004-1.016) in the full cohort. There was a high degree of institutional variation, with single-unit O/E ratios ranging between 0.49-1.91. When stratified by time period, there were observable changes in O/E ratios at the individual PICU level over time.

CONCLUSIONS

We derived and validated a model to predict the duration of invasive mechanical ventilation that performed well in aggregated predictions at the PICU and the cohort level. This model could be beneficial in quality improvement and institutional benchmarking initiatives for use at the PICU level and for tracking of performance over time.

Machine Learning for Benchmarking Critical Care Outcomes

OBJECTIVES

Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative review centers on key discoveries and outcomes to aid clinicians and researchers in selecting the optimal methodology for critical care benchmarking using ML.

METHODS

We used PubMed to search the literature from 2003 to 2023 regarding predictive models utilizing ML for mortality (592 articles), length of stay (143 articles), or mechanical ventilation (195 articles). We supplemented the PubMed search with Google Scholar, making sure relevant articles were included. Given the narrative style, papers in the cohort were manually curated for a comprehensive reader perspective.

RESULTS

Our report presents comparative results for benchmarked outcomes and emphasizes advancements in feature types, preprocessing, model selection, and validation. It showcases instances where ML effectively tackled critical care outcome-prediction challenges, including nonlinear relationships, class imbalances, missing data, and documentation variability, leading to enhanced results.

CONCLUSIONS

Although ML has provided novel tools to improve the benchmarking of critical care outcomes, areas that require further research include class imbalance, fairness, improved calibration, generalizability, and long-term validation of published models.

Predictive models for short-term mortality and length of hospital stay among adults with community-onset bacteraemia before and during the COVID-19 pandemic: application of early data dynamics

BACKGROUND

The development of scoring systems to predict the short-term mortality and the length of hospital stay (LOS) in patients with bacteraemia is essential to improve the quality of care and reduce the occupancy variance in the hospital bed.

METHODS

Adults hospitalised with community-onset bacteraemia in the coronavirus disease 2019 (COVID-19) and pre-COVID-19 eras were captured as the validation and derivation cohorts in the multicentre study, respectively. Model I incorporated all variables available on day 0, Model II incorporated all variables available on day 3, and Models III, IV, and V incorporated the variables that changed from day 0 to day 3. This study adopted the statistical and machine learning (ML) methods to jointly determine the prediction performance of these models in two study cohorts.

RESULTS

A total of 3,639 (81.4%) and 834 (18.6%) patients were included in the derivation and validation cohorts, respectively. Model IV achieved the best performance in predicting 30-day mortality in both cohorts. The most frequently identified variables incorporated into Model IV were deteriorated consciousness from day 0 to day 3 and deteriorated respiration from day 0 to day 3. Model V achieved the best performance in predicting LOS in both cohorts. The most frequently identified variables in Model V were deteriorated consciousness from day 0 to day 3, a body temperature ≤ 36.0 °C or ≥ 39.0 °C on day 3, and a diagnosis of complicated bacteraemia.

CONCLUSIONS

For hospitalised adults with community-onset bacteraemia, clinical variables that dynamically changed from day 0 to day 3 were crucial in predicting the short-term mortality and LOS.

Research Hotspots and Trends of Deep Learning in Critical Care Medicine: A Bibliometric and Visualized Study

Background

Interest in the application of deep learning (DL) in critical care medicine (CCM) is growing rapidly. However, comprehensive bibliometric research that analyze and measure the global literature is still lacking.

Objective

The present study aimed to systematically evaluate the research hotspots and trends of DL in CCM worldwide based on the output of publications, cooperative relationships of research, citations, and the co-occurrence of keywords.

Methods

A total of 1708 articles in all were obtained from Web of Science. Bibliometric analysis was performed by Bibliometrix package in R software (4.2.2), Microsoft Excel 2019, VOSviewer (1.6.18), and CiteSpace (5.8.R3).

Results

The annual publications increased steeply in the past five years, accounting for 95.67% (1634/1708) of all the included literature. China and USA contributed to approximately 71.66% (1244/1708) of all publications. Seven of the top ten most productive organizations rank in the top 100 universities globally. Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Convolutional neural networks, long and short-term memory networks, recurrent neural networks, transformer models, and attention mechanisms were all commonly used DL technologies.

Conclusion

Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Extensive collaborative research to improve the maturity and robustness of the model remains necessary to make DL-based model applications sufficiently compelling for conventional CCM practice.

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

Objective

This study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework).

Methods

We analyzed a dataset of patients admitted through the ED to the "Sant"Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%).

Results

A total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6-7 day mean difference between actual and predicted LoS.

Conclusion

Our results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system.

A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease

BACKGROUND

Providing intensive care is increasingly expensive, and the aim of this study was to construct a risk column line graph (nomograms)for prolonged length of stay (LOS) in the intensive care unit (ICU) for patients with chronic obstructive pulmonary disease (COPD).

METHODS

This study included 4,940 patients, and the data set was randomly divided into training (n = 3,458) and validation (n = 1,482) sets at a 7:3 ratio. First, least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Second, a prediction model was constructed using multifactorial logistic regression analysis. Third, the model was validated using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, calibration plots, and decision-curve analysis (DCA), and was further internally validated.

RESULTS

This study selected 11 predictors: sepsis, renal replacement therapy, cerebrovascular disease, respiratory failure, ventilator associated pneumonia, norepinephrine, bronchodilators, invasive mechanical ventilation, electrolytes disorders, Glasgow Coma Scale score and body temperature. The models constructed using these 11 predictors indicated good predictive power, with the areas under the ROC curves being 0.826 (95%CI, 0.809-0.842) and 0.827 (95%CI, 0.802-0.853) in the training and validation sets, respectively. The Hosmer-Lemeshow test indicated a strong agreement between the predicted and observed probabilities in the training (χ2 = 8.21, p = 0.413) and validation (χ2 = 0.64, p = 0.999) sets. In addition, decision-curve analysis suggested that the model had good clinical validity.

CONCLUSION

This study has constructed and validated original and dynamic nomograms for prolonged ICU stay in patients with COPD using 11 easily collected parameters. These nomograms can provide useful guidance to medical and nursing practitioners in ICUs and help reduce the disease and economic burdens on patients.

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

Background

In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.

Materials and methods

A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.

Results

A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.

Conclusion

The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.