Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

AbstractBackgroundParalytic Ileus (PI) patients in the Intensive Care Unit (ICU) are at a significant risk of death. Prediction of at-risk patients for mortality after 24 hours of admission of ICU PI patients is important to increase the life expectancy of PI patients.Methods and ResultsThe proposed framework, DLMP (Deep Learning Model for Mortality Prediction of ICU Patients with PI) is a powerful deep learning model consisting of six total unique clinical lab items and two demographics as inputs to a Neural Network(NN) of only two neuron layers. Using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset of 1,017 ICU PI patients, the DLMP resulted in the best prediction performance with an AUC score of 0.866.ConclusionThe proposed approach is capable of modeling the mortality of ICU patients after 24 hours admission using only six unique total clinical data and two demographics with a simple NN architecture. DLMP framework significantly improves the outcome prediction compared to the process mining and machine learning models. The proposed DLMP has the potential of allowing clinicians to create targeted interventions that reduce mortality for PI patients in an ICU setting.

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Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

BioData Mining ◽

10.1186/s13040-021-00276-5 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Zhixuan Zeng ◽

Shuo Yao ◽

Jianfei Zheng ◽

Xun Gong

Keyword(s):

Machine Learning ◽

Hospital Mortality ◽

Mortality Prediction ◽

Research Database ◽

Training Set ◽

Icu Patients ◽

Test Set ◽

Saps Ii ◽

Severity Scores ◽

Mimic Iii

Abstract Background Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. Methods Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. Results Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. Conclusions The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU.

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Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

BMC Health Services Research ◽

10.1186/s12913-020-05999-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Narayan Sharma ◽

René Schwendimann ◽

Olga Endrich ◽

Dietmar Ausserhofer ◽

Michael Simon

Keyword(s):

Hospital Mortality ◽

Patient Population ◽

Generalized Additive Models ◽

Routine Data ◽

Predictive Performance ◽

Population Based ◽

Mortality Prediction ◽

Additive Models ◽

General Hospitals ◽

Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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Development and internal validation of the HIV In‐hospital Mortality Prediction (HIV‐IMP) risk score

HIV Medicine ◽

10.1111/hiv.13159 ◽

2021 ◽

Author(s):

Abdullah E. Laher ◽

Fathima Paruk ◽

Willem D. F. Venter ◽

Oluwatosin A. Ayeni ◽

Feroza Motara ◽

...

Keyword(s):

Hospital Mortality ◽

Risk Score ◽

Mortality Prediction ◽

Internal Validation

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The Relationship of the ACCP/SCCM Consensus Conference Classification of Sepsis to Mortality and Multiorgan Dysfunction among Medical ICU Patients

Journal of Intensive Care Medicine ◽

10.1177/088506669601100604 ◽

1996 ◽

Vol 11 (6) ◽

pp. 326-334 ◽

Cited By ~ 4

Author(s):

Marin H. Kollef ◽

Paul R. Eisenberg

Keyword(s):

Septic Shock ◽

Severe Sepsis ◽

Hospital Mortality ◽

Consensus Conference ◽

Organ System ◽

Apache Ii ◽

Apache Ii Score ◽

Icu Patients ◽

Multiorgan Dysfunction

To determine the relation between the proposed ACCP/SCCM Consensus Conference classification of sepsis and hospital outcomes, we conducted a single-center, prospective observational study at Barnes Hospital, St. Louis, MO, an academic tertiary care hospital. A total of 324 consecutive patients admitted to the medical intensive care unit (ICU) were studied for prospective patient surveillance and data collection. The main outcome measures were the number of acquired organ system derangements and hospital mortality. Fifty-seven (17.6%) patients died during the study period. The proposed classifications of sepsis (e.g., systemic inflammatory response syndrome [SIRS], sepsis, severe sepsis, septic shock) correlated with hospital mortality ( r = 0.330; p < 0.001) and development of an Organ System Failure Index (OSFI) of 3 or greater ( r = 0.426; p < 0.001). Independent determinants of hospital mortality for this patient cohort ( p < 0.05) were development of an OSFI of 3 or greater (adjusted odds ratio [AOR], 13.9; 95% confidence interval [CI], 6.4–30.2; p < 0.001); presence of severe sepsis or septic shock (AOR, 2.6; 95% CI, 1.2–5.6; p = 0.002), and an APACHE II score ≥ of 18 or greater (AOR, 2.4; 95% CI, 1.0–5.8; p = 0.045). Intra-abdominal infection (AOR, 19.1; 95% CI, 1.6–230.1; p = 0.011), an APACHE II score ≥ of 18 or greater (AOR, 8.9; 95% CI, 4.2–18.6; p < 0.001), and presence of severe sepsis or septic shock (AOR, 2.9; 95% CI, 1.5–5.4; p = 0.001) were independently associated with development of an OSFI of 3 or greater. These data confirm that acquired multiorgan dysfunction is the most important predictor of mortality among medical ICU patients. In addition, they identify the proposed ACCP/SCCM Consensus Conference classification of sepsis as an additional independent determinant of both hospital mortality and multiorgan dysfunction.

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