Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Perng, Jau-Woei; Kao, I-Hsi; Kung, Chia-Te; Hung, Shih-Chiang; Lai, Yi; Su, Chih-Min

doi:10.3390/jcm8111906

Cited by 50 publications

(37 citation statements)

References 41 publications

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“…However, novel machine-learning models can accurately predict sepsis onset beforehand [46]. In accordance with the results of previous studies [38,39], our machine-learning models showed higher accuracy rate of mortality prediction in patients with suspected infection than the qSOFA scores. Therefore, machine-learning based prediction models may be beneficial for physicians in management of sepsis in the future.…”

Section: Discussionsupporting

confidence: 89%

“…Recently, machine-learning models have been applied for predicting diverse outcomes in the ED, e.g., cardiac arrest prediction [24], ED triage [34][35][36], prediction of hospital admission [37], identification of patients with suspected infection [27], screening of sepsis [28] or septic shock [26], and mortality prediction in patients with sepsis [38] or suspected infection [39]. Our study suggests that the ability of machine-learning models for predicting deterioration within three days of patients with suspected infection are superior to the conventional severity illness scores.…”

Section: Discussionmentioning

confidence: 99%

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Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Kwon

Baek

2020

JCM

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The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals (n = 19,353) were used as training-validation datasets and data from one (n = 4234) as the test dataset. Machine-learning algorithms including extreme gradient boosting, light gradient boosting machine, and random forest were used. We assessed the prediction ability of machine-learning models using the area under the receiver operating characteristic (AUROC) curve, and DeLong’s test was used to compare AUROCs between the qSOFA scores and qSOFA-based machine-learning models. A total of 447,926 patients visited EDs during the study period. We analyzed 23,587 patients with suspected infection who were admitted to the EDs. The median age of the patients was 63 years (interquartile range: 43–78 years) and in-hospital mortality was 4.0% (n = 941). For predicting three-day mortality among patients with suspected infection in the ED, the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three -day mortality was higher than that of the qSOFA scores (0.78 [95% CI 0.77–0.79], p < 0.001). For predicting three-day mortality in patients with suspected infection in the ED, the qSOFA-based machine-learning model was found to be superior to the conventional qSOFA scores.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Kwon

Baek

2020

JCM

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“…Four "folds" of similar size were created by splitting this set to facilitate four-fold cross-validation (CV) [40], [41]. The cross-validation technique can handle [43], [44].…”

Section: Clinical Procedures For Detection Indexmentioning

confidence: 99%

A Deep Learning-Based Sepsis Estimation Scheme

Al-Mualemi

2021

IEEE Access

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“…Hence, the first studies emerged that report machine learning-based mortality prediction models using data from patients with sepsis presenting to the ED [15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

Doorn

Borggreve

Schalkwijk

et al. 2020

Preprint

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IntroductionPatients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.MethodsA single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n=1,244) and validation dataset (n=100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality.ResultsA number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80-0.84) and 0.84 (95% CI: 0.81-0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78-0.92) compared to abbMEDS (0.63,0.54-0.73), mREMS (0.63,0.54-0.72) and internal medicine physicians (0.74,0.65-0.82).ConclusionMachine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

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Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Cited by 50 publications

References 41 publications

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

A Deep Learning-Based Sepsis Estimation Scheme

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

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