scholarly journals Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Infection ◽  
2021 ◽  
Author(s):  
Carolin E. M. Jakob ◽  
Ujjwal Mukund Mahajan ◽  
Marcus Oswald ◽  
Melanie Stecher ◽  
Maximilian Schons ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Smoking Habit ◽  
Clinical Score ◽  
Early Warning Score ◽  
Predictive Score ◽  
Good Prediction ◽  
Validation Data ◽  
Patients At Risk ◽  
Predictor Model

Abstract Purpose While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

scholarly journals MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

2020 ◽  
Vol 9 (2) ◽  
pp. 343 ◽  
Author(s):  
Arash Kia ◽  
Prem Timsina ◽  
Himanshu N. Joshi ◽  
Eyal Klang ◽  
Rohit R. Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Area Under The Curve ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Support Vector ◽  
Adult Age ◽  
Machine Learning Model ◽  
Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

scholarly journals Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion

2021 ◽  
pp. 219256822110193
Author(s):  
Kevin Y. Wang ◽  
Ijezie Ikwuezunma ◽  
Varun Puvanesarajah ◽  
Jacob Babu ◽  
Adam Margalit ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Venous Thromboembolism ◽  
Risk Stratification ◽  
Predictive Model ◽  
Predictive Modeling ◽  
Lumbar Fusion ◽  
Posterior Lumbar Fusion ◽  
Single Level ◽  
Patients At Risk

Study Design: Retrospective review. Objective: To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology. Methods: Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic. Results: 13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm ( P > 0.05). Conclusion: Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.

Glucose as an additional parameter to National Early Warning Score (NEWS) in prehospital setting enhances identification of patients at risk of death: an observational cohort study

2020 ◽  
pp. emermed-2018-208309
Author(s):  
Hanna Vihonen ◽  
Mitja Lääperi ◽  
Markku Kuisma ◽  
Jussi Pirneskoski ◽  
Jouni Nurmi
Keyword(s):  
At Risk ◽  
Blood Glucose ◽  
Regression Model ◽  
Early Warning ◽  
Prehospital Setting ◽  
Early Warning Score ◽  
Calibration Model ◽  
Risk Of Death ◽  
Patients At Risk ◽  
National Early Warning Score

BackgroundTo determine if prehospital blood glucose could be added to National Early Warning Score (NEWS) for improved identification of risk of short-term mortality.MethodsRetrospective observational study (2008–2015) of adult patients seen by emergency medical services in Helsinki metropolitan area for whom all variables for calculation of NEWS and a blood glucose value were available. Survival of 24 hours and 30 days were determined. The NEWS parameters and glucose were tested by multivariate logistic regression model. Based on ORs we formed NEWSgluc model with hypoglycaemia (≤3.0 mmol/L) 3, normoglycaemia 0 and hyperglycaemia (≥11.1 mmol/L) 1 points. The scores from NEWS and NEWSgluc were compared using discrimination (area under the curve), calibration (Hosmer-Lemeshow test), likelihood ratio tests and reclassification (continuous net reclassification index (cNRI)).ResultsData of 27 141 patients were included in the study. Multivariable regression model for NEWSgluc parameters revealed a strong association with glucose disturbances and 24-hour and 30-day mortality. Likelihood ratios (LRs) for mortality at 24 hours using a cut-off point of 15 were for NEWSgluc: LR+ 17.78 and LR− 0.96 and for NEWS: LR+ 13.50 and LR− 0.92. Results were similar at 30 days. Risks per score point estimation and calibration model showed glucose added benefit to NEWS at 24 hours and at 30 days. Although areas under the curve were similar, reclassification test (cNRI) showed overall improvement of classification of survivors and non-survivors at 24 days and 30 days with NEWSgluc.ConclusionsIncluding glucose in NEWS in the prehospital setting seems to improve identification of patients at risk of death.

scholarly journals Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning

2020 ◽  
Author(s):  
F. P. Chmiel ◽  
M. Azor ◽  
F. Borca ◽  
M. J. Boniface ◽  
D. K. Burns ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Departments ◽  
Risk Groups ◽  
Machine Learning Algorithms ◽  
Care Indicator ◽  
Number Of Patients ◽  
Increased Risk ◽  
Single Centre Study ◽  
Patients At Risk

ABSTRACTShort-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance can help reduce the number of patients with missed or undertreated illness or injury, and could support appropriate discharges with focused interventions. In this manuscript we present a retrospective, single-centre study where we create and evaluate a machine-learnt classifier trained to identify patients at risk of reattendance within 72 hours of discharge from an emergency department. On a patient hold-out test set, our highest performing classifier obtained an AUROC of 0.748 and an average precision of 0.250; demonstrating that machine-learning algorithms can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. In parallel to our predictive model we train an explanation model, capable of explaining predictions at an attendance level, which can be used to help inform the design of interventional strategies.

SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 37 (10) ◽  
pp. S65
Author(s):  
C Willis ◽  
K Kawamoto ◽  
A Watanabe ◽  
J Biskupiak ◽  
K Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Wild Type ◽  
Patients At Risk

Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis

2019 ◽  
Vol 73 (4) ◽  
pp. 334-344 ◽  
Author(s):  
Ryan J. Delahanty ◽  
JoAnn Alvarez ◽  
Lisa M. Flynn ◽  
Robert L. Sherwin ◽  
Spencer S. Jones
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Early Identification ◽  
Learning Model ◽  
Machine Learning Model ◽  
Patients At Risk

Unsupervised Machine Learning of LGE Patterns on Cardiac MRI Identifies Patients at Risk for Right Ventricular Failure After LVAD

2020 ◽  
Vol 26 (10) ◽  
pp. S146
Author(s):  
Julia M. Simkowski ◽  
Ramsey M. Wehbe ◽  
Jack Goergen ◽  
Allen S. Anderson ◽  
Kambiz Ghafourian ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Right Ventricular ◽  
Cardiac Mri ◽  
Right Ventricular Failure ◽  
Ventricular Failure ◽  
Unsupervised Machine Learning ◽  
Patients At Risk

Can Machine Learning Help Identify Patients at Risk for Recurrent Sexually Transmitted Infections?

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Heather R. Elder ◽  
Susan Gruber ◽  
Sarah J. Willis ◽  
Noelle Cocoros ◽  
Myfanwy Callahan ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Sexually Transmitted Infections ◽  
Sexually Transmitted ◽  
Patients At Risk

scholarly journals Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions

2021 ◽  
pp. 1106-1126
Author(s):  
Dylan J. Peterson ◽  
Nicolai P. Ostberg ◽  
Douglas W. Blayney ◽  
James D. Brooks ◽  
Tina Hernandez-Boussard
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Department ◽  
Emergency Department Visits ◽  
Test Model ◽  
Receiver Operator Curve ◽  
Patients At Risk ◽  
Electronic Health ◽  
Chemotherapy Patients ◽  
Operator Curve

PURPOSE Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data. METHODS Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve. RESULTS Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients. CONCLUSION Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.

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