Machine Learning–Based Mortality Prediction of Patients at Risk During Hospital Admission

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.

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Towards Prevention Of Acute Lung Injury: Identification Of Patients At Risk At The Time Of Hospital Admission

10.1164/ajrccm-conference.2010.181.1_meetingabstracts.a4087 ◽

2010 ◽

Cited By ~ 1

Author(s):

Ognjen Gajic ◽

Ousama Dabbagh ◽

Pauline K. Park ◽

Adebola Adesanya ◽

Steven Y. Chang ◽

...

Keyword(s):

At Risk ◽

Acute Lung Injury ◽

Lung Injury ◽

Hospital Admission ◽

Patients At Risk

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

10.1101/2020.12.02.20239194 ◽

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.

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

Canadian Journal of Cardiology ◽

10.1016/j.cjca.2021.07.134 ◽

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

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Abstract 344: Circulating MicroRNA Signature as a Predictive Biomarker for Postoperative Heart Failure

Circulation Research ◽

10.1161/res.127.suppl_1.344 ◽

2020 ◽

Vol 127 (Suppl_1) ◽

Author(s):

Farhan Rizvi ◽

Stacie Kroboth ◽

Larisa Emelyanova ◽

Gracious R Ross ◽

Maharaj Singh ◽

...

Keyword(s):

Heart Failure ◽

At Risk ◽

Previous History ◽

Surgical Techniques ◽

Principal Component ◽

Mortality Prediction ◽

Circulating Microrna ◽

Cardiac Events ◽

Diagnostic Potential ◽

Patients At Risk

Background: Advancements in cardiac surgical techniques have led to decreasing operative risk. However, postoperative heart failure (PoHF) continues to be a major risk factor for adverse cardiac events in 20-35% of patients after cardiac surgery, with a 10-fold increase in 30-day mortality. Prediction of PoHF is challenging, particularly in patients with preserved ventricular function. Circulating microRNAs (miRNAs) recently were identified to predict HF or AF after surgery, but their role in predicting PoHF is not known. This study aimed to find novel noninvasive circulating biomarkers along with clinical factors that can identify patients at risk of developing PoHF immediately after surgery. Methods: Patients undergoing CABG surgery with no previous history of HF, ventricular or supraventricular tachycardia were recruited, and preoperative blood assessed for circulating levels of protein biomarkers using ELISA. Differences in relative plasma levels of 13 miRNAs between the PoHF and no-PoHF groups were assessed by qPCR. Preoperative echocardiography was obtained. SAS was used for statistical analysis and ROC curve. Results: Out of 68 patients, 13 developed PoHF (19.1%, mean age 64.1±11.6y, 53.8% males), whereas 55 (mean age 68.3±12.4y) remained free of HF. Patients who developed PoHF had lower LVEF (51.4±13.7 vs 58.2±9.9, P<0.05) with no differences in prevalence of hypertension, diabetes, hyperlipidemia, obesity, previous myocardial infarction, stroke, COPD, sleep apnea, or use of cardiac medications. The correlation matrix of all 13 miRNAs was transformed in a principal component (PC), resulting in 3 main clusters with eigenvalue >1. PC cluster2 consisted of miR-23a, -23b, -25 and -26a2, principally involved in oxidatives stress, fibrosis and contractility, and had the strongest association (AUC=0.797; P<0.01) with PoHF. A model combining PC cluster2 with age and LVEF improved sensitivity and specificity of the model to identify patients at risk of PoHF (AUC=0.880; 95% CL, 0.761-0.991; P<0.001) Conclusion: Our study demonstrates that miR-23a, -23b, -25 and -26a2 may be useful predictors of PoHF. Circulating miRNA as biomarkers may have diagnostic potential to preoperatively, noninvasively identify patients at risk of developing PoHF.

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

Journal of Clinical Medicine ◽

10.3390/jcm9020343 ◽

2020 ◽

Vol 9 (2) ◽

pp. 343 ◽

Cited By ~ 4

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.

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