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.

Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose

2021 ◽  
Vol 56 (3) ◽  
pp. 396-403
Author(s):  
Lindsey M. Ferris ◽  
Brendan Saloner ◽  
Kate Jackson ◽  
B. Casey Lyons ◽  
Vijay Murthy ◽  
...  
Keyword(s):  
At Risk ◽  
Predictive Model ◽  
Opioid Overdose ◽  
Patients At Risk ◽  
Model Versus

A Predictive Model of Intrinsic Factors Associated with Long-Stay Nursing Home Care After Hospitalization

2020 ◽  
pp. 105477382098527
Author(s):  
Jane Flanagan ◽  
Marie Boltz ◽  
Ming Ji
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Predictive Model ◽  
Operating Characteristic ◽  
Resource Planning ◽  
Term Care ◽  
Intrinsic Factors ◽  
Patients At Risk ◽  
Receiver Operating

We aimed to build a predictive model with intrinsic factors measured upon admission to skilled nursing facilities (SNFs) post-acute care (PAC) to identify older adults transferred from SNFs to long-term care (LTC) instead of home. We analyzed data from Massachusetts in 23,662 persons admitted to SNFs from PAC in 2013. Explanatory logistic regression analysis identified single “intrinsic predictors” related to LTC placement. To assess overfitting, the logistic regression predictive model was cross-validated and evaluated by its receiver operating characteristic (ROC) curve. A 12-variable predictive model with “intrinsic predictors” demonstrated both high in-sample and out-of-sample predictive accuracy in the receiver operating characteristic ROC and area under the ROC among patients at risk of LTC placement. This predictive model may be used for early identification of patients at risk for LTC after hospitalization in order to support targeted rehabilitative approaches and resource planning.

A Clinical Predictive Model for Risk Stratification of Patients with Severe Acute Lower Gastrointestinal Bleeding

2021 ◽  
Author(s):  
Manraj Singh ◽  
Jayne Chiang ◽  
Andre Seah ◽  
Nan Liu ◽  
Ronnie Mathew ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Model ◽  
Primary Outcome Measure ◽  
Initial Assessment ◽  
Intestinal Bleeding ◽  
Clinical Parameters ◽  
Predictive Values ◽  
Management Algorithm ◽  
Patients At Risk ◽  
Clinical Predictive Model

Abstract Background: Lower Gastro-Intestinal Bleeding (LGIB) is a common presentation of surgical admissions, imposing a significant burden on healthcare costs and resources. There is a paucity of standardised clinical predictive tools available for the initial assessment and risk stratification of patients with LGIB. We propose a simple clinical scoring model to prognosticate patients at risk of severe LGIB and an algorithm to guide management of such patients.Methods: A retrospective cohort study was conducted, identifying consecutive patients admitted to our institution for LGIB over a 1-year period. Baseline demographics, clinical parameters at initial presentation and treatment interventions were recorded. Severe LGIB was the primary outcome measure. Multivariate logistic regression was performed to identify factors predictive of severe LGIB. A clinical management algorithm was developed to discriminate between patients requiring admission, and to guide endoscopic, angiographic and/or surgical intervention.Results: 226/649 (34.8%) patients had severe LGIB. Six variables were entered into a clinical predictive model for risk stratification of LGIB: Tachycardia (HR>100), hypotension (SBP<90mmHg), anemia (Hb<9g/dL), metabolic acidosis, use of antiplatelet/anticoagulants, and active per-rectal bleeding. The optimum cut-off score of >1 had a sensitivity of 91.9%, specificity of 39.8%, and Positive and Negative Predictive Values of 45% and 90.2% respectively for predicting severe LGIB. The Area Under Curve (AUC) was 0.77.BConclusion: Early diagnosis and management of severe LGIB remains a challenge for the acute care surgeon. The predictive model described comprises objective clinical parameters routinely obtained at initial triage to guide risk stratification, disposition and inpatient management of patients.

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

A multiparametric ICD algorithm for heart failure risk stratification and management: an analysis in clinical practice

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
L Calo" ◽  
V Bianchi ◽  
D Ferraioli ◽  
L Santini ◽  
A Dello Russo ◽  
...  
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Clinical Practice ◽  
Risk Stratification ◽  
Management Strategies ◽  
Cardiac Resynchronization ◽  
Office Visits ◽  
Increased Risk ◽  
Patients At Risk ◽  
Observation Period

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The HeartLogic algorithm combines multiple implantable cardioverter defibrillator (ICD) sensors to identify patients at risk of heart failure (HF) events. Purpose We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events. Methods The HeartLogic feature was activated in 366 ICD and cardiac resynchronization therapy ICD patients at 22 centers. The HeartLogic algorithm automatically calculates a daily HF index and identifies periods IN or OUT of an alert state on the basis of a configurable threshold (in this analysis set to 16). Results The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients over a median follow-up of 11 months [25-75 percentile: 6-16]. Overall, the time IN the alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations and 8 patients died of HF (rate: 0.12 events/patient-year) during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (HR: 24.53, 95% CI: 8.55-70.38, p &lt; 0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with a lower rate of HF events (HR: 0.37, 95% CI: 0.14-0.99, p = 0.047). No differences in event rates were observed between in-office and remote alert management. By contrast, verification of HF symptoms during post-alert examination was associated with a higher risk of HF events (HR: 5.23, 95% CI: 1.98-13.83, p &lt; 0.001). Conclusions This multiparametric ICD algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required in order to effectively manage HeartLogic alerts, while post-alert verification of symptoms seemed useful in order to better stratify patients at risk of HF events.

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.

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
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