scholarly journals Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

2020 ◽  
Vol 17 (3) ◽  
pp. 927
Author(s):  
Mei-Chin Su ◽  
Yi-Jen Wang ◽  
Tzeng-Ji Chen ◽  
Shiao-Hui Chiu ◽  
Hsiao-Ting Chang ◽  
...  
Keyword(s):  
Risk Management ◽  
Cost Effectiveness ◽  
Home Care ◽  
Readmission Rate ◽  
Prediction Models ◽  
Medical Center ◽  
Management Tool ◽  
Receiver Operating Curve ◽  
Limited Information ◽  
Net Reclassification Improvement

The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488–0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582–0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068–0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician’s model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.

scholarly journals LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

2021 ◽  
Vol 18 (3) ◽  
pp. 1135
Author(s):  
Mei-Chin Su ◽  
Yu-Chun Chen ◽  
Mei-Shu Huang ◽  
Yen-Hsi Lin ◽  
Li-Hwa Lin ◽  
...  
Keyword(s):  
Risk Management ◽  
Home Care ◽  
Readmission Rate ◽  
Management Tool ◽  
Predictive Score ◽  
Care Providers ◽  
Real World Evidence ◽  
Before And After ◽  
Reduction Effect

Background: Effectively predicting and reducing readmission in long-term home care (LTHC) is challenging. We proposed, validated, and evaluated a risk management tool that stratifies LTHC patients by LACE predictive score for readmission risk, which can further help home care providers intervene with individualized preventive plans. Method: A before-and-after study was conducted by a LTHC unit in Taiwan. Patients with acute hospitalization within 30 days after discharge in the unit were enrolled as two cohorts (Pre-Implement cohort in 2017 and Post-Implement cohort in 2019). LACE score performance was evaluated by calibration and discrimination (AUC, area under receiver operator characteristic (ROC) curve). The clinical utility was evaluated by negative predictive value (NPV). Results: There were 48 patients with 87 acute hospitalizations in Pre-Implement cohort, and 132 patients with 179 hospitalizations in Post-Implement cohort. These LTHC patients were of older age, mostly intubated, and had more comorbidities. There was a significant reduction in readmission rate by 44.7% (readmission rate 25.3% vs. 14.0% in both cohorts). Although LACE score predictive model still has room for improvement (AUC = 0.598), it showed the potential as a useful screening tool (NPV, 87.9%; 95% C.I., 74.2–94.8). The reduction effect is more pronounced in infection-related readmission. Conclusion: As real-world evidence, LACE score-based risk management tool significantly reduced readmission by 44.7% in this LTHC unit. Larger scale studies involving multiple homecare units are needed to assess the generalizability of this study.

Parameter framework - a risk management tool to improve the safety of tailings facilities

2007 ◽  
Vol 15 (2) ◽  
pp. 223-233 ◽  
Author(s):  
J. Engels ◽  
D. Dixon-Hardy ◽  
C. McDonald ◽  
K. Kreft-Burman
Keyword(s):  
Risk Management ◽  
Management Tool

Risk management tool to define a corrective storage to enhance Salmonella inactivation in dry fermented sausages

2021 ◽  
pp. 109160
Author(s):  
Cristina Serra-Castelló ◽  
Sara Bover-Cid ◽  
Margarita Garriga ◽  
Tina Beck Hansen ◽  
Annemarie Gunvig ◽  
...  
Keyword(s):  
Risk Management ◽  
Management Tool ◽  
Fermented Sausages ◽  
Dry Fermented Sausages

scholarly journals A Call for a Reform of the Influenza Immunization Program in Mexico: Epidemiologic and Economic Evidence for Decision Making

Vaccines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 286
Author(s):  
Roberto Tapia-Conyer ◽  
Miguel Betancourt-Cravioto ◽  
Alejandra Montoya ◽  
Jorge Abelardo Falcón-Lezama ◽  
Myrna María Alfaro-Cortes ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Influenza Vaccination ◽  
Vaccination Coverage ◽  
Decision Analytic Model ◽  
Limited Information ◽  
Vaccination Policy ◽  
Cross Sectional ◽  
Influenza Case ◽  
One Year ◽  
Effectiveness Studies

Limited information is available to determine the effectiveness of Mexico’s national influenza vaccination guidelines and inform policy updates. We aim to propose reforms to current influenza vaccination policies based on our analysis of cost-effectiveness studies. This cross-sectional epidemiological study used influenza case, death, discharge and hospitalization data from several influenza seasons and applied a one-year decision-analytic model to assess cost-effectiveness. The primary health outcome was influenza cases avoided; secondary health outcomes were influenza-related events associated with case reduction. By increasing vaccination coverage to 75% in the population aged 12–49 years with risk factors (diabetes, high blood pressure, morbid obesity, chronic renal failure, asthma, pregnancy), and expanding universal vaccination coverage to school-aged children (5–11 years) and adults aged 50–59 years, 7142–671,461 influenza cases; 1–15 deaths; 7615–262,812 healthcare visits; 2886–154,143 emergency room admissions and 2891–97,637 hospitalizations could be prevented (ranges correspond to separate age and risk factor groups), with a net annual savings of 3.90 to 111.99 million USD. Such changes to the current vaccination policy could potentially result in significant economic and health benefits. These data could be used to inform the revision of a vaccination policy in Mexico with substantial social value.

scholarly journals Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 268-269
Author(s):  
Jaime Speiser ◽  
Kathryn Callahan ◽  
Jason Fanning ◽  
Thomas Gill ◽  
Anne Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Learning Methods ◽  
Life Study ◽  
Fall Injury ◽  
Machine Learning Methods

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

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