scholarly journals Risk of 30-Day Hospital Readmission Among Patients Discharged to Skilled Nursing Facilities: Development and Validation of a Risk-Prediction Model

2019 ◽  
Vol 20 (4) ◽  
pp. 444-450.e2 ◽  
Author(s):  
Anupam Chandra ◽  
Parvez A. Rahman ◽  
Amelia Sneve ◽  
Rozalina G. McCoy ◽  
Bjorg Thorsteinsdottir ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Hospital Readmission ◽  
Risk Prediction Model ◽  
Skilled Nursing Facilities ◽  
Nursing Facilities ◽  
Day Hospital ◽  
Skilled Nursing ◽  
Development And Validation

scholarly journals Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

2019 ◽  
Author(s):  
Sameh N. Saleh ◽  
Anil N. Makam ◽  
Ethan A. Halm ◽  
Oanh Kieu Nguyen
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Risk Prediction ◽  
Hospital Readmission ◽  
Model Performance ◽  
Risk Prediction Model ◽  
Day Hospital ◽  
Baseline Characteristics ◽  
Substantial Change ◽  
Readmission Risk

AbstractDespite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day readmission prediction model predicts 7-day readmissions. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We compared model performance and compared differences in strength of model factors between the 7-day model to the 30-day model. While there was no substantial change in model performance between the original 30-day and the re-derived 7-day model, there was significant change in strength of predictors. Characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to the day of discharge.

Hospital Readmission From Skilled Nursing Facilities (SNFs): Perspectives of Hospital and SNF Providers

2019 ◽  
Vol 20 (8) ◽  
pp. 1050-1051
Author(s):  
Karl E. Minges ◽  
Meredith Campbell Britton ◽  
Bennett W. Clark ◽  
Gregory M. Ouellet ◽  
Beth Hodshon ◽  
...  
Keyword(s):  
Hospital Readmission ◽  
Skilled Nursing Facilities ◽  
Nursing Facilities ◽  
Skilled Nursing

Development and Validation of a Risk Prediction Model for Patients with COVID-19 Using Linked Population Health Data (Preprint)

2021 ◽  
Author(s):  
Nikolaos Mastellos ◽  
Richard Betteridge ◽  
Prasanth Peddaayyavarla ◽  
Andrew Moran ◽  
Jurgita Kaubryte ◽  
...  
Keyword(s):  
Health Care ◽  
Prediction Model ◽  
Population Health ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Risk Prediction Model ◽  
Adult Patients ◽  
Internal Validation ◽  
Temporal Validation ◽  
Development And Validation

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

Development and Validation of a Risk Prediction Model for Cesarean Delivery After Labor Induction

2020 ◽  
Vol 29 (5) ◽  
pp. 656-669 ◽  
Author(s):  
Valery A. Danilack ◽  
Jennifer A. Hutcheon ◽  
Elizabeth W. Triche ◽  
David D. Dore ◽  
Janet H. Muri ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cesarean Delivery ◽  
Risk Prediction ◽  
Labor Induction ◽  
Risk Prediction Model ◽  
Development And Validation
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