scholarly journals A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Shahid Ali Choudhry ◽  
Jing Li ◽  
Darcy Davis ◽  
Cole Erdmann ◽  
Rishi Sikka ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Hospital Readmission ◽  
Public Private Partnership ◽  
Risk Prediction Model ◽  
Day Hospital ◽  
Private Partnership ◽  
Readmission Risk

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.

scholarly journals Derivation and External Validation of an Early Readmission Risk Prediction Model

2016 ◽  
Vol 19 (7) ◽  
pp. A809
Author(s):  
SR Dorajoo ◽  
V See ◽  
CT Chan ◽  
ZY Tan ◽  
N Koomanan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
External Validation ◽  
Risk Prediction Model ◽  
Early Readmission ◽  
Readmission Risk

Acute Myocardial Infarction Readmission Risk Prediction Model in Admit and Discharge patients

2021 ◽  
Author(s):  
Rupanjali Chaudhuri ◽  
Vadim Khotilovich ◽  
Monica Gaur ◽  
KV Chetan ◽  
Will Zimmerman
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Readmission Risk

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

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sameh N. Saleh ◽  
Anil N. Makam ◽  
Ethan A. Halm ◽  
Oanh Kieu Nguyen
Keyword(s):  
Prediction Model ◽  
Hospital Readmission ◽  
Model Performance ◽  
Day Hospital ◽  
North Texas ◽  
Point Estimates ◽  
C Statistic ◽  
Split Sample ◽  
Baseline Characteristics ◽  
Readmission Risk

Abstract Background Despite 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 EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors. Methods We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50–50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients. Results Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive. Conclusion A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; 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 day of discharge.

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model

A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study

2020 ◽  
Author(s):  
Liwen Zhang ◽  
Di Dong ◽  
Wenjuan Zhang ◽  
Xiaohan Hao ◽  
Mengjie Fang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Multicenter Study ◽  
Risk Prediction Model

Wound dehiscence after radical cystectomy: A novel risk-prediction model

2021 ◽  
Vol 79 ◽  
pp. S1112-S1113
Author(s):  
A.A. Nasrallah ◽  
M. Mansour ◽  
C.H. Ayoub ◽  
N. Abou Heidar ◽  
J.A. Najdi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Radical Cystectomy ◽  
Risk Prediction ◽  
Wound Dehiscence ◽  
Risk Prediction Model
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