Overstay2 scoring model for Grace Hospital: Difference between revisions
No edit summary |
|||
| Line 2: | Line 2: | ||
== Data elements and assigned coefficients == | == Data elements and assigned coefficients == | ||
The binary logistic regression Y (Overstay GE 10 Days) which gave the AUC C-index =0. | The binary logistic regression Y (Overstay GE 10 Days) which gave the AUC C-index =0.832 implying better predicted model and Hosmer and Lemeshow Goodness of fit test Chi-Square value of 5.049 (prob=0.7523) is : | ||
LN(OS/1-OS) = Y | LN(OS/1-OS) = Y | ||
| Line 10: | Line 10: | ||
!Parameter !! Estimate !! Prob > ChiSq | !Parameter !! Estimate !! Prob > ChiSq | ||
|- | |- | ||
| Intercept || - | | Intercept || -10.1922 || <.0001 | ||
|- | |- | ||
| Age || 0. | | Age || 0.0309 || <.0001 | ||
|- | |- | ||
| Homeless || | | Homeless || 0.8355 || 0.093 | ||
|- | |- | ||
| PCH || -3.6064 || <.0001 | | PCH || -3.6064 || <.0001 | ||
Revision as of 14:06, 27 June 2025
This page describes the Overstay2 scoring model for Grace Hospital for the Project Overstay2, one of the site-based Overstay2 scoring models. The model was developed in Re-analysis and generation of Overstay2 model and is used to generate the Overstay2 colour, which in turn drives the Overstay2 processes on the units to reduce discharge delay.
Data elements and assigned coefficients
The binary logistic regression Y (Overstay GE 10 Days) which gave the AUC C-index =0.832 implying better predicted model and Hosmer and Lemeshow Goodness of fit test Chi-Square value of 5.049 (prob=0.7523) is :
LN(OS/1-OS) = Y
Y = Intercept Estimate + sum of all the rest of (the Parameter values multiplied by the Estimate) listed below:
| Parameter | Estimate | Prob > ChiSq |
|---|---|---|
| Intercept | -10.1922 | <.0001 |
| Age | 0.0309 | <.0001 |
| Homeless | 0.8355 | 0.093 |
| PCH | -3.6064 | <.0001 |
| OutsideWPG | 2.0009 | 0.0311 |
| InterImplantDevice | 0.9103 | 0.0004 |
| Opioids | -1.6117 | 0.0363 |
| Dementia | 1.1715 | <.0001 |
| CHF | 0.2820 | 0.0223 |
| Plegia | 0.2515 | 0.0649 |
| GCS_Motor | 0.4485 | 0.0133 |
| ADL_bath | 0.3843 | <.0001 |
| ADL_toilet | 0.1612 | 0.0027 |
| ADL_continence | 0.1567 | 0.0001 |
| ADL_Adlmean_age | -0.00064 | 0.01 |
Where OS is the likelihood to overstay 10 days and beyond.
How to calculate OS:
- OS = exp(Y) / (1 + exp(Y)
- Homeless, PCH, OutsideWPG, InterImplantDevice, Opioids, Dementia, are binary categorical variables (present/yes=1, absent/no =0) .
- CHF, Plegia, GCS_Motor, ADL_bath, ADL_toilet, ADl_continence are points or scores.
The model gave a 0.077 cut-off probability for OS. "A cut-off point of 0.077, or a predicted probability of (Overstay >= 10 days) of 7.7%, maybe be used to classify patients into two groups; Overstay >= 10 days and Overstay < 10 days. See more discussion on Overstay2 colour.
The model has been validated using the data from the validation set. The AUC C-index = 0.815, the Hosmer and Lemeshow Goodness of fit test Chi-Square value = 9.3015 (prob=0.327) and the cut-off probability for OS = 0.08.
The model was again validated using the data from Jan to June 17, 2025. The AUC C-index = 0.781, the Hosmer and Lemeshow Goodness of fit test Chi-Square value = 15.315 (prob=0.0545) and the cut-off probability for OS = 0.089.
Model files
- files describing the model and records of discussion about it are at Template:S:\MED\CCMED
Log
- 2025-06-18 - the May 20, 2025 model has been validated using recent data from Jan 1 to June 17, 2025.
- 2025-05-20 - the Feb 21, 2025 model has been revisited and refitted deleting the factor Age*Age. Found a model from the training data set and also validated using a different data set of the same period Oct 2020 to Dec 2024.
- 2025-02-21 - 2025-02-21 JM DR Overstay model decision and cut-off - We will use Model 8 at cut-off of 0.069
Related articles
| Related articles: |