Overstay2 scoring model for Grace Hospital: Difference between revisions
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This page describes the {{PAGENAME}} for the [[Project Overstay2]], one of the site-based [[Overstay2 scoring models]]. The model | This page describes the {{PAGENAME}} for the [[Project Overstay2]], one of the site-based [[Overstay2 scoring models]]. The model and the [[Data definition for factor candidates for the Overstay2 project]] it uses were 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 == | == Data elements and assigned coefficients == | ||
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LN(OS/1-OS) = Y | LN(OS/1-OS) = Y | ||
Y = Intercept Estimate + sum of all the rest of (the Parameter values multiplied by the | Y = Intercept Estimate + sum of all the rest of (the Parameter values multiplied by the Estimate) listed below: | ||
{| class="wikitable notsortable" | {| class="wikitable notsortable" | ||
!Parameter !! Estimate !! Prob > ChiSq | !Parameter !! Estimate !! Prob > ChiSq | ||
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| Intercept || -10.1922 || <.0001 | | Intercept || -10.1922 || <.0001 | ||
|- | |- | ||
| Age || 0.0309 || <.0001 | | {{OSDD|Age}} || 0.0309 || <.0001 | ||
|- | |- | ||
| Homeless || 0.8355 || 0.093 | | {{OSDD|Homeless}} || 0.8355 || 0.093 | ||
|- | |- | ||
| PCH || -3.6008 || <.0001 | | {{OSDD|PCH}} || -3.6008 || <.0001 | ||
|- | |- | ||
| FromNonWpgFacility || 1.7445 || 0.0612 | | {{OSDD|FromNonWpgFacility}} || 1.7445 || 0.0612 | ||
|- | |- | ||
| InterImplantDevice || 0.9099 || 0.0004 | | {{OSDD|InterImplantDevice}} || 0.9099 || 0.0004 | ||
|- | |- | ||
| Dementia || 1.1677 || <.0001 | | {{OSDD|Dementia}} || 1.1677 || <.0001 | ||
|- | |- | ||
| CHF || 0.2739 || 0.0263 | | {{OSDD|CHF}} || 0.2739 || 0.0263 | ||
|- | |- | ||
| Plegia || 0.2615 || 0.0547 | | {{OSDD|Plegia}} || 0.2615 || 0.0547 | ||
|- | |- | ||
| GCS_Motor || 0.4489 || 0.0131 | | {{OSDD|GCS_Motor}} || 0.4489 || 0.0131 | ||
|- | |- | ||
| ADL_bath || 0.3868 || <.0001 | | {{OSDD|ADL_bath}} || 0.3868 || <.0001 | ||
|- | |- | ||
| ADL_toilet || 0.1596 || 0.0029 | | {{OSDD|ADL_toilet}} || 0.1596 || 0.0029 | ||
|- | |- | ||
| ADL_continence || 0.1565 || <.0001 | | {{OSDD|ADL_continence}} || 0.1565 || <.0001 | ||
|- | |- | ||
| ADL_Adlmean_age || -0.00065 || 0.0087 | | {{OSDD|ADL_Adlmean_age}} || -0.00065 || 0.0087 | ||
|} | |} | ||
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* Homeless, PCH, FromNonWpgFacility, InterImplantDevice, Opioids, Dementia, are binary categorical variables (present/yes=1, absent/no =0) . | * Homeless, PCH, FromNonWpgFacility, 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. | * CHF, Plegia, GCS_Motor, ADL_bath, ADL_toilet, ADl_continence are points or scores. | ||
{{JM| | |||
* can we just leave the two bullets above on [[Data definition for factor candidates for the Overstay2 project]] to avoid duplicating definitions? [[User:Ttenbergen|Ttenbergen]] 00:21, 30 June 2025 (CDT) | |||
}} | |||
*The model gave a 0.076 optimum cut-off probability for OS GE 10 Days with sensitivity = 74.2%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d and with specificity = 74.3%, the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This cut-off point of 0.076 will result to 24.1% rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d'''. Increasing the cut off predicted probability of (Overstay >= 10 days) to 0.094 resulted to sensitivity = 67.2%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d''' to 19.7%. Thus '''a predicted probability of (Overstay >= 10 days) of 0.094''' is going to be used to classify patients into two groups; Overstay >= 10 days and Overstay < 10 days. See more discussion on [[Overstay2 colour]]. | *The model gave a 0.076 optimum cut-off probability for OS GE 10 Days with sensitivity = 74.2%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d and with specificity = 74.3%, the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This cut-off point of 0.076 will result to 24.1% rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d'''. Increasing the cut off predicted probability of (Overstay >= 10 days) to 0.094 resulted to sensitivity = 67.2%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d''' to 19.7%. Thus '''a predicted probability of (Overstay >= 10 days) of 0.094''' is going to be used to classify patients into two groups; Overstay >= 10 days and Overstay < 10 days. See more discussion on [[Overstay2 colour]]. | ||
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== Log == | == Log == | ||
* 2025-06-25 - The definition of | * 2025-06-25 - The definition of {{OSDD|Opioids}} factor has been revisited and broken done between Chronic Addiction and Acute/Overdose Toxicity. The new fitted model from the training data set did not show significant estimates on these factors. The new model is similar to the May 20, 2025 model but without the factor Chronic Addiction and validated in the two data sets of Oct 2020 to Dec 2024 and Jan -June 2025 periods. | ||
* 2025-06-18 - the May 20, 2025 model has been validated using recent data from Jan 1 to June 17, 2025. | * 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. | * 2025-05-20 - 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 - Initial decision on a model - We will use Model 8 at cut-off of 0.069 | ||
== Related articles == | == Related articles == | ||