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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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Published on: January 27, 2010

Procedure-based postoperative risk prediction using NSQIP data.

David E Clark¹, Timothy L Fitzgerald², Albert W Dibbins²

¹Department of Surgery, Maine Medical Center, Portland, Maine; Center for Outcomes Research and Evaluation, Maine Medical Center, Portland, Maine.

The Journal of Surgical Research

|December 13, 2017

Summary

This summary is machine-generated.

Procedure-based hierarchical models accurately predict surgical patient mortality and morbidity using National Surgical Quality Improvement Program data. These models offer valuable insights for improving surgical risk assessment and patient outcomes.

Keywords:

Hierarchical Morbidity Mortality Multilevel NSQIP Prediction Surgical Risk Calculator

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Area of Science:

Health Services Research
Surgical Outcomes Research
Biostatistics

Background:

The National Surgical Quality Improvement Program (NSQIP) proposed procedure-based hierarchical models for predicting adverse surgical outcomes.
The development approach for the NSQIP Surgical Risk Calculator using these models was unclear.

Purpose of the Study:

To demonstrate the construction of procedure-based hierarchical models.
To describe the results of these models in predicting surgical outcomes.

Main Methods:

Utilized NSQIP data from 2015 to build statistical models for 30-day postoperative mortality and morbidity.
Employed two-level logistic regression with patient-level variables as fixed effects and procedure codes as random intercepts.
Validated model performance using 2014 NSQIP data.

Main Results:

Models analyzed 885,502 patients, predicting 1.0% mortality and 11.8% morbidity.
Most comorbidities increased risk, while overweight/obesity decreased it.
Validation C-statistics were 0.940 for mortality and 0.833 for morbidity.

Conclusions:

Procedure-based hierarchical logistic regression models demonstrated satisfactory performance for NSQIP outcomes.
Model specifications and results are provided for further refinement and improvement.
The study suggests potential areas for enhancing these predictive surgical risk models.