Dynamic risk adjustment of prediction models using statistical process control methods

Introduction. Models that represent mathematical relationships between clinical outcomes and their predictors are useful to the decision making process in patient care. Many models, such as the score of neonatal physiology (SNAP II) that predicts in-hospital mortality, have been well validated on several large populations. However, the performance profile of such models in the midst of changing predictor-outcome relationships or newly appearing outcome predictors have not been well studied. We address this problem using statistical process control (SPC) techniques in a novel way. Although widely used in the manufacturing industry to maintain high quality in critical processes, SPC's value to healthcare has begun only recently to gain attention from decision makers. It has been used to construct risk-adjusted charts to track outcomes in the intensive care unit and the surgical arena, and to monitor hospital acquired infections. However, there are no reports of using SPC techniques to scrutinize the performance quality of a clinical model over time. The series of experiments in this manuscript show that the deterioration of a model's performance can be a useful indicator of unexpected changes in the environment that it represents; therefore, defining when a model is statistically not performing according to expectations is the first step towards determining the causes of clinical variations that might impact patient healthcare. Methods. We obtained a database of 3437 newborns admitted to 7 Neonatal Intensive Care Units in the New England area from October 1994 to January 1996. We chronologically arranged the patients by birthday and grouped them into 14 sequential periods; thereby establishing a time-sequenced database to be used in our SPC experiments.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Chuo, John, 1969-

Advisor dc:contributor.advisor

• Lucila Ohno-Machado.

• Harvard University--MIT Division of Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/28583

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/28583