Results Summary

What was the research about?

Electronic health records store a lot of data about a patient. These data often include race, age, health problems, current medicines, and lab results. Looking at these data may help doctors treating patients after a trauma predict how likely it is that they will respond well to a treatment and survive. This information can help doctors make better treatment decisions. But first, researchers need to figure out how to combine and analyze data to make accurate predictions.

In this study, the research team created new statistical methods to combine data from patient records. They used these methods to predict patient health outcomes. Then the team used health record data collected from patients in hospital trauma centers to test their predictions.

What were the results?

The research team made three new computer programs. The first program combined data from patient records to automatically predict patient outcomes. Using test data, the program predicted how likely trauma patients were to stop bleeding and to survive.

The second program looked at whether the new method could be used to show what information was likely to be important for predicting outcomes. For example, in a test case, head injury and blood clotting problems were linked with lower chances of survival among trauma patients.

The third program combined data from patients’ records to automatically suggest a treatment choice. In a test case, the team looked at what would happen if patients received the treatments that the program chose. Using the treatments recommended by the program didn’t lead to better patient outcomes.

What did the research team do?

The research team used math equations to combine data from trauma patients. The data included gender, age, weight, blood pressure, medicines, trauma severity, trauma site, blood clotting, and lab readings. Then the team used the equations to create the computer programs.

Next, the team tested the programs using data created to mimic real data. Then they tested the programs with data from medical records of patients with severe trauma.

What were the limits of the study?

The data used to create the computer programs weren’t always complete. Missing information may have made the predictions less accurate. Also, the data didn’t include information taken over time, such as hourly blood pressure readings. If the programs had included these data, they might have predicted different results.

Future research could look at adding other types of data to the programs. Studies could also test the computer programs with new patients who have had severe trauma or with patients with other health problems.

How can people use the results?

Researchers could continue to test and improve the computer programs. The programs may eventually help doctors make treatment decisions based on patients’ traits and other health problems.

Final Research Report

View this project's final research report.

Peer-Review Summary

Peer review of PCORI-funded research helps make sure the report presents complete, balanced, and useful information about the research. It also assesses how the project addressed PCORI’s Methodology Standards. During peer review, experts read a draft report of the research and provide comments about the report. These experts may include a scientist focused on the research topic, a specialist in research methods, a patient or caregiver, and a healthcare professional. These reviewers cannot have conflicts of interest with the study.

The peer reviewers point out where the draft report may need revision. For example, they may suggest ways to improve descriptions of the conduct of the study or to clarify the connection between results and conclusions. Sometimes, awardees revise their draft reports twice or more to address all of the reviewers’ comments. 

Peer reviewers commented, and the researchers made changes or provided responses. Those comments and responses included the following:

  • Reviewers asked for additional information about when simulation studies were necessary in developing new methods for this study. The researchers explained that they found simulations were unnecessary for some of the algorithms because other methods like cross validation had been used or previous studies had used simulations, so the outcomes were well known. In addition, the researchers indicated that they did not have the time and resources to perform additional simulations given the extensive work on algorithm development.
  • Reviewers asked the researchers to revise the report for this methods development project to make it easier to understand for a general-scientist audience. The researchers moved the more- technical sections into the appendix.

Conflict of Interest Disclosures

Project Information

Alan Hubbard, PhD
University of California Berkeley
Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research

Key Dates

December 2013
September 2018

Study Registration Information


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Last updated: April 11, 2024