1. Henry Domenico Vanderbilt University Medical Center

2.  Guidelines for a Data Driven Project ◦ What can Biostatistics do for a Project? ◦ Defining a Question ◦ Data Collection ◦ Analysis ◦ Conclusion  Readmissions at Vanderbilt ◦ Background ◦ Identifying Important Factors ◦ Predicting the Probability of Readmission ◦ Future Work

3.  Statistics is a powerful tool.  Statistics should be part of improving the care we give, not just for research.  My focus is on using statistics to… ◦ Answer questions about quality issues ◦ Understand where we are and where we’re going ◦ Identify the driving force behind the issues we face ◦ Develop new strategies for improving care ◦ Understand the effectiveness of an intervention

4.  Understand your population  Decide if previously held notions are correct  Discover what factors are important to your problem  Make Predictions  Test an Intervention  Present your findings in a convincing way Wealth of data collected at Vanderbilt

5.  Biostatistics can be built into each stage of a project. ◦ Question ◦ Data Collection ◦ Analysis ◦ Conclusions

6.  You can make your life easier by starting with a carefully defined question. ◦ What factors lead to increased patient satisfaction?  Clearly identify the population.  What are the possible factors?  How will the response be measured?  What is a clinically significant result?

7.  Data should be collected from the population of interest.  Ideally would like to have any variable that effects the response.  Observational or Experimental?  At what level should the data be gathered?  A lot of effort can go into gathering data that won’t answer your question.

8.  If the question is carefully defined and data is gathered correctly, analysis becomes the easy part.  Identify important factors.  Determine statistical/clinical significance.  Account for confounding factors.

9.  We want to present our results in a way that is convincing to others.  Use the results of the analysis to present a clear picture of what is occurring.  Directly answer the original question.

10.  Hospital Readmission rate is being viewed as a quality metric.  In the near future, Vanderbilt will see financial penalties for patients discharged and readmitted within 30 days.

12.  Can we identify which patients are at risk of being readmitted?  Can we model a patient’s probability of being readmitted within 30 days?  Can we present this information to providers at the point of decision?  Is there an intervention that will reduce the probability that a patient is readmitted?

13.  Working with a data set of all 2009 Inpatients.  Created a readmission flag.  Demographic and diagnostic variables are included.  Missing lab and vital sign information.

16.  Can examine each DRG’s readmission rate.  Several have only one observation.  May be more meaningful to examine which had a higher number of readmissions than average.

17. DRGDescriptionResidual 280AMI Disc. Alive6 847Chemo5.5 293Heart Failure5.1 291Heart Failure4.5 216Cardiac Valve3.6

18.  Used same method to determine:  Patients from Davidson County had a higher readmission rate.  ICD-9’s 780( Malaise and Fatigue) and 789.09(Symptoms involving abdomen and Pelvis) had higher readmission rates.  Patients on Blue Cross/Blue Shield were at higher risk.

19.  Logistic regression models the probability of readmission based on a patient’s explanatory variables.  Used logistic regression to model the odds ratio for different factors.  The odds ratio tells us the increased probability of readmission associated with these factors.

20.  Again using logistic regression, we can develop a model that will provide each patient’s readmission probability.  Specify which variables we want to use to make predictions.  Use a statistical software package to build a model.

21.  Model a patient’s probability of readmission based on: ◦ Age ◦ Length of Stay ◦ Number of Medications ◦ DRG  Model should only be applied to patients from same population used to build the model.

22.  Alternative non-parametric approach to logistic regression.  Breaks population into subsets and identifies factors important to each subset.  Provides predicted probabilities like logistic regression.

24.  Our goal is to be able to inform providers of a patient’s probability of readmission.  Let them know what factors need to be addressed before discharge.  Goal is to reduce preventable readmissions.

25.  Obtain a more comprehensive data set. ◦ All admitted patients ◦ Gender ◦ Lab Values ◦ Vital Signs ◦ Readmission Status  Develop specific models for individual DRGs.

26.  Eventual goal is to work with subject matter experts to develop an intervention that reduces readmissions.  Show effectiveness using a Randomized Controlled Trial.

27.  This is just one example of how statistics can improve a project.  Hopefully demonstrates the value of being data driven and using statistics.  Biostatistics Free Clinic

28. Thank You Questions?