Using Statistical Methods to Improve Disease Classification Ryan Sieberg Advisor: Rebecca Nugent Abstract In this research project, we combined statistical grouping algorithms (“clustering”) with decision-making guided by medical expertise in order to improve pulmonary disease classification. This interdisciplinary research is in collaboration with pulmonary physicians in the Department of Internal Medicine at Texas Tech University Health Sciences Center (TTUHSC) in Lubbock, Texas. With their help we plan to develop an algorithm that allows us to examine the sensitivity of patient classification: balancing correctly classifying patients with pulmonary disease at the expense of misclassifying healthy patients. By doing so we would lower current misclassification rates as well as save hospitals and patients time and money. Data The data come from the Department of Internal Medicine at TTUHSC. It is comprised of the Spirometry data of all patients in the year A Spirometer is one of the most common Pulmonary Function Tests used in medicine and is used to obtain measures of an individual’s lung function. For our purposes, the most useful measures are: Forced Vital Capacity (FVC) – The amount of air that an individual can force out of his/her lungs. Forced Expiratory Volume 1 (FEV1) – The amount of air that an individual breathes in the first second of exhalation. FEV1/FVC – A ratio of the two above measures. In order to compare individuals across gender, race, and age, we use standardized values of these measures. They are standardized as percent predicted. So if a 6 foot, 200 pound, 25 year old white male is predicted to have an FVC of 2 liters but in fact has an FVC of 1.5, his standardized FVC would be 75%. Spirometer Image Common Pulmonary Disease Characteristics Obstructive – Reduced Airflow (Tumor) Restrictive – Reduced Lung Volume (Asthma) Verification of Standardization To ensure that this standardization removed height, weight, and other correlation effects, we performed some linear regressions. Currently, physicians use loosely defined rules to classify patients into disease type, using 70% as a guideline for “Normal.” Model: FVC% = BMI + Height + Weight Estimate Std. Error t value Pr(>|t|) (Intercept) * BMI Height Weight F-statistic: on 3 and 877 DF, p-value: PatientsFVCFEV1FEV1/FVC Normal ObstructiveNormalLow RestrictiveLow Normal Cutoff for Normal Proportion Normal Proportion Diseased 65% % % Clustering In order to address issue 2, we introduce a new measure: standardized midflow. Midflow is the amount of air exhaled during the middle 50% of the exhalation. It has been proposed that midflow can be used to further classify the restrictive group into restrictive (short midflow time) and restrictive with small airway disease (long midflow time). We used three different clustering algorithms: K-means – Uses a Euclidean radius to cluster. Hierarchical Clustering – Uses statistical criteria to create a hierarchical tree of groupings. Model Based Clustering – Optimally fits normal distributions to the data by statistical criteria. This algorithm was the most promising to us (flexibility). Diagnosis1234 Normal Restrictive Obstructive10110 NLN LNN01503 LLL00470 Why is 70% the cutoff for normal? We explored the sensitivity of the cutoff. Example In our regressions, we saw that the relationship between these potential confounders and the spirometry measures had been removed by the standardization (no correlation). Issues 1.) Classifying patients by rule of thumb; misclassifying patients is costly. 2.) Rules of thumb do not allow for further classification. For example, restrictive with small airway disease is not identified under current classification rules of thumb. Conclusion There are no obvious partitions. We plan to develop an algorithm that minimizes misclassification rates, while balancing emotional and monetary costs.. Black – Healthy (Normal) Red – Restrictive Green – Obstructive Other – No classification