Date Published: April 16, 2019
Publisher: Public Library of Science
Author(s): Yuya Shinkawa, Takashi Yoshida, Yohei Onaka, Makoto Ichinose, Kazuo Ishii, Antonio Palazón-Bru.
Cerebral white matter lesions are ischemic symptoms caused mainly by microangiopathy; they are diagnosed by MRI because they show up as abnormalities in MRI images. Because patients with white matter lesions do not have any symptoms, MRI often detects the lesions for the first time. Generally, head MRI for the diagnosis and grading of cerebral white matter lesions is performed as an option during medical checkups in Japan. In this study, we develop a mathematical model for the prediction of white matter lesions using data from routine medical evaluations that do not include a head MRI. Linear discriminant analysis, logistic discrimination, Naive Bayes classifier, support vector machine, and random forest were investigated and evaluated by ten-fold cross-validation, using clinical data for 1,904 examinees (988 males and 916 females) from medical checkups that did include the head MRI. The logistic regression model was selected based on a comparison of accuracy and interpretability. The model variables consisted of age, gender, plaque score (PS), LDL, systolic blood pressure (SBP), and administration of antihypertensive medication (odds ratios: 2.99, 1.57, 1.18, 1.06, 1.12, and 1.52, respectively) and showed Areas Under the ROC Curve (AUC) 0.805, the model displayed sensitivity of 72.0%, and specificity 75.1% when the most appropriate cutoff value was used, 0.579 as given by the Youden Index. This model has shown to be useful to identify patients with a high-risk of cerebral white matter lesions, who can then be diagnosed with a head MRI examination in order to prevent dementia, cerebral infarction, and stroke.
In Japan, it is generally recognized that the increase in national health expenditure that accompanies aging is a serious social problem . Although the number of deaths by stroke has decreased drastically due to preventive treatment, stroke still ranks at the top of the health care expenditures in Japan . It is estimated that 2 million people are currently bedridden, and this number will increase to 3 million by 2025 . Therefore, the early detection and prevention of cerebrovascular diseases is important for the reduction of health care expenditures .
In the assessment of questionnaire in Table 1, though smoking habits showed significance (p<0.001) in the between-groups comparison, the ratio of non-smoking patients with white matter lesions (86.7%) was higher than that of smokers with white matter lesions with smoking habits (13.3%). Generally, it is considered that smoking is a risk factor for cerebrovascular disorders , but this study showed the opposite result. Although it is said that smoking is a risk factor of cerebrovascular disorder , smoking itself is not a direct cause of ischemic change due to microangiopathy, that is, white matter lesions. It is possible that other indirect and complex factors including changes in blood constituents, such as a decrease of “good” HDL cholesterol due to smoking habits, could lead to their expression. Therefore, it is probably necessary to investigate the detailed temporal changes in smoking amounts in order to complete an effective evaluation of smoking habits. In the specific health examination questionnaire in this study, the question regarding smoking habits only asked whether the participant was a heavy smoker, defined as having smoked a total of over 100 cigarettes or have smoked over a period of 6 months, not considering the past history or cigarettes smoked or/and the number of cigarettes smoked in a day. It was considered that the index could become a more meaningful variable regarding smoking, if it included detailed information such as the smoking index represented by the product of the number of cigarettes smoked per day and the smoking history (years). However, in Japan, specific health examination questionnaires are utilized in medical checkups to check the patient's past medical history and lifestyle and to aid in patient consultation by using the similar format of questionnaires at most of the hospitals. Therefore, it is considered an important part of the patient consultation in medical checkups. Therefore, in this modeling, the specific health examination questionnaires were regarded as an important factor from the viewpoint of its practical usage. To predict cerebral white matter lesions based on clinical examination data, a logistic regression model was selected from some candidate models, created by various algorithms, based on a comparison of accuracy and interpretability. The explanatory variables of the model were age, gender, PS, LDL, SBP, and the administration of antihypertensive medication. Variable selection was important to the establishment of a high accuracy model, but the model algorithm did not significantly influence the discrimination performance if an appropriate variable selection was conducted while constructing the prediction model. This model will allow clinicians to discriminate a risk group in subjects who have not received a head MRI test. Source: http://doi.org/10.1371/journal.pone.0215142