{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:12:19Z","timestamp":1789704739888,"version":"4.0.1"},"reference-count":56,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["headachejournal.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Headache"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and internally validate a multivariable predictive model for days with new\u2010onset migraine headaches based on patient self\u2010prediction and exposure to common trigger factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate real\u2010time forecasting of one\u2019s daily risk of migraine attack could help episodic migraine patients to target preventive medications for susceptible time periods and help decrease the burden of disease. Little is known about the predictive utility of common migraine trigger factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We recruited adults with episodic migraine through online forums to participate in a 90\u2010day prospective daily\u2010diary cohort study conducted through a custom research application for iPhone. Every evening, participants answered questions about migraine occurrence and potential predictors including stress, sleep, caffeine and alcohol consumption, menstruation, and self\u2010prediction. We developed and estimated multivariable multilevel logistic regression models for the risk of a new\u2010onset migraine day vs a healthy day and internally validated the models using repeated cross\u2010validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We had 178 participants complete the study and qualify for the primary analysis which included 1870 migraine events. We found that a decrease in caffeine consumption, higher self\u2010predicted probability of headache, a higher level of stress, and times within 2\u00a0days of the onset of menstruation were positively associated with next\u2010day migraine risk. The multivariable model predicted migraine risk only slightly better than chance (within\u2010person C\u2010statistic: 0.56, 95% CI: 0.54, 0.58).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this study, episodic migraine attacks were not predictable based on self\u2010prediction or on self\u2010reported exposure to common trigger factors. Improvements in accuracy and breadth of data collection are needed to build clinically useful migraine prediction models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/head.13960","type":"journal-article","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T17:45:36Z","timestamp":1602006336000},"page":"2364-2379","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Development and Internal Validation of a Multivariable Prediction Model for Individual Episodic Migraine Attacks Based on Daily Trigger Exposures"],"prefix":"10.1111","volume":"60","author":[{"given":"Katherine K.","family":"Holsteen","sequence":"first","affiliation":[{"name":"Department of Epidemiology &amp; Population Health Stanford University School of Medicine Stanford CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Hittle","sequence":"additional","affiliation":[{"name":"Department of 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