{"id":2460,"date":"2014-10-19T06:46:53","date_gmt":"2014-10-18T19:46:53","guid":{"rendered":"http:\/\/blog.panicola.com\/?p=2460"},"modified":"2014-10-19T06:46:53","modified_gmt":"2014-10-18T19:46:53","slug":"nyt-can-big-data-tell-us-what-clinical-trials-dont","status":"publish","type":"post","link":"https:\/\/blog.panicola.com\/?p=2460","title":{"rendered":"NYT: Can Big Data Tell Us What Clinical Trials Don\u2019t?"},"content":{"rendered":"<p>&nbsp;<\/p>\n<p>http:\/\/www.nytimes.com\/2014\/10\/05\/magazine\/can-big-data-tell-us-what-clinical-trials-dont.html?src=twr<\/p>\n<p><a href=\"http:\/\/www.nytimes.com\/pages\/magazine\/index.html\">MAGAZINE<\/a><\/p>\n<header id=\"story-header\" class=\"story-header\">\n<div class=\"story-meta\">\n<h1 class=\"story-heading\">Can Big Data Tell Us What Clinical Trials Don\u2019t?<\/h1>\n<div class=\"story-meta-footer\">\n<p class=\"byline-dateline\"><time class=\"dateline\" datetime=\"2014-10-03\">OCT. 3, 2014<\/time><\/p>\n<div class=\"inside-story\"><\/div>\n<\/div>\n<\/div>\n<\/header>\n<div class=\"lede-container\">\n<figure id=\"media-\" class=\"media photo lede layout-large-vertical\" data-media-action=\"modal\"><span class=\"visually-hidden\">Photo<\/span><\/p>\n<div class=\"image\"><img decoding=\"async\" class=\"media-viewer-candidate\" src=\"http:\/\/static01.nyt.com\/images\/2014\/10\/05\/magazine\/05eureka\/mag-05Eureka-t_CA0-master495.jpg\" alt=\"\" data-mediaviewer-src=\"http:\/\/static01.nyt.com\/images\/2014\/10\/05\/magazine\/05eureka\/mag-05Eureka-t_CA0-superJumbo.jpg\" data-mediaviewer-caption=\"\" data-mediaviewer-credit=\"Illustration by Christopher Brand\" \/><\/p>\n<div class=\"media-action-overlay\"><\/div>\n<\/div><figcaption class=\"caption\"><span class=\"credit\"><span class=\"visually-hidden\">Credit<\/span>Illustration by Christopher Brand<\/span><\/figcaption><\/figure>\n<div class=\"lede-container-ads\"><\/div>\n<\/div>\n<div class=\"extended-byline no-thumb\">\n<p class=\"byline-column\">Eureka<\/p>\n<p class=\"byline\">By <span class=\"byline-author\" data-byline-name=\"Veronique Greenwood\">VERONIQUE GREENWOOD<\/span><\/p>\n<\/div>\n<div id=\"sharetools-story\" class=\"sharetools theme-classic  sharetools-story  \" data-shares=\"email,facebook|Share,twitter|Tweet,pinterest|Pin,save,show-all|more,ad\" data-url=\"http:\/\/www.nytimes.com\/2014\/10\/05\/magazine\/can-big-data-tell-us-what-clinical-trials-dont.html\" data-title=\"Can Big Data Tell Us What Clinical Trials Don\u2019t?\" data-media=\"http:\/\/static01.nyt.com\/images\/2014\/10\/05\/magazine\/05eureka\/mag-05Eureka-t_CA0-jumbo.jpg\" data-description=\"To uncover patterns that might steer care, doctors and scientists are dipping into the medical records of thousands of patients.\" data-publish-date=\"October 3, 2014\" data-share-tools-initialized=\"1\"><a class=\"visually-hidden skip-to-text-link\" href=\"http:\/\/www.nytimes.com\/2014\/10\/05\/magazine\/can-big-data-tell-us-what-clinical-trials-dont.html?src=twr#story-continues-1\">Continue reading the main story<\/a><span class=\"sharetools-label visually-hidden\">Share This Page<\/span><\/p>\n<ul>\n<li class=\"sharetool email-sharetool\"><a data-share=\"email\"><i class=\"icon\"><\/i><span class=\"sharetool-text \">Email<\/span><\/a><\/li>\n<li class=\"sharetool facebook-sharetool\"><a data-share=\"facebook\"><i class=\"icon\"><\/i><span class=\"sharetool-text \">Share<\/span><\/a><\/li>\n<li class=\"sharetool twitter-sharetool\"><a data-share=\"twitter\"><i class=\"icon\"><\/i><span class=\"sharetool-text \">Tweet<\/span><\/a><\/li>\n<li class=\"sharetool pinterest-sharetool\"><a data-share=\"pinterest\"><i class=\"icon\"><\/i><span class=\"sharetool-text \">Pin<\/span><\/a><\/li>\n<li class=\"sharetool save-sharetool\"><a data-share=\"save\"><i class=\"icon\"><\/i><span class=\"sharetool-text \">Save<\/span><\/a><\/li>\n<li class=\"sharetool show-all-sharetool\"><a data-share=\"show-all\"><i class=\"icon\"><\/i><span class=\"sharetool-text \">More<\/span><\/a><\/li>\n<\/ul>\n<\/div>\n<p id=\"story-continues-1\" class=\"story-body-text story-content\" data-para-count=\"795\" data-total-count=\"795\">When a helicopter rushed a 13-year-old girl showing symptoms suggestive of kidney failure to Stanford\u2019s Packard Children\u2019s Hospital, Jennifer Frankovich was the rheumatologist on call. She and a team of other doctors quickly diagnosed lupus, an autoimmune disease. But as they hurried to treat the girl, Frankovich thought that something about the patient\u2019s particular combination of lupus symptoms \u2014 kidney problems, inflamed pancreas and blood vessels \u2014 rang a bell. In the past, she\u2019d seen lupus patients with these symptoms develop life-threatening blood clots. Her colleagues in other specialties didn\u2019t think there was cause to give the girl anti-clotting drugs, so Frankovich deferred to them. But she retained her suspicions. \u201cI could not forget these cases,\u201d she says.<\/p>\n<p class=\"story-body-text story-content\" data-para-count=\"623\" data-total-count=\"1418\">Back in her office, she found that the scientific literature had no studies on patients like this to guide her. So she did something unusual: She searched a database of all the lupus patients the hospital had seen over the previous five years, singling out those whose symptoms matched her patient\u2019s, and ran an analysis to see whether they had developed blood clots. \u201cI did some very simple statistics and brought the data to everybody that I had met with that morning,\u201d she says. The change in attitude was striking. \u201cIt was very clear, based on the database, that she could be at an increased risk for a clot.\u201d<\/p>\n<p class=\"story-body-text story-content\" data-para-count=\"378\" data-total-count=\"1796\">The girl was given the drug, and she did not develop a clot. \u201cAt the end of the day, we don\u2019t know whether it was the right decision,\u201d says Chris Longhurst, a pediatrician and the chief medical information officer at Stanford Children\u2019s Health, who is a colleague of Frankovich\u2019s. But they felt that it was the best they could do with the limited information they had.<\/p>\n<p class=\"story-body-text story-content\" data-para-count=\"511\" data-total-count=\"2307\">A large, costly and time-consuming clinical trial with proper controls might someday prove Frankovich\u2019s hypothesis correct. But large, costly and time-consuming clinical trials are rarely carried out for uncommon complications of this sort. In the absence of such focused research, doctors and scientists are increasingly dipping into enormous troves of data that already exist \u2014 namely the aggregated medical records of thousands or even millions of patients to uncover patterns that might help steer care.<\/p>\n<p id=\"story-continues-2\" class=\"story-body-text story-content\" data-para-count=\"933\" data-total-count=\"3240\"><strong>The Tatonetti Laboratory<\/strong> at Columbia University is a nexus in this search for signal in the noise. There, Nicholas Tatonetti, an assistant professor of biomedical informatics \u2014 an interdisciplinary field that combines computer science and medicine \u2014 develops algorithms to trawl medical databases and turn up correlations. For his doctoral thesis, he mined the F.D.A.\u2019s records of adverse drug reactions to identify pairs of medications that seemed to cause problems when taken together. He found an interaction between two very commonly prescribed drugs: The antidepressant paroxetine (marketed as Paxil) and the cholesterol-lowering medication pravastatin were connected to higher blood-sugar levels. Taken individually, the drugs didn\u2019t affect glucose levels. But taken together, the side-effect was impossible to ignore. \u201cNobody had ever thought to look for it,\u201d Tatonetti says, \u201cand so nobody had ever found it.\u201d<\/p>\n<p id=\"story-continues-3\" class=\"story-body-text story-content\" data-para-count=\"952\" data-total-count=\"4192\">The potential for this practice extends far beyond drug interactions. In the past, researchers noticed that being born in certain months or seasons appears to be linked to a higher risk of some diseases. In the Northern Hemisphere, people with multiple sclerosis tend to be born in the spring, while in the Southern Hemisphere they tend to be born in November; people with schizophrenia tend to have been born during the winter. There are numerous correlations like this, and the reasons for them are still foggy \u2014 a problem Tatonetti and a graduate assistant, Mary Boland, hope to solve by parsing the data on a vast array of outside factors. Tatonetti describes it as a quest to figure out \u201chow these diseases could be dependent on birth month in a way that\u2019s not just astrology.\u201d Other researchers think data-mining might also be particularly beneficial for cancer patients, because so few types of cancer are represented in clinical trials.<\/p>\n<aside class=\"marginalia comments-marginalia  selected-comment-marginalia\" data-marginalia-type=\"sprinkled\" data-skip-to-para-id=\"story-continues-4\"><a class=\"visually-hidden\" href=\"http:\/\/www.nytimes.com\/2014\/10\/05\/magazine\/can-big-data-tell-us-what-clinical-trials-dont.html?src=twr#story-continues-4\">Continue reading the main story<\/a><\/p>\n<header>\n<h2 class=\"module-heading\"><i class=\"icon\"><\/i>RECENT COMMENTS<\/h2>\n<\/header>\n<div class=\"comments-view\">\n<article class=\"comment\" data-permid=\"12980881\">\n<header>\n<h2 class=\"commenter\">B<\/h2>\n<p><time class=\"comment-time\" datetime=\"\">9 days ago<\/time><\/header>\n<p class=\"comment-text\">The standard journalistic formula to grab reader interest &#8211; start with a heart rending anecdote about an individual, then make a general&#8230;<\/p>\n<\/article>\n<article class=\"comment\" data-permid=\"12980465\">\n<header>\n<h2 class=\"commenter\">Joseph Ting<\/h2>\n<p><time class=\"comment-time\" datetime=\"\">9 days ago<\/time><\/header>\n<p class=\"comment-text\">A clinician currently assesses for the possibility of a serious complication of a rare illness, or the side effect of a new therapy, by&#8230;<\/p>\n<\/article>\n<article class=\"comment\" data-permid=\"12980153\">\n<header>\n<h2 class=\"commenter\">Discussant<\/h2>\n<p><time class=\"comment-time\" datetime=\"\">9 days ago<\/time><\/header>\n<p class=\"comment-text\">This progress is exciting. More robust evidence, data utilization and tracking, and accountability for long-term outcomes is especially&#8230;<\/p>\n<\/article>\n<\/div>\n<footer>\n<ul class=\"comment-actions\">\n<li class=\"comment-count\">SEE ALL COMMENTS<\/li>\n<\/ul>\n<\/footer>\n<\/aside>\n<p id=\"story-continues-4\" class=\"story-body-text story-content\" data-para-count=\"659\" data-total-count=\"4851\"><strong>As with so much<\/strong> network-enabled data-tinkering, this research is freighted with serious privacy concerns. If these analyses are considered part of treatment, hospitals may allow them on the grounds of doing what is best for a patient. But if they are considered medical research, then everyone whose records are being used must give permission. In practice, the distinction can be fuzzy and often depends on the culture of the institution. After Frankovich wrote about her experience in The New England Journal of Medicine in 2011, her hospital warned her not to conduct such analyses again until a proper framework for using patient information was in place.<\/p>\n<p id=\"story-continues-5\" class=\"story-body-text story-content\" data-para-count=\"651\" data-total-count=\"5502\">In the lab, ensuring that the data-mining conclusions hold water can also be tricky. By definition, a medical-records database contains information only on sick people who sought help, so it is inherently incomplete. Also, they lack the controls of a clinical study and are full of other confounding factors that might trip up unwary researchers. Daniel Rubin, a professor of bioinformatics at Stanford, also warns that there have been no studies of data-driven medicine to determine whether it leads to positive outcomes more often than not. Because historical evidence is of \u201cinferior quality,\u201d he says, it has the potential to lead care astray.<\/p>\n<p class=\"story-body-text story-content\" data-para-count=\"850\" data-total-count=\"6352\">Yet despite the pitfalls, developing a \u201clearning health system\u201d \u2014 one that can incorporate lessons from its own activities in real time \u2014 remains tantalizing to researchers. Stefan Thurner, a professor of complexity studies at the Medical University of Vienna, and his researcher, Peter Klimek, are working with a database of millions of people\u2019s health-insurance claims, building networks of relationships among diseases. As they fill in the network with known connections and new ones mined from the data, Thurner and Klimek hope to be able to predict the health of individuals or of a population over time. On the clinical side, Longhurst has been advocating for a button in electronic medical-record software that would allow doctors to run automated searches for patients like theirs when no other sources of information are available.<\/p>\n<p class=\"story-body-text story-content\" data-para-count=\"335\" data-total-count=\"6687\">With time, and with some crucial refinements, this kind of medicine may eventually become mainstream. Frankovich recalls a conversation with an older colleague. \u201cShe told me, \u2018Research this decade benefits the next decade,\u2019 \u201d Frankovich says. \u201cThat was how it was. But I feel like it doesn\u2019t have to be that way anymore.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; http:\/\/www.nytimes.com\/2014\/10\/05\/magazine\/can-big-data-tell-us-what-clinical-trials-dont.html?src=twr MAGAZINE Can Big Data Tell Us What Clinical Trials Don\u2019t? OCT. 3, 2014 Photo CreditIllustration by Christopher Brand Eureka By VERONIQUE GREENWOOD Continue reading the main storyShare This Page Email Share Tweet Pin Save More When a helicopter rushed a 13-year-old girl showing symptoms suggestive of kidney failure to Stanford\u2019s Packard Children\u2019s Hospital, &hellip; <a href=\"https:\/\/blog.panicola.com\/?p=2460\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">NYT: Can Big Data Tell Us What Clinical Trials Don\u2019t?<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,5,9,3],"tags":[],"class_list":["post-2460","post","type-post","status-publish","format-standard","hentry","category-complex-adaptive-systems","category-data-saving-lives","category-healthcare","category-rapid-learning-health-systems"],"_links":{"self":[{"href":"https:\/\/blog.panicola.com\/index.php?rest_route=\/wp\/v2\/posts\/2460","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.panicola.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.panicola.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.panicola.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.panicola.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2460"}],"version-history":[{"count":1,"href":"https:\/\/blog.panicola.com\/index.php?rest_route=\/wp\/v2\/posts\/2460\/revisions"}],"predecessor-version":[{"id":2461,"href":"https:\/\/blog.panicola.com\/index.php?rest_route=\/wp\/v2\/posts\/2460\/revisions\/2461"}],"wp:attachment":[{"href":"https:\/\/blog.panicola.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2460"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.panicola.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2460"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.panicola.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2460"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}