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But up to 78% of them are undiagnosed or “silent”. This means a large fraction of people with heart attack never get the cocktail of drugs known to save lives, by preventing future heart attacks and sudden death. Today, doctors can order tests (like MRIs or ultrasounds) to diagnose patients when they suspect a prior heart attack. But the reason so many heart attacks remain silent is precisely because doctors and patients don’t even suspect a heart attack has happened. Finding new ways to diagnose these undiagnosed heart attacks at scale could dramatically expand access to life-saving medications. And because of our close partnership with the county health system that sourced these data, algorithms developed on the platform, once validated, have a clear pathway for making it into clinical use and helping real patients. Electrocardiograms (ECGs) are a cheap, widespread test done everywhere in the health care system: during annual checkups, ER visits, before surgical procedures, etc. Doctors have learned to diagnose some limited signs of prior heart attack on ECGs (like ‘Q waves’), but these coarse findings still miss about 80% of prior heart attacks. We know that algorithms can match human performance on ECG interpretation—but could they do better, by systematically mining ECG waveforms for signals that might identify prior heart attacks? We don’t know, because there have not historically been datasets linking ECGs to high-quality labels on prior heart attack.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://docs.ngsci.org/datasets/silent-cchs-ecg","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-02-08T23:49:54Z","registered":"2022-02-08T23:49:55Z","published":null,"updated":"2023-08-15T17:14:58Z"},"relationships":{"client":{"data":{"id":"cdl.ucb","type":"clients"}}}},{"id":"10.48815/n5rp44","type":"dois","attributes":{"doi":"10.48815/n5rp44","identifiers":[],"creators":[{"name":"Lungren, Matthew","givenName":"Matthew","familyName":"Lungren","affiliation":[],"nameIdentifiers":[]},{"name":"Kim, Johanna","givenName":"Johanna","familyName":"Kim","affiliation":[],"nameIdentifiers":[]},{"name":"Bogdan, Stephanie","givenName":"Stephanie","familyName":"Bogdan","affiliation":[],"nameIdentifiers":[]},{"name":"Lane, William","givenName":"William","familyName":"Lane","affiliation":[],"nameIdentifiers":[]},{"name":"Risley, Josh","givenName":"Josh","familyName":"Risley","affiliation":[],"nameIdentifiers":[]},{"name":"Haynes, Katy","givenName":"Katy","familyName":"Haynes","affiliation":[],"nameIdentifiers":[]},{"name":"Obermeyer, Ziad","givenName":"Ziad","familyName":"Obermeyer","affiliation":[],"nameIdentifiers":[]}],"titles":[{"lang":"en","title":"Predicting fractures and pain using chest x-rays"},{"lang":"en","title":"A Nightingale Open Science dataset","titleType":"Subtitle"}],"publisher":"Nightingale Open Science","container":{},"publicationYear":2021,"subjects":[],"contributors":[],"dates":[{"date":"2021","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"De-Identified Medical Data","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"latest","rightsList":[],"descriptions":[{"lang":"en","description":"For many older patients—and some younger ones—a fracture marks the beginning of the end. The fracture itself is seldom fatal; but it sets off a downward spiral of pain, decreased mobility, physical deconditioning, debility, and ultimately death. This is why screening for osteoporosis, recommended today for women starting at age 65, is so critical: the appearance of bones on a special type of x-ray (called a DEXA scan) shows us who is at high risk of fractures, and lets us start treatments to prevent them before they happen. Given the massive costs of fractures—to both patients, and the health care system, which a recent report put at nearly $60 billion for fractures in US Medicare patients alone—it’s clear that our current screening strategies are not adequate. For one thing, despite established guidelines calling for universal screening over age 65, the vast majority of women don’t get it—not to mention the fact that many fractures occur in men and younger people, for whom guidelines don’t recommend screening. So it would be very useful to find another way to predict fractures at scale, using routinely available data. The chest x-ray is, by far, the most commonly-performed radiological study in the world, done when patients see their doctor for a cough, chest or back pain, before surgery, in the ER, on admission to the hospital, and in a variety of other settings. An interesting fact about the ‘chest’ x-ray is that it also gets a very clear view of the spine, from neck to the upper lumbar area. 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Or is close monitoring in the hospital, or even the ICU, needed? Getting this right is critical not just to save lives, but also to optimize scarce hospital resources. Reports from the front lines of the Covid-19 pandemic indicate that the current state of medical knowledge is failing here. Empirically, many patients are admitted to the hospital, but ultimately do not require advanced care—a waste of beds. Other patients look well enough to be sent home, only to deteriorate rapidly, returning to the ER in profound respiratory distress—or not returning at all. The key to solving this problem could lie in the chest x-ray, a rapid, cheap diagnostic that nearly all patients with respiratory complaints get in the ER. It's clear to front-line doctors that there is a signal in the x-ray image for predicting impending pulmonary collapse. But this signal can be devilishly hard to find. 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