Acute congestive heart failure

今日，CDE官网显示，由华夏生生药业（北京）有限公司申报的硝普钠注射液拟纳入优先审评，拟定适应症为：用于在高血压危象中立即降低成年人和儿童患者的血压。应同时服用长效降压药，以尽量减少硝普钠的治疗时间。也可用于可控降压，以减少手术中的出血。也适用于治疗急性充血性心力衰竭。 根据CDE公布的拟纳入优先审评理由显示，经加快上市申请专家论证会议定，本品是临床急需的短缺药品，目前尚无硝普钠注射液仿制药上市申请纳入优先审评，并请申请人尽快提交上市申请。 据公开资料显示，硝普钠注射液是一种血管扩张剂，2022年4月，普利制药的硝普钠注射液以化学药品新注册分类3类获批上市，视同通过一致性评价。至今，硝普钠注射液在国内仍是普利制药的独家品种。 普利制药的硝普钠注射液仿制药成功研发后，相继提交了国内外多个国家的注册申请，2020年8月获得美国食药监局（FDA）批准，2020年10月获得加拿大卫生部批准，今年2月，硝普钠注射液获得德国上市许可 。 据药融云数据库显示，硝普钠注射液2023年全国医院（全终端）销售额近5000万元，相较于2022年销售额大增326%。

Prescription medication adherence is a problem. And an expensive one. The Centers for Disease Control and Prevention (CDC) finds that “the costs of nonadherence to prescribed medications are high and place significant financial strains on the health care system as a whole.” For example, the CDC cites a study showing that higher adherence to prescribed medications lowered healthcare costs among people taking medications for congestive heart failure by an estimated $7,800 per person annually. For individuals taking blood pressure medication, the cost is around $3,900 annually, and about $1,250 each year for those taking medication to manage high cholesterol. Prescription medication adherence is especially a problem in underserved areas, and it becomes even more of a problem when a public health disease such as tuberculosis (TB) is involved. TB treatment often requires Directly Observed Therapy (DOT), in which healthcare professionals watch patients take their medications and monitor for responses to treatments. And TB hits underserved communities the hardest. CDC data shows that about 88% of the TB cases reported in America occur among ethnic and racial minority groups. To reduce the number of healthcare professionals needed for in-person DOT, the CDC recommends telehealth technology with video-enabled devices that facilitate remote interactions between patients and healthcare workers. And AI may help reduce that burden on healthcare workers even further. A new study led by researchers at the University of Georgia shows that artificial intelligence evaluates patient-submitted videos to ensure medication adherence with accuracy. The study looked at TB treatment in low-resource communities in Uganda, where a program dubbed DOT Selfie generates thousands of videos of people taking their medications. But instead of healthcare workers watching all the videos, the study tasked AI-enabled programs with reviewing the videos. The results? The best of the four AI models used identified patients taking their medication 85% of the time, a percentage comparable to humans performing the same task. Though humans aren’t left out of the process entirely. University of Georgia researcher Juliet Sekandi says, “AI is really an accelerator of that process because then a nurse will not be worried that they have to watch all the 10,000 videos, but maybe watch only a few that need verification, say 100 out of 10,000.” While the study only considered AI-assisted DOT among TB patients, it’s easy to see how this technology could apply to a wide range of treatments that may benefit from observed medication adherence.

Butterfly Network's latest program automates the counting of what are known as B-lines: the appearance of bright, vertical artifacts in an ultrasound image that come with changes in lung density. Hand-held ultrasound developer Butterfly Network has obtained a new clearance from the FDA for an artificial intelligence program to help front-line technicians scan for impaired lung function. Using a six-second video clip, the software aims to spot the signs of excess wetness in the lungs that can hinder the ability to breathe—a potential harbinger of acute infections like pneumonia and COVID-19 or long-term conditions such as chronic obstructive pulmonary disease and congestive heart failure. “Our goal at Butterfly is to give healthcare practitioners, and eventually consumers, a real time full color, annotated, window into the human body” through the use of AI, Jonathan Rothberg, Ph.D., Butterfly’s founder and interim CEO, said in a release. To train its algorithms, Butterfly tapped into its cloud-based repository of more than 3.5 million de-identified ultrasound clips gathered from across the U.S.  The latest program automates the counting of what are known as B-lines: the appearance of bright, vertical artifacts in an ultrasound image that come with changes in lung density. A higher number of B-line echoes can indicate the presence of fluids as well as less air within the lung pockets. Previously a manual and more subjective process, Butterfly’s Auto B-line Counter algorithm assigns numbers to the B-lines and compares them to the ultrasound shadows produced by the rib cage. Butterfly said it hopes to launch its Auto B-line Counter in early summer. Butterfly previously teamed up with ultrasound AI developer Caption Health to bring automated programs to its hand-held, smartphone-connected probe, including guidance programs that coach users through conducting a heart scan. That software will be wrapped up in Butterfly's Blueprint enterprise platform for hospitals, which also integrates the probes with electronic medical records. “The desired end state is Butterfly is as ubiquitous as a stethoscope,” Darius Shahida, the company’s strategy and business development chief, told Fierce Medtech in an interview at the J.P. Morgan Healthcare Conference earlier this year.