In December 2019, the Indian Council of Medical Research (ICMR) has released a draft
document to guide physicians on decisions not to provide cardiopulmonary resuscitation
(CPR) to patients. The draft document narrates that the death is inevitable in certain
situations at the terminal stage of certain diseases and providing CPR may only increase
the suffering of such patients. The draft document recommends that physicians should
provide complete information to patients or their family members about circumstances
under which CPR would in the best judgment of physicians be considered medically inappropriate.
It emphasizes the need to explain such circumstances to patients and family members,
so they understand the implications of CPR in the context of the incurability of the
disease and the nearly impossible opportunity for any meaningful survival.
The draft document issued by ICMR provides information such as an algorithm, with
a flowchart guiding the physicians on how best to proceed, and in which circumstances
CPR should be provided. It will guide treating physicians to take the decision whether
or not to perform the CPR on the background of incurable disease where the patient’s
chances of survival are extremely low. The flowchart provided by ICMR is provided
in [Figure 1].{Figure 1}
Figure 1: Algorithm for do not attempt resuscitation decision making (from draft document
issued by the Indian Council of Medical Research)
ICMR has invited feedback and comments on the Draft ICMR Position Paper on “Do Not
Attempt Resuscitation (DNAR)” from relevant stakeholders. It is requested that comments/feedback
pertaining to the same may be conveyed in the prescribed format by E-mail to Dr. Roli
Mathur (icmr.bioethics@gmail.com).
The guidelines on such topics are very sensitive topic, as there will always be those
who oppose the decision. Leaving an individual to pass away will cause distress and
unhappiness for family and friends. However, the draft document does highlight the
importance of providing detailed yet simply understood reasoning to the family of
the affected individual through every step of the decision-making process.
Until now, there has been no formal classification or guidelines provided by any government
body to establish an appropriate course of action in such a circumstance. This places
physicians in a difficult situation in which neglecting to perform CPR can have terrible
repercussions both to their career and, indeed, to their safety.
The question which will also be debated is priority when the patient requires CPR
(especially in patients who are brought to emergency department); whether the physician
shall spend time in explaining patient’s relatives about DNAR rather than actually
providing CPR to the patient. The time spent at that time by the physician on DNAR
may be looked differently leading arguments.
Considering the sheer volume of violence committed against physicians, many are in
a situation where they may feel threatened in opting not to perform CPR. Although
following guidelines on the practice will, of course, not alleviate the threat of
violence in its entirety, it can, at the very least, help inform the family as to
decisions made in terms of giving CPR.
It will be crucial to know interest taken by physicians in reporting comments on the
draft document to ICMR and contents of finalized document once released by ICMR post
addressing comments.
Google Ai Platform Aids in Breast Cancer Screenings
Google Ai Platform Aids in Breast Cancer Screenings
The year 2020 started with a big news in health-care system mainly from the perspective
of positive implications of artificial intelligence (AI) on cancer detection. Google
Health announced the results of a study which indicates AI could be more effective
in spotting breast cancer. The findings, published in nature, indicate that Google’s
AI model spotted breast cancer in deidentified screening mammograms with greater accuracy,
with fewer false positives and false negatives than experts.
Google’s London-based AI subsidiary DeepMind worked with Cancer Research UK Imperial
Centre, Northwestern University and Royal Surrey County Hospital to train and deploy
the AI model. The UK test set consisted of screening mammograms that were collected
between 2012 and 2015 from 25,856 women at two screening centers in England, where
women are screened every 3 years. This was a random sample of 10% of all women with
screening mammograms at these sites during this time period. The UK cohort resembled
the broader screening population in age and disease characteristics. The test set
from the USA, where women are screened every 1–2 years, consisted of screening mammograms
that were collected between 2001 and 2018 from 3097 women at one academic medical
center.
The study showed that the AI system could identify cancers with a similar level of
accuracy to expert radiologists. At the same time, it reduced the number of false
positive results by 5.7% in the American patients and 1.2% in the British patients.
It also cut the number of false negatives, where tests are wrongly listed as normal,
by 9.4% in the American group and 2.7% in the British group. These results show differences
in how mammograms are read. In the USA, only one radiologist reads the results and
the tests are done every 1–2 years. In Britain, the tests are done every 3 years,
and each is read by two radiologists. When they disagree, a third radiologist reads
it.
The study has some limitations. Most of the tests were done using the same type of
imaging equipment, and the USA group had a lot of patients with confirmed breast cancers.
The next challenge lies in getting regulatory approval which may take few years.
The use of machine learning technologies in breast cancer screening could have huge
implications, as spotting and diagnosing breast cancer early remains a challenge with
radiologists.
This is not the first time AI has shown big promise for better mammography detection.
In 2016, researchers at Houston Methodist developed (Houston Methodist Research Institute,
Houston, USA) an AI software which could improve readings to 99% accuracy by analyzing
values from X-ray images and the text of clinical reports. In April 2019, CureMetrix
(health-care technology company based in California, USA) has received the Food and
Drug Administration (FDA) approval to sell its software “triage” service to screen
mammograms for signs of cancer. The AI-based triage software for mammography named
as cmTriage is a workflow optimization tool that enables a radiologist to customize,
sort, and prioritize their mammography worklist based on cases that may need immediate
attention. Rather than today’s method of first in, first out for reading through a
screening mammography worklist, radiologists can now use cmTriage to identify which
cases are suspicious and focus on those first. They can also use cmTriage to optimize
their clinical workflow by sending the suspicious cases to their most senior radiologists
or reading the suspicious cases immediately before the patient has even left the clinic.
This workflow optimization tool enables a radiologist to customize their mammography
worklist based on cases that may need immediate attention.
The opportunities for clinical improvements using AI are broad and diverse, and the
technology stands to continue making big advances across health care in 2020. For
instance, AI technology based on a deep learning model has also shown promise of helping
cardiologists to predict irregular heart rhythm, atrial fibrillation, before it develops.
The medical fraternity will be looking forward to future applications; there are some
promising signs that the model could potentially increase the accuracy and efficiency
of screening programs and reduce wait times and stress for patients. However, ensuring
its reach to treating physicians post conduct of clinical trials and regulatory approvals
will be critical in acceptance and use of such technologies.
Trends to Watch Out for in 2020
Trends to Watch Out for in 2020
Direct-to-consumer drug delivery – set to become more common in 2020
The pharma companies in India are under lot of pricing pressure and expected to think
innovative ways to reduce cost to patients. Choosing direct-to-consumer (DTC) may
help pharma companies in reducing margins to be shared with distributors and effectively
reduce the cost. DTC drug delivery firms could give pharma firms more direct access
to consumers and their medication-related habits. Using DTC startups working in the
drug delivery to consumer space would be a smart way for pharma.
Big pharma companies probably would not embark on acquisitions of such companies which
provide DTC services for a revenue boost as most of these DTC companies are start-ups.
Few names in the space of DTC such as Nurx, Hims and Hers, and capsule have all grabbed
funding this year and consumers are becoming more and more convenience-driven, which
will likely increase demand for fast and reliable drug delivery services.
Emphasis on targeted therapies
Several years ago, personalized medicine was an emerging concept rather than a concrete
part of the health-care system. However, last year, 42% of newly approved drugs, agents,
or therapeutic biologics were classified as personalized medicines.
Personalized medicine refers to therapies that are designed based on the genetic and
molecular makeup of a patient. In principle, an individualized treatment should be
more effective at treating a patient compared to a nonspecific drug.
With the advancement of genetic techniques and a greater understanding of disease
pathology, the cancer drug development industry, in particular, has witnessed a major
shift toward targeted therapies. In the last 3 years alone, the US FDA approved eight
targeted therapies for acute myeloid leukemia.
Rise in biosimilars and generics
A biosimilar is highly similar to and has no clinically meaningful differences from
an existing FDA-approved reference product, according to the FDA. In other words,
biosimilars do not necessarily contain the same active ingredients as the reference
product. In contrast, generic drugs do contain the same active ingredients as those
of the brand name drugs. Biosimilars and generics are often cheaper than brand-name
drugs, but are each regulated differently.
The industry has seen a rise in biosimilar and generic drugs as decades-old patents
begin to expire. The Brazilian, Russian, Indian, and Chinese drug markets are each
expected to spend about double the amount on generics compared to patented products.
Reimagined clinical trials
In 2020, sponsors can look forward to new innovations into the clinical trial space,
including more virtual or hybrid trials, adaptive trials, and other designs that better
meet patient needs. In fact, companies are recognizing the value of patient insights
(and other stakeholders) for successful trial execution.
Trials are also becoming more complex than ever before. The amount of data that is
available to sponsors comes as a blessing in disguise – more insights can lead to
more effective treatment, yet the disparate sources of data present a significant
challenge for accurate and timely analysis. Sophisticated digitization tools promise
to streamline clinical data management, reduce costs, and accelerate study completion
times.