The Only Hydroxychloroquine Story You Need to Read
Dr. Adalja is focused on emerging infectious disease, pandemic preparedness, and biosecurity. He has served on US government panels tasked with developing guidelines for the treatment of plague, botulism, and anthrax in mass casualty settings and the system of care for infectious disease emergencies, and as an external advisor to the New York City Health and Hospital Emergency Management Highly Infectious Disease training program, as well as on a FEMA working group on nuclear disaster recovery. Dr. Adalja is an Associate Editor of the journal Health Security. He was a coeditor of the volume Global Catastrophic Biological Risks, a contributing author for the Handbook of Bioterrorism and Disaster Medicine, the Emergency Medicine CorePendium, Clinical Microbiology Made Ridiculously Simple, UpToDate's section on biological terrorism, and a NATO volume on bioterrorism. He has also published in such journals as the New England Journal of Medicine, the Journal of Infectious Diseases, Clinical Infectious Diseases, Emerging Infectious Diseases, and the Annals of Emergency Medicine. He is a board-certified physician in internal medicine, emergency medicine, infectious diseases, and critical care medicine. Follow him on Twitter: @AmeshAA
In the early days of a pandemic caused by a virus with no existing treatments, many different compounds are often considered and tried in an attempt to help patients.
It all relates back to a profound question: How do we know what we know?
Many of these treatments fall by the wayside as evidence accumulates regarding actual efficacy. At that point, other treatments become standard of care once their benefit is proven in rigorously designed trials.
However, about seven months into the pandemic, we're still seeing political resurrection of a treatment that has been systematically studied and demonstrated in well-designed randomized controlled trials to not have benefit.
The hydroxychloroquine (and by extension chloroquine) story is a complicated one that was difficult to follow even before it became infused with politics. It is a simple fact that these drugs, long approved by the Food and Drug Administration (FDA), work in Petri dishes against various viruses including coronaviruses. This set of facts provided biological plausibility to support formally studying their use in the clinical treatment and prevention of COVID-19. As evidence from these studies accumulates, it is a cognitive requirement to integrate that knowledge and not to evade it. This also means evaluating the rigor of the studies.
In recent days we have seen groups yet again promoting the use of hydroxychloroquine in, what is to me, a baffling disregard of the multiple recent studies that have shown no benefit. Indeed, though FDA-approved for other indications like autoimmune conditions and preventing malaria, the emergency use authorization for COVID-19 has been rescinded (which means the government cannot stockpile it). Still, however, many patients continue to ask for the drug, compelled by political commentary, viral videos, and anecdotal data. Yet most doctors (like myself) are refusing to write the prescriptions outside of a clinical trial – a position endorsed by professional medical organizations such as the American College of Physicians and the Infectious Diseases Society of America. Why this disconnect?
It all relates back to a profound question: How do we know what we know? In science, we use the scientific method – the process of observing reality, coming up with a hypothesis about what might be true, and testing that hypothesis as thoroughly as possible until we discover the objective truth.
The confusion we're seeing now stems from an inability to distinguish between anecdotes reported by physicians (observational data) and an actual evidence base. This is understandable among the general public but when done by a healthcare professional, it reveals a disdain for reason, logic, and the scientific method.
The Difference Between Observational Data and Randomized Controlled Trials
The power of informal observation is crucial. It is part of the scientific method but primarily as a basis for generating hypotheses that we can test. How do we conduct medical tests? The gold standard is the double-blind, randomized, placebo-controlled trial. This means that neither the researchers nor the volunteers know who is getting a drug and who is getting a sugar pill. Then both groups of the trial, called arms, can be compared to determine whether the people who got the drug fared better. This study design prevents biases and the placebo effect from confounding the data and undermining the veracity of the results.
For example, a seemingly beneficial effect might be seen in an observational study with no blinding and no control group. In such a case, all patients are openly given the drug and their doctors observe how they do. A prime example is the 36-patient single-arm study from France that generated a tremendous amount of interest after President Trump tweeted about it. But this kind of a study by its nature cannot answer the critical question: Was the positive effect because of hydroxychloroquine or just the natural course of the illness? In other words, would someone have recovered in a similar fashion regardless of the drug? What is the role of the placebo effect?
These are reasons why it is crucial to give a placebo to a control group that is as similar in every respect as possible to those receiving the intervention. Then we attempt to find out by comparing the two groups: What is the side effect profile of the drug? Are the groups large enough to detect a relatively rare safety concern? How long were the patients followed for? Was something else responsible for making the patients get better, such as the use of steroids (as likely was the case in the Henry Ford study)?
Looking at the two major hydroxychloroquine trials, it is apparent that, when studied using the best tools of clinical trials, no benefit is likely to occur.
All of these considerations amount to just a fraction of the questions that can be answered more definitively in a well-designed large randomized controlled trial than in observational studies. Indeed, an observational study from New York failed to show any benefit in hospitalized patients, showing how unclear and disparate the results can be with these types of studies. A New York retrospective study (which examined patient outcomes after they were already treated) had similar results and included the use of azithromycin.
When evaluating a study, it is also important to note whether conflicts of interest exist, as well as the quality of the peer review and the data itself. In the case of the French study, for example, the paper was published in a journal in which one of the authors was editor-in-chief, and it was accepted for publication after 24 hours. Patients who fared poorly on hydroxychloroquine were also left out of the study altogether, skewing the results.
What Randomized Controlled Trials Have Shown
Looking at the two major hydroxychloroquine trials, it is apparent that, when studied using the best tools of clinical trials, no benefit is likely to occur. The most important of these studies to announce results was part of the Recovery trial, which was designed to test multiple interventions in the treatment of COVID-19. This trial, which has yet to be formally published, was a randomized controlled trial that involved over 1500 hospitalized patients being administered hydroxychloroquine compared to over 3000 who did not receive the medication. Clinical testing requires large numbers of patients to have the power to demonstrate statistical significance -- the threshold at which any apparent benefit is more than you would expect by random chance alone.
In this study, hydroxychloroquine provided no mortality benefit or even a benefit in hospital length of stay. In fact, the opposite occurred. Hydroxychloroquine patients were more likely to stay in the hospital longer and were more likely to require mechanical ventilation. Additionally, smaller randomized trials conducted in China have not shown benefit either.
Another major study involved the use of hydroxychloroquine to prevent illness in people who were exposed to COVID-19. These results, published in The New England Journal of Medicine, included over 800 patients who were studied in a randomized double-blind controlled trial and also failed to show any benefit.
But what about adding the antibiotic azithromycin in conjunction with hydroxychloroquine? A three-arm randomized controlled study involving over 500 patients hospitalized with mild to moderate COVID-19 was conducted. Its results, also published in The New England Journal of Medicine, failed to show any benefit – with or without azithromycin – and demonstrated evidence of harm. Those who received these treatments had elevations of their liver function tests and heart rhythm abnormalities. These findings hold despite the retraction of an observational study showing similar results.
Additionally, when used in combination with remdesivir – an experimental antiviral – hydroxychloroquine has been shown to be associated with worse outcomes and more side effects.
But what about in mildly ill patients not requiring hospitalization? There was no benefit found in a randomized double-blind placebo-controlled trial of 400 patients, the majority of whom were given the drug within one day of symptoms.
Some randomized controlled studies have yet to report their findings on hydroxychloroquine in non-hospitalized patients, with the use of zinc (which has some evidence in the treatment of the common cold, another ailment that can be caused by coronaviruses). And studies have yet to come out regarding whether hydroxychloroquine can prevent people from getting sick before they are even exposed. But the preponderance of the evidence from studies designed specifically to find benefit for treating COVID-19 does not support its use outside of a research setting.
Today – even with some studies (including those with zinc) still ongoing – if a patient asked me to prescribe them hydroxychloroquine for any severity or stage of illness, with or without zinc, with or without azithromycin, I would refrain. I would explain that, based on the evidence from clinical trials that has been amassed, there is no reason to believe that it will alter the course of illness for the better.
Failing to recognize the reality of the situation runs the risk of crowding out other more promising treatments and creating animosity where none should exist.
What has been occurring is a continual shifting of goalposts with each negative hydroxychloroquine study. Those in favor of the drug protest that a trial did not include azithromycin or zinc or wasn't given at the right time to the right patients. While there may be biological plausibility to treating illness early or combining treatments with zinc, it can only be definitively shown in a randomized, controlled prospective study.
The bottom line: A study that only looks at past outcomes in one group of patients – even when well conducted – is at most hypothesis generating and cannot be used as the sole basis for a new treatment paradigm.
Some may argue that there is no time to wait for definitive studies, but no treatment is benign. The risk/benefit ratio is not the same for every possible use of the drug. For example, hydroxychloroquine has a long record of use in rheumatoid arthritis and systemic lupus (whose patients are facing shortages because of COVID-19 related demand). But the risk of side effects for many of these patients is worth taking because of the substantial benefit the drug provides in treating those conditions.
In COVID-19, however, the disease apparently causes cardiac abnormalities in a great deal of many mild cases, a situation that should prompt caution when using any drugs that have known effects on the cardiac system -- drugs like hydroxychloroquine and azithromycin.
My Own Experience
It is not the case that every physician was biased against this drug from the start. Indeed, most of us wanted it to be shown to be beneficial, as it was a generic drug that was widely available and very familiar. In fact, early in the pandemic I prescribed it to hospitalized patients on two occasions per a hospital protocol. However, it is impossible for me as a sole clinician to know whether it worked, was neutral, or was harmful. In recent days, however, I have found the hydroxychloroquine talk to have polluted the atmosphere. One recent patient was initially refusing remdesivir, a drug proven in large randomized trials to have effectiveness, because he had confused it with hydroxychloroquine.
Moving On to Other COVID Treatments: What a Treatment Should Do
The story of hydroxychloroquine illustrates a fruitless search for what we are actually looking for in a COVID-19 treatment. In short, we are looking for a medication that can decrease symptoms, decrease complications, hasten recovery, decrease hospitalizations, decrease contagiousness, decrease deaths, and prevent infection. While it is unlikely to find a single antiviral that can accomplish all of these, fulfilling even just one is important.
For example, remdesivir hastens recovery and dexamethasone decreases mortality. Definitive results of the use of convalescent plasma and immunomodulating drugs such as siltuxamab, baricitinib, and anakinra (for use in the cytokine storms characteristic of severe disease) are still pending, as are the trials with monoclonal antibodies.
While it was crucial that the medical and scientific community definitively answer the questions surrounding the use of chloroquine and hydroxychloroquine in the treatment of COVID-19, it is time to face the facts and accept that its use for the treatment of this disease is not likely to be beneficial. Failing to recognize the reality of the situation runs the risk of crowding out other more promising treatments and creating animosity where none should exist.
Dr. Adalja is focused on emerging infectious disease, pandemic preparedness, and biosecurity. He has served on US government panels tasked with developing guidelines for the treatment of plague, botulism, and anthrax in mass casualty settings and the system of care for infectious disease emergencies, and as an external advisor to the New York City Health and Hospital Emergency Management Highly Infectious Disease training program, as well as on a FEMA working group on nuclear disaster recovery. Dr. Adalja is an Associate Editor of the journal Health Security. He was a coeditor of the volume Global Catastrophic Biological Risks, a contributing author for the Handbook of Bioterrorism and Disaster Medicine, the Emergency Medicine CorePendium, Clinical Microbiology Made Ridiculously Simple, UpToDate's section on biological terrorism, and a NATO volume on bioterrorism. He has also published in such journals as the New England Journal of Medicine, the Journal of Infectious Diseases, Clinical Infectious Diseases, Emerging Infectious Diseases, and the Annals of Emergency Medicine. He is a board-certified physician in internal medicine, emergency medicine, infectious diseases, and critical care medicine. Follow him on Twitter: @AmeshAA
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.