The researchers followed hospitalized COVID-19 patients with and without cancer from March 15th to May 14th, 2020, tracking their viral load. Their goal was to observe how many died during that time period, and if those patients tended to have higher loads. Two different tests were used to assess the relationship between viral load at the time of hospital admission and in-hospital mortality – the “COBAS SARS-CoV-2 Assay” and the “Xpert Xpress SARS-CoV-2 Assay.” These are lab tests that count virus particles.

The researchers accessed data from 100 cancer patients and 2,914 patients with no cancer diagnosis from three different hospitals across New York City during the height of the pandemic in March of 2020. The researchers compared cancer patients with non-cancer patients to see if their COVID-19 treatment outcomes differed. They took into account whether the cancer was a solid tumor or a blood cancer, and if the patients had any other chronic illnesses. 

How do scientists and doctors determine a patient’s viral load? This is done using a technique called PCR (polymerase chain reaction). In most organisms, genetic information is stored in a molecule called DNA. However, SARS-CoV-2 has a genome made of a similar but distinct molecule, RNA. It has one strand instead of two. The number of virus-specific RNA molecules counted in the patient’s blood sample tells them how much COVID-19 virus is present. 

To perform this test, the viral RNA is first doubled, so it looks like DNA. Then, the PCR technique is used to make lots of copies of a specific piece of that DNA, making a new copy each time the reaction cycle runs. Finally, the researchers asked how many cycles of PCR are required for the number of DNA copies to reach a certain threshold value. The more viral RNA is present to begin with, the fewer cycles it takes to reach the threshold value. When less virus is present, the more cycles it takes. Therefore, the cycle threshold value indicates how much of the virus is present in the sample to start with.

Patients with active cancer had similar outcomes compared with patients without cancer. Cancer patients were more likely to be non-Hispanic, white, and former smokers and to have coronary artery disease as well as congestive heart failure compared to non-cancer patients. 

Patients with blood cancers (leukemia, lymphoma, and multiple myeloma) had higher SARS-CoV-2 viral loads than patients with other cancers and those without cancer. This makes sense because blood cancers make the individual more prone to other infections, including COVID-19. Typical PCR protocols run around 20-35 cycles. The lower the cycle threshold, the higher the load. Blood cancer patients scores centered around 25, while cancer patients were at 29.2, and patients with no cancer were at 28.4

Among non-cancer patients, 38.8% with a high viral load died during their hospitalization, 24.1% with a medium viral load died, and 15.3% with a low viral load died. In cancer patients, 45.2% with a high viral load died while hospitalized, compared to 28.0% in patients with a medium viral load, and 12.1% with a low viral load. This data supports a difference in high, medium, and low viral loads between non-cancer and cancer patients. 

It turns out, viral load can predict whether or not you die from COVID-19, whether you have cancer or not. Patients with blood cancers had higher viral loads upon admission to the hospital than patients without cancer. Patients with solid tumors had similar viral loads as patients without cancer. The amount of virus in one’s body is related to chances of dying of COVID-19.

No difference was shown between patients with hematological cancers, other cancers, or no cancer. The authors explained that other studies found clearer differences in mortality between cancer and non-cancer patients, which may be because cancer patients made up a larger portion of the previous study sample.

Reporting the viral load for hospitalized COVID-19 patients with or without cancer can provide vital information that healthcare workers can use to help identify patients who may need more intensive and thorough monitoring.