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FINDINGS FROM A RETROSPECTIVE CASE STUDY OF COVID-19 INFECTION IN CANCER PATIENTS IN WUHAN: AN EMPHASIS ON SEVERE EVENTS

administrator / 04/03/2020

Analysis of risk factors associated with admission to an intensive care unit, the use of mechanical ventilation or deathDate:30 Mar 2020Topics:Cancer in Special Situations / Population A study team composed from clinicians from three hospitals in Wuhan, China, led by Dr Min Zhou of the Department of the Respiratory and Critical Care Medicine, Tongji Hospital, performed a retrospective case study of clinical features among 28 patients with cancer and COVID-19 infection. In particular, they analyzed the risk factors associated with admission to an intensive care unit, the use of mechanical ventilation or death. Their series was associated with poor outcomes. There was increased risk of developing severe events, especially if the last antitumour treatment was within 14 days of COVID-19 diagnosis. The findings are published on 23 March 2020 in the Annals of Oncology. Like the other coronaviruses, SARS-CoV-2 primarily causes respiratory tract infection. The study team wrote that data published in The Lancet Oncology on 18 patients with cancer diagnosis from a cohort of 2007 cases with COVID-19 disease in China represents a relatively small sample with limited clinical information and high heterogeneity of the course of the disease. Therefore, critical issues concerning treatment principles for cancer patients with COVID-19 infection are unclear. There is an urgent need to answer whether cancer patients with COVID-19 infection will have distinct clinical course and worse outcomes, such as death from the infection or severe pneumonia, and whether cancer patients should receive antitumour treatments as usual. The study team aimed to explore these issues by conducting a rapid retrospective case study on critical COVID-19-infected cancer patients. They analyzed cancer patients with laboratory confirmed COVID-19. The clinical data were collected from medical records from 13 January 2020 to 26 February 2020. In total, 28 cancer patients with COVID-19 infection were included. Of those, 17 patients (60.7%) were male. Median age was 65.0 years. Lung cancer was the most frequent cancer type in the cohort with 7 patients (25.0%), followed by oesophageal cancer in 4 patients (14.3%) and breast cancer in 3 patients (10.7%). Ten patients (35.7%) were diagnosed with stage IV cancer. Eight patients (28.6%) were suspected to be from hospital-associated transmission. All the patients had a history of antitumour therapy. Within 14 days of COVID-19 diagnosis, 6 patients (21.4%) were treated by at least one kind of antitumour therapy, such as chemotherapy in 3 patients (10.7%), targeted therapy in 2 patients (7.1%), radiotherapy in 1 patient (3.6%), immunotherapy in 1 patient (3.6%); one of them received treatment combining chemotherapy and immunotherapy. There were two main clusters of patients: 8 patients (28.6%) who developed COVID-19 undergoing antitumour therapy in hospitals and 20 patients (71.4%) in their communities. In addition to cancer, 11 patients (39.2%) had at least one or more comorbidities. The following clinical features were present in studied cohort: fever in 23 patients (82.1%), dry cough in 22 patients (81%) and dyspnoea in 14 patients (50.0%), along with lymphopaenia in 23 patients (82.1%), high level of high-sensitivity C-reactive protein in 23 patients (82.1%), anaemia in 21 patients (75.0%) and hypoproteinaemia in 25 patients (89.3%). The common chest CT findings were ground-glass opacity in 21 patients (75.0%) and patchy consolidation in 13 patients (46.3%). In total, 22 patients (78.6%) received oxygen therapy, 10 patients (35.7%) were put on invasive mechanical ventilation, with 2 patients (7.1%) requiring endotracheal intubation and invasive ventilation because of progressive hypoxia. Significantly more severe cases were subjected to mechanical ventilation (non-invasive: 53.3% vs 0%, p < 0.001; invasive: 13.3% vs 0%, p < 0.001) as compared to non-severe cases. The median period of mechanical ventilation for non-invasive and invasive ventilation was 2.5 days. None of the severe patients received extracorporeal membrane oxygenation. In the series, 20 patients (71.4%) were prescribed at least one antiviral agent, while 9 patients (32.1%) received combinations of antiviral agents. Empirical antibiotic therapy was given to 23 patients (82.1%). Systemic corticosteroids were given to 15 patients (53.6%). Administration of corticosteroids was more frequent in patients with severe events (12/15, 80%) than non-severe cases (3/13, 23.1%). Seven of 8 patients with acute respiratory distress syndrome (ARDS) received systemic corticosteroids. The dosage and period of corticosteroids in the severe cases was higher than in non-severe cases, but the difference was not significant. Moreover, intravenous immunoglobin was prescribed to 12 patients (35.7%). As of 26 February 2020, 15 patients (53.6%) developed severe clinical events, 6 patients (21.4%) were admitted to intensive care unit, 10 patients (35.7%) had life-threatening complications and 8 patients (28.6%) died. Of the 10 stage IV cancer patients, 7 patients (70%) had developed severe events, while 44.4% of the non-stage IV patients had such events. Among 6 cancer patients who received antitumor treatment within 14 days of COVID-19 disease diagnosis, 5 patients (83%) developed severe events. Additionally, 11 of 13 patients (84.6%) with patchy consolidation on CT on admission had developed severe events. The most common complication was ARDS in 8 patients (28.6%), followed by septic shock in 1 patient (3.6%), and acute myocardial infarction (AMI) in 1 patient (3.6%). Two patients (7.1%) were suspected to have pulmonary embolism. Ten of 28 patients (35.7%) had been discharged with a median hospital stay of 13.5 days; 10 patients (35.7%) were still inpatients with a median stay of 19.0 days. Of the 28 patients, 8 patients (28.6%) died, with a median time of 16.0 days from admission to death. The cause of death included ARDS in 5 patients (62.5%), followed by pulmonary embolism in 1 patient (12.5%), septic shock in 1 patient (12.5%) and AMI in 1 patient (12.5%). In univariate Cox proportional hazards model, cancer patients who received antitumour treatment within 14 days of COVID-19 diagnosis had a higher risk of developing severe events. Moreover, patchy consolidation on the first CT on admission suggested an elevated risk of developing severe events than those cases without consolidation. Similar results were observed in the multivariate-adjusted Cox proportional hazards model after being adjusted for age and gender. If the last antitumour treatment was within 14 days, it significantly increased the risk

COVID-19: WHY WE SHOULD ALL WEAR MASKS — THERE IS NEW SCIENTIFIC RATIONALE

The official recommendation in the United States (and other Western countries) that the public should not wear face masks was motivated by the need to save respirator masks for health care workers. There is no scientific support for the statement that masks worn by non-professionals are “not effective”. In contrary, in view of the stated goal to “flatten the curve”, any additional, however partial reduction of transmission would be welcome — even that afforded by the simple surgical masks or home-made (DIY) masks (which would not exacerbate the supply problem). The latest biological findings on SARS-Cov-2 viral entry into human tissue and sneeze/cough-droplet ballistics suggest that the major transmission mechanism is not via the fine aerosols but large droplets, and thus, warrant the wearing of surgical masks by everyone. The surgeon general tweeted: “STOP BUYING MASK, they are not effective…”. The Center for Disease Controls (CDC) states that surgical masks offer far less protection than the N95 respirator masks (which also must be perfectly fitted and only professionals can do it). The CDC recommends that healthy persons should not wear masks at all, only the sick ones. These guidelines are not rooted in scientific rationales but were motivated by the need to save the valuable masks for health professionals in view of a shortage. But they may have had unintended consequences: stigmatizing those that wear masks in the public (you are a hoarder, or you are contagious!) Contrast this with the cultural habit, the encouragement, or even mandate to wear masks in Asian countries — which have now “flattened the curve” or even have had a flatter curve from the beginning. Sure, surgical masks, and improperly worn N95 respirator masks, do not offer perfect protection. But if the stated goal is to “flatten” the curve (as opposed to eradication of the virus), we have to abandon the black-and-white thinking, and embrace shades of grey. We cannot any longer claim that masks “are not effective”. We cannot allow the perfect to be the enemy of the good. What if a however partial protection afforded by leaky surgical or even self-made masks reduces transmission probability to an extent that is similar to that of the recommended (equally imperfect) distancing by more than 6 feet from each other or “not touching your face”? It could then double the impact of non-pharmacological intervention (NPI) on flattening the curve (FIG. 1). Since the CDC provides no scientific evidence for its statement that masks worn by the public “are not effective”, here we review the scientific support for protection conferred by surgical masks. We focus on mechanistic rationale (as opposed to epidemiological-phenomenological evidence). We conclude, by considering cough droplet ballistics and the latest research findings on the biology of transmission of the SARS-CoV2 virus (which causes COVID-19) that any physical barrier, as provided even by make-shift masks, may substantially reduce the spread of COVID 19. If we are soon to yield to the pressure to loosen lockdowns and allow limited social interactions to revive the economy, then public masks should have a role and could facilitate a middle-of-the-road approach. The official recommendation by CDC, FDA and others that masks worn by the non-health-care professionals are ineffective is incorrect at three levels: In the logic, in the mechanics of transmission, and in the biology of viral entry. I. THE LOGIC Of course no mask, be it the tight-fitting NIOSH approved N95 respirator mask or the loosely worn surgical mask, provide perfect (“100%”) protection. But imperfect protection does not mean “completely useless”, much as a glass not full need not be empty: I would gladly accept a glass of water filled to 60 % when I am thirsty. Absence of evidence (of protection) is not evidence of absence. But in our binary world, the official message that surgical masks are “not effective” may have sent the wrong message: that they are absolutely useless. Sadly, with the black-and-white picture painted by officials, the discussion about the effectiveness of masks has been stifled, and with it the possibility of incentivizing industry to ramp up production of these 75 cents-a-piece protective devices. But with the declared goal to “flatten the curve” (and not to totally eliminate the virus) we have a “relative” as opposed to absolute goal, which places the notion of “partial protection” in a new light. In principle, one could compute the extent Y of flattening of the curve given a partial protection by X % as conferred by mask. But for that we need to first understand the mechanics and biology of transmission in detail. II. THE MECHANICS How viruses that cause airborne diseases are carried by droplets from person to person is a complicated, understudied matter. Droplets can (for this discussion) be crudely divided in two large categories based on size (FIG. 2): (a) Droplets below a diameter of 10 um (micrometer), the upper size limit for the definition of ‘aerosol’ (particles so light as to be able to float in the air). For brevity, let us call this category across rooms. What makes N95 facial masks different from the surgical masks is that the former are designed (as per regulatory requirement) to stop aerosols: they have to filter out 95% of droplets smaller than 0.3 um. (b) Droplets larger than 10um (micrometer), reaching 100um or more. Let us call these large particles “spray droplets” here. (For a more detailed discussion, see Nicas and Jones, 2009). Of course, droplets can be even larger, up to a size visible to the naked eye in the spray generated by coughing or sneezing (0.1 um diameter to above). Calculations by Xie et al suggest that if exhaled, the >0.1 um droplets may evaporate or fall to a surface within 2m, depending on size, air humidity and temperature. But coughing or sneezing can shoot them like projectiles out of the mouth with a “muzzle velocity” of 50 meters/second (for sneezing) or 10 m/s (for coughing), and droplets can reach distances as far as 6m away. If so, then the much mentioned “safe distance” of 6 feet in social encounters may not suffice — except you wear a (simple) mask –more on that later. Here is the central