Apr 3, 2020 10:21 PM
Ventilators Are No Panacea For Critically Ill COVID-19 Patients (data)
https://www.npr.org/sections/health-shot...9-patients
INTRO: Most coronavirus patients who end up on ventilators go on to die, according to several small studies from the U.S., China and Europe. And many of the patients who continue to live can't be taken off the mechanical breathing machines.
"It's very concerning to see how many patients who require ventilation do not make it out of the hospital," says Dr. Tiffany Osborn, a critical care specialist at Washington University in St. Louis who has been caring for coronavirus patients at Barnes-Jewish Hospital.
That concern is echoed by Negin Hajizadeh, a pulmonary critical care doctor at the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell on Long Island, N.Y. "We have had several patients between the hospitals across the Northwell system that have come off the breathing machine," Hajizadeh says. "But the vast majority are unable to."
The largest study so far to look at mortality among coronavirus patients on ventilators was done by the Intensive Care National Audit & Research Centre in London. It found that among 98 ventilated patients in the U.K., just 33 were discharged alive.
The numbers from a study of Wuhan, China, are even grimmer. Only 3 of 22 ventilated patients survived. And a study of 18 ventilated patients in Washington state found that nine were still alive when the study ended, but only six had recovered enough to breathe on their own. All the early research suggests that once coronavirus patients are placed on a ventilator, they will probably need to stay on it for weeks. And the longer patients remain on a breathing machine, the more likely they are to die... (MORE)
Coronavirus: country comparisons are pointless unless we account for these biases in testing (statistics)
https://theconversation.com/coronavirus-...ing-135464
INTRO: Suppose we wanted to estimate how many car owners there are in the UK and how many of those own a Ford Fiesta, but we only have data on those people who visited Ford car showrooms in the last year. If 10% of the showroom visitors owned a Fiesta, then, because of the bias in the sample, this would certainly overestimate the proportion of Ford Fiesta owners in the country.
Estimating death rates for people with COVID-19 is currently undertaken largely along the same lines. In the UK, for example, almost all testing of COVID-19 is performed on people already hospitalised with COVID-19 symptoms. At the time of writing, there are 29,474 confirmed COVID-19 cases (analogous to car owners visiting a showroom) of whom 2,352 have died (Ford Fiesta owners who visited a showroom). But it misses out all the people with mild or no symptoms.
Concluding that the death rate from COVID-19 is on average 8% (2,352 out of 29,474) ignores the many people with COVID-19 who are not hospitalised and have not died (analogous to car owners who did not visit a Ford showroom and who do not own a Ford Fiesta). It is therefore equivalent to making the mistake of concluding that 10% of all car owners own a Fiesta.
There are many prominent examples of this sort of conclusion. The Oxford COVID-19 Evidence Service have undertaken a thorough statistical analysis. They acknowledge potential selection bias, and add confidence intervals showing how big the error may be for the (potentially highly misleading) proportion of deaths among confirmed COVID-19 patients.
They note various factors that can result in wide national differences – for example the UK’s 8% (mean) “death rate” is very high compared to Germany’s 0.74%. These factors include different demographics, for example the number of elderly in a population, as well as how deaths are reported. For example, in some countries everybody who dies after having been diagnosed with COVID-19 is recorded as a COVID-19 death, even if the disease was not the actual cause, while other people may die from the virus without actually having been diagnosed with COVID-19.
However, the models fail to incorporate explicit causal explanations in their modelling that might enable us to make more meaningful inferences from the available data, including data on virus testing. [...] different countries may appear to have different death rates, but only because they have applied different sampling and reporting policies. It is not necessarily because they are managing the virus any better or that the virus has infected fewer or more people... (MORE - details)
https://www.npr.org/sections/health-shot...9-patients
INTRO: Most coronavirus patients who end up on ventilators go on to die, according to several small studies from the U.S., China and Europe. And many of the patients who continue to live can't be taken off the mechanical breathing machines.
"It's very concerning to see how many patients who require ventilation do not make it out of the hospital," says Dr. Tiffany Osborn, a critical care specialist at Washington University in St. Louis who has been caring for coronavirus patients at Barnes-Jewish Hospital.
That concern is echoed by Negin Hajizadeh, a pulmonary critical care doctor at the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell on Long Island, N.Y. "We have had several patients between the hospitals across the Northwell system that have come off the breathing machine," Hajizadeh says. "But the vast majority are unable to."
The largest study so far to look at mortality among coronavirus patients on ventilators was done by the Intensive Care National Audit & Research Centre in London. It found that among 98 ventilated patients in the U.K., just 33 were discharged alive.
The numbers from a study of Wuhan, China, are even grimmer. Only 3 of 22 ventilated patients survived. And a study of 18 ventilated patients in Washington state found that nine were still alive when the study ended, but only six had recovered enough to breathe on their own. All the early research suggests that once coronavirus patients are placed on a ventilator, they will probably need to stay on it for weeks. And the longer patients remain on a breathing machine, the more likely they are to die... (MORE)
Coronavirus: country comparisons are pointless unless we account for these biases in testing (statistics)
https://theconversation.com/coronavirus-...ing-135464
INTRO: Suppose we wanted to estimate how many car owners there are in the UK and how many of those own a Ford Fiesta, but we only have data on those people who visited Ford car showrooms in the last year. If 10% of the showroom visitors owned a Fiesta, then, because of the bias in the sample, this would certainly overestimate the proportion of Ford Fiesta owners in the country.
Estimating death rates for people with COVID-19 is currently undertaken largely along the same lines. In the UK, for example, almost all testing of COVID-19 is performed on people already hospitalised with COVID-19 symptoms. At the time of writing, there are 29,474 confirmed COVID-19 cases (analogous to car owners visiting a showroom) of whom 2,352 have died (Ford Fiesta owners who visited a showroom). But it misses out all the people with mild or no symptoms.
Concluding that the death rate from COVID-19 is on average 8% (2,352 out of 29,474) ignores the many people with COVID-19 who are not hospitalised and have not died (analogous to car owners who did not visit a Ford showroom and who do not own a Ford Fiesta). It is therefore equivalent to making the mistake of concluding that 10% of all car owners own a Fiesta.
There are many prominent examples of this sort of conclusion. The Oxford COVID-19 Evidence Service have undertaken a thorough statistical analysis. They acknowledge potential selection bias, and add confidence intervals showing how big the error may be for the (potentially highly misleading) proportion of deaths among confirmed COVID-19 patients.
They note various factors that can result in wide national differences – for example the UK’s 8% (mean) “death rate” is very high compared to Germany’s 0.74%. These factors include different demographics, for example the number of elderly in a population, as well as how deaths are reported. For example, in some countries everybody who dies after having been diagnosed with COVID-19 is recorded as a COVID-19 death, even if the disease was not the actual cause, while other people may die from the virus without actually having been diagnosed with COVID-19.
However, the models fail to incorporate explicit causal explanations in their modelling that might enable us to make more meaningful inferences from the available data, including data on virus testing. [...] different countries may appear to have different death rates, but only because they have applied different sampling and reporting policies. It is not necessarily because they are managing the virus any better or that the virus has infected fewer or more people... (MORE - details)
