Commentary

A very long time ago, when I ran clinical labs, one of the most ordered tests was the “sed rate” (aka ESR, the erythrocyte sedimentation rate). Easy, quick, and low cost, with high sensitivity but very low specificity. If the sed rate was normal, the patient probably did not have an infectious or inflammatory disease. If it was elevated, they probably did, but no telling what. Later, the C-reactive protein (CRP) test came into common use. Same general inferences: If the CRP was low, the patient was unlikely to have an inflammatory process; if high, they were sick, but we didn’t know what with.

Could the heart rate variability (HRV) score come to be thought of similarly? Much as the sed rate and CRP are sensitivity indicators of infectious or inflammatory diseases, might the HRV score be a sensitivity indicator for nervous system (central and autonomic) and cardiovascular (especially heart rhythm) malfunctions?

A substantial and relatively old body of heart rhythm literature ties HRV alterations to posttraumatic stress disorder, physician occupational stress, sleep disorders, depression, autonomic nervous system derangements, various cardiac arrhythmias, fatigue, overexertion, medications, and age itself.

More than 100 million Americans are now believed to use smartwatches or personal fitness monitors. Some 30%-40% of these devices measure HRV. So what? Credible research about this huge mass of accumulating data from “wearables” is lacking.
 

What is HRV?

HRV is the variation in time between each heartbeat, in milliseconds. HRV is influenced by the autonomic nervous system, perhaps reflecting sympathetic-parasympathetic balance. Some devices measure HRV 24/7. My Fitbit Inspire 2 reports only nighttime measures during 3 hours of sustained sleep. Most trackers report averages; some calculate the root mean squares; others calculate standard deviations. All fitness trackers warn not to use the data for medical purposes.

Normal values (reference ranges) for HRV begin at an average of 100 msec in the first decade of life and decline by approximately 10 msec per decade lived. At age 30-40, the average is 70 msec; age 60-70, it’s 40 msec; and at age 90-100, it’s 10 msec.

As a long-time lab guy, I used to teach proper use of lab tests. Fitness trackers are “lab tests” of a sort. We taught never to do a lab test unless you know what you are going to do with the result, no matter what it is. We also taught “never do anything just because you can.” Curiosity, we know, is a frequent driver of lab test ordering.

That underlying philosophy gives me a hard time when it comes to wearables. I have been enamored of watching my step count, active zone minutes, resting heart rate, active heart rate, various sleep scores, and breathing rate (and, of course, a manually entered early morning daily body weight) for several years. I even check my “readiness score” (a calculation using resting heart rate, recent sleep, recent active zone minutes, and perhaps HRV) each morning and adjust my behaviors accordingly.
 

Why monitor HRV?

But what should we do with HRV scores? Ignore them? Try to understand them, perhaps as a screening tool? Or monitor HRV for consistency or change? “Monitoring” is a proper and common use of lab tests.

Some say we should improve the HRV score by managing stress, getting regular exercise, eating a healthy diet, getting enough sleep, and not smoking or consuming excess alcohol. Duh! I do all of that anyway.

The claims that HRV is a “simple but powerful tool that can be used to track overall health and well-being” might turn out to be true. Proper study and sharing of data will enable that determination.

To advance understanding, I offer an n-of-1, a real-world personal anecdote about HRV.

I did not request the HRV function on my Fitbit Inspire 2. It simply appeared, and I ignored it for some time.

A year or two ago, I started noticing my HRV score every morning. Initially, I did not like to see my “low” score, until I learned that the reference range was dramatically affected by age and I was in my late 80s at the time. The vast majority of my HRV readings were in the range of 17 msec to 27 msec.

Last week, I was administered the new Moderna COVID-19 Spikevax vaccine and the old folks’ influenza vaccine simultaneously. In my case, side effects from each vaccine have been modest in the past, but I never previously had both administered at the same time. My immune response was, shall we say, robust. Chills, muscle aches, headache, fatigue, deltoid swelling, fitful sleep, and increased resting heart rate.

My nightly average HRV had been running between 17 msec and 35 msec for many months. WHOA! After the shots, my overnight HRV score plummeted from 24 msec to 10 msec, my lowest ever. Instant worry. The next day, it rebounded to 28 msec, and it has been in the high teens or low 20s since then.

Off to PubMed. A recent study of HRV on the second and 10th days after administering the Pfizer mRNA vaccine to 75 healthy volunteers found that the HRV on day 2 was dramatically lower than prevaccination levels and by day 10, it had returned to prevaccination levels. Some comfort there.

Another review article has reported a rapid fall and rapid rebound of HRV after COVID-19 vaccination. A 2010 report demonstrated a significant but not dramatic short-term lowering of HRV after influenza A vaccination and correlated it with CRP changes.

Some believe that the decline in HRV after vaccination reflects an increased immune response and sympathetic nervous activity.

I don’t plan to receive my flu and COVID vaccines on the same day again.

So, I went back to review what happened to my HRV when I had COVID in 2023. My HRV was 14 msec and 12 msec on the first 2 days of symptoms, and then returned to the 20 msec range.

I received the RSV vaccine this year without adverse effects, and my HRV scores were 29 msec, 33 msec, and 32 msec on the first 3 days after vaccination. Finally, after receiving a pneumococcal vaccine in 2023, I had no adverse effects, and my HRV scores on the 5 days after vaccination were indeterminate: 19 msec, 14 msec, 18 msec, 13 msec, and 17 msec.

Of course, correlation is not causation. Cause and effect remain undetermined. But I find these observations interesting for a potentially useful screening test.

George D. Lundberg, MD, is the Editor in Chief of Cancer Commons.

A version of this article first appeared on Medscape.com.

A very long time ago, when I ran clinical labs, one of the most ordered tests was the “sed rate” (aka ESR, the erythrocyte sedimentation rate). Easy, quick, and low cost, with high sensitivity but very low specificity. If the sed rate was normal, the patient probably did not have an infectious or inflammatory disease. If it was elevated, they probably did, but no telling what. Later, the C-reactive protein (CRP) test came into common use. Same general inferences: If the CRP was low, the patient was unlikely to have an inflammatory process; if high, they were sick, but we didn’t know what with.

Could the heart rate variability (HRV) score come to be thought of similarly? Much as the sed rate and CRP are sensitivity indicators of infectious or inflammatory diseases, might the HRV score be a sensitivity indicator for nervous system (central and autonomic) and cardiovascular (especially heart rhythm) malfunctions?

A substantial and relatively old body of heart rhythm literature ties HRV alterations to posttraumatic stress disorder, physician occupational stress, sleep disorders, depression, autonomic nervous system derangements, various cardiac arrhythmias, fatigue, overexertion, medications, and age itself.

More than 100 million Americans are now believed to use smartwatches or personal fitness monitors. Some 30%-40% of these devices measure HRV. So what? Credible research about this huge mass of accumulating data from “wearables” is lacking.
 

What is HRV?

HRV is the variation in time between each heartbeat, in milliseconds. HRV is influenced by the autonomic nervous system, perhaps reflecting sympathetic-parasympathetic balance. Some devices measure HRV 24/7. My Fitbit Inspire 2 reports only nighttime measures during 3 hours of sustained sleep. Most trackers report averages; some calculate the root mean squares; others calculate standard deviations. All fitness trackers warn not to use the data for medical purposes.

Normal values (reference ranges) for HRV begin at an average of 100 msec in the first decade of life and decline by approximately 10 msec per decade lived. At age 30-40, the average is 70 msec; age 60-70, it’s 40 msec; and at age 90-100, it’s 10 msec.

As a long-time lab guy, I used to teach proper use of lab tests. Fitness trackers are “lab tests” of a sort. We taught never to do a lab test unless you know what you are going to do with the result, no matter what it is. We also taught “never do anything just because you can.” Curiosity, we know, is a frequent driver of lab test ordering.

That underlying philosophy gives me a hard time when it comes to wearables. I have been enamored of watching my step count, active zone minutes, resting heart rate, active heart rate, various sleep scores, and breathing rate (and, of course, a manually entered early morning daily body weight) for several years. I even check my “readiness score” (a calculation using resting heart rate, recent sleep, recent active zone minutes, and perhaps HRV) each morning and adjust my behaviors accordingly.
 

Why monitor HRV?

But what should we do with HRV scores? Ignore them? Try to understand them, perhaps as a screening tool? Or monitor HRV for consistency or change? “Monitoring” is a proper and common use of lab tests.

Some say we should improve the HRV score by managing stress, getting regular exercise, eating a healthy diet, getting enough sleep, and not smoking or consuming excess alcohol. Duh! I do all of that anyway.

The claims that HRV is a “simple but powerful tool that can be used to track overall health and well-being” might turn out to be true. Proper study and sharing of data will enable that determination.

To advance understanding, I offer an n-of-1, a real-world personal anecdote about HRV.

I did not request the HRV function on my Fitbit Inspire 2. It simply appeared, and I ignored it for some time.

A year or two ago, I started noticing my HRV score every morning. Initially, I did not like to see my “low” score, until I learned that the reference range was dramatically affected by age and I was in my late 80s at the time. The vast majority of my HRV readings were in the range of 17 msec to 27 msec.

Last week, I was administered the new Moderna COVID-19 Spikevax vaccine and the old folks’ influenza vaccine simultaneously. In my case, side effects from each vaccine have been modest in the past, but I never previously had both administered at the same time. My immune response was, shall we say, robust. Chills, muscle aches, headache, fatigue, deltoid swelling, fitful sleep, and increased resting heart rate.

My nightly average HRV had been running between 17 msec and 35 msec for many months. WHOA! After the shots, my overnight HRV score plummeted from 24 msec to 10 msec, my lowest ever. Instant worry. The next day, it rebounded to 28 msec, and it has been in the high teens or low 20s since then.

Off to PubMed. A recent study of HRV on the second and 10th days after administering the Pfizer mRNA vaccine to 75 healthy volunteers found that the HRV on day 2 was dramatically lower than prevaccination levels and by day 10, it had returned to prevaccination levels. Some comfort there.

Another review article has reported a rapid fall and rapid rebound of HRV after COVID-19 vaccination. A 2010 report demonstrated a significant but not dramatic short-term lowering of HRV after influenza A vaccination and correlated it with CRP changes.

Some believe that the decline in HRV after vaccination reflects an increased immune response and sympathetic nervous activity.

I don’t plan to receive my flu and COVID vaccines on the same day again.

So, I went back to review what happened to my HRV when I had COVID in 2023. My HRV was 14 msec and 12 msec on the first 2 days of symptoms, and then returned to the 20 msec range.

I received the RSV vaccine this year without adverse effects, and my HRV scores were 29 msec, 33 msec, and 32 msec on the first 3 days after vaccination. Finally, after receiving a pneumococcal vaccine in 2023, I had no adverse effects, and my HRV scores on the 5 days after vaccination were indeterminate: 19 msec, 14 msec, 18 msec, 13 msec, and 17 msec.

Of course, correlation is not causation. Cause and effect remain undetermined. But I find these observations interesting for a potentially useful screening test.

George D. Lundberg, MD, is the Editor in Chief of Cancer Commons.

A version of this article first appeared on Medscape.com.

A very long time ago, when I ran clinical labs, one of the most ordered tests was the “sed rate” (aka ESR, the erythrocyte sedimentation rate). Easy, quick, and low cost, with high sensitivity but very low specificity. If the sed rate was normal, the patient probably did not have an infectious or inflammatory disease. If it was elevated, they probably did, but no telling what. Later, the C-reactive protein (CRP) test came into common use. Same general inferences: If the CRP was low, the patient was unlikely to have an inflammatory process; if high, they were sick, but we didn’t know what with.

Could the heart rate variability (HRV) score come to be thought of similarly? Much as the sed rate and CRP are sensitivity indicators of infectious or inflammatory diseases, might the HRV score be a sensitivity indicator for nervous system (central and autonomic) and cardiovascular (especially heart rhythm) malfunctions?

A substantial and relatively old body of heart rhythm literature ties HRV alterations to posttraumatic stress disorder, physician occupational stress, sleep disorders, depression, autonomic nervous system derangements, various cardiac arrhythmias, fatigue, overexertion, medications, and age itself.

More than 100 million Americans are now believed to use smartwatches or personal fitness monitors. Some 30%-40% of these devices measure HRV. So what? Credible research about this huge mass of accumulating data from “wearables” is lacking.
 

What is HRV?

HRV is the variation in time between each heartbeat, in milliseconds. HRV is influenced by the autonomic nervous system, perhaps reflecting sympathetic-parasympathetic balance. Some devices measure HRV 24/7. My Fitbit Inspire 2 reports only nighttime measures during 3 hours of sustained sleep. Most trackers report averages; some calculate the root mean squares; others calculate standard deviations. All fitness trackers warn not to use the data for medical purposes.

Normal values (reference ranges) for HRV begin at an average of 100 msec in the first decade of life and decline by approximately 10 msec per decade lived. At age 30-40, the average is 70 msec; age 60-70, it’s 40 msec; and at age 90-100, it’s 10 msec.

As a long-time lab guy, I used to teach proper use of lab tests. Fitness trackers are “lab tests” of a sort. We taught never to do a lab test unless you know what you are going to do with the result, no matter what it is. We also taught “never do anything just because you can.” Curiosity, we know, is a frequent driver of lab test ordering.

That underlying philosophy gives me a hard time when it comes to wearables. I have been enamored of watching my step count, active zone minutes, resting heart rate, active heart rate, various sleep scores, and breathing rate (and, of course, a manually entered early morning daily body weight) for several years. I even check my “readiness score” (a calculation using resting heart rate, recent sleep, recent active zone minutes, and perhaps HRV) each morning and adjust my behaviors accordingly.
 

Why monitor HRV?

But what should we do with HRV scores? Ignore them? Try to understand them, perhaps as a screening tool? Or monitor HRV for consistency or change? “Monitoring” is a proper and common use of lab tests.

Some say we should improve the HRV score by managing stress, getting regular exercise, eating a healthy diet, getting enough sleep, and not smoking or consuming excess alcohol. Duh! I do all of that anyway.

The claims that HRV is a “simple but powerful tool that can be used to track overall health and well-being” might turn out to be true. Proper study and sharing of data will enable that determination.

To advance understanding, I offer an n-of-1, a real-world personal anecdote about HRV.

I did not request the HRV function on my Fitbit Inspire 2. It simply appeared, and I ignored it for some time.

A year or two ago, I started noticing my HRV score every morning. Initially, I did not like to see my “low” score, until I learned that the reference range was dramatically affected by age and I was in my late 80s at the time. The vast majority of my HRV readings were in the range of 17 msec to 27 msec.

Last week, I was administered the new Moderna COVID-19 Spikevax vaccine and the old folks’ influenza vaccine simultaneously. In my case, side effects from each vaccine have been modest in the past, but I never previously had both administered at the same time. My immune response was, shall we say, robust. Chills, muscle aches, headache, fatigue, deltoid swelling, fitful sleep, and increased resting heart rate.

My nightly average HRV had been running between 17 msec and 35 msec for many months. WHOA! After the shots, my overnight HRV score plummeted from 24 msec to 10 msec, my lowest ever. Instant worry. The next day, it rebounded to 28 msec, and it has been in the high teens or low 20s since then.

Off to PubMed. A recent study of HRV on the second and 10th days after administering the Pfizer mRNA vaccine to 75 healthy volunteers found that the HRV on day 2 was dramatically lower than prevaccination levels and by day 10, it had returned to prevaccination levels. Some comfort there.

Another review article has reported a rapid fall and rapid rebound of HRV after COVID-19 vaccination. A 2010 report demonstrated a significant but not dramatic short-term lowering of HRV after influenza A vaccination and correlated it with CRP changes.

Some believe that the decline in HRV after vaccination reflects an increased immune response and sympathetic nervous activity.

I don’t plan to receive my flu and COVID vaccines on the same day again.

So, I went back to review what happened to my HRV when I had COVID in 2023. My HRV was 14 msec and 12 msec on the first 2 days of symptoms, and then returned to the 20 msec range.

I received the RSV vaccine this year without adverse effects, and my HRV scores were 29 msec, 33 msec, and 32 msec on the first 3 days after vaccination. Finally, after receiving a pneumococcal vaccine in 2023, I had no adverse effects, and my HRV scores on the 5 days after vaccination were indeterminate: 19 msec, 14 msec, 18 msec, 13 msec, and 17 msec.

Of course, correlation is not causation. Cause and effect remain undetermined. But I find these observations interesting for a potentially useful screening test.

George D. Lundberg, MD, is the Editor in Chief of Cancer Commons.

A version of this article first appeared on Medscape.com.

It’s upper respiratory infection (URI) season. The following is a clinical scenario drawn from my own practice. I’ll tell you what I plan to do, but I’m most interested in crowdsourcing a response from all of you to collectively determine best practice. So please answer the polling questions and contribute your thoughts in the comments, whether you agree or disagree with me.

The patient

The patient is a 69-year-old woman with a 3-day history of cough, nasal congestion, malaise, tactile fever, and poor appetite. She has no sick contacts. She denies dyspnea, presyncope, and chest pain. She has tried guaifenesin and ibuprofen for her symptoms, which helped a little.

She is up to date on immunizations, including four doses of COVID-19 vaccine and the influenza vaccine, which she received 2 months ago.

The patient has a history of heart failure with reduced ejection fraction, coronary artery disease, hypertension, chronic kidney disease stage 3aA2, obesity, and osteoarthritis. Current medications include atorvastatin, losartan, metoprolol, and aspirin.

Her weight is stable at 212 lb, and her vital signs today are:

• Temperature: 37.5° C
• Pulse: 60 beats/min
• Blood pressure: 150/88 mm Hg
• Respiration rate: 14 breaths/min
• SpO₂: 93% on room air

What information is most critical before deciding on management?

Your peers chose: 

• The patient’s history of viral URIs

 14%

 

• Whether her cough is productive and the color of the sputum

 38%

 

• How well this season’s flu vaccine matches circulating influenza viruses

 8%

 

• Local epidemiology of major viral pathogens (e.g., SARS-CoV-2, influenza, RSV)

 40%
 

Dr. Vega’s take

To provide the best care for our patients when they are threatened with multiple viral upper respiratory pathogens, it is imperative that clinicians have some idea regarding the epidemiology of viral infections, with as much local data as possible. This knowledge will help direct appropriate testing and treatment.

Modern viral molecular testing platforms are highly accurate, but they are not infallible. Small flaws in specificity and sensitivity of testing are magnified when community viral circulation is low. In a U.K. study conducted during a period of low COVID-19 prevalence, the positive predictive value of reverse-transcriptase polymerase chain reaction (RT-PCR) testing was just 16%. Although the negative predictive value was much higher, the false-positive rate of testing was still 0.5%. The authors of the study describe important potential consequences of false-positive results, such as being temporarily removed from an organ transplant list and unnecessary contact tracing.
 

Testing and treatment

Your county public health department maintains a website describing local activity of SARS-CoV-2 and influenza. Both viruses are in heavy circulation now.

What is the next best step in this patient’s management?

Your peers chose: 

• Treat empirically with ritonavir-boosted nirmatrelvir

 7%

 

• Treat empirically with oseltamivir or baloxavir

 14%

 

• Perform lab-based multiplex RT-PCR testing and wait to treat on the basis of results

 34%

 

• Perform rapid nucleic acid amplification testing (NAAT) and treat on the basis of results

 45%

Every practice has different resources and should use the best means available to treat patients. Ideally, this patient would undergo rapid NAAT with results available within 30 minutes. Test results will help guide not only treatment decisions but also infection-control measures.

The Infectious Diseases Society of America has provided updates for testing for URIs since the onset of the COVID-19 pandemic. Both laboratory-based and point-of-care rapid NAATs are recommended for testing. Rapid NAATs have been demonstrated to have a sensitivity of 96% and specificity of 100% in the detection of SARS-CoV-2. Obviously, they also offer a highly efficient means to make treatment and isolation decisions.

There are multiple platforms for molecular testing available. Laboratory-based platforms can test for dozens of potential pathogens, including bacteria. Rapid NAATs often have the ability to test for SARS-CoV-2, influenza, and respiratory syncytial virus (RSV). This functionality is important, because these infections generally are difficult to discriminate on the basis of clinical information alone.

The IDSA clearly recognizes the challenges of trying to manage cases of URI. For example, they state that testing of the anterior nares (AN) or oropharynx (OP) is acceptable, even though testing from the nasopharynx offers increased sensitivity. However, testing at the AN/OP allows for patient self-collection of samples, which is also recommended as an option by the IDSA. In an analysis of six cohort studies, the pooled sensitivity of patient-collected nasopharyngeal samples from the AN/OP was 88%, whereas the respective value for samples taken by health care providers was 95%.

The U.S. Centers for Disease Control and Prevention also provides recommendations for the management of patients with acute upper respiratory illness. Patients who are sick enough to be hospitalized should be tested at least for SARS-CoV-2 and influenza using molecular assays. Outpatients should be tested for SARS-CoV-2 with either molecular or antigen testing, and influenza testing should be offered if the findings will change decisions regarding treatment or isolation. Practically speaking, the recommendations for influenza testing mean that most individuals should be tested, including patients at high risk for complications of influenza and those who might have exposure to individuals at high risk.

Treatment of COVID-19 should only be provided in cases of a positive test within 5 days of symptom onset. However, clinicians may treat patients with anti-influenza medications presumptively if test results are not immediately available and the patient has worsening symptoms or is in a group at high risk for complications.

What are some of the challenges that you have faced during the COVID-19 pandemic regarding the management of patients with acute URIs? What have you found in terms of solutions, and where do gaps in quality of care persist? Please add your comments. I will review and circle back with a response. Thank you!

A version of this article first appeared on Medscape.com.

It’s upper respiratory infection (URI) season. The following is a clinical scenario drawn from my own practice. I’ll tell you what I plan to do, but I’m most interested in crowdsourcing a response from all of you to collectively determine best practice. So please answer the polling questions and contribute your thoughts in the comments, whether you agree or disagree with me.

The patient

The patient is a 69-year-old woman with a 3-day history of cough, nasal congestion, malaise, tactile fever, and poor appetite. She has no sick contacts. She denies dyspnea, presyncope, and chest pain. She has tried guaifenesin and ibuprofen for her symptoms, which helped a little.

She is up to date on immunizations, including four doses of COVID-19 vaccine and the influenza vaccine, which she received 2 months ago.

The patient has a history of heart failure with reduced ejection fraction, coronary artery disease, hypertension, chronic kidney disease stage 3aA2, obesity, and osteoarthritis. Current medications include atorvastatin, losartan, metoprolol, and aspirin.

Her weight is stable at 212 lb, and her vital signs today are:

• Temperature: 37.5° C
• Pulse: 60 beats/min
• Blood pressure: 150/88 mm Hg
• Respiration rate: 14 breaths/min
• SpO₂: 93% on room air

What information is most critical before deciding on management?

Your peers chose: 

• The patient’s history of viral URIs

 14%

 

• Whether her cough is productive and the color of the sputum

 38%

 

• How well this season’s flu vaccine matches circulating influenza viruses

 8%

 

• Local epidemiology of major viral pathogens (e.g., SARS-CoV-2, influenza, RSV)

 40%
 

Dr. Vega’s take

To provide the best care for our patients when they are threatened with multiple viral upper respiratory pathogens, it is imperative that clinicians have some idea regarding the epidemiology of viral infections, with as much local data as possible. This knowledge will help direct appropriate testing and treatment.

Modern viral molecular testing platforms are highly accurate, but they are not infallible. Small flaws in specificity and sensitivity of testing are magnified when community viral circulation is low. In a U.K. study conducted during a period of low COVID-19 prevalence, the positive predictive value of reverse-transcriptase polymerase chain reaction (RT-PCR) testing was just 16%. Although the negative predictive value was much higher, the false-positive rate of testing was still 0.5%. The authors of the study describe important potential consequences of false-positive results, such as being temporarily removed from an organ transplant list and unnecessary contact tracing.
 

Testing and treatment

Your county public health department maintains a website describing local activity of SARS-CoV-2 and influenza. Both viruses are in heavy circulation now.

What is the next best step in this patient’s management?

Your peers chose: 

• Treat empirically with ritonavir-boosted nirmatrelvir

 7%

 

• Treat empirically with oseltamivir or baloxavir

 14%

 

• Perform lab-based multiplex RT-PCR testing and wait to treat on the basis of results

 34%

 

• Perform rapid nucleic acid amplification testing (NAAT) and treat on the basis of results

 45%

Every practice has different resources and should use the best means available to treat patients. Ideally, this patient would undergo rapid NAAT with results available within 30 minutes. Test results will help guide not only treatment decisions but also infection-control measures.

The Infectious Diseases Society of America has provided updates for testing for URIs since the onset of the COVID-19 pandemic. Both laboratory-based and point-of-care rapid NAATs are recommended for testing. Rapid NAATs have been demonstrated to have a sensitivity of 96% and specificity of 100% in the detection of SARS-CoV-2. Obviously, they also offer a highly efficient means to make treatment and isolation decisions.

There are multiple platforms for molecular testing available. Laboratory-based platforms can test for dozens of potential pathogens, including bacteria. Rapid NAATs often have the ability to test for SARS-CoV-2, influenza, and respiratory syncytial virus (RSV). This functionality is important, because these infections generally are difficult to discriminate on the basis of clinical information alone.

The IDSA clearly recognizes the challenges of trying to manage cases of URI. For example, they state that testing of the anterior nares (AN) or oropharynx (OP) is acceptable, even though testing from the nasopharynx offers increased sensitivity. However, testing at the AN/OP allows for patient self-collection of samples, which is also recommended as an option by the IDSA. In an analysis of six cohort studies, the pooled sensitivity of patient-collected nasopharyngeal samples from the AN/OP was 88%, whereas the respective value for samples taken by health care providers was 95%.

The U.S. Centers for Disease Control and Prevention also provides recommendations for the management of patients with acute upper respiratory illness. Patients who are sick enough to be hospitalized should be tested at least for SARS-CoV-2 and influenza using molecular assays. Outpatients should be tested for SARS-CoV-2 with either molecular or antigen testing, and influenza testing should be offered if the findings will change decisions regarding treatment or isolation. Practically speaking, the recommendations for influenza testing mean that most individuals should be tested, including patients at high risk for complications of influenza and those who might have exposure to individuals at high risk.

Treatment of COVID-19 should only be provided in cases of a positive test within 5 days of symptom onset. However, clinicians may treat patients with anti-influenza medications presumptively if test results are not immediately available and the patient has worsening symptoms or is in a group at high risk for complications.

What are some of the challenges that you have faced during the COVID-19 pandemic regarding the management of patients with acute URIs? What have you found in terms of solutions, and where do gaps in quality of care persist? Please add your comments. I will review and circle back with a response. Thank you!

A version of this article first appeared on Medscape.com.

It’s upper respiratory infection (URI) season. The following is a clinical scenario drawn from my own practice. I’ll tell you what I plan to do, but I’m most interested in crowdsourcing a response from all of you to collectively determine best practice. So please answer the polling questions and contribute your thoughts in the comments, whether you agree or disagree with me.

The patient

The patient is a 69-year-old woman with a 3-day history of cough, nasal congestion, malaise, tactile fever, and poor appetite. She has no sick contacts. She denies dyspnea, presyncope, and chest pain. She has tried guaifenesin and ibuprofen for her symptoms, which helped a little.

She is up to date on immunizations, including four doses of COVID-19 vaccine and the influenza vaccine, which she received 2 months ago.

The patient has a history of heart failure with reduced ejection fraction, coronary artery disease, hypertension, chronic kidney disease stage 3aA2, obesity, and osteoarthritis. Current medications include atorvastatin, losartan, metoprolol, and aspirin.

Her weight is stable at 212 lb, and her vital signs today are:

• Temperature: 37.5° C
• Pulse: 60 beats/min
• Blood pressure: 150/88 mm Hg
• Respiration rate: 14 breaths/min
• SpO₂: 93% on room air

What information is most critical before deciding on management?

Your peers chose: 

• The patient’s history of viral URIs

 14%

 

• Whether her cough is productive and the color of the sputum

 38%

 

• How well this season’s flu vaccine matches circulating influenza viruses

 8%

 

• Local epidemiology of major viral pathogens (e.g., SARS-CoV-2, influenza, RSV)

 40%
 

Dr. Vega’s take

To provide the best care for our patients when they are threatened with multiple viral upper respiratory pathogens, it is imperative that clinicians have some idea regarding the epidemiology of viral infections, with as much local data as possible. This knowledge will help direct appropriate testing and treatment.

Modern viral molecular testing platforms are highly accurate, but they are not infallible. Small flaws in specificity and sensitivity of testing are magnified when community viral circulation is low. In a U.K. study conducted during a period of low COVID-19 prevalence, the positive predictive value of reverse-transcriptase polymerase chain reaction (RT-PCR) testing was just 16%. Although the negative predictive value was much higher, the false-positive rate of testing was still 0.5%. The authors of the study describe important potential consequences of false-positive results, such as being temporarily removed from an organ transplant list and unnecessary contact tracing.
 

Testing and treatment

Your county public health department maintains a website describing local activity of SARS-CoV-2 and influenza. Both viruses are in heavy circulation now.

What is the next best step in this patient’s management?

Your peers chose: 

• Treat empirically with ritonavir-boosted nirmatrelvir

 7%

 

• Treat empirically with oseltamivir or baloxavir

 14%

 

• Perform lab-based multiplex RT-PCR testing and wait to treat on the basis of results

 34%

 

• Perform rapid nucleic acid amplification testing (NAAT) and treat on the basis of results

 45%

Every practice has different resources and should use the best means available to treat patients. Ideally, this patient would undergo rapid NAAT with results available within 30 minutes. Test results will help guide not only treatment decisions but also infection-control measures.

The Infectious Diseases Society of America has provided updates for testing for URIs since the onset of the COVID-19 pandemic. Both laboratory-based and point-of-care rapid NAATs are recommended for testing. Rapid NAATs have been demonstrated to have a sensitivity of 96% and specificity of 100% in the detection of SARS-CoV-2. Obviously, they also offer a highly efficient means to make treatment and isolation decisions.

There are multiple platforms for molecular testing available. Laboratory-based platforms can test for dozens of potential pathogens, including bacteria. Rapid NAATs often have the ability to test for SARS-CoV-2, influenza, and respiratory syncytial virus (RSV). This functionality is important, because these infections generally are difficult to discriminate on the basis of clinical information alone.

The IDSA clearly recognizes the challenges of trying to manage cases of URI. For example, they state that testing of the anterior nares (AN) or oropharynx (OP) is acceptable, even though testing from the nasopharynx offers increased sensitivity. However, testing at the AN/OP allows for patient self-collection of samples, which is also recommended as an option by the IDSA. In an analysis of six cohort studies, the pooled sensitivity of patient-collected nasopharyngeal samples from the AN/OP was 88%, whereas the respective value for samples taken by health care providers was 95%.

The U.S. Centers for Disease Control and Prevention also provides recommendations for the management of patients with acute upper respiratory illness. Patients who are sick enough to be hospitalized should be tested at least for SARS-CoV-2 and influenza using molecular assays. Outpatients should be tested for SARS-CoV-2 with either molecular or antigen testing, and influenza testing should be offered if the findings will change decisions regarding treatment or isolation. Practically speaking, the recommendations for influenza testing mean that most individuals should be tested, including patients at high risk for complications of influenza and those who might have exposure to individuals at high risk.

Treatment of COVID-19 should only be provided in cases of a positive test within 5 days of symptom onset. However, clinicians may treat patients with anti-influenza medications presumptively if test results are not immediately available and the patient has worsening symptoms or is in a group at high risk for complications.

What are some of the challenges that you have faced during the COVID-19 pandemic regarding the management of patients with acute URIs? What have you found in terms of solutions, and where do gaps in quality of care persist? Please add your comments. I will review and circle back with a response. Thank you!

A version of this article first appeared on Medscape.com.

The XXXIII Olympic Summer Games will take place in Paris, France, from July 26 to August 11, 2024, and a variety of outdoor sporting events (eg, surfing, cycling, beach volleyball) will be included. Participation in the Olympic Games is a distinct honor for athletes selected to compete at the highest level in their sports.

Because of their training regimens and lifestyles, Olympic athletes face unique health risks. One such risk appears to be skin cancer, a substantial contributor to the global burden of disease. Taken together, basal cell carcinoma, squamous cell carcinoma, and melanoma account for 6.7 million cases of skin cancer worldwide. Squamous cell carcinoma and malignant skin melanoma were attributed to 1.2 million and 1.7 million life-years lost to disability, respectively.¹

Olympic athletes are at increased risk for sunburn from UVA and UVB radiation, placing them at higher risk for both melanoma and nonmelanoma skin cancers.^2,3 Sweating increases skin photosensitivity, sportswear often offers inadequate sun protection, and sustained high-intensity exercise itself has an immunosuppressive effect. Athletes competing in skiing and snowboarding events also receive radiation reflected off snow and ice at high altitudes.³ In fact, skiing without sunscreen at 11,000-feet above sea level can induce sunburn after only 6 minutes of exposure.⁴ Moreover, sweat, water immersion, and friction can decrease the effectiveness of topical sunscreens.⁵

World-class athletes appear to be exposed to UV radiation to a substantially higher degree than the general public. In an analysis of 144 events at the 2020 XXXII Olympic Summer Games in Tokyo, Japan, the highest exposure assessments were for women’s tennis, men’s golf, and men’s road cycling.⁶ In a 2020 study (N=240), the rates of sunburn were as high as 76.7% among Olympic sailors, elite surfers, and windsurfers, with more than one-quarter of athletes reporting sunburn that lasted longer than 24 hours.⁷ An earlier study reported that professional cyclists were exposed to UV radiation during a single race that exceeded the personal exposure limit by 30 times.⁸

Regrettably, the high level of sun exposure experienced by elite athletes is compounded by their low rate of sunscreen use. In a 2020 survey of 95 Olympians and super sprint triathletes, approximately half rarely used sunscreen, with 1 in 5 athletes never using sunscreen during training.⁹ In another study of 246 elite athletes in surfing, windsurfing, and sailing, nearly half used inadequate sun protection and nearly one-quarter reported never using sunscreen.¹⁰ Surprisingly, as many as 90% of Olympic athletes and super sprint competitors understood the importance of using sunscreen.⁹

What can we learn from these findings?

First, elite athletes remain at high risk for skin cancer because of training regimens, occupational environmental hazards, and other requirements of their sport. Second, despite awareness of the risks of UV radiation exposure, Olympic athletes utilize inadequate photoprotection. Athletes with darker skin are still at risk for skin cancer, photoaging, and pigmentation disorders—indicating a need for photoprotective behaviors in athletes of all skin types.¹¹

Therefore, efforts to promote adequate sunscreen use and understanding of the consequences of UV radiation may need to be prioritized earlier in athletes’ careers and implemented according to evidence-based guidelines. For example, the Stanford University Network for Sun Protection, Outreach, Research and Teamwork (Sunsport) provided information about skin cancer risk and prevention by educating student-athletes, coaches, and trainers in the National Collegiate Athletic Association in the United States. The Sunsport initiative led to a dramatic increase in sunscreen use by student-athletes as well as increased knowledge and discussion of skin cancer risk.¹²

The XXXIII Olympic Summer Games will take place in Paris, France, from July 26 to August 11, 2024, and a variety of outdoor sporting events (eg, surfing, cycling, beach volleyball) will be included. Participation in the Olympic Games is a distinct honor for athletes selected to compete at the highest level in their sports.

Because of their training regimens and lifestyles, Olympic athletes face unique health risks. One such risk appears to be skin cancer, a substantial contributor to the global burden of disease. Taken together, basal cell carcinoma, squamous cell carcinoma, and melanoma account for 6.7 million cases of skin cancer worldwide. Squamous cell carcinoma and malignant skin melanoma were attributed to 1.2 million and 1.7 million life-years lost to disability, respectively.¹

Olympic athletes are at increased risk for sunburn from UVA and UVB radiation, placing them at higher risk for both melanoma and nonmelanoma skin cancers.^2,3 Sweating increases skin photosensitivity, sportswear often offers inadequate sun protection, and sustained high-intensity exercise itself has an immunosuppressive effect. Athletes competing in skiing and snowboarding events also receive radiation reflected off snow and ice at high altitudes.³ In fact, skiing without sunscreen at 11,000-feet above sea level can induce sunburn after only 6 minutes of exposure.⁴ Moreover, sweat, water immersion, and friction can decrease the effectiveness of topical sunscreens.⁵

World-class athletes appear to be exposed to UV radiation to a substantially higher degree than the general public. In an analysis of 144 events at the 2020 XXXII Olympic Summer Games in Tokyo, Japan, the highest exposure assessments were for women’s tennis, men’s golf, and men’s road cycling.⁶ In a 2020 study (N=240), the rates of sunburn were as high as 76.7% among Olympic sailors, elite surfers, and windsurfers, with more than one-quarter of athletes reporting sunburn that lasted longer than 24 hours.⁷ An earlier study reported that professional cyclists were exposed to UV radiation during a single race that exceeded the personal exposure limit by 30 times.⁸

Regrettably, the high level of sun exposure experienced by elite athletes is compounded by their low rate of sunscreen use. In a 2020 survey of 95 Olympians and super sprint triathletes, approximately half rarely used sunscreen, with 1 in 5 athletes never using sunscreen during training.⁹ In another study of 246 elite athletes in surfing, windsurfing, and sailing, nearly half used inadequate sun protection and nearly one-quarter reported never using sunscreen.¹⁰ Surprisingly, as many as 90% of Olympic athletes and super sprint competitors understood the importance of using sunscreen.⁹

What can we learn from these findings?

First, elite athletes remain at high risk for skin cancer because of training regimens, occupational environmental hazards, and other requirements of their sport. Second, despite awareness of the risks of UV radiation exposure, Olympic athletes utilize inadequate photoprotection. Athletes with darker skin are still at risk for skin cancer, photoaging, and pigmentation disorders—indicating a need for photoprotective behaviors in athletes of all skin types.¹¹

Therefore, efforts to promote adequate sunscreen use and understanding of the consequences of UV radiation may need to be prioritized earlier in athletes’ careers and implemented according to evidence-based guidelines. For example, the Stanford University Network for Sun Protection, Outreach, Research and Teamwork (Sunsport) provided information about skin cancer risk and prevention by educating student-athletes, coaches, and trainers in the National Collegiate Athletic Association in the United States. The Sunsport initiative led to a dramatic increase in sunscreen use by student-athletes as well as increased knowledge and discussion of skin cancer risk.¹²

The XXXIII Olympic Summer Games will take place in Paris, France, from July 26 to August 11, 2024, and a variety of outdoor sporting events (eg, surfing, cycling, beach volleyball) will be included. Participation in the Olympic Games is a distinct honor for athletes selected to compete at the highest level in their sports.

Because of their training regimens and lifestyles, Olympic athletes face unique health risks. One such risk appears to be skin cancer, a substantial contributor to the global burden of disease. Taken together, basal cell carcinoma, squamous cell carcinoma, and melanoma account for 6.7 million cases of skin cancer worldwide. Squamous cell carcinoma and malignant skin melanoma were attributed to 1.2 million and 1.7 million life-years lost to disability, respectively.¹

Olympic athletes are at increased risk for sunburn from UVA and UVB radiation, placing them at higher risk for both melanoma and nonmelanoma skin cancers.^2,3 Sweating increases skin photosensitivity, sportswear often offers inadequate sun protection, and sustained high-intensity exercise itself has an immunosuppressive effect. Athletes competing in skiing and snowboarding events also receive radiation reflected off snow and ice at high altitudes.³ In fact, skiing without sunscreen at 11,000-feet above sea level can induce sunburn after only 6 minutes of exposure.⁴ Moreover, sweat, water immersion, and friction can decrease the effectiveness of topical sunscreens.⁵

World-class athletes appear to be exposed to UV radiation to a substantially higher degree than the general public. In an analysis of 144 events at the 2020 XXXII Olympic Summer Games in Tokyo, Japan, the highest exposure assessments were for women’s tennis, men’s golf, and men’s road cycling.⁶ In a 2020 study (N=240), the rates of sunburn were as high as 76.7% among Olympic sailors, elite surfers, and windsurfers, with more than one-quarter of athletes reporting sunburn that lasted longer than 24 hours.⁷ An earlier study reported that professional cyclists were exposed to UV radiation during a single race that exceeded the personal exposure limit by 30 times.⁸

Regrettably, the high level of sun exposure experienced by elite athletes is compounded by their low rate of sunscreen use. In a 2020 survey of 95 Olympians and super sprint triathletes, approximately half rarely used sunscreen, with 1 in 5 athletes never using sunscreen during training.⁹ In another study of 246 elite athletes in surfing, windsurfing, and sailing, nearly half used inadequate sun protection and nearly one-quarter reported never using sunscreen.¹⁰ Surprisingly, as many as 90% of Olympic athletes and super sprint competitors understood the importance of using sunscreen.⁹

What can we learn from these findings?

First, elite athletes remain at high risk for skin cancer because of training regimens, occupational environmental hazards, and other requirements of their sport. Second, despite awareness of the risks of UV radiation exposure, Olympic athletes utilize inadequate photoprotection. Athletes with darker skin are still at risk for skin cancer, photoaging, and pigmentation disorders—indicating a need for photoprotective behaviors in athletes of all skin types.¹¹

Therefore, efforts to promote adequate sunscreen use and understanding of the consequences of UV radiation may need to be prioritized earlier in athletes’ careers and implemented according to evidence-based guidelines. For example, the Stanford University Network for Sun Protection, Outreach, Research and Teamwork (Sunsport) provided information about skin cancer risk and prevention by educating student-athletes, coaches, and trainers in the National Collegiate Athletic Association in the United States. The Sunsport initiative led to a dramatic increase in sunscreen use by student-athletes as well as increased knowledge and discussion of skin cancer risk.¹²

More than 1 in 10 Americans over age 60 years will be diagnosed with cancer, according to the National Cancer Institute, making screening for the disease in older patients imperative. Much of the burden of cancer screening falls on primary care physicians. This news organization spoke recently with William L. Dahut, MD, chief scientific officer of the American Cancer Society, about the particular challenges of screening in older patients.

Question: How much does cancer screening change with age? What are the considerations for clinicians – what risks and comorbidities are important to consider in older populations?

Answer: We at the American Cancer Society are giving a lot of thought to how to help primary care practices keep up with screening, particularly with respect to guidelines, but also best practices where judgment is required, such as cancer screening in their older patients.

We’ve had a lot of conversations recently about cancer risk in the young, largely because data show rates are going up for colorectal and breast cancer in this population. But it’s not one size fits all. Screening for young women who have a BRCA gene, if they have dense breasts, or if they have a strong family history of breast cancer should be different from those who are at average risk of the disease.

But statistically, there are about 15 per 100,000 breast cancer diagnoses in women under the age of 40 while over the age of 65 it’s 443 per 100,000. So, the risk significantly increases with age but we should not have an arbitrary cut-off. The life expectancy of a woman at age 75 is about 13.5 years. If you’re over the age of 70 or 75, then it’s going to be comorbidities that you look at, as well as individual patient decisions. Patients may say, “I don’t want to ever go through a mammogram again, because I don’t want to have a biopsy again, and I’m not going to get treated.” Or they may say, “My mom died of metastatic breast cancer when she was 82 and I want to know.”
 

Q: How should primary care physicians interpret conflicting guidance from the major medical groups? For example, the American College of Gastroenterology and your own organization recommend colorectal cancer screening start at age 45 now. But the American College of Physicians recently came out and said 50. What is a well-meaning primary care physician supposed to do?

A: We make more of guideline differences than we should. Sometimes guideline differences aren’t a reflection of different judgments, but rather what data were available when the most recent update took place. For colorectal cancer screening, the ACS dropped the age to begin screening to 45 in 2018 based on a very careful consideration of disease burden data and within several years most other guideline developers reached the same conclusion.

However, I think it’s good for family practice and internal medicine doctors to know that significant GI symptoms in a young patient could be colorectal cancer. It’s not as if nobody sees a 34-year-old or 27-year-old with colorectal cancer. They should be aware that if something goes away in a day or two, that’s fine, but persistent GI symptoms need a cancer workup – colonoscopy or referral to a gastroenterologist. So that’s why I think age 45 is the time when folks should begin screening.
 

Q: What are the medical-legal issues for a physician who is trying to follow guideline-based care when there are different guidelines?

A: Any physician can say, “We follow the guidelines of this particular organization.” I don’t think anyone can say that an organization’s guidelines are malpractice. For individual physicians, following a set of office-based guidelines will hopefully keep them out of legal difficulty.

Q: What are the risks of overscreening, especially in breast cancer where false positives may result in invasive testing?

A: What people think of as overscreening takes a number of different forms. What one guideline would imply is overscreening is recommended screening by another guideline. I think we would all agree that in an average-risk population, beginning screening before it is recommended would be overscreening, and continuing screening when a patient has life-limiting comorbidities would constitute overscreening. Screening too frequently can constitute overscreening.

For example, many women report that their doctors still are advising a baseline mammogram at age 35. Most guideline-developing organizations would regard this as overscreening in an average-risk population.

I think we are also getting better, certainly in prostate cancer, about knowing who needs to be treated and not treated. There are a lot of cancers that would have been treated 20-30 years ago but now are being safely followed with PSA and MRI. We may be able to get to that point with breast cancer over time, too.
 

Q: Are you saying that there may be breast cancers for which active surveillance is appropriate? Is that already the case?

A: We’re not there yet. I think some of the DCIS breast cancers are part of the discussion on whether hormonal treatment or surgeries are done. I think people do have those discussions in the context of morbidity and life expectancy. Over time, we’re likely to have more cancers for which we won’t need surgical treatments.
 

Q: Why did the American Cancer Society change the upper limit for lung cancer screening from 75 to 80 years of age?

 A: For an individual older than 65, screening will now continue until the patient is 80, assuming the patient is in good health. According to the previous guideline, if a patient was 65 and more than 15 years beyond smoking cessation, then screening would end. This is exactly the time when we see lung cancers increase in the population and so a curable lung cancer would not previously have been detected by a screening CT scan. *  

Q: What role do the multicancer blood and DNA tests play in screening now?

A: As you know, the Exact Sciences Cologuard test is already included in major guidelines for colorectal cancer screening and covered by insurance. Our philosophy on multicancer early detection tests is that we’re supportive of Medicare reimbursement when two things occur: 1. When we know there’s clinical benefit, and 2. When the test has been approved by the FDA.

The multicancer early detection tests in development and undergoing prospective research would not now replace screening for the cancers with established screening programs, but if they are shown to have clinical utility for the cancers in their panel, we would be able to reduce deaths from cancers that mostly are diagnosed at late stages and have poor prognoses.

There’s going to be a need for expertise in primary care practices to help interpret the tests. These are new questions, which are well beyond what even the typical oncologist is trained in, much less primary care physicians. We and other organizations are working on providing those answers.
 

Q: While we’re on the subject of the future, how do you envision AI helping or hindering cancer screening specifically in primary care?

A: I think AI is going to help things for a couple of reasons. The ability of AI is to get through data quickly and get you information that’s personalized and useful. If AI tools could let a patient know their individual risk of a cancer in the near and long term, that would help the primary care doctor screen in an individualized way. I think AI is going to be able to improve both diagnostic radiology and pathology, and could make a very big difference in settings outside of large cancer centers that operate at high volume every day. The data look very promising for AI to contribute to risk estimation by operating like a second reader in imaging and pathology.
 

Q: Anything else you’d like to say on this subject that clinicians should know?

A: The questions about whether or not patients should be screened is being pushed on family practice doctors and internists and these questions require a relationship with the patient. A hard stopping point at age 70 when lots of people will live 20 years or more doesn’t make sense.

There’s very little data from randomized clinical trials of screening people over the age of 70. We know that cancer risk does obviously increase with age, particularly prostate and breast cancer. And these are the cancers that are going to be the most common in your practices. If someone has a known mutation, I think you’re going to look differently at screening them. And first-degree family members, particularly for the more aggressive cancers, should be considered for screening.

My philosophy on cancer screening in the elderly is that I think the guidelines are guidelines. If patients have very limited life expectancy, then they shouldn’t be screened. There are calculators that estimate life expectancy in the context of current age and current health status, and these can be useful for decision making and counseling. Patients never think their life expectancy is shorter than 10 years. If their life expectancy is longer than 10 years, then I think, all things being equal, they should continue screening, but the question of ongoing screening needs to be periodically revisited.

*This story was updated on Nov. 1, 2023.
 

More than 1 in 10 Americans over age 60 years will be diagnosed with cancer, according to the National Cancer Institute, making screening for the disease in older patients imperative. Much of the burden of cancer screening falls on primary care physicians. This news organization spoke recently with William L. Dahut, MD, chief scientific officer of the American Cancer Society, about the particular challenges of screening in older patients.

Question: How much does cancer screening change with age? What are the considerations for clinicians – what risks and comorbidities are important to consider in older populations?

Answer: We at the American Cancer Society are giving a lot of thought to how to help primary care practices keep up with screening, particularly with respect to guidelines, but also best practices where judgment is required, such as cancer screening in their older patients.

We’ve had a lot of conversations recently about cancer risk in the young, largely because data show rates are going up for colorectal and breast cancer in this population. But it’s not one size fits all. Screening for young women who have a BRCA gene, if they have dense breasts, or if they have a strong family history of breast cancer should be different from those who are at average risk of the disease.

But statistically, there are about 15 per 100,000 breast cancer diagnoses in women under the age of 40 while over the age of 65 it’s 443 per 100,000. So, the risk significantly increases with age but we should not have an arbitrary cut-off. The life expectancy of a woman at age 75 is about 13.5 years. If you’re over the age of 70 or 75, then it’s going to be comorbidities that you look at, as well as individual patient decisions. Patients may say, “I don’t want to ever go through a mammogram again, because I don’t want to have a biopsy again, and I’m not going to get treated.” Or they may say, “My mom died of metastatic breast cancer when she was 82 and I want to know.”
 

Q: How should primary care physicians interpret conflicting guidance from the major medical groups? For example, the American College of Gastroenterology and your own organization recommend colorectal cancer screening start at age 45 now. But the American College of Physicians recently came out and said 50. What is a well-meaning primary care physician supposed to do?

A: We make more of guideline differences than we should. Sometimes guideline differences aren’t a reflection of different judgments, but rather what data were available when the most recent update took place. For colorectal cancer screening, the ACS dropped the age to begin screening to 45 in 2018 based on a very careful consideration of disease burden data and within several years most other guideline developers reached the same conclusion.

However, I think it’s good for family practice and internal medicine doctors to know that significant GI symptoms in a young patient could be colorectal cancer. It’s not as if nobody sees a 34-year-old or 27-year-old with colorectal cancer. They should be aware that if something goes away in a day or two, that’s fine, but persistent GI symptoms need a cancer workup – colonoscopy or referral to a gastroenterologist. So that’s why I think age 45 is the time when folks should begin screening.
 

Q: What are the medical-legal issues for a physician who is trying to follow guideline-based care when there are different guidelines?

A: Any physician can say, “We follow the guidelines of this particular organization.” I don’t think anyone can say that an organization’s guidelines are malpractice. For individual physicians, following a set of office-based guidelines will hopefully keep them out of legal difficulty.

Q: What are the risks of overscreening, especially in breast cancer where false positives may result in invasive testing?

A: What people think of as overscreening takes a number of different forms. What one guideline would imply is overscreening is recommended screening by another guideline. I think we would all agree that in an average-risk population, beginning screening before it is recommended would be overscreening, and continuing screening when a patient has life-limiting comorbidities would constitute overscreening. Screening too frequently can constitute overscreening.

For example, many women report that their doctors still are advising a baseline mammogram at age 35. Most guideline-developing organizations would regard this as overscreening in an average-risk population.

I think we are also getting better, certainly in prostate cancer, about knowing who needs to be treated and not treated. There are a lot of cancers that would have been treated 20-30 years ago but now are being safely followed with PSA and MRI. We may be able to get to that point with breast cancer over time, too.
 

Q: Are you saying that there may be breast cancers for which active surveillance is appropriate? Is that already the case?

A: We’re not there yet. I think some of the DCIS breast cancers are part of the discussion on whether hormonal treatment or surgeries are done. I think people do have those discussions in the context of morbidity and life expectancy. Over time, we’re likely to have more cancers for which we won’t need surgical treatments.
 

Q: Why did the American Cancer Society change the upper limit for lung cancer screening from 75 to 80 years of age?

 A: For an individual older than 65, screening will now continue until the patient is 80, assuming the patient is in good health. According to the previous guideline, if a patient was 65 and more than 15 years beyond smoking cessation, then screening would end. This is exactly the time when we see lung cancers increase in the population and so a curable lung cancer would not previously have been detected by a screening CT scan. *  

Q: What role do the multicancer blood and DNA tests play in screening now?

A: As you know, the Exact Sciences Cologuard test is already included in major guidelines for colorectal cancer screening and covered by insurance. Our philosophy on multicancer early detection tests is that we’re supportive of Medicare reimbursement when two things occur: 1. When we know there’s clinical benefit, and 2. When the test has been approved by the FDA.

The multicancer early detection tests in development and undergoing prospective research would not now replace screening for the cancers with established screening programs, but if they are shown to have clinical utility for the cancers in their panel, we would be able to reduce deaths from cancers that mostly are diagnosed at late stages and have poor prognoses.

There’s going to be a need for expertise in primary care practices to help interpret the tests. These are new questions, which are well beyond what even the typical oncologist is trained in, much less primary care physicians. We and other organizations are working on providing those answers.
 

Q: While we’re on the subject of the future, how do you envision AI helping or hindering cancer screening specifically in primary care?

A: I think AI is going to help things for a couple of reasons. The ability of AI is to get through data quickly and get you information that’s personalized and useful. If AI tools could let a patient know their individual risk of a cancer in the near and long term, that would help the primary care doctor screen in an individualized way. I think AI is going to be able to improve both diagnostic radiology and pathology, and could make a very big difference in settings outside of large cancer centers that operate at high volume every day. The data look very promising for AI to contribute to risk estimation by operating like a second reader in imaging and pathology.
 

Q: Anything else you’d like to say on this subject that clinicians should know?

A: The questions about whether or not patients should be screened is being pushed on family practice doctors and internists and these questions require a relationship with the patient. A hard stopping point at age 70 when lots of people will live 20 years or more doesn’t make sense.

There’s very little data from randomized clinical trials of screening people over the age of 70. We know that cancer risk does obviously increase with age, particularly prostate and breast cancer. And these are the cancers that are going to be the most common in your practices. If someone has a known mutation, I think you’re going to look differently at screening them. And first-degree family members, particularly for the more aggressive cancers, should be considered for screening.

My philosophy on cancer screening in the elderly is that I think the guidelines are guidelines. If patients have very limited life expectancy, then they shouldn’t be screened. There are calculators that estimate life expectancy in the context of current age and current health status, and these can be useful for decision making and counseling. Patients never think their life expectancy is shorter than 10 years. If their life expectancy is longer than 10 years, then I think, all things being equal, they should continue screening, but the question of ongoing screening needs to be periodically revisited.

*This story was updated on Nov. 1, 2023.
 

More than 1 in 10 Americans over age 60 years will be diagnosed with cancer, according to the National Cancer Institute, making screening for the disease in older patients imperative. Much of the burden of cancer screening falls on primary care physicians. This news organization spoke recently with William L. Dahut, MD, chief scientific officer of the American Cancer Society, about the particular challenges of screening in older patients.

Question: How much does cancer screening change with age? What are the considerations for clinicians – what risks and comorbidities are important to consider in older populations?

Answer: We at the American Cancer Society are giving a lot of thought to how to help primary care practices keep up with screening, particularly with respect to guidelines, but also best practices where judgment is required, such as cancer screening in their older patients.

We’ve had a lot of conversations recently about cancer risk in the young, largely because data show rates are going up for colorectal and breast cancer in this population. But it’s not one size fits all. Screening for young women who have a BRCA gene, if they have dense breasts, or if they have a strong family history of breast cancer should be different from those who are at average risk of the disease.

But statistically, there are about 15 per 100,000 breast cancer diagnoses in women under the age of 40 while over the age of 65 it’s 443 per 100,000. So, the risk significantly increases with age but we should not have an arbitrary cut-off. The life expectancy of a woman at age 75 is about 13.5 years. If you’re over the age of 70 or 75, then it’s going to be comorbidities that you look at, as well as individual patient decisions. Patients may say, “I don’t want to ever go through a mammogram again, because I don’t want to have a biopsy again, and I’m not going to get treated.” Or they may say, “My mom died of metastatic breast cancer when she was 82 and I want to know.”
 

Q: How should primary care physicians interpret conflicting guidance from the major medical groups? For example, the American College of Gastroenterology and your own organization recommend colorectal cancer screening start at age 45 now. But the American College of Physicians recently came out and said 50. What is a well-meaning primary care physician supposed to do?

A: We make more of guideline differences than we should. Sometimes guideline differences aren’t a reflection of different judgments, but rather what data were available when the most recent update took place. For colorectal cancer screening, the ACS dropped the age to begin screening to 45 in 2018 based on a very careful consideration of disease burden data and within several years most other guideline developers reached the same conclusion.

However, I think it’s good for family practice and internal medicine doctors to know that significant GI symptoms in a young patient could be colorectal cancer. It’s not as if nobody sees a 34-year-old or 27-year-old with colorectal cancer. They should be aware that if something goes away in a day or two, that’s fine, but persistent GI symptoms need a cancer workup – colonoscopy or referral to a gastroenterologist. So that’s why I think age 45 is the time when folks should begin screening.
 

Q: What are the medical-legal issues for a physician who is trying to follow guideline-based care when there are different guidelines?

A: Any physician can say, “We follow the guidelines of this particular organization.” I don’t think anyone can say that an organization’s guidelines are malpractice. For individual physicians, following a set of office-based guidelines will hopefully keep them out of legal difficulty.

Q: What are the risks of overscreening, especially in breast cancer where false positives may result in invasive testing?

A: What people think of as overscreening takes a number of different forms. What one guideline would imply is overscreening is recommended screening by another guideline. I think we would all agree that in an average-risk population, beginning screening before it is recommended would be overscreening, and continuing screening when a patient has life-limiting comorbidities would constitute overscreening. Screening too frequently can constitute overscreening.

For example, many women report that their doctors still are advising a baseline mammogram at age 35. Most guideline-developing organizations would regard this as overscreening in an average-risk population.

I think we are also getting better, certainly in prostate cancer, about knowing who needs to be treated and not treated. There are a lot of cancers that would have been treated 20-30 years ago but now are being safely followed with PSA and MRI. We may be able to get to that point with breast cancer over time, too.
 

Q: Are you saying that there may be breast cancers for which active surveillance is appropriate? Is that already the case?

A: We’re not there yet. I think some of the DCIS breast cancers are part of the discussion on whether hormonal treatment or surgeries are done. I think people do have those discussions in the context of morbidity and life expectancy. Over time, we’re likely to have more cancers for which we won’t need surgical treatments.
 

Q: Why did the American Cancer Society change the upper limit for lung cancer screening from 75 to 80 years of age?

 A: For an individual older than 65, screening will now continue until the patient is 80, assuming the patient is in good health. According to the previous guideline, if a patient was 65 and more than 15 years beyond smoking cessation, then screening would end. This is exactly the time when we see lung cancers increase in the population and so a curable lung cancer would not previously have been detected by a screening CT scan. *  

Q: What role do the multicancer blood and DNA tests play in screening now?

A: As you know, the Exact Sciences Cologuard test is already included in major guidelines for colorectal cancer screening and covered by insurance. Our philosophy on multicancer early detection tests is that we’re supportive of Medicare reimbursement when two things occur: 1. When we know there’s clinical benefit, and 2. When the test has been approved by the FDA.

The multicancer early detection tests in development and undergoing prospective research would not now replace screening for the cancers with established screening programs, but if they are shown to have clinical utility for the cancers in their panel, we would be able to reduce deaths from cancers that mostly are diagnosed at late stages and have poor prognoses.

There’s going to be a need for expertise in primary care practices to help interpret the tests. These are new questions, which are well beyond what even the typical oncologist is trained in, much less primary care physicians. We and other organizations are working on providing those answers.
 

Q: While we’re on the subject of the future, how do you envision AI helping or hindering cancer screening specifically in primary care?

A: I think AI is going to help things for a couple of reasons. The ability of AI is to get through data quickly and get you information that’s personalized and useful. If AI tools could let a patient know their individual risk of a cancer in the near and long term, that would help the primary care doctor screen in an individualized way. I think AI is going to be able to improve both diagnostic radiology and pathology, and could make a very big difference in settings outside of large cancer centers that operate at high volume every day. The data look very promising for AI to contribute to risk estimation by operating like a second reader in imaging and pathology.
 

Q: Anything else you’d like to say on this subject that clinicians should know?

A: The questions about whether or not patients should be screened is being pushed on family practice doctors and internists and these questions require a relationship with the patient. A hard stopping point at age 70 when lots of people will live 20 years or more doesn’t make sense.

There’s very little data from randomized clinical trials of screening people over the age of 70. We know that cancer risk does obviously increase with age, particularly prostate and breast cancer. And these are the cancers that are going to be the most common in your practices. If someone has a known mutation, I think you’re going to look differently at screening them. And first-degree family members, particularly for the more aggressive cancers, should be considered for screening.

My philosophy on cancer screening in the elderly is that I think the guidelines are guidelines. If patients have very limited life expectancy, then they shouldn’t be screened. There are calculators that estimate life expectancy in the context of current age and current health status, and these can be useful for decision making and counseling. Patients never think their life expectancy is shorter than 10 years. If their life expectancy is longer than 10 years, then I think, all things being equal, they should continue screening, but the question of ongoing screening needs to be periodically revisited.

*This story was updated on Nov. 1, 2023.
 

This transcript has been edited for clarity.

As some of you may know, I do a fair amount of clinical research developing and evaluating artificial intelligence (AI) models, particularly machine learning algorithms that predict certain outcomes.

A thorny issue that comes up as algorithms have gotten more complicated is “explainability.” The problem is that AI can be a black box. Even if you have a model that is very accurate at predicting death, clinicians don’t trust it unless you can explain how it makes its predictions – how it works. “It just works” is not good enough to build trust.

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

JAMA

JAMA

JAMA

A version of this article first appeared on Medscape.com.

F. Perry Wilson, MD, MSCE, is an associate professor of medicine and public health and director of Yale’s Clinical and Translational Research Accelerator. His science communication work can be found in the Huffington Post, on NPR, and on Medscape. He tweets @fperrywilson and his new book, “How Medicine Works and When It Doesn’t,” is available now.

This transcript has been edited for clarity.

As some of you may know, I do a fair amount of clinical research developing and evaluating artificial intelligence (AI) models, particularly machine learning algorithms that predict certain outcomes.

A thorny issue that comes up as algorithms have gotten more complicated is “explainability.” The problem is that AI can be a black box. Even if you have a model that is very accurate at predicting death, clinicians don’t trust it unless you can explain how it makes its predictions – how it works. “It just works” is not good enough to build trust.

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

JAMA

JAMA

JAMA

A version of this article first appeared on Medscape.com.

F. Perry Wilson, MD, MSCE, is an associate professor of medicine and public health and director of Yale’s Clinical and Translational Research Accelerator. His science communication work can be found in the Huffington Post, on NPR, and on Medscape. He tweets @fperrywilson and his new book, “How Medicine Works and When It Doesn’t,” is available now.

This transcript has been edited for clarity.

As some of you may know, I do a fair amount of clinical research developing and evaluating artificial intelligence (AI) models, particularly machine learning algorithms that predict certain outcomes.

A thorny issue that comes up as algorithms have gotten more complicated is “explainability.” The problem is that AI can be a black box. Even if you have a model that is very accurate at predicting death, clinicians don’t trust it unless you can explain how it makes its predictions – how it works. “It just works” is not good enough to build trust.

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

F. Perry Wilson, MD, MSCE

JAMA

JAMA

JAMA

A version of this article first appeared on Medscape.com.

F. Perry Wilson, MD, MSCE, is an associate professor of medicine and public health and director of Yale’s Clinical and Translational Research Accelerator. His science communication work can be found in the Huffington Post, on NPR, and on Medscape. He tweets @fperrywilson and his new book, “How Medicine Works and When It Doesn’t,” is available now.

For this month’s column, I wanted to share some frustrations I have had about the current state of primary care. We all find those things that are going on in medicine that seem crazy and we just have to find a way to adapt to them. It is good to be able to share some of these thoughts with a community as distinguished as you readers. I know some of these are issues that you all struggle with and I wanted to give a voice to them. I wish I had answers to fix them.

Faxes from insurance companies

I find faxes from insurance companies immensely annoying. First, it takes time to go through lots of unwanted faxes but these faxes are extremely inaccurate. Today I received a fax telling me I might want to consider starting a statin in my 64-year-old HIV patient who has hypertension. He has been on a statin for 10 years.

Another fax warned me to not combine ACE inhibitors and angiotensin II receptor blockers (ARBs) in a patient who was switched from an ACE inhibitor in July to an ARB because of a cough. The fax that was sent to me has a documented end date for the ACE inhibitor before the start date of the ARB.

We only have so much time in the day and piles of faxes are not helpful.

Speaking of faxes: Why do physical therapy offices and nursing homes fax the same form every day? Physicians do not always work in clinic every single day and it increases the workload and burden when you have to sort through three copies of the same fax. I once worked in a world where these would be sent by mail, and mailed back a week later, which seemed to work just fine.
 

Misinformation

Our patients have many sources of health information. Much of the information they get comes from family, friends, social media posts, and Internet sites. The accuracy of the information is often questionable, and in some cases, they are victims of intentional misinformation.

It is frustrating and time consuming to counter the bogus, unsubstantiated information patients receive. It is especially difficult when patients have done their own research on proven therapies (such as statins) and do not want to use them because of the many websites they have looked at that make unscientific claims about the dangers of the proposed therapy. I share evidence-based websites with my patients for their research; my favorite is medlineplus.gov.
 

Access crisis

The availability of specialty care is extremely limited now. In my health care system, there is up to a 6-month wait for appointments in neurology, cardiology, and endocrinology. This puts the burden on the primary care professional to manage the patient’s health, even when the patient really needs specialty care. It also increases the calls we receive to interpret the echocardiograms, MRIs, or lab tests ordered by specialists who do not share the interpretation of the results with their patients.

What can be done to improve this situation? Automatic consults in the hospital should be limited. Every patient who has a transient ischemic attack with a negative workup does not need neurology follow-up. The same goes for patients who have chest pain but a negative cardiac workup in the hospital – they do not need follow-up by a cardiologist, nor do those who have stable, well-managed coronary disease. We have to find a way to keep our specialists seeing the patients whom they can help the most and available for consultation in a timely fashion.

Please share your pet peeves with me. I will try to give them voice in the future. Hang in there, you are the glue that keeps this flawed system together.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and he serves as third-year medical student clerkship director at the University of Washington. Contact Dr. Paauw at [email protected].

For this month’s column, I wanted to share some frustrations I have had about the current state of primary care. We all find those things that are going on in medicine that seem crazy and we just have to find a way to adapt to them. It is good to be able to share some of these thoughts with a community as distinguished as you readers. I know some of these are issues that you all struggle with and I wanted to give a voice to them. I wish I had answers to fix them.

Faxes from insurance companies

I find faxes from insurance companies immensely annoying. First, it takes time to go through lots of unwanted faxes but these faxes are extremely inaccurate. Today I received a fax telling me I might want to consider starting a statin in my 64-year-old HIV patient who has hypertension. He has been on a statin for 10 years.

Another fax warned me to not combine ACE inhibitors and angiotensin II receptor blockers (ARBs) in a patient who was switched from an ACE inhibitor in July to an ARB because of a cough. The fax that was sent to me has a documented end date for the ACE inhibitor before the start date of the ARB.

We only have so much time in the day and piles of faxes are not helpful.

Speaking of faxes: Why do physical therapy offices and nursing homes fax the same form every day? Physicians do not always work in clinic every single day and it increases the workload and burden when you have to sort through three copies of the same fax. I once worked in a world where these would be sent by mail, and mailed back a week later, which seemed to work just fine.
 

Misinformation

Our patients have many sources of health information. Much of the information they get comes from family, friends, social media posts, and Internet sites. The accuracy of the information is often questionable, and in some cases, they are victims of intentional misinformation.

It is frustrating and time consuming to counter the bogus, unsubstantiated information patients receive. It is especially difficult when patients have done their own research on proven therapies (such as statins) and do not want to use them because of the many websites they have looked at that make unscientific claims about the dangers of the proposed therapy. I share evidence-based websites with my patients for their research; my favorite is medlineplus.gov.
 

Access crisis

The availability of specialty care is extremely limited now. In my health care system, there is up to a 6-month wait for appointments in neurology, cardiology, and endocrinology. This puts the burden on the primary care professional to manage the patient’s health, even when the patient really needs specialty care. It also increases the calls we receive to interpret the echocardiograms, MRIs, or lab tests ordered by specialists who do not share the interpretation of the results with their patients.

What can be done to improve this situation? Automatic consults in the hospital should be limited. Every patient who has a transient ischemic attack with a negative workup does not need neurology follow-up. The same goes for patients who have chest pain but a negative cardiac workup in the hospital – they do not need follow-up by a cardiologist, nor do those who have stable, well-managed coronary disease. We have to find a way to keep our specialists seeing the patients whom they can help the most and available for consultation in a timely fashion.

Please share your pet peeves with me. I will try to give them voice in the future. Hang in there, you are the glue that keeps this flawed system together.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and he serves as third-year medical student clerkship director at the University of Washington. Contact Dr. Paauw at [email protected].

For this month’s column, I wanted to share some frustrations I have had about the current state of primary care. We all find those things that are going on in medicine that seem crazy and we just have to find a way to adapt to them. It is good to be able to share some of these thoughts with a community as distinguished as you readers. I know some of these are issues that you all struggle with and I wanted to give a voice to them. I wish I had answers to fix them.

Faxes from insurance companies

I find faxes from insurance companies immensely annoying. First, it takes time to go through lots of unwanted faxes but these faxes are extremely inaccurate. Today I received a fax telling me I might want to consider starting a statin in my 64-year-old HIV patient who has hypertension. He has been on a statin for 10 years.

Another fax warned me to not combine ACE inhibitors and angiotensin II receptor blockers (ARBs) in a patient who was switched from an ACE inhibitor in July to an ARB because of a cough. The fax that was sent to me has a documented end date for the ACE inhibitor before the start date of the ARB.

We only have so much time in the day and piles of faxes are not helpful.

Speaking of faxes: Why do physical therapy offices and nursing homes fax the same form every day? Physicians do not always work in clinic every single day and it increases the workload and burden when you have to sort through three copies of the same fax. I once worked in a world where these would be sent by mail, and mailed back a week later, which seemed to work just fine.
 

Misinformation

Our patients have many sources of health information. Much of the information they get comes from family, friends, social media posts, and Internet sites. The accuracy of the information is often questionable, and in some cases, they are victims of intentional misinformation.

It is frustrating and time consuming to counter the bogus, unsubstantiated information patients receive. It is especially difficult when patients have done their own research on proven therapies (such as statins) and do not want to use them because of the many websites they have looked at that make unscientific claims about the dangers of the proposed therapy. I share evidence-based websites with my patients for their research; my favorite is medlineplus.gov.
 

Access crisis

The availability of specialty care is extremely limited now. In my health care system, there is up to a 6-month wait for appointments in neurology, cardiology, and endocrinology. This puts the burden on the primary care professional to manage the patient’s health, even when the patient really needs specialty care. It also increases the calls we receive to interpret the echocardiograms, MRIs, or lab tests ordered by specialists who do not share the interpretation of the results with their patients.

What can be done to improve this situation? Automatic consults in the hospital should be limited. Every patient who has a transient ischemic attack with a negative workup does not need neurology follow-up. The same goes for patients who have chest pain but a negative cardiac workup in the hospital – they do not need follow-up by a cardiologist, nor do those who have stable, well-managed coronary disease. We have to find a way to keep our specialists seeing the patients whom they can help the most and available for consultation in a timely fashion.

Please share your pet peeves with me. I will try to give them voice in the future. Hang in there, you are the glue that keeps this flawed system together.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and he serves as third-year medical student clerkship director at the University of Washington. Contact Dr. Paauw at [email protected].

In 2021, diabetes and related complications was the 8th leading cause of death in the United States.¹ As of 2022, more than 11% of the U.S. population had diabetes and 38% of the adult U.S. population had prediabetes.² Diabetes is the most expensive chronic condition in the United States, where $1 of every $4 in health care costs is spent on care.³

Where this is most concerning is diabetes in pregnancy. While childbirth rates in the United States have decreased since the 2007 high of 4.32 million births⁴ to 3.66 million in 2021,⁵ the incidence of diabetes in pregnancy – both pregestational and gestational – has increased. The rate of pregestational diabetes in 2021 was 10.9 per 1,000 births, a 27% increase from 2016 (8.6 per 1,000).⁶ The percentage of those giving birth who also were diagnosed with gestational diabetes mellitus (GDM) was 8.3% in 2021, up from 6.0% in 2016.⁷

Diabetes in pregnancy not only increases risks of adverse events for mother and fetus: Increasing research suggests the condition signals longer-term risks for the mother and child throughout their lifetimes. Adverse outcomes for an infant born to a mother with diabetes include a higher risk of obesity and diabetes as adults, potentially leading to a forward-feeding cycle.

We and our colleagues established the Diabetes in Pregnancy Study Group of North America in 1997 because we had witnessed too frequently the devastating diabetes-induced pregnancy complications in our patients. The mission we set forth was to provide a forum for dialogue among maternal-fetal medicine subspecialists. The three main goals we set forth to support this mission were to provide a catalyst for research, contribute to the creation and refinement of medical policies, and influence professional practices in diabetes in pregnancy.⁸

In the last quarter century, DPSG-NA, through its annual and biennial meetings, has brought together several hundred practitioners that include physicians, nurses, statisticians, researchers, nutritionists, and allied health professionals, among others. As a group, it has improved the detection and management of diabetes in pregnant women and their offspring through knowledge sharing and influencing policies on GDM screening, diagnosis, management, and treatment. Our members have shown that preconceptional counseling for women with diabetes can significantly reduce congenital malformation and perinatal mortality compared with those women with pregestational diabetes who receive no counseling.^9,10

We have addressed a wide variety of topics including the paucity of data in determining the timing of delivery for women with diabetes and the Institute of Medicine/National Academy of Medicine recommendations of gestational weight gain and risks of not adhering to them. We have learned about new scientific discoveries that reveal underlying mechanisms to diabetes-related birth defects and potential therapeutic targets; and we have discussed the health literacy requirements, ethics, and opportunities for lifestyle intervention.^11-16

But we need to do more.

Two risk factors are at play: Women continue to choose to have babies at later ages and their pregnancies continue to be complicated by the rising incidence of obesity (see Figure 1 and Figure 2).

Dr. Reece and Dr. Miodovnik

The global obesity epidemic has become a significant concern for all aspects of health and particularly for diabetes in pregnancy.

Dr. Reece and Dr. Miodovnik

In 1990, 24.9% of women in the United States were obese; in 2010, 35.8%; and now more than 41%. Some experts project that by 2030 more than 80% of women in the United States will be overweight or obese.²¹

If we are to stop this cycle of diabetes begets more diabetes, now more than ever we need to come together and accelerate the research and education around the diabetes in pregnancy. Join us at this year’s DPSG-NA meeting Oct. 26-28 to take part in the knowledge sharing, discussions, and planning. More information can be found online at https://events.dpsg-na.com/home.

Dr. Miodovnik is adjunct professor of obstetrics, gynecology, and reproductive sciences at University of Maryland School of Medicine. Dr. Reece is professor of obstetrics, gynecology, and reproductive sciences and senior scientist at the Center for Birth Defects Research at University of Maryland School of Medicine.

References

1. Xu J et al. Mortality in the United States, 2021. NCHS Data Brief. 2022 Dec;(456):1-8. PMID: 36598387.

2. Centers for Disease Control and Prevention, diabetes data and statistics.

3. American Diabetes Association. The Cost of Diabetes.

4. Martin JA et al. Births: Final data for 2007. Natl Vital Stat Rep. 2010 Aug 9;58(24):1-85. PMID: 21254725.

5. Osterman MJK et al. Births: Final data for 2021. Natl Vital Stat Rep. 2023 Jan;72(1):1-53. PMID: 36723449.

6. Gregory ECW and Ely DM. Trends and characteristics in prepregnancy diabetes: United States, 2016-2021. Natl Vital Stat Rep. 2023 May;72(6):1-13. PMID: 37256333.

7. QuickStats: Percentage of mothers with gestational diabetes, by maternal age – National Vital Statistics System, United States, 2016 and 2021. MMWR Morb Mortal Wkly Rep. 2023 Jan 6;72(1):16. doi: 10.15585/mmwr.mm7201a4.
 

8. Langer O et al. The Diabetes in Pregnancy Study Group of North America – Introduction and summary statement. Prenat Neonat Med. 1998;3(6):514-6.

9. Willhoite MB et al. The impact of preconception counseling on pregnancy outcomes. The experience of the Maine Diabetes in Pregnancy Program. Diabetes Care. 1993 Feb;16(2):450-5. doi: 10.2337/diacare.16.2.450.

10. McElvy SS et al. A focused preconceptional and early pregnancy program in women with type 1 diabetes reduces perinatal mortality and malformation rates to general population levels. J Matern Fetal Med. 2000 Jan-Feb;9(1):14-20. doi: 10.1002/(SICI)1520-6661(200001/02)9:1<14::AID-MFM5>3.0.CO;2-K.

11. Rosen JA et al. The history and contributions of the Diabetes in Pregnancy Study Group of North America (1997-2015). Am J Perinatol. 2016 Nov;33(13):1223-6. doi: 10.1055/s-0036-1585082.

12. Driggers RW and Baschat A. The 12th meeting of the Diabetes in Pregnancy Study Group of North America (DPSG-NA): Introduction and overview. J Matern Fetal Neonatal Med. 2012 Jan;25(1):3-4. doi: 10.3109/14767058.2012.626917.

13. Langer O et al. The proceedings of the Diabetes in Pregnancy Study Group of North America 2009 conference. J Matern Fetal Neonatal Med. 2010 Mar;23(3):196-8. doi: 10.3109/14767050903550634.

14. Reece EA et al. A consensus report of the Diabetes in Pregnancy Study Group of North America Conference, Little Rock, Ark., May 2002. J Matern Fetal Neonatal Med. 2002 Dec;12(6):362-4. doi: 10.1080/jmf.12.6.362.364.

15. Reece EA and Maulik D. A consensus conference of the Diabetes in Pregnancy Study Group of North America. J Matern Fetal Neonatal Med. 2002 Dec;12(6):361. doi: 10.1080/jmf.12.6.361.361.

16. Gabbe SG. Summation of the second meeting of the Diabetes in Pregnancy Study Group of North America (DPSG-NA). J Matern Fetal Med. 2000 Jan-Feb;9(1):3-9.

17. Vital Statistics of the United States 1990: Volume I – Natality.

18. Martin JA et al. Births: final data for 2000. Natl Vital Stat Rep. 2002 Feb 12;50(5):1-101. PMID: 11876093.

19. Martin JA et al. Births: final data for 2010. Natl Vital Stat Rep. 2012 Aug 28;61(1):1-72. PMID: 24974589.

20. CDC Website. Normal weight, overweight, and obesity among adults aged 20 and over, by selected characteristics: United States.

21. Wang Y et al. Has the prevalence of overweight, obesity, and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic. Int J Epidemiol. 2020 Jun 1;49(3):810-23. doi: 10.1093/ije/dyz273.

In 2021, diabetes and related complications was the 8th leading cause of death in the United States.¹ As of 2022, more than 11% of the U.S. population had diabetes and 38% of the adult U.S. population had prediabetes.² Diabetes is the most expensive chronic condition in the United States, where $1 of every $4 in health care costs is spent on care.³

Where this is most concerning is diabetes in pregnancy. While childbirth rates in the United States have decreased since the 2007 high of 4.32 million births⁴ to 3.66 million in 2021,⁵ the incidence of diabetes in pregnancy – both pregestational and gestational – has increased. The rate of pregestational diabetes in 2021 was 10.9 per 1,000 births, a 27% increase from 2016 (8.6 per 1,000).⁶ The percentage of those giving birth who also were diagnosed with gestational diabetes mellitus (GDM) was 8.3% in 2021, up from 6.0% in 2016.⁷

Diabetes in pregnancy not only increases risks of adverse events for mother and fetus: Increasing research suggests the condition signals longer-term risks for the mother and child throughout their lifetimes. Adverse outcomes for an infant born to a mother with diabetes include a higher risk of obesity and diabetes as adults, potentially leading to a forward-feeding cycle.

We and our colleagues established the Diabetes in Pregnancy Study Group of North America in 1997 because we had witnessed too frequently the devastating diabetes-induced pregnancy complications in our patients. The mission we set forth was to provide a forum for dialogue among maternal-fetal medicine subspecialists. The three main goals we set forth to support this mission were to provide a catalyst for research, contribute to the creation and refinement of medical policies, and influence professional practices in diabetes in pregnancy.⁸

In the last quarter century, DPSG-NA, through its annual and biennial meetings, has brought together several hundred practitioners that include physicians, nurses, statisticians, researchers, nutritionists, and allied health professionals, among others. As a group, it has improved the detection and management of diabetes in pregnant women and their offspring through knowledge sharing and influencing policies on GDM screening, diagnosis, management, and treatment. Our members have shown that preconceptional counseling for women with diabetes can significantly reduce congenital malformation and perinatal mortality compared with those women with pregestational diabetes who receive no counseling.^9,10

We have addressed a wide variety of topics including the paucity of data in determining the timing of delivery for women with diabetes and the Institute of Medicine/National Academy of Medicine recommendations of gestational weight gain and risks of not adhering to them. We have learned about new scientific discoveries that reveal underlying mechanisms to diabetes-related birth defects and potential therapeutic targets; and we have discussed the health literacy requirements, ethics, and opportunities for lifestyle intervention.^11-16

But we need to do more.

Two risk factors are at play: Women continue to choose to have babies at later ages and their pregnancies continue to be complicated by the rising incidence of obesity (see Figure 1 and Figure 2).

Dr. Reece and Dr. Miodovnik

The global obesity epidemic has become a significant concern for all aspects of health and particularly for diabetes in pregnancy.

Dr. Reece and Dr. Miodovnik

In 1990, 24.9% of women in the United States were obese; in 2010, 35.8%; and now more than 41%. Some experts project that by 2030 more than 80% of women in the United States will be overweight or obese.²¹

If we are to stop this cycle of diabetes begets more diabetes, now more than ever we need to come together and accelerate the research and education around the diabetes in pregnancy. Join us at this year’s DPSG-NA meeting Oct. 26-28 to take part in the knowledge sharing, discussions, and planning. More information can be found online at https://events.dpsg-na.com/home.

Dr. Miodovnik is adjunct professor of obstetrics, gynecology, and reproductive sciences at University of Maryland School of Medicine. Dr. Reece is professor of obstetrics, gynecology, and reproductive sciences and senior scientist at the Center for Birth Defects Research at University of Maryland School of Medicine.

References

1. Xu J et al. Mortality in the United States, 2021. NCHS Data Brief. 2022 Dec;(456):1-8. PMID: 36598387.

2. Centers for Disease Control and Prevention, diabetes data and statistics.

3. American Diabetes Association. The Cost of Diabetes.

4. Martin JA et al. Births: Final data for 2007. Natl Vital Stat Rep. 2010 Aug 9;58(24):1-85. PMID: 21254725.

5. Osterman MJK et al. Births: Final data for 2021. Natl Vital Stat Rep. 2023 Jan;72(1):1-53. PMID: 36723449.

6. Gregory ECW and Ely DM. Trends and characteristics in prepregnancy diabetes: United States, 2016-2021. Natl Vital Stat Rep. 2023 May;72(6):1-13. PMID: 37256333.

7. QuickStats: Percentage of mothers with gestational diabetes, by maternal age – National Vital Statistics System, United States, 2016 and 2021. MMWR Morb Mortal Wkly Rep. 2023 Jan 6;72(1):16. doi: 10.15585/mmwr.mm7201a4.
 

8. Langer O et al. The Diabetes in Pregnancy Study Group of North America – Introduction and summary statement. Prenat Neonat Med. 1998;3(6):514-6.

9. Willhoite MB et al. The impact of preconception counseling on pregnancy outcomes. The experience of the Maine Diabetes in Pregnancy Program. Diabetes Care. 1993 Feb;16(2):450-5. doi: 10.2337/diacare.16.2.450.

10. McElvy SS et al. A focused preconceptional and early pregnancy program in women with type 1 diabetes reduces perinatal mortality and malformation rates to general population levels. J Matern Fetal Med. 2000 Jan-Feb;9(1):14-20. doi: 10.1002/(SICI)1520-6661(200001/02)9:1<14::AID-MFM5>3.0.CO;2-K.

11. Rosen JA et al. The history and contributions of the Diabetes in Pregnancy Study Group of North America (1997-2015). Am J Perinatol. 2016 Nov;33(13):1223-6. doi: 10.1055/s-0036-1585082.

12. Driggers RW and Baschat A. The 12th meeting of the Diabetes in Pregnancy Study Group of North America (DPSG-NA): Introduction and overview. J Matern Fetal Neonatal Med. 2012 Jan;25(1):3-4. doi: 10.3109/14767058.2012.626917.

13. Langer O et al. The proceedings of the Diabetes in Pregnancy Study Group of North America 2009 conference. J Matern Fetal Neonatal Med. 2010 Mar;23(3):196-8. doi: 10.3109/14767050903550634.

14. Reece EA et al. A consensus report of the Diabetes in Pregnancy Study Group of North America Conference, Little Rock, Ark., May 2002. J Matern Fetal Neonatal Med. 2002 Dec;12(6):362-4. doi: 10.1080/jmf.12.6.362.364.

15. Reece EA and Maulik D. A consensus conference of the Diabetes in Pregnancy Study Group of North America. J Matern Fetal Neonatal Med. 2002 Dec;12(6):361. doi: 10.1080/jmf.12.6.361.361.

16. Gabbe SG. Summation of the second meeting of the Diabetes in Pregnancy Study Group of North America (DPSG-NA). J Matern Fetal Med. 2000 Jan-Feb;9(1):3-9.

17. Vital Statistics of the United States 1990: Volume I – Natality.

18. Martin JA et al. Births: final data for 2000. Natl Vital Stat Rep. 2002 Feb 12;50(5):1-101. PMID: 11876093.

19. Martin JA et al. Births: final data for 2010. Natl Vital Stat Rep. 2012 Aug 28;61(1):1-72. PMID: 24974589.

20. CDC Website. Normal weight, overweight, and obesity among adults aged 20 and over, by selected characteristics: United States.

21. Wang Y et al. Has the prevalence of overweight, obesity, and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic. Int J Epidemiol. 2020 Jun 1;49(3):810-23. doi: 10.1093/ije/dyz273.

In 2021, diabetes and related complications was the 8th leading cause of death in the United States.¹ As of 2022, more than 11% of the U.S. population had diabetes and 38% of the adult U.S. population had prediabetes.² Diabetes is the most expensive chronic condition in the United States, where $1 of every $4 in health care costs is spent on care.³

Where this is most concerning is diabetes in pregnancy. While childbirth rates in the United States have decreased since the 2007 high of 4.32 million births⁴ to 3.66 million in 2021,⁵ the incidence of diabetes in pregnancy – both pregestational and gestational – has increased. The rate of pregestational diabetes in 2021 was 10.9 per 1,000 births, a 27% increase from 2016 (8.6 per 1,000).⁶ The percentage of those giving birth who also were diagnosed with gestational diabetes mellitus (GDM) was 8.3% in 2021, up from 6.0% in 2016.⁷

Diabetes in pregnancy not only increases risks of adverse events for mother and fetus: Increasing research suggests the condition signals longer-term risks for the mother and child throughout their lifetimes. Adverse outcomes for an infant born to a mother with diabetes include a higher risk of obesity and diabetes as adults, potentially leading to a forward-feeding cycle.

We and our colleagues established the Diabetes in Pregnancy Study Group of North America in 1997 because we had witnessed too frequently the devastating diabetes-induced pregnancy complications in our patients. The mission we set forth was to provide a forum for dialogue among maternal-fetal medicine subspecialists. The three main goals we set forth to support this mission were to provide a catalyst for research, contribute to the creation and refinement of medical policies, and influence professional practices in diabetes in pregnancy.⁸

In the last quarter century, DPSG-NA, through its annual and biennial meetings, has brought together several hundred practitioners that include physicians, nurses, statisticians, researchers, nutritionists, and allied health professionals, among others. As a group, it has improved the detection and management of diabetes in pregnant women and their offspring through knowledge sharing and influencing policies on GDM screening, diagnosis, management, and treatment. Our members have shown that preconceptional counseling for women with diabetes can significantly reduce congenital malformation and perinatal mortality compared with those women with pregestational diabetes who receive no counseling.^9,10

We have addressed a wide variety of topics including the paucity of data in determining the timing of delivery for women with diabetes and the Institute of Medicine/National Academy of Medicine recommendations of gestational weight gain and risks of not adhering to them. We have learned about new scientific discoveries that reveal underlying mechanisms to diabetes-related birth defects and potential therapeutic targets; and we have discussed the health literacy requirements, ethics, and opportunities for lifestyle intervention.^11-16

But we need to do more.

Two risk factors are at play: Women continue to choose to have babies at later ages and their pregnancies continue to be complicated by the rising incidence of obesity (see Figure 1 and Figure 2).

Dr. Reece and Dr. Miodovnik

The global obesity epidemic has become a significant concern for all aspects of health and particularly for diabetes in pregnancy.

Dr. Reece and Dr. Miodovnik

In 1990, 24.9% of women in the United States were obese; in 2010, 35.8%; and now more than 41%. Some experts project that by 2030 more than 80% of women in the United States will be overweight or obese.²¹

If we are to stop this cycle of diabetes begets more diabetes, now more than ever we need to come together and accelerate the research and education around the diabetes in pregnancy. Join us at this year’s DPSG-NA meeting Oct. 26-28 to take part in the knowledge sharing, discussions, and planning. More information can be found online at https://events.dpsg-na.com/home.

Dr. Miodovnik is adjunct professor of obstetrics, gynecology, and reproductive sciences at University of Maryland School of Medicine. Dr. Reece is professor of obstetrics, gynecology, and reproductive sciences and senior scientist at the Center for Birth Defects Research at University of Maryland School of Medicine.

References

1. Xu J et al. Mortality in the United States, 2021. NCHS Data Brief. 2022 Dec;(456):1-8. PMID: 36598387.

2. Centers for Disease Control and Prevention, diabetes data and statistics.

3. American Diabetes Association. The Cost of Diabetes.

4. Martin JA et al. Births: Final data for 2007. Natl Vital Stat Rep. 2010 Aug 9;58(24):1-85. PMID: 21254725.

5. Osterman MJK et al. Births: Final data for 2021. Natl Vital Stat Rep. 2023 Jan;72(1):1-53. PMID: 36723449.

6. Gregory ECW and Ely DM. Trends and characteristics in prepregnancy diabetes: United States, 2016-2021. Natl Vital Stat Rep. 2023 May;72(6):1-13. PMID: 37256333.

7. QuickStats: Percentage of mothers with gestational diabetes, by maternal age – National Vital Statistics System, United States, 2016 and 2021. MMWR Morb Mortal Wkly Rep. 2023 Jan 6;72(1):16. doi: 10.15585/mmwr.mm7201a4.
 

8. Langer O et al. The Diabetes in Pregnancy Study Group of North America – Introduction and summary statement. Prenat Neonat Med. 1998;3(6):514-6.

9. Willhoite MB et al. The impact of preconception counseling on pregnancy outcomes. The experience of the Maine Diabetes in Pregnancy Program. Diabetes Care. 1993 Feb;16(2):450-5. doi: 10.2337/diacare.16.2.450.

10. McElvy SS et al. A focused preconceptional and early pregnancy program in women with type 1 diabetes reduces perinatal mortality and malformation rates to general population levels. J Matern Fetal Med. 2000 Jan-Feb;9(1):14-20. doi: 10.1002/(SICI)1520-6661(200001/02)9:1<14::AID-MFM5>3.0.CO;2-K.

11. Rosen JA et al. The history and contributions of the Diabetes in Pregnancy Study Group of North America (1997-2015). Am J Perinatol. 2016 Nov;33(13):1223-6. doi: 10.1055/s-0036-1585082.

12. Driggers RW and Baschat A. The 12th meeting of the Diabetes in Pregnancy Study Group of North America (DPSG-NA): Introduction and overview. J Matern Fetal Neonatal Med. 2012 Jan;25(1):3-4. doi: 10.3109/14767058.2012.626917.

13. Langer O et al. The proceedings of the Diabetes in Pregnancy Study Group of North America 2009 conference. J Matern Fetal Neonatal Med. 2010 Mar;23(3):196-8. doi: 10.3109/14767050903550634.

14. Reece EA et al. A consensus report of the Diabetes in Pregnancy Study Group of North America Conference, Little Rock, Ark., May 2002. J Matern Fetal Neonatal Med. 2002 Dec;12(6):362-4. doi: 10.1080/jmf.12.6.362.364.

15. Reece EA and Maulik D. A consensus conference of the Diabetes in Pregnancy Study Group of North America. J Matern Fetal Neonatal Med. 2002 Dec;12(6):361. doi: 10.1080/jmf.12.6.361.361.

16. Gabbe SG. Summation of the second meeting of the Diabetes in Pregnancy Study Group of North America (DPSG-NA). J Matern Fetal Med. 2000 Jan-Feb;9(1):3-9.

17. Vital Statistics of the United States 1990: Volume I – Natality.

18. Martin JA et al. Births: final data for 2000. Natl Vital Stat Rep. 2002 Feb 12;50(5):1-101. PMID: 11876093.

19. Martin JA et al. Births: final data for 2010. Natl Vital Stat Rep. 2012 Aug 28;61(1):1-72. PMID: 24974589.

20. CDC Website. Normal weight, overweight, and obesity among adults aged 20 and over, by selected characteristics: United States.

21. Wang Y et al. Has the prevalence of overweight, obesity, and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic. Int J Epidemiol. 2020 Jun 1;49(3):810-23. doi: 10.1093/ije/dyz273.