Complex patterns of multimorbidity associated with severe COVID-19 and long COVID, 2024, Pietzner et al.

[SNT Gatchaman]

Complex patterns of multimorbidity associated with severe COVID-19 and long COVID
Pietzner, Maik; Denaxas, Spiros; Yasmeen, Summaira; Ulmer, Maria A.; Nakanishi, Tomoko; Arnold, Matthias; Kastenmüller, Gabi; Hemingway, Harry; Langenberg, Claudia

BACKGROUND
Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses are scarce but may help to understand severe COVID-19 among patients at supposedly low risk.

METHODS
We systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID.

RESULTS
Here we identify 679 diseases associated with an increased risk for severe COVID-19 (n = 672) and/or Long COVID (n = 72) that span almost all clinical specialties and are strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we establish consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This includes a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observe partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis or rheumatoid arthritis, possibly indicating a segregation of disease mechanisms.

CONCLUSIONS
Our results provide a unique reference that demonstrates how 1) complex cooccurrence of multiple – including non-fatal – conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.



Link | PDF (Nature Communications Medicine) [Open Access]

 

[SNT Gatchaman]

Among the diseases for which we observed consistent evidence from survival and genetic analysis to be linked to severe COVID-19 were multiple examples that have been rarely if at all reported. For example, we observed consistent evidence that symptoms of malaise and fatigue, as well as chronic fatigue, predisposeto severe COVID-19. While the vast amount of literature currently discusses or reported these symptoms and disease as characteristics for COVID-19 and its post-acute sequelae, little to nothing is known why patients reporting fatigue might be at higher risk.

While our definition of ‘malaise and fatigue’ covered a broad range of partially unspecific medical codes with most cases (n = 83,316 out of 87,908, 92.4%) originating from primary care, we observed consistent evidence for the refined diagnosis of chronic fatigue classified as post-viral fatigue symptom (Supplementary Data 2). A hypothesis might be, that patients that are already suffering from post-viral symptoms are at a greater risk in general to suffer from more severe courses of viral infections through yet to be identified mechanisms, that may well comprise an altered immune response.

 

[Hutan]

In contrast, pre-existing diseases associated with an increased risk for Long COVID only partially overlapped with those increasing the risk for severe COVID-19. Most notably, we replicated associations with anxiety disorders28 (HR: 2.59; 95%-CI: 2.09–3.20; p-value:1.8 x 10−18) and other mental health symptoms, but most prominently with symptoms of malaise and fatigue (HR: 2.78; 95%-CI: 2.29–3.37; p-value:1.5 x 10−25) that are hallmarks of Long COVID and were also strongly associated with severe COVID-19.

 

[rvallee]

So, following this analysis, COVID causes a huge number of cases where fatigue and malaise are prominent disabling symptoms, symptoms which themselves represent higher risk factors for severe acute COVID and Long Covid, as well as across the board worsening of all conditions, which also represent higher risk factors for worsening, hence feeding into a massive crisis of disability. But of course decades of mislabeling of many of those risk factors as anxiety and mental health is feeding into the beliefs about self-perpetuating behavioral conditions, so it's unlikely that a course correction will happen any time soon, since that massive crisis of disability is simply blamed on people being lazy, based on incorrect data and flawed assumptions.

Sounds very not smart to have chosen a strategy of endless mass reinfections, having lied their ass off that it would do no such thing, that in fact it would be beneficial for health, or whatever. But of course it was such an incompetent and obviously failed decision that there is almost no way to backtrack on it, as it would embarrass far too many people in a profession that values conforming to doing the wrong thing above doing the right thing without authorization.

Glad to see we're in such good hands with great leadership acting in good faith, and all that. But at least billionaires and multimillionaires, our modern aristocracy in all but name, are doing better than ever, and that has to be worth all of this. We're even back to public sacrifices, just without the ceremony and stuff. Well, I guess there's a lot of cheering and rejoicing, it's just happening independently of it.

 

[Hutan]

Our findings show the value of using primary care health records and the need to consider all the medical history of patients to identify those in need of special protection.

This sort of a study (looking at medical records and predicting risks of future illness) is a goldmine for health insurers as well as for health service providers.

Pietzner, Maik 1,2, 3; Denaxas, Spiros 4,5,6,7; Yasmeen, Summaira 1; Ulmer, Maria A. 8; Nakanishi, Tomoko 2; Arnold, Matthias 8,9; Kastenmüller, Gabi 8; Hemingway, Harry 4,5,7; Langenberg, Claudia 1,2,3


1 Computational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany.
2 Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
3 MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
4 Institute of Health Informatics, University College London, London, UK.
5 Health Data Research UK, London, UK.
6 British Heart Foundation Data Science Centre, London, UK.
7 National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK.
8 Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
9 Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA. 10

 

[Hutan]

Here, we collate millions of health records from primary care, hospi- talisations and cancer registrations, and death records among ~500,000 participants of the UK Biobank (UKB) into medical diagnosis concept terms9, so-called ‘phecodes’10, to systematically assess the risk for severe COVID-19 and its post-acute sequalae, Long COVID, across the breadth of medical diagnosis. Apart from well-recognized high-risk patient groups, such as those with chronic kidney disease or those with compromised immune function, we demonstrate consistent evidence for the possible role of less recognized diseases and symptoms, including malaise and fatigue, based on survival and genetic susceptibility analyses.

To define ‘Long COVID’ we used primary care data released by UKB (covid19_emis_gp_clinical.txt, cov- id19_tpp_gp_clinical.txt) searching for codes indicating suspected diagnosis

[CTV3:
Y2b89 – “Referral to post-COVID assessment clinic”,
Y2b8a – “Referral to Your COVID Recovery rehabilitation platform”,
Y2b87 – “Post-COVID-19 syndrome”, and
Y2b88 – “Signposting to Your COVID Recovery”;

SNOMED-CT:
1325161000000102 – “Post-COVID- 19 syndrome”,
1325031000000108 – “Referral to post-COVID assessment clinic”,
1325041000000104 – “Newcastle post-COVID syndrome Follow- up Screening Questionnaire”,
1325181000000106 – “Referral to Your COVID Recovery rehabilitation platform”,
1325021000000106 – “Ongoing symptomatic disease caused by severe acute respiratory syndrome coronavirus 2”, 1325141000000103 – “Signposting to Your COVID Recovery”,
1325081000000107 – “Assessment using Post-COVID-19 Functional Status Scale structured interview”,
1325061000000103 – “Assessment using COVID-19 Yorkshire Rehabilitation Screening tool”,
1325071000000105 – “Assessment using Newcastle post-COVID syndrome Follow-up Screening Questionnaire”, 1325051000000101 – “COVID-19 Yorkshire Rehabilita- tion Screening tool”].

In principle, I like the idea of looking very broadly for risk factors in medical records. But, we know how bad medical records can be. The definition of Long Covid, which is a vague term at best, will include people with all sorts of health issues. Unfortunately, it's probably going to be a garbage-in garbage out situation.

 

[Hutan]

We identified a total of 7507 (hospitalisation), 662 (respiratory failure), and 1546 cases (death), with first cases occurring end of January 2020. Due to restricted availability of primary care data, we only included records up until 30/09/2021 to identify 470 cases of Long COVID.

I think it's worth noting the much smaller sample size for the Long Covid analysis.



Looking a bit closer at Figure 2, I see that the y axes are different for each of the four analyses.
Hospitalisation 0-300
Respiratory failure 0-50
Death 0-200
Long Covid 0-25

The y axis is log10 (p value), so it's a measure of how certain they are that there is an effect there. It isn't a measure of the estimated hazard ratio. The maximum strength of the associations are much lower for Long Covid, and yet, with the variable y-axes, they are presented as being equivalent. They definitely aren't.

It's a shame that they don't report the hazard ratios for the most significant correlations in the main paper.

 

[Hutan]

There's a lot of interesting data in Supplementary Information 2 (bearing in mind the problems with the definition of Long Covid and the accuracy of the medical records).

Mental disorders:
Not significant and trending towards reducing risk of Long Covid

Obsessive-compulsive disorders
Major depressive disorder
Schizophrenia - not significant
Alzheimer's, some other dementias - not significant
Personality disorders - not significant
Substance addiction and disorders - not significant
(note, all of the above trended to less risk of Long Covid)

Not significant and trending towards increasing risk of Long Covid

Alcohol-related disorders
Hallucinations
Aphasia/speech disturbance
Altered mental status
Adjustment reaction
Tics and stuttering
Psychosis
Somatoform disorder HR 2.05 p 0.08
Developmental delays and disorders
Conduct disorders
Specific nonpsychotic mental disorders due to brain damage
Transient mental disorders due to conditions classified elsewhere
Antisocial/borderline personality disorder
Anorexia nervosa
Eating disorder

Significant (Hazard ratio and p value)

Other signs and symptoms involving emotional state - 1.55, 0.045
Tobacco use disorder, 1.60, ^-5
Alcoholism, 1.64, 0.027
Agoraphobia, 1.67, ^-8
Generalised anxiety disorder, 1.96, ^-3
Memory loss, 1.98, ^-3
Bipolar, 2.05, 0.044
Sexual and gender identity disorders, 2.12, ^-3
Tension headache, 2.33, ^-6
Symptoms involving head and neck (sic), 2.37, ^-3
Anxiety disorder, 2.4, ^-17
Other mental disorder, 2.45, ^-14
Dysthymic disorder, 2.55, 0.023 (I think that's mild depression?)
Anxiety disorders, 2.59, ^-18
Phobia, 2.67, ^-5
Suicide/self-inflicted injury, 2.7, 0.016
Neurological disorders (sic), 2.74, ^-3
Psychogenic disorder, 2.74, ^-6
Posttraumatic stress disorder, 2.96, ^-3
Alteration of consciousness, 3.18, ^-3
Vascular dementia, 5.23, ^-3
Symbolic dysfunction, 5.70, 0.014

No data - rows show "NA"

Dementia with cerebral degenerations
Paranoid disorders
Mood disorders (?)
Dissociative disorder
Speech and language disorder
Other persistent mental disorders due to conditions classified elsewhere
Swelling, mass, or lump in head or neck (yes, that's classified as a mental disorder)
Decreased libido
mental retardation
acute reaction to stress
autism
learning disorder
psychogenic and somatoform disorders
impulse control disorder
pervasive developmental disorders

What to conclude from all of that? First, the data is messy. And, there's a really weird inconsistency in the attribution of 'mental disorder' or 'neurological' to the items. Alzheimers, for example, is classed as a mental disorder. Parkinsons is 'neurological'.

There are a whole lot of disorders for which there is no information and it's not clear why. For example, it should have been possibly to identify 'autism' in someone's medical records and it's common enough that there should have been some data to present.

There's a possibility that some disorders reduced the risk of getting Covid-19 during the period of the study e.g. OCD. And, if you don't get Covid-19, in theory you don't get Long Covid. A diagnosis of somatoform disorder was not significant for the risk of later having a diagnosis of Long Covid, but a diagnosis of psychogenic disorder was. Having a medical history of depression seemed to reduce the risk of later being diagnosed with Long Covid - was that because symptoms were just attributed to depression, or because people with depression don't go to super spreader events so much?

Anxiety and its various forms in medical histories seems to be a major risk for a Long Covid diagnosis. (e.g. Anxiety Disorder HR 2.4, with an extremely small p value of 3.85 ^-17). How to explain it?

Some of the people had diagnoses of Chronic fatigue syndrome and symptoms of fatigue and psychogenic disorder in their records, so they already had symptoms that might later qualify them for a Long Covid diagnosis. If people had pre-Covid fatigue and orthostatic intolerance symptoms and had been trying to get their symptoms investigated, it's likely that they would have ended up with at least one doctor putting their symptoms down to anxiety.

Perhaps women in general are more likely to get a diagnosis of anxiety in their records, and so a correlation between anxiety and Long Covid is just partly a reflection of women being more likely to get ME/CFS-like Long Covid?


There are other things to look at in the data. There seems to be quite a few skin infection, urinary tract disorder and musculoskeletal correlations.

 

[Sean]

But of course decades of mislabeling of many of those risk factors as anxiety and mental health is feeding into the beliefs about self-perpetuating behavioral conditions,

I have no doubt this problem has massively skewed and corrupted attempts to understand a whole bunch of health issues and their causes, including mental health.