Changing patterns of multimorbidity among patients with ambiguous diagnoses: The case of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

doi:10.21203/rs.3.rs-4000390/v1

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Changing patterns of multimorbidity among patients with ambiguous diagnoses: The case of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

https://doi.org/10.21203/rs.3.rs-4000390/v1

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Introduction:

Increased attention has been given to multimorbidity. This phenomenon often has been explored through associations between diseases based on the frequency of combinations of diagnoses. A more patient-focused strategy, together with a trajectory of the multimorbidity situation, has recently attracted increased interest. Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a state that per se includes multimorbidity and is preceded by numerous ambiguous diagnoses. A history of infectious disease is considered the most common denominator and pathophysiological base of ME/CFS, even though no biomarker of such infection has been found. Exploring changing patterns of multimorbidity might contribute to the knowledge about the unknown pathophysiology of this disease.

Methods and materials:

This was a retrospective register-based cohort study of 549 patients from 2015–2020 in Region Stockholm, Sweden. We processed the number and type of diagnoses during each year according to the Adjusted Clinical Groups© case-mix system from Johns Hopkins University. The images were visualized with Excel and “Gapminder” software.

Results

Fifty percent of all patients had more than eight diagnoses—up to 32 diagnoses per year—during the last twelve months of the study period. The most frequent diagnosis clusters were “General Signs and Symptoms”, “Musculoskeletal”, “Psychosocial” and “Neurologic”. The cluster “Infections” was found as number fifteen in order. The number of patients with psychiatric or psychosomatic disorders was lower after the registration of ME/CFS diagnoses.

Discussion and conclusion:

This study illustrates the complexity of the pattern of multimorbidity over time, depicting overlapping diagnoses. Analyses of trajectories of different patterns of multimorbidity might pave the way for a broader understanding of triggers for more complex health conditions, as seen in the case of ME/CFS. The traditional view that ME/CFS is the result of an infectious disease was not confirmed in our cohort. The drive toward patient-oriented analyses may lead to findings of subcategories of patients with similar patterns of multimorbidity over time.

Multimorbidity

Myalgic encephalomyelitis

Chronic fatigue syndrome

Patient-Centered Care

Adjusted Clinical Groups©

Data visualization

Postviral fatigue syndrome

Chronic pain

Multimorbidity has attracted increased amounts of attention from health care providers and policy makers due to the increasing age, patient expectations, diagnostic challenges, and rising costs of these diseases. The concept has been described as the presence of multiple chronic diseases, often with a cutoff of two or more, in one individual (1). Most methods thus far have explored multimorbidity through associations between diseases based on the frequency of combinations of diagnoses (2). Various statistical analyses, mostly exploratory factor analysis (EFA), have been used, rendering groups with various clusters of diagnoses (3,4,5). Recently, this approach has been complemented by a more patient-oriented strategy, and the trajectory of the multimorbidity situation has attracted increased interest (6).

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) includes multiple morbidities and comorbidities and is often preceded by numerous other, often ambiguous, diagnoses over long periods of time. ME/CFS is classified by the World Health Organization as a neurological disease of postviral origin (ICD-10 Code G93.3) according to the specified observation criteria. Healthcare in Sweden has chosen to follow the Canada Consensus Criteria, with severe chronic fatigue, postexertional malaise, pain, and neurological and immunological dysfunction serving as key criteria for ME/CFS (7).

The prevalence of ME/CFS has previously been reported to be between 0.2 and 1.6% (8), and a recent meta-analysis reported a prevalence of 0.9% according to the most commonly used case definition CDC-1994 (9). Despite decades of research, the etiology and pathophysiology of ME/CFS are still unknown, and further research is needed. The leading theory suggests that viral infections such as chronic Epstein–Barr virus infection instigate disease development by triggering neuroinflammation and/or autoimmunity (10). If the viral model proves correct, the incidence of ME/CFS is expected to increase substantially in rice as a consequence of the COVID-19 pandemic (11,12). Long COVID-19 symptomatology is indeed very similar to that of ME/CFS (13). However, other theoretical explanations for ME/CFS have been proposed, such as genetic predispositions for metabolic dysfunctions (14) and craniocervical obstructions leading to disturbed cerebrovascular flow/intracranial hypertension (15,16), all of which have pathophysiological consequences.

ME/CFS is a multiorgan system disease with some variability among patients, with dominant symptoms differing between patients (17). To our knowledge, the first study to offer a broad epidemiologic overview of a cohort of patients with ME/CFS was published by the Stanford ME/CFS Initiative (18). The evolution of symptoms was proposed to provide new interpretations of earlier findings and thereby offer insight and solutions for the diagnosis, etiology, pathophysiology, and treatment of these conditions.

Aim

The overall aim of this study was to elucidate the multimorbidity patterns among patients with ME/CFS. More specifically, elucidating, analyzing and visualizing patterns of diagnoses over time will contribute to the knowledge of changes in multimorbidity and improve the understanding of ME/CFS complexity.

Study design

This was a retrospective registry-based cohort study. At the request of the Stockholm County Council in Sweden, now Stockholm Region, an outpatient, tertiary specialty clinic was established in 2017, aimed at ME/CFS diagnosis, treatment, and research. All participants in this study were recruited from this clinic. The inclusion criterion was enrollment in the ME/CFS specialist unit at the time of September 2020. The data in our study were retrieved from a health care database in the Stockholm region and included all diagnoses of all patients from January 2015 to the end of June 2020. The database covers all registered contacts with public caregivers in the region of Stockholm. In total, 795 patients provided consent to participate. Of those, 246 patients were excluded due to their dwelling outside the region, resulting in 549 patients in our study.

The Adjusted Clinical Groups® (ACG®)

Diagnoses were processed by the Adjusted Clinical Groups© case-mix system from Johns Hopkins University (ACG©), version 12.1 (19). With a person-oriented perspective of multimorbidity in mind, we used the ACG case-mix system, where the subject of the grouping was the patient, yielding approximately 100 mutually exclusive patient categories. The outcome measures were as follows:

Number of diagnoses per patient. Unique to each patient, no doubles.
The type of diagnosis cluster, “Expanded Diagnosis Cluster” (EDC), was also merged into major groups (MEDC). A total of 286 EDCs were classified as belonging to a cluster of similar diagnoses and were grouped into 27 MEDCs.
“Rescaled ACG Concurrent Risk”. The estimated risk of needing health care resources for each individual depends on the mix of each patient’s type of diagnosis.

The origin and rationale for clustering diagnoses into expanded diagnosis clusters are shown in the supplementary file, where a complete list of all 286 EDCs is given with full descriptions (Supplementary Data 1).

The algorithm used to calculate the ACG Concurrent Risk is embedded in the ACG system, mirroring the burden of illness in the population based on each patient’s estimated need for resources from health care. The score was rescaled to set the mean to 1.0 for the defined population.

Analysis and visualization

In addition to the descriptive and analysis tools embedded in the ACG system, Excel was used. Visualizations were made by Excel and the “Gapminder” program, allowing us to show trajectories in four dimensions (20).

Study population characteristics

Our study population cohort consisted of 549 patients. Among those, 505 patients were diagnosed during 2015, and approximately 10 patients were diagnosed each year. The age and sex distributions are shown in Fig. 1. The age range was between 6 and 72 years. Female patients were most common, representing 83% of the cohort.

Frequency of diagnoses and clusters of diagnoses

Patients in our examined cohort presented a high degree of multimorbidity, with numerous diagnoses. More than half of all patients had more than eight diagnoses during the last twelve months of the study period, and up to 32 diagnoses were the most common. More than 15% of the patients had fifteen or more diagnoses, and among them, more than one-third had five or more chronic diagnoses.

The distribution of major expanded diagnosis clusters (MEDCs) is shown in Table 1. The ICD-10 diagnosis code for ME/CFS (G93.3) belongs to the “General Signs and Symptoms” cluster. Consequently, “General Signs and Symptoms” represented the most common MEDC in our sample; 215 of the 505 patients were diagnosed during the first year of 2015, and all but five patients were diagnosed at the end of the study period (June 2020). The second most common MEDC was “musculoskeletal”, followed by “psychosocial”. An MEDC covering “Infectious” was less common, with 71 patients in 2015, 83 in 2017, and 64 at the end of the study period.

Table 1

Major Expanded Diagnosis Clusters during 2015–2020. (N = 549)
MEDC description	2015	2016	2017	2018	2019	2019-20
	(n = 505)	(n = 512)	(n = 523)	(n = 535)	(n = 543)	(n = 545)
General Signs and Symptoms	215	256	332	424	527	540
Musculoskeletal	213	227	234	309	365	360
Psychosocial	262	323	331	350	330	298
Administrative	299	308	325	345	331	274
Neurologic	213	225	242	261	242	241
Cardiovascular	58	78	80	93	130	121
Skin	113	134	127	121	127	117
Female Reproductive	119	125	120	123	112	115
General Surgery	116	118	118	103	108	95
Ear, Nose, Throat	128	123	126	112	105	88
Endocrine	65	75	79	85	93	88
Gastrointestinal/Hepatic	67	81	83	71	75	78
Eye	65	69	70	78	74	74
Respiratory	87	85	91	73	69	69
Infections	71	67	83	68	62	64
Allergy	70	62	66	69	62	57
Genito-urinary	60	54	55	50	57	53
Nutrition	18	33	26	27	25	32
Rheumatologic	25	24	29	33	32	29
Hematologic	19	18	16	22	27	19
Reconstructive	10	11	12	9	20	15
Malignancies	10	8	9	8	11	12
Toxic Effects and Adverse Events	13	21	14	6	11	9
Dental	3	12	5	7	10	7
Renal	2	2	5	10	3	5
Genetic	1	3	4	3	3	4

To illustrate the distribution during the five years, the six most frequent clusters are shown in Fig. 2. The “administrative” MEDC was neglected, and the category “general signs and symptoms” was excluded because of inclusion due to the Canada Consensus Criteria.

Patient burden-of-illness categories

The ACG system generates an individual “need score” representing the individual burden-of-illness for each patient. To identify different patterns of multimorbidity, patients were divided into quartiles based on their need scores. The resulting “Rescaled ACG Concurrent Risk” is shown in Table 2, in which the need score is rescaled (the average need score is set to 1.0). In the lowest quartile (Q4), the median age was somewhat lower, as was the proportion of female patients.

Table 2

Distribution of patient need scores by quartile in 2020
Risk quartile	Score range	Mean age	Patients	Female
(N^o)	(#)	(#)	(n)	(%)
Q1	5.08–1.43	42	137	87.6
Q2	1.43–0.76	43	137	87.6
Q3	0.76–0.43	42	137	86.1
Q4	0.43–0.00	40	138	79.7

An analysis of the number of diagnoses over the years showed that the mean was somewhat greater each year for patients in the highest need quartile. However, in the lowest need quartile, the mean rose during the first four years and decreased by 50% during the last year.

Multimorbidity patterns

The clusters of diagnoses in terms of expanded diagnosis clusters (EDCs) are shown for each year in Table 3. The table is limited to 50 or more patients by the year 2020 in the groups displayed. The most frequent diagnosis clusters give signs of ambiguous states throughout the years. The number of patients with psychiatric or psychosomatic disorders decreased during the last period.

Table 3

Annual Expanded Diagnosis Clusters (n = 549)
EDC	EDC name	2015	2016	2017	2018	2019	2020
GSI06	Debility and undue fatigue	119	148	220	371	511	538
MUS01	Musculoskeletal signs and symptoms	100	117	119	180	243	248
ADM05	Adm. concerns and nonspec. lab. abnorm.	232	269	271	250	243	212
GSI01	Nonspecific signs and symptoms	119	128	175	187	220	194
PSY13	Adjustment disorder	133	176	190	206	192	157
PSY01	Anxiety, neuroses	98	118	132	132	122	117
MUS17	Musculoskeletal disorders, other	63	62	66	101	128	114
MUS13	Cervical pain syndromes	44	37	40	87	132	111
NUR01	Neurologic signs and symptoms	94	104	115	120	112	98
PSY09	Depression	109	149	124	131	110	93
MUS14	Low back pain	53	46	54	66	89	75
NUR10	Sleep problems	31	39	43	58	56	55
ADM07	Medical counseling/advice	42	42	47	124	117	54
GSU10	Abdominal pain	70	75	69	61	45	52
PSY05	Attention deficit disorder	37	52	52	56	58	52
EAR11	Acute upper respiratory tract infection	88	90	92	66	61	51
NUR21	Neurologic disorders, other	14	18	20	28	45	51
END04	Hypothyroidism	35	43	43	47	54	50

A number of different analyses have been carried out, combining various groups of frequent MEDCs. Patients with the highest number of diagnoses during the last 12-month period of our study were identified. In total, 22 patients with combinations of all of the MEDCs, “GSI”, “MUS”, “PSY” and “NUR”, and with 22 diagnoses or more each year were compared backward over the years in terms of their number of diagnoses, number of MEDCs and ACG risk score; here, we used an unscaled score because of the low number of patients. The combination of the number of diagnoses and the ACG unscaled risk score over time is shown in Fig. 3 by the “Gapminder” program. The total number of diagnoses is sometimes a false indicator of a high ACG score. The combinations of different types of EDCs might yield a higher ACG risk score than can be achieved with just high numbers of diagnoses.

Main findings

First, our cohort showed a high degree of multimorbidity—although not elderly—and the multimorbidity pattern changed over time. Second, the leading pathophysiological theory of ME/CFS as a postviral infection was not verified. Third, the importance of multimorbidity and comorbidity needs to be discussed from the patient perspective.

Patterns of multimorbidity

A high degree of multimorbidity, with up to 32 diagnoses per year, was found in our patient cohort. More than 15% of the patients had fifteen or more diagnoses per year, many of which were chronic.

These findings can be compared with those of another study from Forslund et al. of the same database in Stockholm County describing multimorbidity in the primary health care population (n = 2 323 667), which included all consultations in primary and specialized outpatient care and all inpatient care. A total of 3.3% had more than 5 diagnoses, and only 0.1% had more than 10 diagnoses during the studied year. (20). The degree of high multimorbidity can be stated as higher than expected from these facts in patients with ME/CFS.

Patterns including combinations of musculoskeletal, psychosocial and neurologic diagnosis clusters were common. Interestingly, we found changing patterns over time, indicating that some ambiguous diagnoses diminished when the patient was diagnosed with ME/CFS. For example, psychosocial diagnoses were very common in earlier years and less frequent in later years. This raises the question of whether patients may have been misdiagnosed or if psychosocial diagnoses were used as a coverup for unknown symptoms of debility and undue fatigue.

The observation that psychiatric diagnoses are common for patients seeking fatigue was shared by Natelson and others after studying 92 patients with fatigue or other medically unexplained syndromes. Current and lifetime psychiatric diagnoses were found in 68% of patients but were not associated with their diagnosis of fatigue. Like us, they conclude that patients with ME/CFS “should be evaluated for psychiatric diagnosis because of their impact on patient quality of life, but they do not act as a symptom multiplier for the illness”. (22)

Viral origin

A possible viral origin of the ME/CFS diagnosis was found in only 25 patients in our cohort based on the relevant diagnosis cluster “INF06” (“virus syndrome”). This EDC cluster was thereby counted as number 26 in order of prevalence among the approximately 200 actual EDCs. The WHO definition of ME/CFS indicating a virus infection (REF) origin could not be confirmed in our cohort. Patterns including musculoskeletal signs and symptoms and cervical spine and neurological diagnoses were considerably more common than any infectious disease.

This was confirmed to some extent in another large study from Barcelona University, where Castro and others also found significant comorbidities and identified five clusters among 1757 subjects with ME/CFS. The major clusters were dominated by pain and connective tissue disorders, and even if there were many symptoms related to the immune system, infections were not described as a major factor. (23)

On the other hand, a recent systematic assessment of 64 studies by Hwang et al. of 4791 patients with ME/CFS and 9221 controls showed an odds ratio greater than 2 for five different viral infections between healthy and diseased patients, suggesting a virus-related etiopathogenesis of ME/CFS. (24)

However, our observation of rather few “virus syndromes” might be explained by patients having significant viral infections not leading to health care visits or diagnosis or having mild infections mistaken for other diagnoses. Reactivated dormant virus infections caused by stress might also be misdiagnosed. A retrospective study over a longer time may reveal other patterns.

Meaning of multimorbidity and comorbidity

Multimorbidity is commonly described as the existence of two or more diagnoses at the same time, often restricted to long-lasting conditions. Comorbidity is commonly described as an index disease with other conditions connected to this index disease in an evidence-based way. The WHO has described multimorbidity as a situation with “discordant” diagnoses and comorbidity as a situation with “concordant” diagnoses. (25)

The diagnostic definition of ME/CFS includes dysfunction of several organ systems. Thus, the diagnosis itself tends to be multimodal. In our opinion, the idea of counting diseases and diagnoses will be meaningless in many patients with ambiguous diagnoses, as well as among diagnoses with criteria-based definitions. Therefore, our study takes a patient perspective.

A shift from a disease to a patient perspective is also essential for preventing the spread of morbidity, as expressed by researchers addressing the complexity approach:

“Health problems and treatments tend to interact in ways that increase the danger of a narrow, conventional biomedical/EBM focus. … can lose sight of the whole and ignore the fact that improving human health requires different approaches than just treating the sum of diseases. … Starting with the person in their family and community context, rather than starting with the disease, enables the doctor to focus on what is most important to the patient. Doing that over time, with empathy, … has beneficial effects for multiple conditions … and avoids harmful interactions among therapies ― it addresses the system of the person-as-a-whole.” (26)

Strengths and weaknesses

One main strength of our study is the patient-oriented approach of grouping individuals with diseases rather than just the diseases or diagnoses themselves. Additionally, we were able to collect every diagnosis that was registered within the region for each of the patients during the five-and-a half-year period. Furthermore, we used a relatively large study cohort, and the register-based study design eliminated recall bias. Most data on diagnoses have been registered as the “main diagnosis”, which is the basis for reimbursement in hospital care. However, in the database of Region Stockholm, we were able to collect all diagnoses registered, though we were aware of the risk of neglecting some diagnoses, judged as conditions of “minor interest”.

Finally, this is one of relatively few studies to visualize the complexity of the pattern of multimorbidity over time. The longitudinal design highlights the changing patterns of overlapping diagnoses and may provide insights into the interactions between them.

One of the weaknesses of our study, due to the design, is the lack of comparisons with other populations. Another weakness is the relatively short period of time needed to find significant changes in morbidity patterns. This research is warranted by initiating a retrospective study with a larger cohort (approximately 2800 patients) with data from a period of twenty years.

Implication for health care

Analyses of the trajectories of various patterns of multimorbidity may pave the way for a broader understanding of triggers for more complex health conditions. This may represent a promising step toward handling care for patients with ME/CFS. Our study might lead to actions to prevent more complex conditions in these patients. The diagnosis of ME/CFS may be considered even if infections are not registered. Additionally, if many diagnoses of ambiguous character are at hand, a ME/CFS diagnosis might be considered, and the different diagnoses may be found to be part of a common pathophysiological entity over time.

Working with high numbers of diagnoses, cluster analysis, and not just statistical analysis may be useful for identifying subgroups of patients with somewhat similar patterns of multimorbidity. The EDC algorithm embedded in the ACG system gave us the opportunity to work with 286 well-defined clusters of diagnoses. Our study presents another perspective on multimorbidity, where the diseases themselves, and the counting them, are of minor interest and where the focus will shift to groups of patients with different combinations of conditions, exploring multimorbidity and comorbidity.

Furthermore, the drive toward a patient-centered perspective can lead to a more holistic view to help patients with ME/CFS cope with their situation. It seems important to have a multidisciplinary approach to treating ME/CFS.

The aim of this study was to elucidate the multimorbidity patterns among patients with ME/CFS by highlighting and visualizing their complex illnesses over time. We found unexpected patterns of multimorbidity where patients had a very high number of diagnoses at the same time. The leading theory of infections preceding the ME/CFS condition was not confirmed. However, combinations of musculoskeletal, psychosocial and neurologic diagnosis clusters were common, and the prevalence of psychosocial diagnoses decreased over time. At the societal level, multimorbidity refers to overlapping diseases; however, from a patient-centered perspective, diseases tend to interact.

Conflict of interest

The authors declare that our research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Funding

Data acquisition was financed by Medect Clinical Trials AB at Bragée Clinics. Open access funding was provided by the Karolinska Institutet.

Author Contributions

The conceptualization and methodology were created by LC, BB and BCB. The ACG software was used by LC, and the resources were arranged by BB. Analyses and validation of the results by BB, LC, PS and BCB. The manuscript was written by BB and LC, reviewed by PS, and edited by BCB. The data were visualized by BB and LC. The project administrator and responsible for funding acquisition was BB. All the authors contributed to the article and approved the submitted version.

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You are reading this latest preprint version

Changing patterns of multimorbidity among patients with ambiguous diagnoses: The case of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Status:

Version 1

Abstract

Introduction:

Methods and materials:

Results

Discussion and conclusion:

Figures

Introduction

Aim

Methods and Materials

Study design

The Adjusted Clinical Groups® (ACG®)

Analysis and visualization

Results

Study population characteristics

Frequency of diagnoses and clusters of diagnoses

Patient burden-of-illness categories

Multimorbidity patterns

Discussion

Main findings

Patterns of multimorbidity

Viral origin

Meaning of multimorbidity and comorbidity

Strengths and weaknesses

Implication for health care

Conclusion

Declarations

Conflict of interest

Funding

Author Contributions

References

Supplementary Data 1

Status:

Version 1