A biopsychosocial analysis of risk factors for long-term physical, cognitive, and psychological functioning in previously hospitalized post-COVID-19 patients

doi:10.21203/rs.3.rs-5071522/v1

A significant number of COVID-19 survivors continue to experience persistent physical, cognitive, and psychological symptoms up to one year after discharge. This study aimed to examine the frequency, severity, and progression of these symptoms, along with contributing factors. This single-centre retrospective cohort study included 126 COVID-19 patients admitted to the VieCuri Medical Centre between 2020 and 2022, with follow-ups at 3 and 12 months post-discharge. Assessments involved pulmonary function tests, CT scans, bioimpedance analysis, and questionnaires on physical, cognitive, and psychological symptoms. At both follow-ups, 31–32% of patients reported moderate to severe physical symptoms, 26–27% reported multiple cognitive symptoms, and 14–18% experienced depressive or post-traumatic stress symptoms (PTSS). Only anxiety symptoms significantly decreased from 22% at 3 months to 12% at 12 months (p = .014).

Persistent symptoms at 12 months were significantly associated with premorbid conditions (chronic respiratory disease, multiple comorbidities), injury severity (infection during the third wave), physical factors (COVID-related pulmonary abnormalities, lower total lung capacity, dyspnoea), and cognitive and psychological factors (cognitive symptoms, anxiety, depression, and PTSS) (p < .05). These findings suggest that a significant portion of COVID-19 survivors continue to experience persistent symptoms influenced by biopsychosocial factors, emphasizing the need for a biopsychosocial approach in early screening and treatment.

Health sciences/Diseases/Infectious diseases

Health sciences/Diseases/Psychiatric disorders/Anxiety

Health sciences/Diseases/Psychiatric disorders/Depression

Health sciences/Diseases/Psychiatric disorders/Post traumatic stress disorder

Biological sciences/Psychology

Health sciences/Health care

Health sciences/Risk factors

lung diseases

Post-COVID Conditions

dyspnoea

models

biopsychosocial

anxiety

cognitive dysfunction

In 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), commonly referred to as COVID-19, caused a worldwide pandemic. A cumulative total of about 774 million individuals across the world were infected until now, and nearly 12 million people have died due to the virus [1]. In the Netherlands, an estimated 8.6 million people were infected by the virus [1]. Admissions have been reliably recorded up until March 2023, with nearly 20 thousand requiring admission to the intensive care unit (ICU) and almost 124 thousand to a nursing ward [2]. Research has shown that a subgroup of patients experience persistent physical, cognitive, and psychological symptoms [3–5], posing significant challenges for the patients and for global healthcare systems. The World Health Organization (WHO) have classified symptoms that persist or newly develop within three months after a SARS-CoV-2 infection and last for a minimum of two months as post COVID-19 condition [6]. The global estimated pooled prevalence of post COVID-19 condition is 54% for those hospitalized compared to 34% for those not hospitalized [7]. The most prevalent symptoms reported are dyspnoea (34%), concentration problems (32%), and fatigue (31%) [8].

The mechanisms behind these persistent post-COVID-19 symptoms are multifaceted, encompassing both biological and psychological factors [9, 10]. Research has shown that COVID-19 affects various body systems, including the respiratory, cardiovascular, neurological, gastrointestinal, and musculoskeletal systems [11]. Furthermore, pre-existing chronic respiratory disease including asthma and chronic obstructive pulmonary disease (COPD) has also been shown to be associated with persistent long-term symptoms post-COVID-19 [12, 13]. However, less is understood about potential psychological mechanisms [14, 15], even though associations have been shown between psychological variables, including history of psychiatric diagnosis, depression, anxiety, and post-traumatic stress disorder, and post COVID-19 condition in both hospitalized and non-hospitalized patients [16, 17].

Understanding persistent symptoms post-COVID-19 seems to require a perspective in which biological, psychological, and social factors are integrated [9]. This not only may aid in understanding the aetiology, but also assists in understanding recovery processes, and the development and optimization of multimodal and interdisciplinary treatment strategies to arrange healthcare for post-COVID-19 patients. Identifying modifiable psychological factors could potentially be addressed in medical treatment, thereby enhancing patient resilience [15]. However, most studies predominantly adopt either a biomedical or psychological approach.

The objectives of the current study were to (1) explore the frequency, severity, and course of physical, cognitive, and psychological symptoms in the first year post-discharge in formerly hospitalized COVID-19 patients and (2) identify the differential contribution of biopsychosocial factors that are associated with poor outcomes, that is, persistent physical and cognitive symptoms, depression, anxiety, and post-traumatic stress symptoms (PTSS) at twelve months after discharge.

Design and Participants

This study included a cohort of adult COVID-19 patients admitted to the VieCuri Medical Centre in Venlo, Netherlands, between February 2020 and February 2022 (hospital admission = T0). All discharged patients aged 18 and above with a confirmed SARS-CoV-2 infection, by quantitative polymerase chain reaction, were invited to the post-COVID-19 outpatient clinic at 3 and 12 months post-discharge (T1 and T2, respectively). For inclusion in this study, questionnaires were completed at both follow-up assessments.

Procedure

As part of regular care, all patients were invited to undergo a standardized outpatient 3-months post-discharge follow-up assessment (T1). Patients exhibiting clinical concerns during follow-up were referred to medical and psychological specialists. Patients were treated according to the current guidelines from the Dutch National Institute for Public Health and the Environment [18]. A standardized 12 months follow-up (T2) was scheduled for patients who experienced symptoms at the 3 months follow-up assessment, while the 12 months follow-up was facultative for patients without symptoms at T1. The outpatient follow-up assessment included physical, cognitive, and psychological assessments.

Demographic, premorbid, and COVID-19 illness severity factors (i.e., hospitalization characteristics), as well as the results of the questionnaires, were retrieved from the patients' electronic medical records.

All experimental protocols were approved by Medical Ethical Committee of Maastricht University Medical Centre. Medical Ethical Committee of Maastricht University Medical Centre waived the requirement of informed consent due to the retrospective nature of the study. Patients attending the outpatient clinic were informed that their routinely collected clinical data could be used for research purposes and were given the option to opt out. All methods were conducted in accordance with the research guidelines and regulations of VieCuri Medical Centre.

Measures

The measures are described in the supplemental material.

Outcome measures at T1 and T2

We used the Four-Dimensional Symptom Questionnaire (4DSQ) to assess physical symptoms [19], the Checklist for Cognitive and Emotional Consequences (CLCE-24) to evaluate cognitive symptoms [20], the Hospital Anxiety and Depression Scale (HADS) to measure depression and anxiety [21], and the Post-traumatic Stress Symptoms Checklist-14 (PTSS-14) to assess post-traumatic stress symptoms [22].

Predictor variables at T0

The demographic factors included age and sex. Premorbid factors encompassed body mass index (BMI), chronic respiratory disease, and the Charlson Comorbidity Index (CCI) [23]. COVID-19 illness severity was assessed using the WHO COVID-19 disease severity categorization [24] and the waves of the SARS-CoV-2 infection [25].

Predictor variables at T1

Physical variables included, forced expiratory volume in one second (FEV₁) and forced vital capacity (FVC) measured using spirometry; total lung capacity (TLC) and residual volume (RV) measured using body plethysmography; diffusion capacity of the lungs for carbon monoxide (DLCO) measured using the single-breath method; and maximal inspiratory and expiratory pressure (MIP and MEP). DLCO per unit alveolar volume (DLCO/VA) was calculated. Pulmonary function parameters were expressed as percentage of predicted values [26]. Lower limit of normal (LLN), defined as the 5th percentile according to the standardized multi-ethnic reference values for spirometry from the Global Pulmonary function initiative, was used to report pulmonary function impairments [27, 28]. Of the pulmonary function parameters, we only included TLC, DLCO, and MEP in the models, since these have been shown to be commonly related to impaired health outcomes post-COVID-19 [29].

COVID-related residual pulmonary abnormalities were based on computed tomography (CT) scans and fat-free mass index (FFMI) assessed by bio impedance analysis (Bodystat 500; EuroMedix, Leuven, Belgium). Fatigue and dyspnoea were reported during the consultation, and physical symptoms were assessed using the 4DSQ [19].

Cognitive coping was measured using the Cognitive Emotion Regulation Questionnaire (CERQ) [30]. Psychological and cognitive functioning was assessed using the HADS, PTSS-14, and CLCE-24 [20–22].

Statistical Analysis

We described the study population using the mean (SD) and median [IQR] for normally and non-normally distributed variables, respectively. The numbers and proportions were reported for binary variables and variables with cut-off values.

Paired sample t-tests (parametric data) or Wilcoxon signed rank tests (non-parametric data) were conducted to investigate the course of physical, cognitive, and psychological symptoms between T1 and T2 (4DSQ, CLCE-24, HADS-anxiety, HADS-depression, and PTSS-14).

Bivariable analyses, using Pearson’s correlation coefficients (normally distributed) or Spearman’s correlation coefficient (if one variable was non-normally distributed) were used to find associations between independent variables at T0 and T1 and outcomes at T2 (4DSQ, CLCE-24, HADS-anxiety, HADS-depression, PTSS-14). Chronic respiratory disease was added as a covariate in all models due to the well-known association between COPD and cognitive impairments, as well as psychological distress (i.e. anxiety and depression) [31–33].

Five hierarchical multiple linear regression analyses were conducted with physical, cognitive, and psychological outcomes at T2 as the dependent variables (4DSQ, CLCE-24, HADS-anxiety, HADS-depression, and PTSS-14). Independent variables significantly associated with dependent variables in the bivariable analyses were entered into multivariable analyses. The following blocks were entered (in sequence): 1) demographic and premorbid factors, 2) COVID-19 illness severity, 3) physical factors at T1, and 4) cognitive and psychological factors at T1. The CERQ and 4DSQ results at T1 were not added to the models, as these variables were added at a later stage, resulting in missing data. The assumptions for linear regression modelling were verified. We transformed the dependent variables (log, square root, and polynomial) to satisfy the assumption of homoscedasticity.

Sensitivity analyses were performed to test the robustness of the findings. The analyses explored the contribution of additional physical and psychological variables, specifically the 4DSQ and CERQ subscales, as an additional block. The sensitivity analyses followed the same approach as in the primary analyses.

For all analyses, the significance was assessed at a 2-sided alpha level of 0.05. To facilitate comparisons across studies, significance was also reported at commonly used alpha levels of 0.05, 0.01, and 0.001. SPSS version 26.0 was used for data analysis [34].

A total of 1,176 patients were admitted to the hospital with a SARS-CoV-2 infection between February 2020 and February 2022. Among these patients, 651 attended the outpatient post-COVID-19 clinic 3 months post-discharge. A total of 126 patients completed the questionnaires at both outpatient assessments and were included in the study (Figure 1).

Frequency, severity, and course of physical, cognitive, and psychological symptoms

Table 1 shows the characteristics of the included patients. The median age of the patients was 68 years, and most patients were male (67%). The most common comorbidities were hypertension (42%), obesity (33%), and chronic respiratory disease (29%). Nearly half of the patients were included in the first wave of the study (49%).

Table 1: General characteristics of the hospitalized COVID-19 patients (n=126)

Demographic and premorbid factors	N (%)	Median [IQR]
Age in years		68 [61-76]
Male, n (%)	84 (67)
BMI in kg/m²		27.5 [25-31]
Co-morbidities, n (%) present
Hypertension	53 (42)
Obesity	41 (33)
Chronic respiratory disease	36 (29)
Type 2 diabetes	30 (24)
Chronic cardiac disease	30 (24)
Autoimmune disorder	19 (15)
Chronic neurologic disease	14 (11)
Rheumatologic disorder	12 (10)
Chronic kidney disease	10 (8)
Malignant neoplasm	8 (6)
CCI score		3 [2-4]
COVID-19 illness severity
Hospital stay in days		7 [5-14]
ICU admission, n (%)	22 (18)
Length of ICU stay in days		13 [6-34]
MC admission, n (%)	5 (4)
Length of MC stay in days		3 [1-6]
Days from discharge to T1		109 [99-129]
Days from discharge to T2		372 [351-405]
Oxygen treatments during hospital stay
Nasal oxygen therapy, n (%)	115 (91)
Non-invasive ventilation, n (%)	7 (6)
Invasive ventilation, n (%)	19 (15)
Severity score, n (%)
Moderate	35 (28)
Severe	68 (54)
Critical	23 (18)
Waves, n (%)
First	62 (49)
Second	24 (19)
Third	30 (24)
Fourth	10 (8)

Abbreviations: BMI, body mass index; CCI, Charlson Co-morbidity Index; ICU, intensive care unit; MC, medium care

As shown in Table 2, in total, 31 and 32% of the patients reported moderate-to-severe physical symptoms on the 4DSQ at T1 and T2, respectively. Patients reported a median of three cognitive symptoms at T1 and two at T2 on the CLCE-24, with 26 and 27% reporting six or more cognitive symptoms at T1 and T2, respectively. Additionally, 22 and 12% scored higher than the cut-off on the HADS anxiety T1 and T2, respectively. For HADS depression, 17 and 18% scored higher than the cut-off at T1 and T2, respectively. In total, 15 and 14% scored above the PTSS-14 cut-off at T1 and T2, respectively.

As shown in Table 3, 68% and 57% of the patients reported physical symptoms, that is, fatigue and dyspnoea, respectively. Among pulmonary function tests, DLCO, MEP, and TLC were impaired in 42%, 29%, and 18% of patients, respectively.

Wilcoxon signed rank tests showed that the HADS anxiety score decreased in 54 patients, it remained stable in 26, and increased in 36 patients (T=-2.542, p = 0.014). There were no significant changes in the other outcomes between T1 and T2 (p > .05).

Table 2: Results of outcome measures at T1 and T2

Outcome variables	n	Median [IQR]	>cut-off n(%)	n	Median [IQR]	>cut-off n (%)
	T1			T2
4DSQ	53	5.0 [1.5-13.5]	17 (32)	124	5.5 [2.0-12.0]	38 (31)
CLCE-24	121	3 [0-6]	31 (26)*	116	2 [0-6]	31 (27)*
HADS-depression	118	2 [1-6]	20 (17)	125	3 [1-7]	23 (18)
HADS-anxiety	117	3 [1-7]	26 (22)	125	2 [0-5]	15 (12)
PTSS-14	115	22 [17-34]	17 (15)	118	21 [16-34]	16 (14)

Abbreviations: IQR, interquartile range; 4DSQ, Four-Dimensional Symptom Questionnaire (4DSQ); CLCE-24, Checklist for Cognitive and Emotional Consequences; (HADS), HADS-depression; Hospital Anxiety and Depression Scale-depression subscale; HADS-anxiety, Hospital Anxiety and Depression Scale-anxiety Subscale; PTSS-14, Post-traumatic Stress Symptoms Checklist-14.

* ≥6 physical symptoms

Table 3: Results of predictor variables at T1

Physical variables	Mean±SD, Median [IQR]	n (%)
	% pred	Impaired*
FEV₁	93.9±21.7	17/126 (14)
FVC	96.9±17.1	11/126 (9)
FEV₁/FVC	98 [89-104]	19/126 (15)
TLC	97 [86-108]	22/122 (18)
RV	90 [81-104]	24/121 (20)
DLCO	77.6±0.5	52/125 (42)
DLCO/VA	86.6±20.1	34/125 (27)
MIP	98 [66-122]	12/125 (10)
MEP	89.6±34.0	36/125 (29)
COVID-related residual pulmonary abnormalities		105/123 (85)
FFMI, kg/m²	19 [17-20]
Fatigue		85/126 (68)
Dyspnoea		72/126 (57)
Psychological factors		low ; high **
CERQ Self-blame	4 [4-7]	0 (0) ; 2 (4)
CERQ Acceptance	10 [6-13]	6 (11) ; 1 (2)
CERQ Rumination	7 [5-9]	1 (2) ; 1 (2)
CERQ Positive refocusing	11.9±4.6	0 (0) ; 10 (19)
CERQ Planning	9 [7-13]	11 (21) ; 0 (0)
CERQ Positive reappraisal	10.5 (4.0)	4 (8) ; 0 (0)
CERQ Putting things in perspective	12 [9-16]	0 (0) ; 1 (2)
CERQ Catastrophizing	5 [4-8]	0 (0) ; 5 (9)
CERQ Other blame	4 [4-5]	0 (0) ; 1 (2)

Abbreviations: IQR, interquartile range; FEV₁, forced expiratory volume in one second; FVC, forced vital capacity; TLC, total lung capacity; RV, residual volume; DLCO, diffusion capacity of the lungs for carbon monoxide; DLCO/VA, DLCO per unit alveolar volume; MIP, maximal inspiratory pressure; MEP, maximal expiratory pressure; FFMI, fat free mass index; CERQ, Cognitive Emotion Regulation Questionnaire; * Impaired = below lower limit of normal (LLN) ** low = < 2 SD; high = > 2 SD

Associations with outcomes at 12 months post-discharge

Physical Symptoms

Bivariable analyses identified significant associations between physical symptoms at T2 and chronic respiratory disease (r=.389, p<.001), COVID-related residual pulmonary abnormalities, fatigue, dyspnoea, and physical symptoms at T1 (r values ranging from .329 to .771, p<.001). Additionally, cognitive symptoms, anxiety, depression, PTSS at T1 (r values from .601 to .714, p<.001), and coping strategies (i.e. acceptance, catastrophizing, and rumination) (r values from .405 to .465, p<.01) were significantly associated with physical symptoms at T2. Hierarchical regression (Table 4) showed that chronic respiratory disease, dyspnoea, and higher anxiety at T1 were significantly associated with physical symptoms at T2, explaining 65.6% of variance.

Table 4. Regression analysis with 4DSQ at T2 as dependent variable (n=100)

	β
Independent variables	Model 1	Model 2	Model 3	Model 4
Demographic and premorbid factors
Chronic respiratory disease	0.36***	NE	0.26**	0.20**
Physical factors
Fatigue	NE	NE	0.30***	0.03
Dyspnoea	NE	NE	0.24*	0.15 *
COVID-related residual pulmonary abnormalities	NE	NE	0.03	0.02
Cognitive and psychological factors
CLCE-24	NE	NE	NE	0.16
HADS-depression	NE	NE	NE	-0.12
HADS-anxiety	NE	NE	NE	0.54 ***
PTSS-14	NE	NE	NE	0.12
R²	0.13		0.31	0.66
Adjusted R²	0.12		0.28	0.63
F change	14.79***		8.03***	22.68***

We used the square root transformation of 4DSQ as dependent variable

Abbreviations: 4DSQ, Four-Dimensional Symptom Questionnaire (4DSQ); CLCE-24, Checklist for Cognitive and Emotional Consequences; (HADS), HADS-depression; Hospital Anxiety and Depression Scale-depression subscale; HADS-anxiety, Hospital Anxiety and Depression Scale-anxiety Subscale; PTSS-14, Post-traumatic Stress Symptoms Checklist-14.

*p<.05; ** p<.01; ***p<.001

Cognitive Symptoms

Bivariable analyses indicated that cognitive symptoms at T2 were significantly associated with BMI, CCI, chronic respiratory disease (r=.198 to .368, p<.05), wave 3 vs. wave 1 (r=.275, p<.01), dyspnoea, fatigue, physical symptoms at T1 (r=.266 to .539, p<.01), and several psychological measures (i.e. cognitive symptoms, anxiety, depression, PTSS, and rumination) (r values from .293 to .708, p<.05). Hierarchical regression (Table 5) showed that chronic respiratory disease, wave 3 vs. wave 1, and cognitive symptoms at T1 were significantly associated with more cognitive symptoms at T2, explaining 63.2% of variance.

Table 5. Regression analysis with CLCE-24 at T2 as dependent variable (n=93)

	β
Independent variables	Model 1	Model 2	Model 3	Model 4
Demographic and premorbid factors
- BMI	0.20*	0.21*	0.16	0.04
- CCI	0.13	0.14	0.19*	0.16*
- Chronic respiratory disease	0.32**	0.31 **	0.22*	0.20**
COVID-19 illness severity
- Wave 3 versus wave 1		0.29**	0.27**	0.17*
Physical factors
- Fatigue	NE	NE	0.34***	0.15
- Dyspnoea	NE	NE	0.21*	0.13
Psychological factors			NE
- CLCE-24	NE	NE	NE	0.50***
- HADS-depression	NE	NE	NE	-0.00
- HADS-anxiety	NE	NE	NE	-0.16
- PTSS-14	NE	NE	NE	0.22
R²	0.17	0.25	0.43	0.63
Adjusted R²	0.14	0.22	0.39	0.59
F change	6.00***	10.06**	13.28***	11.28***

We used the log transformation of CLCE-24 as dependent variable

Abbreviations: CLCE-24, Checklist for Cognitive and Emotional Consequences; (HADS), HADS-depression; Hospital Anxiety and Depression Scale-depression subscale; HADS-anxiety, Hospital Anxiety and Depression Scale-anxiety Subscale; PTSS-14, Post-traumatic Stress Symptoms Checklist-14.

*p<.05; ** p<.01; ***p<.001

Depression

Bivariable analyses found that depressive symptoms at T2 were significantly associated with chronic respiratory disease, wave 3 vs. wave 1 (r=.274 to .347, p<.01), TLC, dyspnoea, fatigue, physical symptoms at T1 (r=-.204 to .460, p<.05), cognitive symptoms, anxiety, depression, PTSS at T1 (r values from .536 to .716, p<.001), and coping strategies (i.e. rumination, positive refocusing, putting things in perspective) (r=.277 to -.389, p<.05). Regression analysis (Table 6) revealed that chronic respiratory disease, lower TLC, depressive symptoms, and PTSS at T1 were significantly associated with more depressive symptoms at T2, explaining 62.0% of variance.

Anxiety

Bivariable analyses showed significant associations between levels of anxiety at T2 and wave 3 vs. wave 1 (r=.198, p<.05), fatigue, physical symptoms, TLC, COVID-related pulmonary abnormalities (r values from -.193 to .472, p<.001), cognitive symptoms, anxiety, depression, PTSS at T1 (r=.497 to .716, p<.001), and rumination and catastrophizing (r=.377 to .490, p<.01). Regression analysis (Table 6) identified that higher anxiety at T1 was significantly associated with higher anxiety levels at T2, explaining 56.3% of variance.

Table 6. Regression analysis with HADS-depression (n=103) and HADS-anxiety (n=106) at T2 as dependent variable

HADS-depression at T2
	β
Independent variables	Model 1	Model 2	Model 3	Model 4
- Chronic respiratory disease	0.34***	0.35***	0.32***	0.19*
Wave 3 vs wave 1	NE	0.23*	0.22*	0.13
Fatigue	NE	NE	0.19*	0.01
Dyspnoea	NE	NE	0.14	0.05
TLC	NE	NE	-0.25**	-0.16*
- CLCE-24	NE	NE	NE	-0.05
- HADS-depression	NE	NE	NE	0.34**
- HADS-anxiety	NE	NE	NE	-0.01
- PTSS-14	NE	NE	NE	0.38**
R²	0.12	0.17	0.32	0.62
Adjusted R²	0.11	0.15	0.29	0.58
F change	12.53**	5.92*	6.96***	17.24***
HADS-anxiety at T2
	β
Independent variables	Model 1	Model 2	Model 3	Model 4
Chronic respiratory disease	0.23*	0.24*	0.25*	0.12
- Wave 3 vs wave 1		0.19	0.17	0.09
- Fatigue			0.19	-0.06
- TLC			-0.20	-0.13
- COVID-related residual pulmonary abnormalities			-0.01	-0.02
- CLCE-24	NE	NE	NE	-0.07 )
- HADS-depression	NE	NE	NE	0.06
- HADS-anxiety	NE	NE	NE	0.60***
- PTSS-14	NE	NE	NE	0.10
R²	0.05	0.09	0.18	0.56
Adjusted R²	0.05	0.07	0.14	0.52
F change	5.53*	3.75	3.52*	19.05***

We used the square root transformation of HADS-depression and log transformation of HADS-anxiety and as dependent variable

Abbreviations: CLCE-24, Checklist for Cognitive and Emotional Consequences; (HADS), HADS-depression; Hospital Anxiety and Depression Scale-depression subscale; HADS-anxiety, Hospital Anxiety and Depression Scale-anxiety Subscale; PTSS-14, Post-traumatic Stress Symptoms Checklist-14.
*p<.05; ** p<.01; ***p<.001

PTSS

Bivariable analyses showed significant associations between PTSS at T2 and chronic respiratory disease (r=.241, p<.01), wave 3 vs. wave 1 (r=.193, p<.05), COVID-related residual pulmonary abnormalities, fatigue, dyspnoea, and physical symptoms at T1 (r values from .214 to .596, p<.05). Depression, anxiety, cognitive symptoms, and PTSS (r values from .581 and .762, p<.001), and coping strategies (i.e. acceptance, rumination, and catastrophizing) (r values from .318 to .552, p<.001) at T1 were also significant. Regression analysis (Table 7) indicated that COVID-related residual pulmonary abnormalities, higher anxiety, and PTSS at T1 were significantly associated with increased PTSS at T2, explaining 68.3% of variance.

Table 7. Regression analysis with PTSS-14 at T2 as dependent variable (n=98)

	β
Independent variables	Model 1	Model 2	Model 3	Model 4
Demographic and premorbid factors
- Chronic respiratory disease	-0.26*	-0.25*	-0.18	-0.08
COVID-19 illness severity
- Wave 3 versus wave 1	NE	-0.18	-0.15	-0.10
Physical factors at T1
- Fatigue	NE	NE	-0.36***	-0.09
- Dyspnoea	NE	NE	-0.13	-0.07
- COVID-related residual pulmonary abnormalities	NE	NE	-0.11	-0.13*
Cognitive and psychological factors at T1
- CLCE-24	NE	NE	NE	0.01
- HADS-depression	NE	NE	NE	-0.02
- HADS-anxiety	NE	NE	NE	-0.34*
- PTSS-14	NE	NE	NE	-0.39**
R²	0.07	0.10	0.28	0.68
Adjusted R²	0.06	0.08	0.24	0.65
F change	6.58*	3.26	7.78***	27.08***

We used the polynomial transformation of PTSS-14 as dependent variable

Abbreviations: CLCE-24, Checklist for Cognitive and Emotional Consequences; (HADS), HADS-depression; Hospital Anxiety and Depression Scale-depression subscale; HADS-anxiety, Hospital Anxiety and Depression Scale-anxiety Subscale; PTSS-14, Post-traumatic Stress Symptoms Checklist-14.

*p<.05; ** p<.01; ***p<.001

Sensitivity Analysis

Adding a fifth predictor block (physical symptoms and coping strategies at T1) did not significantly increase variance explained in the outcomes. However, more physical symptoms at T1 were associated with more physical symptoms at T2 (β=.639, 95% CI [.028-.165], p=.008).

This study illustrates that nearly one-fourth of hospitalized COVID-19 patients reported persistent physical, cognitive, and/or psychological symptoms at 3 and 12 months post-discharge. Only anxiety levels significantly decreased in the first year post-discharge. More physical, cognitive, and psychological symptoms 12 months post-discharge were associated with premorbid conditions (chronic respiratory disease, higher CCI), injury severity (being infected during the third wave), physical variables (COVID-related pulmonary abnormalities, lower TLC, dyspnoea), and cognitive and psychological variables (cognitive symptoms, anxiety, depressive symptoms, and PTSS levels). Furthermore, more persistent symptoms at 12 months were associated with higher levels of rumination, catastrophizing, and acceptance, as well as lower levels of positive refocusing and putting things into perspective, along with more physical symptoms at three months.

Despite a significant decrease in anxiety symptoms over time, a substantial proportion of patients continued to experience clinically significant levels of anxiety, depression, and PTSS at both time points, with prevalence rates ranging from 15% to 22%. In the literature, these rates fluctuate, with some studies finding similar rates of psychological symptoms [35], while other studies find higher rates. For example, when compared to non-hospitalized patients using similar measures [36], psychological symptom prevalence rates in the present study were lower but still higher than in the general population pre-COVID [37,38]. In the present study, patients were invited to the COVID-19 clinic regardless of symptoms, unlike in previous studies where patients were often included due to persistent symptoms [36,39].

Similar prevalence rates of cognitive symptoms have been reported in other studies. A meta-analysis of 43 studies in both hospitalized and non-hospitalized patients revealed that approximately one in five COVID-19 patients reported cognitive symptoms 12 or more weeks after infection [40]. These symptoms and related cognitive disabilities do not necessarily indicate the presence of cognitive impairments. Klinkhammer et al. [35] found that 8-10 months post-discharge, 62% of ICU and general ward survivors with COVID-19 reported three or more cognitive symptoms, while standard neuropsychological testing showed cognitive dysfunction in 12% of patients. Moreover, around one-third of the patients reported moderate to severe physical symptoms, consistent with findings of a review on post-COVID-19 physical symptoms [4]. These symptoms resemble chronic symptomatology after other viral infections. Similar to post-COVID, after viral infections, the majority of patients completely recover within several weeks, while a small subgroup experiences persistent sequelae. The cognitive behavioural model of Chronic Fatigue Syndrome (CFS) may offer an explanatory framework for how chronic symptoms can develop through reciprocal interactions between physiology, cognition, emotion, and behaviour after a viral infection [41].

The study identified several prognostic factors associated with physical, cognitive, and psychological functioning at twelve months post-discharge. Notably, pre-existing chronic respiratory disease emerged as a significant predictor of these symptoms, whereas demographic factors and COVID-19 illness severity did not predict long-term outcomes. In line, a recent meta-analysis has already shown that chronic respiratory disease including asthma and COPD are associated with persistent long-term symptoms post-COVID-19 [12]. Besides, it is well-known that patients with COPD generally experience higher levels of cognitive impairments and psychological distress (i.e. anxiety and depression) compared to the general population, which may arguably translate in the higher risk of these long-term symptoms post-COVID-19 [31-33]. There are mixed findings regarding the severity of the acute infection as a potential risk factor for long-term outcome. A meta-analysis of 10 studies showed that patients who required ICU admission during the acute phase of SARS-CoV-2 infection had more than twice the risk of developing persistent symptoms compared to those who did not require ICU admission [12]. On the contrary, other studies did not find an association between the severity of acute infection and long-term neurological and cognitive functioning, emotional distress, and wellbeing [35]. In the present study, only 18% of patients had been admitted to the ICU, which may explain the absence of an association within our cohort. Notably, COVID-19 illness severity in hospitalized patients is often approximated by differentiating between ICU and general ward admissions. While this categorization provides an indication of infection severity, utilizing standardized severity scores such as the WHO COVID-19 disease severity categorization [24], as in the present study, can offer a more differentiated measure of disease severity, distinguishing between various levels of severity.

Of the physical symptoms assessed at three months post-discharge, both dyspnoea and fatigue were associated with physical, psychological, and/or cognitive outcomes in the models that did not include psychological and cognitive factors. However, after correcting for psychological and cognitive factors, only dyspnoea was found to be associated with physical symptoms. The lack of association between fatigue and outcomes in the final models may be explained by the overlap between fatigue and cognitive and psychological symptoms. This overlap was also observed in a recently published study, where fatigue was not associated with outcomes after correcting for other factors such as depression [42].

The finding that TLC and COVID-related pulmonary abnormalities were linked to psychological outcomes contrasts with previous research, which indicated that pulmonary function impairments and anomalies detected on chest CT scans at three months were not associated with enduring symptoms at twelve months, including cognitive impairments and physical and psychological symptoms post-COVID-19 [43,44]. However, in our study, the associations were only confined to depressive and PTSS symptomatology, while other lung function variables also showed no correlation with outcomes. Our contrasting findings could arguably be explained by the higher number of patients with chronic respiratory disease in our study, as compared to others, potentially influencing the found association between TLC impairments and depressive symptomatology. Nevertheless, these findings underscore the intricate aetiology of the multifaceted symptoms following SARS-CoV-2 infection, extending beyond the biological system.

This study confirmed the impact of psychological factors at three months post-discharge, including depression, anxiety, and PTSS, on physical and psychological symptoms at twelve months. Moreover, the regression models showed a considerable increase in the amount of explained variance when psychological factors were added to the models for all the outcomes. Similarly, cognitive factors at three months were associated with cognitive outcomes at twelve months. These findings are consistent with prior research indicating connections between psychological factors such as anxiety and depression, as well as personality traits such as neuroticism, openness, and conscientiousness, and outcomes [12,45]. Considering the possible impact of acute infection on physical, cognitive, and psychological functioning, these factors may interact, leading to a detrimental cycle in which physical, cognitive, and psychological factors reinforce each other, resulting in persistent symptoms. Coping likely plays a significant role herein, as has been found in other patient populations, such as those with Lyme disease, fibromyalgia, and mild traumatic brain injury [46,47]. Rumination and catastrophizing were identified as maladaptive, while positive refocusing and putting things into perspective were recognized as adaptive strategies. However, the finding that acceptance was associated with maladaptive outcomes is not consistent with previous research. Studies conducted during the pandemic in various populations have shown positive effects of acceptance strategies on quality of life, resilience, and psychological functioning [48,49]. However, to our knowledge, studies investigating coping in formerly hospitalized COVID-19 patients are scarce [50]. Acceptance reflects acknowledging the reality of the situation [51]. A potential explanation for the negative association between acceptance and physical and psychological symptoms, as observed in the present study, is that the uncertainty of the situation during the COVID-19 pandemic may have led to feelings of hopelessness when accepted. It is unclear whether patients, besides accepting the situation, committed to living according to their values and resumed daily activities. The avoidance of these activities may explain the persistent symptoms. However, this finding warrants further investigation.

The current study had several limitations. First, only patients who completed both outcome assessments were included, thus constraining the sample size. Patients who had recovered at three months may have been less inclined to return for the 12-month follow-up appointment. This may have led to higher percentages of symptoms, possibly resulting in overestimation, as most patients with complaints returned at twelve months, and ultimately, not everyone was systematically followed up. All hospitalized patients were invited to the clinic and the data were registered as part of regular care to mitigate selection bias. Despite this, the extent to which these findings can be generalized to a broader population of post-COVID-19 patients remains unclear. Second, factors found to be associated with persistent symptoms in previous studies, such as psychiatric history and race, were not assessed. Data collection occurred during the early stages of the COVID-19 pandemic, when studies on the associations between potential determinants and outcomes were scarce.

An inherent strength of this study is the broad spectrum of factors that were included in the analysis and the adoption of a biopsychosocial perspective in comprehending the persistent sequelae post-COVID, offering significant clinical and research implications.

These findings underscore the persistent physical, cognitive, and psychological symptoms experienced by post-COVID-19 patients, highlighting the need for targeted interventions to address these sequelae. This study demonstrated that the interplay between biopsychosocial factors and symptomatology post-COVID-19 emphasizes the importance of incorporating biopsychosocial aspects into post-COVID-19 patient care. Understanding these interactions is crucial for effective interventions [9,52]. Based on individualized case conceptualization that includes premorbid, physical, psychological, and cognitive factors, a personalized treatment plan can be devised. The finding that symptoms remain relatively stable over time and that early symptoms predict long-term outcomes supports the need for early screening of patients at risk of long-term problems, which could be targeted with treatment.

Evidence supports the effectiveness of multidisciplinary treatments that target biological, psychological, and/or social factors in alleviating post-COVID-19 symptomatology. For instance, a recent study demonstrated that an inpatient multidisciplinary rehabilitation program incorporating cognitive behavioural therapy and exercise led to reduced symptom severity, improved self-efficacy, and enhanced activity and participation [53]. Additionally, cognitive behavioural therapy (CBT) has been shown to effectively alleviate fatigue post-COVID-19 and enhance disease coping both in a feasibility study and randomized controlled trial [10,54]. Moreover, the reduction of PTSS following rehabilitation was associated with decreased fatigue [55]. Further research employing a biopsychosocial perspective is warranted to deepen our understanding of the aetiology and treatment of persistent symptoms.

In conclusion, this study underscores the persistent physical, cognitive, and psychological symptoms experienced by COVID-19 patients post-discharge and the need for targeted interventions. The biopsychosocial perspective provides insights into the complex interplay of factors influencing post-COVID-19 sequelae, emphasizing the importance of personalized interventions that focus on biological and psychological factors. These findings have implications for improving the long-term outcomes and mental health of COVID-19 survivors.

Competing interest statement

All authors have no conflicts of interest related to this work.

Acknowledgements

The authors would like to thank all clinical physicians and psychologists of the COVID-19 outpatient clinic at the VieCuri Medical Centre for their time and effort.

Data availability statement

Datasets and scripts used in this study are available from the corresponding author upon request.

Financial support

This research received financial support from the Science and Innovation Fund of VieCuri Medical Centre, Netherlands, and Maastricht University, Netherlands. This funding source had no role in the study design, collection, analysis and interpretation of data, writing of the report, and in the decision to submit the article for publication.

Author contributions

G.C.: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing; D.G.: Methodology, Writing – original draft, Writing – review & editing; F.H.M.v.O.: Methodology, Writing – review & editing

D.V.: Data curation, Writing – review & editing; J.P.v.d.B.: Data curation, Writing – review & editing; V.v.K.: Data curation; R.J.H.C.G.B.: Writing – review & editing; A.M.W.J.S.: Writing – review & editing; E.v.B.: Conceptualization, Data curation, Writing – review & editing; C.M.v.H.: Conceptualization, Methodology, Writing – review & editing;

All authors have approved the submitted version.

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No competing interests reported.

ScienticReportsSupplementaryMaterialClaessens.docx

A biopsychosocial analysis of risk factors for long-term physical, cognitive, and psychological functioning in previously hospitalized post-COVID-19 patients

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Abstract

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Introduction

Methods

Design and Participants

Procedure

Measures

Outcome measures at T1 and T2

Predictor variables at T0

Predictor variables at T1

Statistical Analysis

Results

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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Version 1