Poor prognosis in patients with prolonged post-COVID syndrome and predictors of its development

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Introduction

Although the epidemic of the novel coronavirus infection was officially declared over more than two years ago, the SARS-CoV-2 virus persists, mutates, creates new strains, and reminds us of itself with periodic outbreaks of COVID-19. In 2025, information appeared about a new strain of the SARS-CoV-2 STRATUS (XFG 1) virus, which is highly contagious and spreads rapidly, but is characterized mainly by a mild course similar to that of an acute respiratory viral infection. However, a common feature of all SARS-CoV-2 strains is the development of long-term post-COVID syndrome in 30–80% of patients who have had COVID-19, with a wide variety of symptoms and syndromes that can last from several months to several years, significantly impairing patients' quality of life and negatively impacting global health care [1; 2].

Data on the impact of long COVID syndrome on mortality and adverse clinical outcomes are contradictory and extremely limited, especially in young and middle-aged patients without a history of underlying conditions. For example, a study was conducted in Japan to assess excess mortality from all causes in patients with novel coronavirus infection (NCVI) and after recovery [3]. Despite initial success in reducing excess mortality in 2023, Japan saw an increase in excess mortality not only as the pandemic progressed, but with its continued rise after the end of the emergency. The results of this study underscore the importance of continuously monitoring excess mortality as a key indicator of public health trends. One observational study showed that patients with long COVID syndrome had a higher need for medical care after discharge, including hospitalizations, than patients without long-term COVID-19 symptoms [4]. However, in a meta-analysis of 63 controlled cohort studies covering more than 96 million participants, a decrease in overall quality of life was found during three years of follow-up among people with confirmed SARS-CoV-2 infection and long COVID syndrome compared to control groups, without an increase in overall mortality and urgent hospitalizations [5].

Thus, assessing adverse events in patients with long-term post-COVID syndrome will allow us to determine the contribution of this complication of NCVI not only to quality of life, but also its impact on prognosis, which makes it possible to develop a system of preventive measures and justify an algorithm for managing patients to prevent the negative consequences of NCVI.

Materials and Methods

The study was conducted in accordance with Good Clinical Practice standards and the principles of the Declaration of Helsinki. The study protocol was approved by the local ethics committee of the Perm Regional Clinical Hospital for War Veterans (No. 137 dated April 21, 2020). Written informed consent was obtained from all participants prior to their inclusion in the study. A two-stage prospective clinical study was conducted. The first stage was a one-time screening study in which 878 patients who sought outpatient care three months or more after suffering from NCVI were screened over a three-year period to identify patients with long-term post-COVID syndrome and its clinical features. The second stage was a prospective observational study in which all fatal events and hospitalizations of patients included in the study in the second stage were recorded. The second stage of the study included 305 patients who had suffered from NCVI three months or more ago. Patients in the second stage had to meet the inclusion criteria and not have any exclusion criteria.

Criteria for inclusion in the second stage: outpatients who have had COVID-19, confirmed by polymerase chain reaction testing and a swab test for SARS-CoV-2 coronavirus more than three months ago.

The criteria for exclusion from the study were: NCVI less than three months ago, acute respiratory viral infection or pneumonia with a negative PCR test for NCVI; acute coronary syndrome, angina pectoris, cerebral strokes and transient coronary events, chronic heart failure in history and at address; pulmonary embolism, chronic pulmonary heart disease in history and at address; severe pneumofibrosis in history and at address, valvular heart disease, severe liver disease in history (chronic hepatitis, cirrhosis), cancer in history and less than five years ago, chronic kidney disease stage 4–5, including dialysis, transplantation; a history of severe blood disorders and autoimmune diseases; a history of uncompensated thyroid dysfunction; type 1 diabetes mellitus; acute inflammatory and infectious diseases, including a history of pulmonary tuberculosis; severe dementia and mental disorders that prevent the signing of informed consent and contact with the patient.

In the second stage, patients were divided into two groups: the first group consisted of 200 patients with symptoms of long COVID; the second group consisted of 105 patients who had recovered from NCVI and did not meet the diagnostic criteria for long COVID.

To identify predictors of poor prognosis in patients with long COVID (n = 200), at the end of the study, they were divided into two subgroups based on prognosis: the first subgroup included 85 patients who had adverse events, and the second included 115 patients without adverse events during the observation period, with a retrospective assessment of their characteristics at the onset of the second stage.

Long COVID syndrome was defined according to the criteria of the UK National Institute for Health and Care Excellence, which are the presence of signs and symptoms developing during or after infection consistent with COVID-19, lasting more than 12 weeks in the absence of other alternative diagnoses*.

The study included patients with the following most common symptoms of long COVID syndrome according to meta-analyses of observational studies: persistent weakness (fatigue), shortness of breath during physical activity, cognitive impairment, pain syndrome of any location, insomnia, and symptoms of depression and anxiety [6; 7].

All patients included in the second stage of the study underwent clinical, laboratory, and instrumental examinations when they visited the polyclinic, with an assessment of their outpatient records and other medical documents.

Ferrokinetic parameters were examined: serum iron, total iron-binding capacity of blood, ferritin concentration, transferrin in blood serum, transferrin saturation coefficient with iron.

To assess the structural and functional parameters of the heart, echocardiography (ECG) was performed using a Vivid S5 ultrasound scanner (General Electric, USA) in accordance with the recommendations of the American and European Societies of Echocardiography.

To assess the severity of myocardial stress, the concentration of N-terminal brain natriuretic propeptide (Nt-proBNP) in the blood was determined using the ELISA immunoassay method on the Expert Plus Microplate reader (Biochrom Ltd., UK) using the Nt-proBNP – ELISA – BEST reagent kit (Russia, Novosibirsk).

To assess renal filtration function, blood creatinine and cystatin C concentrations were determined, GFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPIcre and CKD-EPIcys) formula with an online calculator, and the ratio of albumin/urine protein to urine creatinine in the morning sample was determined. The concentration of cystatin C in blood serum was determined by ELISA on an Expert Plus Microplate reader (Biochrom Ltd., UK) using the Cystatin C – ELISA-BEST reagent kit (Vector Best, Russia, Novosibirsk).

To assess the severity of nonspecific inflammation, tumor necrosis factor alpha (TNF-α) and interleukin-1β were determined in the blood by ELISA using a reagent kit from Vector-Best (Russia) on a Lazurite analyzer (Dynex Technologies Inc., USA).

To assess apoptosis, caspase-6 concentration was determined by ELISA using the SEA 552Hu reagent kit from Cloud-Clone Corp. (USA–China) on a Stat Fax 2100 photometer (Awareness Technology, USA).

For an integrated assessment of fibrosis, the concentration of tissue inhibitor of matrix metalloproteinases type 1 (TIMP1) was determined by ELISA using the SEA 552Hu kit from Cloud-Clone Corp. (USA–China) on a Stat Fax 2100 photometer (Awareness Technology, USA).

The Hospital Anxiety and Depression Scale (HADS) was used to assess anxiety and depression.

The Mini Mental State Examination (MMSE) was used to identify cognitive impairment).

Statistical data processing was performed using the Statistica 10.0 software package. When performing statistical data processing, the critical value of statistical significance when testing null hypotheses was set at 0.05. The normality of the distribution of characteristics in groups was tested using the Shapiro–Wilk and Kolmogorov–Smirnov criteria. For quantitative characteristics that followed a normal distribution, arithmetic means and standard deviations (M ± SD) were calculated. For distributions that did not follow a normal distribution, the median with lower and upper quartiles (Med [LQ; UQ]) or a 95% confidence interval (CI) was determined. For qualitative characteristics, the absolute frequency of occurrence of the characteristic and the frequency of occurrence of the characteristic as a percentage (%) were calculated. When comparing quantitative indicators, Student's t-test was used; when comparing indicators that did not correspond to the normal distribution law, the Mann–Whitney U test was used for statistical analysis, and for qualitative indicators, the χ² chi-square test was used. To determine the existence of a relationship between quantitative characteristics in a normal distribution of indicators, Pearson's correlation analysis was used; between quantitative and ordinal indicators, Spearman's rank correlation analysis was used; between qualitative characteristics, A.A. Chuprov's mutual conjugacy coefficient was used. In accordance with the recommendations of Rea and Parker, the level of significance of the obtained correlations was determined: with a criterion value of < 0.1 – very weak, 0.1 < 0.2 – weak, 0.2 < 0.4 – moderate, 0.4 < 0.6 – relatively strong, 0.6 < 0.8 – strong, 0.8–1.0 – very strong. To determine the odds ratio (OR), relative risk (RR), and 95% CI for OR and RR of developing newly diagnosed CHF in long COVID syndrome, 2 × 2 contingency tables were compiled, χ² was calculated with the achieved significance level adjusted for continuity using Yates' correction. A level of p < 0.05 was accepted as the critical level of statistical significance for the null hypotheses when assessing the relationship.

To analyze the occurrence of adverse events, we used the nonparametric Kaplan-Meier method with data presented in graphical form.

Results and Discussion

The observation period for patients in the study was 24.6 [12.4; 47.7] months. The average age of patients included in the study was 59.6 [31.3; 64.7] years. The groups of patients with and without long COVID did not differ significantly in terms of gender, age, cardiovascular risk factors, comorbidities, ongoing therapy, or severity of NCVI in the acute phase. Among 200patients with long COVID syndrome, 89 adverse clinical outcomes were identified in 85 (44.5%) patients. 22 events were recorded in 22 (21.0%) patients in the group of patients without long-term COVID-19 symptoms among 105examined patients.

The overall mortality rate in the first group over 24.6 months of observation was 4.5%, and in the second group, 1.9% (p = 0.406). In the first group, 9 patients died during the entire observation period: 2 from sudden cardiac death, one from myocardial infarction, one from ischemic stroke, 2 from terminal heart failure, 2 from cancer, and one from a life-threatening injury. In the second group, two patients died: one from cirrhosis of the liver and one from sudden death. Figure 1 shows the curves of fatal outcomes in patients who underwent NCVI, by group of examinees.

 

Fig. 1. Kaplan–Meier survival curves by groups of examinees who had NCVI (n = 305). Note: PPCS – prolonged post-COVID syndrome

 

Survival analysis showed no statistically significant differences in the frequency of fatal events between the groups (p = 0.252).

In the first group, 80 hospitalizations (40.0%) were recorded during the entire observation period: 51 hospitalizations were related to cardiovascular diseases, 14 to diseases of the bronchopulmonary system, 9 to neurological problems, and 6 to other conditions. In the second group, there were 20 hospitalizations (19.0%): 8 hospitalizations were related to cardiovascular diseases, 4 to diseases of the bronchopulmonary system, 6 to surgical and traumatic problems, and 2 to other causes (p = 0.001 between groups). Fig. 2 shows the curves for the frequency and time of hospitalization in patients who have undergone NCVI, by group of examinee.

 

Fig. 2. Kaplan–Meier curves for hospitalizations by groups of examinees who had NCVI (n = 305). Note: PPCS – prolonged post-COVID syndrome

 

Analysis of the incidence of hospitalizations showed statistically significant differences between the groups (p = 0.0004). It was determined that the development of long-term post-COVID syndrome in patients who had undergone NCVI increases the OR of the need for hospitalization by 2.110 times [95% CI 1.393; 3.193].

A combined analysis of survival and hospitalization rates is presented in Fig. 3.

 

Fig. 3. Kaplan–Meier survival and hospitalization curves by groups of examinees who had NCVI (n = 305). Note: PPCS – prolonged post-concussion syndrome

 

A combined analysis of survival and hospitalization showed statistically significant differences between the groups (p < 0.001). It was determined that the development of long-term post-COVID syndrome in patients who had undergone NCVI increases the OR of death and the need for hospitalization by 2.197 times [95%CI 1.481; 3.257].

To determine predictors of adverse events (combined survival and hospitalization rates) in long COVID syndrome, patients in the first group were divided into two subgroups based on prognosis, and a retrospective comparative analysis of all indicators by subgroup, as well as clinical outcomes during the observation period, was performed. When parameters that differed statistically significantly between subgroups were identified, their relationship with the development of adverse events was assessed.

88 adverse clinical outcomes were identified in 85 (42.5%) patients among 200 patients with long-term post-COVID syndrome.

As a result, Table 1 presents indicators that differ statistically significantly between subgroups, and correlation analysis showed their relationship with an unfavorable prognosis. Among 124 parameters, 20 indicators demonstrated predictive significance. For indicators not presented in Table 1, the subgroups did not differ statistically significantly, or these indicators were not associated with fatal events or hospitalizations.

 

Table 1. Comparative characteristics of patients with long-term post-COVID syndrome by subgroups of examinees, n = 200

Parameter	First subgroup (PP, n = 85)	Second subgroup (FP, n = 115)	p
Hospitalization during NCVI, abs./%	56/65.9	32/27.8	< 0.001
Lung damage on X-ray > 45% during NCVI, abs./%	30/35.3	16/13.9	0.002
Presence of five or more symptoms of long COVID, abs./%	61/71.8	48/41.7	0.001
MMSE 10–28 points, abs./%	21/24.7	8/7.0	0.002
HADS > 7, abs./%	42/49.4	31/27.0	0.003
BMI > 30 kg/m2, abs./%	34/40.0	26/22.6	0.013
Non-dipper/Night-peaker ABPM, abs./%	29/34.1	13/11.3	< 0.001
LVH, abs./%	22/25.9	11/9.6	0.005
CAVI1 > 9, abs./%	52/61.2	35/30.4	< 0.001
TAPSE/ PASP < 0,8, abs./%	15/17.6	8/7.0	0.034
PWT < 60 ml/min/1.75 m2, abs.	36/42.4	32/27.8	0.046
α-TNF > 8.1 pg/mL, abs./%	18/21.2	11/9.6	0.036
Caspase 6 > 28.2 pg/ml, abs./%	42/49.4	14/12.2	< 0.001
Nt-proBNP > 125 pg/ml, abs./%	31/36.5	24/20.9	0.023
TIMP1 > 138 ng/ml, abs./%	53/62.4	28/24.3	< 0.001
MI/stroke during the observation period, abs./%	10/11.8	3/2.6	0.022
Atrial fibrillation, abs./%	11/12.9	4/3.5	0.026
IGT/SP 2nd type, abs./%	10/11.8	4/3.5	0.047
Anemia, abs./%	12/14.1	5/4.3	0.029
COPD /BA, abs./%	36/42.4	27/23.5	0.008

Note: NCVI – novel coronavirus infection, PF – poor prognosis, FP – favorable prognosis, MMSE – Mini-mental State Examination, HADS – Hospital Anxiety and Depression Scale, BMI – body mass index, ABPM – ambulatory blood pressure monitoring, LVH – left ventricular hypertrophy, CAVI1 – Cardio-Ankle Vascular Index 1, PWT – pulse wave velocity, TNF – tumor necrosis factor, Nt-proBNP – N-terminal pro-brain natriuretic peptide, TIMP1 – tissue inhibitor of matrix metalloproteinases type 1, MI – myocardial infarction, IGT – impaired glucose tolerance, DM – diabetes mellitus, COPD – chronic obstructive pulmonary disease, BA – bronchial asthma.

 

The odds ratio and risk ratio for the development of an unfavorable prognosis in patients with prolonged post-COVID syndrome, depending on 20 interrelated parameters, are presented in Table 2.

 

Table 2. OR and RR for the development of an unfavorable prognosis in patients with prolonged post-COVID syndrome (n = 200)

Parameter	OR (95% CI)	RR (95% CI)
Hospitalization during NCVI, abs./%	5,009 (2,619–9,636)	2,368 (1,689–3,297)
Lung damage on X-ray > 45% during NCVI, abs./%	3,375 (1,608–7,146)	2,537 (1,438–4,579)
Presence of five or more symptoms of long COVID, abs	3,548 (1,868–6,775)	1,720 (1,319–2,195)
MMSE 10–28 points, abs./%	4,389 (1,718–11,531)	3,552 (1,586–8,435
HADS > 7, abs./%	2,647 (1,405–5,003)	1,834 (1,239–2,710)
BMI > 30 kg/m2, abs./%	2,283 (1,180–4,430)	1,770 (1,122–2,798)
Non-dipper/Night-peaker ABPM, abs./%	4,064 (1,853–9,024)	3,019 (1,623–5,808)
LVH, abs./%	3,302 (1,412–7,837)	2,706 (1,332–5,693)
CAVI1 > 9, abs./%	3,602 (1,917–6,798)	2,011 (1,434–2,798)
TAPSE/ PASP < 0,8, abs./%	2,867 (1,071–7,846)	2,537 (1,062–6,320)
PWT < 60 ml/min/1.75 m2, abs.	1,906 (1,010–3,602)	1,523 (1,007–2,293)
α-TNF > 8.1 pg/mL, abs./%	2,541 (1,058–6,174)	2,214 (1,049–4,800)
Caspase 6 > 28.2 pg/ml, abs./%	7,047 (3,318–15,166)	4,059 (2,349–7,294)
Nt-proBNP > 125 pg/ml, abs./%	2,177 (1,107–4,295)	1,748 (1,077–2,849)
TIMP1 > 138 ng/ml, abs./%	5,147 (2,675–9,964)	2,561 (1,770–3.707)
MI/stroke during the observation period, abs./%	4,978 (1,209–23,671)	4,510 (1,194–20,419)
Atrial fibrillation, abs./%	4,126 (1,155–16,046)	3,721 (1,143–13,630)
IGT/SP 2nd type, abs./%	3,701 (1,017–14,616)	3,383 (1,016–12,591)
Anemia, abs./%	3,617 (1,120–12,355)	3,248 (1,109–10,347)
COPD /BA, abs./%	2,395 (1,247–4,614)	1,804 (1,163–2,807)

Note: OR – odds ratio, RR – relative risk, CI – confidence interval, NCVI – new coronavirus infection, PЗ– poor prognosis, FP – favorable prognosis, MMSE – Mini-mental State Examination, HADS – Hospital Anxiety and Depression Scale, BMI – body mass index, ABPM – ambulatory blood pressure monitoring, LVH – left ventricular hypertrophy, CAVI1 – Cardio-Ankle Vascular Index 1, PWT – pulse wave velocity, TNF – tumor necrosis factor, Nt-proBNP – N-terminal pro-brain natriuretic peptide, TIMP1 – tissue inhibitor of matrix metalloproteinases type 1, MI – myocardial infarction, IGT – impaired glucose tolerance, DM – diabetes mellitus, COPD – chronic obstructive pulmonary disease, BA – bronchial asthma.

 

An overall mortality rate of 4.5% was observed in our study over a 24.6-month follow-up period in patients with long COVID syndrome, which was not statistically significantly different from that in patients who had recovered from NCVI without long-term COVID-19 symptoms. However, the frequency of hospitalizations, as well as the combined indicator with mortality, were statistically significantly higher in patients with long COVID: 40.0% versus 19.0% (p = 0.001 for both indicators). It should be noted that our study mainly included individuals of working age, and we deliberately excluded patients with a history of cardiovascular, neurological, and oncological conditions, which can have an independent negative impact on prognosis. Thus, in one of the American Heart Association's cardiovascular disease registries, COVID-19 survivors hospitalized with prolonged post-COVID syndrome were found to have an increased HR of cardiovascular events (HR, 1.28 [95% CI 1.19–1.37]), which disappeared after adjusting for a history of CVD (HR, 0.97 [95% CI 0.89–1.04) [8]. We also excluded patients who developed acute cardiovascular and cerebrovascular diseases during the acute phase of NCVI, which could have been the cause of the unfavorable prognosis. This is confirmed by a study by K. Tateishi et al., who demonstrated that cardiovascular and cerebrovascular complications associated with hospitalization for COVID-19 were significant independent predictors of long-term mortality, especially ischemic stroke [9].

In an observational study by V. Vera-Delgado et al., patients with long-term post-COVID syndrome had higher overall mortality rates than in our study during the 10-month observation period, at 25.6%, which corresponded to the mortality rate during hospitalization with COVID-19 [10]. Such a high mortality rate in this study was due to the fact that the authors included in the analysis all patients who died both during the index hospitalization with NCVI and within 10 months. If mortality is not taken into account, the mortality rate in this study is significantly lower – 11.4% – but nevertheless exceeds our figures. The authors demonstrated that NT-proBNP values > 503.5 pg/mL [OR = 5.00 (3.06–8.19)], urea > 37 mg/dL [HR 3.51 (1.97–6.27)], cognitive impairment [HR = 1.96 (1.30–2.95)], cancer [HR = 2.23 (1.36–3.68)], and leukocytes > 6330/mm³ [HR = 1.64 (1.08–2.50)] were independently associated with long-term mortality in patients with prolonged post-COVID syndrome during 10 months of follow-up. Another observational study showed that in patients with long-term post-COVID syndrome during a year of follow-up, high concentrations of troponin T and NT-proBNP significantly increased the HR of major cardiovascular events (HR 2.85, 95% CI 1.58–5.12), all-cause mortality (HR 5.56, 95% CI 1.51–20.52), cardiovascular death (HR 11.97, 95% CI 1.40–102.46), and repeat hospitalizations related to CVD (HR 2.38, 95% CI 1.28–4.42) [11].

The predictors of the combined indicator of unfavorable prognosis identified by us are consistent with the literature data on a number of parameters. In our study, an increase in the blood concentration of NT-proBNP above 125pg/ml also increases the HR of unfavorable prognosis by 1.75 times. We suggest that this is associated with the development of CHF after NCVI, which often remains unrecognized due to the variety of symptoms of long COVID syndrome. A meta-analysis of 5 retrospective studies involving 1,628,424 patients who had COVID-19 infection assessed the risk of developing CHF [12]. Recovered COVID-19 patients showed an increased risk of developing newly diagnosed CHF (HR 1.90, 95% CI: 1.54–3.24, p < 0.0001, I2 = 96.5%).

We confirmed the findings of S. Yokoyama et al. that hyperglycemic conditions (impaired glucose tolerance and type 2 diabetes) are associated with delayed recovery from long COVID-19 [13] and, in our study, with a worse prognosis. One observational study found that glycemic conditions and advanced age (over 60 years) are predictors of adverse renal outcomes with the development of CKD in one in three patients with long COVID-19 syndrome [14]. In our study, PWT < 60 ml/min/1.75 m² increases the OR of an unfavorable prognosis by 1.52 times.

According to C. Sabanoglu et al., in 916 patients with long-term post-COVID syndrome, after multivariate analysis, NT-proBNP, high-sensitivity troponin I, CKD, atrial fibrillation, diabetes mellitus, and coronary artery disease were independent predictors of in-hospital mortality and one-year mortality [15].

In our study, the development of CVD during the observation period increased the OR of death and hospitalization by 4.5 times, the presence of atrial fibrillation by 3.7 times, and IGT/SP by 3.4 times in patients with prolonged post-COVID syndrome. G. Krljanac et al. share the same opinion [16]. The authors noted that patients with prolonged post-COVID syndrome and cardiovascular manifestations were hospitalized more often (88.5% vs. 75.9%) and stayed in the hospital longer. The researchers suggest that the development of CVD after NCVI is associated with cardiac remodeling. Echocardiography showed statistically significant changes not only in left ventricular ejection fraction, but also in longitudinal deformation in the subendocardial and intramyocardial layers (-20.9% vs. -22.0% and -18.6% vs.-19.95%). In addition, pathological magnetic resonance findings were observed in 58.2% of the group of patients with long COVID-19 and cardiovascular manifestations. In our study, an unfavorable prognosis in patients with long post-COVID syndrome was associated with LVH and right ventricular dysfunction. We suggest that the process of myocardial remodeling with the formation of a hypertrophic phenotype with fibrosis begins already in the acute period of NCVI, regardless of the level of blood pressure. Thus, in a systematic review by R. Almamlouk et al. (2022), in which a pathological-anatomical examination of the cardiovascular system of patients who died from NCVI was conducted, it was found that myocyte hypertrophy (median: 69.0%; IQR 46.8–92.1%) and fibrosis (median: 35.0%; IQR 35.0–90.5%) were the most common chronic changes [17]. Data from a meta-analysis of 21 observational studies confirm the association of post-COVID syndrome with increased LVM and right ventricular dysfunction [18].

Long-term complications of COVID-19 in patients with anemia are currently being investigated, as these complications may play a decisive role in predicting patient prognosis. There is evidence that patients with anemia are at higher risk not only of developing severe COVID-19, but also of adverse clinical outcomes in long COVID due to several contributing pathophysiological mechanisms, including thrombotic, hemorrhagic, and autoimmune mechanisms [19].

Thus, the multifaceted nature of long COVID, revealing a wide range of symptoms, various risk factors, and the complex interaction of physiological mechanisms underlying this condition, places significant pressure on healthcare systems as a whole, not only by reducing quality of life and increasing days of incapacity for work, but also as a result of high hospitalization costs and mortality among patients of working age [20]. Combating long COVID requires a comprehensive management strategy that combines clinical care, social support, and policy initiatives. The findings underscore the need for greater cooperation in research and health planning to address the complex challenges of long COVID.

The limitations of this study include the lack of information on clinical outcomes in long-term post-COVID syndrome depending on age and aggravated medical history, which requires a larger number of patients included and their data prior to NCVI.

Conclusions

The observation period for patients in the study was 24.6 [12.4; 47.7] months. The average age of patients included in the study was 59.6 [31.3; 64.7] years. Among 200 patients with long COVID syndrome, 89 adverse clinical outcomes were identified in 85 (44.5%) patients, while in the group without long COVID-19 symptoms, among 105 examinees, 22 events were recorded in 22 (21.0%) people. Survival analysis showed no statistically significant differences in the incidence of fatal events between the groups. The incidence of hospitalizations, as well as the incidence of the combined endpoint of mortality and hospitalizations, was statistically significantly higher in the group of patients with post-COVID syndrome. It has been determined that the development of prolonged post-COVID syndrome in patients who have had NCVI increases the OR of the need for hospitalizations by 2.110 times, and the OR of fatal outcomes and hospitalizations by 2.197 times. Predictive value in the development of an unfavorable prognosis in patients with prolonged post-COVID syndrome was demonstrated by indicators reflecting severe NCVI course in the acute phase, polysymptomatism of prolonged post-COVID syndrome, symptoms of anxiety, depression, cognitive impairment, the presence of certain comorbidities and risk factors without a history of disease prior to verification of long COVID, cardiac and arterial remodeling with high myocardial stress, nonspecific inflammation, fibrosis, and apoptosis.

Funding. The study had no external funding.

Conflict of interest. The authors declare no conflict of interest.

Author contributions:

Masalkina O.V. – conducting research, working with data, analysis, preparing and writing the draft manuscript.

Koziolova N.A. – defining the concept, methodology, visualization, reviewing and editing the manuscript.

All authors approved the manuscript (version for publication) and agreed to be responsible for all aspects of this work, guaranteeing proper consideration and resolution of issues related to the accuracy and integrity of any part of it.

Study limitations. The study complies with the standards of the Declaration of Helsinki and has been approved by the local ethics committee of the Perm Regional Clinical Hospital for War Veterans (No. 137 dated April 21, 2020). Before the start of the study, all patients confirmed their participation by signing a written informed consent form.

[*] NICE. COVID-19 rapid guideline: managing the long-term effects of COVID-19. Published March 11, 2022. Accessed January 19 (2024), available at:  https://www.nice.org.uk/guidance/ng188/resources/covid19-rapid-guideline-managing-the-longterm-effects-of-covid19-pdf-51035515742

About the authors

O. V. Masalkina

Ye.A. Vagner Perm State Medical University

Author for correspondence.
Email: omasalkina@mail.ru
ORCID iD: 0009-0006-3364-0591

PhD (Medicine), Associate Professor of the Department of Internal Diseases and Cardiology

Russian Federation, Perm

N. A. Koziolova

Ye.A. Vagner Perm State Medical University

Email: omasalkina@mail.ru
ORCID iD: 0000-0001-7003-5186

DSc (Medicine), Professor, Head of the Department of Internal Diseases and Cardiology

Russian Federation, Perm

References

Supplementary files