Prognostic value of clinical and microbiological parameters in COVID-19: the COMEPA study

From Top Italian Scientists Journal
Published
January 12, 2024
Title
Prognostic value of clinical and microbiological parameters in COVID-19: the COMEPA study
Authors
Nicola Veronese(1), Marianna Noale(2), Anna La Carrubba(1), Luca Carruba(1), Stefano Ciriminna(1), Francesco Pollicino(1), Dario Saguto(1), Simona De Grazia(1), Federica Cacioppo(1), Giovanni M. Giammanco(1), Claudio Costantino(1), Francesco Vitale(1), Marco Affronti(3), Maria Chiara Morgante(3), Giusi Randazzo(3), Ligia J. Dominguez(1,4), Stefania Maggi(2), Mario Barbagallo(1), and the COMEPA study authors
Keywords
COVID-19; clusters; long COVID; prognosis
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Nicola Veronese(1), Marianna Noale(2), Anna La Carrubba(1), Luca Carruba(1), Stefano Ciriminna(1), Francesco Pollicino(1), Dario Saguto(1), Simona De Grazia(1), Federica Cacioppo(1), Giovanni M. Giammanco(1), Claudio Costantino(1), Francesco Vitale(1), Marco Affronti(3), Maria Chiara Morgante(3), Giusi Randazzo(3), Ligia J. Dominguez(1,4), Stefania Maggi(2), Mario Barbagallo(1), and the COMEPA study authors

(1) Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties "G. D'Alessandro", University of Palermo, 90127 Palermo, Italy.

(2) Consiglio Nazionale delle Ricerche, Neuroscience Institute, Padova, 35100, Italy.

(3) Geriatric Unit, Azienda Ospedaliera Universitaria Policlinico Paolo Giaccone, Palermo, Italy.

(4) School of Medicine and Surgery, University of Enna "Kore", 94100, Enna, Italy;

Corresponding author: Nicola Veronese. Geriatric Unit, Department of Internal Medicine and Geriatrics, University of Palermo, Via del Vespro, 141, 90127 Palermo, Italy. Email: nicola.veronese@unipa.it

Abstract

Purpose

Clusters’ analysis may indicate distinct phenotypes and symptom profiles potentially due to differing pathophysiology and needing different clinical approaches in COVID-19. However, the research about clusters combining clinical and microbiological information is still limited. The purpose of our study was to examine the prognostic role of clusters, including clinical and microbiological parameters in terms of severity of lung involvement, in-hospital mortality, and the occurrence of long COVID.

Methods

Information regarding COVID-19, mortality, severity of lung involvement derived from medical records; long COVID symptomatology was ascertained using phone calls. A k-means clustering method was considered to partition data into clusters considering typical symptoms of COVID-19 present at hospital admission and SarsCov2 variants.

Results

Our analysis identified among 414 patients (mean age: 65 years; males: 59.9%) four different clusters. Cluster 1: higher prevalence of respiratory COVID symptoms at hospital admission; Cluster 2: higher frequency of non-respiratory COVID symptoms and a higher prevalence of the Alpha variant; Cluster 3: older subjects and more frequently men, reporting more severe medical conditions and with a higher prevalence of Wild type variant; Cluster 4: patients that more often reported general and gastrointestinal COVID symptoms at the admission. From a prognostic point of view, patients in cluster 3 more frequently died and were admitted in a nursing home, with significantly lower presence of long COVID symptomatology.

Conclusions

Clusters combining clinical and microbiological information in individuals hospitalized with COVID-19 that had different not only different profiles, but also different prognostic values, also in terms of long COVID.

Introduction

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus.[1] Early SARS-CoV-2 variants caused low respiratory tract infections, with high morbidity and mortality and symptoms evolved with the emergence of new variants.[2] Since the appearance of SARS-CoV-2 in December 2019, it has evolved more than ten variant strains.[2] Among these variants, five of them (Alpha, Beta, Gamma, Delta, and Omicron) were thought to be more transmissible and/or more lethal than the original Wuhan strain and have been designated as variants of concern by the World Health Organization (WHO).[3]

Long COVID is defined as the continuation or development of new symptoms three months after the initial SARS-CoV-2 infection, with these symptoms lasting for at least two months with no other explanation.[4] While common symptoms of long COVID can include fatigue, shortness of breath and cognitive dysfunction, over 200 different symptoms have been reported that can have an impact on everyday functioning.[5] Studies show that around 10–20% of people infected by SARS-CoV-2 may go on to develop symptoms that can be diagnosed as long COVID.[3] Although exact numbers of those living with the condition are uncertain, it is believed that more than 17 million people across the WHO European Region may have experienced it during the first two years of the pandemic (2020/21).[6]

At the same time, the interaction between vaccinations and Sars-CoV-2 variants have modified the clinical course of COVID-19 during these years of pandemic[7], indicating the possibility to have different presentations of COVID-19, i.e., clusters. Clusters’ analysis may better indicate distinct phenotypes and symptom profiles potentially due to differing pathophysiology and needing different clinical approaches. Even if this kind of analysis is highly encouraged to better individualize causes and potential treatments for each patient[8], to the best of our knowledge, only a few studies have explored the importance of clusters in COVID-19 and mainly with data regarding long COVID.[9] Given this background, the purpose of our study was to examine the prognostic role of clusters including clinical and microbiological parameters in patients affected by COVID-19, in terms of severity of lung involvement, in-hospital mortality and the occurrence of long COVID.

Materials and methods

Study population

The study was conducted at the University Hospital of Palermo, which is one of the largest University Hospital in Sicily with a total of 604 hospital beds, 542 of which are ordinary beds and 62 Day Hospitals beds, 2,100 employees (including healthcare professionals and administrative staff), 1,120 medical residents and over 1,500 trainees of healthcare university courses (medicine and healthcare professions). The Palermo University Hospital is the only Hospital in Western Sicily to have an outpatient vaccination unit for the vaccination of healthcare workers, employees, hospitalized patients and the general population at high-risk (for severe comorbidities and allergic diseases).

All patients aged ³18 years hospitalized in the Internal Medicine or Geriatrics Wards from the 1st of September 2020 to 31st May 2021 at the University Hospital ‘P. Giaccone’ from Palermo, Italy were enrolled.[10] No other inclusion criteria were considered to better represent a real-life scenario. The study was approved by the Local Ethical Committee during the session of the 28th of April 2021 (protocol number 04/2021). For hygienic reasons, the informed consent to participate to the study was collected orally and reported in the medical records.

Clustering assessment and phenotype profiling

A k-means clustering method, a type of unsupervised machine learning algorithm, was considered to partition data into clusters considering typical symptoms of COVID-19 present at hospital admission (fever, cough, asthenia, headache, dyspnea, anorexia, anosmia, ageusia, myalgia, arthralgia, diarrhoea, nausea, vomiting, other symptoms) and SarsCov2 variants. K-means method assigns clusters to observations in order to minimize the distance between observations and cluster centroids, allowing to obtain observations that are similar to each other in the same cluster and dissimilar to other observations in other clusters.[11] Non-overlapping clusters are defined with an iterative approach by reassigning cluster membership and cluster centroids until the solution reaches a local optimum. The optimal number of clusters was determined by user comparing pseudo F statistic and cubic clustering criterion (CCC) for models with different number of clusters (0 to 10).[12]

Outcomes

The primary outcomes were mortality during hospital stay, the presence of long COVID during the follow-up, and severity of lung involvement. Information regarding mortality was collected using clinical records and death certificates. Long COVID-19 symptomatology was defined using the World Health Organization (WHO) indications, i.e., “a condition that occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis”.[4] Accordingly, the presence of long COVID was assessed after a median of 17 months (range: 13-22) from hospital discharge through phone calls, using a method largely made in other works.[13][14][15][16] We considered as signs or symptoms of long COVID those indicated in several systematic reviews[13][14][15][16], i.e., neurological, respiratory, mobility impairment, heart, digestive, skin, or general signs and symptoms that can be attributable to COVID-19 infection. All the questions were posed as yes/no questions by phone. Psychiatric conditions, considered as secondary outcomes of the work, were assessed using the Post-traumatic Stress Disorder (PTSD) Checklist (PCL)-5[17] and the Hospital Anxiety and Depression Scale (HADS), a validated tool for evaluating anxiety and depression among adults.[18] Respiratory failure was defined as a partial pressure of oxygen (PaO2) < 60 mmHg with a normal or decreased partial pressure of carbon dioxide (PaCO2).[19] The severity of lung involvement was made according to the Berlin Criteria[20] in mild (200 > PaO2/FiO2 ≤ 300), moderate (100 > PaO2/FiO2 ≤ 200) or severe (PaO2/FiO2 ≤ 100).

Covariates

Among the parameters collected in the COMEPA study[10], for the aim of the present study we used the information potentially affecting the association between clusters and the outcomes of interest mentioned before. Therefore, we included as factors: age; gender; comorbidities evaluated in terms of presence and the severity using the Cumulative Illness Rating Scale (CIRS)[21] [21] that estimates the severity of pathology in each of 13 systems, with a grade from 0 to 4, with a value > 2 indicating the presence of, at least, a moderate disease.[21] Moreover, the presence of signs or symptoms typical of COVID-19 and present at hospital admission was recorded using medical records, physical examination, and recent medical history.

COVID-19 Vaccination history was recorded using the National Vaccination Registry (NVR). The COVID-19 NVR started in February 2021 and allowed to all Health care professionals that administered COVID-19 vaccines to general population to charge data on a web portal through personal Username and password https://www.governo.it/it/cscovid19/report-vaccini; last access 10th of March 2023).

COVID-19 variants were ascertained using the SARS-CoV-2 Extended ELITe MGB® Kit able to detect and discriminate the mutations L452R, E484K, E484Q and N501Y of the S gene of SARS-CoV-2 through Reverse Transcription and Real-Time Polymerase Chain Reaction and melting curve analysis. Finally, the length of stay in hospital was also included as covariate in the analysis.

Statistical analysis

Participants’ characteristics are presented as counts and percentages for categorical variables, and as means ± standard deviation (SD) for quantitative measures. No imputation of missing values was made. Comparisons were performed considering the Chi-squared or the Fisher exact tests for categorical variables or generalized linear model testing for homoschedasticity (Levene’s test) for quantitative variables.

A cluster analysis based on k-means method was applied to classify participants into clusters considering their symptoms. Characteristics of participants in different clusters were compared considering Chi-squared, Fisher exact tests or generalized linear model, as appropriate. Associations between clusters and different outcomes (severity of lung involvement, dichotomized into no vs mild, moderate or severe; long COVID symptoms; death during follow-up) were evaluated considering multivariable logistic regression models adjusted for age, sex, comorbidity and duration of the hospitalization, and results were presented as odds ratio (OR) and 95% confidence interval (CI). In relation to post-traumatic stress disorder score according to PTSD and to anxiety/depression score according to HADS, generalized linear models with a Poisson distribution family and a log link function were considered.

All statistical tests were two-tailed and statistical significance was assumed for p-value <0.05. The analyses were performed using SAS, V.9.4 (SAS Institute, Cary, NC).

Results

Among 530 patients initially included in the COMEPA study, 414 were analyzed since, for 116 individuals, no sufficient information was available for the aims of this study. Overall, as shown in Supplementary Table 1, the patients aged a mean of 65.0±15.2 years and they were prevalently males (59.9%). Over a median of 17 months (range: 13–22) from hospital discharge, 115 (=27.8%) reported a long COVID symptomatology. Supplementary Figure 1 reports the prevalence of the single long COVID signs and symptoms, overall showing that the most frequent was weakness.

Overall, 6 out 414 patients (coverage rate: 1,4%) were vaccinated against COVID-19 before hospitalization, and the impact of vaccination was not significant on our clusters (data not shown in table).

Patients having long COVID were significantly younger (p<0.001) than their counterparts. Regarding medical conditions, people with long COVID had, during hospitalization, a lower prevalence of cardiac (p=0.003) and renal (p=0.019) conditions. No significant differences emerged in terms of COVID-19 variants (Supplementary Table 1). Patients reporting long COVID during follow-up had a significantly higher prevalence of COVID symptoms when admitted (p=0.030), in particular fever, asthenia, arthralgia (p<0.05 for all these comparisons). No different regarding the severity of lung involvement was observed, whilst patients reporting long COVID were discharged more frequently at home (Supplementary Material 1).

Table 1 shows the baseline characteristics by clusters. Briefly, Cluster 1 was composed by people who tend to be older, who have had cough or dyspnea as COVID symptoms at hospital admission, with frequent moderate or severe respiratory failure, discharged especially at home or in nursing home. Cluster 2 included subjects that tend to be younger with a higher prevalence of the Alpha variant. In cluster 2, the diseases at the admission were less frequent and less serious according to the CIRS. In cluster 2, COVID symptoms at the hospital admission included fever, cough, asthenia, anosmia, ageusia, myalgia and arthralgia, with frequent moderate or severe respiratory failure during the hospitalization. Patients included in cluster 3 tend to be older and more frequently men, reporting more severe medical conditions according to the CIRS. The wild variant was more frequent in this cluster. In cluster 3, patients had fewer COVID symptoms after hospital admission, but reported more often altered mental status. In cluster 3, the discharge to nursing home or death following hospitalization were more frequent. Finally, cluster 4 included patients that more often reported other COVID symptoms at the admission, such as abdominal or chest pain, dizziness, or syncope, even if they reported less frequently respiratory failure. Overall, regarding long COVID, cluster 1 reported higher values of HADS; cluster 2 had the highest presence of long COVID, such as weakness and hair loss, and reaching high HADS scores.

Table 2 shows the association between the four clusters and the incidence of long COVID, death and severity of lung involvement. After adjusting the analyses for age, sex, severity of medical conditions, according to the CIRS, and duration of the hospitalization, patients in cluster 3 reported a significantly lower presence of long COVID symptomatology (OR=0.40; 95%CI: 0.17-0.96; p=0.041) or severity of lung involvement (OR=0.44; 95%CI:0.21-0.93; p=0.031) than patients in cluster 2. On the contrary no significant differences emerged for mortality across the clusters (Table 2). Similarly, when considering psychiatric conditions during follow-up as outcome, patients in cluster 4 reported a significantly lower presence of PTSD than in cluster 2 (RR=0.41: 95%CI: 0.19-0.92; p=0.030), whilst subjects in cluster 1 reported more frequently than cluster 2 evidence of depression/anxiety (RR=1.57; 95%CI: 1.29-1.92; p<0.001) (Table 3).

Discussion

In our study, we profiled phenotypes of hospitalized patients affected by COVID-19, overall identifying four different symptom profiles (or clusters) which varied in number and symptom combinations. Altogether these findings may indicate the existence of heterogeneous profiles that finally lead to a different prognosis. The main clusters of our analysis were composed by one group of people with a higher prevalence of respiratory COVID symptoms at hospital admission; another group with a higher frequency of non-respiratory COVID symptoms and a higher prevalence of the Alpha variant; a third group with subjects older and more frequently men, reporting more severe medical conditions and with a higher prevalence of Wild type variant. A last cluster included patients that more often reported general and gastrointestinal COVID symptoms at the admission. In our work, we reported that these clusters have a different impact on the outcomes of our research, such as mortality, respiratory failure and long COVID symptomatology. The cluster are graphically reported in Figure 1.

Regarding the importance of identifying clusters having different prognostic role, we can mention other important studies. First, the Post-hospitalization COVID-19 (PHOSP) study that evaluated long-term symptoms in hospitalized patients, that identified four clusters with different physical and health impairment profiles.[22] However, this study did not incorporate the role of SarsCov2 variants and COVID symptoms. At the same, a study from the National Core Study for Health and Wellbeing incorporated individuals from the general population identifying two clusters in individuals more than 12 weeks after SarSCov2 infection, i.e., high and low symptom burden clusters.[23] Finally, another most recent work, made in the general population using a phone app, reported the presence of three main clusters, i.e., one cluster dominated by central neurological symptoms, a second cluster dominated by cardiorespiratory symptoms, and a third more heterogeneous cluster showing systemic and inflammatory symptoms.[7]

We believe that our study completes these important findings since other factors were explored in the clusters and we added some outcomes of clinical importance such as in-hospital mortality, respiratory failure during hospitalization, and long COVID symptomatology. In detail, patients included in cluster 3 (older, more frequently men, with more severe chronic medical conditions, and with a higher prevalence of Wild variant) died more frequently and were admitted more frequently in a nursing home. At the same time, the patients included in the cluster 3 reported a significantly lower presence of long COVID symptomatology than other patients and respiratory failure during hospitalization. Overall, these findings that can be ambiguous may suggest that older people died more frequently for acute complications of medical chronic conditions that they had before hospitalization that for COVID-19 per se.[24] At the same time, patients in cluster 2, i.e., younger patients with a higher frequency of several COVID symptoms at hospital admission, had a significantly higher prevalence of English type variant, confirming the findings of the previous study reporting this kind of association.[25] Of importance, this cluster was associated with a significantly higher presence of long COVID symptomatology that is, in our opinion, of public health importance since these patients were the youngest in age among those considered in our analyses and overall confirming that the presence of long COVID is of particular importance in adults more than in older people.[5] When considering psychiatric conditions during follow-up, patients in cluster 1 (i.e., old subjects with a higher prevalence of cough and dyspnea at hospital admission) had, more frequently, respiratory failure during hospitalization. Of importance, this cluster reported a higher prevalence of depression/anxiety during follow-up. Maybe, we can justify this finding due to the use of invasive and non-invasive respiratory supports that seem to be associated with unfavorable mental health outcomes.[26][27]

We also analyze vaccination status against COVID-19 in these clusters and the possible impact on clinical outcomes. Unfortunately, only six patients over 414 were vaccinated before the hospitalization, probably because during the enrollment period (from 1st of September 2020 to 31st May 2021) COVID-19 vaccination rate among general population were still low among general population under 80 years old. Vaccination of HCPs started in January 2021, vaccination of the elderly and of extremely vulnerable patients started at the end of February 2021 and, due to limited doses of mRNA vaccines, at least 50% of subjects older than 60 y.o. received in Italy only the first dose of adenovirus vector vaccines against COVID since the end of March 2021.

(Ref: Istituto Superiore di Sanità - EpiCentro - Epidemiology for public health. National COVID-19 vaccination plan. Available online: https://www.epicentro.iss.it/en/vaccines/covid-19-vaccination-plan). At the same time, vaccination is important not only because lowers mortality[28], but also because it seems to be associated with a lower risk of long COVID[29] and therefore could significantly modify the associations that we found.

The findings of our study must be interpreted within its limitations. First, only hospitalized people were included probably introducing a selection bias in our findings. Second, as mentioned before, the important role of vaccinations was not explored due to a limited number of patients vaccinated before the hospitalization. Lastly, our model only used the symptoms assessed by our daily clinical practice and we did not consider symptoms reported as free text; thus, we might ignore other symptoms that could affect the clusters.

In conclusion, we identified clusters combining clinical and microbiological information in individuals hospitalized with COVID-19 that had not only different profiles, but also different prognostic values. We believe that our findings may have relevance to individuals previously affected by COVID-19, their general practitioners, and in a public health perspective, providing other information to validate their illness and manage their expectations. Our findings may add to the emerging evidence that long COVID may have sub-types, possibly with differing pathophysiology.[7] Further investigation also using artificial intelligence models can better integrate not only clinical information, but also others (such as laboratory, radiological and genetic) that can further explain the reasons of different prognostic profiles, also in terms of long COVID symptomatology.

Acknowledgements

The COMEPA group includes (alphabetical order): Affronti Marco, Amodeo Simona, Barbagallo Mario, Briganò Vincenza Maria, Cacioppo Federica, Capitano Walter M., Carruba Luca, Cavaleri Francesco, Catanese Giuseppina, Citarrella Roberto, Di Bella Giovanna, Di Franco Giuseppina, Di Prazza Agnese, Dominguez Ligia Juliana, Giannitrapani Lydia, Grasso Giulia, Immordino Federico, Licata Anna, La Carruba Anna, Mansueto Pasquale, Mirarchi Luigi, Morgante Maria Chiara, Parinello Alessandra, Pecoraro Emanuela, Peralta Marco, Polizzotto Carla, Pollicino Francesco, Quartetti Federico, Randazzo Giusi, Rizzo Angelo, Rizzo Giuseppina, Sanfilippo Valeria, Soresi Maurizio, Malerba Valentina, Vernuccio Laura, Veronese Nicola, Zerbo Maddalena.

Funding

None.

Conflict of Interest

The Authors declare that there is no conflict of interest.

Data Availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. The study was approved by the Local Ethical Committee during the session of the 28th of April 2021 (protocol number 04/2021).

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Figure 1

Figure 1. Clusters of clinical and microbiological parameters in the COMEPA study. Click the icon Download Figure 1 to download Figure 1.

Table 1

Table 1. Baseline participants’ characteristics by clusters. Click the icon Download Table 1 to download Table 1.

Table 2

Table 2. Association between clusters and different outcomes (logistic regression models). Download Table 2 to download Table 2.

Table 3

Table 3. Clusters and post-traumatic stress disorder, and anxiety/depression (generalized linear models with a Poisson distribution family and log link function).Click the icon Download Table 3 to download Table 3.

Supplementary material

Click the icon Download the supplementary material to download the supplementary material.

References

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  16. ↑ 16.0 16.1 Han, Q.; Zheng, B.; Daines, L.; Sheikh, A. Long-Term sequelae of COVID-19: A systematic review and meta-analysis of one-year follow-up studies on post-COVID symptoms. Pathogens 2022, 11, 269.
  17. ↑ Blevins, C.A.; Weathers, F.W.; Davis, M.T.; Witte, T.K.; Domino, J.L. The posttraumatic stress disorder checklist for DSM‐5 (PCL‐5): Development and initial psychometric evaluation. Journal of traumatic stress 2015, 28, 489-498.
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  22. ↑ Evans, R.A.; McAuley, H.; Harrison, E.M.; Shikotra, A.; Singapuri, A.; Sereno, M.; Elneima, O.; Docherty, A.B.; Lone, N.I.; Leavy, O.C. Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study. The Lancet Respiratory Medicine 2021, 9, 1275-1287.
  23. ↑ Davis, H.E.; Assaf, G.S.; McCorkell, L.; Wei, H.; Low, R.J.; Re'em, Y.; Redfield, S.; Austin, J.P.; Akrami, A. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine 2021, 38, 101019.
  24. ↑ West, E.; Moore, K.; Kupeli, N.; Sampson, E.L.; Nair, P.; Aker, N.; Davies, N. Rapid review of decision-making for place of care and death in older people: lessons for COVID-19. Age and Ageing 2021, 50, 294-306.
  25. ↑ Sadegi, K.; Soofi, D.; Mirshekarpour, H.; Pormasoumi, H. Investigating the Return Possibility of Covid-19 Mutated Strains and Role of Vaccination in Present and Future. Journal of Pharmaceutical Negative Results 2022, 2043-2047.
  26. ↑ Pedersen, M.L.; Handberg, C.; Dreyer, P. Mental health reported in adult invasive home mechanical ventilation through a tracheostomy: A scoping review. International Journal of Nursing Studies Advances 2022, 100110.
  27. ↑ Arulkumaran, N.; Brealey, D.; Howell, D.; Singer, M. Use of non-invasive ventilation for patients with COVID-19: a cause for concern? The Lancet Respiratory Medicine 2020, 8, e45.
  28. ↑ Talic, S.; Shah, S.; Wild, H.; Gasevic, D.; Maharaj, A.; Ademi, Z.; Li, X.; Xu, W.; Mesa-Eguiagaray, I.; Rostron, J. Effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis. bmj 2021, 375.
  29. ↑ Venkatesan, P. Do vaccines protect from long COVID? The Lancet Respiratory Medicine 2022, 10, e30.
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