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Year : 2022  |  Volume : 9  |  Issue : 4  |  Page : 522-529

Pattern of comorbidities and clinical profile of young adults who died due to severe coronavirus disease: A descriptive study

1 Department of Medicine, Government Medical College, Manjeri, Kerala, India
2 St. John's Research Institute, St. Johnís Medical College Hospital, (SJMCH), Bengaluru, Karnataka, India
3 Madurai Medical College, Madurai, Tamil Nadu, India

Date of Submission21-Sep-2022
Date of Acceptance31-Oct-2022
Date of Web Publication29-Dec-2022

Correspondence Address:
Dr. N C Mary Grace
Department of Medicine, Government Medical College, Manjeri 676121, Kerala
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/mgmj.mgmj_169_22

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Introduction: Mortality due to coronavirus disease (COVID-19) is a significant problem among the non-elderly population. Aims: The primary objective was to estimate the prevalence of comorbidities among the deceased in the age group of 18–64 years and the secondary objective, was to describe their clinical profile. Settings and Design: The study setting was a tertiary care center catering to COVID-19 patients. This was a record-based descriptive study. Materials and Methods: Sampling strategy and Sample size: The formula used for sample size calculation is Z2 * P(1 – P) / d2. The sample size required was 97. The study period was from July 1 to November 30, 2021. Data collection: Demographic data including comorbidities, and clinical and laboratory features were studied. The laboratory investigations were done on the day of admission, and either on the last day or one day before death were taken for analysis. Statistical analysis used: Statistical analysis was carried out in IBM SPSS Version 26. Chi-square and Fisher’s exact tests, Mann–Whitney U and Kruskal–Wallis tests, Wilcoxon signed rank test, and Correlation tests were used for analysis. Results: The majority of the patients had more than one comorbid condition. The parameters which showed significant variation as the illness progressed were lymphocyte count, total protein, and albumin. Conclusion: Special vigilance should be kept while managing young patients with comorbidities. Lymphocyte count and serum albumin can be helpful in prognostication.

Keywords: Comorbidities, COVID-19, diabetes mellitus, hyperproteinemia, hypoalbuminemia, lymphopenia

How to cite this article:
Mary Grace N C, Babu S, Joseph A, Jacob D, Benjamin AS, Anaghajyothi V, Pulpandi S, Jacob C. Pattern of comorbidities and clinical profile of young adults who died due to severe coronavirus disease: A descriptive study. MGM J Med Sci 2022;9:522-9

How to cite this URL:
Mary Grace N C, Babu S, Joseph A, Jacob D, Benjamin AS, Anaghajyothi V, Pulpandi S, Jacob C. Pattern of comorbidities and clinical profile of young adults who died due to severe coronavirus disease: A descriptive study. MGM J Med Sci [serial online] 2022 [cited 2023 Feb 6];9:522-9. Available from: http://www.mgmjms.com/text.asp?2022/9/4/522/365973

  Introduction Top

In the coronavirus disease (COVID-19) pandemic, the majority of hospitalized patients are in the age group of 18–65 years.[1] Mortality is a significant problem among them.[2],[3],[4] What is the reason for their mortality? Despite receiving identical treatments, some patients go on to have a bad outcome. What is the reason for this discrepancy? The studies done so far show conflicting results.

The primary objective of this study was to estimate the prevalence of comorbidities among the deceased in the age group of 18- 64 years and the secondary objective was to describe their clinical profile.

  Materials and methods Top

Study setting

The study was done in a tertiary care center, which catered to the treatment of moderate and severe COVID-19 patients.

Study design

This was a descriptive study based on the analysis of the case records. The diagnosis of COVID was confirmed based on the Rapid Antigen Test or RT PCR of the nasopharyngeal or oropharyngeal samples. (World Health Organization. Recommendations for national SARS-CoV-2 testing strategies and diagnostic capacities: interim guidance, 25 June 2021). Adults in the age group of 18- 64 years, who died of COVID-19, were studied.

Sampling strategy and sample size

The formula used for sample size calculation is Z2 * P (1 – P) / d2 where Z is the standard normal variate, P is the expected prevalence or proportion, and d is the accepted margin of error/ precision. For a 95% Confidence Interval, Z = 1.96, considering prevalence as 50% and a margin of error of d=10%, the total sample size required was 97.[5],[6] Taking into account the confounders and incomplete recording of details, we decided to study 150 case records. The study period was from July 1st to November 30, 2021.

Inclusion criteria

Patients of the age group of 18–64 years who died of COVID-19.

Exclusion criteria

Incomplete records in terms of clinical and laboratory data. Patients who were readmitted one month after they recovered from COVID-19.

Data collection

The study was started after getting approval from the Ethical Committee. Oxygenation status on admission, as assessed by SpO2 in room air, was used to classify patients into moderate or severe diseases. SpO2 of less than 94% was taken as one criterion for moderate disease and less than 90% for severe disease. All moderate and severe COVID cases were treated in ICU settings.

Demographic data that were collected included age and sex. The age groups were sub-classified into 3 strata, 18- 49 years,50–59 years, and 60–64 years. The comorbidities that were studied included Systemic Hypertension, Diabetes Mellitus, Ischemic heart Disease, Cerebrovascular Disease, Neurodegenerative disorders like Parkinson’s disease, Epilepsy, Chronic Obstructive Airway Disease, Chronic Kidney Disease, Psychiatric illness, Preexisting Malignancy, Hypothyroidism. The diagnosis of these conditions was based on either recorded evidence or patients who were on medications for these disorders. The history taken from the patient or reliable bystander was also utilized to come to a diagnosis of such preexisting comorbidities.

The symptoms which were analyzed included fever, rhinorrhea, cough, hemoptysis, and breathlessness. Any other symptoms like myalgia, vomiting, diarrhea, seizures, and altered level of consciousness were also studied. The duration of these symptoms from onset to hospital admission was noted. The pulse rate, blood pressure readings, and oxygen saturation recorded at the time of admission and during the hospital stay were noted.

The laboratory investigations which were studied included complete blood count, inflammatory markers like ESR, CRP, ferritin and LDH, D dimer, blood sugar, renal and liver function tests, and serum electrolytes - Sodium and Potassium. The laboratory investigations were done on the day of admission, and either on the last day or one day before death whichever was available, were taken for analysis [Table 1].
Table 1: Normal values of the laboratory parameters

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Hyperglycemia was considered as newly detected when there was no preexistent diabetes mellitus but the blood sugar value on admission and /or thereafter was >200 mg% on at least two occasions. Blood sugar values were considered uncontrolled when the majority (>50%) of the capillary blood sugar values, during the hospital stay, was more than 200 mg% despite receiving glucose-lowering therapy or in those cases with only one value available if the recording was more than 250 mg %.

AKI (acute kidney injury) was diagnosed based on a change of .3 mg % from the baseline value. In those cases where only one value was available, creatinine of more than 2 mg % along with a ratio of blood urea nitrogen to creatinine of more than 20:1 was considered AKI.

An increase in ALT (Alanine aminotransferase), AST (Aspartate aminotransferase), and ALP (Alkaline phosphatase) were considered significant if the rise was more than 2.5 times the upper limit of normal and non-significant if the rise was less than 2.5 times the upper limit of normal.

Data analysis

The raw data were converted to Microsoft Excel and statistical analysis was carried out in IBM SPSS Version 26. Chi-square and fisher’s exact tests were used to study the association between demographic and other characteristic variables such as symptoms. All clinical information was studied separately in age and gender categories. Since the clinical measures were not following the assumption of normality, a Non-Parametric testing procedure was carried out. Mann-Whitney U and Kruskal-Wallis tests were used to compare clinical variables between gender and age categories. Wilcoxen signed rank test was used to study if there exist significant differences between baseline and post-clinical measurements. Correlation analysis was conducted to study if there exist relationship patterns between clinical variables/ laboratory features measured.

  Results Top

Nearly ¾ th of the subjects belonged to the age group above 50 years. The mean age in the subgroups of 18–49 years,50–59 years, and 60–64 years were 42.46 years, 55.27 years, and 62.4 years respectively. The comorbidities which were present in the descending order of frequency were Diabetes Mellitus, Systemic Hypertension, Ischemic heart disease, Chronic Kidney Disease, and Cerebrovascular disease [Table 2].
Table 2: Demographic characteristics of the data

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Diabetes Mellitus, Chronic Kidney Disease, Psychiatric illness, and Systemic Hypertension were present in considerable proportions in patients below 50 years. All the comorbidities other than chronic kidney disease did not show any gender difference in their occurrence [Table 3].
Table 3: Association between age categories and comorbidities

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The most frequently reported symptoms were breathlessness, fever, and cough. Concerning the age distribution, fever was more common among the younger population (p-value 0.048) while altered mentation was more above the age of 50 years (p-value 0.06). The mean duration of hospital stay was 10.7 days and the mean duration from onset of symptoms to death was 17.05 days. Both these timelines were more among women (p values 0.032 and 0.05 respectively) [Table 4].
Table 4: Descriptive measures of study participants’ symptoms, vital signs, and timelines

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As the illness progressed the significant changes which were noticed were rising total leucocyte count with polymorphonuclear predominance and falling lymphocyte count along with hypoproteinemia and hypoalbuminemia. Among the inflammatory markers, LDH showed a rising trend, while ESR and CRP did not show any significant change from the baseline values [Table 5].
Table 5: Related samples wilcoxon signed rank test to study if laboratory features differ between baseline and final assessments

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There was a negative correlation between age and lymphocyte count and hemoglobin levels at the time of admission [Table 6]. On the days before death, the lymphocyte and platelet counts had a significant correlation with protein and albumin levels. Hypoalbuminemia was more as age advances [Table 7].
Table 6: Correlation coefficient for variables on the day of admission

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Table 7: Correlation coefficient for variables on the day before death

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  Discussion Top

Do comorbid conditions contribute to mortality among the nonelderly population? Are there any peculiarities in the clinical and laboratory features of the younger population? In this cohort of non-elderly individuals, the majority were above 50 years of age.81% of the subjects had one or more comorbidities while 61% had two or more comorbidities. The relevant laboratory abnormalities could be summarized as lymphopenia, raised inflammatory markers (ESR, CRP, Ferritin, LDH, D dimer), uncontrolled hyperglycemia, hypoproteinemia, hypoalbuminemia, elevated blood urea, and serum creatinine.

The elderly people are the ones at the highest risk of death in the COVID-19 pandemic.[7],[8],[9],[10] As per the CDC report, updated on January 31, 2022, as compared to the reference age group of 18- 29 years, the risk of mortality in the age groups 30–39 years,40–49 years, and 50–64 years is 4,10 and 25 times respectively. O’ Driscoll et al. estimate an increase in a case fatality rate of nearly .6% for each 5-year increment above the age of 10 years.[11] The outcome in younger individuals is similar to that of the elderly, in the presence of comorbidities.

Diabetes mellitus is the most common preexistent comorbidity in this cohort. A further 10% had newly detected hyperglycemia. Almost all of the newly detected diabetics had preexisting other comorbidities. The reasons for this new onset of diabetes could be the stress of the current illness or the beta cell dysfunction caused by the SARS CoV 2 infection, or a previously undetected diabetes mellitus or steroid-induced hyperglycemia. The association between diabetes and mortality in COVID and the various postulations for the same is well established.[12],[13],[14],[15],[16] The morbidity due to diabetes increases if the blood sugar is not properly controlled. Even with strict vigil, adequate control of hyperglycemia could be achieved in only very few patients. The fact that hyperglycemia is difficult to manage in COVID-19 has been often observed.[17]

Keeping in mind the implications of uncontrolled blood sugar control, it would be a good option to consider insulin infusion therapy in COVID-19 infection, especially in the more severe cases. In addition to the acute complications, the new onset hyperglycemia may also have long-term effects. Montefusco et al. observed that around 2% of the newly detected diabetic patients went on to develop full-fledged diabetes.[17]

Unlike Ischemic heart disease and Cerebrovascular disease which were more prevalent above 60 years of age, a considerable proportion of young patients had Psychiatric illness, Chronic Kidney Disease, Diabetes Mellitus, and Systemic Hypertension.

The notable abnormalities we observed in the liver function tests could be summarized as follows - transaminitis, with more rise in aspartate aminotransferase when compared to alanine aminotransferase and a decrease in protein and albumin levels. It is widely accepted that COVID-19 infection does not affect a healthy liver adversely. This is explained by the paucity of ACE 2 receptors in the hepatocytes.[18],[19],[20],[21]

The laboratory parameters which showed a significant trend from the time of admission to death were persistently high blood sugars and worsening lymphopenia, hypoproteinemia, and hypoalbuminemia. The mean levels of total protein and albumin were reduced, to begin with and both experienced further fall during the hospital stay. Both hypoproteinemia and hypoalbuminemia are striking findings, even among the younger population in this cohort. The prognostic role of hypoalbuminemia is well-known in several diseases,[22],[23],[24] and also in COVID-19.[25],[26] The mechanisms leading to hypoalbuminemia, in general, can be decreased production of albumin, decreased half-life of albumin, increased loss of albumin, increased volume of distribution, or increased capillary permeability. There was no evidence of increased loss or increased volume of distribution. To attribute this to decreased food intake is also not plausible because the majority of the patients in this cohort were on non-invasive ventilation and feeding was relatively uninterrupted. The increased capillary permeability secondary to a hyperinflammatory state or increased consumption may be held responsible.

Numerous studies have observed that lymphopenia and thrombocytopenia are predictors of poor outcomes. A rise in lymphocyte count from a nadir, which is attained about one week after the onset of symptoms, is an indication of recovery.[27],[28],[29] We observed a very low lymphocyte count on the day of admission and it was followed by a further decrease. Thrombocytopenia was not a significant finding in this study. There was a significant positive correlation between platelet count and inflammatory markers at the time of admission (ESR and LDH) and also later in the disease (ESR). As the disease progressed albumin showed a significant correlation with age, lymphocyte count, and LDH.

Strengths and limitations of this study

The sample size is adequate and missing data is few. We have been able to collect the laboratory data at the time of admission and also follow-up data till the time of death. This has helped to assess the changes in the laboratory parameters. Complications that have occurred have not been properly studied. A comparison group would have helped to pick up the predictors of a bad outcome.

The risk of adverse outcomes increases substantially above the age of 50 years, especially in the presence of at least one other comorbid condition. Special efforts should be made to detect Diabetes and Chronic Kidney disease in the younger population. In a pandemic situation, with the scarcity of resources, what are the investigations which would help in risk stratification? The presence of a hyper-inflammatory state once established by the elevated inflammatory markers does not rise much further as the disease worsens. On the other hand, lymphocyte count, and albumin levels continue to fall and they can be used in the follow-up.

  Conclusion Top

Special vigilance should be kept while managing COVID-19 in young patients who have comorbidities. Once the hyperinflammatory state of COVID-19 has been established, cheaper investigations can be helpful in the prognostication of severe cases.

Ethical consideration

The Institutional Ethics Committee, Government Medical College, Manjeri, Kerala, India has approved the proposal to undertake the research study on “Pattern of Comorbidities and Clinical Profile of Young Adults who died due to severe COVID-19: A descriptive Study” vide their letter no. UEC/GMCM/98 dated December 31, 2021.

Informed consent

Being a record-based study, there was no need for informed consent.

Financial support and sponsorship

Not applicable.

Conflicts of interest

There are no conflicts of interest.

  References Top

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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]


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