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Table of Contents
ORIGINAL ARTICLE
Year : 2022  |  Volume : 10  |  Issue : 3  |  Page : 174-177

Patient-related risk factors for predicting severity of COVID-19 infection admitted to a tertiary care hospital in Telangana


Department of General Medicine, Malla Reddy Institute of Medical Sciences, Hyderabad, Telangana, India

Date of Submission21-Jul-2021
Date of Decision31-Jul-2021
Date of Acceptance21-Aug-2021
Date of Web Publication23-Feb-2022

Correspondence Address:
Dr. Krishna Chaitanya Alam
Department of General Medicine, Malla Reddy Institute of Medical Sciences, Suraram, Quthbullapur Municipality, Hyderabad, Telangana
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ajim.ajim_77_21

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  Abstract 


Background: Study of factors/predictors leading to the disease severity is important. They help us for the early identification of the patients who are susceptible to develop severe form of disease. Cases with a set of unfavorable factors can be given priority attention for the management thereby it may be possible to reduce the mortality rates. Objective: The objective of this study was to study the patient-related risk factors for predicting severity of coronavirus disease 2019 (COVID-19) infection admitted to a tertiary care hospital. Methods: A hospital-based retrospective study was carried out among 305 cases of COVID-19. Hospital records of these cases were studied. Sociodemographic variables and presence of comorbidities were noted. Disease severity was classified as per the standard guidelines. It was classified as mild, moderate, and severe. Univariate and multivariate analysis was carried out. Results: Majority, i.e., 42.3% had severe disease. On univariate analysis, advanced age, coming from rural area, preexisting hypertension, being obese were significant risk factors for severe disease (P < 0.05). Those with severe disease had total risk factors score of 3.36 ± 1.68 compared to 2.48 ± 1.67 for mild or moderate disease, and this difference was statistically significant (P < 0.05). In the final model, coming from rural area and advanced age were the significant predictors of severe disease in the present study (P < 0.05). Conclusion: Advanced age and being from rural area were the significant predictors of severe disease in the present study.

Keywords: Coronavirus disease 2019, patient-related risk factors, predictors, severity


How to cite this article:
Dasari D, Mallela VR, Reddy K V, Alam KC. Patient-related risk factors for predicting severity of COVID-19 infection admitted to a tertiary care hospital in Telangana. APIK J Int Med 2022;10:174-7

How to cite this URL:
Dasari D, Mallela VR, Reddy K V, Alam KC. Patient-related risk factors for predicting severity of COVID-19 infection admitted to a tertiary care hospital in Telangana. APIK J Int Med [serial online] 2022 [cited 2022 Aug 11];10:174-7. Available from: https://www.ajim.in/text.asp?2022/10/3/174/338152




  Introduction Top


Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 2019 (COVID-19) which is now pandemic. It is a global crisis affecting not only health of the population worldwide but also affecting the education and economy globally.[1]

COVID-19 spreads rapidly. In some cases, it can cause severe degree of disease which can be fatal. Certain population groups are highly susceptible for mortality. In the absence of prompt diagnosis and treatment, disease is more severe, and consequently, the mortality is high.[2]

However, there is dilemma regarding the prognosis of the disease on the basis of the clinical features. It is due to a wide variety of presentation of the cases. This makes it difficult to predict the severity of the disease and thus making the management of the cases problematic. This is directly related to the high degree of death rate among these cases. Clinical outcome may be different in patients with similar clinical features and similar management methods.[3]

Older age,[4] the presence of comorbidities such as diabetes, hypertension, cardiovascular disease, chronic lung disease, chronic kidney disease, immunosuppression, overweight, and obesity are considered as the factors that favor toward severity of the disease.[5]

Hence, the study of factors/predictors leading to the disease severity is important. They help us for the early identification of the patients who are susceptible to develop severe form of disease. Cases with set of unfavorable factors can be given priority attention for the management thereby it may be possible to reduce the mortality rates. It will have a great public health impact. Proper understanding of clinical progression not only help clinicians but also gives a public health message that some cases of COVID-19 can be actually severe and needs appropriate management.[6]

With this background, the present study was carried out to study the patient-related risk factors for predicting the severity of COVID-19 infection admitted to a tertiary care hospital.


  Methods Top


A hospital-based retrospective study was carried out. This was a record-based study of 305 individuals diagnosed with COVID-19 and were admitted in the study hospital. Hospital records of patients from January 2021 to June 2021 were studied. As this was a hospital-based record study and we did not use any patient identifying information, Institution Ethics Committee permission was not taken.

Patients with missing data were excluded. Only patients with complete variable information as per the study protocol were included in the present study.

Severity of COVID-19 was assessed by using the Indian Council of Medical Research criteria: Mild if: (“Upper respiratory tract symptoms (and/or fever) without shortness of breath or hypoxia”). Moderate if: (“Any one of: 1. Respiratory rate >24/min, breathlessness”), and in case they were admitted in intensive care unit (ICU) or required ventilator they were classified as having severe disease (“Any one of: 1. Respiratory rate >30/min, breathlessness, SpO2 <90% on room air”).[7]

Other factors such as age, sex, residence, presence of comorbidities, and addictions were noted down from the records in the per designed, semistructured study questionnaire.

The data were analyzed using the SPSS software v. 16. Proportions and means with two standard deviations were calculated for univariate analysis. Student's t-test for mean values and Yate's corrected Chi-square test for proportions was used to test the difference in two groups. Only significantly associated variables on univariate analysis were entered in the final model of multivariate analysis. Predictive accuracy of the model increased from 57.7% to 68.5%. The model was able to explain 20.8% of the variation in the outcome, i.e., disease severity (Nagelkerke R2 = 0.208). The model was fit to predict the dependent variable, i.e., disease severity (Hosmer and Lemeshow test was not significant; P = 0.589). Two tailed P < 0.05 was taken as statistically significant.


  Results Top


23.3% of the cases had mild form of disease while 34.4% had moderate disease. Majority, i.e., 42.3% had severe disease [Table 1].
Table 1: Distribution of study participants as per disease severity

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On univariate analysis, it was observed that advanced age, coming from rural area, preexisting hypertension, being obese were the significant risk factors for severe disease compared to mild and moderate disease (P < 0.05). When total scores of all risk factors were compared in two groups, it was found that those with severe disease has mean score of 3.36 compared to 2.48 for mild or moderate disease and this difference was found to be statistically significant (P < 0.05). Other factors were not found to be associated with the severity of the disease [Table 2].
Table 2: Risk factors of disease severity: Univariate analysis

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It was observed that in the final model, being from rural area and advanced age were the significant predictors of severe disease in the present study (P < 0.05) [Table 3].
Table 3: Predictors of disease severity: Multivariate analysis

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  Discussion Top


In the present study, 23.3% of the cases had mild form of disease while 34.4% had moderate disease. Majority, i.e., 42.3% had severe disease. Bhargava A et al.[8] noted that the severe COVID-19 infection was seen in 37.6% of the cases which is comparable to the present study. However, Liu L et al.[9] from their study reported a low proportion of severe cases, i.e., 18.9%. These differences in the proportion of severity of the disease depend on several factors such as age structure of the population, literacy, attitude, and health infrastructure.

On univariate analysis, it was observed that advanced age, coming from rural area, preexisting hypertension, being obese were the significant risk factors for severe disease compared to mild and moderate disease (P < 0.05). It was observed that in the final model, being from rural area and advanced age were the significant predictors of severe disease in the present study (P < 0.05). Bhargava A et al.[8] observed that age more than 60 years was significant risk factors for severe infection. They also noted that having preexisting diabetes, chronic lung disease and renal disease were more likely to have severe infection. We found that patients with hypertension had more chances of severe disease and we did not find any association of disease severity with diabetes, chronic lung disease, and renal disease. Liu L et al.[9] from their study found that the factors associated with intensification of the disease severity were age, time between onset and diagnosis, fever, C-reactive protein (CRP), damage to lungs. Mudatsir et al.[6] from their systematic review and meta-analysis noted that there were 62 risk factors of disease severity. However, after final analysis, 30 risk factors were found to be significantly associated with risk of severe disease.

The systematic review and meta-analysis by Mudatsir et al.[6] included 19 studies with 1934 mild cases and 1644 sever cases. They identified 62 risk factors for the meta-analysis. They found that more than 30 risk factors were associated with risk of severe disease compared to the mild cases namely morbidities such as diabetes, hypertension, cardiovascular disease, chronic respiratory disease; severe cases had more frequency of clinical features like anorexia, dyspnea, high systolic blood pressure, increased in the rate of respiration; among the laboratory markers they identified that lymphocytopenia, anemia, leukocytosis, increased levels of aspartate aminotransferase, creatinine, urea, creatine kinase, CRP, interleukin-6, high sensitivity troponin, D-dimer, lactate dehydrogenase, ferritin, erythrocyte sedimentation rate, and procalcitonin were associated with severity of COVID-19.

Shi Y et al.[10] observed that the factors associated with severe disease were old age, being male, presence of hypertension, diabetes, cardiovascular diseases, malignancy, not exposed to epidemic area, a greater number of infected family members were significant risk factors on univariate analysis. After multivariable analysis, old age, being male and hypertension were found to be independently associated with severe disease. We also found on multivariate analysis that old age was indecently associated with severe disease but not being male and hypertension. However, most important factor which we reported is very crucial, i.e., being from rural area.

Gong J et al.[11] found that the incidence of severe disease was 19.4% which is lower compared to the present study. They found that old age was significantly associated with severity of the disease which we also noted that old age was independently associated with severe disease.

Jain V et al.[12] carried out a systematic review and meta-analysis of predictive symptoms and co-morbidities for severe COVID-19 infection. They identified 2259 studies but based on their eligibility criteria they finally analyzed only seven studies which had 1813 cases. They found that advanced age, and being male were the significant risk factors for ICU admission. They also reported that the dyspnea was the only symptom which predicted severity of the disease as well as ICU admission. Among the comorbidities, chronic obstructive pulmonary disease was the strongest predictor followed by hypertension and cardiovascular disease of severity of the disease as well as ICU admission. Whereas, we found that only old age and being from rural area were independently associated with severity of the disease. We could not make out other factors as our sample size was only 305 compared to 1813 from this study and that could have made a difference in the identification of the predictive factors.

Li L et al.[13] compared deteriorated versus stabilized patients at 2 weeks. The deteriorated group was found to be older, number of smokers, more proportion of respiratory failure cases, increased CRP, low albumin. On multivariate analysis, they noted that age, smoking, respiratory failure, higher body temperature at admission, albumin, and CRP were significant risk factors of disease progression.

Shukla UB et al.[14] studied clinical characteristics and predictors of 800 patients with COVID-19. They reported that advanced age, presence of symptoms at admission, increased neutrophil and lymphocyte count, and increased markers of inflammation were significant risk factors for severe disease.

Compared to other studies which reported a greater number of risk factors for the severity of the COVID-19 disease, we did not find more risk factors. This difference may be because this study was a hospital record-based study where the records may not have been properly maintained and significant factors are missed. Another reason is that this study was a single center while other studies which are reporting a greater number of risk factors are multi-centric studies. Third reason may be the limited number of cases included in this study.

In the present study, when total scores of all risk factors were compared in two groups, it was found that those with severe disease has mean score of 3.36 compared to 2.48 for mild or moderate disease and this difference was found to be statistically significant (P < 0.05). Other factors were not found to be associated with the severity of the disease.

Rural residence was found to be an independent predictor of disease severity in the present study. It reflects the lack of appropriate health infrastructure in rural areas of India. The attitude and negligence of rural population toward any disease adds to this misfortune. These factors make the delayed diagnosis and start of treatment compared to their urban counterpart and COVID is no exception as reported from the present study. Hence, attention should be paid to this important risk factor to prevent severity and adverse outcome of the COVID-19.

Implications of the study

In this study, since advanced age and travelling from rural area were the only risk patient related risk factors elderly patients and rural patients should stay in COVID care centers so that shift to tertiary care centers can be arranged. Alternatively for the 3rd wave district hospitals and community health centers can be better equipped with oxygen, high-flow nasal oxygen and ventilators, and workforce can be trained to deliver oxygen and high flow nasal oxy gen or through NIV thereby preventing severity.


  Conclusion Top


Advanced age and being from rural area were the significant predictors of severe disease in the present study. Providing care at rural areas with facilities for tele rounds by tertiary care centers would reduce mortality significantly. Since the disease is moving toward rural population in the second wave rural center preparedness is important.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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Wang L, Li J, Guo S, Xie N, Yao L, Cao Y, et al. Real-time estimation and prediction of mortality caused by COVID-19 with patient information-based algorithm. Sci Total Environ. 2020;727:138394.  Back to cited text no. 3
    
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Cecconi M, Piovani D, Brunetta E, Aghemo A, Greco M, Ciccarelli M, et al. Early predictors of clinical deterioration in a cohort of 239 patients hospitalized for Covid-19 infection in Lombardy, Italy. J Clin Med Res. 2020;9:1548.  Back to cited text no. 4
    
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Mudatsir M, Fajar JK, Wulandari L, Soegiarto G, Ilmawan M, Purnamasari Y, et al. Predictors of COVID-19 severity: A systematic review and meta-analysis. Version 2. F1000Res 2020;9:1107.  Back to cited text no. 6
    
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AIIMS/ ICMR-COVID-19 National Task Force/Joint Monitoring Group (Dte.GHS) Ministry of Health & Family Welfare, Government of India Clinical Guidance For Management Of Adult Covid-19 Patients. 22-4-2021. Available from: HYPERLINK “https://www.icmr.gov.in/pdf/covid/techdoc/COVID19_Management_Algorithm_22042021_v1.pdf%20Accessed%20on%2025-5-2021” https://www.icmr.gov.in/pdf/covid/techdoc/COVID19_Management_Algorithm_22042021_v1.pdf. [Last accessed on 2021 May 25].  Back to cited text no. 7
    
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Bhargava A, Fukushima EA, Levine M, Zhao W, Tanveer F, Szpunar SM, et al. Predictors of severe COVID-19 infection. Clin Infect Dis 2020;71:1962-8.  Back to cited text no. 8
    
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Liu W, Tao ZW, Wang L, Yuan ML, Liu K, Zhou L. Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel corona virus disease. Chin Med J (Engl) 2020;133:1032-8.  Back to cited text no. 9
    
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Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Crit Care 2020;24:108.  Back to cited text no. 10
    
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Gong J, Ou j, Qiu X, Jie Y, Chen Y, Yuan L, et al. A Tool to Early Predict Severe Corona Virus Disease 2019 (COVID-19): A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China. Clin Infect Dis 2020 Apr 16: ciaa443.  Back to cited text no. 11
    
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Jain V, Yuan JM. Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis. Int J Public Health 2020:1-14.  Back to cited text no. 12
    
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Li L, Sun W, Han M, Ying Y, Wang Q. A Study on the Predictors of Disease Severity of COVID-19. Med Sci Monit 2020;26:e927167-1–e927167-8.  Back to cited text no. 13
    
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Shukla UB, Shukla SR, Palve SB, Yeravdekar RC, Natarajan VM, Tiwari P, et al. Characteristics of COVID-19 patients admitted to a tertiary care hospital in Pune, India and Predictors of Requirement for Intensive Care treatment. J Assoc Physicians India 2021;69:33-9.  Back to cited text no. 14
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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