ABSTRACT
Laboratory testing has been extremely helpful in determining the severity and determining the course of treatment for COVID-19 patients. Our aim has been to look for variables of patient’s clinical and laboratory profile for two weeks and to observe their significance. Observational, Cross-sectional study. Data from the clinic and laboratory were compiled on Google form after informed consent from the patient. Statistical analysis was done using the Mann-Whitney U and unpaired t test. Population statistics included 202 patients (1st week) and 161 patients (2nd week), with the mean age of 61 ± 18 years. Most patients fell under the mild category (SPO2 >94%). High body mass index (n = 119) and hypertensive (n = 98) were the most common comorbidities observed. Diabetes, cardiovascular and respiratory diseases are the other comorbidities studied in this study. Hypoalbuminemia (n = 194) is the most deranged laboratory parameter in mild category, followed by lymphopenia (n = 109). In severe category also, hypoalbuminemia (n = 13) was deranged more. Other laboratory parameters included are CRP, D-Dimer, neutrophil and lymphocyte count. This study showed that albumin is a good predictor for estimating the severity of COVID-19 patients especially in the first week of their admission.
KEYWORDS: Albumin, COVID-19, CRP, D-dimer, lymphocyte count, neutrophils, observational study
INTRODUCTION
The first pneumonia case with an unidentified microbiological cause was reported in Wuhan, China, in December 2019. Genomic studies were employed when more cases traced to the same locality was identified, and the virus was initially named novel coronavirus (2019-nCoV), and subsequently identified as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19.[1]
Numbers have increased exorbitantly and it has gone on to become the global pandemic. WHO declared it as a global pandemic on 11 March 2020.[2] A student who had just returned from Wuhan, China, in Thrissur, Kerala, reported the first case in India on 30 January 2020.[3] For the next two years since it was first detected, it has continued to evolve and has affected lives, mortality and livelihood of all individuals. Early detection, transmission control by isolation and prevention of morbidity and mortality have been the main modalities in containing this pandemic.[3]
Quite early in the dynamics of the pandemic, comorbidities such as hypertension, diabetes, cardiovascular disease, chronic lung disease, cancer and elderly age were labelled as risk factors for mortality.[4] In other research, smoking and obesity were linked to higher risks. Risk factors identification is beneficial in reducing overall mortality.[5]
Pneumonia associated with coagulopathy has poor prognosis in patients. C Reactive Protein (CRP) is cost effective marker in COVID-19 patients and D-dimer for coagulopathy.[6] Blood sugar levels are important for the prognosis irrespective of the diabetic status, and steroid treatment received by the patients is another vital factor in patient’s mortality status. Disease severity was found to be more in elderly (>52 years) and in those with comorbidities.[7]
Laboratory parameters, clinical severity assessment and radiological scoring are the main modalities in assessing COVID-19 severity and planning its treatment. Among the biomarkers and haematology parameters CRP, interleukin, D-dimer, ferritin, ESR, neutrophils and lymphocytes are the common parameters that have been found to be useful in most studies.[8]
Online search in PubMed on the number of studies done on laboratory parameters in COVID-19 patients in south India did not yield many results. We have undertaken this study with the objective of studying the laboratory profile of COVID-19 patients in south India.
MATERIALS AND METHODS
Population
Information on COVID-19 patients was collected for 3 months, 202 patients were covered in the study and of which 161 patients stayed on for the next week, and their data were compiled. The patients were warded to the tertiary care hospital COVID-19 ward and ICU, from mid-September to mid-December and were enrolled in our study. Patients were discovered with SARS-CoV-2 following a positive RTPCR test with a nasopharyngeal swab. An informed consent was taken of all the patients to collect their clinical and laboratory data. Research committee and Ethics committee approval was taken for the publication of anonymised publication of data for this hospital-based observational cross-sectional study. Adult patients who did not provide consent were kept out from the study. The objective of the study is to study the clinical severity and the laboratory profile of COVID-19 patients admitted in our hospital and to generate a predictive score using the data from the laboratory parameters collected.
Data collection
After the informed consent was collected, the patient’s data were collected weekly for a period of two weeks using Google form. Baseline laboratory parameters and clinical severity indicators were collected.
Clinical severity was measured based on MOHFW guidelines—10 May 2020 and the patients were classified into mild, moderate or severe infection.[9] Mild is with respiratory symptoms without hypoxia or pneumonia. Moderate is with respiratory symptoms with SpO2 90–94 on room air with respiratory rate ≥24 breaths/min. Severe is SpO2 <90 on room air and respiratory rate ≥30 breaths/min.
Patients with comorbidities that is diabetes, hypertension, cardiovascular disease, chronic kidney disease, respiratory disease and high body mass index (BMI >24.9 kg/m2) were included in the study. Laboratory variables included the routinely done tests—CRP (mg/ml), D-dimer (mg/ml) and Alb (g/dl), and haematological tests that are neutrophil and lymphocytes in cells/mm3 or %.
Data Analysis
Clinical severity with 1. Respiratory rate (≤24/min-mild and >24/min moderate severity), 2. Heart rate (≤100/min-mild and >100/min- moderate severity), 3. Intensive care unit (ICU)/ward admission and 4. Comorbidities data were compiled weekly for two weeks. Laboratory parameters and clinical severity were estimated using Mann-Whitney U test. For neutrophils, unpaired t test was used. D-dimer was divided into normal (<500 mg/ml), high (>500- 1600 mg/ml) and very high (>1600 mg/ml) category and was analysed with the above parameters using Chi-squared test. The parameters are expressed as median and interquartile range. The mean of properly distributed data was examined using the independent t test. For statistical examination of skewed continuous variables, the Mann-Whitney U test was used. A value of double-sided P < 0.05 was considered as statistically significant. The SPSS 20.0 software was used for the analysis for the variables.
Missing data
Data collection tool were using a Google form for four weeks, but most patients got discharged by the second week of their admission, so due to lack of adequate data for 4 weeks of the study duration, data have been limited to that of the first and second week. Data for albumin were available only for the first week.
RESULTS
Population statistics
Total number of patients included in the study is 202 in the first week and 161 in the second week. The second week saw a drop in number of patients due to discharge after a week of hospital stay or referrals and in some patients; it was due to inadequate data. The mean age of patients during both weeks was 61 ± 18 years with the oldest patient being 92 years. 105 were male patients of which 79% fell in the mild COVID-19 category. Mild category had the highest number of patients for all parameters. Based on SPO2 levels, the patients were classified as mild, moderate and severe—mild (SPO2 >94%), moderate (SPO2 90-94%), and with the severe category (SPO2 <90%). The severe category had the least number of patients (5.7%) as seen in Table 1.
Table 1.
Baseline characteristics of study cohort in the first week
| Mild Covid SPO2 >94% n (%) | Moderate Covid SPO2 (90-94%) n (%) | Severe Covid SPO2 (<90%) n (%) | |
|---|---|---|---|
| Males | 83 (79.0) | 16 (15.2) | 6 (5.7) |
| Diabetics | 60 (76.9) | 12 (15.4) | 6 (7.7) |
| Hypertension | 78 (79.6) | 12 (12.2) | 8 (8.2) |
| Cardio vascular disease | 41 (83.7) | 5 (10.2) | 3 (6.1) |
| Overweight | 89 (74.8) | 24 (20.2) | 6 (5.0) |
| Respiratory disorder | 25 (78.1) | 4 (12.5) | 3 (9.4) |
| CRP-Greater than 5 | 50 (74.6) | 13 (19.4) | 4 (6.0) |
| D- dimer- Greater than 500 | 48 (84.2) | 5 (8.8) | 4 (7.0) |
| Albumin- Less than 3.5 | 150 (77.3) | 31 (16.0) | 13 (6.7) |
| Neutrophilia >70% | 73 (83.9) | 11 (12.6) | 3 (3.4) |
| Neutropenia <50% | 20 (80.0) | 3 (12.0) | 2 (8.0) |
| Lymphophilia >40% | 10 (83.3) | 2 (16.7) | 0 (0.0) |
| Lymphopenia <20% | 90 (82.6) | 15 (13.8) | 4 (3.7) |
Data has been shown as total number (n) and also in percentage (%). The upper limit of normal range was taken for neutrophilia and lymphophilia, likewise the lower limit for neutropenia and lymphopenia. Patients were divided into categories based on MOHFW guidelines- 10th May 2020.[9]
Overweight (n = 89) represented the most common comorbidity, and 75% of the total 119 overweight patients fell in the mild COVID-19 category. Overweight was also the most common comorbidity in patients having moderate COVID-19. Hypertension (n = 8) was the most common comorbidity in severe category [Table 1].
Among the biochemical laboratory parameters hypoalbuminemia was the most commonly observed with 194 patients having albumin less than 3.5 g/dl. High CRP (>5 mg/ml) was observed in 67 patients of which majority (75%) were in the mild COVID-19 category. Among the haematological parameters lymphopenia (n = 109) was more common than lymphophilia (n = 12), likewise neutrophilia (n = 87) was observed to be more than neutropenia (n = 25) in Table 1. Lymphopenia and Neutrophilia were more in mild category of patients.
The most prominent comorbidity in severe COVID-19 was hypertension. Hypoalbuminemia was the most common deranged biochemical laboratory parameter, and lymphopenia the most commonly observed haematological parameter among the severe category [Table 1].
T test was employed to check the significance of neutrophils (%) with various clinical and comorbid parameters in Table 2. In the first week, neutrophils (%) was found to be significant with respiratory rate (P value = 0.028), cardiovascular disorders and in BMI (P value = 0.012). And in the second week, respiratory rate (P value = 0.034) and heart rate (P value = 0.012) were found to have significance with neutrophils.
Table 2.
t test for neutrophils (%) in both weeks
| (Mean±SD) | Neutrophil (%) | |
|---|---|---|
|
| ||
| First week | Second week | |
| Respiratory rate ≤24/min | 67.271±14.716 | 75.713±16.588 |
| Respiratory rate >24/min | 77.870±15.746 | 86.645±9.721 |
| P | 0.028 | 0.034 |
| Heart rates ≤100/min | 67.630±15.005 | 75.318±16.354 |
| Heart rates >100/min | 68.575±14.668 | 92.010±3.936 |
| P | 0.729 | 0.002 |
| ICU | 67.360±16.155 | 75.318±16.354 |
| Ward | 67.743±14.400 | 82.716±13.085 |
| P | 0.876 | 0.082 |
| Diabetics | 69.362±14.329 | 76.492±16.812 |
| Non diabetic | 66.817±15.244 | 77.073±16.060 |
| P | 0.243 | 0.848 |
| Hypertensive | 69.557±14.757 | 77.755±15.399 |
| Non-Hypertensive | 66.192±14.940 | 75.844±17.314 |
| P | 0.113 | 0.525 |
| Cardiovascular disorder | 71.814±13.628 | 81.029±14.065 |
| No cardiovascular disorder | 66.524±15.117 | 75.142±16.922 |
| P | 0.032 | 0.075 |
| Chronic kidney disease | 69.618±10.412 | 79.422±14.414 |
| No kidney disease | 67.700±15.147 | 76.611±16.505 |
| P | 0.680 | 0.621 |
| Overweight | 65.585±14.709 | 75.673±15.149 |
| Normal | 70.950±14.712 | 78.527±17.938 |
| P | 0.012 | 0.351 |
| Respiratory disease | 69.722±15.837 | 78.010±13.426 |
| No respiratory disease | 72.996±73.360 | 76.584±16.892 |
| P | 0.805 | 0.723 |
Data indicated as mean and standard deviation (SD)
DISCUSSION
Biomarkers and the prognostic indicators are continuously evolving in the COVID-19 pandemic. It has never been more important to formulate effective testing strategies for early detection and treatment.
Our data show the pattern observed in Indian patients from north Kerala. In Table 2, laboratory parameters (CRP, albumin and lymphocyte count) were compared to respiratory rate with a cut off of 24/min, heart rate with a cut off of 100/min, ICU/ward and comorbidities using the Mann-Whitney U test. The median/inter quartile range (IQR), the mean with the standard deviation was evaluated.
The laboratory parameter which showed most significance during the first week of admission is albumin (P value<0.05). It has significant relationship to respiratory rate (P value = 0.038) which is an indicator of clinical severity and with comorbid conditions like hypertension (P value = 0.010), cardiovascular disorders (P value = 0.001) and BMI (P value = 0.033) in Table 3. Previous research indicated that hypoalbuminemia predicted respiratory failure in MERS-CoV-2 patients.[10] Bats M-L et al.[11] found albumin, CRP and ferritin variables to be very significant to the appearance of symptoms and hospitalisation.
Table 3.
Independent discriminators (predictors) of disease severity
| Parameter Median (IQR) | CRP (mg/ml) | Albumin (g/dl) | Lymphocytes (%) |
|---|---|---|---|
| First week | |||
| Respiratory rate ≤24/min | 1.675 (0.61-5.84) | 3.800 (3.45-4.10) | 18.900 (10.50-28.95) |
| Respiratory rate >24/min | 3.135 (0.20-7.54) | 3.250 (2.57-3.87) | 10.950 (7.37-21.75) |
| P | 0.701 | 0.038 | 0.180 |
| Heart rates ≤100/min | 1.610 (0.59-6.08) | 3.800 (3.40-4.10) | 68.575±14.668 |
| Heart rates >100/min | 2.420 (0.75-5.69) | 3.900 (3.50-4.20) | 19.000 (8.90-30.30) |
| P | 0.403 | 0.294 | 0.829 |
| ICU | 1.110 (0.60-3.60) | 3.900 (3.35-4.12) | 20.600 (5.95-30.05) |
| Ward | 1.850 (0.63-6.55) | 3.800 (3.30-4.10) | 17.700 (10.60-28.72) |
| P | 0.253 | 0.383 | 0.923 |
| Diabetics | 1.805 (0.73-5.33) | 3.750 (3.40-4.07) | 17.500 (8.50-27.55) |
| Non diabetic | 1.660 (0.56-6.97) | 3.900 (3.40-4.20) | 19.000 (11.35-31.75) |
| P | 0.747 | 0.165 | 0.244 |
| Hypertensive | 1.730 (0.60-6.02) | 3.700 (3.12-4.00) | 16.200 (9.30-26.90) |
| Non-Hypertensive | 1.705 (0.61-7.06) | 3.900 (3.50-4.20) | 20.500 (11.10-31.90) |
| P | 0.763 | 0.010 | 0.069 |
| Cardiovascular disorder | 2.420 (0.95-7.51) | 3.500 (3.00-4.00) | 13.000 (8.50-22.80) |
| No cardiovascular disorder | 1.610 (0.59-5.38) | 3.900 (3.50-4.20) | 19.150 (10.67-31.52) |
| P | 0.134 | 0.001 | 0.030 |
| Chronic kidney disease | 0.950 (0.46-5.26) | 3.500 (2.60-4.00) | 14.700 (8.50-27.30) |
| No kidney disease | 1.740 (0.61-6.55) | 3.800 (3.40-4.10) | 18.700 (10.40-29.80) |
| P | 0.458 | 0.058 | 0.392 |
| Respiratory disease | 1.640 (0.82-5.07) | 3.700 (3.20-4.10) | 16.200 (8.50-26.50) |
| No respiratory disease | 1.730 (0.58-7.06) | 3.800 (3.42-4.10) | 18.500 (10.50-29.80) |
| P | 0.960 | 0.297 | 0.235 |
| Overweight (BMI >22.9) | 1.550 (0.66-5.06) | 3.900 (3.50-4.20) | 20.600 (11.70-31.80) |
| Normal (BMI 18.5-22.9) | 2.235 (0.56-7.29) | 3.700 (3.10-4.10) | 14.350 (9.80-25.17) |
| P | 0.342 | 0.033 | 0.037 |
Data shown as IQR Abbreviation IQR: inter quartile range
Likewise, lymphocyte count was found to be important in cardiovascular patients (P value = 0.030) and also in relation with BMI (P value = 0.037). Shi S et al.[12] discovered that SARS-CoV-2 infection might cause cardiac damage, which is associated with a greater risk of in-hospital mortality.
CRP was very significant in patients of O2 support [Table 4]. CRP is a systemic positive acute-phase response marker for inflammation, infection and tissue damage, and it has the potential to be employed as an inflammatory indicator. This is validated by many studies which show an increase in inflammatory markers especially CRP in COVID-19 patients and especially in those with severe COVID-19,[13,14] whereas some of these results are controversial since some other studies like Zeng F et al.[15]
Table 4.
CRP and oxygen support status of patient
| CRP (mg/ml) First week | |
|---|---|
| Patient on O2 suport (Median (IQR) | 1.300 (0.51-5.23) |
| Patient not on O2 support (Median (IQR)) | 4.025 (1.61-10.44) |
| P | 0.001 |
Our research offers numerous advantages. A professional team of physicians collected our data utilising a technology tool called Google forms. All patient data were collected and examined by medical investigators, and it was completely cleaned, resulting in high-quality data. We also have access to a variety of patient variables such as comorbidities, clinical complaints and routine laboratory indicators. And the patients were followed up in the second week. However, our study may have some limitations, only 202 and 162 (2nd week) patients were included, and a larger cohort research could confirm our findings. Secondly, we did not include more parameters like IL-6, BNP, LDH and PCT since it was not routinely done in our laboratory.
Authors contribution
SH, PD, BK, HP, SSR, SS and CS planned the study and collected the data. KS cleaned up the data and analysed the data. SH, PD and BK wrote the manuscript. SH, PD, BK, HP, SSR, SS and CS read and reviewed the final manuscript.
Data availability statement
The authors declare that the data have not been made available publicly.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
We acknowledge Krishnaveni—Statistician, Department of Community Medicine, Malabar Medical Hospital and Research Center for her contribution in data cleaning and analysis.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The authors declare that the data have not been made available publicly.
