Abstract
Background
Malnutrition, as defined by the World Health Organization (WHO), includes undernutrition. In the Philippines, malnutrition is common due to several factors. The nutritional biomarkers can be used as an alternative indicator of dietary intake and nutritional status that can detect deficiencies in support to clinical management of COVID-19 patients. Apart from that, biomarkers are potentially useful for screening, clinical management, and prevention of serious complications of COVID-19 patients. Serum albumin, c-reactive protein (CRP), leukocyte count, lymphocyte count, blood urea nitrogen (BUN) to compute the nutritional prognostic indices (Prognostic nutritional index (PNI) score, BUN/Albumin ratio (BAR) and CRP/Albumin ratio (CAR).
Objectives
To compare the nutritional biomarkers of patients with COVID-19 based on case severity and determine the nutritional prognostic indices and associate to patients’ clinical outcome during hospital stay.
Methods
A single center, cross-sectional study was performed between June 2021 to August 2021 in a COVID-19 designated referral center in CALABARZON which comprised of 167 patients as part of the study. Clinicodemographic profile including patients' age, sex, co-morbidities, weight, height, laboratory, and serum biomarkers during the first 48 h of admission (serum albumin, leukocyte count, lymphocytes count, CRP, and BUN) were collated wherein the nutritional prognostic indices were computed and analyzed. Clinical outcomes of the patients were based on the patients’ final diagnoses (recovered, length of hospital stay (LOHS), progression of severity and mortality).
Results
167 non-critically ill COVID-19 patients were included in the analysis, of which 52.7% are admitted under the COVID-19 severe group and 47.3% for COVID-19 Mild/Moderate. Mostly are male (53.3%) with an average body mass index (BMI) of 24.26 (SD = 3.52) and have hypertension (55.1%) and diabetes (42.5%). Among the nutritional biomarker, albumin (p = 0.028; p = 0.004), total lymphocyte count (TLC) (p = 0.013; p = 0.005) and BUN (p = 0.001; p=<0.001) were shown to be significantly associated with progression of severity and mortality. Univariate logistic regression analysis showed the following nutritional prognostic score were correlated. (1.) progression of COVID-19 severity: PNI score (OR 0.928, 95% CI 0.886, 0.971, p=<0.001), and BAR value (OR 1.130, 95% CI 1.027, 1.242, p = 0.012); (2.) Mortality: PNI score (OR 0.926, 95% CI 0.878, 0.977, p = 0.005), CAR (OR 1.809, 95% CI 1.243, 2.632, p = 0.002), and BAR (OR 1.180, 95% CI 1.077, 1.292, p=<0.001). The average LOHS of COVID-19 patients was 12 days (SD = 7.72). However, it does not show any significant correlation between any nutritional biomarker, prognostic indices and LOHS.
Conclusion
This study demonstrated that deranged level of nutritional biomarkers can affect patient's COVID-19 severity and associated with patient's clinical outcome. Low albumin (≤2.5 g/dL), low level of TLC (≤1500 cells/mm3), elevated BUN (≥7.1 mmol/L) are associated with patient's case severity progression and mortality while low PNI score (<42.49), high BAR value (≥2.8) and CAR value (≥2.04) provided an important nutritional prognostic information and could predict mortality which can be a useful parameter in admission, hence it is recommended to screen all COVID-19 patients to reduce mortality.
Keywords: COVID-19, Non-critically ill, Malnutrition, Nutritional biomarker, Prognostic nutritional index (PNI) score, C-reactive protein/albumin ratio (CAR), Blood urea nitrogen/albumin ratio (BAR)
1. Introduction
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and was first recognized in Wuhan, China during the latter part of December 2019, an enveloped, single-stranded RNA viruses, positive-sense that can infect a wide range of vertebrates, and believed that the important reservoir of these virus are bats that infect the human [1,2]. In the Philippines, as of December 2021, the COVID-19 has affected more than 2.8 million people in the Philippines with 51, 504 related deaths based on Department of Health COVID-19 case bulletin number 657 [3]. The immune and nutritional status is associated with susceptibility and severity of the COVID-19 infection, hence remain critically importance [2].
Malnutrition as defined by the WHO, includes undernutrition (underweight, stunting, wasting), insufficient minerals or vitamins, overweight, and resulting diet-related noncommunicable diseases [4]. Globally in 2020, 462 million are underweight [5]. A cross-sectional study done at Wuhan, China by Li et al. evaluating the prevalence of malnutrition and its related factors in elderly patients with COVID-19 showed that 27.5% of patients included in the study have malnutrition risk and 52.7% were malnourished [6].
In the Philippines, malnutrition is common due to several factors. Based from the MalnutriCoV study by Larrazabal et al., the prevalence of malnutrition among adult patients admitted with COVID-19 was 71.83%, most of these patients are elderly. Severity of pneumonia and presence of chronic kidney disease are some of the risk factors of malnutrition [7].
Thus, malnutrition in hospital-admitted patients with COVID-19 has been associated with higher rates of mortality and longer LOHS than in patients with normal nutrition and lead to delay in recovery, increased rates of minor and major complications, and increased financial burden on the health care system [[8], [9], [10], [11]].
Given the broad scope of this health issue, one initiative is to properly screen nutritional status of patients in order to track, monitor and alleviate existing problems, hence improving the clinical outcome of COVID-19 patients. The nutritional biomarkers, such as serum albumin, BUN, TLC, and CRP, can objectively measure the different biological samples and can be used as an alternative indicator of dietary intake and nutritional status which can detect abnormality or deficiency in support to clinical management. However, breakthrough of biomarkers of nutritional intake, status, critical evaluation and the known biomarkers are essential in the continuing progress of nutritional epidemiology. In addition, biomarkers are potentially useful for screening, clinical management, and prevention of serious complications that can used in COVID-19 patients [[12], [13], [14]].
Some international studies used the nutritional prognostic indices computed from the biomarkers mentioned above in predicting morbidity and mortality among critically ill patients but few of studies used these indices in patients with COVID-19 especially in our local setting. Hence, this study aims to compare and determine the nutritional status using the nutritional biomarkers and nutritional prognostic indices (PNI score, CRP/Albumin ratio (CAR), and BUN/Albumin ratio (BAR)) of patients with COVID-19 who were admitted under the Department of Internal Medicine with mild to severe classification based on the WHO clinical severity and to distinguish its association with the clinical outcomes during hospital stay. Also, it seeks whether the study population discharge has improved or recovered, has developed a severe or critically illness, or has prolonged hospital stay and/or mortality. This is a preliminary study in the Philippines that aims to analyze the nutritional prognostic indices as predictor of clinical outcomes of COVID-19 in our setting. The data collected can aid in the early recognition of malnutrition which could improve outcomes and could increase the effectiveness of any future action plans and/or strategies that seek to reestablish good nutritional status in hospitalized COVID-19 patients. This will be the initial study regarding the COVID-19 nutrition in Batangas Medical Center.
2. Objectives
2.1. General objective
To compare the nutritional biomarkers of patients with COVID-19 based on case severity and its association with their clinical outcome during hospital stay.
2.2. Specific objectives
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1.
To identify the clinicodemographic profile of patients with COVID-19.
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2.
To associate the nutritional biomarkers of patients with BMI.
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3.To associate the nutritional biomarkers, nutritional prognostic indices (PNI score, BAR and CAR) with case severity (Mild/Moderate versus Severe) of COVID-19 patients and their clinical outcome.
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3.1.Progression of severity
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3.2Length of hospital stay
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3.3.Recovered
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3.4Mortality
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3.1.
3. Methods
3.1. Study design, setting and duration
This study was conducted in Batangas Medical Center, a tertiary government hospital and was designated by the Department of Health as one of the COVID-19 referral center in Cavite, Laguna, Batangas, Rizal, and Quezon (CaLaBaRZon). A retrospective cross sectional study was performed on 167 COVID-19 patients who were then admitted in our institution from June 2021 to August 2021. The study protocol was approved by the Department of Internal Medicine Technical Review Board followed by Batangas Medical Center Research Ethics Review Committee (BATMC RERC 2021–024).
3.2. Study population
The study does not involve intervention, procedures and interactions with the subject. A total of 271 COVID-19 patients had been admitted, but only 167 patients were included in the study, which comprises of all adult patients aged 19 years old and above with non-critically ill COVID-19 were group into two (a) Mild/Moderate, and (b) Severe, based on WHO case definition and clinical severity. Furthermore, the investigator evaluated the pertinent laboratory results done to the patients during their forty eight (48) hours of admission (serum albumin, CRP, leukocyte count, lymphocyte count, BUN). There were 104 patients out of 271 who were excluded in the study due to the following factors: (a) admitted and classified as COVID-19 critically ill; (b) patients who did not receive Remdesivir or Dexamethasone as investigational drug; (c) patients who were discharged against medical advice or were transferred to other institution, and (d) admitted patients with missing information on medical records including patients height and weight.
3.3. Study procedure
All data were collected and analyzed retrospectively through medical chart review at the Batangas Medical Center Record section. Letter of Request was made and addressed to the following: (a) Medical Center Chief; (b) Chief of Medical Professional Staff (CMPS); (c) Chief of Professional Education Training and Research (PETRO), and (d) Supervising Administrative Officer of Health Information Management, for approval to access the patients' medical record at Hospital Information Management Section (HIMS). All non-critically ill admitted COVID-19 patients were pooled and included in the list for chart retrieval. All patients’ chart were reviewed based on the inclusion and exclusion criteria.
The following information were reviewed from patient's medical chart: age, sex, co-morbidities, weight, height, laboratory/serum biomarkers during the first 48 h of admission (serum albumin, leukocyte count, lymphocytes count, CRP, and BUN) were collated. TLC (cells/mm3) was calculated by leukocyte count x lymphocyte count x 1000, PNI score was calculated based on serum albumin (g/dL) and TLC [ 10 × serum albumin (g/dL) + 0.005 × TLC (cell/mm3)], CAR was computed by CRP (mg/L)/albumin (g/L), while BAR was computed by BUN (mg/dL)/albumin (g/L). Clinical outcomes of the patients were based on the patients' final diagnoses, whether the patient was discharged improved/recovered or the clinical severity of the patient or the patient died. All data were interpreted and analyzed to determine the relationship of the variables.
3.4. Statistical analyses
The study population were characterized by descriptive analysis, namely frequency tables for the categorical variables and measures of position and dispersion for the continuous variables. The Chi–Square test and Fisher–Freeman Halton test were applied to determine the relationships or compared proportions using a significance level of 5%. One-way ANOVA was performed to compare the LOHS of patients with different nutritional biomarkers and case severity. Independent t-test/Mann–Whitney U test was performed to compare the nutritional prognostic scores between COVID-19 mild/moderate and COVID-19 severe patients. To determine the relationship of three nutritional prognostic indices (PNI Score, CAR, BAR) with LOHS, linear regression performed. Moreover, univariate logistic regression was used to determine the relationship of the nutritional prognostic scores with progression of case severity and mortality. The ROC analysis was also be performed to investigate the predictability of three nutritional prognostic indices.
4. Results
A total of 167 patients admitted and diagnosed with non-critically ill COVID-19 were included in the study. Table 1 shows the profile of the patients in the study. Majority of them are 61 years old and above (40.1%), while the average age is 56 years old (SD = 16.82). It shows that there is a higher distribution of male patients (53.3%) was observed as compared to female patients (46.7%). The average BMI is 24.26 (SD = 3.52), in which most of the patients were normal (33.5%), obese I (32.3%), and overweight (26.3%). Most of the COVID-19 patients had hypertension and diabetes comprising 55.1% and 42.5% of the patients, respectively. Other comorbidities were also identified namely thyroid disorder, malignancy, arthritis, cerebrovascular disease and other neurologic disorders. Additionally, the average day of COVID-19 illness prior to admission is seven days (SD = 3.68) and majority of the patients admitted under the COVID-19 severe group with 52.7% compared to COVID-19 Mild/Moderate of 47.3%.
Table 1.
Clinicodemographic profile of patients with COVID-19.
| Clinicodemographic data | N = 167 |
|---|---|
| Age | 55.99 ± 16.82 |
| 19–30 years old | 20 (12.0%) |
| 31–40 years old | 13 (7.8%) |
| 41–50 years old | 26 (15.6%) |
| 51–60 years old | 41 (24.6%) |
| 61 years old and above | 67 (40.1%) |
| Sex | |
| Male | 89 (53.3%) |
| Female | 78 (46.7%) |
| BMIa(kg/m2) | 24.26 ± 3.52 |
| Underweight (<18.5) | 2 (1.2%) |
| Normal range (18.5–22.9) | 56 (33.5%) |
| Overweight (23–24.9) | 44 (26.3%) |
| Obese I (25–29.9) | 54 (32.3%) |
| Obese II (>30) | 11 (6.6%) |
| Co Morbidities | |
| None | 29 (17.4) |
| Hypertension | 92 (55.1) |
| Diabetes | 71 (42.5) |
| Chronic Kidney Disease | 14 (8.4) |
| Liver Disease | 3 (1.8) |
| Bronchial Asthma | 9 (5.4) |
| Chronic Obstructive Pulmonary Disease | 6 (3.6) |
| Heart Disease | 8 (4.8) |
| Others | 57 (34.1) |
| Day of illness on Admission | 7.19 ± 3.68 |
| ≤48 h | 11 (6.6%) |
| >48 h | 156 (93.4%) |
| Clinical Severity of COVID-19 on admission | |
| COVID-19 Mild/Moderate | 79 (47.3%) |
| COVID-19 Severe | 88 (52.7%) |
BMI: body mass index; based on World Health Organization – Asia–Pacific guidelines.
Most of the patients included in the study had a normal BMI as presented in Table 1. However, our study shows that there were no significant associations between the different nutritional biomarkers and the patients’ BMI classification as shown in Table 2 . Furthermore, as shown in Table 3 on the association of nutritional biomarkers with malnutrition, serum albumin, CRP, TLC are not statistically significant except for BUN with p value of 0.018.
Table 2.
Association of nutritional biomarkers and body mass index classification.
| Variables | Body Mass Index Classificationa |
p-value | ||||
|---|---|---|---|---|---|---|
| Under weight n (%) | Normal n (%) | Over weight n (%) | Obese I n (%) | Obese II n (%) | ||
| Serum Albuminb | 36.0 ± 5.7 | 38.0 ± 5.8 | 38.4 ± 5.6 | 40.2 ± 6.4 | 39.9 ± 4.8 | |
| >3.4 g/dL | 1 (0.7) | 44 (31.7) | 38 (27.3) | 46 (33.1) | 10 (7.2) | 0.683 |
| 2.5–3.4 g/dL | 1 (4.0) | 10 (40.0) | 6 (24.0) | 7 (28.0) | 1 (4.0) | |
| <2.5 g/dL | 0 | 2 (66.7) | 0 | 1 (33.3) | 0 | |
| C-reactive proteinb | 4.2 ± 0.8 | 5.1 ± 3.1 | 5.4 ± 3.1 | 5.7 ± 3.1 | 5.6 ± 3.4 | |
| Above 1 mg/dL | 2 (1.3) | 48 (32.2) | 39 (26.2) | 49 (32.9) | 11 (7.4) | 0.719 |
| Below 1 mg/dL | 0 | 8 (44.4) | 5 (27.8) | 5 (27.8) | 0 | |
| Total lymphocyte countb | 1003.4 ± 206.6 | 1186.8 ± 843.2 | 1024.1 ± 432.5 | 1291.5 ± 789.3 | 1427.7 ± 746.2 | |
| >1500 cells/mm 3 | 0 | 11 (31.4) | 7 (20.0) | 14 (40.0) | 3 (8.6) | 0.720 |
| 900–1500 cells/mm 3 | 1 (1.4) | 25 (34.2) | 17 (23.3) | 24 (32.9) | 6 (8.2) | |
| <900 cells/mm 3 | 1 (1.7) | 20 (33.9) | 20 (33.9) | 16 (27.1) | 2 (3.4) | |
| Blood Urea Nitrogenb | 7.7 ± 2.5 | 10.8 ± 11.9 | 7.8 ± 7.9 | 7.2 ± 6.9 | 7.5 ± 5.3 | |
| <2 mmol/L | 0 | 4 (57.1) | 1 (14.3) | 2 (28.6) | 0 | 0.272 |
| 2.1–7.1 mmol/L | 1 (1.0) | 26 (25.5) | 29 (28.4) | 38 (37.3) | 8 (7.8) | |
| >7.2 mmol/L | 1 (1.7) | 26 (44.8) | 14 (24.1) | 14 (24.1) | 3 (5.2) | |
| COVID Severity on admissionb | ||||||
| COVID-19 Mild/Moderate | 1 (1.3) | 26 (32.9) | 16 (20.3) | 30 (38.0) | 6 (7.6) | 0.383 |
| COVID-19 Severe | 1 (1.1) | 30 (34.1) | 28 (31.8) | 24 (27.3) | 5 (5.7) | |
Body mass index; based on World Health Organization – Asia–Pacific guidelines.
Fisher's Exact test/Fisher–Freeman–Halton test.
Table 3.
Association of nutritional biomarkers with malnutrition and normal body mass index.
| Variables | Malnutrition |
p-value | |
|---|---|---|---|
| Normal n (%) | Malnourished n (%) | ||
| Serum Albuminc | 38.0 ± 5.8 | 39.4 ± 6.0 | |
| >3.4 g/dL | 44 (31.7) | 95 (68.3) | 0.326 |
| 2.5–3.4 g/dL | 10 (40.0) | 15 (60.0) | |
| <2.5 g/dL | 2 (66.7) | 1 (33.3) | |
| C-reactive proteinb | 5.06 ± 3.10 | 5.5 ± 3.1 | |
| Above 1 mg/dL | 48 (32.2) | 101 (67.8) | 0.299 |
| Below 1 mg/dL | 8 (44.4) | 10 (55.6) | |
| Total lymphocyte countb | 1186.8 ± 843.2 | 1193.8 ± 668.3 | |
| >1500 cells/mm 3 | 11 (31.4) | 24 (68.6) | 0.956 |
| 900–1500 cells/mm 3 | 25 (34.2) | 48 (65.8) | |
| <900 cells/mm 3 | 20 (33.9) | 39 (66.1) | |
| Blood Urea Nitrogenc | 10.8 ± 11.9 | 7.5 ± 7.1 | |
| <2 mmol/L | 4 (57.1) | 3 (42.9) | 0.018a |
| 2.1–7.1 mmol/L | 26 (25.5) | 76 (74.5) | |
| >7.2 mmol/L | 26 (44.8) | 32 (55.2) | |
| COVID Severity on admissionb | |||
| COVID-19 Mild/Moderate | 26 (32.9) | 53 (67.1) | 0.872 |
| COVID-19 Severe | 30 (34.1) | 58 (65.9) | |
Significant at 0.05 level of significance.
Chi-square test of independence.
Fisher's Exact test/Fisher–Freeman–Halton test.
Our study shows higher distribution with COVID-19 severe as observed in all patients with different levels of serum albumin (Table 4 ). Nonetheless, there was no sufficient evidence to conclude that there is a significant association between serum albumin and COVID-19 severity (p = 1.000) at 0.05 level of significance. Also, there was no statistically significant association between blood urea nitrogen and COVID-19 severity (p = 0.165).
Table 4.
Association of nutritional biomarkers with COVID severity.
| Variables | COVID Severity |
p-value | |
|---|---|---|---|
| COVID-19 Mild/Moderate n (%) | COVID-19 Severe n (%) | ||
| Serum Albuminc | 39.8 ± 6.2 | 38.1 ± 5.6 | |
| >3.4 g/dL | 66 (47.5) | 73 (52.5) | 1.000 |
| 2.5–3.4 g/dL | 12 (48.0) | 13 (52.0) | |
| <2.5 g/dL | 1 (33.3) | 2 (66.7) | |
| C-reactive proteinb | 4.0 ± 2.8 | 6.6 ± 2.8 | |
| Above 1 mg/dL | 66 (44.3) | 83 (55.7) | 0.025a |
| Below 1 mg/dL | 13 (72.2) | 5 (27.8) | |
| Total lymphocyte countb | 1334.5 ± 755.7 | 1063.0 ± 683.3 | |
| >1500 cells/mm 3 | 25 (71.4) | 10 (28.6) | 0.005a |
| 900–1500 cells/mm 3 | 31 (42.5) | 42 (57.5) | |
| <900 cells/mm 3 | 23 (39.0) | 36 (61.0) | |
| Blood Urea Nitrogenc | 7.6 ± 9.1 | 9.5 ± 9.0 | |
| <2 mmol/L | 2 (28.6) | 5 (71.4) | 0.165 |
| 2.1–7.1 mmol/L | 54 (52.9) | 48 (47.1) | |
| >7.2 mmol/L | 23 (39.7) | 35 (60.3) | |
Significant at 0.05 level of significance.
Chi-square test of independence.
Fisher's Exact test/Fisher–Freeman–Halton test.
More than half of the patients with CRP of above 1 mg/dL were COVID-19 severe. On the other hand, 72.2% of the patients with below 1 mg/dL CRP were COVID-19 mild/moderate. With a resulting p-value of 0.025, it can be concluded that there was a significant association between CRP and COVID-19 severity.
In terms of the TLC, most of the patients with >1500 cells/mm3 were COVID-19 mild/moderate (71.4%). While, most of the patients with below 1500 cells/mm3 were COVID-19 severe. At 0.05 level of significance, there was sufficient evidence to conclude that there was a significant association between TLC and COVID-19 severity (p = 0.005).
As shown in Table 5 , the majority of patients with serum albumin of >3.4 g/dL did not experience disease progression during admission (52.5%), while the majority of patients with serum albumin of less than 3.4 g/dL experienced progression to more severe disease (68% of patients with serum albumin 2.5–3.4 g/dL, and every patient with albumin <2.5 g/dL). Thus it can be concluded that there was a statistically significant association between serum albumin and COVID-19 severity progression (p = 0.028).
Table 5.
Association of nutritional biomarkers and case severity with progression of severity.
| Variables | Progression of Severity |
p-value | |
|---|---|---|---|
| Progressed n (%) | Did not progressed n (%) | ||
| Serum Albuminc | 37.6 ± 6.2 | 40.4 ± 5.3 | |
| >3.4 g/dL | 66 (47.5) | 73 (52.5) | 0.028∗ |
| 2.5–3.4 g/dL | 17 (68.0) | 8 (32.0) | |
| <2.5 g/dL | 3 (100.0) | 0 | |
| C-reactive proteinb | 5.5 ± 3.1 | 5.2 ± 3.1 | |
| Above 1 mg/dL | 78 (52.3) | 71 (47.7) | 0.526 |
| Below 1 mg/dL | 8 (44.4) | 10 (55.6) | |
| Total lymphocyte countb | 1060.5 ± 685.3 | 1330.5 ± 752.3 | |
| >1500 cells/mm 3 | 11 (31.4) | 24 (68.6) | 0.013a |
| 900–1500 cells/mm 3 | 38 (52.1) | 35 (47.9) | |
| <900 cells/mm 3 | 37 (62.7) | 22 (37.3) | |
| Blood Urea Nitrogenc | 10.3 ± 10.2 | 6.8 ± 7.4 | |
| <2 mmol/L | 2 (28.6) | 5 (71.4) | 0.001a |
| 2.1–7.1 mmol/L | 43 (42.2) | 59 (57.8) | |
| >7.2 mmol/L | 41 (70.7) | 17 (29.3) | |
| COVID Severity on admissionb | |||
| COVID-19 Mild/Moderate | 49 (62.0) | 30 (38.0) | 0.010a |
| COVID-19 Severe | 37 (42.0) | 51 (58.0) | |
Significant at 0.05 level of significance.
Chi-square test of independence.
Fisher's Exact test/Fisher–Freeman–Halton test.
The TLC of COVID-19 patients were also significantly associated with the progression of COVID-19 severity (p = 0.013). Twenty-four out of thirty-five patients with TLC of greater than 1500 cells/mm3 did not experience COVID-19 severity progression (68.6% versus 31.4%), while there was an observable trend of increasing proportion of patients who experienced disease progression as the TLC decreased to 900–1500 cells/mm3 and further to <900 cells/mm3 (52.1% versus 47.9% and 62.7% versus 37.3%, respectively).
In terms of BUN, most patients with BUN greater than 7.2 mmol/L suffered disease progression (70.7% versus 29.3%), while most COVID-19 patients with BUN less than 7.1 mmol/L did not have disease progression (71.4% versus 28.6%). At 5% level of significance, there was sufficient evidence to state that BUN and COVID-19 severity progression had a statistically significant association (p = 0.001).
There was also a significant association between COVID-19 severity on admission and its progression (p = 0.010). The severity of 62.0% of COVID-19 mild/moderate patients progressed, while only 42% of COVID-19 severe patients had experienced progression.
There was no observed significant association between CRP measurements and progression of disease severity.
The average length of stay of COVID-19 patients was 12 days (SD = 7.72). As shown in Table 6 , there were no significant associations between any nutritional biomarker level and average LOHS.
Table 6.
Comparison of nutritional biomarkers and case severity in terms of length of hospital stay (LOHS).
| Variables | Mean LOHSa | SDb | p-value |
|---|---|---|---|
| Serum Albumin | 12.13 | 7.72 | |
| >3.4 g/dL | 11.96 | 7.75 | 0.740 |
| 2.5–3.4 g/dL | 13.20 | 7.71 | |
| <2.5 g/dL | 11.00 | 8.72 | |
| C-reactive protein | 12.13 | 7.72 | |
| Above 1 mg/dL | 12.11 | 8.00 | 0.907 |
| Below 1 mg/dL | 12.33 | 5.06 | |
| Total lymphocyte count | 12.13 | 7.72 | |
| >1500 cells/mm 3 | 11.46 | 6.06 | 0.838 |
| 900–1500 cells/mm 3 | 12.40 | 7.48 | |
| <900 cells/mm 3 | 12.20 | 8.91 | |
| Blood Urea Nitrogen | 12.13 | 7.72 | |
| <2 mmol/L | 13.86 | 5.84 | 0.353 |
| 2.1–7.1 mmol/L | 11.43 | 6.56 | |
| >7.2 mmol/L | 13.16 | 9.57 | |
| COVID Severity on admission | 12.13 | 7.72 | |
| COVID-19 Mild/Moderate | 12.15 | 6.21 | 0.974 |
| COVID-19 Severe | 12.11 | 8.90 |
LOHS: length of hospital stay.
Significant at 0.05 level of significance.
In Table 7 , it shows that among the different variables, only the CRP was not significantly associated with increase in mortality. It can be concluded that the serum albumin of COVID-19 patients was significantly associated with mortality with a p-value of 0.004. Most of the patients with serum albumin of ≥2.5 g/dL (≥25 g/L) recovered, while all of the patients with <2.5 g/dL (<25 g/L) expired.
Table 7.
Association of nutritional biomarkers and case severity with mortality.
| Variables | Outcome |
p-value | |
|---|---|---|---|
| Recovered n (%) | Expired n (%) | ||
| Serum Albuminc | 39.7 ± 5.5 | 36.7 ± 6.6 | |
| >3.4 g/dL | 107 (77.0) | 32 (23.0) | 0.004∗ |
| 2.5–3.4 g/dL | 15 (60.0) | 10 (40.0) | |
| <2.5 g/dL | 0 | 3 (100.0) | |
| C-reactive proteinc | 5.0 ± 3.0 | 6.4 ± 3.0 | |
| Above 1 mg/dL | 106 (71.1) | 43 (28.9) | 0.159 |
| Below 1 mg/dL | 16 (88.9) | 2 (11.1) | |
| Total lymphocyte countb | 1247.3 ± 661.2 | 1040.0 ± 878.1 | |
| >1500 cells/mm 3 | 31 (88.6) | 4 (11.4) | 0.005a |
| 900–1500 cells/mm 3 | 56 (76.7) | 17 (23.3) | |
| <900 cells/mm 3 | 35 (59.3) | 24 (40.7) | |
| Blood Urea Nitrogenb | 6.8 ± 6.7 | 13.4 ± 12.4 | |
| <2 mmol/L | 7 (100.0) | 0 | <0.001a |
| 2.1–7.1 mmol/L | 86 (84.3) | 16 (15.7) | |
| >7.2 mmol/L | 29 (50.0) | 29 (50.0) | |
| COVID Severity on admissionb | |||
| COVID-19 Mild/Moderate | 68 (86.1) | 11 (13.9) | <0.001a |
| COVID-19 Severe | 54 (61.4) | 34 (38.6) | |
Significant at 0.05 level of significance.
Chi-square test of independence.
Fisher's Exact test/Fisher–Freeman–Halton test.
Although most of the patients in each group of TLC levels recovered, it was observed that there was a higher proportion of patients with TLC >1500 cells/mm3 who recovered (88.6%) as compared to patients with TLC 900–1500 cells/mm3 and <900 cells/mm3 (76.7% and 59.3%, respectively). Thus there was sufficient evidence to conclude that TLC was significantly associated with mortality (p = 0.005).
In terms of BUN, all seven patients with <2 mmol/L and 84.3% of patients with 2.1–7.1 mmol/L recovered from COVID-19. On the other hand, only 50% of patients with >7.1 mmol/L recovered from COVID-19. At 5% level of significance, it can be concluded that there was a significant association between BUN and mortality (p = <0.001).
Furthermore, there was a significant association between COVID-19 severity on admission and patients’ outcome (p = <0.001). A higher proportion of patients who had mild/moderate COVID-19 disease recovered (86.1%) as compared to COVID-19 severe patients on admission (61.4%).
The mean PNI scores of patients with mild/moderate and severe COVID severity were 46.55 (SD = 8.44) and 43.38 (SD = 7.16), respectively. At 0.05 level of significance, it can be concluded that the mean PNI scores of the two groups was significantly different with a mean difference of 3.17. In terms of the average CAR, there was also a statistically significant difference between the two severity groups (p=<0.001). There was a mean difference of 0.73, in which the mean CAR of patients with mild/moderate COVID-19 severity is lower as compared to COVID-19 severe patients. On the other hand, there was no significant difference between the mean BAR of COVID-19 mild/moderate and severe patients (p = 0.164). (see Table 8 )
Table 8.
Comparison of the nutritional prognostic indices of the COVID severity groups.
| Variables | Mean Score (SD) | Mean Difference | p-value |
|---|---|---|---|
| PNI Score | |||
| COVID Mild/Moderate | 46.55 (8.44) | 3.17 | 0.010a |
| COVID Severe | 43.38 (7.16) | ||
| CAR | |||
| COVID Mild/Moderate | 1.08 (0.86) | −0.73 | <0.001a |
| COVID Severe | 1.81 (0.92) | ||
| BAR | |||
| COVID Mild/Moderate | 3.68 (4.72) | −1.23 | 0.164 |
| COVID Severe | 4.92 (6.44) | ||
Significant at 0.05 level of significance.
The patients’ PNI score was statistically and significantly associated with the progression of severity (p = 0.001). An increase in the PNI score was associated with a decrease likelihood of COVID-19 severity progression by 0.072 times. The BAR value was also associated with the COVID-19 severity progression (p = 0.012), in which an increase in the BAR value was associated with an increase likelihood of progression by 1.130 times. On the contrary, the CAR value was not associated with COVID-19 severity progression Table 9 .
Table 9.
Association of PNI Score, CAR and BAR value with progression of severity.
| Variables | Odds Ratio (OR) | 95% CI - OR | p-value |
|---|---|---|---|
| PNI Score | 0.928 | 0.886, 0.971 | 0.001a |
| CAR value | 1.293 | 0.934, 1.789 | 0.121 |
| BAR value | 1.130 | 1.027, 1.242 | 0.012a |
Significant at 0.05 level of significance.
The PNI score, CAR, and BAR values were not significantly associated with the patients’ LOHS (Table 10 ).
Table 10.
Association of PNI Score, CAR and BAR value with LOHS
| Variables | Coefficient | 95% CI | p-value |
|---|---|---|---|
| PNI Score | −0.121 | −0.269, 0.028 | 0.111 |
| CAR value | −0.207 | −1.446, 1.031 | 0.742 |
| BAR value | 0.127 | −0.080, 0.334 | 0.228 |
∗Significant at 0.05 level of significance.
The PNI score, CAR value, and BAR value were statistically and significantly associated with mortality (Table 11 ). An increase in the PNI score was associated with a decrease likelihood of death (p = 0.005). On the other hand, an increase in the CAR value was associated with an increase likelihood of mortality (p = 0.002). Increasing the BAR value was associated with 1.18 times increase likelihood of mortality (p = <0.001).
Table 11.
Association of PNI Score, CAR and BAR value with Mortality.
| Variables | Odds Ratio (OR) | 95% CI - OR | p-value |
|---|---|---|---|
| PNI Score | 0.926 | 0.878, 0.977 | 0.005a |
| CAR value | 1.809 | 1.243, 2.632 | 0.002a |
| BAR value | 1.180 | 1.077, 1.292 | <0.001a |
Significant at 0.05 level of significance.
The PNI score had an area under the curve (AUC) of 0.664 (Table 12 ). A resulting p-value of <0.001 suggests that PNI score can significantly predict the progression of severity. The optimal threshold was 44.81, in which 70.9% of non-progression would be correctly predicted if the PNI score is greater than or equal to 44.81. The BAR value can also significantly predict the progression of COVID severity (AUC = 0.675, p=<0.001). A BAR value of greater than or equal to 2.81 would correctly predict 62.8% of COVID severity progression. However, an AUC of 0.556 of the CAR value was not significant (see Fig. 1).
Table 12.
AUC and optimal threshold of nutritional prognostic indices according to progression of severity.
| Nutritional Prognostic Scores | AUC | p-value | Optimal threshold | Sensitivity | Specificity |
|---|---|---|---|---|---|
| PNI Score | 0.664 | <0.001a | 44.81 | 70.9% | 56.8% |
| CAR | 0.556 | 0.215 | 1.055 | 59.3% | 43.2% |
| BAR | 0.675 | <0.001a | 2.81 | 62.8% | 69.1% |
Significant at 0.05 level of significance.
Fig. 1.
Receiver-operating characteristic curve of PNI score, CAR, and BAR in predicting progression of severity.
Results of the ROC analysis showed that the PNI score (AUC = 0.668), CAR (AUC = 0.649), and BAR (AUC = 0.776) can significantly predict the mortality of COVID-19 patients (Table 13 ). A PNI score of greater than or equal to 42.49 can correctly predict 55.6% of recovery, while a CAR value of greater than or equal to 2.04 can correctly predict 55.6% of mortality. Among the three nutritional prognostic indices, the 0.776 AUC of BAR suggests an acceptable discrimination and a BAR value of greater than or equal to 2.88 can predict 77.8% of mortality correctly (see Fig. 2).
Table 13.
AUC and optimal threshold of nutritional prognostic indices according to mortality.
| Nutritional Prognostic Scores | AUC | p-value | Optimal threshold | Sensitivity | Specificity |
|---|---|---|---|---|---|
| PNI Score | 0.668 | 0.001a | 42.49 | 55.6% | 69.7% |
| CAR | 0.649 | 0.003a | 2.04 | 55.6% | 72.1% |
| BAR | 0.776 | <0.001a | 2.88 | 77.8% | 66.4% |
Significant at 0.05 level of significance.
Fig. 2.
Receiver-operating characteristic curve of PNI score, CAR, and BAR in predicting mortality.
5. Discussion
The WHO defined malnutrition as imbalances, excesses and deficiencies in a person's intake of nutrients and/or energy. Around 462 million adults worldwide are underweight, while 1.9 Billion are overweight [4]. European Society for Clinical Nutrition and Metabolism aims to provide guidance for nutritional management of COVID-19 including prevention, diagnosis and treatment since based on their studies, patients with worst outcomes and higher mortality are reported in immunocompromised patients, concise of older adults, with multiple comorbidity including malnourished individuals [15,36].
According to Rouget et al., in a Nutricov study on the prevalence of malnutrition in hospitalized patients with COVID-19, out of 80 patients, 37.5% had criteria for malnutrition, and 57.5% need for ICU admission, of these numbers, 3.75% were malnourished and died [16]. In a local study done at Philippine General Hospital on prevalence of malnutrition among admitted adult COVID-19 confirmed patients was 71.83% and they found out that the malnutrition is linked with poorer outcomes among these patients. They also concluded that older age, severity of pneumonia and presence of chronic kidney disease are the risk factors for malnutrition among COVID-19 patients [7]. In addition, according to Yue et al., the risk factors of high mortality and poor prognosis in COVID-19 confirmed patients are old age and multiple basic diseases [17]. Malnutrition in hospitalized patients are assessed to affect variably at admission, poor nutritional status or decline of nutritional status during the hospital stay was known to adversely affect the clinical outcomes of the patients affecting mainly the immune response, and lead to a delayed recovery [18]. Comparing to our study, only 1.2% are underweight and 109 out of 167 with BMI of greater than 23 (overweight, obese I and obese II) as shown in Table 1, the nutritional biomarkers as mentioned, only elevated BUN is associated with malnutrition in COVID-19, these findings is similar to study of Li et al., they analyzed kidney function of 193 COVID-19 patients and they found that 31% of sample population had an elevated level of BUN may be due to kidney involvement during COVID-19 [19].
A nutritional serum biomarker defined as any biological specimen that can be a value of nutritional status, it can be used to validate the dietary instruments, substitute indicators of dietary intake, and integrate methods of nutritional status for a nutrient which reflect not only intake but probably affect the disease processes [12]. COVID-19 patients with evident malnutrition have longer length of hospitalization than in patients with normal nutrition due to slow absorption of inflammation and poor resistance to disease [20]. Also, it has been associated with increased rates of minor and major complications [21]. Our study, revealed that individual nutritional biomarker are not associated with length of hospitalization including the nutritional prognostic indices (PNI score, CAR and BAR value).
As to the nutrition-related indicators, the level of albumin in critically ill COVID-19 patients were significantly lower than those of severe COVID-19 patients. In relation to our study, there was no enough evidence to say that albumin is associated with COVID-19 severity as presented in Table 3, but the low level of albumin was associated with COVID-19 severity progression. Furthermore, hypoalbuminemia of less than 2.5 g/dL was associated with mortality, including the low level of TLC (less than 1500 cells/mm3) and elevated BUN of greater than 7.1 mmol/L were also associated with poor prognosis and mortality of patients with COVID-19. Similar study of Herrmann et al., the serum albumin within forty eight (48) hours of hospitalization for acute illness they predicted that 14% of the population results to in-hospital death with low albumin compared to 4% with normal albumin level, also it was robust predictor of length of hospitalization, and patients’ readmission [22]. Hence, it was recommended that all COVID-19 confirmed patients with malnutrition or nutritional risk should start nutritional support treatment as soon as possible [20].
The PNI score is an objective assessment index reflecting the immune-nutritional status of the patients. It is calculated using serum albumin and total lymphocyte count. It is a valuable screening tool for patients prognosis. In our study, low PNI score was associated with patients poor prognosis and mortality. Similar study of Wei et al., to worse prognosis in severe COVID-19 patients is related to lower PNI score (less than 43), in comparison to our study, PNI score of less than 42.49 can predict the worst outcome in patients with COVID-19. Similar study by Hu et al., in China that the decreased PNI score is independently associated with the severity of COVID-19 [[23], [24], [25]].
Moreover, the levels of BUN were increased obviously in critically ill COVID-19 patients [26]. The same is true in this study, wherein high level of BUN are associated with progression of case severity and mortality. In addition, from the study of Kermali, M. et al. They demonstrated significantly higher levels of renal biomarkers such as blood urea nitrogen in severe cases of COVID-19 [27]. Hence, the reduction of albumin and prealbumin and the elevation of blood urea nitrogen warned that critically ill COVID-19 patients set them at a tremendous nutritional risk [26].
In addition, BAR value has been found to be positively correlated with severity of community acquired pneumonia and associated with mortality among these patients. In the study of Gemcioglu et al., BAR provide prognostic information for decision making in patients with COVID-19. High BAR shows better predictor of severity of COVID-19 infection during hospital admission [28]. Similar to our study was observed, higher BAR value greater than 2.8 can predict patients worst outcome including progression of COVID-19 severity and mortality. In comparison, study in Konya, Turkey, a total of 602 COVID-19 patients seen at emergency department from March to September 2020 included int the study wherein they concluded that BUN, albumin and BAR levels were significantly reliable as an in-hospital mortality predictors among COVID-19 patients, but BAR levels was found to be more reliable predictor than the serum albumin and BUN [29].
The TLC is an added serum marker with worth for determining nutritional status. It has been shown to vary with the degree of malnutrition. TLC levels of less than 1500/mm3 is associated well with malnutrition, and those less than 900/mm3 signify severe malnutrition [30]. Good biomarkers of nutritional intake and status are considerably important for clinical practice that need to address [12]. Low lymphocyte levels were noted in patients with severe COVID-19 compared with those with mild COVID-19. In this study, most of the patients with low level of TLC have an increased risk of more severe COVID-19 classification that can lead to progression of the disease severity and even mortality. In the study of Mudatsir et al., they found out that the lymphocyte subsets are known to play an important role in the action against viral infections; therefore, the levels of circulating lymphocytes should increase, but, the immunological response in COVID-19 is unique and remains unclear [31].
Also, elevated CRP was also link or associated with progression of disease and mortality in patients with COVID-19 infection. But it can be due to CRP is a plasma protein made by the liver and induced by inflammatory mediators. In COVID-19 infection, CRP levels were significantly higher during early periods of severe cases and proved to be a more sensitive biomarker in reflecting disease development [21]. Hence, it is a useful marker and gauge of inflammation and it plays an important role in host defense against infection, and an early rise of C-reactive protein indicates strong association with mechanical ventilation or COVID-19 mortality [32]. In a retrospective cohort study, it was found that the likelihood of progressing to severe COVID-19 disease increased in patients with CRP levels >41.8 mg/L, it suggests that the CRP levels is a strong indicator to reflect the presence and severity of COVID-19 infection [27]. In terms of nutrition, it can be used to observe stress-response during the acute phase. As CRP decreases, the visceral proteins can be used to monitor the nutritional state [21]. As opposed to the result of the study that, in patients with COVID-19 infection as shown in Table 2, CRP is not associated with the BMI, which can be one of the baseline of patient's nutrition. The manifest elevation of CRP in most of the patients may be related with the inflammatory response of the body to infection (COVID-19). With this, CRP cannot be considered as a good indicator of COVID patient's nutrition.
Studies suggest that CAR as a prognostic markers has also been undertaken in hospitalized patients who is mechanically ventilated and admitted at Intensive care unit and showed that elevated CAR score is associated to poor prognosis. Moreover, CAR value reflects both inflammation and malnutrition and it may be a useful biomarker for predicting prognosis and seem to be good predictor of length of hospital stay and mortality for hospitalized COVID-19 patients [[33], [34], [35]]. However in this study, CAR value was merely observed to predict patient's mortality and not in the case severity progression and length of hospitalization.
This study involves several limitations. First, it is a single-centered study, which we can assume that more severe to critical cases are admitted. Second, this study may have caused selection bias as only those admitted had the opportunity to be included. Lastly, this is a retrospective study which involves chart review of only with those available data, laboratory results in the patients’ medical charts during the first 48 h of admission, including the anthropometric measurements (weight and height). Patients vaccination status were not included in the study.
6. Conclusions
This study demonstrated that deranged level of nutritional biomarkers can affect patient's COVID-19 severity and associated with patient's clinical outcome. Only elevated BUN is related to patient's malnutrition. None of the nutritional biomarkers including the prognostic scoring is associated with the LOHS. However, the study concluded that low albumin (≤2.5 g/dL), low level of TLC (≤1500 cells/mm3), elevated BUN (≥7.1 mmol/L) are associated with patient's case severity progression and mortality. Furthermore, all three nutritional prognostic indices can predict progression of severity and mortality, except the CAR value which has no role in predicting COVID-19 case severity progression. The low PNI score (≤42.49), high BAR value (≥2.8) and CAR value (≥2.04) provided an important nutritional prognostic information and could predict mortality which can be a useful parameter in admission, hence it is recommended to screen all COVID-19 patients to reduce mortality. Moreover, to prevent the progression of case severity and mortality, patients' nutrition should be addressed as early as possible. Additionally, it can be gauged as a preventive marker rather than a disease marker. Lastly, nutrition is one of the key importance affecting the risk of the disease.
7. Recommendations
The primary investigator recommends further studies with adjusted and larger sample size or even a multi-center study involving other institutions to better investigate the association of nutritional biomarkers and nutritional prognostic indices used in the study. Along with this, a prospective study is recommended, including the available nutritional screening tool to assess the nutritional status objectively and further research to establish the functional effects of nutritional biomarkers on the health and disease relationship. The systematic use of the nutritional prognostic indices could help to identify and classify COVID-19 patients who are at risk and prevent complications and poor outcomes.
Grants and funding
This study did not receive any grant or funds from the private and government organizations.
Author contributions
Ferdinand M. Anzo: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Visualization, Writing – original draft and Writing – review and editing.
Maribeth B. Mayo: Conceptualization, Investigation, Resources, Supervision and Validation.
Disclosure
The views expressed in the submitted research study are the author's own and do not reflect the views of the institution to which he is affiliated. The information gathered throughout this research will remain confidential, and protected from unauthorized disclosure, tampering, or damage. The author declares that there were no conflicts of interest that arose from the conduct and publication of this study.
Declaration of competing interest
The principal investigator have no relevant conflict of interest.
Acknowledgment
The investigators would like to acknowledge the Technical Review Board Committee of the Batangas Medical Center – Department of Internal Medicine headed by Dr. Eddieson Gonzales, and its members, Dr. Kenedy Cruzat, Dr. Donnazon Reyes-Macasaet, Dr. Allan Lanzon, and Dr. Florence Amorado-Santos, also, Ms. Jenina Rose Bicol and Dr. Christine Jew Baldovino for sharing their expert advice. We would also like to acknowledge the unwavering support of the chairman of the Department of Internal Medicine, Dr. Andrew Gonzales and the administration of the Batangas Medical Center.
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