Abstract
Objective
To determine whether nutritional status is related to the incidence of thrombosis and mortality in patients with coronavirus disease 2019 (COVID-19).
Methods
A total of 496 consecutive patients who were admitted and diagnosed with COVID-19 between April 2020 and March 2023 were retrospectively analyzed. The geriatric nutritional risk index (GNRI) on admission was calculated as follows: 14.89×serum albumin (g/dL)+41.7×body mass index/22. Patients were divided into two groups according to the median GNRI values. The endpoint of this study was a composite of in-hospital thrombotic events and mortality.
Results
The median GNRI value was 99.3. Patients in the low GNRI (≤99.3) group were older (75±21 vs. 51±20 years, p＜0.001) and more likely to be female (55.6% vs. 41.1%, p＜0.05). In addition, patients with a low GNRI often exhibited hypertension (43.5% vs. 28.2%, p＜0.001) and had a history of cardiovascular disease (34.3% vs. 14.5%, p＜0.001). Under these conditions, the median D-dimer levels on admission were significantly higher in patients with a low GNRI (0.90 μg/mL; interquartile range (IQR), 0.49-1.64 μg/mL) than those with high GNRI (0.36 μg/mL; IQR, 0.26-0.51 μg/mL, p＜0.001). During hospitalization, the composite endpoint was observed in 32 patients. In the logistic regression analysis, a low GNRI was significantly associated with the composite endpoint adjusted using inverse probability of treatment weighting (odds ratio, 3.24; 95% confidence interval: 1.51-6.93, p＜0.05).
Conclusion
Assessment of the GNRI provides useful information for predicting in-hospital thrombosis and mortality in COVID-19 patients.
Keywords: COVID-19, nutrition, prognosis, thrombosis
Introduction
Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2, and its clinical presentation varies widely from asymptomatic to severe respiratory failure (1). Although the majority of patients hospitalized with COVID-19 do not initially require critical care, including mechanical ventilation support and extracorporeal membrane oxygenation, some patients experience worsening of disease severity during their disease course (2). Thus, identifying individuals at a higher risk of disease progression in this population may further improve both health and economic outcomes.
Although pulmonary manifestations, such as cough and dyspnea, are typical symptoms of severe acute respiratory syndrome coronavirus 2 infection, patients with COVID-19 have been reported to have hemostatic abnormalities and increased coagulation activation, resulting in the development of thrombosis (3). Considering that D-dimer reflects the activation of coagulation and fibrinolysis, several investigations have highlighted D-dimer as a predictor of adverse clinical outcomes in patients with COVID-19 (4,5). In contrast, recent studies have reported that the nutritional status of geriatric patients is significantly associated with thrombotic events through the mechanisms of malnutrition, inflammation, and atherosclerosis syndrome (6-8).
However, the relationship between malnutrition, thrombotic events, and mortality in patients with COVID-19 has not yet been fully elucidated.
We conducted this study with two main objectives: first, to compare the incidence of thrombotic events and mortality in patients with COVID-19 based on differences in nutritional status, and second, to assess whether malnutrition was associated with increased adverse clinical events, composed of thrombotic events and mortality in patients with COVID-19.
Materials and Methods
Study population
Between April 2020 and March 2023, a total of 987 consecutive patients who were admitted to Takaoka City Hospital and diagnosed with COVID-19 with a positive polymerase chain reaction test or antigen testing of nasopharyngeal swabs were assessed retrospectively. Among them, the following patients were excluded from the analysis: 50 pediatric patients; 32 patients without an assessment by chest X-ray or computed tomography due to the risk of radiation exposure during pregnancy; 119 patients with missing body mass index (BMI) information; and 290 patients lacking serum albumin. The final sample size was 496 (Fig. 1).
Figure 1.
Study enrollment flowchart. BMI: body mass index, CT: computed tomography, COVID-19: coronavirus disease 2019, GNRI: geriatric nutritional risk index
To evaluate the effect of nutritional status on clinical outcomes, we divided the patients into two groups based on the geriatric nutritional risk index (GNRI). As previously illustrated, the GNRI on admission was calculated using the formula: GNRI=14.89×serum albumin (g/dL)+41.7×BMI/22 (9,10).
In accordance with the Guidelines for the Clinical Practice of Novel Coronavirus Infections, Version 10.0, formulated by the Ministry of Health, Labour and Welfare, patients were divided into four groups based on disease severity: mild, moderate I, moderate II, and severe. When patients experienced worsening of COVID-19 severity during hospitalization, the most advanced category was assigned.
This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the ethics committee of the Takaoka City Hospital.
Comorbidities
Hypertension was defined as a blood pressure ≥140/90 mmHg treatment with antihypertensive medication. Diabetes mellitus was defined as HbA1c ≥6.5% or treatment with insulin or hypoglycemic agents. Chronic kidney disease was defined as the presence of proteinuria, serum creatinine ≥1.3 mg/dL, or estimated glomerular filtration rate ≤60 mL/min/1.73 m2 (11). A thorough chart review, including referral letters and inhalation medication use, was performed to identify patients with chronic obstructive pulmonary disease. Cardiovascular disease was diagnosed when the patient had a history of heart failure, angina pectoris, myocardial infarction, peripheral artery disease, or cerebrovascular disease. At least one diagnosis of cancer before the index admission was defined as a history of cancer. Comorbidities included hypertension, diabetes mellitus, chronic kidney disease, chronic obstructive pulmonary disease, cardiovascular disease, and a history of cancer. We initially treated comorbidities as categorical variables and subsequently grouped them according to the number of comorbidities (12). Multiple comorbidities were defined as ≥2 comorbidities.
In the current study, obesity was defined as a BMI ＞25 kg/m2 (13). According to the diagnostic criteria of the Global Leadership Initiative on Malnutrition, a low BMI was defined as ＜18.5 kg/m2 if ＜70 years, or ＜20 kg/m2 if ≥70 years of age (14).
D-dimer measurement
In the current study, a subgroup of patients underwent evaluation of their serum D-dimer levels upon admission. Plasma D-dimer levels were measured using a latex-enhanced photometric immunoassay (Hexamate D-dimer; Roche Diagnostics). The laboratory reference range was 0 to 0.5 μg/mL. D-dimer results were expressed in μg/mL fibrinogen equivalent units.
Outcome measurement
The patients were followed up until discharge or in-hospital death. The primary outcome of this study was a composite of in-hospital thrombotic events and mortality during the index admission. Thrombotic events included venous thromboembolism, ischemic stroke, myocardial infarction, and systemic arterial thromboembolism. Venous thromboembolism was defined as pulmonary embolism and/or deep vein thrombosis objectively confirmed by imaging or autopsy (15).
Statistical analysis
Continuous variables were expressed as the mean±standard deviation or median [interquartile range (IQR)] and were compared using the unpaired t-test or Mann-Whitney U test, as appropriate. A frequency analysis was performed using the chi-square test, as appropriate. We explored whether the clinical and laboratory variables collected at admission were associated with the outcome of interest using a logistic regression analysis. Owing to the small number of events, we developed three models under the restriction of selected variables that could be entered in the multivariate analyses: Model 1 (adjusted for age and sex), Model 2 (adjusted for multiple comorbidities and oral anticoagulants), and Model 3 (adjusted for COVID-19 severity and oral anticoagulants). Serum albumin and BMI were not entered into the multivariate models, as these parameters were included in the calculation of the GNRI. In addition, the inverse probability of treatment weighting (IPTW) method based on propensity scoring was used. We adjusted for confounding factors without reducing the sample size by using the estimated propensity scores to assign weights to the data (16). The variables included in the IPTW analysis were age, sex, hypertension, diabetes mellitus, chronic kidney disease, chronic obstructive pulmonary disease, cardiovascular disease, history of cancer, vaccination status, and oral anticoagulant use. Continuous variables were dichotomized as medians or clinically relevant values. Statistical significance was set at p＜0.05. Statistical analyses were performed using JMP Pro Version 12 (SAS Institute, Cary, USA) and EZR [Jichi Medical University Saitama Medical Center (17)], a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria, version 4.2.0).
Results
Baseline characteristics
Using the median GNRI value (99.3), the patients were divided into a low-GNRI group (n=248) and a high-GNRI group (n=248) (Fig. 1). The distribution of GNRI is shown in Fig. 2. The median (25th, 75th percentile) GNRI and the mean±standard deviation of the GNRI were 99.3 (90.1, 109.3) and 99.1±14.4, respectively.
Figure 2.
Histogram of the GNRI. GNRI: geriatric nutritional risk index
The baseline patient characteristics are presented in Table 1. The mean patient age was 63±24 years, and 51.6% of the patients were male. Patients in the low GNRI (≤99.3) group were older (75±21 vs. 51±20 years, p＜0.001) and more likely to be women (55.6% vs. 41.1%, p＜0.05). The prevalence of hypertension (43.5% vs. 28.2%, p＜0.001), chronic kidney disease (44.4% vs. 16.9%, p＜0.001), chronic obstructive pulmonary disease (4.8% vs. 0.8%, p＜0.05), cardiovascular disease (34.3% vs. 14.5%, p＜0.001), and cancer history (14.5% vs. 7.3%, p＜0.05) was significantly higher in the low GNRI group than in the high GNRI group. There was a significant difference in the number of comorbidities between groups. Notably, significantly higher levels of C-reactive protein were observed in patients with a low GNRI in comparison to their counterparts (2.38 mg/dL; IQR, 0.81-5.32 mg/dL vs. 0.82 mg/dL; IQR, 0.26-2.19 mg/dL, p＜0.001). There were significant differences in the disease severity in COVID-19 between the two groups. In the present study, patients with a low GNRI were frequently prescribed antithrombotic medications, including aspirin (11.3% vs. 5.2%, p＜0.05), thienopyridines (6.0% vs. 1.6%, p＜0.05), and oral anticoagulants (10.5% vs. 3.2%, p＜0.05).
Table 1.
Clinical Characteristics of Patients with COVID-19
| All (n=496) | Low GNRI (GNRI ≤99.3) (n=248) |
High GNRI GNRI >99.3 (n=248) |
p value | |
|---|---|---|---|---|
| Age, years | 63±24 | 75±21 | 51±20 | <0.001 |
| Female gender | 240 (48.4) | 138 (55.6) | 102 (41.1) | <0.05 |
| Body mass index, kg/m2 | 22.7±4.6 | 20.2±3.3 | 25.3±4.4 | <0.001 |
| Obesitya | 126 (25.4) | 13 (5.2) | 113 (45.6) | <0.001 |
| Low body mass indexb | 102 (20.6) | 96 (38.7) | 6 (2.4) | <0.001 |
| Comorbidities | ||||
| Hypertension | 178 (35.9) | 108 (43.5) | 70 (28.2) | <0.001 |
| Diabetes mellitus | 91 (18.3) | 50 (20.2) | 41 (16.5) | 0.30 |
| Chronic kidney disease | 152 (30.6) | 110 (44.4) | 42 (16.9) | <0.001 |
| Chronic obstructive pulmonary disease | 14 (2.8) | 12 (4.8) | 2 (0.8) | <0.05 |
| Cardiovascular disease | 121 (24.4) | 85 (34.3) | 36 (14.5) | <0.001 |
| Cancer history | 54 (10.9) | 36 (14.5) | 18 (7.3) | <0.05 |
| Number of comorbidities | <0.001 | |||
| 0 | 204 (41.1) | 70 (28.2) | 134 (54.0) | |
| 1 | 106 (21.4) | 54 (21.8) | 52 (21.0) | |
| ≥2 | 186 (37.5) | 124 (50.0) | 62 (25.0) | |
| Laboratory data | ||||
| Total protein, g/dL | 6.8±0.8 | 6.4±0.7 | 7.3±0.5 | <0.001 |
| Albumin, g/dL | 3.8±0.6 | 3.3±0.5 | 4.2±0.4 | <0.001 |
| CRP, mg/dL | 1.27 (0.42-3.73) | 2.38 (0.81-5.32) | 0.82 (0.26-2.19) | <0.001 |
| LDH, IU/L | 213 (181-259) | 216 (184-269) | 210 (181-249) | 0.07 |
| Vaccination status | 0.09 | |||
| Yes | 67 (13.5) | 40 (16.1) | 27 (10.9) | |
| No | 429 (86.5) | 208 (83.9) | 221 (89.1) | |
| COVID-19 severity | <0.001 | |||
| Mild | 259 (52.2) | 124 (50.0) | 135 (54.4) | |
| Moderate I | 148 (29.8) | 61 (24.6) | 87 (35.1) | |
| Moderate II | 79 (15.9) | 56 (22.6) | 23 (9.3) | |
| Severe | 10 (2.0) | 7 (2.8) | 3 (1.2) | |
| Treatments | ||||
| Aspirin | 41 (8.3) | 28 (11.3) | 13 (5.2) | <0.05 |
| Thienopyridines | 19 (3.8) | 15 (6.0) | 4 (1.6) | <0.05 |
| Oral anticoagulants | 34 (6.9) | 26 (10.5) | 8 (3.2) | <0.05 |
| Warfarin | 9 (1.8) | 7 (2.8) | 2 (0.8) | 0.08 |
| DOAC | 25 (5.0) | 19 (7.7) | 6 (2.4) | <0.05 |
| Prophylactic unfractionated heparin | 26 (5.2) | 10 (4.0) | 16 (6.5) | 0.22 |
| Systemic corticosteroids | 90 (18.1) | 38 (15.3) | 52 (21.0) | 0.10 |
| Remdesivir | 117 (23.6) | 56 (22.6) | 61 (24.6) | 0.60 |
Categorical variables are presented as n (%) and continuous variables are presented as the mean±SD or median (interquartile range). COVID-19: coronavirus disease 2019, CRP: C-reactive protein, DOAC: direct oral anticoagulants, GNRI: geriatric nutritional risk index, LDH: lactate dehydrogenase
aObesity was defined as body mass index >25 kg/m2.
bLow body mass index was defined as <18.5 kg/m2 if <70 years, or <20 kg/m2 if ≥70 years.
In the current study, the D-dimer level at admission was assessed in 90 and 92 patients with low and high GNRI, respectively (36.3% vs. 37.1%, p=0.82). The median D-dimer level was significantly higher in patients with low a GNRI (0.90 μg/mL; IQR, 0.49-1.64 μg/mL) than in those with high GNRI (0.36 μg/mL; IQR, 0.26-0.51 μg/mL, p＜0.001) (Fig. 3).
Figure 3.
Comparison of the D-dimer level at admission according to GNRI. GNRI: geriatric nutritional risk index
Clinical outcomes
Under these conditions, the median duration of hospital stay was 9 days (IQR: 6-13 days). During hospitalization, the composite endpoint was observed in 32 patients. Specifically, 10 (2.0%) in-hospital thrombotic events occurred. The causes of thrombosis included ischemic stroke (n=4), venous thromboembolism (n=2), myocardial infarction (n=2), and systemic arterial thromboembolism (n=2). In addition, 24 (4.8%) in-hospital all-cause deaths occurred. The causes of death were pneumonia (n=12), septic shock (n=4), sudden death (n=3), renal failure (n=2), and other causes (n=3). The relationship between the incidence of in-hospital thrombotic events, all-cause death, and the composite endpoint according to the GNRI status is shown in Fig. 4. In-hospital thrombotic events (3.2% vs. 0.8%, p＜0.05), all-cause death (8.5% vs. 1.2%, p＜0.001), and composite endpoint (11.3% vs. 1.6%, p＜0.001) were significantly higher in the low GNRI group than in the high GNRI group.
Figure 4.
In-hospital adverse outcomes according to the GNRI. GNRI: geriatric nutritional risk index
In the univariate analysis for the composite endpoint, low GNRI, age ＞65 years, low BMI, hypertension, chronic kidney disease, cardiovascular disease, cancer history, multiple comorbidities, albumin ＜3.5 g/dL, C-reactive protein ＞1 mg/dL, lactate dehydrogenase ＞240 IU/L, and COVID-19 severity were major predictors (Table 2). The odds ratios (ORs) of each factor according to the 3 models are presented in Table 3. In all models, a low GNRI was found to have significant associations with the composite endpoint of in-hospital thrombotic events and mortality [model 1: OR 3.59, 95% confidence interval (CI) 1.27-12.99, p＜0.05; model 2: OR 5.06, 95% CI 1.89-17.60, p＜0.001; model 3: OR 4.91, 95% CI 1.79-17.33, p＜0.05]. After the IPTW analysis, a low GNRI was also significantly associated with the composite endpoint (OR 3.24, 95% CI 1.51-6.93, p＜0.05).
Table 2.
Univariate Analysis of Association between Clinical Characteristics and the Composite Endpoint.
| Factor | Univariate | |
|---|---|---|
| OR (95% CI) | p value | |
| Low GNRI (GNRI ≤99.3) | 7.76 (2.99-26.52) | <0.001 |
| Age >65 years | 10.45 (3.65-44.04) | <0.001 |
| Female gender | 1.61 (0.78-3.40) | 0.20 |
| Obesity | 0.66 (0.24-1.55) | 0.36 |
| Low body mass index | 3.32 (1.57-6.92) | <0.05 |
| Hypertension | 4.34 (2.06-9.80) | <0.001 |
| Diabetes mellitus | 1.03 (0.37-2.42) | 0.95 |
| Chronic kidney disease | 4.85 (2.32-10.70) | <0.001 |
| Chronic obstructive pulmonary disease | 1.12 (0.06-5.91) | 0.92 |
| Cardiovascular disease | 4.51 (2.18-9.52) | <0.001 |
| Cancer history | 5.13 (2.25-11.18) | <0.001 |
| Multiple comorbidities (versus none/single comorbidity) | 10.36 (4.25-31.02) | <0.001 |
| Albumin <3.5 g/dL | 4.19 (2.02-8.93) | <0.001 |
| CRP >1 mg/dL | 4.54 (1.87-13.58) | <0.001 |
| LDH >240 IU/L | 3.04 (1.47-6.38) | <0.05 |
| Post vaccination | 0.65 (0.15-1.89) | 0.46 |
| COVID-19 severity (Severe/Moderate II versus Mild/Moderate I) | 18.42 (8.25-45.40) | <0.001 |
| Aspirin | 1.65 (0.47-4.49) | 0.40 |
| Thienopyridines | 2.90 (0.65-9.33) | 0.15 |
| Oral anticoagulants | 2.78 (0.89-7.23) | 0.08 |
CI: confidence interval, OR: odds ratio. Other abbreviations as in Table 1.
Table 3.
Logistic Regression Analysis of Low GNRI for the Composite Endpoint.
| OR (95% CI) | p value | |
|---|---|---|
| Low GNRI (GNRI ≤99.3) | ||
| Model 1 | 3.59 (1.27-12.99) | <0.05 |
| Model 2 | 5.06 (1.89-17.60) | <0.001 |
| Model 3 | 4.91 (1.79-17.33) | <0.05 |
| IPTW-adjusted | 3.24 (1.51-6.93) | <0.05 |
Model 1: adjusted for age and sex.
Model 2: adjusted for multiple comorbidities and oral anticoagulants.
Model 3: adjusted for COVID-19 severity and oral anticoagulants.
The IPTW analysis was executed using propensity scores, derived from a multiple regression models based on age, sex, hypertension, diabetes mellitus, chronic kidney disease, chronic obstructive pulmonary disease, cardiovascular disease, cancer history, vaccination status, and oral anticoagulants.
IPTW: inverse probability of treatment weighting. Other abbreviations as in Table 1-2.
Discussion
The present study, based on hospitalized patients with COVID-19, demonstrated that patients with low GNRI values had more comorbidities, including hypertension, chronic kidney disease, chronic obstructive pulmonary disease, cardiovascular disease, and cancer history. Additionally, there were significant differences in the occurrence of in-hospital thrombotic events and all-cause death according to the GNRI status. Finally, low GNRI values were independently associated with an increased risk of the composite endpoint of in-hospital thrombosis and mortality in patients with COVID-19. These results suggest that the GNRI, which is easy to assess in clinical practice, could be a reliable biomarker for predicting thrombosis and mortality in patients with COVID-19.
Changes in coagulation parameters are common in patients with COVID-19, as immuno-thrombosis has been implicated in acute COVID-19 pathogenesis (18). Recent systematic reviews and meta-analyses have shown that thrombotic events in patients with COVID-19 vary greatly depending on the severity of COVID-19 (19,20). Moreover, previous studies have demonstrated that increased D-dimer levels are associated with significantly worse outcomes in COVID-19 (21). Thus, D-dimer levels have been evaluated in patients with COVID-19 (22,23). In the current study, we found that D-dimer levels were significantly higher in patients with a low GNRI than in those with a high GNRI. Although the underlying mechanisms between malnutrition and thrombus formation are not completely understood, recent studies have reported that the assessment of GNRI can predict cardiovascular events (24,25). Indeed, BMI, a component of GNRI measurement, is influenced by several factors, including systemic inflammation, atherosclerosis, and frailty, which can predispose patients to thrombotic events (26-28). Moreover, low serum albumin levels could contribute to pulmonary edema formation and lead to hypoxia-related hypercoagulability in patients hospitalized for COVID-19 (29). These findings might partially explain why low GNRI values in patients with COVID-19 are associated with thrombotic events and mortality.
Multiple predictors of thrombosis in patients with COVID-19 have been previously reported (30,31). However, these studies have focused on venous thrombosis, and less attention has been paid to thrombosis of arterial origin. With the assessment of both arterial and venous thrombosis, the incidence of thrombotic events in the present study was 2.0%, which is consistent with previous data from hospitalized COVID-19 patients in Japan (32). Although the available evidence suggests an association between nutritional status and risk of mortality in patients with COVID-19 (33), the current study extends these findings to the occurrence of thrombosis.
The present study was associated with several limitations. First, this was an observational study restricted to Japanese patients. Therefore, further investigations are necessary to examine whether similar results can be obtained in patients of other ethnicities. Second, although the GNRI was originally established as an index for patients of ≥65 years of age, a low GNRI indicates malnutrition and might be associated with worse clinical outcomes regardless of age. This led to the inclusion of patients aged ＜65 years in the present study. However, including a broad age group might have contributed to the different median GNRI values relative to the original study. Indeed, previous studies in some clinical settings have demonstrated various cutoff values for the GNRI (10,24,34). Therefore, further studies are needed to elucidate the optimal cut-off points for GNRI values in patients with COVID-19. Third, we could not assess D-dimer levels in all patients. Moreover, the use of antiviral agents, glucocorticoids, or pharmacological thromboprophylaxis was determined by attending physicians. Therefore, a future study with a more rigorous protocol is warranted. Fourth, the prevalence of severe COVID-19 requiring mechanical ventilation was low in the current study; therefore, our results may not apply to all patients with COVID-19. Fifth, even after the multivariate analysis, the GNRI remained an independent predictor of the outcomes of interest; however, the statistical power may have been low and confoundable. Therefore, a large-scale study is required to confirm our results. Finally, this study demonstrated an association, but not causality, in the relationship between nutritional status and in-hospital adverse clinical events. Future studies are required to determine whether nutritional interventions contribute to better outcomes in malnourished patients with COVID-19.
Conclusion
The assessment of the GNRI on admission is useful for predicting adverse clinical outcomes, including in-hospital thrombotic events and mortality, in hospitalized patients with COVID-19.
The authors state that they have no Conflict of Interest (COI).
References
- 1.Mody A, Lyons PG, Vazquez Guillamet C, et al. The clinical course of coronavirus disease 2019 in a US hospital system: a multistate analysis. Am J Epidemiol 190: 539-552, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Ninomiya T, Otsubo K, Hoshino T, et al. Risk factors for disease progression in Japanese patients with COVID-19 with no or mild symptoms on admission. BMC Infect Dis 21: 850, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Horiuchi H, Morishita E, Urano T, Yokoyama K; the Questionnaire-survey Joint Team on The COVID-19-related thrombosis . COVID-19-related thrombosis in Japan: final report of a questionnaire-based survey in 2020. J Atheroscler Thromb 28: 406-416, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost 18: 844-847, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Zhang L, Yan X, Fan Q, et al. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J Thromb Haemost 18: 1324-1329, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Takahashi H, Ito Y, Ishii H, et al. Geriatric nutritional risk index accurately predicts cardiovascular mortality in incident hemodialysis patients. J Cardiol 64: 32-36, 2014. [DOI] [PubMed] [Google Scholar]
- 7.Jia Z, El Moheb M, Nordestgaard A, et al. The geriatric nutritional risk index is a powerful predictor of adverse outcome in the elderly emergency surgery patient. J Trauma Acute Care Surg 89: 397-404, 2020. [DOI] [PubMed] [Google Scholar]
- 8.Wu L, Wang W, Gui Y, et al. Nutritional status as a risk factor for new-onset atrial fibrillation in acute myocardial infarction. Clin Interv Aging 18: 29-40, 2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bouillanne O, Morineau G, Dupont C, et al. Geriatric nutritional risk index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr 82: 777-783, 2005. [DOI] [PubMed] [Google Scholar]
- 10.Kinugasa Y, Kato M, Sugihara S, et al. Geriatric nutritional risk index predicts functional dependency and mortality in patients with heart failure with preserved ejection fraction. Circ J 77: 705-711, 2013. [DOI] [PubMed] [Google Scholar]
- 11.Sawano M, Yamaji K, Kohsaka S, et al. Contemporary use and trends in percutaneous coronary intervention in Japan: an outline of the J-PCI registry. Cardiovasc Interv Ther 35: 218-226, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhou H, Wang J, Asghar N, Liang B, Song Q, Zhou X. Impact of comorbidity on the duration from symptom onset to death in patients with coronavirus disease 2019: a retrospective study of 104,753 cases in Pakistan. Diseases 11: 176, 2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Zheng KI, Gao F, Wang XB, et al. Obesity as a risk factor for greater severity of COVID-19 in patients with metabolic associated fatty liver disease. Metabolism 108: 154244, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Cederholm T, Jensen GL, Correia MITD, et al. GLIM criteria for the diagnosis of malnutrition - a consensus report from the global clinical nutrition community. Clin Nutr 38: 1-9, 2019. [DOI] [PubMed] [Google Scholar]
- 15.Ikeda S, Ueno Y, Maemura K, et al. Association between the development of thrombosis and worsening of disease Severity in patients with moderate COVID-19 on admission - from the CLOT-COVID Study. Circ J 87: 448-455, 2023. [DOI] [PubMed] [Google Scholar]
- 16.Austin PC. The performance of different propensity score methods for estimating marginal hazard ratios. Stat Med 32: 2837-2849, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kanda Y. Investigation of the freely available easy-to-use software ‘EZR' for medical statistics. Bone Marrow Transplant 48: 452-458, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Nicosia RF, Ligresti G, Caporarello N, Akilesh S, Ribatti D. COVID-19 vasculopathy: mounting evidence for an indirect mechanism of endothelial injury. Am J Pathol 191: 1374-1384, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Zhang R, Ni L, Di X, et al. Systematic review and meta-analysis of the prevalence of venous thromboembolic events in novel coronavirus disease-2019 patients. J Vasc Surg Venous Lymphat Disord 9: 289-298.e5, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Jiménez D, García-Sanchez A, Rali P, et al. Incidence of VTE and bleeding among hospitalized patients with coronavirus disease 2019: a systematic review and meta-analysis. Chest 159: 1182-1196, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395: 1054-1062, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Paliogiannis P, Mangoni AA, Dettori P, Nasrallah GK, Pintus G, Zinellu A. D-dimer concentrations and COVID-19 severity: a systematic review and meta-analysis. Front Public Health 8: 432, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Varikasuvu SR, Varshney S, Dutt N, et al. D-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies. Sci Rep 11: 21888, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Minamisawa M, Miura T, Motoki H, et al. Geriatric nutritional risk index predicts cardiovascular events in patients at risk for heart failure. Circ J 82: 1614-1622, 2018. [DOI] [PubMed] [Google Scholar]
- 25.Mii S, Guntani A, Kawakubo E, Shimazoe H, Ishida M. Impact of the geriatric nutritional risk index on the long-term outcomes of patients undergoing open bypass for intermittent claudication. Circ J 83: 1349-1355, 2019. [DOI] [PubMed] [Google Scholar]
- 26.Kosuge M, Kimura K, Kojima S, et al. Impact of body mass index on in-hospital outcomes after percutaneous coronary intervention for ST segment elevation acute myocardial infarction. Circ 72: 521-525, 2008. [DOI] [PubMed] [Google Scholar]
- 27.Takeji Y, Yamaji K, Tomoi Y, et al. Impact of frailty on clinical outcomes in patients with critical limb ischemia. Circ Cardiovasc Interv 11: e006778, 2018. [DOI] [PubMed] [Google Scholar]
- 28.Yao W, Zhang K, Lv Q, Deng Z, Ding W. D-dimer-albumin ratio (DAR) as a new biomarker for predicting preoperative deep vein thrombosis after geriatric hip fracture patients. J Orthop Surg Res 18: 645, 2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Loffredo L, Pignatelli P, Pirro M, et al. Association between PaO2/FiO2 ratio and thrombotic events in COVID-19 patients. Intern Emerg Med 18: 889-895, 2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Middeldorp S, Coppens M, van Haaps TF, et al. Incidence of venous thromboembolism in hospitalized patients with COVID-19. J Thromb Haemost 18: 1995-2002, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Cohen SL, Gianos E, Barish MA, et al. Prevalence and predictors of venous thromboembolism or mortality in hospitalized COVID-19 patients. Thromb Haemost 121: 1043-1053, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Shibahashi E, Jujo K, Kuroda S, et al. Assessment of thromboembolism risk in COVID-19 patients with cardiovascular disease risk factors: analysis of a Japanese nationwide registry. Thromb Res 216: 90-96, 2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Recinella G, Marasco G, Serafini G, et al. Prognostic role of nutritional status in elderly patients hospitalized for COVID-19: a monocentric study. Aging Clin Exp Res 32: 2695-2701, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Yoshida R, Ishii H, Morishima I, et al. Impact of nutritional and inflammation status on long-term bleeding in patients undergoing percutaneous coronary intervention with an oral anticoagulant. J Atheroscler Thromb 26: 728-737, 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]