key: cord-0843290-21by2ioh authors: Hu, Xiang; Deng, Huihui; Wang, Yuxia; Chen, Lingqiao; Gu, Xuemei; Wang, Xiaobo title: The predictive value of prognostic nutritional index for the severity of coronavirus disease 2019 date: 2020-12-18 journal: Nutrition DOI: 10.1016/j.nut.2020.111123 sha: 5d689d57a9212d31023900046b629a2a341629af doc_id: 843290 cord_uid: 21by2ioh Background & Aims: Malnutrition plays a critical role in the onset and progress of coronavirus disease 2019 (COVID-19). The present study was to explore the association of prognostic nutritional index (PNI) with the severity of COVID-19 and its predictive value of the severe form of COVID-19. Methods: The clinical data were collected from 122 patients with COVID-19 hospitalized in the Sixth People's hospital of Wenzhou, which is the specialized infectious hospital affiliated to Wenzhou Central Hospital. PNI = serum albumin (g/L) + 5 × total lymphocyte count (/nL). Results: The study population consisted of 105 patients with common form (86.1%) and 17 patients with severe form (13.9%) of COVID-19. PNI was significantly declined from patients with common to severe form of COVID-19 (P = 0.029) regardless of gender, age range, and body mass index (BMI)-category. After adjustment of gender, age, indexes of liver and renal function, C-reactive protein, and current smoking, PNI remained to be independently and inversely associated with the severity of COVID-19 (odd ratio = 0.797, P = 0.030). Receiver operating characteristic showed PNI had similar accuracy for prediction of severe form of COVID-19 compared to its combination with gender, age, and BMI (P = 0.402). PNI < 49 were defined as a cut-off value for predicting the severe form of COVID-19. Conclusions: Poorer nutritional status predisposed patients with COVID-19 to severe form. Independently associated with the severity of COVID-19, PNI could serve as a simple, fast, and effective predictor among patients with different gender, age, and BMI. Coronavirus disease 2019 , emerging at the end of 2019, has developed into a pandemic worldwide, posing tremendous challenges and threats to individual's health and healthcare systems, as well as the social stability and development [1] . Since the declaration of COVID-19 as pandemic from the World Health Organization [2] , the rate of diagnosed cases is changing every day, and the number of confirmed cases reached as many as 22,179,934 on Aug 19,2020, with 781,932 deaths [3] . The clinical spectrum of COVID-19 disease ranges from asymptomatic infection, mild upper respiratory tract infection, to severe pneumonia, which might progress to respiratory failure, further resulting in multi-organ failure [4, 5] . The majority of deaths has involved older and polymorbid patients, among whom, malnutrition is likely to be common [5, 6] . A previous study has provided the evidence supporting malnutrition as one of the leading predictors of mortality in viral infections [7] . With the severe acute respiratory syndrome coronavirus 2 as pathogen, it is warranted to suppose that malnutrition and its related weakened immune response play a critical role in the onset and progress of COVID-19. The importance of assessment of nutritional status has been advocated and emphasized in expert statements and practical guidance for nutritional management of COVID-19 endorsed by the European Society for Clinical Nutrition and Metabolism (ESPEN) Council [8] . Therefore, a simple and effective index should be identified to evaluate the nutritional status of patients with COVID-19. The prognostic nutritional index (PNI), first introduced as a nutritional assessment for non-emergency general surgical patients, was simplified as a calculation of the peripheral blood lymphocyte count and serum albumin (Alb) concentration and able to reflect the immune-nutritional status of patients [9] . Recently, evidence has been accumulating that PNI was able to predict clinical outcomes in some types of cancers, including lung cancer, gastrointestinal cancer, breast cancer, gynecological cancer, and so on [10] [11] [12] [13] . Up till now, however, no study has been concentrated on the role of PNI in assessment and prediction of the severity of COVID-19. Furthermore, critically ill patients usually suffered a drastic reduction of food intake because of severe inflammation and anorexia, who were more predisposed to develop respiratory failure. Therefore, nutritional assessment and further derangements should be systematically and urgently managed in patients affected by COVID-19 [14, 15] . For patients with the most severe manifestations of the infection, however, assessments of nutritional status with the common tools can be quite difficult because of physical constraints and difficulties in collecting anthropometric and dietary information. Therefore, rapid screening instruments to assess nutritional like PNI should be considered. The present study was to explore the association of PNI with the severity of COVID-19, as well as its predictive value of the severe form of COVID-19. The present single-centered, retrospective study were conducted in the Sixth People's hospital of Wenzhou, which is the specialized infectious hospital affiliated to Wenzhou Central Hospital. Data was derived from 122 patients admitted to the hospital between January 17, 2020 to February 21, 2020. All the patients were diagnosed as COVID-19 and categorized into mild, common, severe, and extremely severe form during hospitalization according to the standards for "Diagnosis and This study was approved by the Institutional Review Board of Wenzhou Central Hospital. Due to the retrospective nature of the study, informed consent was waived. Every patient underwent a physical examination after their admission, which included anthropometric measurements (body height and weight) and blood pressure. Body height and weight were used to calculate body mass index (BMI) as follows: BMI=weight (kg)/height 2 (m 2 ). Overweight and obesity were defined as BMI ≥24kg/m 2 and 28kg/m 2 , respectively [17] . Systolic and total bilirubin [TBil]), serum creatinine (Scr), and C-reactive protein (CRP) were measured using standard methods. PNI was determined with the following formula: PNI = serum albumin (g/L) + 5 × total lymphocyte count (/nL) [9] . The statistical analyses were conducted using the statistical software package version 16.0 (SPSS Inc., Chicago, IL, USA). One-sample Kolmogorov-Smirnov test determined the normality of data distribution. According to commonor skewed distribution, continuous variables were expressed as mean ± standard deviation or median with interquartile range. Categorical variables were expressed as numbers with percentage. Inter-group comparisons of normally and skewed distributed data were carried out by the unpaired student's t test and Mann-Whitney U test, respectively. The chi-square test was used for inter-group comparisons of categorical variables. Multivariate analyses of variance were applied to exploring the interference of age, gender, and BMI with the association between PNI and severity of COVID-19. Multivariate logistic regression analyses were used to identify the independent factor associated with the severity of COVID-19. Receiver operating characteristic (ROC) analyses were carried out to evaluate the predictive ability for severity of COVID-19, and determine the cut-off value of PNI. All reported (95% CI: 0.733-0.901, P < 0.001), and 0.883 (95% CI: 0.792-0.975, P < 0.001), respectively. Compared to the combined index, only -PNI showed similar accuracy for prediction of severe form of COVID-19 (P = 0.402, Figure 2 ), while the other indexes' accuracies were significantly decreased (all P < 0.05). PNI < 49 were defined as a cut-off value for predicting the severe form of COVID-19 with a sensitivity of 100.0% (specificity = 56.2%). The present study revealed that PNI decreased with the progress of COVID-19 from common to severe forms. The association of PNI with gender, age, and BMI did not interfered with its association with the severity of COVID-19. PNI was an inverse and independent factor associated with the severe form of COVID-19. Since the accuracy of PNI was similar to its combination with age, gender, and BMI, PNI could serve as a predictor of the severity of COVID-19 independently, with a cut-off value of 49. The present study extended the age range of the study population, and found nutritional status was worsen in the patients with severe form of COVID-19, which was consistent with the previous study. Furthermore, the cut-off value of 49 was selected for PNI to predict the severe form of COVID-19, which is close to the cut-off value of 50 for PNI to predict the pulmonary complications and long-term outcomes after curative resection of lung cancer [19] . Therefore, with no need of the fully understanding of the history of weight loss, nutritional intake, cognitive status, and other health conditions, PNI could be a simple, fast, and effective index to evaluate the nutritional status and predict the disease progression, especially applicable to the children, elderly people, and those paying less attention to their own health conditions, who cannot recall and describe their previous health conditions clearly. Recently, a large prospective cohort study of 20 133 hospital inpatients with COVID-19 uncovered that advanced age, gender of male, and obesity were independent risk factors of mortality of COVID-19. In terms of nutritional status, older adults are at risk of protein-energy malnutrition, of which, the predictor and determinants were sex-specific [20] . The older adults were vulnerable to nutritional deficiency, with published data showing that malnourishment occurred in 35-65% of hospitalized elderly patients and 25-60% of institutionalized older adults [18, 21] . And the obesity, owing to overnutrition or excess storage of fats relative to energy expenditure, is another form of malnutrition [22] . The present study discovered that the different nutritional status between men and women, the young and the elderly, and the thin and the obese, did not interfere with the association of PNI with the severity of COVID-19, suggesting that as a predictor of severity of COVID-19, PNI is suitable for both men and women with varied age and BMI. The mechanism underlying the association of PNI and severity of COVID-19 remained unclear. As a combination of peripheral blood lymphocyte count and Alb concentration, PNI links nutritional status to immune response for patients [9] . It is acknowledged that nutrition plays a critical role in regulation of immune responses. Protein-energy malnutrition is associated with immunodeficiency [22] , manifested as an impairment of cell-mediated immunity, function of phagocyte, complement system, and cytokine production, which have been observed in patients with severe form of COVID-19 with decreases in lymphocytes and T cell, and increases in inflammatory cytokines [23] . There are some limitations in the present study which might bring some potential bias. First, it was a retrospective, single center, and small sample-sized study, from which the findings should be confirmed and generalized in a larger cohort. Second, without data of weight and diet of patients before admitted to hospital, the present study could not obtain some other classical nutritional index such as Nutrition Risk Screening 2002, Mini Nutrition Assessment Shortcut, and Nutrition Risk Index, and compare the predictive ability for the severity of COVID-19 between PNI and these indexes. Third, although serum levels of albumin have historically been utilized as surrogates for undernourishment, there are some other pathologic states that also contributed to decrease in serum albumin levels, such as inflammation, acute or chronic liver dysfunction, catabolism, nephrotic syndrome, and protein-losing enteropathy. The utility of preoperative serum levels of albumin might be limited to the provision of prognostic information [24] . However, PNI, incorporating serum albumin and lymphocyte levels, has been used in many patient populations to predict the prognostic outcomes [10] [11] [12] [13] 25] . As a conclusion, poorer nutritional status predisposed patients with COVID-19 to severe form. As an independent factor associated the severity of COVID-19, PNI could serve as a simple, fast, and effective predictor among patients with different gender, age, and BMI. A novel coronavirus from patients with pneumonia in China Geneva: World Health Organization. (2020) WHO Director-General's opening remarks at the media briefing on COVID-19 -11 Johns Hopkins University & Medicine. 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Continuous data are expressed as means ± SD or median 05 versus normal form. **P < 0.01 versus normal form