Red blood cell distribution width: A severity indicator in patients with COVID-19.

Red blood cell distribution width (RDW) was frequently assessed in COVID-19 infection and reported to be associated with adverse outcomes. However, there was no consensus regarding the optimal cutoff value for RDW. Records of 98 patients with COVID-19 from the First People's Hospital of Jingzhou were reviewed. They were divided into two groups according to the cutoff value for RDW on admission by receiver operator characteristic curve analysis: ≤11.5% (n = 50) and >11.5% (n = 48). The association of RDW with the severity and outcomes of COVID-19 was analyzed. The receiver operating characteristic curve indicated that the RDW was a good discrimination factor for identifying COVID-19 severity (area under the curve = 0.728, 95% CI: 0.626-0.830, p < 0.001). Patients with RDW > 11.5% more frequently suffered from critical COVID-19 than those with RDW ≤ 11.5% (62.5% vs. 26.0%, p < 0.001). Multivariate logistic regression analysis showed RDW to be an independent predictor for critical illness due to COVID-19 (OR = 2.40, 95% CI: 1.27-4.55, p = 0.007). A similar result was obtained when we included RDW > 11.5% into another model instead of RDW as a continuous variable (OR = 5.41, 95% CI: 1.53-19.10, p = 0.009). RDW, as an inexpensive and routinely measured parameter, showed promise as a predictor for critical illness in patients with COVID-19 infection. RDW > 11.5% could be the optimal cutoff to discriminate critical COVID-19 infection.

Nutritional screening based on objective indices at admission predicts in-hospital mortality in patients with COVID-19.

BACKGROUND: Could nutritional status serve as prognostic factors for coronavirus disease 2019 (COVID-19)? The present study evaluated the clinical and nutritional characteristics of COVID-19 patients and explored the relationship between risk for malnutrition at admission and in-hospital mortality. METHODS: A retrospective, observational study was conducted in two hospitals in Hubei, China. Confirmed cases of COVID-19 were typed as mild/moderate, severe, or critically ill. Clinical data and in-hospital death were collected. The risk for malnutrition was assessed using the geriatric nutritional risk index (GNRI), the prognostic nutritional index (PNI), and the Controlling Nutritional Status (CONUT) via objective parameters at admission. RESULTS: Two hundred ninety-five patients were enrolled, including 66 severe patients and 41 critically ill patients. Twenty-five deaths were observed, making 8.47% in the whole population and 37.88% in the critically ill subgroup. Patients had significant differences in nutrition-related parameters and inflammatory biomarkers among three types of disease severity. Patients with lower GNRI and PNI, as well as higher CONUT scores, had a higher risk of in-hospital mortality. The receiver operating characteristic curves demonstrated the good prognostic implication of GNRI and CONUT score. The multivariate logistic regression showed that baseline nutritional status, assessed by GNRI, PNI, or CONUT score, was a prognostic indicator for in-hospital mortality. CONCLUSIONS: Despite variant screening tools, poor nutritional status was associated with in-hospital death in patients infected with COVID-19. This study highlighted the importance of nutritional screening at admission and the new insight of nutritional monitoring or therapy.

Predictive Value of Prognostic Nutritional Index on COVID-19 Severity.

Background: The prognostic nutritional index (PNI) has been described as a simple risk-stratified tool for several diseases. We explored the predictive role of the PNI on coronavirus disease 2019 (COVID-19) severity. Methods: A total of 101 patients with COVID-19 were included in this retrospective study from January 2020 to March 2020. They were divided into two groups according to COVID-19 severity: non-critical (n = 56) and critical (n = 45). The PNI was calculated upon hospital admission: 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (/mm3). Critical COVID-19 was defined as having one of the following features: respiratory failure necessitating mechanical ventilation; shock; organ dysfunction necessitating admission to the intensive care unit (ICU). The correlation between the PNI with COVID-19 severity was analyzed. Results: The PNI was significantly lower in critically ill than that in non-critically ill patients (P < 0.001). The receiver operating characteristic curve indicated that the PNI was a good discrimination factor for identifying COVID-19 severity (P < 0.001). Multivariate logistic regression analysis showed the PNI to be an independent risk factor for critical illness due to COVID-19 (P = 0.002). Conclusions: The PNI is a valuable biomarker that could be used to discriminate COVID-19 severity.

Critical Care Response to the Outbreak of COVID-19: The Experience From Guangdong Province, China.

In December 2019, human infection with a novel coronavirus, known as SARS-CoV-2, was confirmed in Wuhan, China, and spread rapidly beyond Wuhan and around the world. By 7 May 2020, a total of 84,409 patients were infected in mainland China, with 4,643 deaths, according to a Chinese Center for Disease Control and Prevention report. Recent studies reported that critically ill patients were presented with high mortality. However, the clinical experiences of patients with coronavirus disease 2019 (COVID-19) have not been described in Guangdong Province, where by 7 May 2020, 1,589 people had been confirmed as having COVID-19 but with a very low mortality of 8 death (0.5%). Here, we describe the experience of critical care response to the outbreak of SARS-CoV-2 in Guangdong Province in the following points: Early intervention by the government, Establishment of a Multidisciplinary Working Group, Prompt intensive care interventions, Adequate ICU beds and Human resource in ICU, Infection control practices.

Clinical features of severe patients infected with 2019 novel coronavirus: a systematic review and meta-analysis.

BACKGROUND: 2019 novel coronavirus disease (COVID-19) has posed significant threats to public health. To identify and treat the severe and critical patients with COVID-19 is the key clinical problem to be solved. The present study aimed to evaluate the clinical characteristics of severe and non-severe patients with COVID-19. METHODS: We searched independently studies and retrieved the data that involved the clinical characteristics of severe and non-severe patients with COVID-19 through database searching. Two authors independently retrieved the data from the individual studies, assessed the study quality with Newcastle-Ottawa Scale and analyzed publication bias by Begg's test. We calculated the odds ratio (OR) of groups using fixed or random-effect models. RESULTS: Five studies with 5,328 patients confirmed with COVID-19 met the inclusion criteria. Severe patents were older and more common in dyspnea, vomiting or diarrhea, creatinine >104 µmol/L, procalcitonin ≥0.05 ng/mL, lymphocyte count <1.5×109/L and bilateral involvement of chest CT. Severe patents had higher risk on complications including acute cardiac injury (OR 13.48; 95% CI, 3.60 to 50.47, P<0.001) or acute kidney injury (AKI) (OR 11.55; 95% CI, 3.44 to 38.77, P<0.001), acute respiratory distress syndrome (ARDS) (OR 26.12; 95% CI, 11.14 to 61.25, P<0.001), shock (OR 53.17; 95% CI, 12.54 to 225.4, P<0.001) and in-hospital death (OR 45.24; 95% CI, 19.43 to 105.35, P<0.001). Severe group required more main interventions such as received antiviral therapy (OR 1.69; 95% CI, 1.23 to 2.32, P=0.001), corticosteroids (OR 5.07; 95% CI, 3.69 to 6.98, P<0.001), CRRT (OR 37.95; 95% CI, 7.26 to 198.41, P<0.001) and invasive mechanical ventilation (OR 129.35; 95% CI, 25.83 to 647.68, P<0.001). CONCLUSIONS: Severe patients with COVID-19 had more risk of clinical characteristics and multiple system organ complications. Even received more main interventions, severe patients had higher risk of mortality.