Can prognostic nutritional index predict mortality in intensive care patients with COVID-19?

Development and validation of a prognostic nomogram including inflammatory indicators for overall survival in hepatocellular carcinoma patients treated primarily with surgery or loco-regional therapy: A single-center retrospective study

Hepatocellular carcinoma (HCC) is among the most prevalent malignant tumors, but the current staging system has limited efficacy in predicting HCC prognosis. The authors sought to develop and validate a nomogram model for predicting overall survival (OS) in HCC patients primarily undergoing surgery or loco-regional therapy. Patients diagnosed with HCC from January 2017 to June 2023 were enrolled in the study. The data were randomly split into a training cohort and a validation cohort. Utilizing univariate and multivariate Cox regression analyses, independent risk factors for OS were identified, and a nomogram model was constructed to predict patient survival. Therapy, body mass index, portal vein tumor thrombus, leukocyte, γ-glutamyl transpeptidase to platelet ratio, monocyte to lymphocyte ratio, and prognostic nutritional index were used to build the nomogram for OS. The nomogram demonstrated strong predictive ability, with high C-index values (0.745 for the training cohort and 0.650 for the validation cohort). ROC curves, calibration plots, and DCA curves all indicated satisfactory performance of the nomogram. Kaplan-Meier curve analysis showed a significant difference in prognosis between patients in the low- and high- risk groups. This nomogram provides precise survival predictions for HCC patients and helps identify individuals with varying prognostic risks, emphasizing the need for individualized follow-up and treatment plans.

Predictivity of the Prognostic Nutritional Index and Systemic Inflammation Index for All-Cause In-Hospital Mortality in Geriatric and Adult COVID-19 Inpatients

Background: The prognostic nutritional index (PNI) and the systemic immune inflammation index (SII) have been used as simple risk-stratification predictors for COVID-19 severity and mortality in the general population. However, the associations between these indices and mortality might differ due to age-related changes such as inflammaging and several comorbid conditions in older patients. Therefore, we aimed to compare the predictivity of the PNI and SII for mortality among hospitalized older patients and patients under 65 years old. Methods: Patients hospitalized with COVID-19 from March 2020 to December 2020 were retrospectively included. The PNI and SII were calculated from hospital records within the first 48 h after admission. Data were evaluated in the whole group and according to age groups (≥65 < years). Receiver operating characteristic curves were drawn to evaluate the predictivity of the PNI and SII. Results: Out of 407 patients included in this study, 48.4% (n = 197) were older patients, and 51.6% (n = 210) were under 65 years old. For mortality, the area under the curve (AUC) of the PNI and SII in the adult group (<65 years) was 0.706 (95% CI 0.583-0.828) (p = 0.003) and 0.697 (95% CI 0.567-0.827) (p < 0.005), respectively. The AUC of the PNI and SII in the older group was 0.515 (95% CI 0.427-0.604) (p = 0.739) and 0.500 (95% CI 0.411-0.590) (p = 0.993). Conclusions: The accuracy of the PNI and SII in predicting mortality in adult COVID-19 patients seemed to be fair, but no association was found in geriatric patients in this study. The predictivity of the PNI and SII for mortality varies according to age groups.

The role of nutritional risk evaluation in predicting adverse outcomes among patients with severe COVID-19 in Vietnam

Introduction

As sufficient nutrition helps alleviate catabolic stress and modulate the systemic inflammatory response of the body, it plays an indispensable role in the good prognosis of critically ill patients. Thus, this study aimed to investigate the malnutrition of patients with severe COVID-19 and its association with adverse treatment outcomes.

Methods

We conducted a retrospective cross-sectional study in two provincial hospitals in Hanoi from February to April 2022. Participants were patients with severe COVID-19 admitted to the Intensive Care Unit (ICU). Malnutrition risk were evaluated by Nutritional Risk Screening-2002 (NRS), Global Leadership Initiative on Malnutrition (GLIM), Prognostic Nutritional Index (PNI), and the adverse prognosis was assessed by Acute Physiology and Chronic Health Evaluation II (APACHE II). The multivariate receiver-operating characteristic (ROC) curve was applied to estimate the predictive ability of those criteria regarding worse treatment results.

Results

The percentages of malnutrition measured by NRS, GLIM, PNI, and BMI were 62.6, 51.5, 42.9, and 16.6%, respectively. Patients with more severe malnutrition assessed by GLIM, PNI, and having above target fasting blood glucose (FBG) (≥10.0 mmol/L) were more likely to have higher APACHE scores. PNI had a better diagnostic performance than NRS and BMI (AUC = 0.84, 0.81, and 0.82, respectively). In addition, FBG revealed a good prognostic implication (AUC = 0.84).

Conclusion

A relatively high percentage of patients experienced moderate and severe malnutrition regardless of screening tools. Individuals at higher risk of malnutrition and high FBG were predicted to have more adverse treatment outcomes. It is recommended that nutritional screening should be conducted regularly, and personalizing nutritional care strategies is necessary to meet patients' nutrient demands and prevent other nutrition-related complications.

Association of the quantitative oral health status of patients with COVID-19 with their organ status and immunity: A single-centre observational study

Objectives

To evaluate the prevalence of oral complications in patients with severe COVID-19; investigate the association between their oral health, organ status, and immunity; and determine whether the resazurin disc test is an effective substitute for the Oral Assessment Guide.

Research methodology/design

A single-centre observational study.

Setting

Intensive care unit with restricted access specialising in extracorporeal membrane oxygenation for COVID-19 treatment.

Main outcome measures

We investigated the oral health of 13 patients with COVID-19 receiving extracorporeal membrane oxygenation therapy between April and December 2021 using the Oral Assessment Guide and colour reactive resazurin disc test. The Sequential Organ Failure Assessment and Prognostic Nutritional Index were used to assess organ status and immunity, respectively. The correlation of oral health status with organ status and immunity was investigated.

Results

High bacterial levels, revealed by the resazurin disc test, were associated with elevated Oral Assessment Guide scores, indicating oral health deterioration, particularly in terms of teeth and dentures. Increased Sequential Organ Failure Assessment scores and decreased Prognostic Nutritional Index were correlated with poor oral health, revealed by the Oral Assessment Guide and resazurin disc test.

Conclusion

Poor oral health is an important risk factor for severe COVID-19 complications in patients admitted to an intensive care unit. The Oral Assessment Guide and resazurin disc test can evaluate oral conditions; however, the resazurin disc test is quantitative and does not require salivary specimens to be transferred outside the patient ward for evaluation. The resazurin disc test can be a useful substitute for the Oral Assessment Guide in intensive care units with restricted access.

Implications for clinical practice

The resazurin disc test can be used for quantitative assessment of patients’ oral condition in isolation wards. Multidisciplinary management of patients with COVID-19 should be promoted and involve oral healthcare providers such as dentists and dental hygienists.

Association of Prognostic Nutritional Index with Severity and Mortality of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

The associations of prognostic nutritional index (PNI) with disease severity and mortality in patients with coronavirus disease 2019 (COVID-19) remain unclear. Electronic databases, including MEDLINE, EMBASE, Google scholar, and Cochrane Library, were searched from inception to 10 May 2022. The associations of PNI with risk of mortality (primary outcome) and disease severity (secondary outcome) were investigated. Merged results from meta-analysis of 13 retrospective studies (4204 patients) published between 2020 and 2022 revealed a lower PNI among patients in the mortality group [mean difference (MD): −8.65, p < 0.001] or severity group (MD: −5.19, p < 0.001) compared to those in the non-mortality or non-severity groups. A per-point increase in PNI was associated with a reduced risk of mortality [odds ratio (OR) = 0.84, 95% CI: 0.79 to 0.9, p < 0.001, I2 = 67.3%, seven studies] and disease severity (OR = 0.84, 95% CI: 0.77 to 0.92, p < 0.001, I2 = 83%, five studies). The pooled diagnostic analysis of mortality yielded a sensitivity of 0.76, specificity of 0.71, and area under curve (AUC) of 0.79. Regarding the prediction of disease severity, the sensitivity, specificity, and AUC were 0.8, 0.61, and 0.65, respectively. In conclusion, this study demonstrated a negative association between PNI and prognosis of COVID-19. Further large-scale trials are warranted to support our findings.

Nutritional Risk Assessment Scores Effectively Predict Mortality in Critically Ill Patients with Severe COVID-19

Background: Malnutrition predicts a worse outcome for critically ill patients. However, quick, easy-to-use nutritional risk assessment tools have not been adequately validated. Aims and Methods: The study aimed to evaluate the role of four biological nutritional risk assessment instruments (the Prognostic Nutritional Index—PNI, the Controlling Nutritional Status Score—CONUT, the Nutrition Risk in Critically Ill—NUTRIC, and the modified NUTRIC—mNUTRIC), along with CT-derived fat tissue and muscle mass measurements in predicting in-hospital mortality in a consecutive series of 90 patients hospitalized in the intensive care unit for COVID-19-associated ARDS. Results: In-hospital mortality was 46.7% (n = 42/90). Non-survivors had a significantly higher nutritional risk, as expressed by all four scores. All scores were independent predictors of mortality on the multivariate regression models. PNI had the best discriminative capabilities for mortality, with an area under the curve (AUC) of 0.77 for a cut-off value of 28.05. All scores had an AUC above 0.72. The volume of fat tissue and muscle mass were not associated with increased mortality risk. Conclusions: PNI, CONUT, NUTRIC, and mNUTRIC are valuable nutritional risk assessment tools that can accurately predict mortality in critically ill patients with COVID-19-associated ARDS.

Can prognostic nutritional index predict mortality in intensive care patients with COVID-19?

OBJECTIVES

PNI is a calculated parameter using the albumin and the lymphocyte count from the CBC, which demonstrates the immunological and nutritional status of the patient. The aim of this study is to show the relationship between PNI and mortality in COVID-19 patients and to reveal a PNI cut-off value for mortality.

MATERIALS AND METHODS

Data of 690 PCR positive COVID-19 ICU patients were recorded. COVID-19 ICU patients were divided into two groups; the first group consisted of survivors, while the second group consisted of patients who died in the ICU. Patients were also evaluated in two groups according to the PNI cut-off value that predicted mortality (PNI ≤ 42.00, PNI ≥ 43) and were compared in terms of demographics, laboratory parameters, clinical findings and mortality rates.

RESULTS

When 690 COVID-19 patients were divided into two groups as survivors (50.6%) and deceased (49.4%) in intensive care, PNI value was significantly lower in the deceased group compared to the surviving group (P < .001). The PNI cut-off value predicting mortality was determined as ≤42. Patients were classified into two groups according to the PNI cut-off value. PNI ≤42 was determined as an independent risk factor for mortality (OR:2.9 P < .001). AUC values for PNI, albumin, and lymphocyte were 0.628, 0.612, and 0.590, respectively; P < .001 for all.

CONCLUSION

PNI is an inexpensive method that can be easily calculated on the basis of routine laboratory parameters. We believe that the PNI value of COVID-19 patients on admission to the ICU may be an independent factor to predict mortality.