Can prognostic nutritional index and systemic immune-inflammatory index predict disease severity in COVID-19?

Low Prognostic Nutritional Index (PNI) Level is Associated with an Increased Risk of Neonatal Respiratory Distress Syndrome in Preterm Infants with Different Gestational Ages: A Retrospective Study

Background

Neonatal respiratory distress syndrome (NRDS) is common in preterm infants. Prognostic nutritional index (PNI)((albumin (g/L)+(5×total lymphocyte count (109/L)) is a comprehensive indicator of nutritional and immune levels, and associated with several diseases. The relationship between PNI and the risk of NRDS in newborns of different gestational ages remains unclear.

Methods

A total of 2722 preterm infants were included in this retrospective study. PNI level and clinical records of these neonates (adverse pregnancy and birth history, amniotic fluid contamination, nuchal cord, placental abnormality, mode of delivery, gender and birth weight of neonates, Apgar scores) were collected. The clinical features of the infants with and without NRDS were compared. Logistic regression analysis was used to evaluate the relationship between PNI and NRDS in newborns with different gestational ages.

Results

There were 1226 neonates with NRDS and 1496 without NRDS. The differences in the proportions of placenta abnormality, cesarean section, and small for gestational age (SGA) among infants with 34-37 weeks, 28+1-33+6 weeks, and ≤28 weeks gestational age were statistically significant. Logistic analysis showed that cesarean section (odds ratio (OR): 1.550, 95% confidence interval (CI): 1.197-2.007, p=0.001), and low PNI (OR: 1.417, 95% CI: 1.110-1.808, p=0.005) were associated with NRDS in infants born at 34-37 weeks gestational. Adverse pregnancy and birth history (OR: 1.507, 95% CI: 1.124-2.019, p=0.006), SGA (OR: 1.994, 95% CI: 1.455-2.733, p<0.001), and low PNI (OR: 1.626, 95% CI: 1.230-2.149, p=0.001) were associated with NRDS in infants with 28+1-33+6 weeks gestational age. Low PNI (OR: 5.512, 95% CI: 1.555-19.536, p=0.008) was associated with NRDS in infants with ≤28 weeks gestational age.

Conclusion

The risk factors for NRDS in preterm infants with different gestational ages were different. But the low PNI level is associated with an increased risk of NRDS in preterm infants with all different gestational ages.

A retrospective cohort study on the association between nutritional status and prognosis in COVID-19 patients with severe and critical infection

OBJECTIVE

To evaluate the relationship between nutritional scoring systems, nutritional support methods, and the prognosis of severe and critically ill patients infected with the Omicron variant of coronavirus disease 2019 (COVID-19).

METHODS

Patients with confirmed Omicron variant severe and critical COVID-19, who were admitted to Chongqing Medical University First Hospital between December 2022 and January 2023, were enrolled into this retrospective study. Clinical data of patients who survived for 28 days were compared with those who died during the same period. Nutritional status was assessed using the 2002 Nutrition Risk Screening (NRS) tool and Prognostic Nutritional Index (PNI). Factors influencing patient mortality were identified by multivariate logistic regression, and the relationship between patient nutrition and mortality as the disease progressed was illustrated using Kaplan-Meier curves. The study was registered on the ChiCTR platform (No. ChiCTR2300067595).

RESULTS

A total of 508 patients were included (349 survivors and 159 non-survivors). Significant differences were found in sex, age, NRS score, PNI score, albumin level, lymphocyte count, chronic comorbidities, mechanical ventilation, neutrophil count, procalcitonin, and platelet count between survivors and non-survivors. Multivariate analysis revealed that high NRS score (OR 3.87, 95% CI, 1.97, 7.63), fourth-level nutritional support (combined enteral and parenteral nutrition; OR 7.89, 95% CI, 1.32, 47.28), chronic comorbidities (OR 4.03, 95% CI, 1.91, 8.51), and mechanical ventilation (OR 6.03, 95% CI, 3, 12.13) were risk factors for mortality (OR > 1). The malnutrition rate among patients with NRS ≥ 3 was 41.93%. The median (interquartile range) PNI score was 38.20 (35.65, 41.25) for survivors versus 32.65 (29.65, 36.58) for non-survivors. The mortality rate was higher in patients with high nutritional risk within 28 days of hospitalization. The descending order for mortality rate in patients receiving different nutritional support was: dual parenteral and enteral nutrition, no nutritional intervention, single enteral nutrition, and single parenteral nutrition.

CONCLUSION

A high proportion of severe and critically ill patients with COVID-19 experience malnutrition, and various factors are associated with their prognosis. High nutritional risk is significantly related to patient mortality. Early assessment using NRS or PNI is crucial for these patients, and personalized interventions should be implemented to improve overall nutritional status, maintain organ function, and enhance the body's antiviral defence.

Evaluation of malnutrition by objective nutritional indexes and predictors in hospitalized patients with COVID-19

Nutritional information on hospitalized patients with COVID-19 is limited. We aimed to (1) investigate the prevalence of nutrition risk defined by the Scored Nutritional Risk Screening (NRS 2002) and malnutrition assessed by prognostic nutritional index (PNI) and controlling nutritional status score (CONUT), (2) observe the nutritional intervention, and (3) explore the predictors of critical condition and mortality. Nutritional risk was 53.00% and the prevalence of malnutrition was 79.09% and 88.79% among 464 patients based on PNI and CONUT, respectively. The area under the receiver operating characteristic curve for hypersensitivity C-reactive protein (hs-CRP), platelet-to-lymphocyte ratio (PLR), PNI, neutrophil/lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and CONUT were 0.714, 0.677, 0.243, 0.778, 0.742, and 0.743, respectively, in discerning critical patients. The mortality-related area under the curve of hs-CRP, PLR, PNI, NLR, SII, and CONUT were 0.740, 0.647, 0.247, 0.814, 0.758, and 0.767, respectively. The results showed that CONUT and NLR were significantly correlated with the critical conditions. Our study revealed a high prevalence of nutritional risk and malnutrition among hospitalized patients with COVID-19. NLR, PLR, hs-CRP, SII, and CONUT are independent predictors of critical conditions and mortality. CONUT and NLR could assist clinicians in discerning critical cases.

Prognostic Nutritional Index (PNI) and Systemic Immune-Inflammatory Index (SII) as markers of severity among patients having COVID-19 infection

Background

Prognostic nutritional index (PNI) and systemic immune-inflammatory index (SII) are two novel markers that have emerged as potential candidates as an early indication of the severity of the disease in coronavirus disease 2019 (COVID-19) patients.

Objective

The objective of the study is to assess the utility of the prognostic nutritional index (PNI) and systemic immune-inflammatory index (SII) as markers of severity among patients with COVID-19 infection.

Methods

This is a retrospective study conducted in a tertiary care centre in South India. A total of 80 patients diagnosed with COVID-19 were included in the study. The patients were divided into mild, moderate, and severe groups based on the clinical parameters as per Indian Council of Medical Research guidelines. Lab values taken at admission were obtained from patient records, using which the PNI and SII were calculated using standard formulae. These markers were correlated with the severity of the COVID-19 illness.

Results

PNI and SII were significantly elevated in the patients with severe COVID-19 illness as compared with mild COVID-19 illness. The mean PNI among subjects with mild COVID-19 and severe COVID-19 being 46.62 ± 6.51 and 34.09 ± 5.81, respectively. The mean SII among subjects with mild COVID-19 was 9,52,287.2 ± 1,42,113, and among subjects with severe COVID-19 was 15,39,461 ± 8,04,285. The cut-off value for PNI and SII for predicting severity of COVID-19 illness was 35.93 and 5,82,400, respectively. The sensitivity for PNI was 87.5, and the SII was 95.

Conclusion

The present study showed a significant correlation between the SII and PNI as markers used to determine the severity of COVID-19. Based on these findings, it can be effectively used independently of other markers to predict critical illness among COVID-19 patients.

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.

Prognostic value of the neutrophil-to-lymphocyte ratio and C-reactive-protein-to-prealbumin ratio in hospitalized older patients with coronavirus disease 2019

The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein-to-prealbumin ratio (CPAR) are novel markers of inflammation. The CPAR is an indicator of inflammation and malnutrition. We evaluated NLR and CPAR in combination as indicators of disease severity and prognosis in hospitalized older patients with coronavirus disease 2019 (COVID-19). A total of 222 hospitalized patients with COVID-19 (aged > 60 years) were divided into non-severe and severe groups. The severe group was subdivided into the surviving and deceased subgroups. We retrospectively assessed the predictive power of NLR and CPAR in combination (NLR + CPAR) to determine the prognosis of hospitalized older patients with COVID-19. The NLR and CPAR were significantly higher in the severe group than in the non-severe group (P < .001). Furthermore, the NLR and CPAR were higher in the deceased subgroup than in the surviving subgroup (P < .001). Pearson correlation analysis showed a highly significant positive correlation between NLR and CPAR (P < .001, r = 0.530). NLR + CPAR showed an area under the curve of 0.827 and sensitivity of 83.9% in the severe group; the area under the curve was larger (0.925) and sensitivity was higher (87.1%) in the deceased subgroup. The receiver operating characteristic curve of NLR + CPAR was significantly different from the receiver operating characteristic curves of either biomarker alone (P < .001). Kaplan-Meier analysis showed that patients in the severe group with elevated NLR + CPAR had a significantly lower 90-day survival rate than patients who lacked this finding (odds ratio 7.87, P < .001). NLR + CPAR may enable early diagnosis and assessment of disease severity in hospitalized older patients with COVID-19. This may also enable the identification of high-risk older patients with COVID-19 at the time of admission.

Predictive Value of Nutrition and Inflammation-Related Indices on Prognosis in Type 2 Diabetes Mellitus Patients with Coronavirus Disease-2019

Objectives

This study aimed to demonstrate how the prognostic nutritional index (PNI) and systemic immune-inflammatory index (SII) help predict the severity and prognosis of patients with type 2 diabetes (T2DM) and coronavirus disease (COVID-19).

Methods

This retrospective cohort study included 501 T2DM patients (male, 42.1%; female, 57.9%) who were hospitalized due to COVID-19 between April 2020 and December 2020. The patients were divided into survivors and non-survivors. After comparing demographic and laboratory data between the groups, the correlation of PNI and SII with clinical and laboratory data was evaluated.

Results

The median (interquartile) ages of the non-survivor and survivor groups were 74 (15) and 69 (14) years, respectively, and the difference was significant (p<0.001). The PNI was significantly lower in the non-survivor group than in the survivor group (p<0.001). The SII was significantly higher in the non-survivor group than in the survivor group (p<0.001). PNI was negatively correlated with glucose levels (r=-0.115, p=0.011). If the cut-off PNI value of 29.1 was used, it had a sensitivity and specificity of 76.2% and 76.3%, respectively, in predicting the severity of the illness and the risk of death in T2DM patients.

Conclusion

Consequently, the PNI and SII levels are effective in predicting survival and disease severity in patients with COVID-19 and T2DM.

Anti-inflammatory Effect of Batroxobin Combined With Anticoagulation in Patients With Cerebral Venous Thrombosis

Inflammation is pivotal in the pathogenesis and development of cerebral venous thrombosis (CVT). Herein, we aimed to assess the anti-inflammatory effects of batroxobin combined with anticoagulation in CVT. Participants were categorized into the batroxobin group (batroxobin combined with anticoagulation) and the control group (anticoagulation only). Regression analysis was employed to explore the association between the number of episodes of batroxobin administration and the fluctuation of inflammatory indicators, as well as the proportion of patients with inflammatory indicators that were reduced after batroxobin use. Twenty-three cases (age: 39.9 ± 13.8 years, female: 39.1%) in the batroxobin group and 36 cases (40.3 ± 9.6 years, 52.8%) in the control group were analyzed. Compared to the control group, batroxobin combined with anticoagulation significantly decreased fibrinogen (P < .001), platelet-lymphocyte ratio (PLR) (P = .016) and systemic immune-inflammation index (SII) (P = .008), and increased the proportion of the patients with lower fibrinogen (P < .001), neutrophil-lymphocyte ratio (NLR) (P = .005), PLR (P = .026), and SII (P = .006). Linear analysis showed that as the number of episodes of batroxobin administration increased, the fibrinogen (P < .001), the PLR (P = .001), and the SII (P = .020) significantly decreased. Logistic regression analysis showed as the number of episodes of batroxobin administration increased, the ratio of the patients with decreased NLR (P = .008) and PLR (P = .015), as well as SII (P = .013), significantly increased. Batroxobin could decrease NLR, PLR, and SII in CVT. The effect was related to the number of episodes of batroxobin administration. Besides reducing fibrinogen and indirect thrombolysis effects, this may be another critical benefit of batroxobin for CVT.

Association of Malnutrition with Risk of Acute Kidney Injury: A Systematic Review and Meta-Analysis

Background

Acute kidney injury (AKI) is a complex clinical syndrome of hospitalization that may be affected by undernutrition and metabolic changes. The aim of this meta-analysis was to systematically assess the association between malnutrition and the risk of prevalent AKI.

Materials and Methods

We searched PubMed, Embase, Ovid MEDLINE, Web of Science, and Chinese databases (WANFANG, VIP, and CKI) from database inception until May 1, 2023, for studies evaluating the association of malnutrition with the risk of AKI. Summary odds ratios (ORs) were estimated using a random-effects model.

Results

We identified 17 observational studies, which included 273,315 individuals. Compared with patients with normal nutritional status, those with malnutrition had a 125% increased risk of prevalent AKI (pooled ORs, 2.25; 95% confidence interval, 1.80-2.82). Malnutrition was also significantly associated with prevalent AKI across all subgroups when subgroup analyses were performed on covariates such as region, study design, age, sample size, malnutrition assessment method, patient characteristics, covariate adjustment degree, and risk of bias. Meta-regression models demonstrated no significant differences in AKI risk between patients with malnutrition and without malnutrition.

Conclusions

Our results suggest that malnutrition may be a potential target for AKI prevention. However, well-designed studies with ethnically or geographically diverse populations are needed to evaluate strategies and interventions to prevent or slow the development and progression of AKI in malnourished individuals.

Hematocytological, biochemical, and hemostasis parameters' role in predicting the possibility of the various forms of the COVID-19 course in hospitalized Ukrainian patients: A cross-sectional study

Background and Aims

The main objective of this study is to establish the characteristics of blood laboratory parameters in hospitalized coronavirus disease 2019 (COVID-19) Ukrainian patients and the significance of the above-mentioned parameters for predicting the course of the disease.

Methods

Hematocytological, biochemical, and hemostasis methods of research have been used. Groups of patients with different forms of the coronavirus disease course have been analyzed (lethality - recovery, recovery with a severe and mild course).

Results and Conclusion

Age is one of the risk factors for COVID-19 mortality. Absolute values of neutrophils, neutrophil-lymphocyte ratio (NLR), systemic inflammation index, d-dimer, C-reactive protein, and soluble fibrin complex can be used by clinicians to effectively differentiate between two possible outcomes (lethality vs. recovery). A higher number of stab leukocytes, d-NLR, and platelets concentration have been recorded in patients with severe COVID-19 cases, compared to mild ones. The risk of adverse COVID-19 outcome (lethality) is significantly linked with d-dimer and NLR (odds ratio 1.42). The risk of a severe course of the disease was significantly associated with the count of leukocytes (odds ratio 4.96).

Correlation of systemic inflammation biomarkers and disease severity in pregnant women with COVID-19

OBJECTIVE

The aim of this study was to evaluate the prognostic value of whole blood parameters, systemic inflammatory indices, and systemic inflammatory markers in pregnant women with COVID-19.

METHODS

In this cross-sectional study, the demographic, clinical, and laboratory data (i.e., whole blood parameters, C-reactive protein, procalcitonin, ferritin, and D-dimer) of 464 pregnant women with COVID-19 who attended a tertiary hospital between January and April 2021 were reviewed. Systemic inflammatory indices (i.e., neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, platelet/neutrophil ratio, and systemic immune inflammation index) were calculated. Asymptomatic and mildly symptomatic pregnant women were classified as Group 1 (n=413), and those with severe disease were classified as Group 2 (n=51).

RESULTS

Lymphocyte count and lymphocyte percentage in whole blood parameters were significantly lower (p<0.05), and C-reactive protein, ferritin, and procalcitonin values were higher in Group 2 (p<0.05). Systemic inflammatory indices [neutrophil/lymphocyte ratio (4.7±2.9 (1.1-21.2) vs 7.5±4.7 (2.13-23.2)), platelet/lymphocyte ratio (191.1±104.3 (53.0-807.1) vs 269.5±118.9 (105.0-756.0)), systemic immune inflammation index (1,000±663 (209-5,231) vs 1,630±1,314 (345-7,006))] were found statistically significantly higher in severe disease group (p<0.001).

CONCLUSION

Evidence in this study indicates that neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and systemic immune inflammation index at first admission are simple, rapid, and inexpensive indices in predicting the prognosis of COVID-19 in pregnant women.

Systemic inflammation index, disease severity, and mortality in patients with COVID-19: a systematic review and meta-analysis

Introduction

An excessive systemic pro-inflammatory state increases the risk of severe disease and mortality in patients with coronavirus disease 2019 (COVID-19). However, there is uncertainty regarding whether specific biomarkers of inflammation can enhance risk stratification in this group. We conducted a systematic review and meta-analysis to investigate an emerging biomarker of systemic inflammation derived from routine hematological parameters, the systemic inflammation index (SII), in COVID-19 patients with different disease severity and survival status.

Methods

A systematic literature search was conducted in PubMed, Web of Science, and Scopus, between the 1st of December 2019 and the 15th of March 2023. Risk of bias and certainty of evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Grades of Recommendation, Assessment, Development and Evaluation, respectively (PROSPERO registration number: CRD42023420517).

Results

In 39 studies, patients with a severe disease or non-survivor status had significantly higher SII values on admission compared to patients with a non-severe disease or survivor status (standard mean difference (SMD)=0.91, 95% CI 0.75 to 1.06, p<0.001; moderate certainty of evidence). The SII was also significantly associated with the risk of severe disease or death in 10 studies reporting odds ratios (1.007, 95% CI 1.001 to 1.014, p=0.032; very low certainty of evidence) and in six studies reporting hazard ratios (1.99, 95% CI 1.01 to 3.92, p=0.047; very low certainty of evidence). Pooled sensitivity, specificity, and area under the curve for severe disease or mortality were 0.71 (95% CI 0.67 to 0.75), 0.71 (95% CI 0.64 to 0.77), and 0.77 (95% CI 0.73 to 0.80), respectively. In meta-regression, significant correlations were observed between the SMD and albumin, lactate dehydrogenase, creatinine, and D-dimer.

Discussion

Our systematic review and meta-analysis has shown that the SII on admission is significantly associated with severe disease and mortality in patients with COVID-19. Therefore, this inflammatory biomarker derived from routine haematological parameters can be helpful for early risk stratification in this group.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420517.

The utility of systemic immune-inflammation index and systemic immune-response index in the prediction of adverse outcomes in pregnant women with coronavirus disease 2019: Analysis of 2649 cases

AIM

To investigate the association of systemic immune-inflammation index (SII) and systemic immune-response index (SIRI) with adverse perinatal outcomes in pregnant women with coronavirus disease 2019 (COVID-19).

METHODS

The cases were divided into (1) the Mild-moderate COVID-19 group (n = 2437) and (2) the Severe-critical COVID-19 group (n = 212). Clinical characteristics, perinatal outcomes, SII (neutrophilXplatelet/lymphocyte), and SIRI (neutrophilXmonocyte/lymphocyte) were compared between the groups. Afterward, SII and SIRI values were compared between subgroups based on pregnancy complications, neonatal intensive care unit (NICU) admission, and maternal mortality. A receiver operator characteristic analysis was performed for the determination of optimal cutoff values for SII and SIRI in the prediction of COVID-19 severity, pregnancy complications, NICU admission, and maternal mortality.

RESULTS

Both SII and SIRI were significantly higher in complicated cases (p < 0.05). Cutoff values in the prediction of severe-critical COVID-19 were 1309.8 for SII, and 2.3 for SIRI. For pregnancy complications, optimal cutoff values were 973.2 and 1.6. Cutoff values of 1045.4 and 1.8 were calculated for the prediction of NICU admission. Finally, cut-off values of 1224.2 and 2.4 were found in the prediction of maternal mortality.

CONCLUSION

SII and SIRI might be used in combination with other clinical findings in the prediction of poor perinatal outcomes.

Evaluation of Inflammation-Based Prognostic Risk Scores in Predicting in-Hospital Mortality Risk in COVID-19 Patients: A Cross-Sectional Retrospective Study

Objective

Systemic inflammatory parameters are predictors of poor prognosis in COVID-19 patients. This study evaluated whether the prognostic nutritional index, which was also related to nutrition risk and other inflammation-based prognostic scores, was predictive of in-hospital mortality in COVID-19 patients.

Materials and Methods

This was a retrospective cross-sectional single-center study. Based on the exclusion criteria, 151 patients over 18 years old diagnosed with COVID-19 and hospitalized in the intensive care unit between March 2020 and December 2020 were eligible for this study. Multivariable logistic regression analysis was performed to evaluate the predictive value of the Glasgow Prognostic Score (GPS), Prognostic Index (PI), Prognostic Nutritional Index (PNI), and Systemic Inflammatory Index (SII).

Results

In the univariate analyses, age, diabetes mellitus (DM), chronic kidney disease, acute kidney injury, hypothyroidism, hospitalization stay, lactate dehydrogenase (LDH), aspartate aminotransferase (AST), D-dimer, ferritin, C-reactive protein (CRP), albumin, hemoglobin level, platelet count, urea, creatinine level, PNI, GPS were significantly associated with mortality. However, in the multivariable logistic regression analysis of the inflammation-based prognostic scores, only PNI was statistically significant in predicting in-hospital mortality (OR=0.83; [95% CI=0.71-0.97]; p =0.019).

Conclusion

PNI is a more useful and powerful tool among these inflammation-based prognostic risk scores in predicting in-hospital mortality in COVID-19 patients.

Using inflammatory indices for assessing malnutrition among COVID-19 patients: A single-center retrospective study

BACKGROUND

Coronavirus disease 2019 (COVID-19) causes malnutrition in infected patients. This study aimed to investigate the use of systemic immune-inflammatory index (SII), platelet-to-lymphocyte ratio (PLR), the Glasgow Prognostic Score (GPS), and neutrophil-to-lymphocyte ratio (NLR) for malnutrition assessment among COVID-19 inpatients.

METHODS

This is a single-center retrospective study on 108 hospitalized COVID-19 patients; 14 were admitted to the intensive care unit (ICU). Data were collected from patients' profiles while NLR, PLR, GPS, and SII were calculated. Inflammatory indices' predictive power was analyzed using the receiver operating characteristic curve (ROC). A P-value of < 0.05 was considered statistically significant.

RESULTS

Hospitalization days, neutrophils count, C-reactive protein (CRP), and serum urea levels were significantly higher in ICU patients. None of SII, PLR, and NLR were significantly different between ICU and non-ICU groups. Also, albumin and GPS showed a higher sensitivity level (100.0), followed by PLR and SII (78.57 and 71.34, respectively). Regarding ROC curves, even though NLR, PLR, and SII provided the largest area under the curve (AUC) (0.687, 0.682, 0.645; respectively), they have shown a poor discrimination ability, while GPS and albumin were ineffective in predicting malnutrition in COVID-19 patients.

CONCLUSION

NLR, SII, and PLR showed poor predicting ability for malnutrition among COVID-19 inpatients. Additional consideration should be taken for using inflammatory parameters (SII, PLR, GPS, and NLR) to predict malnutrition in COVID-19 inpatients.

Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients' Mortality

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Background

Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.

Methods

Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.

Results

The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.

Conclusion

The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

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.

Role of hemogram-derived ratios in predicting intensive care unit admission in COVID-19 patients: a multicenter study

Purpose

As hyperinflammation is recognized as a driver of severe COVID-19 disease, checking markers of inflammation is gaining more attention. Our study aimed to evaluate the utility of cost-effective hemogram-derived ratios in predicting intensive care unit (ICU) admission in COVID-19 patients.

Methods

This multicenter retrospective study included hospitalized COVID-19 patients from four dedicated COVID-19 hospitals in Sylhet, Bangladesh. Data on demographics, clinical characteristics, laboratory parameters and survival outcomes were analyzed. Logistic regression analysis was used to identify the significance of each hemogram-derived ratio in predicting ICU admission.

Results

Of 442 included patients, 98 (22.17%) required ICU admission. At the time of admission, patients requiring ICU had a higher neutrophil count and lower lymphocyte and platelet counts than patients not requiring ICU. Peripheral capillary oxygen saturation at admission was significantly lower in those who subsequently required ICU admission. Neutrophil-to-lymphocyte ratio, derived neutrophil-to-lymphocyte ratio, neutrophil-to-platelet ratio, and systemic immune-inflammation index were significant predictors of ICU admission.

Conclusion

Hemogram-derived ratios can be an effective tool in facilitating the early categorization of at-risk patients, enabling timely measures to be taken early in the disease course.

Predicting progression to severe COVID-19 using the PAINT score

Objectives

One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19.

Methods

A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19.

Results

Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16⁺/CD56⁺ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value.

Conclusions

The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07466-4.

Multi-inflammatory Index as a Novel Mortality Predictor in Critically Ill COVID-19 Patients

Aim

Systemic inflammation has a crucial role in the pathogenesis and mortality of Coronavirus disease 2019 (COVID-19). Multi-inflammatory index (MII) is a novel index related with systemic inflammation. In this study, we investigated the relationship between MII and in-hospital mortality in COVID-19 patients admitted to the intensive care unit (ICU).

Methods

We retrospectively analyzed the medical records of COVID-19 patients followed-up in the ICU of our institution between 01.04.2020 and 01.10.2021. Patients were classified into two groups according to mortality status as survivors and non-survivors. Various inflammatory parameters of the groups were compared and their efficacy in predicting mortality was investigated.

Results

Out of 348 study patients, 86 cases (24.7%) were in the survived group and 262 cases (75.3%) were in the dead group. The median age of the mortal group was significantly higher than that of the survived group (65.5 vs 76, P < .001). Multiple logistic regression analysis revealed that among all the included inflammatory parameters, MII showed the best efficacy for predicting mortality (OR: 0.999; 95% CI: 0.9991-0.9998; P = .003).

Conclusion

MII, a new combination of Neutrophil to lymphocyte ratio (NLR) and C-reactive protein (CRP), is a simple and practical biomarker that can help us in the prediction of mortality in COVID-19 patients followed-up in the ICU.

Systemic immune-inflammation index to predict placenta accreta spectrum and its histological subtypes

AIM

In this study, we aimed to investigate the role of systemic immune-inflammation index (SII) and other inflammatory parameters in the diagnosis of placenta accreta spectrum (PAS) and its histological subtypes.

METHODS

This retrospective case-control study included patients who underwent surgery for placenta previa (PP). Case group (patients with PAS) included pregnant women diagnosed with histologically confirmed PAS, whereas control group (patients with PP) included pregnant women who underwent cesarean section with a PP diagnosis, required no additional intervention during the operation. Both groups were compared with respect to their demographic data, clinical characteristics, SII, and other laboratory parameters. Cut-off values that can predict PAS were calculated. The PAS group was separated into subgroups based on histology findings, and inflammatory parameters were compared between subgroups.

RESULTS

In this study, data of 273 patients were analyzed. Of these, 68 (24.9%) were included in the PAS group and 205 (75.1%) patients were included in the PP group. Significant differences were observed in SII, platelet distribution width, mean platelet volume, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio (p = 0.000, p = 0.006, p = 0.002, p = 0.000, and p = 0.000, respectively). The best SII cut-off value was 985.02109/L (57.4% sensitivity and 72.2% specificity). There was no significant association between the histologic subtypes of PAS and inflammatory parameters.

CONCLUSION

SII can be used to predict PAS in pregnant women with PP. The relationship between the histologic subtypes of PAS and inflammatory parameters should be investigated in more comprehensive studies.

Assessment of Cancer-Associated Cachexia - How to Approach Physical Function Evaluation

PURPOSE OF REVIEW

Cachexia is a devastating syndrome that impacts a majority of cancer patients. Early assessment of cachexia is critical to implementing cachexia treatments. Our aim was to summarize the existing cachexia assessment tools for their utility in both symptom and function evaluation.

RECENT FINDINGS

Several tools now exist that provide a symptom-based approach for evaluating weight change, appetite, and nutrition impact symptoms in cancer patients with cachexia. However, current instruments used to assess physical function changes related to cachexia are limited in depth and breadth. Instead, we recommend a tiered approach to cachexia-related functional assessment that involves evaluation of activities of daily living, general mobility, and exercise tolerance in a prioritized sequence. Current tools for cancer-associated cachexia assessment are adept at symptom evaluation. New approaches to physical function evaluation are needed that efficiently and broadly evaluate the diverse functional needs of cachexia patients.

The Role of Different Inflammatory Indices in the Diagnosis of COVID-19

AIM

To assess the role of different inflammatory indices in the diagnosis of COVID-19 infection.

METHODS

The neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), derived NLR (dNLR), neutrophil to lymphocyte, platelet ratio (NLPR), systemic inflammation index (SII), aggregate index of systemic inflammation (AISI), systemic inflammation response index (SIRI) and C-reactive protein-to-lymphocyte ratio (CRP/L) were assessed in 88 COVID-19 patients compared to 41 healthy control subjects.

RESULTS

The NLR, PLR, NLPR, SIRI, and CRP/L were significantly increased, while LMR was significantly decreased in COVID-19 patients compared to the control group (P = 0.008, 0.011, <0.001, 0.032, 0.002 and P < 0.001; respectively). The AUC for the assessed indices was LMR (0.738, P = 0.008), NLPR (0.721, P < 0.001), CRP/L (0.692, P = 0.002), NLR (0.649, P < 0.001), PLR (0.643, P = 0.011), SIRI (0.623, P = 0.032), dNLR (0.590, P = 0.111), SII (0.571, P = 0.207), and AISI (0.567, P-0.244). Multivariate analysis showed that NLPR >0.011 (OR: 38.751, P = 0.014), and CRP/L >7.6 (OR: 7.604, P = 0.022) are possible independent diagnostic factors for COVID-19 infection.

CONCLUSION

NLPR and CRP/L could be potential independent diagnostic factors for COVID-19 infection.

Can prognostic nutritional index and systemic immune-inflammatory index predict disease severity in COVID-19?

BACKGROUND

Prognostic nutritional index (PNI) and systemic immune-inflammatory index (SII) are inflammation-based novel markers that predict the prognosis in various patient populations. We have investigated the relationship between the disease severity in COVID-19, and the PNI and SII scores in the present study.

MATERIALS AND METHODS

This cross-sectional retrospective study included 118 hospitalised patients with a confirmed diagnosis of COVID-19. The patients were divided into two groups as those who were hospitalised at the intensive care unit (ICU) and those who had been internalised at the clinic (non-ICU).

RESULTS

Of the 118 patients, 50.8% were male. The mean age was 57.7 ± 17.5 years in non-ICU patients and 70.3 ± 11.7 years in ICU patients and the difference was statistically significant (P < .001). The lymphocyte count and the albumin levels were significantly lower in ICU patients (P < .001, P < .001, respectively). The PNI score was significantly lower in ICU patients compared with non-ICU patients (P < .001). The SII score was found to be significantly higher in ICU patients compared with non-ICU patients (P < .001). The value of PNI and SII scores in prediction of the disease severity in COVID-19 was evaluated with the ROC analysis (PNI: AUC = 0.796, 95%CI: 0.715-0.877, P < .001; SII: AUC =0.689, 95% CI: 0.559-0.819, P=.004). When the cut-off value was taken as ≤36.7 for the PNI score, it was found to have 73.4% sensitivity and 70.8% specificity for predicting of the disease severity and ICU admission probability was 4.4 times higher. When the cut-off value was taken as ≥813.6 for SII score, it was found to have 70.8% sensitivity and 66.0% specificity for predicting of the disease severity and ICU admission probability was six times higher.

CONCLUSION

The PNI and the SII scores are independent predictors of the prognosis and the disease severity in COVID-19 patients who require hospitalisation at the ICU.