The Prognostic Nutritional Index is associated with mortality of COVID-19 patients in Wuhan, China

The prognostic nutritional index as a risk factor for severe COVID-19 infection among hospitalized patients: A multicenter historical cohort study

INTRODUCTION

Malnutrition is a critical prognostic factor in COVID-19, affecting up to 50 % of hospitalized patients and increasing their mortality risk tenfold compared to well-nourished patients. The prognostic nutritional index (PNI) assesses nutritional and immune status and can help gauge the severity of COVID-19.

OBJECTIVE

To evaluate whether PNI was independently associated with the severity of COVID-19 infection among hospitalized patients in the United States.

METHODS

This study was a historical cohort study of adult patients with COVID-19 hospitalized in five hospitals in southeast Michigan. Data collected from the electronic medical record were analyzed using SPSS v. 29.0, and a p-value <0.05 was considered statistically significant.

RESULTS

Data were included on 286 patients, with a mean age of 58.7 ± 17.5 years, 53.5 % (153/286) female, and 48.3 % (138/286) black/African American. The most common comorbidities were hypertension (62.9 %), obesity (54.2 %) and type 2 diabetes mellitus (32.1 %). Of the 286 patients, 144 (50.3) had severe/ critical disease. Patients with severe COVID-19 had significantly lower mean PNI levels than those with mild to moderate disease (35.1 ± 5.2 vs 37.7 ± 6.4, p < 0.001). After controlling for smoking status, vaccination status, race, and home steroid use, PNI remained an independent predictor for severe/ critical COVID-19 (OR=0.92, p < 0.001).

CONCLUSIONS

This study demonstrated that PNI is an independent predictor of severe COVID-19. The PNI score can be easily calculated from routine blood tests for every patient and helps risk stratify hospitalized COVID-19 patients. Additional research is needed to confirm these results.

The ability of the prognostic nutritional index to predict short-term mortality in geriatric acute heart failure

BACKGROUND

Heart failure is a critical cardiovascular condition, necessitating comprehensive treatment approaches and contributing to elevated mortality rates. This study aimed to evaluate the effect of the prognostic nutritional index (PNI) on the prognosis of geriatric patients diagnosed with acute heart failure.

RESULTS

A total of 104 patients were included and evaluated retrospectively in this study; 57.7% of them were females, and 19.24% of the patients died. A statistically significant difference was identified between high (≥ 35.6) and low PNI (< 35.6) groups in terms of lymphocyte count, neutrophil-lymphocyte ratio, C-reactive protein, and albumin (p values: < 0.001, < 0.001, 0.011, and < 0.001, respectively). The area under the curve (AUC) value for albumin was 0.53 (95% CI: 0.30-0.83) with a cutoff value of 3.1 g/dL; for lymphocyte count, it was 0.61 (95% CI: 0.57-0.84) with a cutoff value of 0.34 × 103/µL; and for PNI, it was 0.58 (95% CI: 41.18-85.06) with a cutoff value of 34.6.

CONCLUSION

The low PNI group exhibited a significantly higher mortality rate; nonetheless, PNI alone does not hold clinical significance as a prognostic marker. However, when combined with other clinical parameters, it can contribute to a more comprehensive assessment of patients.

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.

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 prognostic value of prognostic nutritional index and renal function indicators for mortality prediction in severe COVID-19 elderly patients: A retrospective study

Identifying prognostic factors in elderly patients with severe coronavirus disease 2019 (COVID-19) is crucial for clinical management. Recent evidence suggests malnutrition and renal dysfunction are associated with poor outcome. This study aimed to develop a prognostic model incorporating prognostic nutritional index (PNI), estimated glomerular filtration rate (eGFR), and other parameters to predict mortality risk. This retrospective analysis included 155 elderly patients with severe COVID-19. Clinical data and outcomes were collected. Logistic regression analyzed independent mortality predictors. A joint predictor "L" incorporating PNI, eGFR, D-dimer, and lactate dehydrogenase (LDH) was developed and internally validated using bootstrapping. Decreased PNI (OR = 1.103, 95% CI: 0.78-1.169), decreased eGFR (OR = 0.964, 95% CI: 0.937-0.992), elevated D-dimer (OR = 1.001, 95% CI: 1.000-1.004), and LDH (OR = 1.005, 95% CI: 1.001-1.008) were independent mortality risk factors (all P < .05). The joint predictor "L" showed good discrimination (area under the curve [AUC] = 0.863) and calibration. The bootstrapped area under the curve was 0.858, confirming model stability. A combination of PNI, eGFR, D-dimer, and LDH provides useful prognostic information to identify elderly patients with severe COVID-19 at highest mortality risk for early intervention. Further external validation is warranted.

High CRP/PNI levels predict an unfavorable prognosis in severe fever with thrombocytopenia syndrome: A propensity score matching study

BACKGROUND

This study aimed to identify a novel inflammatory index and construct a nomogram for predicting in-hospital mortality due to severe fever with thrombocytopenia syndrome (SFTS).

METHODS

This cohort included 610 patients with SFTS hospitalized in Wuhan Union Hospital between March 2017 and November 2022. The ratio of C-reactive protein (CRP) to the prognostic nutritional index (PNI) was calculated and used to reflect patients' inflammatory status. Propensity score matching (PSM) was utilized to balance confounding factors between the low- and high-CRP/PNI groups. SFTS individuals from Jinyinhu Hospital were used as the validation cohort.

RESULTS

Patients with SFTS and high CRP/PNI were significantly correlated with a higher percentage of severe and critical SFTS types and higher in-hospital mortality rates than those with low CRP/PNI. CRP/PNI was the potent risk indicator for in-hospital mortality in individuals with SFTS. The CRP/PNI nomogram showed a good predictive value for in-hospital mortality in patients with SFTS. After PSM, the predictive performance of CRP/PNI for 28-day mortality was excellent. Finally, the CRP/PNI could still assess patients with SFTS at different risks based on SFTS data from another medical center.

CONCLUSION

The CPR/PNI ratio exhibited a strong positive correlation with the SFTS disease type and could predict in-hospital mortality in the early stages of SFTS. The CPR/PNI ratio could substantially help clinicians facilitate the early identification of patients with high-risk SFTS and the timely initiation of intensive therapy.

The Role of Sequentially Monitored Laboratory Values and Inflammatory Biomarkers in Assessing the Severity of COVID-19

With the onset of the pandemic in 2020, COVID-19 pneumonia has become a common cause for hospitalization and is associated with high mortality rates. Inflammatory biomarkers play a crucial role in understanding and monitoring the progression of various diseases, including COVID-19. The objective of this study was to assess the significance of sequentially monitored standard laboratory tests, including complete blood cell count, D-dimer, fibrinogen, ferritin, albumin, C-reactive protein (CRP), as well as newly calculated inflammatory biomarkers in predicting the severity and prognosis of COVID-19 pneumonia. This single-center retrospective study included 194 patients hospitalized due to COVID-19 pneumonia. Patients were grouped based on the severity of their clinical symptoms, with 134 categorized as severe disease and 60 as mild-moderate disease. The patients' demographic data and laboratory values at hospital admission and on the third day of hospitalization were comparatively evaluated. In the severe illness group, there were more complaints about shortness of breath and a significant drop in the SPO2 value was observed at the time of application (p =0.005 and p<0.001, respectively). The overall mortality rate in all patients was 9% (18/194), and all deaths occurred within the severe disease group. All laboratory parameters, with the exception of platelet count and ferritin levels, were significantly associated and correlated with the severity of the disease during the hospitalization period. Among the biomarkers, there was no significant difference in neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) on the first day, a significant increase was observed on the third day of hospitalization in the severe disease group (p=0.050 vs. 0.003 and p=0.073 vs. 0.020, respectively). No significant difference was observed only in the PNR (platelet/neutrophil ratio) value among the inflammatory biomarkers (p=0.090 vs. p=0.354). In conclusion, the SPO2 level of COVID-19 patients at admission and the subsequent laboratory parameters examined show a significant relationship with the severity of the disease. In addition, simple inflammation biomarkers derived from laboratory values have shown a very significant relationship and correlation in the diagnosis and follow-up of the disease. In both admission and follow-up evaluation, a more significant association was observed with CRP-related biomarkers such as CRP/albumin ratio and CRP/lymphocyte ratio rather than NLR and PLR, which are widely used in the literature, in showing the severity of COVID-19. In patients with pneumonia, the laboratory assessment made on the third day of hospitalization reflects the severity of the disease more clearly than on the first day.

Cancer patients with COVID-19: does prior nutritional risk associated with cancer indicate a poor prognosis for COVID-19?

OBJECTIVE

To verify whether the presence of related nutritional risk indicators prior to COVID-19 diagnosis is associated with poor survival in patients with cancer.

METHODS

We retrospectively analyzed the data of hospitalized cancer patients who tested positive for COVID-19 between March 2020 and February 2021. Nutritional risk was defined as the presence of one of the following characteristics: body mass index <20kg/m 2 , scored Patient-generated Subjective Global Assessment ≥9 points or classification B, albumin level <3.5g/dL, and C-reactive protein level ≥10mg/L, evaluated between 7 and 60 days prior to the date of patient inclusion. The endpoint measure was all-cause mortality within 30 days of COVID-19 diagnosis.

RESULTS

A total of 253 patients were included, most of whom were elderly (62.4%) and female (63.6%). Overall, 45.4% of the patients were at nutritional risk. Survival was significantly lower in patients at nutritional risk (8 days; interquartile range [IQR]: 3-29) than in patients not at nutritional risk (16 days; IQR: 6-30) (p<0.001). The presence of prior nutritional risk was associated with increased 30-day mortality (HR: 1.42; 95%CI: 1.03-1.94), regardless of age, gender, tumor site or stage, and other risk factors, and the model had good discrimination accuracy (concordance statistic: 0.744).

CONCLUSION

The presence of prior nutritional risk indicators is related to poor prognosis in patients with cancer and COVID-19, emphasizing the importance of nutritional care, notably during this pandemic.

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.

Onodera's prognostic nutritional index: Comparison of its role in the severity and outcomes of patients with COVID‑19 during the periods of alpha, delta and omicron variant predominance

Coronavirus disease 2019 (COVID-19) has posed a severe public health threat worldwide, affecting the function of multiple organs in affected individuals, in addition to respiratory function. Several strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been circulating worldwide since it first arose, with some of these having the ability to escape from natural or vaccine-mediated immunity. The Onodera's prognostic nutritional index (OPNI), which is derived from the peripheral lymphocyte count and serum albumin, has been reported to be significantly associated with a poor survival rate and post-operative complications in patients with various diseases and in some studies on patients with COVID-19. The aim of the present retrospective study was to evaluate and compare the efficacy of OPNI as a prognostic indicator in patients with COVID-19 during the periods of alpha, delta and omicron variant predominance. Adult patients who visited or were hospitalized due to SARS-CoV-2 infection were included, covering the second, third (alpha variant), fourth (delta variant) and fifth (omicron variant) pandemic waves. According to the results obtained, OPNI exhibited a statistically significant difference among patients with mild/moderate, severe and critical disease, with the lowest values observed in patients with critical disease in all the pandemic waves examined. Moreover, OPNI was found to be an independent prognostic biomarker of intubation and mortality in patients with COVID-19, according to multivariate logistic regression analysis, including as confounders an age >65 years, the male sex and the presence of comorbidities in all periods examined.

THE IMPORTANCE OF INFLAMMATORY MARKERS IN PREDICTION OF MORTALITY IN COVID-19 PATIENTS

ÖZET GİRİŞ: Çin’de 2019 Aralık ayı sonunda ortaya çıkan COVID-19 kısa sürede tüm dünyaya yayılarak Dünya Sağlık Örgütü tarafından 11 Mart 2020’de pandemi olarak kabul edilmiştir. Pandemi hala değişen varyantlarıyla ve vaka sayılarıyla güncelliğini korumaktadır. Bu çalışmada COVID-19 tanılı yatan hastalarda 1. ve 5. gün bakılan hematolojik, inflamatuvar ve biyokimyasal belirteçlerin mortaliteyi öngörmede önemini belirlemeyi amaçladık. GEREÇ ve YÖNTEM: Araştırma retrospektif ve kesitsel çalışma olarak tasarlanmıştır. Çalışmaya 1 Kasım 2020-30 Nisan 2021 tarihleri arasında Kahramanmaraş Sütçü İmam Üniversitesi Hastanesi’ne COVID-19 şüpheli semptomları ile başvuran COVID-19 RT-PCR testi ile tanısı doğrulanarak yatışı yapılan 18 yaş üstü 200 hasta dahil edilmiştir. Hastaların yaş, cinsiyet, eşlik eden komorbid hastalıkları, 1. ve 5. gün laboratuvar parametreleri ve sonlanım durumları kaydedildi. Hastalar taburcu ve ölen şeklinde iki gruba ayrıldı. BULGULAR: Çalışmaya alınan 200 hastanın %75’i (n=150) taburcu olan, %25’i ise (n=50) 28 gün içinde ölen hastalardan oluşmaktaydı. Hastaların %63.5 (n=127) erkek, %36.5’i (n=73) kadındı. Hastaların yaş ortalaması 63±17.2 yıldı. Hastaların %50.5’i hafif, %31’i orta, %18.5’i ise ağır klinik tabloya sahipti. COVID-19 hastalarının prognozunu etkileyen faktörleri belirlemek amacıyla lojistik regresyon analizi yapıldı. Prognoz ile ilişkili faktörler erkek cinsiyet, diyabetes mellitus, KOAH, hipertansiyon varlığı, ateş, nefes darlığı ve öksürük olarak bulundu. Hastaların 1. ve 5.gün bakılan lenfosit (lenfopeni), C-Reaktif Protein (CRP), Prokalsitonin (PCT),nötrofil7lenfosit oranı (NLO), platelet /lenfosit oranı (PLO), D-Dimer değerleri tanı anında ve takiplerde mortalite öngörücüsü olarak tespit edildi (p

Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of thromboembolic events 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), platelet to lymphocyte ratio (PLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI)] and the CT Severity Score in acute limb ischemia (ALI) risk, intensive unit care (ICU) admission, and mortality in COVID-19 patients.; Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients older than 18 years of age with a diagnosis of COVID-19 infection, confirmed through real time-polymerase chain reaction (RT-PCR), and admitted to the County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2020 and December 2021. Results: Non-Survivors and “ALI” patients were associated with higher incidence of cardiovascular disease [atrial fibrillation (AF) p = 0.0006 and p = 0.0001; peripheral arterial disease (PAD) p = 0.006 and p < 0.0001], and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, PLR, SII, SIRI, AISI, and the CT Severity Score independent predictor of adverse outcomes for all recruited patients (all p < 0.0001). Moreover, the presence of AF and PAD was an independent predictor of ALI risk and mortality. Conclusions: According to our findings, higher MLR, NLR, PLR, SII, SIRI, AISI, and CT Severity Score values at admission strongly predict ALI risk, ICU admission, and mortality. Moreover, patients with AF and PAD had highly predicted ALI risk and mortality but no ICU admission.

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules

Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients′ age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.

Platelet-to-Lymphocyte Ratio (PLR) Is Not a Predicting Marker of Severity but of Mortality in COVID-19 Patients Admitted to the Emergency Department: A Retrospective Multicenter Study

(1) Introduction: In the present study, we investigate the prognostic value of platelet-to-lymphocyte ratio (PLR) as a marker of severity and mortality in COVID-19 infection. (2) Methods: Between 1 March and 30 April 2020, we conducted a multicenter, retrospective cohort study of patients with moderate to severe coronavirus 19 (COVID-19), all of whom were hospitalized after being admitted to the emergency department (ED). (3) Results: A total of 1035 patients were included in our study. Neither lymphocytes, platelets or PLR were associated with disease severity. Lymphocyte count was significantly lower and PLR values were significantly higher in the group of patients who died, and both were associated with mortality in the univariate analysis (OR: 0.524, 95% CI: (0.336−0.815), p = 0.004) and (OR: 1.001, 95% CI: (1.000−1.001), p = 0.042), respectively. However, the only biological parameter significantly associated with mortality in the multivariate analysis was platelet count (OR: 0.996, 95% CI: (0.996−1.000), p = 0.027). The best PLR value for predicting mortality in COVID-19 was 356.6 (OR: 3.793, 95% CI: (1.946−7.394), p < 0.001). (4) Conclusion: A high PLR value is however associated with excess mortality.

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.

Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study

Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79-0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81-0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.

Role of platelet-to-lymphocyte count ratio (PLR), as a prognostic indicator in COVID-19: A systematic review and meta-analysis

Prognostic predictors are of paramount interest for prompt intervention and optimal utilization of the healthcare system in the ongoing context of the COVID-19 pandemic. The platelet-to-lymphocyte count ratio (PLR), has emerged as a potential tool for risk stratification of critically ill patients with sepsis. The current systematic review explores the utility of PLR as a prognostic predictor of COVID-19 patients. We screened the electronic databases until May 15, 2021 after enrolling in PROSPERO (CRD42021220269). Studies evaluating the association between PLR on admission and outcomes in terms of mortality and severity among COVID-19 patients were included. We retrieved 32 studies, with a total of 2768 and 3262 COVID-19 patients for mortality and disease severity outcomes. Deceased and critically ill patients had higher PLR levels on admission in comparison to survivors and non-severe patients (mean differences [MD] = 66.10; 95% confidence interval [CI]: 47.75-84.44; p < 0.00001 and MD = 86.74; 95% CI: 67.7-105.7; p < 0.00001, respectively). A higher level of PLR on admission in COVID-19 patients is associated with increased morbidity and mortality. However, the evidence is of low quality and further studies regarding the cut-off value of PLR are the need of the hour.

Efficacy of Ninjin'yoeito in treating severe coronavirus disease 2019 in patients in an intensive care unit

Coronavirus Disease-2019 (COVID-19), an infectious disease associated with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a global emergency with high mortality. There are few effective treatments, and many severe patients are treated in an intensive care unit (ICU). The purpose of this study was to evaluate whether the Japanese Kampo medicine ninjin'yoeito (NYT) is effective in treating ICU patients with COVID-19. Nine patients with confirmed SARS-CoV-2 infection admitted to the ICU were enrolled in this study. All patients underwent respiratory management with invasive mechanical ventilation (IMV) and enteral nutrition. Four patients received NYT (7.5 g daily) from an elemental diet tube. We retrospectively examined the prognostic nutritional index (PNI), length of IMV, length of ICU stay, length of hospital stay, rate of tracheostomy, and mortality rate. The median age of the enrolled participants was 60.0 years (4 men and 5 women). The median body mass index was 27.6. The most common comorbidity was diabetes (4 patients, 44%), followed by hypertension (3 patients, 33%) and chronic kidney disease (2 patients, 22%). The median length of IMV, ICU stay, and hospital stay were all shorter in the NYT group than in the non-NYT group (IMV; 4.0 days vs 14.3 days, ICU; 5.3 days vs 14.5 days, hospital stay; 19.9 days vs 28.2 days). In the NYT and non-NYT groups, the median PNI at admission was 29.0 and 31.2, respectively. One week after admission, the PNI was 30.7 in the NYT group and 24.4 in non-NYT group. PNI was significantly (p = 0.032) increased in the NYT group (+13.6%) than in the non-NYT group (-22.0%). The Japanese Kampo medicine NYT might be useful for treating patients with severe COVID-19 in ICU. This study was conducted in a small number of cases, and further large clinical trials are necessary.

Prognostic Nutritional Index and CRP, age, platelet count, albumin level score in predicting mortality and intensive care unit admission for COVID-19

Aim: In our study, we investigated the efficiency of the prognostic nutritional index (PNI) score and the CRP, age, platelet count, albumin level (CAPA) score predicting mortality and intensive care unit (ICU) admission in COVID-19 disease. Materials & methods: PNI and CAPA score of patients confirmed with COVID-19 calculated by using the complete blood count and biochemical parameters at admission to the hospital, in predicting the COVID-19-associated mortality and ICU admission were analyzed. Results: PNI and CAPA scores in predicting mortality were detected as AUC: 0.67 (p < 0.001), AUC: 0.71 (p < 0.001), respectively. For predicting ICU admission AUC was 0.66 (p < 0.001), AUC was 0.77 (p < 0.001), respectively. Conclusion: PNI and CAPA scores are effective scores in COVID-19, with CAPA score being better in predicting mortality and ICU admission.

Food Access, Diet Quality, and Nutritional Status of Older Adults During COVID-19: A Scoping Review

Background: COVID-19 has imposed challenges for older adults to access food, particularly in minority, lower income, and rural communities. However, the impact of COVID-19 on food access, diet quality, and nutrition of diverse older adult populations has not been systematically assessed. Objective: To examine changes in food access, diet quality, and nutritional status among older adults during the COVID-19 pandemic and the potential differential impacts of the COVID-19 pandemic on these nutrition-related outcomes using the framework of the socio-ecological model. Methods: An electronic search was conducted on 3 databases (PubMed, CINAHL, and Web of Science) on March 7, 2021. Original, peer-reviewed English-language studies published 10/1/2019-3/1/2021 were considered for which the mean age of participants was 50 years and older. In order to be considered, studies must have examined food access, food security, or nutrition constructs as an outcome. Results: The initial search yielded 13,628 results, of which 9,145 were duplicates. Of the remaining 4,483 articles, 13 articles were in scope and therefore selected in the final analysis, which can be characterized as descriptive (n = 5), analytical (n = 6), and correlational (n = 2). Studies were conducted among community-dwelling older adult populations (n = 7) as well as those temporarily residing in hospital settings (n = 6) in 10 countries. None of the in-scope studies examined the impact of food programs or specific public policies or disaggregated data by race/ethnicity. Conclusions: More research is needed to examine the impact of COVID-19 on food access/security and the differential barriers experienced by older adult populations.

[Nutritional risk and clinical outcomes in patients diagnosed with COVID-19 in a high-complexity hospital network]

INTRODUCTION

the identification of nutritional risk at hospital admission is important to establish timely interventions in the COVID-19 patient care cycle, due to a high risk of it being associated with complications.

OBJECTIVE

to determine the association between the level of nutritional risk upon admission and in-hospital mortality at 28 days in patients diagnosed with COVID-19 treated between March and October 2020 in two hospital institutions in Colombia.

METHODS

a retrospective, observational study. Hospitalized patients with a diagnosis of COVID-19 were included and assessed by the Nutrition Service using the nutritional risk identification in emergencies scale, adapted from the NRS 2002 scale. In-hospital mortality at 28 days was analyzed as the primary endpoint, and hospital stay, admission to Intensive Care Unit (ICU), and requirement for mechanical ventilation as secondary endpoints.

RESULTS

a total of 1230 patients were included, with a mean age of 65.43 ± 15.90 years, mainly men (57.1 %, n = 702). A high nutritional risk (≥ 2 points) was identified in 74.3 % (n = 914). Patients with a high nutritional risk had a greater probability of in-hospital death at 28 days (HRadj: 1.64; 95 % CI: 1.11-2.44), and a greater risk of requiring mechanical ventilation (OR = 1.78; 95 % CI: 1.11-2.86) or ICU admission (OR = 1.478; 95 % CI: 1.05-2.09), as well as hospital stay longer than 7 days (OR = 1.91; 95 % CI: 1.47-2.48).

CONCLUSIONS

patients with a diagnosis of COVID-19 at high nutritional risk had a significantly higher in-hospital mortality at 28 days and a higher probability of requiring mechanical ventilation, ICU admission, and prolonged hospital stay.

Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19.

OBJECTIVE

To evaluate the prognostic value of the NLR in patients diagnosed with COVID-19.

METHODS

We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (ie, at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs). Small study effects were assessed with the Egger's test.

RESULTS

We analysed 61 studies (n = 15 522 patients), 58 cohorts, and 3 case-control studies. An increase of one unit of NLR was associated with higher odds of severity (OR 6.22; 95%CI 4.93 to 7.84; P < .001) and higher odds of all-cause mortality (OR 12.6; 95%CI 6.88 to 23.06; P < .001). In our sensitivity analysis, we found that 41 studies with low risk of bias and moderate heterogeneity (I2  = 53% and 58%) maintained strong association between NLR values and both outcomes (severity: OR 5.36; 95% CI 4.45 to 6.45; P < .001; mortality: OR 10.42 95% CI 7.73 to 14.06; P = .005).

CONCLUSIONS

Higher values of NLR were associated with severity and all-cause mortality in hospitalised COVID-19 patients.

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.

Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis

Coronavirus disease 2019 (COVID-19) has resulted in a global pandemic, challenging both the medical and scientific community for the development of novel vaccines and a greater understanding of the effects of the SARS-CoV-2 virus. COVID-19 has been associated with a pronounced and out-of-control inflammatory response. Studies have sought to understand the effects of inflammatory response markers to prognosticate the disease. Herein, we aimed to review the evidence of 11 groups of systemic inflammatory markers for risk-stratifying patients and prognosticating outcomes related to COVID-19. Numerous studies have demonstrated the effectiveness of neutrophil to lymphocyte ratio (NLR) in prognosticating patient outcomes, including but not limited to severe disease, hospitalization, intensive care unit (ICU) admission, intubation, and death. A few markers outperformed NLR in predicting outcomes, including 1) systemic immune-inflammation index (SII), 2) prognostic nutritional index (PNI), 3) C-reactive protein (CRP) to albumin ratio (CAR) and high-sensitivity CAR (hsCAR), and 4) CRP to prealbumin ratio (CPAR) and high-sensitivity CPAR (hsCPAR). However, there are a limited number of studies comparing NLR with these markers, and such conclusions require larger validation studies. Overall, the evidence suggests that most of the studied markers are able to predict COVID-19 prognosis, however NLR seems to be the most robust marker.

Clinical significance of prognostic nutrition index in hospitalized patients with COVID-19: Results from single-center experience with systematic review and meta-analysis

BACKGROUND

We aimed to ascertain risk indicators of in-hospital mortality and severity as well as to provide a comprehensive systematic review and meta-analysis to investigate the prognostic significance of the prognostic nutrition index (PNI) as a predictor of adverse outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients.

METHODS

In this cross-sectional study, we studied patients with COVID-19 who were referred to our hospital from February 16 to November 1, 2020. Patients with either a real-time reverse-transcriptase polymerase chain reaction test that was positive for COVID-19 or high clinical suspicion based on the World Health Organization (WHO) interim guidance were enrolled. A parallel systematic review/meta-analysis (in PubMed, Embase, and Web of Science) was performed.

RESULTS

A total of 504 hospitalized COVID-19 patients were included in this study, among which 101 (20.04%) patients died during hospitalization, and 372 (73.81%) patients were categorized as severe cases. At a multivariable level, lower PNI, higher lactate dehydrogenase (LDH), and higher D-dimer levels were independent risk indicators of in-hospital mortality. Additionally, patients with a history of diabetes, lower PNI, and higher LDH levels had a higher tendency to develop severe disease. The meta-analysis indicated the PNI as an independent predictor of in-hospital mortality (odds ratio [OR] = 0.80; P < .001) and disease severity (OR = 0.78; P = .009).

CONCLUSION

Our results emphasized the predictive value of the PNI in the prognosis of patients with COVID-19, necessitating the implementation of a risk stratification index based on PNI values in hospitalized patients with COVID-19.

Comparison of systemic immune-inflammation index (SII), early warning score (ANDC) and prognostic nutritional index (PNI) in hospitalized patients with malignancy, and their influence on mortality from COVID-19

Introduction

We evaluated several biological indicators based on inflammation and/or nutritional status, such as systemic immune-inflammation index (SII), early warning score (ANDC) and prognostic nutritional index (PNI) in hospitalized COVID-19 patients with and without malignancies for a prognostic significance.

Methodology

This is a retrospective and observational study on 186 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 75 patients had various malignancies, and the rest (111), having a similar age and comorbidity profile based on propensity score matching, had no malignancy.

Results

None of the measures as neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, monocyte to lymphocyte ratio, SII, PNI or ANDC was found to be significantly different between two groups. Odds ratio for the mortality, OR 2.39 (%95 CI 1.80–3.16) was found to be significantly higher for the malignancy group, even though the duration of hospitalization was statistically similar for both groups. PNI was found to be significantly lower for deceased patients compared with survivors in the malignancy group. Contrarily, ANDC was found to be significantly higher for deceased patients in the malignancy group.

Conclusions

PNI and ANDC have independent predictive power on determining the in-hospital death in COVID-19 malignancy cases. It is suggested that ANDC seems to be a more sensitive score than SII in COVID-19 cases with malignancies.

Critical care nutrition and COVID-19: a cause of malnutrition not to be underestimated

Malnutrition in critical care is highly prevalent and well documented to have adverse implications on morbidity and mortality. During the current COVID-19 pandemic, the evolving literature has been able to identify high risk groups in whom unfavourable outcomes are more common, for example, obesity, premorbid status, male sex, members from the Black, Asian and Minority Ethnic (BAME) community and others. Nutritional status and provision precritical and pericritical phase of COVID-19 illness is gaining traction in the literature assessing how this can influence the clinical course. It is therefore of importance to understand and address the challenges present in critical care nutrition and to identify and mitigate factors contributing to malnutrition specific to this patient group. We report a case of significant disease burden and the associated cachexia and evidence of malnutrition in a young 36-year-old male with Somalian heritage with no pre-existing medical conditions but presenting with severe COVID-19 during the first wave of the pandemic (March 2020). We highlight some key nutritional challenges during the critical phase of illness signposting to some of the management instigated to counter this. These considerations are hoped to provide further insight to help continue to evolve nutritional management when treating patients with COVID-19.

Laboratory markers associated with COVID-19 progression in patients with or without comorbidity: A retrospective study

OBJECTIVES

To investigate laboratory markers for COVID-19 progression in patients with different medical conditions.

METHODS

We performed a multicenter retrospective study of 836 cases in Hubei. To avoid the collinearity among the indicators, principal component analysis (PCA) followed by partial least squares discriminant analysis (PLS-DA) was performed to obtain an overview of laboratory assessments. Multivariable logistic regression analysis and multivariable Cox proportional hazards regression analysis were respectively used to explore risk factors associated with disease severity and mortality. Survival analysis was performed in patients with the most common comorbidities.

RESULTS

Lactate dehydrogenase (LDH) and prealbumin were associated with disease severity in patients with or without comorbidities, indicated by both PCA/PLS-DA and multivariable logistic regression analysis. The mortality risk was associated with age, LDH, C-reactive protein (CRP), D-dimer, and lymphopenia in patients with comorbidities. CRP was a risk factor associated with short-term mortality in patients with hypertension, but not liver diseases; additionally, D-dimer was a risk factor for death in patients with liver diseases.

CONCLUSIONS

Lactate dehydrogenase was a reliable predictor associated with COVID-19 severity and mortality in patients with different medical conditions. Laboratory biomarkers for mortality risk were not identical in patients with comorbidities, suggesting multiple pathophysiological mechanisms following COVID-19 infection.

The Prognostic Nutritional Index is associated with mortality of COVID-19 patients in Wuhan, China

BACKGROUND

Declared as pandemic by WHO, the coronavirus disease 2019 (COVID-19) pneumonia has brought great damage to human health. The uncontrollable spread and poor progression of COVID-19 have attracted much attention from all over the world. We designed this study to develop a prognostic nomogram incorporating Prognostic nutritional index (PNI) in COVID-19 patients.

METHODS

Patients confirmed with COVID-19 and treated in Renmin Hospital of Wuhan University from January to February 2020 were included in this study. We used logistic regression analysis to find risk factors of mortality in these patients. A prognostic nomogram was constructed and receiver operating characteristics (ROC) curve was drawn to evaluate the predictive value of PNI and this prognostic model.

RESULTS

Comparison of baseline characteristics showed non-survivors had higher age (P < .001), male ratio (P = .038), neutrophil-to-lymphocyte ratio (NLR) (P < .001), platelet-to-lymphocyte ratio (PLR) (P < .001), and PNI (P < .001) than survivors. In the multivariate logistic regression analysis, independent risk factors of mortality in COVID-19 patients included white blood cell (WBC) (OR 1.285, P = .039), PNI (OR 0.790, P = .029), LDH (OR 1.011, P < .015). These three factors were combined to build the prognostic model. Area under the ROC curve (AUC) of only PNI and the prognostic model was 0.849 (95%Cl 0.811-0.888) and 0.950 (95%Cl 0.922-0.978), respectively. And calibration plot showed good stability of the prognostic model.

CONCLUSION

This research indicates PNI is independently associated with the mortality of COVID-19 patients. Prognostic model incorporating PNI is beneficial for clinicians to evaluate progression and strengthen monitoring for COVID-19 patients.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.