Practical parameters that can be used for nutritional assessment in patients hospitalized in the intensive care unit with the diagnosis of chronic obstructive pulmonary disease: Prognostic nutritional index, neutrophil-to-lymphocyte, platelet-to-lymphocyte, and lymphocyte-to-monocyte ratio

Association between systemic inflammatory markers and chronic obstructive pulmonary disease: A population-based study

Objective

To investigate whether inflammatory indices, including the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), product of platelet and neutrophil count (PPN), and lymphocyte-to-monocyte ratio (LMR), correlate with chronic obstructive pulmonary disease (COPD).

Methods

This was a cross-sectional study from the National Health and Nutrition Examination Survey (NHANES) database 2007-2018. The SII, NLR, PLR, PPN and LMR were calculated based on blood cell counts and were log2-transformed. COPD was diagnosed via a questionnaire or spirometry examination. Multivariate logistic regression, sensitivity analysis, subgroup analyses, and interaction tests were performed to evaluate the relationships.

Results

23,875 participants, including 1000 COPD patients (453 diagnosed via spirometry examination, 547 diagnosed via a questionnaire), were enrolled in this study. Positive associations were observed between SII (OR 1.231, 95 % CI 1.081,1.401), NLR (OR 1.223, 95 % CI 1.064,1.405), PLR (OR 1.325, 95 % CI 1.086,1.617), PPN (OR 1.157, 95 % CI 1.031,1.298) and COPD, while a negative association was obtained between LMR and COPD (OR 0.794, 95 % CI 0.666,0.948) after covariate adjustments. When divided COPD patients into spirometry-based and questionnaire-based, only SII (OR 1.310, 95%CI 1.122,1.529), PLR (OR 1.669, 95%CI 1.272,2.191) and PPN (OR 1.218, 95%CI 1.050,1.412) significantly correlated with spirometry-based COPD, while only NLR (OR 1.303, 95%CI 1.055,1.609) and LMR (OR 0.524, 95%CI 0.406,0.677) significantly correlated with questionnaire-based COPD after covariate adjustments.

Conclusion

Significant associations are observed between different inflammation indices and COPD. Heterogeneity exists between spirometry-based and questionnaire-based COPD patients. Future studies are needed to verify the results.

A Diagnostic Nomogram for Predicting Hypercapnic Respiratory Failure in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Purpose

To develop and validate a nomogram for assessing the risk of developing hypercapnic respiratory failure (HRF) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

Patients and Methods

From January 2019 to August 2023, a total of 334 AECOPD patients were enrolled in this research. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression to determine independent predictors and develop a nomogram. This nomogram was appraised by the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow goodness-of-fit test (HL test), decision curve analysis (DCA), and clinical impact curve (CIC). The enhanced bootstrap method was used for internal validation.

Results

Sex, prognostic nutritional index (PNI), hematocrit (HCT), and activities of daily living (ADL) were independent predictors of HRF in AECOPD patients. The developed nomogram based on the above predictors showed good performance. The AUCs for the training, internal, and external validation cohorts were 0.841, 0.884, and 0.852, respectively. The calibration curves and HL test showed excellent concordance. The DCA and CIC showed excellent clinical usefulness. Finally, a dynamic nomogram was developed (https://a18895635453.shinyapps.io/dynnomapp/).

Conclusion

This nomogram based on sex, PNI, HCT, and ADL demonstrated high accuracy and clinical value in predicting HRF. It is a less expensive and more accessible approach to assess the risk of developing HRF in AECOPD patients, which is more suitable for primary hospitals, especially in developing countries with high COPD-related morbidity and mortality.

Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD

Purpose

Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) exhibit heterogeneous responses to corticosteroid treatment. We aimed to determine whether combining eosinophil levels with other routine clinical indicators can enhance the predictability of corticosteroid treatment outcomes and to come up with a scoring system.

Patients and Methods

Consecutive patients admitted with AECOPD receiving corticosteroid treatment between July 2013 and March 2022 at Beijing Chao-Yang Hospital were retrospectively analyzed. Data on patients' demographics, smoking status, hospitalization for AECOPD in the previous year, comorbidities, blood laboratory tests, in-hospital treatment and clinical outcomes were collected. Least absolute shrinkage and selection operator (LASSO) regression and backward logistic regression were used for predictor selection, and predictive nomograms were developed. The discrimination and calibration of the nomograms were assessed using the area under the receiver operating curve (AUC) and calibration plots. Internal validation was performed using the 500-bootstrap method, and clinical utility was evaluated using decision curve analysis (DCA).

Results

Among the 3254 patients included, 804 (24.7%) had treatment failure. A nomogram of eosinophils, platelets, C-reactive protein (CRP), low density lipoprotein cholesterol, prognostic nutritional index (PNI), hospitalization for AECOPD in the previous year, ischemic heart diseases and chronic hepatic disease was developed to predict treatment failure for patients with a smoking history. For patients without a smoking history, a nomogram of CRP, PNI, ischemic heart diseases and chronic hepatic disease was developed. Although the AUCs of these two nomograms were only 0.644 and 0.647 respectively, they were significantly superior to predictions based solely on blood eosinophil levels.

Conclusion

We developed easy-to-use comprehensive nomograms utilizing readily available clinical biomarkers related to inflammation, nutrition and immunity, offering modestly enhanced predictive value for treatment outcomes in corticosteroid-treated patients with AECOPD. Further investigations into novel biomarkers and additional patient data are imperative to optimize the predictive performance.

The Relationship between Nutritional Risk and the Most Common Chronic Diseases in Hospitalized Geriatric Population from Central Poland

The aim of this study was to assess the relationship between Nutrition Risk Screening 2002 (NRS-2002) and the prevalence of concomitant chronic diseases among hospitalized older adults. This study included 2122 consecutively hospitalized older participants with an average age of 82 years. The criteria to participate were the ability to communicate and give consent. In multivariate design, the prevalence of nutritional risk with at least 3 points in the NRS-2002 score was associated with the presence of stroke, atrial fibrillation, dementia and pressure ulcers. Patients with arterial hypertension, lipid disorders, osteoarthritis and urine incontinence had a significantly lower (better) NRS-2002 score. The explanation of the inverse relationship between some disorders and nutritional risk may be their occurrence in relatively earlier age and the relationship with body mass index. In conclusion, the study revealed which medical conditions coexist with the increased nutritional risk in a “real-world” hospitalized geriatric population. The hospital admission of an older subject with stroke, atrial fibrillation, dementia or pressure ulcers should primarily draw attention to the nutritional risk of the patient.

Logistic Regression Analysis of Clinical Characteristics for Differentiation of Chronic Obstructive Pulmonary Disease Severity

Background

This study aimed to investigate the predictive value of general clinical data, blood test indexes, and ventilation function test indexes on the severity of chronic obstructive pulmonary disease (COPD).

Methods

A total of 141 clinical characteristics of COPD patients admitted to our hospital were collected. A mild-to-moderate group and a severe group were classified depending on the severity of COPD, and their baseline data were compared. The predictive factors of severe COPD were analyzed by univariate and multivariate logistic regression, and the nomogram model of severe COPD was constructed. The clinical variables, including gender, height, weight, body mass index (BMI), age, course, diabetes, hypertension, smoking history, WBC, NEUT, lymphocyte count (LY), MONO, eosinophil count (EOS), PLT, mean platelet volume (MPV), platelet distribution width (PDW), partial pressure of oxygen (PaO₂), and PaCO₂, were collected.

Results

There were 67 mild-to-moderate COPD patients and 74 severe COPD patients in this study cohort. Severe COPD had a higher white blood cell count (WBC), neutrophil count (NEUT), monocyte count (MONO), platelet count (PLT), neutrophil to lymphocyte ratio (NLR), and a lower partial pressure of carbon dioxide (PaCO₂). Univariate logistic regression analysis showed that WBC, NEUT, MONO, PLT, and NLR were contributing factors of severe COPD, while PaCO₂ was an unfavorable factor of severe COPD. Enter, forward, backward, and stepwise multivariate logistic regression analyses all showed that NEUT and PLT were independent contributing factors to severe COPD. Moreover, the nomogram model had good predictive ability, with an area under the curve (AUC) of the receiver operating characteristic (ROC) curve being 0.881. Good calibration and clinical utility were validated through the calibration plot and the decision curve analysis (DCA) plot, respectively.

Conclusion

The severity of COPD was correlated with NEUT and PLT, and the nomogram model based on these factors had good predictive performance.

Predictive value of the hemoglobin-albumin-lymphocyte-platelet (HALP) index for ICU mortality in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD)

The combined index of hemoglobin, albumin, lymphocyte, and platelet (HALP) is a novel indicator reflecting systemic inflammation and nutritional status. To explore the relationship between HALP score and ICU mortality risk in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). A total of 1533 AECOPD patients from the eICU Collaborative Research Database (eICU-CRD) between 2014 and 2015 were included in this retrospective cohort study. Univariate and multivariate Cox proportional hazards models were utilized to investigate the association of HALP score, platelet-to-lymphocyte ratio (PLR) score, and lymphocyte-to-monocyte ratio (LMR) score with the ICU mortality risk in patients with AECOPD. Stratified analyses were performed based on patients' ICU admission type, body mass index (BMI), and Acute Physiology, Age and Chronic Health Evaluation IV (APACHE IV) score. Of these 1533 AECOPD patients, 123 (8.00%) patients died in the ICU. Low HALP score [hazard ratio (HR) = 1.69; 95% confidence interval (CI) 1.14-2.53] and low LMR score (HR = 1.60; 95% CI 1.07-2.39) were associated with an increased ICU mortality risk in patients with AECOPD after adjusting for all confounders. Stratified analyses indicated that low HALP score were still associated with a higher ICU mortality risk in patients admitted to ICU by emergency (HR = 1.81; 95% CI 1.11-2.96), obese patients (HR = 2.81; 95% CI 1.29-6.10), and patients with low APACHE scores (HR = 2.87; 95% CI 1.75-4.69). Low HALP score was associated with an increased risk of ICU mortality in patients with AECOPD, suggesting that the HALP score may be a novel prognostic predictor in patients with AECOPD.

The Role of the Pediatric Yorkhill Malnutrition Score (PYMS), Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in Malnutrition Prediction of Hospitalized Children

Malnutrition in hospitalized children represents a significant burden with occasionally detrimental consequences. In this retrospective analysis of pediatric patients aged one to 16 years old, who were hospitalized in the children’s cantonal hospital of Aarau, Switzerland, we investigated the utilization of PYMS (Pediatric Yorkhill Malnutrition Score) as a routine screening tool for malnutrition in pediatric inpatients. Additionally, we explored the correlation between PYMS and NLR (neutrophil−lymphocyte ratio) and PLR (platelet−lymphocyte ratio), which are two novel biomarkers. Various parameters were analyzed from the medical records of the patients. Most of the sample (n = 211, 77.3%) was characterized by a low PYMS of 0−1 point. Greater NLR and PLR values were significantly associated with greater PYMS (p = 0.030 and p = 0.004, respectively). ROC (receiver operating characteristic curves) analysis revealed that PLR had a significant predictive ability for having PYMS > 1 (AUC = 0.59; 95% CI: 0.51−0.68; p = 0.024). The optimal cut-off was 151 with sensitivity of 51.6% (95% CI: 38.6−64.5%) and specificity of 67.3% (95% CI: 60.5−73.6%). Furthermore, 37% of the children (n = 101) yielded a PLR over 151. Our results support a promising value of PLR as a predictive marker for moderate to severe malnutrition in hospitalized children.