Neutrophil-to-Lymphocyte Ratio Predicts Mortality in Adult Renal Transplant Recipients with Severe Community-Acquired Pneumonia

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study

BACKGROUND

Severe community-acquired pneumonia is one of the most lethal forms of CAP with high mortality. For rapid and accurate decisions, we developed a mortality prediction model specifically tailored for elderly SCAP patients.

METHODS

The retrospective study included 2365 elderly patients. To construct and validate the nomogram, we randomly divided the patients into training and testing cohorts in a 70% versus 30% ratio. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were used in the training cohort to identify independent risk factors. The robustness of this model was assessed using the C index, ROC and AUC. DCA was employed to evaluate the predictive accuracy of the model.

RESULTS

Six factors were used as independent risk factors for in-hospital mortality to construct the prediction model, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet, and BUN. The C index was 0.743 (95% CI 0.719-0.768) in the training cohort and 0.731 (95% CI 0.694-0.768) in the testing cohort. The ROC curves and AUC for the training cohort and testing cohort (AUC = 0.742 vs. 0.728) indicated a robust discrimination. And the calibration plots showed a consistency between the prediction model probabilities and observed probabilities. Then, the DCA demonstrated great clinical practicality.

CONCLUSIONS

The nomogram incorporated six risk factors, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet and BUN, which had great predictive accuracy and robustness, while also demonstrating clinical practicality at ICU admission.

Development of a new prognostic index PNPI for prognosis prediction of CKD patients with pneumonia at hospital admission

Background

The aim of this study was to investigate the relationship between pneumonia and chronic kidney disease (CKD), to elucidate potential risk factors, and to develop a new predictive model for the poor prognosis of pneumonia in CKD patients.

Method

We conducted a retrospective observational study of CKD patients admitted to Tongji Hospital between June 2012 and June 2022. Demographic information, comorbidities or laboratory tests were collected. Applying univariate and multivariate logistic regression analyses, independent risk factors associated with a poor prognosis (i.e., respiratory failure, shock, combined other organ failure, and/or death during hospitalization) for pneumonia in CKD patients were discovered, with nomogram model subsequently developed. Predictive model was compared with other commonly used pneumonia severity scores.

Result

Of 3,193 CKD patients with pneumonia, 1,013 (31.7%) met the primary endpoint during hospitalization. Risk factors predicting poor prognosis of pneumonia in CKD patients were selected on the result of multivariate logistic regression models, including chronic cardiac disease; CKD stage; elevated neutrophil to lymphocyte ratio (NLR) and D-dimer; decreased platelets, PTA, and chloride iron; and significant symptom presence and GGO presentation on CT. The nomogram model outperformed other pneumonia severity indices with AUC of 0.82 (95% CI: 0.80, 0.84) in training set and 0.83 (95% CI: 0.80, 0.86) in testing set. In addition, calibration curve and decision curve analysis (DCA) proved its efficiency and adaptability.

Conclusion

We designed a clinical prediction model PNPI (pneumonia in nephropathy patients prognostic index) to assess the risk of poor prognosis in CKD patients with pneumonia, which may be generalized after more external validation.

Predictive value of arterial blood lactate to serum albumin ratio for in-hospital mortality of patients with community-acquired pneumonia admitted to the Intensive Care Unit

OBJECTIVE

To investigate the predictive value of the arterial blood lactate to serum albumin ratio (LAR) on in-hospital mortality of patients with community-acquired pneumonia (CAP) admitted to the Intensive Care Unit (ICU).

METHODS

Clinical datasets of 1720 CAP patients admitted to ICU from MIMIC-IV database were retrospectively analyzed. Patients were randomly assigned to the training cohort (n=1204) and the validation cohort (n=516) in a ratio of 7:3. X-tile software was used to find the optimal cut-off value for LAR. The receiver operating curve (ROC) analysis was conducted to compare the performance between LAR and other indicators. Univariate and multivariate Cox regression analyses were applied to select prognostic factors associated with in-hospital mortality. Based on the observed prognostic factors, a nomogram model was created in training cohort, and the validation cohort was utilized to further validate the nomogram.

RESULTS

The optimal cut-off value for LAR in CAP patients admitted to ICU was 1.6 (the units of lactate and albumin were, respectively, 'mmol/L' and 'g/dL'). The ROC analysis showed that the discrimination abilities of LAR were superior to other indicators except Sequential Organ Failure Assessment score and Simplified acute physiology score (SAPSII), which had the same abilities. Age, mean arterial pressure, SpO2, heart rate, SAPSII score, neutrophil-to-lymphocyte ratio, and LAR were found to be independent predictors of poor overall survival in the training cohort by multivariate Cox regression analysis and were incorporated into the nomogram for in-hospital mortality as independent factors. The nomogram model, exhibiting medium discrimination, had a C-index of 0.746 (95% CI = 0.715-0.777) in the training cohort and 0.716 (95% CI = 0.667-0.765) in the validation cohort.

CONCLUSION

LAR could predict in-hospital mortality of patients with CAP admitted to ICU independently as a readily accessible biomarker. The nomogram that included LAR with other independent factors performed well in predicting in-hospital mortality.

Performance of Derived Laboratory Biomarkers with Regard to 30-Day Mortality in Kidney Transplant Recipients with COVID-19

There are limited data on the performance of laboratory-derived biomarkers in kidney transplant recipients (KTR) with COVID-19. This observational study enrolled 65 KTR with COVID-19 who were treated at the University Hospital of Split up to March 2022. Laboratory-derived biomarkers (neutrophile-to-lymphocyte (NLR) ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, De Ritis ratio, C-reactive protein (CRP)-to-albumin ratio, lactate dehydrogenase (LDH)-to-hemoglobin ratio, CRP-to-lymphocyte ratio, red cell distribution width-to-albumin ratio, platelet-to-albumin ratio, D-Dimer-to-albumin ratio, D-Dimer-to-NLR ratio, LDH-to-albumin ratio, and LDH-to-white blood cell (WBC) ratio) were calculated, and their performance with regard to 30-day mortality was determined. Mortality events occurred in 12 patients (18.5%), which was significantly associated with increased De Ritis (HR 3.83, 95% CI 1.57-9.35, p = 0.003), CRP-to-albumin (HR 1.36, 95% CI 1.13-1.64, p = 0.001), LDH-to-hemoglobin (HR 1.44, 95% CI 1.07-1.92, p = 0.015), CRP-to-lymphocyte (HR 1.03, 95% CI 1.01-1.07, p = 0.003), D-dimer-to-albumin (HR 4.94, 95% CI 1.38-7.24, p = 0.038), LDH-to-albumin (HR 1.20, 95% CI 1.05-1.36, p = 0.008), and LDH-to-WBC (HR 1.03 95% CI 1.01-1.05, p = 0.024) ratios. Out of these, the best area-under-the-curve (AUC) values were achieved with De Ritis (AUC 0.691), CRP-to-albumin (AUC 0.764), LDH-to-hemoglobin (AUC 0.877), CRP-to-lymphocyte (AUC 0.739), and LDH-to-albumin (AUC 0.827) ratios, while the best discrimination displayed LDH-to-hemoglobin ratio (Harrell's C 0.808 and Somers' D 0.616). The overall calibration was satisfactory for all models. Derived laboratory biomarkers such as the de Ritis, CRP-to-albumin, LDH-to-hemoglobin, CRP-to-lymphocyte, and LDH-to-albumin ratios show significant association and discrimination with all-cause mortality in KTR with COVID-19, suggesting its potential risk stratification role.

Steroids Therapy in Patients With Severe COVID-19: Association With Decreasing of Pneumonia Fibrotic Tissue Volume

Background

We use longitudinal chest CT images to explore the effect of steroids therapy in COVID-19 pneumonia which caused pulmonary lesion progression.

Materials and Methods

We retrospectively enrolled 78 patients with severe to critical COVID-19 pneumonia, among which 25 patients (32.1%) who received steroid therapy. Patients were further divided into two groups with severe and significant-severe illness based on clinical symptoms. Serial longitudinal chest CT scans were performed for each patient. Lung tissue was segmented into the five lung lobes and mapped into the five pulmonary tissue type categories based on Hounsfield unit value. The volume changes of normal tissue and pneumonia fibrotic tissue in the entire lung and each five lung lobes were the primary outcomes. In addition, this study calculated the changing percentage of tissue volume relative to baseline value to directly demonstrate the disease progress.

Results

Steroid therapy was associated with the decrease of pneumonia fibrotic tissue (PFT) volume proportion. For example, after four CT cycles of treatment, the volume reduction percentage of PFT in the entire lung was −59.79[±12.4]% for the steroid-treated patients with severe illness, and its p-value was 0.000 compared to that (−27.54[±85.81]%) in non-steroid-treated ones. However, for the patient with a significant-severe illness, PFT reduction in steroid-treated patients was −41.92[±52.26]%, showing a 0.275 p-value compared to −37.18[±76.49]% in non-steroid-treated ones. The PFT evolution analysis in different lung lobes indicated consistent findings as well.

Conclusion

Steroid therapy showed a positive effect on the COVID-19 recovery, and its effect was related to the disease severity.

Usage of compromised lung volume in monitoring steroid therapy on severe COVID-19

BACKGROUND

Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19) and for monitoring its progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19.

METHODS

Between February 7 and February 17, 2020, 72 patients with severe COVID-19 were retrospectively enrolled. All 300 chest CT scans from these patients were collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3-7 days); Stage 3 (8-14 days); Stage 4 (15-21 days); and Stage 5 (22-31 days). QCT was performed using a threshold-based quantitative analysis to segment the lung according to different Hounsfield unit (HU) intervals. The primary outcomes were changes in percentage of compromised lung volume (%CL, - 500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders.

RESULTS

Of 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (- 3.27% [95% CI, - 5.86 to - 0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P < 0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P < 0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (P < 0.05), but not in the low CL group.

CONCLUSIONS

Steroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247.

Clinical characteristics and risk factors associated with mortality in patients with severe community-acquired pneumonia and type 2 diabetes mellitus

BACKGROUND

The present study was performed to investigate the impacts of type 2 diabetes mellitus (T2DM) on severe community-acquired pneumonia (SCAP) and to develop a novel prediction model for mortality in SCAP patients with T2DM.

METHODS

This was a retrospective observational study conducted in consecutive adult patients with SCAP admitted to the intensive care unit (ICU) of West China Hospital, Sichuan University, China, between September 2011 and September 2019. The primary outcome was hospital mortality. A propensity score matching (PSM) analysis model with a 1:2 ratio was used for the comparisons of clinical characteristics and outcomes between T2DM and nondiabetic patients. The independent risk factors were identified via univariate and then multivariable logistic regression analysis and were then used to establish a nomogram.

RESULTS

In total, 1262 SCAP patients with T2DM and 2524 matched patients without T2DM were included after PSM. Patients with T2DM had longer ICU length of stay (LOS) (13 vs. 12 days, P = 0.016) and higher 14-day mortality (15% vs. 10.8%, P < 0.001), 30-day mortality (25.7% vs. 22.7%, P = 0.046), ICU mortality (30.8% vs. 26.5%, P = 0.005), and hospital mortality (35.2% vs. 31.0%, P = 0.009) than those without T2DM. In SCAP patients with T2DM, the independent risk factors for hospital mortality were increased numbers of comorbidities and diabetes-related complications; elevated C-reactive protein (CRP), neutrophil to lymphocyte ratio (NLR), brain natriuretic peptide (BNP) and blood lactate; as well as decreased blood pressure on admission. The nomogram had a C index of 0.907 (95% CI: 0.888, 0.927) in the training set and 0.873 (95% CI: 0.836, 0.911) in the testing set, which was superior to the pneumonia severity index (PSI, AUC: 0.809, 95% CI: 0.785, 0.833). The calibration curve and decision curve analysis (DCA) also demonstrated its accuracy and applicability.

CONCLUSIONS

SCAP patients with T2DM had worse clinical outcomes than nondiabetic patients. The nomogram has good predictive performance for hospital mortality and might be generally applied after more external validations.

Neutrophile-to-Lymphocyte Ratio as a Predictor of Mortality and Response to Treatment in Invasive Aspergillosis among Heart Transplant Recipients—Exploratory Study

Background and objective: Aspergillus pulmonary infections are potentially life-threatening complications that can occur after heart transplantation. The aim of the study was to find an easily available mortality predictor during Aspergillosis infection therapy following heart transplantation. Materials and methods: This study involved 15 heart recipients with the mean age of 55 ± 6 years who were diagnosed with invasive aspergillosis (IA) in a mean time of 80 ± 53 (19–209) days after orthotropic heart transplantation. Results: Out of fifteen patients diagnosed with IA, five died. The mean time from diagnosis to death in the deceased group was 28 ± 18 days. They were diagnosed with IA in a mean time of 80 ± 53 (19–209) days after orthotropic heart transplantation. During the initial seven days of therapy, the neutrophil to lymphocyte ratio (NLR) significantly differed between the two groups on day three and day seven, with median values of 10.8 [4.3–17.0] vs. 20.2 [17.4–116.8] (p = 0.0373) and 5.2 [3.2–8.1] vs. 32.2 [13.5–49.9] (p = 0.0101) in the survivor and the deceased group, respectively. The NLR was a significant predictor of death both on day three (cut-off point 17.2) and day seven (cut-off point 12.08) of therapy. Conclusions: Findings in our study indicate that NLR may be of predictive value in the estimation of mortality risk or response to treatment among patients with invasive aspergillosis following heart transplantation.

Two new inflammatory markers related to the CURB-65 score for disease severity in patients with community-acquired pneumonia: The hypersensitive C-reactive protein to albumin ratio and fibrinogen to albumin ratio

Background

The objective of this study was to investigate the relationship among hypersensitive C-reactive protein to albumin ratio (CAR), fibrinogen to albumin ratio (FAR), and the CURB-65 score for community-acquired pneumonia (CAP) severity.

Methods

Clinical data and laboratory indicators of 82 patients with CAP and 40 healthy subjects were retrospectively analysed. The relationship among CAR, FAR, and the severity of CAP was then analysed.

Results

CAR and FAR in patients with low-risk CAP were significantly higher than those in the normal control group (P < 0.05). CAR and FAR in patients with medium-high-risk CAP were further increased compared with those in patients with low-risk CAP (P < 0.05). CAR and FAR were positively correlated with hypersensitive C-reactive protein, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and CURB-65 scores (P < 0.05). In the receiver operating characteristic curve for predicting severe CAP, the area under the curve of combining four biomarkers (CAR + FAR + NLR + PLR) was the largest. CAR was also an independent risk factor for severe CAP (OR = 8.789, 95% CI: 1.543-50.064, P = 0.014).

Conclusions

CAR and FAR may be used as the inflammatory markers for CAP severity evaluation.

Predictive and Prognostic Utility of the Serum Level of Resistin-Like Molecule Beta for Risk Stratification in Patients with Community-Acquired Pneumonia

Despite progress in intensive care, the morbidity and mortality of patients with community-acquired pneumonia (CAP) remains high. Furthermore, the predictive and prognostic utility of resistin-like molecule beta (RELM-β) in patients with CAP is uncertain. This study investigated the role of RELM-β in patients with CAP and evaluated its correlation with disease severity and the risk of death. A prospective, multicenter study was conducted in 2017, and admission serum levels of RELM-β were detected using quantitative enzyme-linked immunosorbent assay. A total of 114 and 112 patients with severe CAP (SCAP) and non-severe CAP (NSCAP) were enrolled, respectively, with 15 healthy controls. Patients with SCAP, especially non-survivors, had significantly higher levels of serum RELM-β than patients with NSCAP. RELM-β levels positively correlated with severity scores and consistently predicted SCAP in patients with CAP (area under the curve = 0.794). Increased levels of RELM-β were closely related to the severity and prognosis of patients with CAP. The accuracy of 30-day mortality predictions of CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years) can be significantly improved when combined with RELM-β levels. The level of RELM-β can assist clinicians in risk stratification of patients with CAP in early stages.