Mortality and Prognostic Prediction in Very Elderly Patients With Severe Pneumonia

Multiplex PCR Pneumonia Panel in Critically Ill Patients Did Not Modify Mortality: A Cohort Study

In critically ill patients, identification of the pathogen may allow for the timely adjustment of antibiotics and improved outcomes. Background/Objectives: The aim of the study was to assess whether performing a multiplex PCR pneumonia panel (PN-panel) in patients with pneumonia in the intensive care unit (ICU) had any effect on mortality or other important clinical outcomes. Methods: A retrospective cohort study was conducted on adult patients with pneumonia who required ICU admission in four institutions in Bogotá between November 2019 and June 2023. Mortality at 30 days, the length of the hospital and ICU stay, the duration of antibiotics, and their association with the PN-panel performance were evaluated using an inverse probability of the treatment weighting to adjust for covariates and potential confounders. Results: A total of 304 patients were included, including 150 with PN-panel, with a mean age of 65.0 years (SD 14.6). SARS-CoV-2 was the primary etiologic agent in 186 (61.2%) patients, and 256 (84.2%) patients had community-acquired pneumonia. No association was found between 30-day mortality and the PN-panel, with a HR of 1.14 (CI 95% 0.76-1.70), although the assessment by an infectious disease specialist was associated with a lower mortality HR of 0.29 (CI 95% 0.19-0.45). There was no association between the PN-panel and antimicrobial therapy duration or other clinical outcomes. Conclusions: The use of the PN-panel was not associated with changes in mortality, the duration of antibiotics, or hospital and ICU stays. To acquire greater rational decision-making, microbiological data produced by this test should be interpreted with aid of an antimicrobial stewardship program oriented by an infectious disease team that could take the clinical data and integrate the information provided.

Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

Pneumonia is a serious disease that can be fatal, particularly among children and the elderly. The accuracy of pneumonia diagnosis can be improved by combining artificial-intelligence technology with X-ray imaging. This study proposes X-ODFCANet, which addresses the issues of low accuracy and excessive parameters in existing deep-learning-based pneumonia-classification methods. This network incorporates a feature coordination attention module and an omni-dimensional dynamic convolution (ODConv) module, leveraging the residual module for feature extraction from X-ray images. The feature coordination attention module utilizes two one-dimensional feature encoding processes to aggregate feature information from different spatial directions. Additionally, the ODConv module extracts and fuses feature information in four dimensions: the spatial dimension of the convolution kernel, input and output channel quantities, and convolution kernel quantity. The experimental results demonstrate that the proposed method can effectively improve the accuracy of pneumonia classification, which is 3.77% higher than that of ResNet18. The model parameters are 4.45M, which was reduced by approximately 2.5 times. The code is available at https://github.com/limuni/X-ODFCANET .

Pneumonia in Geriatric Patients and Prediction of Mortality Based on the Pneumonia Severity Index (PSI), CURB-65, Frailty Index (FI), and FI-Lab21 Scores

Background Elderly individuals have higher rates of morbidity, death, and financial burden due to community-acquired pneumonia (CAP). Objectives The study aimed to assess the outcomes of geriatric pneumonia patients and the prediction of mortality based on the pneumonia severity index (PSI), CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65-year-old score), frailty index (frailty index), and FI-Lab21 (21-item frailty index based on laboratory) scores. Methods A prospective observational study was conducted on 100 elderly patients (≥ 65 years) with CAP. PSI, CURB-65, FI, and FI-Lab21 scores were determined. The outcome measures were 30-day mortality and the risk factors of mortality. The mortality predictive value of scores were compared. Results The mean age of the study subjects was 72.14 ± 6.1 years. Specifically, 76 (76%) were male, and 24 (24%) were females. During the follow-up, there was a 30-day mortality rate of 57%. On performing multivariate regression, the PSI score and severely frail were significant independent risk factors of mortality, with an odds ratio of 1.046 and 52.213, respectively. Area under the ROC curve (AUC) showed that the performance of the PSI score (AUC: 0.952; 95% CI: 0.910-0.994), CURB-65 score (AUC: 0.936; 95% CI: 0.893-0.978), and severely frail (AUC: 0.907; 95% CI: 0.851-0.962) was outstanding, while FI-Lab21 (AUC: 0.515; 95% CI: 0.400-0.631) was non-significant. Among all the parameters, the PSI score was the best predictor of mortality at the cutoff points of >121 with a diagnostic accuracy of 92%. Conclusion CAP in the elderly carries a high mortality rate. Out of PSI, CURB-65, FI, and FI-Lab21 scores, the PSI holds the best predicting ability for mortality.

Risk stratification and survival time of patients with gram-negative bacillary pneumonia in the intensive care unit

Introduction

Pneumonia is a common infection in the intensive care unit (ICU), and gram-negative bacilli are the most common bacterial cause. The purpose of the study was to investigate the risk factors for 30-day mortality in patients with gram-negative bacillary pneumonia in the ICU, construct a predictive model, and stratify patients based on risk to assess their short-term survival.

Methods

Patients admitted to the ICU with gram-negative bacillary pneumonia at Fujian Medical University Affiliated First Hospital between January 2018 and September 2020 were selected. Patients were divided into deceased and survivor groups based on whether death occurred within 30 days. Multifactorial logistic regression analysis was used to identify independent risk factors for 30-day mortality in these patients, and a predictive nomogram model was constructed based on these factors. Patients were categorized into low-, medium-, and high-risk groups according to the model's predicted probability, and Kaplan-Meier survival curves were plotted to assess short-term survival.

Results

The study included 305 patients. Lactic acid (odds ratio [OR], 1.524, 95% CI: 1.057-2.197), tracheal intubation (OR: 4.202, 95% CI: 1.092-16.169), and acute kidney injury (OR:4.776, 95% CI: 1.632-13.978) were identified as independent risk factors for 30-day mortality. A nomogram prediction model was established based on these three factors. Internal validation of the model showed a Hosmer-Lemeshow test result of X2=5.770, P=0.834, and an area under the ROC curve of 0.791 (95% CI: 0.688-0.893). Bootstrap resampling of the original data 1000 times yielded a C-index of 0.791, and a decision curve analysis indicated a high net benefit when the threshold probability was between 15%-90%. The survival time for low-, medium-, and high-risk patients was 30 (30, 30), 30 (16.5, 30), and 17 (11, 27) days, respectively, which were significantly different.

Conclusion

Lactic acid, tracheal intubation, and acute kidney injury were independent risk factors for 30-day mortality in patients in the ICU with gram-negative bacillary pneumonia. The predictive model constructed based on these factors showed good predictive performance and helped assess short-term survival, facilitating early intervention and treatment.

The relationship between lactate dehydrogenase and Apolipoprotein A1 levels in patients with severe pneumonia

Background

To investigate the relationship between lactate dehydrogenase and apolipoprotein A1 levels and the condition and prognosis of patients with severe pneumonia.

Methods

Data was collected from 204 patients with severe pneumonia who were hospitalized from January 1, 2019 to December 1, 2021 in Zhaotong First People's Hospital (respiratory intensive care unit (RICU)), and divided into survival group (160 patients) and death group (44 patients) according to their hospitalization outcome. The relationship between lactate dehydrogenase and apolipoprotein A1 levels and general information, disease, and treatment needs of patients with severe pneumonia was analyzed, and lactate dehydrogenase, apolipoprotein A1, neutrophil-to-lymphocyte ratio, hematocrit, C-reactive protein, calcitoninogen, D-dimer, Acute Physiology and Chronic Health Status Rating System II, and Pneumonia Severity Index scores were compared between the survival and death groups. The value of these indicators in determining the prognosis of patients was analyzed using subject operating characteristic (ROC) curves. Logistic regression was used to analyze the risk factors for death from severe pneumonia.

Predictive Value of Lysophosphatidylcholine for Determining the Disease Severity and Prognosis of Elderly Patients with Community-Acquired Pneumonia

Purpose

To investigate the clinical value of serum lysophosphatidylcholine (LPC) as a predictive biomarker for determining disease severity and mortality risk in hospitalized elderly patients with community-acquired pneumonia (CAP).

Methods

This prospective, single-center study enrolled 208 elderly patients, including 67 patients with severe CAP (SCAP) and 141 with non-SCAP between November 1st, 2020, and November 30th, 2021 at the Qingdao Municipal Hospital, Shandong Province, China. The demographic and clinical parameters were recorded for all the included patients. Serum LPC levels were measured on day 1 and 6 after admission using ELISA. Propensity score matching (PSM) was used to balance the baseline variables between SCAP and non-SCAP patient groups. Receiver operative characteristic (ROC) curve analysis was used to compare the predictive performances of LPC and other clinical parameters in discriminating between SCAP and non-SCAP patients and determining the 30-day mortality risk of the hospitalized CAP patients. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with SCAP. Cox proportional hazard regression analysis was used to determine if serum LPC was an independent risk factor for the 30-day mortality of CAP patients.

Results

The serum LPC levels at admission were significantly higher in the non-SCAP patients than in the SCAP patients (P = 0.011). Serum LPC level <24.36 ng/mL, and PSI score were independent risk factors for the 30-day mortality in the elderly patients with CAP. The risk of 30-day mortality in the elderly CAP patients with low serum LPC levels (< 24.36ng/mL) was >5-fold higher than in the patients with high serum LPC levels (≥ 24.36ng/mL).

Conclusion

Low serum LPC levels were associated with significantly higher disease severity and 30-day mortality in the elderly patients with CAP. Therefore, serum LPC is a promising predictive biomarker for the early identification of elderly CAP patients with poor prognosis.

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 and validation of a predictive model for 30-day mortality in patients with severe community-acquired pneumonia in intensive care units

Background

Based on the high prevalence and fatality rates associated with severe community-acquired pneumonia (SCAP), this study endeavored to construct an innovative nomogram for early identification of individuals at high risk of all-cause death within a 30-day period among SCAP patients receiving intensive care units (ICU) treatment.

Methods

In this single-center, retrospective study, 718 SCAP patients were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for the development of a predictive model. A total of 97 patients eligible for inclusion were included from Chongqing General Hospital, China between January 2020 and July 2023 for external validation. Clinical data and short-term prognosis were collected. Risk factors were determined using the least absolute shrinkage and selection operator (LASSO) and multiple logistic regression analysis. The model's performance was evaluated through area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

Eight risk predictors, including age, presence of malignant cancer, heart rate, mean arterial pressure, albumin, blood urea nitrogen, prothrombin time, and lactate levels were adopted in a nomogram. The nomogram exhibited high predictive accuracy, with an AUC of 0.803 (95% CI: 0.756-0.845) in the training set, 0.756 (95% CI: 0.693-0.816) in the internal validation set, 0.778 (95% CI: 0.594-0.893) in the external validation set concerning 30-day mortality. Meanwhile, the nomogram demonstrated effective calibration through well-fitted calibration curves. DCA confirmed the clinical application value of the nomogram.

Conclusion

This simple and reliable nomogram can help physicians assess the short-term prognosis of patients with SCAP quickly and effectively, and could potentially be adopted widely in clinical settings after more external validations.

Effect of oral probiotics on clinical efficacy and intestinal flora in elderly severe pneumonia patients

Complex microbial ecosystems in both gastrointestinal and respiratory systems have been found to have a significant impact on human health. Growing evidence has demonstrated that intestinal dysbiosis can increase vulnerability to pulmonary infections. However, changes in the composition and activity of the intestinal flora after probiotic supplementation may alter the disease state of the host. The effects of probiotics on the improvement of diseases, such as severe pneumonia (SP), in intensive care units (ICUs) remain controversial. We retrospectively included 88 patients diagnosed with severe pneumonia between April 2021 and June 2022. The patients were divided into 2 groups: a probiotic group (n = 40) and a control group (n = 48). In addition, changes in CRP, PCT, WBC, IL-6, Clostridium difficile toxin, and PSI pneumonia scores were assessed. Changes in the gut microbiome of the patients were assessed using amplicon sequencing. Compared to the control group, a significant reduction in the incidence of length of hospital stay was observed in the probiotic group, but there were no significant differences in the mortality rate, duration of fever, diarrhea, and constipation. After probiotic treatment, CRP, PCT, WBC, and PSI score were significantly lower than before, and better clinical efficacy was achieved in the probiotic group for the duration of antibiotic therapy. Gut microbiota analysis revealed that the abundance of opportunistic pathogens (e.g., Massilia) increased remarkably at the genus level in the control group, and a significant increase in Erysipelotrichaceae_ge was observed after probiotic intervention. The control group showed an increase in opportunistic pathogens (Citrobacter, Massilia) during the antibiotic treatment. Probiotics interventions inhibit the growth of opportunistic pathogens. In addition, we found that the population of butyrate-producing bacteria (e.g., Ruminococcaceae UCG-005) increased following probiotic treatment.

Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit

BACKGROUND

Scores for predicting the long-term mortality of severe pneumonia are lacking. The purpose of this study is to use machine learning methods to develop new pneumonia scores to predict the 1-year mortality and hospital mortality of pneumonia patients on admission to the intensive care unit (ICU).

METHODS

The study population was screened from the MIMIC-IV and eICU databases. The main outcomes evaluated were 1-year mortality and hospital mortality in the MIMIC-IV database and hospital mortality in the eICU database. From the full data set, we separated patients diagnosed with community-acquired pneumonia (CAP) and ventilator-associated pneumonia (VAP) for subgroup analysis. We used common shallow machine learning algorithms, including logistic regression, decision tree, random forest, multilayer perceptron and XGBoost.

RESULTS

The full data set of the MIMIC-IV database contained 4697 patients, while that of the eICU database contained 13760 patients. We defined a new pneumonia score, the "Integrated CCI-APS", using a multivariate logistic regression model including six variables: metastatic solid tumor, Charlson Comorbidity Index, readmission, congestive heart failure, age, and Acute Physiology Score III. The area under the curve (AUC) and accuracy of the integrated CCI-APS were assessed in three data sets (full, CAP, and VAP) using both the test set derived from the MIMIC-IV database and the external validation set derived from the eICU database. The AUC value ranges in predicting 1-year and hospital mortality were 0.784-0.797 and 0.691-0.780, respectively, and the corresponding accuracy ranges were 0.723-0.725 and 0.641-0.718, respectively.

CONCLUSIONS

The main contribution of this study was a benchmark for using machine learning models to build pneumonia scores. Based on the idea of integrated learning, we propose a new integrated CCI-APS score for severe pneumonia. In the prediction of 1-year mortality and hospital mortality, our new pneumonia score outperformed the existing score.

Erector spinae muscle-based nomogram for predicting in-hospital mortality among older patients with severe community-acquired pneumonia

BACKGROUND

No multivariable model incorporating erector spinae muscle (ESM) has been developed to predict clinical outcomes in older patients with severe community-acquired pneumonia (SCAP). This study aimed to construct a nomogram based on ESM to predict in-hospital mortality in patients with SCAP.

METHODS

Patients aged ≥ 65 years with SCAP were enrolled in this prospective observational study. Least absolute selection and shrinkage operator and multivariable logistic regression analyses were used to identify risk factors for in-hospital mortality. A nomogram prediction model was constructed. The predictive performance was evaluated using the concordance index (C-index), calibration curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis.

RESULTS

A total of 490 patients were included, and the in-hospital mortality rate was 36.1%. The nomogram included the following independent risk factors: mean arterial pressure, peripheral capillary oxygen saturation, Glasgow Coma Scale score (GCS), lactate, lactate dehydrogenase, blood urea nitrogen levels, and ESM cross-sectional area. Incorporating ESM into the base model with other risk factors significantly improved the C-index from 0.803 (95% confidence interval [CI], 0.761-0.845) to 0.836 (95% CI, 0.798-0.873), and these improvements were confirmed by category-free NRI and IDI. The ESM-based nomogram demonstrated a high level of discrimination, good calibration, and overall net benefits for predicting in-hospital mortality compared with the combination of confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years (CURB-65), Pneumonia Severity Index (PSI), Acute Physiology and Chronic Health Evaluation II (APACHEII), and Sequential Organ Failure Assessment (SOFA).

CONCLUSIONS

The proposed ESM-based nomogram for predicting in-hospital mortality among older patients with SCAP may help physicians to promptly identify patients prone to adverse outcomes.

TRIAL REGISTRATION

This study was registered at www.chictr.org.cn (registration number Chi CTR-2300070377).

Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study

Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854-0.917, p < 0.000), 0.912 (95% CI 0.880-0.938, p < 0.000) and 0.872 (95% CI 0.833-0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66-75/ 76-85/ over 85 years).

Association of admission lactate with mortality in adult patients with severe community-acquired pneumonia

PURPOSE

The present study was conducted to investigate the association of admission lactate with mortality in severe community-acquired pneumonia (SCAP).

METHODS

We performed a retrospective, observational, cohort study on adult SCAP patients admitted to intensive care unit (ICU) in West China Hospital of Sichuan University between December 2011 and December 2018. The primary outcome was hospital mortality. Univariate and then multivariate analysis were performed to identify independent risk factors for hospital mortality. The association of admission lactate categories with hospital mortality was examined in three logistic regression models and Kaplan-Meier plots. We also applied restricted cubic splines to estimate the potential non-linear associations.

RESULTS

In total, 2275 SCAP patients were included. Admission lactate remained a significant factor for mortality after multivariate regression (OR: 1.085; 95% CI: 1.033,1.141; by continuous variable). After lactate was categorized into quartiles and the confounders were fully adjusted, compared with the quartile 1, ORs (95% CIs) of hospital mortality for quartile 2, quartile 3 and quartile 4 were 1.001 (0.759-1.321), 1.153 (0.877-1.516) and 1.593 (1.202-2.109), respectively (P for trend =0.001). Survival curves indicated that elevated lactate was associated with poor prognosis (P < 0.001). Moreover, this association was non-linear, indicating that increased lactate has the most notable impact on mortality within the range of 1.5 to 4 mmol/L (P non-linear: 0.029 for hospital mortality; 0.004 for ICU mortality).

CONCLUSION

Elevated admission lactate has a significant, independent, and potentially non-linear association with increased mortality in SCAP patients.

Development and validation of a survival prediction model in elder patients with community-acquired pneumonia: a MIMIC-population-based study

BACKGROUND

To develop a prediction model predicting in-hospital mortality of elder patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU).

METHODS

In this cohort study, data of 619 patients with CAP aged ≥ 65 years were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001-2012 database. To establish the robustness of predictor variables, the sample dataset was randomly partitioned into a training set group and a testing set group (ratio: 6.5:3.5). The predictive factors were evaluated using multivariable logistic regression, and then a prediction model was constructed. The prediction model was compared with the widely used assessments: Sequential Organ Failure Assessment (SOFA), Pneumonia Severity Index (PSI), systolic blood pressure, oxygenation, age and respiratory rate (SOAR), CURB-65 scores using positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), area under the curve (AUC) and 95% confidence interval (CI). The decision curve analysis (DCA) was used to assess the net benefit of the prediction model. Subgroup analysis based on the pathogen was developed.

RESULTS

Among 402 patients in the training set, 90 (24.63%) elderly CAP patients suffered from 30-day in-hospital mortality, with the median follow-up being 8 days. Hemoglobin/platelets ratio, age, respiratory rate, international normalized ratio, ventilation use, vasopressor use, red cell distribution width/blood urea nitrogen ratio, and Glasgow coma scales were identified as the predictive factors that affect the 30-day in-hospital mortality. The AUC values of the prediction model, the SOFA, SOAR, PSI and CURB-65 scores, were 0.751 (95% CI 0.749-0.752), 0.672 (95% CI 0.670-0.674), 0.607 (95% CI 0.605-0.609), 0.538 (95% CI 0.536-0.540), and 0.645 (95% CI 0.643-0.646), respectively. DCA result demonstrated that the prediction model could provide greater clinical net benefits to CAP patients admitted to the ICU. Concerning the pathogen, the prediction model also reported better predictive performance.

CONCLUSION

Our prediction model could predict the 30-day hospital mortality in elder patients with CAP and guide clinicians to identify the high-risk population.

Combining a Frailty Index Based on Laboratory Data and Pneumonia Severity Assessments to Predict In-Hospital Outcomes in Older Adults with Community-Acquired Pneumonia

OBJECTIVES

Due to the increased morbidity, mortality, and cost of community-acquired pneumonia (CAP) in older people, strategies directed at improving disease evaluation and prevention are imperative. We independently compared the 30-day in-hospital mortality prediction ability of a frailty index based on laboratory data (FI-Lab) with that of the CURB-65 and the Pneumonia Severity Index (PSI) and then proposed combining them to further improve prediction efficiency.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

Patients aged ≥ 65 years (n = 2039) with CAP who were admitted to Jiangsu Provincial People's Hospital of Nanjing Medical University and Jiangsu Provincial Hospital of Chinese Medicine from January 2019 to June 2022.

MEASURES

The 29-item FI-Lab, PSI and, CURB-65 were administered at admission. We defined frailty by the cut-off value of the FI-Lab score (> 0.43). Multivariable logistic regression analysis, together with the calculation of the area under the receiver operating characteristic curve (ROC-AUC), was conducted to identify stratified risks and relationships between the three indices and 30-day mortality. Participants were divided into the following three groups based on age: 65-74 years, 75-84 years, and ≥ 85 years. Hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality due to frailty were calculated.

RESULTS

A total of 495 participants ranging from 65 to 100 years of age were ultimately included and divided into age groups (65-74 years, n = 190, 38.4%; 75-84 years, n = 183, 37.0%; ≥ 85 years, n = 122, 24.6%). A total of 142 (28.7%) of the 495 patients were defined as having frailty. All three scores tested in this study were significantly associated with 30-day mortality in the total sample. The ORs were as follows: 1.06 (95% CI: 1.03-1.09, P < 0.001) and 2.33 (95% CI: 1.26-4.31, P = 0.007) for the FI-Lab when the score was treated as a continuous and categorical variable, respectively; 1.04 (95% CI: 1.02-1.05, P < 0.001) for the PSI; and 3.70 (95% CI: 2.48-5.50, P < 0.001) for the CURB-65. In the total sample, the ROC-AUCs were 0.783 (95% CI: 0.744-0.819) for the FI-Lab, 0.812 (95% CI: 0.775-0.845) for the PSI, and 0.799 (95% CI: 0.761-0.834) for the CURB-65 (P < 0.001). The ROC-AUC slightly improved when the FI-Lab was added to the PSI (AUC 0.850, 95% CI: 0.809-0.892, P = 0.031) and to the CURB-65 (AUC 0.839, 95% CI: 0.794-0.885, P = 0.002). Older patients with frailty showed a higher risk of in-hospital mortality, with an HR of 2.25 (95% CI: 1.14-3.58, P < 0.001).

CONCLUSION AND IMPLICATIONS

The FI-Lab seems to generate simple and readily available data, suggesting that it could be a useful complement to the CURB-65 and the PSI as effective predictors of 30-day mortality due to CAP in older populations.

Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison

BACKGROUND

Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support.

METHODS

Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS).

RESULTS

Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores.

CONCLUSIONS

Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia

(1) Background: Biomarker and model development can help physicians adjust the management of patients with community-acquired pneumonia (CAP) by screening for inpatients with a low probability of cure early in their admission; (2) Methods: We conducted a 30-day cohort study of newly admitted adult CAP patients over 20 years of age. Prognosis models to predict the short-term prognosis were developed using random survival forest (RSF) method; (3) Results: A total of 247 adult CAP patients were studied and 208 (84.21%) of them reached clinical stability within 30 days. The soluble form of suppression of tumorigenicity-2 (sST2) was an independent predictor of clinical stability and the addition of sST2 to the prognosis model could improve the performance of the prognosis model. The C-index of the RSF model for predicting clinical stability was 0.8342 (95% CI, 0.8086–0.8598), which is higher than 0.7181 (95% CI, 0.6933–0.7429) of CURB 65 score, 0.8025 (95% CI, 0.7776–8274) of PSI score, and 0.8214 (95% CI, 0.8080–0.8348) of cox regression. In addition, the RSF model was associated with adverse clinical events during hospitalization, ICU admissions, and short-term mortality; (4) Conclusions: The RSF model by incorporating sST2 was more accurate than traditional methods in assessing the short-term prognosis of CAP patients.

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

BACKGROUND

Researchers have linked cardiovascular disease (CVD) with advancing age; however, how it drives disease progression in elderly severe community acquired pneumonia (SCAP) patients is still unclear. This study aims to identify leading risk predictors of in-hospital mortality in elderly SCAP patients with CVD, and construct a comprehensive nomogram for providing personalized prediction.

PATIENTS AND METHODS

The study retrospectively enrolled 2365 elderly patients identified SCAP. Among them, 413 patients were found to have CVD. The LASSO regression and multivariate logistic regression analysis were utilized to select potential predictors of in-hospital mortality in elderly SCAP patients with CVD. By incorporating these features, a nomogram was then developed and subjected to internal validations. Discrimination, calibration, and clinical use of the nomogram were assessed via C-index, calibration curve analysis, and decision plot.

RESULTS

Compared with patients without CVD, elderly SCAP patients with CVD had a significant poor outcome. Further analysis of the CVD population identified 7 independent risk factors for in-hospital mortality in elderly SCAP patients, including age, the use of vasopressor, numbers of primary symptoms, body temperature, monocyte, CRP and NLR. The nomogram model incorporated these 7 predictors showed sufficient predictive accuracy, with the C-index of 0.800 (95% CI 0.758-0.842). High C-index value of 0.781 was obtained in the internal validation via bootstrapping validation. Moreover, the calibration curve indicative a good consistency of risk prediction, and the decision curve manifested that the nomogram had good overall net benefits.

CONCLUSION

An integrated nomogram was developed to facilitate the personalized prediction of in-hospital mortality in elderly SCAP patients with CVD.

Development and Validation of a Nomogram for Predicting 28-Day Mortality on Admission in Elderly Patients with Severe Community-Acquired Pneumonia

Introduction

There were few studies on the mortality of severe community-acquired pneumonia (SCAP) in elderly people. Early prediction of 28-day mortality of hospitalized patients will help in the clinical management of elderly patients (age ≥65 years) with SCAP, but a prediction model that is reliable and valid is still lacking.

Methods

The 292 elderly patients with SCAP met the criteria defined by the American Thoracic Society from 33 hospitals in China. Clinical parameters were analyzed by the use of univariable and multivariable logistic regression analysis. A nomogram to predict the 28-day mortality in elderly patients with SCAP was constructed and evaluated using the area under the receiver operating characteristic curve (AUC) and internally verified using the Bootstrap method.

Results

A total of 292 elderly patients (227 surviving and 65 died within 28 days) were included in the analysis. Age, Glasgow score, blood platelet, and blood urea nitrogen values were found to be significantly associated with 28-day mortality in elderly patients with SCAP. The AUC of the nomogram was 0.713 and the calibration curve for 28-day mortality also showed high coherence between the predicted and actual probability of mortality.

Conclusion

This study provides a nomogram containing age, Glasgow score, blood platelet, and blood urea nitrogen values that can be conveniently used to predict 28-day mortality in elderly patients with SCAP. This model has the potential to assist clinicians in evaluating prognosis of patients with SCAP.

Sequential treatment of severe pneumonia with respiratory failure and its influence on respiratory mechanical parameters and hemodynamics

BACKGROUND

The pathophysiological characteristics of severe pneumonia complicated by respiratory failure comprise pulmonary parenchymal changes leading to ventilation imbalance, alveolar capillary injury, pulmonary edema, refractory hypoxemia, and reduced lung compliance. Prolonged hypoxia can cause acid-base balance disorder, peripheral circulatory failure, blood-pressure reduction, arrhythmia, and other adverse consequences.

AIM

To investigate sequential mechanical ventilation’s effect on severe pneumonia complicated by respiratory failure.

METHODS

We selected 108 patients with severe pneumonia complicated by respiratory failure who underwent mechanical ventilation between January 2018 and September 2020 at the Luhe Hospital’s Intensive Care Unit and divided them into sequential and regular groups according to a randomized trial, with each group comprising 54 patients. The sequential group received invasive and non-invasive sequential mechanical ventilation, whereas the regular group received invasive mechanical ventilation. Blood-gas parameters, hemodynamic parameters, respiratory mechanical parameters, inflammatory factors, and treatment outcomes were compared between the two groups before and after mechanical-ventilation treatment.

RESULTS

The arterial oxygen partial pressure and stroke volume variation values of the sequential group at 24, 48, and 72 h of treatment were higher than those of the conventional group (P < 0.05). The carbon dioxide partial pressure value of the sequential group at 72 h of treatment and the Raw value of the treatment group at 24 and 48 h were lower than those of the conventional group (P < 0.05). The pH value of the sequential group at 24 and 72 h of treatment, the central venous pressure value of the treatment at 24 h, and the Cst value of the treatment at 24 and 48 h were higher than those of the conventional group (P < 0.05). The tidal volume in the sequential group at 24 h of treatment was higher than that in the conventional group (P < 0.05), the measured values of interleukin-6 and tumor necrosis factor-α in the sequential group at 72 h of treatment were lower than those in the conventional group (P < 0.05), and the total time of mechanical ventilation in the sequential group was shorter than that in the conventional group, with a statistically significant difference (P < 0.05).

CONCLUSION

Treating severe pneumonia complicated by respiratory failure with sequential mechanical ventilation is more effective in improving respiratory system compliance, reducing inflammatory response, maintaining hemodynamic stability, and improving patient blood-gas levels; however, from this study’s perspective, it cannot reduce patient mortality.

Soft Exoskeleton Mimics Human Cough for Assisting the Expectoration Capability of SCI Patients

OBJECTIVE

This paper describes the design of a bionic soft exoskeleton and demonstrates its feasibility for assisting the expectoration function rehabilitation of patients with spinal cord injury (SCI).

METHODS

A human-robot coupling respiratory mechanic model is established to mimic human cough, and a synergic inspire-expire assistance strategy is proposed to maximize the peak expiratory flow (PEF), the key metric for promoting cough intensity. The negative pressure module of the exoskeleton is a soft "iron lung" using layer-jamming actuation. It assists inspiration by increasing insufflation to mimic diaphragm and intercostal muscle contraction. The positive pressure module exploits soft origami actuators for assistive expiration; it pressures human abdomen and bionically "pushes" the diaphragm upward.

RESULTS

The maximum increase in PEF ratios for mannequins, healthy participants, and patients with SCI with robotic assistance were 57.67%, 278.10%, and 124.47%, respectively. The soft exoskeleton assisted one tetraplegic SCI patient to cough up phlegm successfully.

CONCLUSION

The experimental results suggest that the proposed soft exoskeleton is promising for assisting the expectoration ability of SCI patients in everyday life scenarios.

SIGNIFICANCE

The proposed soft exoskeleton is promising for advancing the application field of rehabilitation exoskeletons from motor functions to respiratory functions.

Efficacy of phentolamine combined with ambroxol aerosol inhalation in the treatment of pediatric severe pneumonia and its effect on serum IL-10 and CRP levels

BACKGROUND

The aim of the present study was to determine the therapeutic effect of phentolamine combined with Ambroxol aerosol inhalation on pediatric severe pneumonia and its effect on serum interleukin-10 (IL-10) and C-reactive protein (CRP) levels.

METHODS

Eighty-five children with severe pneumonia treated in our hospital from November 2019 to November 2020 were selected as the research participants, and were divided into the routine group (n=41) and treatment group (n=44) according to odd and even admission numbers, respectively. Children in the first group received routine treatment, namely symptomatic treatment such as cough relief (e.g., expectorant) and defervescence, while those in the second group received phentolamine combined with Ambroxol aerosol inhalation. Clinical indexes of both groups before and after treatment were analyzed to determine the therapeutic effect of different treatment methods and serum IL-10 and CRP level changes.

RESULTS

There was no significant difference in general clinical data between the 2 groups (P>0.05). The duration of cough, fever, abnormal lung sound and lung shadow, and hospitalization time in the treatment group was significantly shorter than those in the routine group (P<0.001). The total clinical effective rate in the treatment group was significantly higher than that in the routine group (P<0.05). Forced vital capacity and peak expiratory flow rate levels were higher in both groups after treatment (P<0.05), and these were higher in the treatment group compared with the routine group after treatment (P<0.05). Serum IL-10 and CRP levels at T1 (2 days after treatment), T2 (5 days after treatment), and T3 (7 days after treatment) in the treatment group were significantly lower than those in the routine group (P<0.001). The total incidence of adverse reactions in the treatment group was significantly lower than that in the routine group (P<0.05).

CONCLUSIONS

Phentolamine combined with Ambroxol aerosol inhalation can significantly improve the clinical symptoms of children with severe pneumonia, reduce the body's inflammatory response, and improve lung function safely.

Development and validation of a new scoring system for prognostic prediction of community-acquired pneumonia in older adults

The discriminative power of CURB-65 for mortality in community-acquired pneumonia (CAP) is suspected to decrease with age. However, a useful prognostic prediction model for older patients with CAP has not been established. This study aimed to develop and validate a new scoring system for predicting mortality in older patients with CAP. We recruited two prospective cohorts including patients aged ≥ 65 years and hospitalized with CAP. In the derivation (n = 872) and validation cohorts (n = 1,158), the average age was 82.0 and 80.6 years and the 30-day mortality rate was 7.6% (n = 66) and 7.4% (n = 86), respectively. A new scoring system was developed based on factors associated with 30-day mortality, identified by multivariate analysis in the derivation cohort. This scoring system named CHUBA comprised five variables: confusion, hypoxemia (SpO₂ ≤ 90% or PaO₂ ≤ 60 mmHg), blood urea nitrogen ≥ 30 mg/dL, bedridden state, and serum albumin level ≤ 3.0 g/dL. With regard to 30-day mortality, the area under the receiver operating characteristic curve for CURB-65 and CHUBA was 0.672 (95% confidence interval, 0.607–0.732) and 0.809 (95% confidence interval, 0.751–0.856; P < 0.001), respectively. The effectiveness of CHUBA was statistically confirmed in the external validation cohort. In conclusion, a simpler novel scoring system, CHUBA, was established for predicting mortality in older patients with CAP.

Infections in the older population: what do we know?

The incidence of infections increases with age and results in a higher risk of morbidity and mortality. This rise is not mainly related to chronological age per se but has been linked mostly to individual factors such as immunosenescence; the presence of comorbidities; the occurrence of geriatric syndromes such as poor nutrition, polypharmacy, and cognitive disorders; and the presence of functional impairment concomitant with environmental, healthcare-related and microbiological factors such as the increasing risk of multidrug-resistant microorganisms. The geriatric concept of frailty introduces a new approach for considering the risk of infection; this concept highlights the importance of functional status and is a more comprehensive and multicomponent approach that may help to reverse the vulnerability to stress. The aim of this article is to provide some typical hallmarks of infections among older adults in comparison to younger individuals. The main differences among the older population that are presented are an increased prevalence of infections and potential risk factors, a higher risk of carrying multidrug-resistant microorganisms, an increase in barriers to a prompt diagnosis related to atypical presentations and challenges with diagnostic tools, a higher risk of under- and over-diagnosis, a worse prognosis with a higher risk of acute and chronic complications and a particular need for better communication among all healthcare sectors as they are closely linked together.