Development and Validation of Risk Prediction Model for In-hospital Mortality Among Patients Hospitalized With Acute Exacerbation Chronic Obstructive Pulmonary Disease Between 2015 and 2019

Development and Validation of an In-Hospital Mortality Prediction Model for Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Purpose

Patients with chronic obstructive pulmonary disease (COPD) often face unknown risks during acute exacerbation of the disease (AECOPD), which could potentially result in mortality. This study aimed to develop and validate a nomogram model for predicting the risk of in-hospital mortality in AECOPD patients.

Patients and Methods

Clinical data of patients hospitalized at The Second People's Hospital of Wuhu City for AECOPD between January 2013 and December 2022 were retrospectively collected. Variables underwent selection through LASSO regression and multivariable logistic regression to develop a nomogram model. The model's predictive performance was assessed using the concordance index, calibration curve, and decision curve analysis (DCA), with internal validation conducted using the bootstrap method.

Results

A total of 1224 patients were included in this study, with 98 (8%) deaths occurring during hospitalization. LASSO regression identified 11 variables, used to construct model A. Further multivariable logistic regression was conducted to select variables with P < 0.05 to establish model B. model B was selected as the final model based on discrimination, calibration, and clinical utility, encompassing variables including acute respiratory failure, lung cancer, heart rate, hemoglobin, absolute neutrophil count, serum albumin, blood urea nitrogen, and serum chloride. The nomogram model achieved a concordance index of 0.858. Internal validation of the model was conducted using the bootstrap method with 500 repetitions, resulting in a concordance index of 0.851 (95% CI: 0.805, 0.893). The calibration curve demonstrated a good fit, with a Hosmer-Lemeshow goodness-of-fit test P-value of 0.520. Moreover, DCA findings suggested patient benefit within a threshold probability range of 0.02 to 0.73, with a maximum net benefit of 0.07.

Conclusion

The model constructed in this study has good predictive performance, which helps clinical doctors identify patients at high risk of death early.

Efficacy of auricular acupressure on lung function among chronic obstructive pulmonary disease: A meta-analysis of randomised controlled trials

OBJECTIVES

To systematically evaluate the efficacy of auricular acupressure on lung function, sleep quality and quality of life in chronic obstructive pulmonary disease patients.

BACKGROUND

Auricular acupressure has been increasingly used in chronic obstructive pulmonary disease patients, such as lung function and sleep quality, but the efficacy has not yet been unified.

DESIGN

A meta-analysis of randomised controlled trials.

METHODS

Randomised controlled trials comparing auricular acupressure intervention with non-auricular acupressure intervention in chronic obstructive pulmonary disease patients were included. We searched English databases and Chinese databases from the inception to 26 December 2022. The risk of bias was assessed by the Cochrane risk of bias tool. The PRISMA statement was used to report a meta-analysis.

RESULTS

A total of 12 randomised controlled trials with 987 chronic obstructive pulmonary disease patients were included. The meta-analysis showed that auricular acupressure had significant differences in improving lung function, including FEV1 (MD = 0.29, 95% CI: 0.21 to 0.37, p < .0001), FVC (MD = 0.24, 95% CI: 0.14 to 0.34, p < .0001) and FEV1/FVC (MD = 4.70, 95% CI: 3.63 to 5.78, p < .0001). There was also a positive effect on sleep quality (MD = -0.71, 95% CI: -0.89 to -0.53, p < .0001) and quality of life (MD = -3.20, 95% CI: -3.92 to -2.49, p < .0001).

CONCLUSIONS

The results indicated auricular acupressure had a positive efficacy in chronic obstructive pulmonary disease patients to improve lung function, sleep quality and quality of life, but these results should be treated with caution due to the low quality of included studies. Future researchers need to conduct more high-quality randomised controlled trials to provide a solid basis to demonstrate the efficacy of auricular acupressure in chronic obstructive pulmonary disease patients.

RELEVANT TO CLINICAL PRACTICE

Auricular acupressure has the advantages of being non-invasive, convenient and without significant side effects. This review suggested auricular acupressure could be considered a non-pharmacological intervention for patients. Clinical nurses can teach chronic obstructive pulmonary disease patients to perform auricular acupressure to help self-manage complications.

PATIENT OR PUBLIC CONTRIBUTION

No Patient or Public Contribution.

An International Validation of the "DECAF Score" to Predict Disease Severity and Hospital Mortality in Acute Exacerbation of COPD in the UAE

The DECAF score (the Dyspnea, Eosinopenia, Consolidation, Academia, and Atrial fibrillation score) has been adopted in some hospitals to predict the severity of Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD). However, DECAF score has not been widely evaluated or used in Middle Eastern countries. The present study aimed to validate the DECAF score for predicting in-hospital mortality in patients with AECOPD in the United Arab Emirates (UAE). This was a retrospective, observational study conducted in 19 hospitals in the UAE. Data were retrieved from the electronic records of patients admitted for AECOPD in 17 hospitals across the country. Patients aged more than 35 years who were diagnosed with AECOPD were included in the study. The validation of the DECAF Score for inpatient death, 30-days death, and 90-day readmission was conducted using the Area Under the Receiver Operator curve (AUROC). The AUROCDECAF curves for inpatient death, 30-days death, and 90-day readmission were 0.8 (95% CI: 0.8-0.9), 0.8 (95% CI: 0.7-0.8), and 0.8 (95% CI: 0.8-0.8), respectively. The model was a satisfactory fit to the data (Hosmer-Lemeshow statistic = 0.195, Nagelkerke R2 = 31.7%). There were significant differences in means of length of stay across patients with different DECAF score (P = .008). Patients with a DECAF score of 6 had the highest mean length of stay, which was 29.8 ± 31.4 days. Patients with a DECAF score of 0 had the lowest mean length of stay, which was 3.6 ± 2.0 days. The DECAF score is a strong predictive tool for inpatient death, 30 days mortality and 90-day readmission in UAE hospital settings. The DECAF score is an effective tool for predicating mortality and other disease outcomes in patients with AECOPD in the UAE; hence, clinicians would be more empowered to make appropriate clinical decisions by using the DECAF score.

Global mortality and readmission rates following COPD exacerbation-related hospitalisation: a meta-analysis of 65 945 individual patients

Background

Exacerbations of COPD (ECOPD) have a major impact on patients and healthcare systems across the world. Precise estimates of the global burden of ECOPD on mortality and hospital readmission are needed to inform policy makers and aid preventive strategies to mitigate this burden. The aims of the present study were to explore global in-hospital mortality, post-discharge mortality and hospital readmission rates after ECOPD-related hospitalisation using an individual patient data meta-analysis (IPDMA) design.

Methods

A systematic review was performed identifying studies that reported in-hospital mortality, post-discharge mortality and hospital readmission rates following ECOPD-related hospitalisation. Data analyses were conducted using a one-stage random-effects meta-analysis model. This study was conducted and reported in accordance with the PRISMA-IPD statement.

Results

Data of 65 945 individual patients with COPD were analysed. The pooled in-hospital mortality rate was 6.2%, pooled 30-, 90- and 365-day post-discharge mortality rates were 1.8%, 5.5% and 10.9%, respectively, and pooled 30-, 90- and 365-day hospital readmission rates were 7.1%, 12.6% and 32.1%, respectively, with noticeable variability between studies and countries. Strongest predictors of mortality and hospital readmission included noninvasive mechanical ventilation and a history of two or more ECOPD-related hospitalisations <12 months prior to the index event.

Conclusions

This IPDMA stresses the poor outcomes and high heterogeneity of ECOPD-related hospitalisation across the world. Whilst global standardisation of the management and follow-up of ECOPD-related hospitalisation should be at the heart of future implementation research, policy makers should focus on reimbursing evidence-based therapies that decrease (recurrent) ECOPD.

A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease

OBJECTIVE

The prognosis for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not optimistic, and severe AECOPD leads to an increased risk of mortality. Prediction models help distinguish between high- and low-risk groups. At present, many prediction models have been established and validated, which need to be systematically reviewed to screen out more suitable models that can be used in the clinic and provide evidence for future research.

METHODS

We searched PubMed, EMBASE, Cochrane Library and Web of Science databases for studies on risk models for AECOPD mortality from their inception to 10 April 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Stata software (version 16) was used to synthesize the C-statistics for each model.

RESULTS

A total of 37 studies were included. The development of risk prediction models for mortality in patients with AECOPD was described in 26 articles, in which the most common predictors were age (n = 17), dyspnea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen (BUN, n = 6). The remaining 11 articles only externally validated existing models. All 37 studies were evaluated at a high risk of bias using PROBAST. We performed a meta-analysis of five models included in 15 studies. DECAF (dyspnoea, eosinopenia, consolidation, acidemia and atrial fibrillation) performed well in predicting in-hospital death [C-statistic = 0.91, 95% confidence interval (CI): 0.83, 0.98] and 90-day death [C-statistic = 0.76, 95% CI: 0.69, 0.82] and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) performed well in predicting 30-day death [C-statistic = 0.74, 95% CI: 0.70, 0.77].

CONCLUSIONS

This study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. This pooled analysis of the present study suggests that the DECAF performs well in predicting in-hospital and 90-day deaths. Yet, external validation in different populations is still needed to prove this performance.

Low diastolic blood pressure and adverse outcomes in inpatients with acute exacerbation of chronic obstructive pulmonary disease: A multicenter cohort study

BACKGROUND

Although intensively studied in patients with cardiovascular diseases (CVDs), the prognostic value of diastolic blood pressure (DBP) has little been elucidated in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study aimed to reveal the prognostic value of DBP in AECOPD patients.

METHODS

Inpatients with AECOPD were prospectively enrolled from 10 medical centers in China between September 2017 and July 2021. DBP was measured on admission. The primary outcome was all-cause in-hospital mortality; invasive mechanical ventilation and intensive care unit (ICU) admission were secondary outcomes. Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regressions were used to identify independent prognostic factors and calculate the hazard ratio (HR) and 95% confidence interval (CI) for adverse outcomes.

RESULTS

Among 13,633 included patients with AECOPD, 197 (1.45%) died during their hospital stay. Multivariable Cox regression analysis showed that low DBP on admission (<70 mmHg) was associated with increased risk of in-hospital mortality (HR = 2.16, 95% CI: 1.53-3.05, Z = 4.37, P <0.01), invasive mechanical ventilation (HR = 1.65, 95% CI: 1.32-2.05, Z = 19.67, P <0.01), and ICU admission (HR = 1.45, 95% CI: 1.24-1.69, Z = 22.08, P <0.01) in the overall cohort. Similar findings were observed in subgroups with or without CVDs, except for invasive mechanical ventilation in the subgroup with CVDs. When DBP was further categorized in 5-mmHg increments from <50 mmHg to ≥100 mmHg, and 75 to <80 mmHg was taken as reference, HRs for in-hospital mortality increased almost linearly with decreased DBP in the overall cohort and subgroups of patients with CVDs; higher DBP was not associated with the risk of in-hospital mortality.

CONCLUSION

Low on-admission DBP, particularly <70 mmHg, was associated with an increased risk of adverse outcomes among inpatients with AECOPD, with or without CVDs, which may serve as a convenient predictor of poor prognosis in these patients.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trail Registry, No. ChiCTR2100044625.

A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

Background

Post-operative mortality risk assessment for geriatric patients with hip fractures (HF) is a challenge for clinicians. Early identification of geriatric HF patients with a high risk of post-operative death is helpful for early intervention and improving clinical prognosis. However, a single significant risk factor of post-operative death cannot accurately predict the prognosis of geriatric HF patients. Therefore, our study aims to utilize a machine learning approach, random forest algorithm, to fabricate a prediction model for post-operative death of geriatric HF patients.

Methods

This retrospective study enrolled consecutive geriatric HF patients who underwent treatment for surgery. The study cohort was divided into training and testing datasets at a 70:30 ratio. The random forest algorithm selected or excluded variables according to the feature importance. Least absolute shrinkage and selection operator (Lasso) was utilized to compare feature selection results of random forest. The confirmed variables were used to create a simplified model instead of a full model with all variables. The prediction model was then verified in the training dataset and testing dataset. Additionally, a prediction model constructed by logistic regression was used as a control to evaluate the efficiency of the new prediction model.

Results

Feature selection by random forest algorithm and Lasso regression demonstrated that seven variables, including age, time from injury to surgery, chronic obstructive pulmonary disease (COPD), albumin, hemoglobin, history of malignancy, and perioperative blood transfusion, could be used to predict the 1-year post-operative mortality. The area under the curve (AUC) of the random forest algorithm-based prediction model in training and testing datasets were 1.000, and 0.813, respectively. While the prediction tool constructed by logistic regression in training and testing datasets were 0.895, and 0.797, respectively.

Conclusions

Compared with logistic regression, the random forest algorithm-based prediction model exhibits better predictive ability for geriatric HF patients with a high risk of death within post-operative 1 year.

Efficacy and safety of modified Bushen Yiqi formulas (MBYF) as an add-on to formoterol and budesonide in the management of COPD: study protocol for a multicentre, double-blind, placebo-controlled, parallel-group, randomized clinical trial: FB-MBYF Trial

Background

Inhaled glucocorticoid corticosteroid (ICS), long-acting β2-adrenoceptor agonist (LABA), and other drugs have limited therapeutic effects on COPD with significant individual differences. Traditional Chinese medicine (TCM)-modified Bushen Yiqi formula (MBYF) demonstrates advantages in COPD management in China. This study aims to evaluate the efficacy and safety of MBYF as an add-on to budesonide/formoterol in COPD patients and confirm the related genes affecting the therapeutic effect in the treatment of COPD.

Methods

In this multicentre, randomised, double-blind, placebo-controlled, parallel-group study, eligible patients with COPD will randomly receive a 360-day placebo or MBYF as an adjuvant to budesonide/formoterol in a 1:1 ratio and be followed up with every 2 months. The primary outcomes will be the frequency, times, and severity of acute exacerbation of COPD (AECOPD), COPD assessment test (CAT) score, and pulmonary function tests (PFTs). The secondary outcomes will include the modified Medical Research Council (mMRC) dyspnoea scale, 6-min walking test (6MWT), BODE index, quantitative scores of syndromes classified in TCM, inflammation indices, and hypothalamic-pituitary-adrenaline (HPA) axis function. We will also test the genotype to determine the relationship between drugs and efficacy. All the data will be recorded in case report forms (CRFs) and analysed by SPSS V.20.0.

Discussion

A randomized clinical trial design to evaluate the efficacy and safety of MBYF in COPD is described. The results will provide evidence for the combination therapy of modern medicine and TCM medicine, and individual therapy for COPD.Trial registration.

Trial registration

ID: ChiCTR1900026124, Prospective registration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06057-7.

Clinical factors predictive of appropriate treatment in COPD: a community hospital setting

Background:

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease. The appropriate treatment according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline was 19-60%. However, there are limited data on predictors of appropriate treatment in patients with COPD. This study aimed to evaluate risk factors of appropriate treatment in patients with COPD according to the GOLD guideline in a real-world community setting.

Methods:

This is a retrospective study conducted at a community hospital. Inclusion criteria were adult patients diagnosed as COPD treated at a COPD clinic. The primary outcome was the appropriate treatment, defined by correct pharmacological treatment by the GOLD guideline according to the ABCD severity assessment. Clinical predictors of appropriate treatment were executed by stepwise multivariate logistic regression analysis.

Results:

136 patients with COPD met the study criteria. Of those, 100 patients had inappropriate treatment according to the GOLD guideline. Three factors were independently associated with the appropriate treatment including number of admissions, modified Medical Research Council (mMRC) score, and CAT score. These factors had adjusted odds ratio of 3.11, 2.86, and 1.26, respectively. Causes of inappropriate treatment were unavailability of long-acting muscarinic antagonist (LAMA) (51 patients; 79.69%), treated by inhaled corticosteroid (ICS) alone (12 patients; 18.75%), and treated with only bronchodilator (1 patient; 1.56%).

Conclusions:

Appropriate COPD patients’ treatment according to the GOLD guideline was 26.47% in community setting. Factors associated with severity of COPD were associated with prescribing appropriate treatments.