Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal

Reproducibility of prediction models in health services research

The field of health services research studies the health care system by examining outcomes relevant to patients and clinicians but also health economists and policy makers. Such outcomes often include health care spending, and utilization of care services. Building accurate prediction models using reproducible research practices for health services research is important for evidence-based decision making. Several systematic reviews have summarized prediction models for outcomes relevant to health services research, but these systematic reviews do not present a thorough assessment of reproducibility and research quality of the prediction modelling studies. In the present commentary, we discuss how recent advances in prediction modelling in other medical fields can be applied to health services research. We also describe the current status of prediction modelling in health services research, and we summarize available methodological guidance for the development, update, external validation and systematic appraisal of prediction models.

Deep Learning Prediction of Survival in Patients with Chronic Obstructive Pulmonary Disease Using Chest Radiographs

Background Preexisting indexes for predicting the prognosis of chronic obstructive pulmonary disease (COPD) do not use radiologic information and are impractical because they involve complex history assessments or exercise tests. Purpose To develop and to validate a deep learning-based survival prediction model in patients with COPD (DLSP) using chest radiographs, in addition to other clinical factors. Materials and Methods In this retrospective study, data from patients with COPD who underwent postbronchodilator spirometry and chest radiography from 2011-2015 were collected and split into training (n = 3475), validation (n = 435), and internal test (n = 315) data sets. The algorithm for predicting survival from chest radiographs was trained (hereafter, DLSP_CXR), and then age, body mass index, and forced expiratory volume in 1 second (FEV₁) were integrated within the model (hereafter, DLSP_integ). For external test, three independent cohorts were collected (n = 394, 416, and 337). The discrimination performance of DLSP_CXR was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) at 5-year survival. Goodness of fit was assessed by using the Hosmer-Lemeshow test. Using one external test data set, DLSP_integ was compared with four COPD-specific clinical indexes: BODE, ADO, COPD Assessment Test (CAT), and St George's Respiratory Questionnaire (SGRQ). Results DLSP_CXR had a higher performance at predicting 5-year survival than FEV₁ in two of the three external test cohorts (TD AUC: 0.73 vs 0.63 [P = .004]; 0.67 vs 0.60 [P = .01]; 0.76 vs 0.77 [P = .91]). DLSP_CXR demonstrated good calibration in all cohorts. The DLSP_integ model showed no differences in TD AUC compared with BODE (0.87 vs 0.80; P = .34), ADO (0.86 vs 0.89; P = .51), and SGRQ (0.86 vs 0.70; P = .09), and showed higher TD AUC than CAT (0.93 vs 0.55; P < .001). Conclusion A deep learning model using chest radiographs was capable of predicting survival in patients with chronic obstructive pulmonary disease. © RSNA, 2022 Online supplemental material is available for this article.

Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury

BACKGROUND

Prediction modeling studies often have methodological limitations, which may compromise model performance in new patients and settings. We aimed to examine the relation between methodological quality of model development studies and their performance at external validation.

METHODS

We systematically searched for externally validated multivariable prediction models that predict functional outcome following moderate or severe traumatic brain injury. Risk of bias and applicability of development studies was assessed with the Prediction model Risk Of Bias Assessment Tool (PROBAST). Each model was rated for its presentation with sufficient detail to be used in practice. Model performance was described in terms of discrimination (AUC), and calibration. Delta AUC (dAUC) was calculated to quantify the percentage change in discrimination between development and validation for all models. Generalized estimation equations (GEE) were used to examine the relation between methodological quality and dAUC while controlling for clustering.

RESULTS

We included 54 publications, presenting ten development studies of 18 prediction models, and 52 external validation studies, including 245 unique validations. Two development studies (four models) were found to have low risk of bias (RoB). The other eight publications (14 models) showed high or unclear RoB. The median dAUC was positive in low RoB models (dAUC 8%, [IQR - 4% to 21%]) and negative in high RoB models (dAUC - 18%, [IQR - 43% to 2%]). The GEE showed a larger average negative change in discrimination for high RoB models (- 32% (95% CI: - 48 to - 15) and unclear RoB models (- 13% (95% CI: - 16 to - 10)) compared to that seen in low RoB models.

CONCLUSION

Lower methodological quality at model development associates with poorer model performance at external validation. Our findings emphasize the importance of adherence to methodological principles and reporting guidelines in prediction modeling studies.

The development of the ADO-SQ model to predict 1-year mortality in patients with COPD

BACKGROUND

Goals of end-of-life care must be adapted to the needs of patients with chronic obstructive pulmonary disease (COPD) who are in the last phase of life. However, identification of those patients is limited by moderate performances of existing prognostic models and by limited validation of the often-recommended surprise question.

AIM

To develop a clinical prediction model to predict 1-year mortality in patients with COPD.

DESIGN

Prospective study using logistic regression to develop a model in two steps: (1) external validation of the ADO, BODEX, or CODEX models (A = age; B = body mass index; C = comorbidity; D = dyspnea; EX = exacerbations; O = airflow obstruction); (2) updating of best performing model and extending it with the surprise question. Discriminative performance of the new model was assessed using internal-external validation and measured with area under the curve (AUC). A nomogram and web application were developed.

SETTINGS/PARTICIPANTS

Patients with COPD from five hospitals (September-November 2017).

RESULTS

Of the 358 included patients (median age 69.5 years, 50% male), 63 (17%) died within a year. The ADO index (AUC 0.73) had the best discriminative ability compared to the BODEX (AUC 0.71) or CODEX (AUC 0.68), and was extended with the surprise question. The resulting ADO-surprise question (SQ) model had an AUC of 0.79.

CONCLUSION

The ADO-SQ model offers improved discriminative performance for predicting 1-year mortality compared to the surprise question, ADO, BODEX, or CODEX. A user-friendly nomogram and web application (https://dnieboer.shinyapps.io/copd) were developed. Further external validation of the ADO-SQ in patient groups is needed.

“NEWS2” as an Objective Assessment of Hospitalised COPD Exacerbation Severity

Introduction

There is currently no accepted way to risk-stratify hospitalised exacerbations of chronic obstructive pulmonary disease (COPD). We hypothesised that the revised UK National Early Warning Score (NEWS2) calculated at admission would predict inpatient mortality, need for non-invasive ventilation (NIV) and length-of-stay.

Methods

We included data from 52,284 admissions for exacerbation of COPD. Data were divided into development and validation cohorts. Logistic regression was used to examine relationships between admission NEWS2 and outcome measures. Predictive ability of NEWS2 was assessed using area under receiver operating characteristic curves (AUC). We assessed the benefit of including other baseline data in the prediction models and assessed whether these variables themselves predicted admission NEWS2.

Results

53% of admissions had low risk, 24% medium risk and 23% a high risk NEWS2 in the development cohort. The proportions dying as an inpatient were 2.2%, 3.6% and 6.5% by NEWS2 risk category, respectively. The proportions needing NIV were 4.4%, 9.2% and 18.0%, respectively. NEWS2 was poorly predictive of length-of-stay (AUC: 0.59[0.57–0.61]). In the external validation cohort, the AUC (95% CI) for NEWS2 to predict inpatient death and need for NIV were 0.72 (0.68–0.77) and 0.70 (0.67–0.73). Inclusion of patient demographic factors, co-morbidity and COPD severity improved model performance. However, only 1.34% of the variation in admission NEWS2 was explained by these baseline variables.

Conclusion

The generic NEWS2 risk assessment tool, readily calculated from simple physiological data, predicts inpatient mortality and need for NIV (but not length-of-stay) at exacerbations of COPD. NEWS2 therefore provides a classification of hospitalised COPD exacerbation severity.

Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review

BACKGROUND

With rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending.

METHODS

We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual's health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g., genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field.

DISCUSSION

Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending.

Association Between Extracellular Superoxide Dismutase Activity and 1-Year All-Cause Mortality in Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Prospective Cohort Study

Background and Objectives

Accumulating evidence suggests that oxidative stress is involved in the development of chronic obstructive pulmonary disease (COPD) and its progression. Activity of extracellular superoxide dismutase (ecSOD), the only extracellular enzyme eliminating superoxide radicals, has been reported to decline in acute exacerbations of COPD (AECOPD). However, the association between serum ecSOD activity and 1-year all-cause mortality in AECOPD patients remains unclear. The objective of our study was to explore the usefulness of ecSOD activity on admission in AECOPD as an objective predictor for 1-year all-cause mortality.

Methods

We measured serum ecSOD activity in AECOPD patients on admission in a prospective cohort study. We also recorded their laboratory and clinical data. Multivariate Cox regression was used to analyze the association between ecSOD activity and the risk of 1-year all-cause mortality. Restricted cubic spline curves were used to visualize the relationship between ecSOD activity and the hazard ratio of 1-year all-cause mortality.

Results

A total of 367 patients were followed up for 1 year, and 29 patients died during a 1-year follow-up period. Compared with survivors, the non-survivors were older (79.52 ± 8.39 vs. 74.38 ± 9.34 years old, p = 0.004) and had increased levels of tobacco consumption (47.07 ± 41.67 vs. 33.83 ± 31.79 pack-years, p = 0.037). Having an ecSOD activity ≤ 98.8 U/ml was an independent risk factor of 1-year all-cause mortality after adjustment for baseline differences, clinical variables and comorbidities [hazard ratio = 5.51, 95% confidence interval (CI): 2.35–12.95, p &lt; 0.001].

Conclusion

Lower serum ecSOD activity was a strong and independent predictor of 1-year all-cause mortality in AECOPD patients.

Improving the Reliability Between the BODE Index and the BODS Index in Which the 6-Min Walk Test Was Replaced with the Five-Repetition Sit-to-Stand Test

Purpose

The BODS index has been confirmed to have predictive properties similar to the original BODE index for mortality in COPD. We evaluated the agreement between the BODS index and the BODE and explored with an updated BODS how this agreement could be improved and its ability to correctly discriminate individual participants’ mortality in a prospective cohort study.

Patients and Methods

We included prospectively a consecutive sample of 137 patients with COPD, between 40 and 80 years, during 2014 and followed for 5 years (2014–2019) in the Pneumology section of a public university hospital in Spain. They participated in the baseline data collection, which included BODE- and BODS-related measurements and prognostic factors, and were followed up for 5-year mortality. We used Bland–Altman plots and the kappa coefficient to analyze the agreement between both the original and updated BODS and the BODE index, and we used the areas under ROC curves (AUC) to compare their discriminative abilities for 5-year all-cause mortality.

Results

The original BODS index scores and quartiles had weak agreement with the BODE index, and our updated BODS strengthened these agreements (a small, statistically nonsignificant mean bias [<0.03] with LoAs<2 points, and a substantial Kappa coefficient [k =0.63; IC 95%: 0.53–0.73]). In addition, the updated BODS index scores had better summarized ability than the BODS index in discriminating participants’ mortality during the following 5 years (AUC: 0.768 versus 0.736; p=0.04).

Conclusion

The updated BODS index scores and quartiles may provide prognostic information similar to that provided by the BODE index in COPD. Future research should focus on index improvement through external validation, as well as the assessment of safety and effectiveness in clinical practice by means of impact studies.

Scoring systems for the management of oncological hepato-pancreato-biliary patients

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

Comparison of Clinical Characteristics and Short-Term Prognoses Within Hospitalized Chronic Obstructive Pulmonary Disease Patients Comorbid With Asthma, Bronchiectasis, and Their Overlaps: Findings From the ACURE Registry

Introduction

Real-world evidence and comparison among commonly seen chronic obstructive pulmonary disease (COPD) phenotypes, i.e., asthma–COPD overlap (ACO), bronchiectasis–COPD overlap (BCO), and their coexistence (ABCO) have not been fully depicted, especially in Chinese patients.

Methods

Data were retrieved from an ongoing nationwide registry in hospitalized patients due to acute exacerbation of COPD in China (ACURE).

Results

Of the eligible 4,813 patients with COPD, 338 (7.02%), 492 (10.22%), and 63 (1.31%) were identified as ACO, BCO, and ABCO phenotypes, respectively. Relatively, the ABCO phenotype had a younger age with a median of 62.99 years [interquartile range (IQR): 55.93–69.48] and the COPD phenotype had an older age with a median of 70.15 years (IQR: 64.37–76.82). The BCO and COPD phenotypes were similar in body mass index with a median of 21.79 kg/m² (IQR: 19.47–23.97) and 21.79 kg/m² (IQR: 19.49–24.22), respectively. The COPD phenotype had more male gender (79.90%) and smokers (71.12%) with a longer history of smoking (median: 32.45 years, IQR: 0.00–43.91). The ACO and ABCO phenotypes suffered more prior allergic episodes with a proportion of 18.05 and 19.05%, respectively. The ACO phenotype exhibited a higher level of eosinophil and better lung reversibility. Moreover, the four phenotypes showed no significant difference neither in all-cause mortality, intensive care unit admission, length of hospital stay, and COPD Assessment Test score change during the index hospitalization, and nor in the day 30 outcomes, i.e., all-cause mortality, recurrence of exacerbation, all-cause, and exacerbation-related readmission.

Conclusions

The ACO, BCO, ABCO, and COPD phenotypes exhibited distinct clinical features but had no varied short-term prognoses. Further validation in a larger sample is warranted.

Mastering Prognostic Tools: An Opportunity to Enhance Personalized Care and to Optimize Clinical Outcomes in Physical Therapy

In health care, clinical decision making is typically based on diagnostic findings. Rehabilitation clinicians commonly rely on pathoanatomical diagnoses to guide treatment and define prognosis. Targeting prognostic factors is a promising way for rehabilitation clinicians to enhance treatment decision-making processes, personalize rehabilitation approaches, and ultimately improve patient outcomes. This can be achieved by using prognostic tools that provide accurate estimates of the probability of future outcomes for a patient in clinical practice. Most literature reviews of prognostic tools in rehabilitation have focused on prescriptive clinical prediction rules. These studies highlight notable methodological issues and conclude that these tools are neither valid nor useful for clinical practice. This has raised the need to open the scope of research to understand what makes a quality prognostic tool that can be used in clinical practice. Methodological guidance in prognosis research has emerged in the last decade, encompassing exploratory studies on the development of prognosis and prognostic models. Methodological rigor is essential to develop prognostic tools, because only prognostic models developed and validated through a rigorous methodological process should guide clinical decision making. This Perspective argues that rehabilitation clinicians need to master the identification and use of prognostic tools to enhance their capacity to provide personalized rehabilitation. It is time for prognosis research to look for prognostic models that were developed and validated following a comprehensive process before being simplified into suitable tools for clinical practice. New models, or rigorous validation of current models, are needed. The approach discussed in this Perspective offers a promising way to overcome the limitations of most models and provide clinicians with quality tools for personalized rehabilitation approaches.

IMPACT

Prognostic research can be applied to clinical rehabilitation; this Perspective proposes solutions to develop high-quality prognostic models to optimize patient outcomes.

Associations with Post-Consultation Health-Status in Primary Care Managed Acute Exacerbation of COPD

Background

It has been demonstrated that antibiotic prescribing for Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) can be safely reduced in primary care when general practitioners have access to C-reactive protein (CRP) rapid testing.

Aim

To investigate the factors associated with post-consultation COPD health status in patients presenting with AECOPD in this setting.

Design and Setting

A cohort study of patients enrolled in a randomised controlled trial. Patients aged 40+ years with a clinical diagnosis of COPD who presented in primary care across England and Wales with an AECOPD were included.

Methods

Participants were contacted for follow-up at one- and two-weeks by phone and attended the practice four weeks after the index consultation. The outcome of interest was the Clinical COPD Questionnaire (CCQ) score. Multivariable multilevel linear regression models fitted to examine the factors associated with COPD health status in the four-weeks following consultation for an AECOPD.

Results

A total of 649 patients were included, with 1947 CCQ total scores analysed. Post-consultation CCQ total scores were significantly higher (worse) in participants with diabetes (adjusted mean difference [AMD]=0.26; 95% confidence interval (CI) 0.08–0.45), obese patients compared to those with normal body mass index (AMD = 0.25, 95% CI 0.07–0.43), and those who were prescribed oral antibiotics in the prior 12 months (AMD = 0.26; 95% CI 0.11–0.41), but only the two latter associations remained after adjusting for other sociodemographic variables.

Conclusion

COPD health status was worse in the four weeks following primary care consultation for AECOPD in patients with obesity and those prescribed oral antibiotics in the preceding year.

Impact of the iCODEX tool in routine clinical practice in Spain

INTRODUCTION

The prognosis of COPD patients can be calculated using multidimensional indexes that improve the predictive capacity of the individual variables. The CODEX index can be calculated using iCODEX, a digital support tool available on the web and in an app. The aim of this study was to evaluate how the usefulness and applicability of iCODEX and its recommendations in routine clinical practice are perceived by specialists in internal medicine, pneumology, and primary care.

METHODS

A cross-sectional study was conducted from November 2019 to February 2020 with the participation of specialists in internal medicine, primary care, and pneumology. All respondents completed a survey consisting of 104 questions on their perception of the iCODEX tool.

RESULTS

Overall, 335 physicians responded. Of these, 95.2% had no difficulty accessing the tool and 83.1% were quite or very satisfied with it. Regarding the applicability and implementation of iCODEX recommendations in routine clinical practice, respondents reported that the recommendations were generally applicable: most questions obtained a median score of ≥ 4 out of 5. The recommendations with the greatest expected clinical benefit are listed.

CONCLUSIONS

Our study shows that the iCODEX tool is easy for participating specialists to use and identifies the recommendations that have the greatest clinical impact in areas such as lung obstruction, severe exacerbations, exercise, smoking, and patient follow-up.

Development and validation of a prediction index for recent mortality in advanced COPD patients

The primary barrier to initiating palliative care for advanced COPD patients is the unpredictable course of the disease. We enroll 752 COPD patients into the study and validate the prediction tools for 1-year mortality using the current guidelines for palliative care. We also develop a composite prediction index for 1-year mortality and validate it in another cohort of 342 patients. Using the current prognostic models for recent mortality in palliative care, the best area under the curve (AUC) for predicting mortality is 0.68. Using the Modified Medical Research Council dyspnea score and oxygen saturation to define the combined dyspnea and oxygenation (DO) index, we find that the AUC of the DO index is 0.84 for predicting mortality in the validated cohort. Predictions of 1-year mortality based on the current palliative care guideline for COPD patients are poor. The DO index exhibits better predictive ability than other models in the study.

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.

Objective

The aim of this study is to develop a more accurate model to predict severe COPD exacerbations.

Methods

We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.

Results

The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).

Conclusions

Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Individual participant data meta-analysis with mixed-effects transformation models

One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most important characteristics of the IPD. We present a model class that incorporates general normally distributed random effects into linear transformation models. We discuss extensions to model between-study heterogeneity in baseline risks and covariate effects and also relax the assumption of proportional hazards. Within the proposed framework, data with arbitrary random censoring patterns can be handled. The accompanying \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\textsf{R}$\end{document} package tramME utilizes the Laplace approximation and automatic differentiation to perform efficient maximum likelihood estimation and inference in mixed-effects transformation models. We compare several variants of our model to predict the survival of patients with chronic obstructive pulmonary disease using a large data set of prognostic studies. Finally, a simulation study is presented that verifies the correctness of the implementation and highlights its efficiency compared to an alternative approach.

Development and Validation of Prognostic Models for Oral Squamous Cell Carcinoma: A Systematic Review and Appraisal of the Literature

(1) Background: An accurate prediction of cancer survival is very important for counseling, treatment planning, follow-up, and postoperative risk assessment in patients with Oral Squamous Cell Carcinoma (OSCC). There has been an increased interest in the development of clinical prognostic models and nomograms which are their graphic representation. The study aimed to revise the prognostic performance of clinical-pathological prognostic models with internal validation for OSCC. (2) Methods: This systematic review was performed according to the Cochrane Handbook for Diagnostic Test Accuracy Reviews chapter on searching, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, and the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). (3) Results: Six studies evaluating overall survival in patients with OSCC were identified. All studies performed internal validation, while only four models were externally validated. (4) Conclusions: Based on the results of this systematic review, it is possible to state that it is necessary to carry out internal validation and shrinkage to correct overfitting and provide an adequate performance for optimism. Moreover, calibration, discrimination and nonlinearity of continuous predictors should always be examined. To reduce the risk of bias the study design used should be prospective and imputation techniques should always be applied to handle missing data. In addition, the complete equation of the prognostic model must be reported to allow updating, external validation in a new context and the subsequent evaluation of the impact on health outcomes and on the cost-effectiveness of care.

Impact of Lung Function and Exacerbations on Health-Related Quality of Life in COPD Patients Within One Year: Real-World Analysis Based on Claims Data

PURPOSE

Real-world evidence on the impact of forced expiratory volume in one second (FEV1) and exacerbations on health-related quality of life (HRQoL) in patients with chronic obstructive pulmonary disease (COPD) is sparse especially with regard to GOLD ABCD groups. This study investigates how changes in FEV1 and exacerbations affect generic and disease-specific HRQoL in COPD patients over one year.

METHODS

Using German claims data and survey data, we classified 3016 COPD patients and analyzed their health status by GOLD groups AB and CD. HRQoL was measured with the disease-specific COPD assessment test (CAT) and the visual analog scale (VAS) from the generic Euro-Qol 5D-5L. We applied change score models to assess associations between changes in FEV1 (≥100 mL decrease/no change/≥100 mL increase) or the development of severe exacerbations with change in HRQoL.

RESULTS

FEV1 decrease was associated with a significant but not minimal important difference (MID) deterioration in disease-specific HRQoL (mean change [95% CI]: CAT +0.74 [0.15 to 1.33]), while no significant change was observed in the generic VAS. Experiencing at least one severe exacerbation also had a significant impact on CAT deterioration (+1.58 [0.52 to 2.64]), but again not on VAS. Here, GOLD groups AB showed not only a statistically but also a clinically relevant MID deterioration in CAT (+2.1 [0.88 to 3.32]). These particular patient groups were further characterized by a higher probability of being male, having a higher mMRC and Charlson index, and a lower probability of having higher FEV1 or BMI values.

CONCLUSION

FEV1 decline and the occurrence of ≥1 severe exacerbation are significantly associated with overall deterioration in disease-specific HRQoL. Preventing severe exacerbations particularly in patients without previous severe exacerbations (ABCD groups A and B) may help to stabilize the key patient-reported outcome HRQoL.

Predictors of impaired pulmonary function in people living with HIV in an urban African setting

Background

Studies have associated HIV with an increased risk of obstructive lung disease (OLD).

Objectives

We aimed to identify the predictive factors for impaired lung function in an urban, African, HIV-positive population.

Method

A cross-sectional study was performed in Johannesburg, South Africa, from July 2016 to November 2017. A questionnaire was administered and pre- and post-bronchodilator spirometry conducted. The predictors investigated included age, sex, antiretroviral treatment (ART) duration, body mass index, history of tuberculosis (TB) or pneumonia, occupational exposure, environmental exposure, smoking and symptoms of OLD (cough, wheeze, mucus and dyspnoea). Impaired lung function was defined as a forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC) ratio of < 0.70, or below the 20th percentile of normal.

Results

The 98 ART-naïve participants (mean age = 34.0, standard deviation [s.d.] = 8.2), 85 participants on first-line ART (mean age = 36.9, s.d. = 6.6) and 189 participants on second-line ART (mean age = 43.5, s.d. = 7.9) were predominantly female (65.6%). Of the participants, 64 (17.2%) had impaired lung function and 308 had normal lung function. Linear regression identified age (β = –0.003, P < 0.01), male sex (β = –0.016, P = 0.03) and history of TB or pneumonia (β = –0.024, P < 0.01) as independent predictors of a lower FEV1/FVC ratio. Following logistic regression, only a history of TB or pneumonia (odds ratio = 2.58, 95% confidence interval = 1.47–4.52) was significantly related to impaired lung function (area under the receiver operating characteristic curve = 0.64).

Conclusion

Our data show that a history of TB or pneumonia predicts impaired lung function. In order to improve timely access to spirometry, clinicians should be alert to the possibility of impaired lung function in people with a history of TB or pneumonia.

Consensus document for the selection of lung transplant candidates: An update from the International Society for Heart and Lung Transplantation

Tens of thousands of patients with advanced lung diseases may be eligible to be considered as potential candidates for lung transplant around the world each year. The timing of referral, evaluation, determination of candidacy, and listing of candidates continues to pose challenges and even ethical dilemmas. To address these challenges, the International Society for Heart and Lung Transplantation appointed an international group of members to review the literature, to consider recent advances in the management of advanced lung diseases, and to update prior consensus documents on the selection of lung transplant candidates. The purpose of this updated consensus document is to assist providers throughout the world who are caring for patients with pulmonary disease to identify potential candidates for lung transplant, to optimize the timing of the referral of these patients to lung transplant centers, and to provide transplant centers with a framework for evaluating and selecting candidates. In addition to addressing general considerations and providing disease specific recommendations for referral and listing, this updated consensus document includes an ethical framework, a recognition of the variability in acceptance of risk between transplant centers, and establishes a system to account for how a combination of risk factors may be taken into consideration in candidate selection for lung transplantation.

Long-term weight gain in obese COPD patients participating in a disease management program: a risk factor for reduced health-related quality of life

BACKGROUND

Little is known about how long-term weight gain affects the health perception of COPD patients.

OBJECTIVES

The aim is to evaluate the long-term association of BMI change and health-related quality of life (HRQoL) in obese COPD patients.

METHODS

Claims and survey data from a COPD disease management program were used to match two groups of COPD patients with BMI ≥ 30 who have differing weight trajectories over a 5-year timespan via propensity score and genetic matching. EQ-5D-5L, including visual analog scale (VAS) and COPD Assessment Test (CAT), were used as outcomes of interest. Sociodemographic and disease-based variables were matched.

RESULTS

Out of 1202 obese COPD patients, 126 with a weight increase of four or more BMI points were matched separately with 252 (propensity score matching) and 197 (genetic matching) control subjects who had relatively stable BMI. For the EQ-5D-5L, patients with BMI increase reported significantly worse health perception for VAS and all descriptive dimensions except pain/discomfort. For the CAT, especially the perception of ability to complete daily activities and overall energy results were significantly worse. VAS differences reach the range of minimal important differences. Stopping smoking and already being in obesity class II were the most influential risk factors for BMI increase.

CONCLUSION

Obese COPD patients who gain four or more BMI points over 5 years report significantly lower results in different dimensions of generic and disease-specific HRQoL than their peers with stable BMI. To improve real-world outcomes, tracking and preventing specific BMI trajectories could constitute a clinically relevant aspect of managing COPD patients.

Uptake of Clinical Prognostic Tools in COPD Exacerbations Requiring Hospitalisation

Clinical prognostic tools are used to objectively predict outcomes in many fields of medicine. Whilst over 400 have been developed for use in chronic obstructive pulmonary disease (COPD), only a minority have undergone full external validation and just one, the DECAF score, has undergone an implementation study supporting use in clinical practice. Little is known about how such tools are used in the UK. We distributed surveys at two time points, in 2017 and 2019, to hospitals included in the Royal College of Physicians of London national COPD secondary care audit program. The survey assessed the use of prognostic tools in routine care of hospitalized COPD patients. Hospital response rates were 71/196 in 2017 and 72/196 in 2019. The use of the DECAF and PEARL scores more than doubled in decisions about unsupported discharge (7%-15.3%), admission avoidance (8.1%-17%) and readmission avoidance (4.8%-13.1%); it more than tripled (8.8%-27.8%) in decisions around hospital-at-home or early supported discharge schemes. In other areas, routine use of clinical prognostic tools was uncommon. In palliative care decisions, the use of the Gold Standards Framework Prognostic Indicator Guidance fell (5.6%-1.4%). In 2017, 43.7% of hospitals used at least one clinical prognostic tool in routine COPD care, increasing to 52.1% in 2019. Such tools can help challenge prognostic pessimism and improve care. To integrate these further into routine clinical care, future research should explore current barriers to their use and focus on implementation studies.Supplemental data for this article is available online at https://dx.doi.org/10.1080/15412555.2021.1959540.

Multidimensional indices in the assessment of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a very common disease, is the third leading cause of death worldwide. Due to the significant heterogeneity of clinical phenotypes of COPD there is no single method suitable for predicting patients' health status and outcomes, and therefore multidimensional indices, assessing different components of the disease, were developed and are recommended for clinical practice by international guidelines. Several indices have been widely accepted: BODE and its modifications, ADO, DOSE, CODEX, COTE. They differ in their composition and aim, while they are more accurate and better validated in specific settings and populations. We review the characteristics, strengths and limitations of these indices, and we discuss their role in routine management of patients with COPD, as well as in specific clinical scenarios, such as resuscitation and ceiling of care, or decisions to offer more invasive treatments. This analysis may help clinicians to use those indexes in a more practical and appropriate way.

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

BACKGROUND

Sepsis is associated with high mortality and predictive models can help in clinical decision-making. The objective of this study was to carry out a systematic review of these models.

METHODS

In 2019, we conducted a systematic review in MEDLINE and EMBASE (CDR42018111121:PROSPERO) of articles that developed predictive models for mortality in septic patients (inclusion criteria). We followed the CHARMS recommendations (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), extracting the information from its 11 domains (Source of data, Participants, etc). We determined the risk of bias and applicability (participants, outcome, predictors and analysis) through PROBAST (Prediction model Risk Of Bias ASsessment Tool).

RESULTS

A total of 14 studies were included. In the CHARMS extraction, the models found showed great variability in its 11 domains. Regarding the PROBAST checklist, only one article had an unclear risk of bias as it did not indicate how missing data were handled while the others all had a high risk of bias. This was mainly due to the statistical analysis (inadequate sample size, handling of continuous predictors, missing data and selection of predictors), since 13 studies had a high risk of bias. Applicability was satisfactory in six articles. Most of the models integrate predictors from routine clinical practice. Discrimination and calibration were assessed for almost all the models, with the area under the ROC curve ranging from 0.59 to 0.955 and no lack of calibration. Only three models were externally validated and their maximum discrimination values in the derivation were from 0.712 and 0.84. One of them (Osborn) had undergone multiple validation studies.

DISCUSSION

Despite most of the studies showing a high risk of bias, we very cautiously recommend applying the Osborn model, as this has been externally validated various times.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

OBJECTIVE

To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation.

STUDY DESIGN AND SETTING

We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n=556) and assessed the change in discrimination (dAUC) in external validation cohorts (n=1,147).

RESULTS

PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P<0.001).

CONCLUSION

High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.

Chronic obstructive pulmonary disease and lung cancer: access to palliative care, emergency room visits and hospital deaths

Background

Despite the severe symptoms experienced by dying COPD patients, specialized palliative care (SPC) services focus mainly on cancer patients. We aimed to study the access to SPC that COPD and lung cancer (LC) patients receive and how that access affects the need for acute hospital care.

Methods

A descriptive regional registry study using data acquired through VAL, the Stockholm Regional Council’s central data warehouse, which covers nearly all healthcare use in the county of Stockholm. All the patients who died of COPD or LC from 2015 to 2019 were included. T-tests, chi-2 tests, and univariable and multivariable logistic regression analyses were performed on the accumulated data.

Results

In total, 6479 patients, (2917 with COPD and 3562 with LC) were studied. The patients with LC had more access to SPC during the last three months of life than did those with COPD (77% vs. 18%, respectively; p < .0001), whereas patients with COPD were more likely to be residents of nursing homes than those with LC (32% vs. 9%, respectively; p < .0001). Higher socioeconomic status (SES) (p < .01) and patient age < 80 years (p < .001) were associated with increased access to SPC for LC patients. Access to SPC correlated with fewer emergency room visits (p < .0001 for both COPD and LC patients) and fewer admissions to acute hospitals during the last month of life (p < .0001 for both groups). More COPD patients died in acute hospitals than lung cancer patients, (39% vs. 20%; χ² = 287, p < .0001), with significantly lower figures for those who had access to SPC (p < .0001).

Conclusions

Compared to dying COPD patients, LC patients have more access to SPC. Access to SPC reduces the need for emergency room visits and admissions to acute hospitals.

Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis

BACKGROUND

Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is lacking, the key of KOA treatment is shifting to disease prevention and progression slowing. The prognostic prediction models are called for to guide clinical decision-making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA about three clinical concerns: (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA.

METHODS

The electronic datasets (PubMed, Embase, the Cochrane Library, Web of Science, Scopus, SportDiscus, and CINAHL) and gray literature sources (OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview) will be searched from their inception onwards. Title and abstract screening and full-text review will be accomplished by two independent reviewers. The multivariable prognostic models that concern on (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA will be included. Data extraction instrument and critical appraisal instrument will be developed before formal assessment and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to the TRIPOD statement, CHARMS checklist, and PROBAST tool, respectively. Prognostic prediction models will be summarized qualitatively. Quantitative metrics on the predictive performance of these models will be synthesized with meta-analyses if appropriate.

DISCUSSION

Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation, or impact assessment will be made.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020203543.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal

Objective: This work aims to systematically identify, describe, and appraise all prognostic models for cervical cancer and provide a reference for clinical practice and future research. Methods: We systematically searched PubMed, EMBASE, and Cochrane library databases up to December 2020 and included studies developing, validating, or updating a prognostic model for cervical cancer. Two reviewers extracted information based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies checklist and assessed the risk of bias using the Prediction model Risk Of Bias ASsessment Tool. Results: Fifty-six eligible articles were identified, describing the development of 77 prognostic models and 27 external validation efforts. The 77 prognostic models focused on three types of cervical cancer patients at different stages, i.e., patients with early-stage cervical cancer (n = 29; 38%), patients with locally advanced cervical cancer (n = 27; 35%), and all-stage cervical cancer patients (n = 21; 27%). Among the 77 models, the most frequently used predictors were lymph node status (n = 57; 74%), the International Federation of Gynecology and Obstetrics stage (n = 42; 55%), histological types (n = 38; 49%), and tumor size (n = 37; 48%). The number of models that applied internal validation, presented a full equation, and assessed model calibration was 52 (68%), 16 (21%), and 45 (58%), respectively. Twenty-four models were externally validated, among which three were validated twice. None of the models were assessed with an overall low risk of bias. The Prediction Model of Failure in Locally Advanced Cervical Cancer model was externally validated twice, with acceptable performance, and seemed to be the most reliable. Conclusions: Methodological details including internal validation, sample size, and handling of missing data need to be emphasized on, and external validation is needed to facilitate the application and generalization of models for cervical cancer.

Short physical performance battery as a practical tool to assess mortality risk in chronic obstructive pulmonary disease

RATIONALE

chronic obstructive pulmonary disease (COPD) is a leading cause of mortality and common in older adults. The BODE Index is the most recognised mortality risk score in COPD but includes a 6-minute walk test (6MWT) that is seldom available in practise; the BODE Index may be better adopted if the 6MWT was replaced.

OBJECTIVES

we investigated whether a modified BODE Index in which 6MWT was replaced by an alternative measure of physical capacity, specifically the short physical performance battery (SPPB) or components, retained its predictive ability for mortality in individuals with COPD.

METHODS

we analysed 630 COPD patients from the ERICA cohort study for whom UK Office for National Statistics verified mortality data were available. Variables tested at baseline included spirometry, 6MWT, SPPB and its components (4-m gait speed test [4MGS], chair stand and balance). Predictive models were developed using stratified multivariable Cox regression, and assessed by C-indices and calibration plots with 10-fold cross-validation and replication.

RESULTS

during median 2 years of follow-up, 60 (10%) individuals died. There was no significant difference between the discriminative ability of BODE6MWT (C-index 0.709, 95% confidence interval [CI], 0.680-0.737), BODESPPB (C-index 0.683, 95% CI, 0.647-0.712), BODE4MGS (C-index 0.676, 95% CI, 0.643-0.700) and BODEBALANCE (C-index 0.686, 95% CI, 0.651-0.713) for predicting mortality.

CONCLUSIONS

the SPPB, and its 4MGS and balance components, can potentially be used as an alternative to the 6MWT in the BODE Index without significant loss of predictive ability in all-cause mortality.

Gait speed and adverse outcomes following hospitalised exacerbation of COPD

Four-metre gait speed (4MGS) is a simple physical performance measure and surrogate marker of frailty that is associated with adverse outcomes in older adults. We aimed to assess the ability of 4MGS to predict prognosis in patients hospitalised with acute exacerbations of COPD (AECOPD).213 participants hospitalised with AECOPD (52% male, mean age and FEV₁, 72 years and 35% predicted) were enrolled. 4MGS and baseline demographics were recorded at hospital discharge. All-cause readmission and mortality were collected for 1 y after discharge, and multivariable Cox-proportional hazards regression were performed. Kaplan-Meier and Competing risk analysis was conducted comparing time to all-cause readmission and mortality between 4MGS quartiles.111 participants (52%) were readmitted, and 35 (16%) died during the follow-up period. 4MGS was associated with all-cause readmission, with an adjusted subdistribution hazard ratio of 0.868 (95% CI 0.797-0.945; p=0.001) per 0.1 m·s^-1 increase in gait speed, and with all-cause mortality with an adjusted subdistribution hazard ratio of 0.747 (95% CI: 0.622-0.898; p=0.002) per 0.1 m·s^-1 increase in gait speed. Readmission and mortality models incorporating 4MGS had higher discrimination than age or FEV₁% predicted alone, with areas under the receiver operator characteristic curves of 0.73 and 0.80 respectively. Kaplan-Meier and Competing Risk curves demonstrated that those in slower gait speed quartiles had reduced time to readmission and mortality (log rank both p<0.001).4MGS provides a simple means of identifying at-risk patients with COPD at hospital discharge. This provides valuable information to plan post-discharge care and support.

Lung transplantation: candidate selection and timing of transplant

PURPOSE OF REVIEW

Over the past two decades, lung transplant has become the mainstay of treatment for several end-stage lung diseases. As the field continues to evolve, the criteria for referral and listing have also changed. The last update to these guidelines was in 2014 and several studies since then have changed how patients are transplanted. Our article aims to briefly discuss these updates in lung transplantation.

RECENT FINDINGS

This article discusses the importance of early referral of patients for lung transplantation and the concept of the 'transplant window'. We review the referral and listing criteria for some common pulmonary diseases and also cite the updated literature surrounding the absolute and relative contraindications keeping in mind that they are a constantly moving target. Frailty and psychosocial barriers are difficult to assess with the current assessment tools but continue to impact posttransplant outcomes. Finally, we discuss the limited data on transplantation in acute respiratory distress syndrome (ARDS) due to COVID19 as well as extracorporeal membrane oxygenation bridge to transplantation.

SUMMARY

The findings discussed in this article will strongly impact, if not already, how we select candidates for lung transplantation. It also addresses some aspects of lung transplant such as frailty and ARDS, which need better assessment tools and clinical data.

Referral criteria to palliative care for patients with respiratory disease: a systematic review

BACKGROUND

Advanced non-malignant respiratory diseases are associated with significant patient morbidity, yet access to palliative care occurs late, if at all.

AIM

To examine referral criteria for palliative care among patients with advanced non-malignant respiratory disease, with a view to developing a standardised set of referral criteria.

DESIGN

Systematic review of all studies reporting on referral criteria to palliative care in advanced non-malignant respiratory disease, with a focus on chronic obstructive pulmonary disease and interstitial lung disease.

DATA SOURCES

A systematic review conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses guideline was undertaken using electronic databases (Ovid, MEDLINE, Ovid Embase, and PubMed).

RESULTS

Searches yielded 2052 unique titles, which were screened for eligibility resulting in 62 studies addressing referral criteria to palliative care in advanced non-malignant respiratory disease. Of 18 categories put forward for referral to palliative care, the most commonly discussed factors were hospital use (69% of papers), indicators of poor respiratory status (47%), physical and emotional symptoms (37%), functional decline (29%), need for advanced respiratory therapies (27%), and disease progression (26%).

CONCLUSION

Clinicians consider referral to specialist palliative care for a wide range of disease- and needs-based criteria. Our findings highlight the need to standardise palliative care access by developing consensus referral criteria for patients with advanced non-malignant respiratory illnesses.

The role of elevated red blood cell distribution width in the prognosis of AECOPD patients: A retrospective study

Chronic obstructive pulmonary disease (COPD) is still a constant threat to people's health. We aimed to identify the relationship between increased red cell distribution width (RDW) on admission and length of hospitalization in acute exacerbation of chronic obstructive pulmonary disease patients (AECOPD).Patients with AECOPD were recruited and divided into 3 groups based on RDW tertiles.Two hundred eighty six patients with AECOPD admitted to our department during January 1, 2017 and June 30, 2019 were enrolled in the study. According to the RDW tertiles (≤12.8%, 12.9% to 13.6%, >13.6%), the patients were divided into 3 groups. Length of stay was significantly related to RDW (P < .001) in AECOPD patients. Correlation analysis indicated that RDW was negatively associated with FEV1% predicted (r = -0.142, P = .016). However, RDW was positively associated with prolonged of stay (r = 0.298, P < .001) in AECOPD patients. Multivariate regression analysis discovered that RDW was independently associated with the length of hospitalization (P = .001). Receiver operating characteristic (ROC) curve showed that RDW was a good predictor of prolonged hospital stay in AECOPD patients, and the area under the curve (AUC) was 0.818 (95% CI: 0.769-0.868). The highest sensitivity to predict prolonged hospital stay was 83.8% and the specificity was 71.6% with the cut-off 13.35%.In conclusion, prolonged hospital stay in AECOPD patients was closely associated with increased RDW. Elevated RDW may be an independent predictor for prolonged hospitalization in AECOPD patients.

Prognostic models for amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Increasing prognostic models for amyotrophic lateral sclerosis (ALS) have been developed. However, no comprehensive evaluation of these models has been done. The purpose of this study was to map the prognostic models for ALS to assess their potential contribution and suggest future improvements on modeling strategy.

METHODS

Databases including Medline, Embase, Web of Science, and Cochrane library were searched from inception to 20 February 2021. All studies developing and/or validating prognostic models for ALS were selected. Information regarding modelling method and methodological quality was extracted.

RESULTS

A total of 28 studies describing the development of 34 models and the external validation of 19 models were included. The outcomes concerned were ALS progression (n = 12; 35%), change in weight (n = 1; 3%), respiratory insufficiency (n = 2; 6%), and survival (n = 19; 56%). Among the models predicting ALS progression or survival, the most frequently used predictors were age, ALS Functional Rating Scale/ALS Functional Rating Scale-Revised, site of onset, and disease duration. The modelling method adopted most was machine learning (n = 16; 47%). Most of the models (n = 25; 74%) were not presented. Discrimination and calibration were assessed in 12 (35%) and 2 (6%) models, respectively. Only one model by Westeneng et al. (Lancet Neurol 17:423-433, 2018) was assessed with overall low risk of bias and it performed well in both discrimination and calibration, suggesting a relatively reliable model for practice.

CONCLUSIONS

This study systematically reviewed the prognostic models for ALS. Their usefulness is questionable due to several methodological pitfalls and the lack of external validation done by fully independent researchers. Future research should pay more attention to the addition of novel promising predictors, external validation, and head-to-head comparisons of existing models.

Standardisation of Clinical Assessment, Management and Follow-Up of Acute Hospitalised Exacerbation of COPD: A Europe-Wide Consensus

BACKGROUND

Despite hospitalization for exacerbation being a high-risk event for morbidity and mortality, there is little consensus globally regarding the assessment and management of hospitalised exacerbations of COPD. We aimed to establish a consensus list of symptoms, physiological measures, clinical scores, patient questionnaires and investigations to be obtained at time of hospitalised COPD exacerbation and follow-up.

METHODS

A modified Delphi online survey with pre-defined consensus of importance, feasibility and frequency of measures at hospitalisation and follow-up of a COPD exacerbation was undertaken.

FINDINGS

A total of 25 COPD experts from 18 countries contributed to all 3 rounds of the survey. Experts agreed that a detailed history and examination were needed. Experts also agreed on which treatments are needed and how soon these should be delivered. Experts recommended that a full blood count, renal function, C-reactive protein and cardiac blood biomarkers (BNP and troponin) should be measured within 4 hours of admission and that the modified Medical Research Council dyspnoea scale (mMRC) and COPD assessment test (CAT) should be performed at time of exacerbation and follow-up. Experts encouraged COPD clinicians to strongly consider discussing palliative care, if indicated, at time of hospitalisation.

INTERPRETATION

This Europe-wide consensus document is the first attempt to standardise the assessment and care of patients hospitalised for COPD exacerbations. This should be regarded as the starting point to build knowledge and evidence on patients hospitalised for COPD exacerbations.

The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status

INTRODUCTION

Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734).

METHODS

We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively.

RESULTS

We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening.

DISCUSSION

Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.

Impact of insomnia and obstructive sleep apnea on the risk of acute exacerbation of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide. Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is characterized by worsening of patients' respiratory symptoms that requires a modification in medication. This event could accelerate disease progression and increase the risk of hospital admissions and mortality. Both insomnia and obstructive sleep apnea (OSA) are prevalent in patients with COPD, and are linked to increased susceptibility to AECOPD. Improper treatment of insomnia may increase the risk of adverse respiratory outcomes for patients with COPD, while effective continuous positive airway pressure (CPAP) treatment may reduce the risk of AECOPD and mortality in patients with overlap syndrome. Sleep disorders should be considered in clinical management for COPD.

Natural history and mechanisms of COPD

The natural history of COPD is complex, and the disease is best understood as a syndrome resulting from numerous interacting factors throughout the life cycle with smoking being the strongest inciting feature. Unfortunately, diagnosis is often delayed with several longitudinal cohort studies shedding light on the long 'preclinical' period of COPD. It is now accepted that individuals presenting with different COPD phenotypes may experience varying natural history of their disease. This includes its inception, early stages and progression to established disease. Several scenarios regarding lung function course are possible, but it may conceptually be helpful to distinguish between individuals with normal maximally attained lung function in their early adulthood who thereafter experience faster than normal FEV₁ decline, and those who may achieve a lower than normal maximally attained lung function. This may be the main mechanism behind COPD in the latter group, as the decline in FEV₁ during their adult life may be normal or only slightly faster than normal. Regardless of the FEV₁ trajectory, continuous smoking is strongly associated with disease progression, development of structural lung disease and poor prognosis. In developing countries, factors such as exposure to biomass and sequelae after tuberculosis may lead to a more airway-centred COPD phenotype than seen in smokers. Mechanistically, COPD is characterized by a combination of structural and inflammatory changes. It is unlikely that all patients share the same individual or combined mechanisms given the heterogeneity of resultant phenotypes. Lung explants, bronchial biopsies and other tissue studies have revealed important features. At the small airway level, progression of COPD is clinically imperceptible, and the pathological course of the disease is poorly described. Asthmatic features can further add confusion. However, the small airway epithelium is likely to represent a key focus of the disease, combining impaired subepithelial crosstalk and structural/inflammatory changes. Insufficient resolution of inflammatory processes may facilitate these changes. Pathologically, epithelial metaplasia, inversion of the goblet to ciliated cell ratio, enlargement of the submucosal glands and neutrophil and CD8-T-cell infiltration can be detected. Evidence of type 2 inflammation is gaining interest in the light of new therapeutic agents. Alarmin biology is a promising area that may permit control of inflammation and partial reversal of structural changes in COPD. Here, we review the latest work describing the development and progression of COPD with a focus on lung function trajectories, exacerbations and survival. We also review mechanisms focusing on epithelial changes associated with COPD and lack of resolution characterizing the underlying inflammatory processes.

Development and validation of a nomogram to predict pulmonary function and the presence of chronic obstructive pulmonary disease in a Korean population

BACKGROUND

Early suspicion followed by assessing lung function with spirometry could decrease the underdiagnosis of chronic obstructive pulmonary disease (COPD) in primary care. We aimed to develop a nomogram to predict the FEV₁/FVC ratio and the presence of COPD.

METHODS

We retrospectively reviewed the data of 4241 adult patients who underwent spirometry between 2013 and 2019. By linear regression analysis, variables associated with FEV₁/FVC were identified in the training cohort (n = 2969). Using the variables as predictors, a nomogram was created to predict the FEV₁/FVC ratio and validated in the test cohort (n = 1272).

RESULTS

Older age (β coefficient [95% CI], - 0.153 [- 0.183, - 0.122]), male sex (- 1.904 [- 2.749, - 1.056]), current or past smoking history (- 3.324 [- 4.200, - 2.453]), and the presence of dyspnea (- 2.453 [- 3.612, - 1.291]) or overweight (0.894 [0.191, 1.598]) were significantly associated with the FEV₁/FVC ratio. In the final testing, the developed nomogram showed a mean absolute error of 8.2% between the predicted and actual FEV₁/FVC ratios. The overall performance was best when FEV₁/FVC < 70% was used as a diagnostic criterion for COPD; the sensitivity, specificity, and balanced accuracy were 82.3%, 68.6%, and 75.5%, respectively.

CONCLUSION

The developed nomogram could be used to identify potential patients at risk of COPD who may need further evaluation, especially in the primary care setting where spirometry is not available.

Diffusing capacity in chronic obstructive pulmonary disease assessment: A meta-analysis

To achieve a multidimensional evaluation of chronic obstructive pulmonary disease (COPD) patients, the spirometry measures are supplemented by assessment of symptoms, risk of exacerbations, and CT imaging. However, the measurement of diffusing capacity of the lung for carbon monoxide (DLCO) is not included in most common used models of COPD assessment. Here, we conducted a meta-analysis to evaluate the role of DLCO in COPD assessment.The studies were identified by searching the terms "diffusing capacity" OR "diffusing capacity for carbon monoxide" or "DLCO" AND "COPD" AND "assessment" in Pubmed, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Scopus, and Web of Science databases. The mean difference of DLCO % predict was assessed in COPD patient with different severity (according to GOLD stage and GOLD group), between COPD patients with or without with frequent exacerbation, between survivors and non-survivors, between emphysema dominant and non-emphysema dominant COPD patients, and between COPD patients with or without pulmonary hypertension.43 studies were included in the meta-analysis. DLCO % predicted was significantly lower in COPD patients with more severe airflow limitation (stage II/IV), more symptoms (group B/D), and high exacerbation risk (group C/D). Lower DLCO % predicted was also found in exacerbation patients and non-survivors. Low DLCO % predicted was related to emphysema dominant phenotype, and COPD patients with PH.The current meta-analysis suggested that DLCO % predicted might be an important measurement for COPD patients in terms of severity, exacerbation risk, mortality, emphysema domination, and presence of pulmonary hypertension. As diffusion capacity reflects pulmonary ventilation and perfusion at the same time, the predictive value of DLCO or DLCO combined with other criteria worth further exploration.

Ultra Long-Acting β-Agonists in Chronic Obstructive Pulmonary Disease

INTRODUCTION

Inhaled β-agonists have been foundational medications for maintenance COPD management for decades. Through activation of cyclic adenosine monophosphate pathways, these agents relax airway smooth muscle and improve expiratory airflow by relieving bronchospasm and alleviating air trapping and dynamic hyperinflation improving breathlessness, exertional capabilities, and quality of life. β-agonist drug development has discovered drugs with increasing longer durations of action: short acting (SABA) (4-6 h), long acting (LABA) (6-12 h), and ultra-long acting (ULABA) (24 h). Three ULABAs, indacaterol, olodaterol, and vilanterol, are approved for clinical treatment of COPD.

PURPOSE

This article reviews both clinically approved ULABAs and ULABAs in development.

CONCLUSION

Indacaterol and olodaterol were originally approved for clinical use as monotherapies for COPD. Vilanterol is the first ULABA to be approved only in combination with other respiratory medications. Although there are many other ULABA's in various stages of development, most clinical testing of these novel agents is suspended or proceeding slowly. The three approved ULABAs are being combined with antimuscarinic agents and corticosteroids as dual and triple agent treatments that are being tested for clinical use and efficacy. Increasingly, these clinical trials are using specific COPD clinical characteristics to define study populations and to begin to develop therapies that are trait-specific.

Development and validation of a prognostic index (BODEXS90) for mortality in stable chronic obstructive pulmonary disease

INTRODUCTION

Several multidimensional indices have been proposed to predict mortality in chronic obstructive pulmonary disease (COPD). The BODEX index is simple and easy to use for this purpose in all clinical settings. Only a few prognostic indices have integrated oxygenation variables, with measurement methods that are not practical for real life clinical practice in all settings.

OBJECTIVES

To develop and externally validate a new prognostic index (BODEXS90) that combines the variables included in BODEX index with rest peripheral oxygen saturation measured with finger oximetry (SpO₂) to predict all-cause mortality in stable COPD.

METHOD

Observational, non-intervention, multicenter historic cohort study. The BODEXS90 index was developed in a derivation cohort and externally validated in a validation cohort. Calibration of the index was carried out using Hosmer-Lemeshow test. The discrimination capacity of BODEXS90 and BODEX were compared by means of receiver-operating characteristics curves. Modelling of the index was carried out by crude and adjusted Cox regression analysis.

RESULTS

The derivation and validation cohorts included 787 and 1179 subjects, respectively. SpO₂ predicted all cause-mortality independently of BODEX index. Discrimination capacity of BODEXS90 to predict the outcome was significantly higher than that of BODEX, particularly for more severely affected patients, both in the derivation and in the validation cohorts.

CONCLUSIONS

The new index is potentially useful for designing clinical decision-making algorithms in stable COPD.

Critical appraisal and external validation of a prognostic model for survival of people living with HIV/AIDS who underwent antiretroviral therapy

BACKGROUND

HIV/AIDS remains a leading cause of death worldwide. Recently, a model has been developed in Wenzhou, China, to predict the survival of people living with HIV/AIDS (PLWHA) who underwent antiretroviral therapy (ART). We aimed to evaluate the methodological quality and validate the model in an external population-based cohort.

METHODS

Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the Wenzhou model. Data were from the National Free Antiretroviral Treatment Program database. We included PLWHA treated between February 2004 and December 2019 in a tertiary hospital in Guangzhou city, China. The endpoint was all-cause deaths and assessed until January 2020. We assessed the discrimination performance of the model by Harrell's overall C-statistics and time-dependent C-statistics and calibration by comparing observed survival probabilities estimated with the Kaplan-Meier method versus predicted survival probabilities. To assess the potential prediction value of age and gender which were precluded in developing the Wenzhou model, we compared the discriminative ability of the original model with an extended model added with age and gender.

RESULTS

Based on PROBAST, the Wenzhou model was rated as high risk of bias in three out of the four domains (selection of participants, definition of outcome, and methods for statistical analysis) mainly because of the misuse of nested case-control design and propensity score matching. In the external validation analysis, 16758 patients were included, among whom 743 patients died (mortality rate 11.41 per 1000 person-years) during follow-up (median 3.41 years, interquartile range 1.64-5.62). The predictor of HIV viral load was missing in 14361 patients (85.7%). The discriminative ability of the Wenzhou model decreased in the external dataset, with the Harrell's overall C-statistics being 0.76, and time-dependent C-statistics dropping from 0.81 at 6 months to 0.48 at 10 years after ART initiation. The model consistently underestimated the survival, and the level was 6.23%, 10.02%, and 14.82% at 1, 2, and 3 years after ART initiation, respectively. The overall and time-dependent discriminative ability of the model improved after adding age and gender to the original model.

CONCLUSION

The Wenzhou prognostic model is at high risk of bias in model development, with inadequate model performance in external validation. Thereby, we could not confirm the validity and extended utility of the Wenzhou model. Future prediction model development and validation studies need to comply with the methodological standards and guidelines specifically developed for prediction models.