Changes in health-related quality of life as a marker in the prognosis in COPD patients

[EN] Chronic obstructive pulmonary disease (COPD) is understood as a complex, heterogeneous and multisystem airway obstructive disease. The association of deterioration in health-related quality of life (HRQoL) with mortality and hospitalisation for COPD exacerbation has been explored in general terms. The specific objectives of this study were to determine whether a change in HRQoL is related, over time, to mortality and hospitalisation. Overall, 543 patients were recruited through Galdakao Hospital's five outpatient respiratory clinics. Patients were assessed at baseline, and the end of the first and second year, and were followed up for 3 years. At each assessment, measurements were made of several variables, including HRQoL using the St George's Respiratory Questionnaire (SGRQ). The cohort had moderate obstruction (forced expiratory volume in 1 s 55% of the predicted value). SGRQ total, symptoms, activity and impact scores at baseline were 39.2, 44.5, 48.7 and 32.0, respectively. Every 4-point increase in the SGRQ was associated with an increase in the likelihood of death: "symptoms" domain odds ratio 1.04 (95% CI 1.00-1.08); "activity" domain OR 1.12 (95% CI 1.08-1.17) and "impacts" domain OR 1.11 (95% CI 1.06-1.15). The rate of hospitalisations per year was 5% (95% CI 3-8%) to 7% (95% CI 5-10%) higher for each 4-point increase in the separate domains of the SGRQ. Deterioration in HRQoL by 4 points in SGRQ domain scores over 1 year was associated with an increased likelihood of death and hospitalisation.This work was supported by the Spanish Health Research Fund (FIS grant number PI020510), and by funding from the Dept of Health of the Basque Government (grant number 200111002), Spanish Ministry of Economy and Competitiveness (MTM2016-74931-P and BCAM Severo Ochoa excellence accreditation SEV-2017-0718), Dept of Education, Linguistic Policy and Culture of the Basque Government (IT1294-19 and BERC 2018-2021), and the University of the Basque Country (COLAB20/01)

BODE-Index vs HADO-Score in Chronic Obstructive Pulmonary Disease: Which one to use in general practice?

<p>Abstract</p> <p>Background</p> <p>Forced expiratory volume in one second (FEV₁) is used to diagnose and establish a prognosis in chronic obstructive pulmonary disease (COPD). Using multi-dimensional scores improves this predictive capacity.Two instruments, the BODE-index (<b>B</b>ody mass index, <b>O</b>bstruction, <b>D</b>yspnea, <b>E</b>xercise capacity) and the HADO-score (<b>H</b>ealth, <b>A</b>ctivity, <b>D</b>yspnea, <b>O</b>bstruction), were compared in the prediction of mortality among COPD patients.</p> <p>Methods</p> <p>This is a prospective longitudinal study. During one year (2003 to 2004), 543 consecutively COPD patients were recruited in five outpatient clinics and followed for three years. The endpoints were all-causes and respiratory mortality.</p> <p>Results</p> <p>In the multivariate analysis of patients with FEV₁< 50%, no significant differences were observed in all-cause or respiratory mortality across HADO categories, while significant differences were observed between patients with a lower BODE (less severe disease) and those with a higher BODE (greater severity). Among patients with FEV₁≥ 50%, statistically significant differences were observed across HADO categories for all-cause and respiratory mortality, while differences were observed across BODE categories only in all-cause mortality.</p> <p>Conclusions</p> <p>HADO-score and BODE-index were good predictors of all-cause and respiratory mortality in the entire cohort. In patients with severe COPD (FEV₁< 50%) the BODE index was a better predictor of mortality whereas in patients with mild or moderate COPD (FEV₁≥ 50%), the HADO-score was as good a predictor of respiratory mortality as the BODE-index. These differences suggest that the HADO-score and BODE-index could be used for different patient populations and at different healthcare levels, but can be used complementarily.</p

Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19

[EN] Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer-Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer-Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice. Registration: ClinicalTrials.gov Identifier: NCT04463706.This work was supported in part by grants from the Instituto de Salud Carlos III and the European Regional Development Fund COVID20/00459; the health outcomes group from Galdakao-Barrualde Health Organization; the Kronikgune Institute for Health Service Research; and the thematic network–REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas)–of the Instituto de Salud Carlos III. The funder of the study had no role in study design, data collection, analysis, management or interpretation, or writing of the report

Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer-Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer-Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.Registration: ClinicalTrials.gov Identifier: NCT04463706

Kaplan-Meier estimate of the survival function during the one year period of follow-up stratified by cluster.

<p>Log-rank test for homogeneity (p < 0.001). Significant differences adjusted for multiple comparisons (Bonferroni) were observed between clusters A and C; A and D; and B and D.</p

Map of clusters and distribution of patients.

<p>Map created by the first and second components derived from the MCA is shown at the center. Four circles at the sides show how patients move between clusters after one year of follow-up. Relative positions of the subjects in the planes are represented by different colors, depending on the subtype provided by the cluster analysis. Definition of the axes is suggested based on information provided in appendix Table A1. The horizontal axis, first component, can be defined as an index of the respiratory conditions of the patient, milder (left side) vs. more severe (right side). The vertical axis, second component, can be defined as an index of the systemic status, worse (bottom) vs. better (top).</p

Association of cluster-based classification to GOLD classification and COPD severity score (HADO-AH, BODE and ADO).

<p>Association of cluster-based classification to GOLD classification and COPD severity score (HADO-AH, BODE and ADO).</p

Partial dendrogram obtained from the cluster analysis.

<p>The dendogram represents the results from the cluster analysis performed with the four components obtained from the multiple correspondence analysis. The graphical display includes an easy interpretation of the partition and a brief description of the resulting clusters.</p