CHRONIC OBSTRUCTIVE PULMONARY DISEASE AND COMORBIDITIES: MANAGEMENT OF SOMATOFORM DISORDERS

2021 ◽  
Vol 74 (6) ◽  
pp. 1401-1404
Author(s):  
Liliia V. Burya ◽  
Anna A. Kapustianska ◽  
Nataliia V. Moiseieva ◽  
Andrii V. Vakhnenko ◽  
Mariia O. Rumiantseva ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Somatoform Disorders ◽  
Comprehensive Evaluation ◽  
Pulmonary Ventilation ◽  
Multidisciplinary Care ◽  
Step Test ◽  
Chronic Obstructive ◽  
Copd Exacerbations ◽  
Group D ◽  
Severe Copd

The aim: To perform a comprehensive evaluation of the effect of paroxetine on the degree of somatoform disorders in exacerbation of severe COPD in women. Materials and methods: The study involved 53 female patients with severe COPD (Group D), confirmed by instrumental methods of study. At hospitalization, patients were divided into 2 groups. Patients of Group 1 (n = 21; aged 52.5 ± 0.8 years old) underwent basic exacerbation therapy. Patients of Group 2 (n = 22; aged 57.9 ± 0.4 years old) underwent basic exacerbation therapy supplemented with paroxetine for 14 days, 1 tablet (0.20 g) once a day. Results: The basic therapy for treatment of COPD exacerbations, supplemented with paroxetine, led to a positive clinical effect, confirmed by increase in skeletal and respiratory muscular system, increased parameters of pulmonary ventilation, increased tolerance to physical load, increased oxygen saturation, decreased heart rate and breathing rate. Conclusions: The strategy for choosing an antidepressant to provide multidisciplinary care for somatoform disorders in women with exacerbation of severe COPD (group D) should take into account the efficacy and favorable safety profile and personalization of the drug. In exacerbation of severe COPD, the degree of somatoform disorders in patients correlates with the severity of the main criteria: FVC1, the distance walked during the 6-minute step test, oxygen saturation after the 6-minute step test, end-expiratory pressure in the oral cavity.

scholarly journals The nature of COPD revealed by self-tracking, PRO-data and analysis of contact pattern for participants in a personalized health centered living lab organized according to the Epital Care Model. A longitudinal study. (Preprint)

2020 ◽  
Author(s):  
Klaus Phanareth ◽  
Astrid Laura Dam ◽  
Martin ABC Hansen ◽  
Signe Lindskrog ◽  
Søren Vingtoft ◽  
...  
Keyword(s):  
Risk Factor ◽  
Pulmonary Function ◽  
Oxygen Saturation ◽  
Healthcare Professionals ◽  
Factor Group ◽  
Chronic Obstructive ◽  
Care Model ◽  
Copd Patients ◽  
Group A ◽  
Group D

BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death and is characterized by a progressive loss of pulmonary function over time with intermittent episodes of exacerbations. Rapid and proactive interventions may reduce the burden of the condition for the patients. Telehealth solutions involving self-tracking of vital parameters such as pulmonary function, oxygen saturation, heart rate and temperature with synchronous communication of health data may become a powerful solution as they enable healthcare professionals to react with a pro-active and adequate response. We have taken this idea to the next level in the Epital Care Model (ECM) and organized a person-centered technology assisted ecosystem to provide health services to COPD patients. OBJECTIVE The objective is to reveal the nature of COPD by combining technology with a person-centered design aimed to benefit from interactions based on PRO data and to assess the needed kind of contacts to best treat exacerbations. We wanted to know: 1) What is the incidence of mild, moderate and severe exacerbations in a mixed population of COPD patients? 2) What is the course of the mild, moderate and severe exacerbations? And 3) How is the activity and pattern of contacts to health professionals related to the participants condition? METHODS Convenience sampling during the period November 2013 to December 2015. The participants’ sex, age, FEV1, pulse rate and oxygen saturation were registered at entry. During the study, we registered number of days, number of exacerbations, number of contact notes coded into care and treatment notes. Each participant was classified according to GOLD I-IV and risk factor group A-D. Participants reported their clinical status using a tablet by answering four questions and sending three semi-automated measurements. RESULTS Of the 87 participants, 11 were in risk factor group A, 24 in B, 13 in C and 39 in D. The number of observed days was 31801 days with 12470 measurements and 1397 care notes and 1704 treatment notes. A total of 254 exacerbations were treated and only 18 caused hospitalization. Those in risk factor group D have the highest number of hospitalizations (16), exacerbations (151), and contacts (1910) The initial contacts during the first month declined within three months to 1/3 for care contacts and 1/2 for treatment contacts and reached a plateau after four months. CONCLUSIONS The majority of COPD patients in risk factor group D can be managed virtually and only 13% of those with severe exacerbations required hospitalization. Contact to the healthcare professionals decreases markedly within the first months after enrollment. These results provide a new and detailed insight into the course of COPD. We propose a resilience index for virtual clinical management making it easier to compare results across settings.

COPD Exacerbations: Clinical Management Options

2012 ◽  
Vol 3 ◽  
pp. IJCM.S5476
Author(s):  
Unnati Desai ◽  
Dipti Gothi ◽  
Jyotsna M. Joshi
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Future Research ◽  
Chronic Obstructive ◽  
Evidence Based ◽  
Management Options ◽  
Obstructive Pulmonary Disease ◽  
Progressive Decline ◽  
Copd Exacerbations ◽  
Sputum Production ◽  
Severe Copd

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a sudden and sustained worsening in cough, dyspnoea and/or sputum production in patients with COPD. AECOPD is an infrequent occurrence in mild COPD, but is a common feature of moderate to severe COPD. It is essential to treat AECOPD early to prevent morbidity and mortality related to the disease. The treatment can be administered at home or in hospital depending on the facility and patient condition. The pharmacological and non pharmacological therapies are essential component of management and complimentary to each other. Prevention of further exacerbation after its optimum treatment is as important as the treatment, as these episodes lead to progressive decline in lung function. Evidence based management; newer advances and direction for future research are included in this review.

scholarly journals Once Daily Versus Overnight and Symptom Versus Physiological Monitoring to Detect Exacerbations of Chronic Obstructive Pulmonary Disease: Pilot Randomized Controlled Trial

10.2196/17597 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e17597
Author(s):  
Ahmed M Al Rajeh ◽  
Yousef Saad Aldabayan ◽  
Abdulelah Aldhahir ◽  
Elisha Pickett ◽  
Shumonta Quaderi ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Heart Rate ◽  
Oxygen Saturation ◽  
Pulse Oximetry ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Once Daily ◽  
Copd Exacerbations ◽  
Daily Monitoring ◽  
The Mean

Background Earlier detection of chronic obstructive pulmonary disease (COPD) exacerbations may facilitate more rapid treatment with reduced risk of hospitalization. Changes in pulse oximetry may permit early detection of exacerbations. We hypothesized that overnight pulse oximetry would be superior to once-daily monitoring for the early detection of exacerbations. Objective This study aims to evaluate whether measuring changes in heart rate and oxygen saturation overnight is superior to once-daily monitoring of both parameters and to assess symptom changes in facilitating earlier detection of COPD exacerbations. Methods A total of 83 patients with COPD were randomized to once-daily or overnight pulse oximetry. Both groups completed the COPD assessment test questionnaire daily. The baseline mean and SD for each pulse oximetry variable were calculated from 14 days of stable monitoring. Changes in exacerbation were expressed as Z scores from this baseline. Results The mean age of the patients was 70.6 (SD 8.1) years, 52% (43/83) were female, and the mean FEV1 was 53.0% (SD 18.5%) predicted. Of the 83 patients, 27 experienced an exacerbation. Symptoms were significantly elevated above baseline from 5 days before to 12 days after treatment initiation. Day-to-day variation in pulse oximetry during the stable state was significantly less in the overnight group than in the once-daily group. There were greater relative changes at exacerbation in heart rate than oxygen saturation. An overnight composite score of change in heart rate and oxygen saturation changed significantly from 7 days before initiation of treatment for exacerbation and had a positive predictive value for exacerbation of 91.2%. However, this was not statistically better than examining changes in symptoms alone. Conclusions Overnight pulse oximetry permits earlier detection of COPD exacerbations compared with once-daily monitoring. Monitoring physiological variables was not superior to monitoring symptoms, and the latter would be a simpler approach, except where there is a need for objective verification of exacerbations. Trial Registration ClinicalTrials.gov NCT03003702; https://clinicaltrials.gov/ct2/show/NCT03003702

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

10.2196/28953 ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. e28953
Author(s):  
Siyang Zeng ◽  
Mehrdad Arjomandi ◽  
Yao Tong ◽  
Zachary C Liao ◽  
Gang Luo
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
High Risk ◽  
Pulmonary Disease ◽  
Care Management ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Exacerbations ◽  
Machine Learning Model ◽  
Severe Copd

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

Biofeedback flutter device for managing the symptoms of patients with COPD

2020 ◽  
Vol 28 (5) ◽  
pp. 477-485
Author(s):  
Rekha Kaja ◽  
Anandh Vaiyapuri ◽  
Mohamed Sherif Sirajudeen ◽  
Hariraja Muthusamy ◽  
Radhakrishnan Unnikrishnan ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Chronic Obstructive ◽  
Post Intervention ◽  
Oxygen Saturation Level ◽  
Chronic Obstructive Pulmonary Diseases ◽  
Before And After ◽  
Group A ◽  
Dyspnea Score ◽  
Group B ◽  
Group D

BACKGROUND: Flutter is a device used in removing excess lung secretions. The conventional flutter lacks a biofeedback component to facilitate optimal use by the patients. OBJECTIVE: The current research aims to compare the effects of biofeedback flutter devices with the conventional flutter in managing the symptoms of patients with chronic obstructive pulmonary diseases. METHODS: One hundred and sixty-eight participants were randomly allocated into four groups: Group A (conventional), Group B (visual biofeedback), Group C (auditory biofeedback) and Group D (visual and auditory biofeedback). All groups were treated five days for 20 minutes. Outcome measures included wet sputum weight [during intervention (T1) and 1 hour after intervention (T2)], oxygen saturation and dyspnea score (before and after intervention) on all days. RESULTS: The wet sputum expectorated (T2) by Group B was significantly higher than Group A (P< 0.001), Group C (P< 0.001) and Group D (P< 0.05). The dyspnea score for Group B (P< 0.05), Group C (P< 0.05) and Group D (P< 0.05) was significantly lower than Group A. The post-intervention oxygen saturation level was higher in Group D followed by Groups B, C and A. CONCLUSION: The use of biofeedback flutter is effective in the removal of secretion, reducing dyspnea and improving oxygen saturation when compared to conventional flutter.

scholarly journals Efficacy and safety of single-inhaler extrafine triple therapy versus inhaled corticosteroid plus long-acting beta2 agonist in eastern Asian patients with COPD: the TRIVERSYTI randomised controlled trial

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jinping Zheng ◽  
Simonetta Baldi ◽  
Li Zhao ◽  
Huiping Li ◽  
Kwan-Ho Lee ◽  
...  
Keyword(s):  
Maintenance Therapy ◽  
Dry Powder Inhaler ◽  
Dose Inhaler ◽  
Chronic Obstructive ◽  
Severe Exacerbation ◽  
Efficacy And Safety ◽  
Post Dose ◽  
Exacerbation Rate ◽  
Copd Exacerbations ◽  
Severe Copd

Abstract Background A single-inhaler extrafine triple combination of beclometasone dipropionate (BDP), formoterol fumarate (FF) and glycopyrronium (G) has been developed for maintenance therapy of chronic obstructive pulmonary disease (COPD). This study evaluated the efficacy and safety of BDP/FF/G in patients in three eastern Asian areas: China, Republic of Korea and Taiwan. Methods TRIVERSYTI was a double-blind, randomised, active-controlled, parallel-group study in patients with COPD, post-bronchodilator forced expiratory volume in 1 s (FEV1) < 50% predicted, ≥ 1 exacerbation in the previous 12 months, and receiving inhaled maintenance medication. Patients received either extrafine BDP/FF/G 100/6/10 µg via pressurised metered-dose inhaler, or non-extrafine budesonide/formoterol (BUD/FF) 160/4.5 µg via dry-powder inhaler, both administered as two puffs twice-daily for 24 weeks. The co-primary objectives (analysed in the overall population) were to demonstrate superiority of BDP/FF/G over BUD/FF for change from baseline in pre-dose morning and 2-h post-dose FEV1 at Week 24 (these were analysed as key secondary objectives in the China subgroup). The rate of moderate/severe COPD exacerbations was a secondary endpoint. Results Of 708 patients randomised, 88.8% completed. BDP/FF/G was superior to BUD/FF for pre-dose and 2-h post-dose FEV1 at Week 24 [adjusted mean differences 62 (95% CI 38, 85) mL and 113 (87, 140) mL; both p < 0.001]. The annualised moderate/severe exacerbation rate was 43% lower with BDP/FF/G [rate ratio 0.57 (95% CI 0.42, 0.77); p < 0.001]. Adverse events were reported by 61.1% and 67.0% patients with BDP/FF/G and BUD/FF. Results were similar in the China subgroup. Conclusions In patients with COPD, FEV1 < 50% and an exacerbation history despite maintenance therapy, treatment with extrafine BDP/FF/G improved bronchodilation, and was more effective at preventing moderate/severe COPD exacerbations than BUD/FF. Trial registration CFDA CTR20160507 (registered 7 Nov 2016, http://www.chinadrugtrials.org.cn/index.html).

scholarly journals Impact of COVID-19 lockdown on the incidence and mortality of acute exacerbations of chronic obstructive pulmonary disease: national interrupted time series analyses for Scotland and Wales

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Mohammad A. Alsallakh ◽  
◽  
Shanya Sivakumaran ◽  
Sharon Kennedy ◽  
Eleftheria Vasileiou ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Hospital Admissions ◽  
Interrupted Time Series ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Exacerbations ◽  
Time Series Analyses ◽  
The Uk ◽  
The Impact ◽  
Severe Copd

Abstract Background The COVID-19 pandemic and ensuing national lockdowns have dramatically changed the healthcare landscape. The pandemic’s impact on people with chronic obstructive pulmonary disease (COPD) remains poorly understood. We hypothesised that the UK-wide lockdown restrictions were associated with reductions in severe COPD exacerbations. We provide the first national level analyses of the impact of the COVID-19 pandemic and first lockdown on severe COPD exacerbations resulting in emergency hospital admissions and/or leading to death as well as those recorded in primary care or emergency departments. Methods Using data from Public Health Scotland and the Secure Anonymised Information Linkage Databank in Wales, we accessed weekly counts of emergency hospital admissions and deaths due to COPD over the first 30 weeks of 2020 and compared these to the national averages over the preceding 5 years. For both Scotland and Wales, we undertook interrupted time-series analyses to model the impact of instigating lockdown on these outcomes. Using fixed-effect meta-analysis, we derived pooled estimates of the overall changes in trends across the two nations. Results Lockdown was associated with 48% pooled reduction in emergency admissions for COPD in both countries (incidence rate ratio, IRR 0.52, 95% CI 0.46 to 0.58), relative to the 5-year averages. There was no statistically significant change in deaths due to COPD (pooled IRR 1.08, 95% CI 0.87 to 1.33). In Wales, lockdown was associated with 39% reduction in primary care consultations for acute exacerbation of COPD (IRR 0.61, 95% CI 0.52 to 0.71) and 46% reduction in COPD-related emergency department attendances (IRR 0.54, 95% CI 0.36 to 0.81). Conclusions The UK-wide lockdown was associated with the most substantial reductions in COPD exacerbations ever seen across Scotland and Wales, with no corresponding increase in COPD deaths. This may have resulted from reduced transmission of respiratory infections, reduced exposure to outdoor air pollution and/or improved COPD self-management.

scholarly journals Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study (Preprint)

2021 ◽  
Author(s):  
Siyang Zeng ◽  
Mehrdad Arjomandi ◽  
Yao Tong ◽  
Zachary C Liao ◽  
Gang Luo
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
High Risk ◽  
Pulmonary Disease ◽  
Care Management ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Exacerbations ◽  
Machine Learning Model ◽  
Severe Copd

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 RR2-10.2196/13783

scholarly journals Once Daily Versus Overnight and Symptom Versus Physiological Monitoring to Detect Exacerbations of Chronic Obstructive Pulmonary Disease: Pilot Randomized Controlled Trial (Preprint)

2019 ◽  
Author(s):  
Ahmed M Al Rajeh ◽  
Yousef Saad Aldabayan ◽  
Abdulelah Aldhahir ◽  
Elisha Pickett ◽  
Shumonta Quaderi ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Heart Rate ◽  
Oxygen Saturation ◽  
Pulse Oximetry ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Once Daily ◽  
Copd Exacerbations ◽  
Daily Monitoring ◽  
The Mean

BACKGROUND Earlier detection of chronic obstructive pulmonary disease (COPD) exacerbations may facilitate more rapid treatment with reduced risk of hospitalization. Changes in pulse oximetry may permit early detection of exacerbations. We hypothesized that overnight pulse oximetry would be superior to once-daily monitoring for the early detection of exacerbations. OBJECTIVE This study aims to evaluate whether measuring changes in heart rate and oxygen saturation overnight is superior to once-daily monitoring of both parameters and to assess symptom changes in facilitating earlier detection of COPD exacerbations. METHODS A total of 83 patients with COPD were randomized to once-daily or overnight pulse oximetry. Both groups completed the COPD assessment test questionnaire daily. The baseline mean and SD for each pulse oximetry variable were calculated from 14 days of stable monitoring. Changes in exacerbation were expressed as Z scores from this baseline. RESULTS The mean age of the patients was 70.6 (SD 8.1) years, 52% (43/83) were female, and the mean FEV1 was 53.0% (SD 18.5%) predicted. Of the 83 patients, 27 experienced an exacerbation. Symptoms were significantly elevated above baseline from 5 days before to 12 days after treatment initiation. Day-to-day variation in pulse oximetry during the stable state was significantly less in the overnight group than in the once-daily group. There were greater relative changes at exacerbation in heart rate than oxygen saturation. An overnight composite score of change in heart rate and oxygen saturation changed significantly from 7 days before initiation of treatment for exacerbation and had a positive predictive value for exacerbation of 91.2%. However, this was not statistically better than examining changes in symptoms alone. CONCLUSIONS Overnight pulse oximetry permits earlier detection of COPD exacerbations compared with once-daily monitoring. Monitoring physiological variables was not superior to monitoring symptoms, and the latter would be a simpler approach, except where there is a need for objective verification of exacerbations. CLINICALTRIAL ClinicalTrials.gov NCT03003702; https://clinicaltrials.gov/ct2/show/NCT03003702

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