Assessment of Work of Breathing in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Sniff and reverse-sniff nasal respiratory pressures after exacerbation of chronic obstructive pulmonary disease: A single-center prospective study

BACKGROUND

and objective: This study examined the validity of sniff nasal inspiratory (SNIP) and reverse-sniff nasal expiratory pressures (RSNEP) for estimating respiratory muscle strength and for predicting poor life expectancy following exacerbation in patients with chronic obstructive pulmonary disease (COPD).

METHODS

This prospective study included patients who were admitted for COPD exacerbation and underwent rehabilitation. At hospital discharge, SNIP, RSNEP, and maximum mouth inspiratory (MIP) and expiratory pressures (MEP) were measured, and the body mass index, degree of airflow obstruction, dyspnea, and exercise capacity (BODE) index was calculated by evaluating body mass index, forced expiratory volume in 1 s (FEV1), the Modified Medical Research Council Dyspnea Scale, and 6-min walk distance.

RESULTS

Data from 43 patients (mean age 76.8 years, FEV1 42.8 % predicted) were analyzed. SNIP and RSNEP were moderately correlated with MIP and MEP, respectively. Bland-Altman plot means of SNIP (48.3 ± 17.5) and RSNEP (44.7 ± 23.8 cmH2O) were lower than those of MIP (54.8 ± 19.9) and MEP (76.4 ± 31.2 cmH2O), respectively, and the SNIP-MIP and RSNEP-MEP 95 % limits of agreement were wide. Logistic regression showed that SNIP and RSNEP were significantly associated with BODE score ≥7 (poor life expectancy), and predictive accuracy was 81.4 % when combining SNIP ≤49 and RSNEP ≤42 cmH2O.

CONCLUSION

After exacerbation in patients with COPD, SNIP and RSNEP are useful indicators that complement MIP and MEP. Furthermore, a combined SNIP and RSNEP test may be beneficial in predicting poor life expectancy.

PEEP titration by EIT strategies for patients with ARDS: A systematic review and meta-analysis

OBJECTIVE

To determine which method of Positive End-expiratory Pressure (PEEP) titration is more useful, and to establish an evidence base for the clinical impact of Electrical Impedance Tomography (EIT) based individual PEEP setting which appears to be a promising method to optimize PEEP in Acute Respiratory Distress Syndrome (ARDS) patients.

DESIGN

A systematic review and meta-analysis.

SETTING

4 databases (PUBMED, EMBASE, Web Of Science, and the Cochrane Library) from 1980 to December 2020 were performed.

PARTICIPANTS

Randomized clinical trials patients with ARDS.

MAIN VARIABLES

PaO₂/FiO₂-ratio and respiratory system compliance.

INTERVENSION

The quality of the studies was assessed with the Cochrane risk and bias tool.

RESULTS

8 trials, including a total of 222 participants, were eligible for analysis. Meta-analysis demonstrates a significantly EIT-based individual PEEP setting for patients receiving higher PaO₂/FiO₂ ratio as compared to other PEEP titration strategies [5 trials, 202 patients, SMD 0.636, (95% CI 0.364-0.908)]. EIT-drived PEEP titration strategy did not significantly increase respiratory system compliance when compared to other peep titration strategies, [7 trials, 202 patients, SMD -0.085, (95% CI -0.342 to 0.172)].

CONCLUSIONS

The benefits of PEEP titration with EIT on clinical outcomes of ARDS in placebo-controlled trials probably result from the visible regional ventilation of EIT. These findings offer clinicians and stakeholders a comprehensive assessment and high-quality evidence for the safety and efficacy of the EIT-based individual PEEP setting as a superior option for patients who undergo ARDS.

Application of Artificial Intelligence in Emergency Nursing of Patients with Chronic Obstructive Pulmonary Disease

The research achievements of artificial intelligence technology in the development of chronic obstructive pulmonary disease were explored, and the advantages and problems encountered in the development of intelligent nursing were analyzed. This paper presents the application of artificial intelligence in the emergency care of patients with chronic obstructive pulmonary disease. The method included 447 COPD patients in a randomized controlled trial to observe the improvement of quality of life at 4 and 12 months after artificial intelligence medical intervention. A prospective randomized controlled trial included 101 patients with COPD who underwent a 9-month web-based knowledge exercise on the prevention of acute exacerbation of COPD through artificial intelligence medicine and were randomly divided into two groups: the experimental group and the control group. The results show that, in the experimental group and the control group, after 4 months, the quality of life does not change; after 12 months, compared with controls, the quality of life and emotional and psychological conditions have improved obviously. 29 patients who participated in the experiment and were randomly divided into the experimental group and the control group showed satisfactory results. COPD hospitalized rate and length of hospital stay were decreased in the experimental group than in the control group. For single-factor analysis, artificial intelligence medical intervention has not achieved significant significance, and the experimental results have preliminarily confirmed the effectiveness of artificial intelligence medical treatment.