scholarly journals Early prediction of noninvasive ventilation failure in COPD patients: derivation, internal validation, and external validation of a simple risk score

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jun Duan ◽  
Shengyu Wang ◽  
Ping Liu ◽  
Xiaoli Han ◽  
Yao Tian ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Noninvasive Ventilation ◽  
Scoring System ◽  
External Validation ◽  
Chronic Obstructive ◽  
Internal Validation ◽  
Obstructive Pulmonary Disease ◽  
Derivation Cohort ◽  
Copd Patients ◽  
Early Intubation

Abstract Background Early identification of noninvasive ventilation (NIV) failure is a promising strategy for reducing mortality in chronic obstructive pulmonary disease (COPD) patients. However, a risk-scoring system is lacking. Methods To develop a scale to predict NIV failure, 500 COPD patients were enrolled in a derivation cohort. Heart rate, acidosis (assessed by pH), consciousness (assessed by Glasgow coma score), oxygenation, and respiratory rate (HACOR) were entered into the scoring system. Another two groups of 323 and 395 patients were enrolled to internally and externally validate the scale, respectively. NIV failure was defined as intubation or death during NIV. Results Using HACOR score collected at 1–2 h of NIV to predict NIV failure, the area under the receiver operating characteristic curves (AUC) was 0.90, 0.89, and 0.71 for the derivation, internal-validation, and external-validation cohorts, respectively. For the prediction of early NIV failure in these three cohorts, the AUC was 0.91, 0.96, and 0.83, respectively. In all patients with HACOR score > 5, the NIV failure rate was 50.2%. In these patients, early intubation (< 48 h) was associated with decreased hospital mortality (unadjusted odds ratio = 0.15, 95% confidence interval 0.05–0.39, p < 0.01). Conclusions HACOR scores exhibited good predictive power for NIV failure in COPD patients, particularly for the prediction of early NIV failure (< 48 h). In high-risk patients, early intubation was associated with decreased hospital mortality.

scholarly journals Characteristics and outcomes in acute patients receiving mechanical ventilation in a Coronary Care Unit (CCU)

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
M Rivadeneira Ruiz ◽  
DF Arroyo Monino ◽  
T Seoane Garcia ◽  
MP Ruiz Garcia ◽  
JC Garcia Rubira
Keyword(s):  
Mechanical Ventilation ◽  
Cardiac Arrest ◽  
Hospital Mortality ◽  
Coronary Care Unit ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Funding Sources ◽  
Copd Patients ◽  
The Mean ◽  
Coronary Care

Abstract Funding Acknowledgements Type of funding sources: None. Objectives Mechanical ventilation is the short-term technical support most widely used and cardiac arrest its main indication in a Coronary Care Unit (CCU). However, the knowledge about the specific moment and ventilator mode of onset to avoid the acute lung injury is still equivocal. Our objective is to determine the survival rate and the prognostic factors in patients supported by mechanical ventilation. Methods We conducted a retrospective cohort study of adult patients admitted to the CCU between January 2018 and November 2020 that received mechanical ventilation during the hospital stay. Results We collected 94 patients, 28% females with a median age of 68 ± 11,9. 43% were diabetics and almost one quarter of them had some degree of chronic obstructive pulmonary disease (COPD). Ischemic cardiopathy (33%) and heart failure (31%) were frequent pathologies as well as renal injury (29% patients a filtration rate below 45 mL/min/1,73m2). The reason for initiating mechanical ventilation was cardiac arrest in the half of the patients. Volume-controlled ventilation (73%) was the initial setting mode in most cases. The support with vasoactive drugs were highly necessary in these patients (Infection rate of 48%). In the subgroup analysis, we realized that the number of reintubations and the necessity of non-invasive ventilation were higher in the COPD group (p = 0,01), as well as tracheostomy (p = 0,03). COPD patients also needed higher maintaining PEEP, though this was not statistically significant. The mean length of stay in the intensive care unit of our cohort was 11 days (range: 1-78 days; median: 8 days) and the mean length of mechanical ventilation 6 days (range: 1-64 days; median: 3 days). The in-hospital mortality was 41,4%. Conclusions Cardiac arrest is the most common reason of mechanical ventilation support. Our study showed that COPD patients presented more complications during the weaning and the period after extubation. In-hospital mortality remains high in intubated patients.

scholarly journals Risk Factors Associated with Late Failure of Noninvasive Ventilation in Patients with Chronic Obstructive Pulmonary Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Tao Chen ◽  
Linfu Bai ◽  
Wenhui Hu ◽  
Xiaoli Han ◽  
Jun Duan
Keyword(s):  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Heart Rate ◽  
Hospital Mortality ◽  
Acute Exacerbation ◽  
Noninvasive Ventilation ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Late Failure ◽  
Unit Increase

Background. Risk factors for noninvasive ventilation (NIV) failure after initial success are not fully clear in patients with acute exacerbation of chronic obstructive pulmonary disease (COPD). Methods. Patients who received NIV beyond 48 h due to acute exacerbation of COPD were enrolled. However, we excluded those whose pH was higher than 7.35 or PaCO2 was less than 45 mmHg which was measured before NIV. Late failure of NIV was defined as patients required intubation or died during NIV after initial success. Results. We enrolled 291 patients in this study. Of them, 48 (16%) patients experienced late NIV failure (45 received intubation and 3 died during NIV). The median time from initiation of NIV to intubation was 4.8 days (IQR: 3.4–8.1). Compared with the data collected at initiation of NIV, the heart rate, respiratory rate, pH, and PaCO2 significantly improved after 1–2 h of NIV both in the NIV success and late failure of NIV groups. Nosocomial pneumonia (odds ratio (OR) = 75, 95% confidence interval (CI): 11–537), heart rate at initiation of NIV (1.04, 1.01–1.06 beat per min), and pH at 1–2 h of NIV (2.06, 1.41–3.00 per decrease of 0.05 from 7.35) were independent risk factors for late failure of NIV. In addition, the Glasgow coma scale (OR = 0.50, 95% CI: 0.34–0.73 per one unit increase) and PaO2/FiO2 (0.992, 0.986–0.998 per one unit increase) were independent protective factors for late failure of NIV. In addition, patients with late failure of NIV had longer ICU stay (median 9.5 vs. 6.6 days) and higher hospital mortality (92% vs. 3%) compared with those with NIV success. Conclusions. Nosocomial pneumonia; heart rate at initiation of NIV; and consciousness, acidosis, and oxygenation at 1–2 h of NIV were associated with late failure of NIV in patients with COPD exacerbation. And, late failure of NIV was associated with increased hospital mortality.

scholarly journals Noninvasive ventilation in stable hypercapnic COPD: what is the evidence?

2018 ◽  
Vol 4 (2) ◽  
pp. 00012-2018 ◽  
Author(s):  
Marieke L. Duiverman
Keyword(s):  
Noninvasive Ventilation ◽  
High Intensity ◽  
Randomised Controlled Trials ◽  
Therapy Monitoring ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Positive Effects ◽  
Copd Patients ◽  
Randomised Controlled

Long-term noninvasive ventilation (NIV) to treat chronic hypercapnic respiratory failure is still controversial in severe chronic obstructive pulmonary disease (COPD) patients. However, with the introduction of high-intensity NIV, important benefits from this therapy have also been shown in COPD. In this review, the focus will be on the arguments for long-term NIV at home in patients with COPD. The rise of (high-intensity) NIV in COPD and the randomised controlled trials showing positive effects with this mode of ventilation will be discussed. Finally, the challenges that might be encountered (both in clinical practice and in research) in further optimising this therapy, monitoring and following patients, and selecting the patients who might benefit most will be reviewed.

scholarly journals Prediction of cardiovascular risk in patients with chronic obstructive pulmonary disease: a study of the National Health and Nutrition Examination Survey database

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yun Shi ◽  
Jing Zhang ◽  
Yingshuo Huang
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Logistic Regression Model ◽  
Chronic Obstructive ◽  
Nutrition Examination Survey ◽  
Cvd Risk ◽  
Internal Validation ◽  
Obstructive Pulmonary Disease ◽  
Health And Nutrition ◽  
Copd Patients

Abstract Background Cardiovascular disease (CVD) is a common comorbidity associated with chronic obstructive pulmonary disease (COPD), but few studies have been conducted to identify CVD risk in COPD patients. This study was to develop a predictive model of CVD in COPD patients based on the National Health and Nutrition Examination Survey (NHANES) database. Methods A total of 3,226 COPD patients were retrieved from NHANES 2007–2012, dividing into the training (n = 2351) and testing (n = 895) sets. The prediction models were conducted using the multivariable logistic regression and random forest analyses, respectively. Receiver operating characteristic (ROC) curves, area under the curves (AUC) and internal validation were used to assess the predictive performance of models. Results The logistic regression model for predicting the risk of CVD was developed regarding age, gender, body mass index (BMI), high-density lipoprotein (HDL), glycosylated hemoglobin (HbA1c), family history of heart disease, and stayed overnight in the hospital due to illness last year, which the AUC of the internal validation was 0.741. According to the random forest analysis, the important variables-associated with CVD risk were screened including smoking (NNAL and cotinine), HbA1c, HDL, age, gender, diastolic blood pressure, poverty income ratio, BMI, systolic blood pressure, and sedentary activity per day. The AUC of the internal validation was 0.984, indicating the random forest model for predicting the CVD risk in COPD cases was superior to the logistic regression model. Conclusion The random forest model performed better predictive effectiveness for the cardiovascular risk among COPD patients, which may be useful for clinicians to guide the clinical practice.

scholarly journals Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jihye Yun ◽  
Young Hoon Cho ◽  
Sang Min Lee ◽  
Jeongeun Hwang ◽  
Jae Seung Lee ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Pulmonary Function Tests ◽  
External Validation ◽  
Ct Images ◽  
Chronic Obstructive ◽  
Survival Prediction ◽  
Obstructive Pulmonary Disease ◽  
Copd Patients ◽  
Fully Connected

AbstractHeterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.

scholarly journals Ward-Based Noninvasive Ventilation for Acute Hypercapnic Respiratory Failure Unrelated to Chronic Obstructive Pulmonary Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Bandar M. Faqihi ◽  
Dhruv Parekh ◽  
Samuel P. Trethewey ◽  
Julien Morlet ◽  
Rahul Mukherjee ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Respiratory Failure ◽  
Hospital Mortality ◽  
Pulmonary Disease ◽  
Hospital Discharge ◽  
Noninvasive Ventilation ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Hypercapnic Respiratory Failure ◽  
Acute Hypercapnic Respiratory Failure

Background. The use of ward-based noninvasive ventilation (NIV) for acute hypercapnic respiratory failure (AHRF) unrelated to chronic obstructive pulmonary disease (COPD) remains controversial. This study evaluated the outcomes and failure rates associated with NIV application in the ward-based setting for patients with AHRF unrelated to COPD. Methods. A multicentre, retrospective cohort study of patients with AHRF unrelated to COPD was conducted. COPD was not the main reason for hospital admission, treated with ward-based NIV between February 2004 and December 2018. All AHRF patients were eligible; exclusion criteria comprised COPD patients, age < 18 years, pre-NIV pH < 7.35, or a lack of pre-NIV blood gas. In-hospital mortality was the primary outcome; univariable and multivariable models were constructed. The obesity-related AHRF group included patients with AHRF due to obesity hypoventilation syndrome (OHS), and the non-obesity-related AHRF group included patients with AHRF due to pneumonia, bronchiectasis, neuromuscular disease, or fluid overload. Results. In total, 479 patients were included in the analysis; 80.2% of patients survived to hospital discharge. Obesity-related AHRF was the indication for NIV in 39.2% of all episodes and was the aetiology with the highest rate of survival to hospital discharge (93.1%). In the multivariable analysis, factors associated with a higher risk of in-hospital mortality were increased age (odds ratio, 95% CI: 1.034, 1.017–1.051, P < 0.001 ) and pneumonia on admission (5.313, 2.326–12.131, P < 0.001 ). In the obesity-related AHRF group, pre-NIV pH < 7.15 was associated with significantly increased in-hospital mortality (7.800, 1.843–33.013, P = 0.005 ); however, a pre-NIV pH 7.15–7.25 was not associated with increased in-hospital mortality (2.035, 0.523–7.915, P = 0.305 ). Conclusion. Pre-NIV pH and age have been identified as important predictors of surviving ward-based NIV treatment. Moreover, these data support the use of NIV in ward-based settings for obesity-related AHRF patients with pre-NIV pH thresholds down to 7.15. However, future controlled trials are required to confirm the effectiveness of NIV use outside critical care settings for obesity-related AHRF.

scholarly journals Discontinuing noninvasive ventilation in severe chronic obstructive pulmonary disease exacerbations: a randomised controlled trial

2017 ◽  
Vol 50 (1) ◽  
pp. 1601448 ◽  
Author(s):  
Jacobo Sellares ◽  
Miquel Ferrer ◽  
Antonio Anton ◽  
Hugo Loureiro ◽  
Carolina Bencosme ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Randomised Controlled Trial ◽  
Pulmonary Disease ◽  
Noninvasive Ventilation ◽  
Controlled Trial ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Patients ◽  
Randomised Controlled ◽  
Subsequent Relapse

We assessed whether prolongation of nocturnal noninvasive ventilation (NIV) after recovery from acute hypercapnic respiratory failure (AHRF) in chronic obstructive pulmonary disease (COPD) patients with NIV could prevent subsequent relapse of AHRF.A randomised controlled trial was performed in 120 COPD patients without previous domiciliary ventilation, admitted for AHRF and treated with NIV. When the episode was resolved and patients tolerated unassisted breathing for 4 h, they were randomly allocated to receive three additional nights of NIV (n=61) or direct NIV discontinuation (n=59). The primary outcome was relapse of AHRF within 8 days after NIV discontinuation.Except for a shorter median (interquartile range) intermediate respiratory care unit (IRCU) stay in the direct discontinuation group (4 (2–6)versus5 (4–7) days, p=0.036), no differences were observed in relapse of AHRF after NIV discontinuation (10 (17%)versus8 (13%) for the direct discontinuation and nocturnal NIV groups, respectively, p=0.56), long-term ventilator dependence, hospital stay, and 6-month hospital readmission or survival.Prolongation of nocturnal NIV after recovery from an AHRF episode does not prevent subsequent relapse of AHRF in COPD patients without previous domiciliary ventilation, and results in longer IRCU stay. Consequently, NIV can be directly discontinued when the episode is resolved and patients tolerate unassisted breathing.

scholarly journals Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jiong Li ◽  
Yuntao Chen ◽  
Shujing Chen ◽  
Sihua Wang ◽  
Dingyu Zhang ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Cumulative Incidence ◽  
Prognostic Model ◽  
External Validation ◽  
Predictive Performance ◽  
Prognostic Models ◽  
Internal Validation ◽  
Derivation Cohort ◽  
Proposed Model ◽  
Bootstrap Approach

Abstract Background Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.

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