Risk factors for reintubation and mortality among patients who had unplanned extubation.

BACKGROUND: Unplanned extubation (UE) occurs among 2%-16% of patients with mechanical ventilation (MV). Failed UE requiring reintubation could be associated with several adverse events. AIMS: The aim of this study was to investigate the outcomes and prognostic factors of patients with UE in intensive care units (ICUs). METHODS: We prospectively registered the patients who had UE and retrospectively reviewed the electronic medical records for 96-bed ICUs between 1 January 2009, and 31 December 2020. RESULTS: A total of 392 patients had UE, and 234 patients (59.7%) were ≥65 years (older adult group). The median Acute Physiology and Chronic Health Evaluation (APACHE) II score were 17 and the median Glasgow Coma Scale score was 10. In total, 205 patients (52.3%) were reintubated within 48 h (due to failed UE) and 75 patients (19.1%) died during hospitalization. Multivariate analyses were performed to evaluate those factors predicting failed UE and mortality. These analyses demonstrated that higher positive end-expiratory pressure (PEEP) and the admission APACHE II scores predicted failed UE. A higher fraction of inspiration O2 (FiO2 ) and minute ventilation; lower haemoglobin (Hb); and higher instances of liver cirrhosis, cancer, and failed UE were independently associated with hospital mortality. CONCLUSION: We concluded that among patients who had UE, higher FiO2 or minute ventilation, or under MV or with lower Hb, liver cirrhosis, cancer, and failed UE tended to have higher mortality. RELEVANCE TO CLINICAL PRACTICE: Patients with high disease severity indices who have an increased risk of UE required special attention to techniques to prevent endotracheal tubes from accidental removal.

An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.

Background: For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose: Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods: AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results: A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion: We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.

The Impact of the Pay-for-Performance Program on the Outcome of COPD Patients in Taiwan After One Year.

Objective: To investigate the impact of a multidisciplinary intervention on the clinical outcomes of patients with COPD. Methods: This study retrospectively extracted the data of patients enrolled in the national pay-for-performance (P4P) program for COPD in four hospitals. Only COPD patients who received regular follow-up for at least one year in the P4P program between September 2018 and December 2020 were included. Results: A total of 1081 patients were included in this study. Among them, 424 (39.2%), 287 (26.5%), 179 (16.6%), and 191 (17.7%) patients were classified as COPD Groups A, B, C, and D, respectively. Dual therapy with long-acting ß2-agonist (LABA)/long-acting muscarinic antagonist (LAMA) was the most used inhaled bronchodilator at baseline (n = 477, 44.1%) patients, followed by LAMA monotherapy (n = 195, 18.0%), triple therapy with inhaled corticosteroid (ICS)/LABA/LAMA (n = 184, 17.0%), and ICS/LABA combination (n = 165, 15.3%). After one year of intervention, 374 (34.6%) and 323 (29.9%) patients had their pre- and post-bronchodilator-forced expiratory volume in one second (FEV1) increase of more than 100 mL. Both the COPD Assessment Test (CAT) and modified British Medical Research Council (mMRC) scores had a mean change of -2.2 ± 5.5 and -0.3 ± 0.9, respectively. The improvement in pulmonary function and symptom score were observed across four groups. The decreased number of exacerbations was only observed in Groups C and D, and not in Groups A and B. Conclusion: This real-world study demonstrated that the intervention in the P4P program could help improve the clinical outcome of COPD patients. It also showed us a different view on the use of dual therapy, which has a lower cost in Taiwan.

Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers.

Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed.

Emotional Labor Is Associated With the Mental Health of Respiratory Therapists in Hospitals.

BACKGROUND: Respiratory therapists (RTs) play important roles in providing ventilator support to patients in hospitals. They are on the front line in respiratory cases and work with physicians to help patients survive. However, questions remain regarding whether the mental health conditions at work are protected and secured for RTs. This study aimed to explore the risk factors of mental illness for RTs and to design an app to allow individual RTs to seek assistance at an earlier stage. METHODS: A total of 642 RTs from 107 two-tiered hospitals in Taiwan were randomly selected to complete a 44-item, 5-category questionnaire regarding emotional labor and mental health in 2019. Exploratory factor analysis, the Rasch model, descriptive statistics, the nonparametric Mann-Whitney U test, the Kruskal-Wallis test for unpaired t test, and one-way analysis of variance were performed to examine the demographic characteristics and emotional labor and mental health factors that influence RTs' mental health. An app was then designed to evaluate their mental health status. RESULTS: A total of 352 questionnaires were eligible, with a return rate of 54.8% (352 of 642). About 62.8% came from medical centers and 37.2% from regional hospitals. There were 311 (88.4%) women and 41 (11.6%) men, with a mean ± SD age of 37 ± 9.5 y. Six construct factors were extracted from the responses. The overall reliability of the emotional labor and mental health questionnaire for each subscale beyond 0.70 was evident based on internal consistency and stability in the data. Four risk factors (ie, basic emotional expression, superficial emotional control, emotional diversity extent, and weekly work hours) influenced RT mental health. All findings were applied to design an app for RTs to evaluate their mental health at work. CONCLUSIONS: Four risk factors were verified to influence RT mental health. An app was developed to detect their mental health and allow them to seek assistance at an earlier stage.

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study.

BACKGROUND: Mental illness (MI) is common among those who work in health care settings. Whether MI is related to employees' mental status at work is yet to be determined. An MI app is developed and proposed to help employees assess their mental status in the hope of detecting MI at an earlier stage. OBJECTIVE: This study aims to build a model using convolutional neural networks (CNNs) and fit statistics based on 2 aspects of measures and outfit mean square errors for the automatic detection and classification of personal MI at the workplace using the emotional labor and mental health (ELMH) questionnaire, so as to equip the staff in assessing and understanding their own mental status with an app on their mobile device. METHODS: We recruited 352 respiratory therapists (RTs) working in Taiwan medical centers and regional hospitals to fill out the 44-item ELMH questionnaire in March 2019. The exploratory factor analysis (EFA), Rasch analysis, and CNN were used as unsupervised and supervised learnings for (1) dividing RTs into 4 classes (ie, MI, false MI, health, and false health) and (2) building an ELMH predictive model to estimate 108 parameters of the CNN model. We calculated the prediction accuracy rate and created an app for classifying MI for RTs at the workplace as a web-based assessment. RESULTS: We observed that (1) 8 domains in ELMH were retained by EFA, (2) 4 types of mental health (n=6, 63, 265, and 18 located in 4 quadrants) were classified using the Rasch analysis, (3) the 44-item model yields a higher accuracy rate (0.92), and (4) an MI app available for RTs predicting MI was successfully developed and demonstrated in this study. CONCLUSIONS: The 44-item model with 108 parameters was estimated by using CNN to improve the accuracy of mental health for RTs. An MI app developed to help RTs self-detect work-related MI at an early stage should be made more available and viable in the future.

Outcome of liver cirrhosis patients requiring prolonged mechanical ventilation.

Acute respiratory failure requiring mechanical ventilation is a major indicator of intensive care unit (ICU) admissions in cirrhotic patients and is an independent risk factor for ICU mortality. This retrospective study aimed to investigate the outcome and mortality risk factors in patients with liver cirrhosis (LC) who required prolonged mechanical ventilation (PMV) between 2006 and 2013 from two databases: Taiwan's National Health Insurance Research Database (NHIRD) and a hospital database. The hospital database yielded 58 LC patients (mean age: 65.3 years; men: 65.5%). The in-hospital mortality was significantly higher than in patients without LC. Based on the NHIRD database of PMV cases, patients were age-gender matched in a ratio of 1:2 for patients with and without LC. Model for End-Stage Liver Disease (MELD) score was calculated. The mortality was higher in patients with LC (19.5%) than those without LC (18.12%), though not statistically significant (p = 0.0622). Based on the hospital database, risk factor analysis revealed that patients who died had significant higher MELD score than the survivors (18.9 vs 13.7, p = 0.036) and patients with MELD score of >23 had higher risk of mortality than patients with MELD score of ≤23 (adjusted OR:9.26, 95% CI: 1.96-43.8). In conclusion, the in-hospital mortality of patients with high MELD scores who required PMV was high. MELD scores may be useful predictors of mortality in these patients.

Prognosis of patients with acute respiratory failure and prolonged intensive care unit stay.

BACKGROUND: Reasons for the prolonged critical care support include uncertainty of outcome, the complex dynamic created between physicians with care team members and the patient's family over a general unwillingness to surrender to unfavorable outcomes. The purpose of this study was to investigate outcomes and identify risk factors of patients with acute respiratory failure (ARF) who required a prolonged intensive care unit (ICU) stay (≥21 days). It may provide reference to screen patients who are suitable for hospice care. METHODS: The medical records of all ARF patients with a prolonged ICU stay were retrospectively reviewed. The primary outcome was in-hospital mortality. RESULTS: We identified 1,189 patients. Sepsis (n=896, 75.4%) was the most common cause of prolonged ICU stays, following by renal failure (n=232, 19.5%), and unstable hemodynamic status vasopressors or arrhythmia (n=208, 17.5%). Using multivariable logistic regression, we identified eight risk factors of death: age >75 years, ICU stay for more than 28 days, APACHE II score ≥25, unstable hemodynamic status, renal failure, hepatic failure, massive gastrointestinal tract bleeding, and using a fraction of inspired oxygen (FiO2) ≥40%. The overall in-hospital mortality rate was 53.6% (n=637), and it up to 75.3% (216/287) for patients with at least three risk factors. CONCLUSIONS: The outcome of patients with ARF who required prolonged ICU stay was poor. They had a high risk of in-hospital mortality. Palliative care should be considered as a reasonable option for the patients at high risk of death.

Prognostic factors and outcomes of unplanned extubation.

This study investigated the prognostic factors and outcomes of unplanned extubation (UE) in patients in a medical center's 6 intensive care units (ICUs) and calculated their mortality risk. We retrospectively reviewed the medical records of all adult patients in Chi Mei Medical Center who underwent UE between 2009 and 2015. During the study period, there were 305 episodes of UE in 295 ICU patients (men: 199 [67.5%]; mean age: 65.7 years; age range: 18-94 years). The mean Acute Physiology and Chronic Health Evaluation (APACHE) II score was 16.4, mean therapeutic intervention scoring system (TISS) score was 26.5, and mean Glasgow coma scale score was 10.4. One hundred thirty-six patients (46.1%) were re-intubated within 48 h. Forty-five died (mortality rate: 15.3%). Multivariate analyses showed 5 risk factors-respiratory rate, APACHE II score, uremia, liver cirrhosis, and weaning status-were independently associated with mortality. In conclusion, five risk factors including a high respiratory rate before UE, high APACHE II score, uremia, liver cirrhosis, and not in the process of being weaned-were associated with high mortality in patients who underwent UE.

Establishing failure predictors for the planned extubation of overweight and obese patients.

We investigated failure predictors for the planned extubation of overweight (body mass index [BMI] = 25.0-29.9) and obese (BMI ≥ 30) patients. All patients admitted to the adult intensive care unit (ICU) of a tertiary hospital in Taiwan were identified. They had all undergone endotracheal intubation for > 48 h and were candidates for extubation. During the study, 595 patients (overweight = 458 [77%]); obese = 137 [23%]) with planned extubation after weaning were included in the analysis; extubation failed in 34 patients (5.7%). Their mean BMI was 28.5 ± 3.8. Only BMI and age were significantly different between overweight and obese patients. The mortality rate for ICU patients was 0.8%, and 2.9% for inpatients during days 1-28; the overall in-hospital mortality rate was 8.4%. Failed Extubation group patients were significantly older, had more end-stage renal disease (ESRD), more cardiovascular system-related respiratory failure, higher maximal inspiratory pressure (MIP), lower maximal expiratory pressure (MEP), higher blood urea nitrogen, and higher ICU- and 28-day mortality rates than did the Successful Extubation group. Multivariate logistic regression showed that cardiovascular-related respiratory failure (odds ratio [OR]: 2.60; 95% [confidence interval] CI: 1.16-5.80), ESRD (OR: 14.00; 95% CI: 6.25-31.35), and MIP levels (OR: 0.94; 95% CI: 0.90-0.97) were associated with extubation failure. We conclude that the extubation failure risk in overweight and obese patients was associated with cardiovascular system-related respiratory failure, ESRD, and low MIP levels.

The prognostic value of N-terminal proB-type natriuretic peptide in patients with acute respiratory distress syndrome.

We investigated whether N-terminal proB-type natriuretic peptide (NT-proBNP) predicts the prognosis of patients with acute respiratory distress syndrome (ARDS). Between December 1, 2012, and May 31, 2015, this observational study recruited patients admitted to our tertiary medical center who met the Berlin criteria for ARDS and who had their NT-proBNP measured. The main outcome was 28-day mortality. We enrolled 61 patients who met the Berlin criteria for ARDS: 7 were classified as mild, 29 as moderate, and 25 as severe. The median APACHE II scores were 23 (interquartile range [IQR], 18-28), and SOFA scores were 11 (IQR, 8-13). The median lung injury score was 3.0 (IQR, 2.50-3.25), and the median level of NT-proBNP was 2011 pg/ml (IQR, 579-7216). Thirty-four patients died during this study, and the 28-day mortality rate was 55.7%. Patients who die were older and had significantly (all p < 0.05) higher APACHE II scores and NT-proBNP levels than did patients who survived. Multivariate analysis identified age (HR: 1.546, 95% CI: 1.174-2.035, p = 0.0019) and NT-proBNP (HR: 1.009, 95% CI: 1.004-1.013, p = 0.0001) as significant risk factors of death. NT-proBNP was associated with poor outcomes for patients with ARDS, and its level predicted mortality.

Establishing predictors for successfully planned endotracheal extubation.

The aim of this study was to establish predictors for successfully planned extubation, which can be followed by medical personnel. The patients who were admitted to the adult intensive care unit of a tertiary hospital and met the following criteria between January 2005 and December 2014 were collected retrospectively: intubation > 48 hours; and candidate for extubation. The patient characteristics, including disease severity, rapid shallow breath index (RSBI), maximal inspiratory pressure (MIP), maximal expiratory pressure (MEP), cuff leak test (CLT) before extubation, and outcome, were recorded. The CLT was classified as 2+ with audible flow without a stethoscope, 1+ with audible flow using a stethoscope, and negative (N) with no audible flow, even with a stethoscope. Failure to extubate was defined as reintubation within 48 hours. In total, 6583 patients were enrolled and 403 patients (6.1%) had extubation failures. Male patients dominated the patient cohort (4261 [64.7%]). The mean age was 64.5±16.3 years. The overall in-hospital mortality rate was 11.3%. The extubation failure rate for females was greater than males (7.7% vs 5.3%, P < 0.001). The group of patients who failed extubation were older (66.7 ±â€Š14.4 vs 64.3 ±â€Š16.4, P = 0.002), had higher APACHE II scores (16.8 ±â€Š7.6 vs 15.9 ±â€Š7.8, P = 0.023), lower coma scales (10.3 ±â€Š3.7 vs 10.8 ±â€Š3.7, P = 0.07), a higher RSBI (69.9 ±â€Š37.3 vs 58.6 ±â€Š30.3, P < 0.001), a lower MIP, and MEP (-35.6 ±â€Š15.3 vs -37.8 ±â€Š14.6, P = 0.0001 and 49.6 ±â€Š28.4 vs 58.6 ±â€Š30.2, P < 0.001, respectively), and a higher mortality rate (25.6% vs 10.5%, P < 0.001) compared to the successful extubation group. Based on multivariate logistic regression, a CLT of 2+ (odds ratio [OR] = 2.07, P < 0.001), a MEP ≥ 55 cmH2O (OR = 1.73, P < 0.001), and a RSBI < 68 breath/min/ml (OR = 1.57, P < 0.001) were independent predictors for successful extubation.This study identified 3 independent risk factors for successful extubation after a successful breathing trial, including a CLT of 2+, a MEP ≥ 55 cmH2O, and a RSBI < 68 breath/min/ml. Furthermore, a nomogram integrating these 3 parameters, which represented the combined consideration of the upper airway patentency, cough strength, and respiratory capacity, was developed to better predict extubation success.

Improved survival in patients with severe tuberculosis after exposure to fluoroquinolones.

BACKGROUND: The clinical impact of prior exposure to antibiotics on patients with tuberculosis (TB) is largely unknown. This study investigated the survival of patients with severe TB after exposure to a variety of antibiotics. METHODS: A retrospective cohort study was conducted in TB patients with prior exposure to fluoroquinolones (FQs) (FQ group), to third-generation cephalosporins (CEPH group), and to third-generation penicillins (PCN group). To understand the impact of monotherapy with antibiotics on survival, patients with prior exposure to only moxifloxacin, ceftriaxone, or piperacillin were investigated. RESULTS: Patients in the FQ group (N= 401) had a significantly higher survival rate (82.5%) than patients in the CEPH (N = 210) and PCN (N = 172) groups (67.6% and 62.8%, respectively; both p < 0.0001) at 180 d after TB diagnosis. Adjusted odds ratio (AOR) logistic regression analysis demonstrated that patients in the FQ group had significantly more favourable outcomes than those in the CEPH and PCN groups in terms of intensive care unit (ICU) admission rate (versus CEPH cohort: AOR, 1.70; 95% CI: 1.16-2.50; p = 0.0067, versus PCN cohort: AOR, 3.58; 95% CI: 2.42-5.29; both p < 0.0001), mechanical ventilation rate (versus CEPH cohort: AOR, 1.70; 95% CI: 1.09-2.66; p = 0.0205, versus PCN cohort: AOR, 3.92; 95% CI: 2.54-6.05; both p < 0.0001), and acute respiratory failure rate (versus CEPH cohort: AOR, 1.62; 95% CI: 1.07-2.45; p = 0.0223, versus PCN cohort: AOR, 4.29; 95% CI: 2.86-6.43; both p < 0.0001). TB patients with prior exposure to moxifloxacin (N = 198) had a significantly higher survival rate (85.9%) than that of patients with exposure to ceftriaxone (N = 119) and piperacillin (N = 172) monotherapy (survival rates: 69.8% and 62.8%, respectively; both p < 0.001). CONCLUSIONS: TB patients with prior exposure to FQs had more favourable outcomes compared with patients who had prior exposure to third-generation cephalosporins or third-generation penicillins. This study provides new insights into the impact of previous exposure to FQs on the survival of TB patients.

The Ratio of Partial Pressure Arterial Oxygen and Fraction of Inspired Oxygen 1 Day After Acute Respiratory Distress Syndrome Onset Can Predict the Outcomes of Involving Patients.

The initial hypoxemic level of acute respiratory distress syndrome (ARDS) defined according to Berlin definition might not be the optimal predictor for prognosis. We aimed to determine the predictive validity of the stabilized ratio of partial pressure arterial oxygen and fraction of inspired oxygen (PaO2/FiO2 ratio) following standard ventilator setting in the prognosis of patients with ARDS.This prospective observational study was conducted in a single tertiary medical center in Taiwan and compared the stabilized PaO2/FiO2 ratio (Day 1) following standard ventilator settings and the PaO2/FiO2 ratio on the day patients met ARDS Berlin criteria (Day 0). Patients admitted to intensive care units and in accordance with the Berlin criteria for ARDS were collected between December 1, 2012 and May 31, 2015. Main outcome was 28-day mortality. Arterial blood gas and ventilator setting on Days 0 and 1 were obtained.A total of 238 patients met the Berlin criteria for ARDS were enrolled, and they were classified as mild (n = 50), moderate (n = 125), and severe (n = 63) ARDS, respectively. Twelve (5%) patients who originally were classified as ARDS did not continually meet the Berlin definition, and a total of 134 (56%) patients had the changes regarding the severity of ARDS from Day 0 to Day 1. The 28-day mortality rate was 49.1%, and multivariate analysis identified age, PaO2/FiO2 on Day 1, number of organ failures, and positive fluid balance within 5 days as significant risk factors of death. Moreover, the area under receiver-operating curve for mortality prediction using PaO2/FiO2 on Day 1 was significant higher than that on Day 0 (P = 0.016).PaO2/FiO2 ratio on Day 1 after applying mechanical ventilator is a better predictor of outcomes in patients with ARDS than those on Day 0.