Risk Factors Associated with Mortality among Mechanically Ventilated Patients with Coronavirus Disease 2019 Pneumonia: A Multicenter Cohort Study in Japan (J-RECOVER Study).

Objective Mortality analyses of patients with coronavirus disease 2019 (COVID-19) requiring invasive mechanical ventilation in Japan are limited. The present study therefore determined the risk factors for mortality in patients with COVID-19 requiring invasive mechanical ventilation. Methods This retrospective cohort study used the dataset from the Japanese multicenter research of COVID-19 by assembling real-word data (J-RECOVER) study that was conducted between January 1 and September 31, 2020. Independent risk factors associated with in-hospital mortality were evaluated using a multivariate logistic regression analysis. Kaplan-Meier estimates of the survival were calculated for different age groups. A subgroup analysis was performed to assess differences in survival rates according to additional risk factors, including an older age and chronic pulmonary disease. Patients A total of 561 patients were eligible. The median age was 67 (interquartile range: 56-75) years old, 442 (78.8%) were men, and 151 (26.9%) died in the hospital. Results Age, chronic pulmonary disease, and renal disease were significantly associated with in-hospital mortality. Compared with patients 18-54 years old, the adjusted odds ratios of patients 55-64, 65-74, and 75-94 years old were 3.34 (95% CI, 1.34-8.31), 7.07 (95% CI, 3.05-16.40), and 18.43 (95% CI, 7.94-42.78), respectively. Conclusion Age, chronic pulmonary disease, and renal disease were independently associated with mortality in patients with COVID-19 requiring invasive mechanical ventilation, and age was the most decisive indicator of a poor prognosis. Our results may aid in formulating treatment strategies and allocating healthcare resources.

Reintubation in COVID-19 patients: a multicenter observational study in Japan (J-RECOVER study)

Background Reintubation is not an uncommon occurrence following extubation and discontinuation of mechanical ventilation. In COVID-19 patients, the proportion of reintubation may be higher than that of non-COVID-19 patients. Furthermore, COVID-19 patients may have a higher risk for developing laryngotracheal stenosis, along with a higher proportion of reintubation than in non-COVID-19 patients. Our understanding of the proportion of reintubation in COVID-19 patients is limited in Japan. Additionally, the reasons for reintubation have not been adequately examined in previous studies outside of Japan. Thus, the present study aimed to describe the proportion and causes of reintubation among COVID-19 patients in Japan. Methods This was a multicenter observational study that included 64 participating centers across Japan. This study included mechanically ventilated COVID-19 patients who were discharged between April 1, 2020 and September 30, 2020. The outcomes examined were the proportion and causes of reintubation. Results A total of 373 patients were eligible for inclusion in the current analysis. The median age of patients was 64 years and 80.4% were male. Reintubation was required for 35 patients (9.4%) and the most common causes for reintubation were respiratory failure (71.4%;n = 25) and laryngotracheal stenosis (8.6%;n = 3). Conclusions The proportion of reintubation among COVID-19 patients in Japan was relatively low. Respiratory failure was the most common cause for reintubation. Reintubation due to laryngotracheal stenosis accounted for only a small fraction of all reintubated COVID-19 patients in Japan.

Reintubation in COVID-19 patients: a multicenter observational study in Japan (J-RECOVER study).

BACKGROUND: Reintubation is not an uncommon occurrence following extubation and discontinuation of mechanical ventilation. In COVID-19 patients, the proportion of reintubation may be higher than that of non-COVID-19 patients. Furthermore, COVID-19 patients may have a higher risk for developing laryngotracheal stenosis, along with a higher proportion of reintubation than in non-COVID-19 patients. Our understanding of the proportion of reintubation in COVID-19 patients is limited in Japan. Additionally, the reasons for reintubation have not been adequately examined in previous studies outside of Japan. Thus, the present study aimed to describe the proportion and causes of reintubation among COVID-19 patients in Japan. METHODS: This was a multicenter observational study that included 64 participating centers across Japan. This study included mechanically ventilated COVID-19 patients who were discharged between April 1, 2020 and September 30, 2020. The outcomes examined were the proportion and causes of reintubation. RESULTS: A total of 373 patients were eligible for inclusion in the current analysis. The median age of patients was 64 years and 80.4% were male. Reintubation was required for 35 patients (9.4%) and the most common causes for reintubation were respiratory failure (71.4%; n = 25) and laryngotracheal stenosis (8.6%; n = 3). CONCLUSIONS: The proportion of reintubation among COVID-19 patients in Japan was relatively low. Respiratory failure was the most common cause for reintubation. Reintubation due to laryngotracheal stenosis accounted for only a small fraction of all reintubated COVID-19 patients in Japan.

Relationship between institutional intensive care volume prior to the COVID-19 pandemic and in-hospital death in ventilated patients with severe COVID-19.

We aimed to evaluate the association between ICU patient volume before the COVID-19 pandemic and the outcomes of ventilated COVID-19 patients. We analyzed ventilated patients with COVID-19 aged > 17 years and enrolled in the J-RECOVER study, a retrospective multicenter observational study conducted in Japan between January and September 2020. Based on the number of patients admitted to the ICU between January and December 2019, the top third institutions were defined as high-volume centers, the middle third ones as middle-volume centers, and the bottom third ones as low-volume centers. The primary outcome measure was in-hospital mortality. Multivariate logistic regression analysis for in-hospital mortality and ICU patient volume was performed after adjusting for multiple propensity scores. Among 461 patients, 158, 158, and 145 patients were admitted to low-volume (20 institutions), middle-volume (14 institutions), and high-volume (13 institutions) centers, respectively. Admission to middle- and high-volume centers was not significantly associated with in-hospital death compared with admission to low-volume centers (adjusted odds ratio, 1.11 [95% confidence interval (CI): 0.55-2.25] and adjusted odds ratio, 0.81 [95% CI: 0.31-1.94], respectively). In conclusion, institutional intensive care patient volume prior to the COVID-19 pandemic was not significantly associated with in-hospital death in ventilated COVID-19 patients.

Early intubation and decreased in-hospital mortality in patients with coronavirus disease 2019.

BACKGROUND: Some academic organizations recommended that physicians intubate patients with COVID-19 with a relatively lower threshold of oxygen usage particularly in the early phase of pandemic. We aimed to elucidate whether early intubation is associated with decreased in-hospital mortality among patients with novel coronavirus disease 2019 (COVID-19) who required intubation. METHODS: A multicenter, retrospective, observational study was conducted at 66 hospitals in Japan where patients with moderate-to-severe COVID-19 were treated between January and September 2020. Patients who were diagnosed as COVID-19 with a positive reverse-transcription polymerase chain reaction test and intubated during admission were included. Early intubation was defined as intubation conducted in the setting of ≤ 6 L/min of oxygen usage. In-hospital mortality was compared between patients with early and non-early intubation. Inverse probability weighting analyses with propensity scores were performed to adjust patient demographics, comorbidities, hemodynamic status on admission and time at intubation, medications before intubation, severity of COVID-19, and institution characteristics. Subgroup analyses were conducted on the basis of age, severity of hypoxemia at intubation, and days from admission to intubation. RESULTS: Among 412 patients eligible for the study, 110 underwent early intubation. In-hospital mortality was lower in patients with early intubation than those with non-early intubation (18 [16.4%] vs. 88 [29.1%]; odds ratio, 0.48 [95% confidence interval 0.27-0.84]; p = 0.009, and adjusted odds ratio, 0.28 [95% confidence interval 0.19-0.42]; p < 0.001). The beneficial effects of early intubation were observed regardless of age and severity of hypoxemia at time of intubation; however, early intubation was associated with lower in-hospital mortality only among patients who were intubated later than 2 days after admission. CONCLUSIONS: Early intubation in the setting of ≤ 6 L/min of oxygen usage was associated with decreased in-hospital mortality among patients with COVID-19 who required intubation. Trial Registration None.

Association between mortality and age among mechanically ventilated COVID-19 patients: a Japanese nationwide COVID-19 database study.

BACKGROUND: Only a few studies have reported the association between age and mortality in COVID-19 patients who require invasive mechanical ventilation (IMV). We aimed to evaluate the effect of age on COVID-19-related mortality among patients undergoing IMV therapy. METHODS: This cohort study was conducted using the COVID-19 Registry Japan database, a nationwide multi-centre study of hospitalized patients with laboratory-confirmed COVID-19. Of all 33,808 cases registered between 1 January 2020 to 28 February 2021, we analysed 1555 patients who had undergone IMV. We evaluated mortality rates between age groups using multivariable regression analysis after adjusting for known potential components, such as within-hospital clustering, comorbidities, steroid use, medication for COVID-19, and vital signs on admission, using generalized estimation equation. RESULTS: By age group, the mortality rates in the IMV group were 8.6%, 20.7%, 34.9%, 49.7% and 83.3% for patients in their 50s, 60s, 70s, 80s, and 90s, respectively. Multivariable analysis showed that compared with those for patients aged < 60 years, the odds ratios (95% confidence interval) of death were 2.6 (1.6-4.1), 6.9 (4.2-11.3), 13.2 (7.2-24.1), 92.6 (16.7-515.0) for patients in their 60s, 70s, 80s, and 90s, respectively. CONCLUSIONS: In this cohort study, age had a great effect on mortality in COVID-19 patients undergoing IMV, after adjusting for variables independently associated with mortality. This study suggested that age was associated with higher mortality and that preventing progression to severe COVID-19 in elderly patients may be a great public health issue.

Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19.

BACKGROUND: The coronavirus disease (COVID-19) poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19. METHODS: This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission. Data on patient characteristics and vital signs were collected upon admission. We used seven machine learning algorithms, assessed their capability to predict exacerbation, and analyzed important influencing features using the best algorithm. RESULTS: In total, 210 patients were included in the study. Among them, 43 (19%) required oxygen therapy. Of all the models, the logistic regression model had the highest accuracy and precision. Logistic regression analysis showed that the model had an accuracy of 0.900, precision of 0.893, and recall of 0.605. The most important parameter for predictive capability was SpO2, followed by age, respiratory rate, and systolic blood pressure. CONCLUSION: In this study, we developed a machine learning model that can be used as a triage tool by clinicians to detect high-risk patients and disease progression earlier. Prospective validation studies are needed to verify the application of the tool in clinical practice.