Survival of COVID-19 Patients With Respiratory Failure is Related to Temporal Changes in Gas Exchange and Mechanical Ventilation

Factors associated with mortality of patients with COVID-19 on invasive mechanical ventilation: A retrospective cohort study in a university hospital in Northeastern Brazil

The aim of this study is to identify the factors associated with mortality in patients with COVID-19 undergoing invasive mechanical ventilation at a university hospital in Northeastern Brazil. This is a retrospective cohort from April to August 2020 through an analysis of medical records, considering the demographic profile, comorbidities, complications, supports, respiratory and laboratory parameters. A total of 65 patients required invasive mechanical ventilation, of which 64.6% died in the ICU. They were older, had more comorbidities, shorter length of stay in the intensive care unit, received more support such as palliative care and two vasopressors simultaneously, showed lower levels of pH, hemoglobin and calcium, and higher levels of bicarbonate, lactate, prothrombin time, international normalized ratio, troponin and ferritin at the start of invasive mechanical ventilation. Furthermore, the time course of pH, arterial oxygen partial pressure to fractional inspired oxygen ratio, arterial carbon dioxide partial pressure, lactate, hemoglobin, platelets, lymphocytes, neutrophil-to-lymphocyte ratio, coagulation parameters, calcium, urea, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, ferritin, static compliance, airway resistance, tidal volume, and noradrenaline doses showed association with mortality. There was a high mortality rate in invasively mechanically ventilated COVID-19 patients, with some associated factors identified at the start of invasive mechanical ventilation and others identified over time.

Coronavirus disease 2019-associated persistent cough and Chiari malformation type I resulting in acute respiratory failure: illustrative case

BACKGROUND

Chiari malformation type I (CM-I) is the herniation of cerebellar tonsils through the foramen magnum, potentially resulting in the obstruction of cerebrospinal fluid flow and brainstem compression. Sleep-disordered breathing (SDB) is common in patients with CM-I, and symptomatic exacerbations have been described after Valsalva-inducing stressors. Acute decompensation in the setting of coronavirus disease 2019 (COVID-19) has not been described.

OBSERVATIONS

After violent coughing episodes associated with COVID-19 infection, a 44-year-old female developed several months of Valsalva-induced occipital headaches, episodic bulbar symptoms, and worsening SDB, which led to acute respiratory failure requiring mechanical ventilation. Imaging demonstrated 12 mm of cerebellar tonsillar descent below the foramen magnum, dorsal brainstem compression, and syringobulbia within the dorsal medulla. She underwent posterior fossa and intradural decompression with near-complete resolution of her symptoms 6 months postoperatively.

LESSONS

Although CM-I can remain asymptomatic, Valsalva-inducing stressors, including COVID-19 infection, can initiate or acutely exacerbate symptoms, placing patients at risk for CM-I-associated brainstem dysfunction and, in rare cases, acute respiratory failure. Worsening Valsalva maneuvers can contribute to further cerebellar tonsil impaction, brainstem compression, syringomyelia/syringobulbia, and worsening CM-I intradural pathology. Ventilator support and timely decompressive surgery are paramount, as brainstem compression can reduce central respiratory drive, placing patients at risk for coma, neurological deficits, and/or death.

Ventilatory ratio as a predictor for extubation failure in critical ill patients based on MIMIC-IV database (from 2008 to 2019)

Background: The predictive ability of the ventilatory ratio (VR) for extubation failure risk in critically ill patients on mechanical ventilation is unclear. This study aims to examine the predictive ability of VR for extubation failure risk. Methods: This retrospective study was based on the MIMIC-IV database. The MIMIC-IV database consists of the clinical information of patients who were admitted to the intensive care unit at the Beth Israel Deaconess Medical Center between 2008 and 2019. With extubation failure as the primary outcome and in-hospital mortality as the secondary outcome, we assessed the predictive value of VR 4 hours before extubation using a multivariate logistic regression model. Results: Of 3,569 ventilated patients who were included, the rate of extubation-failure was 12.7% and the median Sequential Organ Failure Assessment (SOFA) score was 6 before extubation. Increased VR, elevated heart rate, greater positive end-expiratory pressure, higher blood urea nitrogen level, higher platelet count, greater SOFA score, decreased pH, decreased tidal volume, presence of chronic pulmonary disease, paraplegia, and metastatic solid tumor were independent predictors for extubation failure. A threshold of 1.595 of VR was associated with prolonged intensive care unit length of stay, higher risk of mortality and extubation failure. The area under the receiver operating characteristic curve (ROC) for VR was 0.669 [0.635-0.703], which was significantly larger than the rapid shallow breathing index [0.510 (0.476-0.545)] and the partial pressure of oxygen to the fraction of inspired oxygen [0.586 (0.551-0.621)]. Conclusion: VR 4 hours before extubation was associated with extubation failure, mortality, and prolonged length of stay in the intensive care unit. VR provides good predictive performance for extubation failure (measured by ROC) than the rapid shallow breathing index. Further prospective studies are warranted to confirm these findings.

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Background

In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.

Methods

Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.

Results

The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.

Conclusion

Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

Longitudinal medical subspecialty follow-up of critically and non-critically ill hospitalized COVID-19 survivors up to 24 months after discharge

Medical specialty usage of COVID-19 survivors after hospital discharge is poorly understood. This study investigated medical specialty usage at 1-12 and 13-24 months post-hospital discharge in critically ill and non-critically ill COVID-19 survivors. This retrospective study followed ICU (N = 89) and non-ICU (N = 205) COVID-19 survivors who returned for follow-up within the Stony Brook Health System post-hospital discharge. Follow-up data including survival, hospital readmission, ongoing symptoms, medical specialty care use, and ICU status were examined 1-12 and 13-24 months after COVID-19 discharge. "New" (not previously seen) medical specialty usage was also identified. Essentially all (98%) patients survived. Hospital readmission was 34%, but functional status scores at discharge were not associated with hospital readmission. Many patients reported ongoing [neuromuscular (50%) respiratory (39%), chronic fatigue (35%), cardiovascular (30%), gastrointestinal (28%), neurocognitive (22%), genitourinary (22%), and mood-related (13%)] symptoms at least once 1-24 months after discharge. Common specialty follow-ups included cardiology (25%), vascular medicine (17%), urology (17%), neurology (16%), and pulmonology (14%), with some associated with pre-existing comorbidities and with COVID-19. Common new specialty visits were vascular medicine (11%), pulmonology (11%), and neurology (9%). ICU patients had more symptoms and follow-ups compared to the non-ICU patients. This study reported high incidence of persistent symptoms and medical specialty care needs in hospitalized COVID-19 survivors 1-24 months post-discharge. Some specialty care needs were COVID-19 related or exacerbated by COVID-19 disease while others were associated with pre-existing medical conditions. Longer follow-up studies of COVID-19 survivor medical care needs are necessary.

Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients

Objectives

To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients.

Methods

This is a retrospective study. Serial pCXR and serial clinical variables were analyzed for data from day 1, day 5, day 1–3, day 3–5, or day 1–5 on IMV (110 IMV survivors and 76 IMV non-survivors). The outcome variables were duration on IMV and mortality. With fivefold cross-validation, the performance of the proposed deep learning system was evaluated by receiver operating characteristic (ROC) analysis and correlation analysis.

Results

Predictive models using 5-consecutive-day data outperformed those using 3-consecutive-day and 1-day data. Prediction using data closer to the outcome was generally better (i.e., day 5 data performed better than day 1 data, and day 3–5 data performed better than day 1–3 data). Prediction performance was generally better for the combined pCXR and non-imaging clinical data than either alone. The combined pCXR and non-imaging data of 5 consecutive days predicted mortality with an accuracy of 85 ± 3.5% (95% confidence interval (CI)) and an area under the curve (AUC) of 0.87 ± 0.05 (95% CI) and predicted the duration needed to be on IMV to within 2.56 ± 0.21 (95% CI) days on the validation dataset.

Conclusions

Deep learning of longitudinal pCXR and clinical data have the potential to accurately predict mortality and duration on IMV in COVID-19 patients. Longitudinal pCXR could have prognostic value if these findings can be validated in a large, multi-institutional cohort.

Impact of asthma on COVID-19 mortality in the United States: Evidence based on a meta-analysis

OBJECTIVE

The aim of this study was to investigate the impact of asthma on the risk for mortality among coronavirus disease 2019 (COVID-19) patients in the United States by a quantitative meta-analysis.

METHODS

A random-effects model was used to estimate the pooled odds ratio (OR) with corresponding 95% confidence interval (CI). I² statistic, sensitivity analysis, Begg's test, meta-regression and subgroup analyses were also performed.

RESULTS

The data based on 56 studies with 426,261 COVID-19 patients showed that there was a statistically significant association between pre-existing asthma and the reduced risk for COVID-19 mortality in the United States (OR: 0.82, 95% CI: 0.74-0.91). Subgroup analyses by age, male proportion, sample size, study design and setting demonstrated that pre-existing asthma was associated with a significantly reduced risk for COVID-19 mortality among studies with age ≥ 60 years old (OR: 0.79, 95% CI: 0.72-0.87), male proportion ≥ 55% (OR: 0.79, 95% CI: 0.72-0.87), male proportion ＜ 55% (OR: 0.81, 95% CI: 0.69-0.95), sample sizes ≥ 700 cases (OR: 0.80, 95% CI: 0.71-0.91), retrospective study/case series (OR: 0.82, 95% CI: 0.75-0.89), prospective study (OR: 0.83, 95% CI: 0.70-0.98) and hospitalized patients (OR: 0.82, 95% CI: 0.74-0.91). Meta-regression did reveal none of factors mentioned above were possible reasons of heterogeneity. Sensitivity analysis indicated the robustness of our findings. No publication bias was detected in Begg's test (P = 0.4538).

CONCLUSION

Our findings demonstrated pre-existing asthma was significantly associated with a reduced risk for COVID-19 mortality in the United States.

Characterizing non-critically ill COVID-19 survivors with and without in-hospital rehabilitation

This study investigated pre-COVID-19 admission dependency, discharge assistive equipment, discharge medical follow-up recommendation, and functional status at hospital discharge of non-critically ill COVID-19 survivors, stratified by those with (N = 155) and without (N = 162) in-hospital rehabilitation. “Mental Status”, intensive-care-unit (ICU) Mobility, and modified Barthel Index scores were assessed at hospital discharge. Relative to the non-rehabilitation patients, rehabilitation patients were older, had more comorbidities, worse pre-admission dependency, were discharged with more assistive equipment and supplemental oxygen, spent more days in the hospital, and had more hospital-acquired acute kidney injury, acute respiratory failure, and more follow-up referrals (p < 0.05 for all). Cardiology, vascular medicine, urology, and endocrinology were amongst the top referrals. Functional scores of many non-critically ill COVID-19 survivors were abnormal at discharge (p < 0.05) and were associated with pre-admission dependency (p < 0.05). Some functional scores were negatively correlated with age, hypertension, coronary artery disease, chronic kidney disease, psychiatric disease, anemia, and neurological disorders (p < 0.05). In-hospital rehabilitation providing restorative therapies and assisting discharge planning were challenging in COVID-19 circumstances. Knowledge of the functional status, discharge assistive equipment, and follow-up medical recommendations at discharge could enable appropriate and timely post-discharge care. Follow-up studies of COVID-19 survivors are warranted as many will likely have significant post-acute COVID-19 sequela.