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

Background: Respiratory failure due to coronavirus disease of 2019 (COVID-19) often presents with worsening gas exchange over a period of days. Once patients require mechanical ventilation (MV), the temporal change in gas exchange and its relation to clinical outcome is poorly described. We investigated whether gas exchange over the first 5 days of MV is associated with mortality and ventilator-free days at 28 days in COVID-19. Methods: In a cohort of 294 COVID-19 patients, we used data during the first 5 days of MV to calculate 4 daily respiratory scores: PaO2/FiO2 (P/F), oxygenation index (OI), ventilatory ratio (VR), and Murray lung injury score. The association between these scores at early (days 1-3) and late (days 4-5) time points with mortality was evaluated using logistic regression, adjusted for demographics. Correlation with ventilator-free days was assessed (Spearman rank-order coefficients). Results: Overall mortality was 47.6%. Nonsurvivors were older (P < .0001), more male (P = .029), with more preexisting cardiopulmonary disease compared to survivors. Mean PaO2 and PaCO2 were similar during this timeframe. However, by days 4 to 5 values for all airway pressures and FiO2 had diverged, trending lower in survivors and higher in nonsurvivors. The most substantial between-group difference was the temporal change in OI, improving 15% in survivors and worsening 11% in nonsurvivors (P < .05). The adjusted mortality OR was significant for age (1.819, P = .001), OI at days 4 to 5 (2.26, P = .002), and OI percent change (1.90, P = .02). The number of ventilator-free days correlated significantly with late VR (-0.166, P < .05), early and late OI (-0.216, P < .01; -0.278, P < .01, respectively) and early and late P/F (0.158, P < .05; 0.283, P < .01, respectively). Conclusion: Nonsurvivors of COVID-19 needed increasing intensity of MV to sustain gas exchange over the first 5 days, unlike survivors. Temporal change OI, reflecting both PaO2 and the intensity of MV, is a potential marker of outcome in respiratory failure due to COVID-19.

Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables.

BACKGROUND: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. METHODS: This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). RESULTS: The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760-0.785]) and 0.844 (95% CI [0.839-0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726-0.729]) and 0.848 (95% CI [0.847-0.849]), respectively. CONCLUSIONS: Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.

Undetectable SARS-CoV-2 in a nasopharyngeal swab but persistent viral RNA from deep lung swabs: findings from an autopsy.

During the global pandemic of COVID-19 accurate diagnosis of the infection by demonstrating SARS-CoV-2 viral RNA by PCR in specimens is crucial for therapeutic and preventative interventions. There have been instances where nasal and throat swabs have been negative despite the patient having typical clinical and radiological findings compatible with the disease. We report a case of a man in his late 50s, brought to the hospital following a cardiac arrest and prolonged unsuccessful resuscitation. The history was typical for COVID-19 with fever for 10 days and worsening shortness of breath. His throat and nasal swabs (after death) were negative for SARS-CoV-2. A limited diagnostic autopsy was performed after 27 days, and lung swabs confirmed presence of SARS-CoV-2. This case highlights the importance of lung swabs when initial upper respiratory tract swabs are negative and proves that the virus can be detected from dead human tissue almost a month later.

Continued In-Hospital Angiotensin-Converting Enzyme Inhibitor and Angiotensin II Receptor Blocker Use in Hypertensive COVID-19 Patients Is Associated With Positive Clinical Outcome.

BACKGROUND: This study investigated continued and discontinued use of angiotensin-converting enzyme inhibitors (ACEi) or angiotensin II receptor blockers (ARB) during hospitalization of 614 hypertensive laboratory-confirmed COVID-19 patients. METHODS: Demographics, comorbidities, vital signs, laboratory data, and ACEi/ARB usage were analyzed. To account for confounders, patients were substratified by whether they developed hypotension and acute kidney injury (AKI) during the index hospitalization. RESULTS: Mortality (22% vs 17%, P > .05) and intensive care unit (ICU) admission (26% vs 12%, P > .05) rates were not significantly different between non-ACEi/ARB and ACEi/ARB groups. However, patients who continued ACEi/ARBs in the hospital had a markedly lower ICU admission rate (12% vs 26%; P = .001; odds ratio [OR] = 0.347; 95% confidence interval [CI], .187-.643) and mortality rate (6% vs 28%; P = .001; OR = 0.215; 95% CI, .101-.455) compared to patients who discontinued ACEi/ARB. The odds ratio for mortality remained significantly lower after accounting for development of hypotension or AKI. CONCLUSIONS: These findings suggest that continued ACEi/ARB use in hypertensive COVID-19 patients yields better clinical outcomes.

Prediction model and risk scores of ICU admission and mortality in COVID-19.

This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.