Admission SpO2 and ROX index predict outcome in patients with COVID-19

O2 Saturation Predicted the ICU Stay of COVID-19 Patients in a Hospital at Altitude: A Low-Cost Tool for Post-Pandemic

Background and Objectives: Patients at high altitudes with COVID-19 may experience a decrease in their partial oxygen saturation (PO2S) levels. The objective was to assess the association between PO2S and intensive care unit (ICU) stay in patients at high altitudes with COVID-19. Materials and Methods: Clinical records of 69 COVID-19 patients (36% women) admitted to the ICU were analyzed. Median values were considered for intra-group categories ("≤11 days" and ">11 days" in the ICU) and for PO2S height categories ("<90%" and "≥90%"). Logistic regression and linear regression models adjusted for confounding variables were used. Results: Patients with >11 days in the ICU had 84% lower odds of having a PO2S ≥ 90% (OR: 0.16 [CI: 0.02, 0.69], p = 0.005) compared to those with ≤11 days in the ICU. An increase in PO2S by 1% reduced ICU stay by 0.22 days (β: -0.22 [CI: -0.33, -0.11], p < 0.001), potentially leading to a reduction of up to 1.44 days. Conclusions: PO2S is a crucial factor in estimating ICU stays for COVID-19 patients at high altitudes and serves as an accessible and cost-effective measure. It should be used in infected patients to complement the prognosis of post-pandemic ICU stay.

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients

BACKGROUND

The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19).

MATERIALS AND METHODS

In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data.

RESULT

The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (β = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (β = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026).

CONCLUSION

The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

Predictive Factors of Oxygen Therapy Failure in Patients with COVID-19 in the Emergency Department

Background

Most patients with coronavirus disease 2019 (COVID-19) pneumonia require oxygen therapy, including standard oxygen therapy and a high-flow nasal cannula (HFNC), in the Emergency Department (ED), and some patients develop respiratory failure. In the COVID-19 pandemic, the intensive care unit (ICU) was overburdening. Therefore, prioritizing patients who require intensive care is important. This study aimed to find predictors and develop a model to predict patients at risk of requiring an invasive mechanical ventilator (IMV) in the ED.

Methods

We performed a retrospective, single-center, observational study. Patients aged ≥18 years who were diagnosed with COVID-19 and required oxygen therapy in the ED were enrolled. Cox regression and Harrell's C-statistic were used to identifying predictors of requiring IMV. The predictive model was developed by calculated coefficients and the ventilator-free survival probability. The predictive model was internally validated using the bootstrapping method.

Results

We enrolled 333 patients, and 97 (29.1%) had required IMV. Most 66 (68.0%) failure cases were initial oxygen therapy with HFNC. Respiratory rate-oxygenation (ROX) index, interleukin-6 (IL-6) concentrations ≥20 pg/mL, the SOFA (Sequential Organ Failure Assessment) score without a respiratory score, and the patient's age were independent risk factors of requiring IMV. These factors were used to develop the predictive model. ROX index and the predictive model at 2 hours showed a good performance to predict oxygen therapy failure; the c-statistic was 0.814 (95% confidence level [CI] 0.767-0.861) and 0.901 (95% CI 0.873-0.928), respectively. ROX index ≤5.1 and the predictive model score ≥8 indicated a high probability of requiring IMV.

Conclusion

The COVID-19 pandemic was limited resources, ROX index, IL-6 ≥20 pg/mL, the SOFA score without a respiratory score, and the patient's age can be used to predict oxygen therapy failure. Moreover, the predictive model is good at discriminating patients at risk of requiring IMV and close monitoring.

The Use of the "Respiratory Rate Oxygenation (ROX) Index" in the Assessment of Respiratory Support and Observation of Its Outcome in COVID-19 Patients With Acute Respiratory Failure

BACKGROUND

COVID-19 infection caused by the SARS-Cov-2 virus may result in severe acute respiratory failure and require respiratory support in the ICU.

OBJECTIVE

The present study was designed to evaluate the role of the respiratory rate oxygenation (ROX) index in the assessment of the adequacy of non-invasive respiratory support the COVID-19 patients with acute respiratory failure and observe its outcome.

MATERIALS AND METHODS

This cross-sectional, observational study was conducted between October 2020 and September 2021 in the Department of Anaesthesia, Analgesia, and Intensive Care Medicine of Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh. A total of 44 patients with a confirmed diagnosis of COVID-19 with acute respiratory failure were enrolled in this study based on inclusion and exclusion criteria. Informed written consent was taken from the patient/patient's guardian. Each patient underwent detailed history taking through physical examination and relevant investigations. All necessary information were recorded in a separate case record form. All the patients receiving high-flow nasal cannula (HFNC) were assessed at two hours, six hours, and 12 hours for variables of the ROX Index. The team of respective physicians was applied responsibly for determining HFNC failure to discontinue or deescalate respiratory support as a part of continuous positive airway pressure (CPAP) ventilation success. Each selected patient was observed for the duration of different types of respiratory support. CPAP failure or success, progression to mechanical ventilation, and data were collected from individual medical records. The patients who were successfully weaned from CPAP were recorded. The diagnostic accuracy of the ROX index was determined.

RESULTS

The mean age of the patients was 65±8.80 years with a majority in the age group 61-70 years (36.4%). A male predominance was observed with 79.5% male and 20.5% female. Of all, HFNC failure was observed in 29.5% of patients. Oxygen saturation (SpO2), respiratory rate (RR), and ROX index were statistically worse at the sixth and 12th hour of initiation of HFNC (P<0.05). At a cut-off value of 3.90, the ROC curve showed 90.3% sensitivity and 76.9% specificity in predicting HFNC success (the area under the curve (AUC) was 0.909). Similarly, 46.2% of patients had CPAP failure. SpO2, RR, and ROX index were found statistically worse among those patients at the sixth and 12th hour of CPAP therapy (P<0.05). The ROC curve showed 85.7% sensitivity and 83.3% specificity at a cut-off value of 2.64 in predicting CPAP success (the AUC was 0.881).

CONCLUSION

The ROX index's clinical score form, which does not require lab findings or sophisticated computation techniques, is its key benefit. The study findings recommend the use of the ROX index to predict the outcome of respiratory support in acute respiratory failure in COVID-19 patients.

A new non-invasive index for the prediction of endotracheal intubation in patients with SARS COVID-19 infection, in the emergency department, pilot study

BACKGROUND

In the current context of the SARS COVID-19 pandemic, where the main cause of death is respiratory failure, and since early recognition would allow timely measures to be implemented and probably improve outcomes, it is important to have tools that allow the emergency room to predict quickly and without the use of large resources which will need invasive mechanical ventilation. This study proposes using a new predictive index of noninvasive characteristics, based on the relationship between oxygenation and work of breathing measured by ultrasound-assessed diaphragmatic function, for the need for invasive mechanical ventilation in patients with SARS-COV2 infection who are admitted to the emergency department.

METHODS

A prospective predictive cohort study was performed, collecting all patients admitted to the emergency room with respiratory failure (not severe or in imminent respiratory arrest) and a confirmed diagnosis of SARS-CoV-2 pneumonia. Diaphragmatic excursion measurements were taken within the first 24 h after admission to the department. The relationship between diaphragmatic excursion and SAFI was calculated, establishing the ultrasound diaphragmatic excursion So2/FiO2 index (U.D.E.S.I). The index's performance was determined by analysis of sensitivity, specificity, and area under the curve (AUC).

RESULTS

This pilot study analyzed the first 100 patients enrolled and found in-hospital mortality of 19%, all patients who died required mechanical ventilation, the right index showed a specificity of 82.4% with a sensitivity of 76.9%, likewise for the left index an overall specificity of 90.5% with a sensitivity of 65.3% was found. The ideal cut-off point for the right index is 1.485, and for the left index, the threshold point was 1.856. AUC of the right index is 0.798 (0.676-0.920) and of the left index 0.793 (0.674-0.911), when comparing them no significant differences were found between these values p = 0.871.

CONCLUSION

The relationship of So2/FiO2 and diaphragm excursion measured by both right and left ultrasound could predict the need for mechanical ventilation of the patient with COVID-19 pneumonia in the emergency room and could constitute a valuable tool since it uses noninvasive parameters and is easily applicable at the patient's bedside. However, a more extensive study is needed to validate these preliminary results.

Factors Associated with COVID-19 Death in a High-Altitude Peruvian Setting during the First 14 Months of the Pandemic: A Retrospective Multicenter Cohort Study in Hospitalized Patients

Risk factors for COVID-19 death in high-altitude populations have been scarcely described. This study aimed to describe risk factors for COVID-19 death in three referral hospitals located at 3399 m in Cusco, Peru, during the first 14 months of the pandemic. A retrospective multicenter cohort study was conducted. A random sample of ~50% (1225/2674) of adult hospitalized patients who died between 1 March 2020 and 30 June 2021 was identified. Of those, 977 individuals met the definition of death by COVID-19. Demographic characteristics, intensive care unit (ICU) admission, invasive respiratory support (IRS), disease severity, comorbidities, and clinical manifestation at hospital admission were assessed as risk factors using Cox proportional-hazard models. In multivariable models adjusted by age, sex, and pandemic periods, critical disease (vs. moderate) was associated with a greater risk of death (aHR: 1.27; 95%CI: 1.14–1.142), whereas ICU admission (aHR: 0.39; 95%CI: 0.27–0.56), IRS (aHR: 0.37; 95%CI: 0.26–0.54), the ratio of oxygen saturation (ROX) index ≥ 5.3 (aHR: 0.87; 95%CI: 0.80–0.94), and the ratio of SatO2/FiO2 ≥ 122.6 (aHR: 0.96; 95%CI: 0.93–0.98) were associated with a lower risk of death. The risk factors described here may be useful in assisting decision making and resource allocation.

Descriptive characteristics of continuous oximetry measurement in moderate to severe covid-19 patients

Non-invasive oxygen saturation (SpO2) is a central vital sign used to shape the management of COVID-19 patients. Yet, there have been no report quantitatively describing SpO2 dynamics and patterns in COVID-19 patients using continuous SpO2 recordings. We performed a retrospective observational analysis of the clinical information and 27 K hours of continuous SpO2 high-resolution (1 Hz) recordings of 367 critical and non-critical COVID-19 patients hospitalised at the Rambam Health Care Campus, Haifa, Israel. An absolute SpO2 threshold of 93% most efficiently discriminated between critical and non-critical patients, regardless of oxygen support. Oximetry-derived digital biomarker (OBMs) computed per 1 h monitoring window showed significant differences between groups, notably the cumulative time below 93% SpO2 (CT93). Patients with CT93 above 60% during the first hour of monitoring, were more likely to require oxygen support. Mechanical ventilation exhibited a strong effect on SpO2 dynamics by significantly reducing the frequency and depth of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected, up to several hours before the initiation of the mechanical ventilation. In summary, OBMs, traditionally used in the field of sleep medicine research, are informative for continuous assessment of disease severity and response to respiratory support of hospitalised COVID-19 patients. In conclusion, OBMs may improve risk stratification and therapy management of critical care patients with respiratory impairment.

A Retrospective Analysis of Ventilatory Strategy Comparing Non-invasive Ventilation (NIV) With Invasive Ventilation in Patients Admitted With Severe COVID-19 Pneumonia

Background The second wave of the COVID-19 pandemic in India saw a sudden upsurge of critically ill patients getting admitted to the ICU. The guidance for respiratory support was unclear in the early phase. But later reports showed lower mortality with non-invasive ventilation (NIV) than with intubation. The aim of this study was to assess the end result of initial methods of ventilation in COVID-19 patients. Methodology Patients admitted to ICU with COVID-19 were categorized as group 1 (IPPV-intubated within 24 hrs of admission), group 2 (NIV -NIV only), group 3 (NIV+ IPPV-intubated after 24 hrs), and group 4 (NRBM - Non-Rebreathing Mask only). All causes in the hospital or 30-day mortality, length of stay in ICU, and incidence of pneumothorax were compared between groups. Logistic regression analysis was done to determine the odds of mortality. Results The overall mortality rate among patients admitted to tertiary care centers was 15% and the rate among patients in ICU was 54.07%. Patients in group 1 and group 3 had significantly high mortality rates of 90.47% and 93.75%, respectively, as compared to 51.28% in group 2 patients. The odds of mortality were high in group 3 (OR 29.57, 95% CI 4.51 and 193.52) and group 1 (OR 8.01, 95% CI 1.35 and 47.48). Conclusion In a resource-limited setting, the use of NIV is associated with higher survival in COVID-19 patients. The prognosis of patients who are intubated early or after a trial of NIV is the same with increased odds of mortality.

Choice of respiratory therapy for COVID-19 patients with acute hypoxemic respiratory failure: a retrospective case series study

Background

In the treatment of acute hypoxemic respiratory failure (AHRF) due to coronavirus 2019 (COVID-19), physicians choose respiratory management ranging from low-flow oxygen therapy to more invasive methods, depending on the severity of the patient's symptoms. Recently, the ratio of oxygen saturation (ROX) index has been proposed as a clinical indicator to support the decision for either high-flow nasal cannulation (HFNC) or mechanical ventilation (MV). However, the reported cut-off value of the ROX index ranges widely from 2.7 to 5.9. The objective of this study was to identify indices to achieve empirical physician decisions for MV initiation, providing insights to shorten the delay from HFNC to MV. We retrospectively analyzed the ROX index 6 hours after initiating HFNC and lung infiltration volume (LIV) calculated from chest computed tomography (CT) images in COVID-19 patients with AHRF.

Methods

We retrospectively analyzed the data for 59 COVID-19 patients with AHRF in our facility to determine the cut-off value of the ROX index for respiratory therapeutic decisions and the significance of radiological evaluation of pneumonia severity. The physicians chose either HFNC or MV, and the outcomes were retrospectively analyzed using the ROX index for initiating HFNC. LIV was calculated using chest CT images at admission.

Results

Among the 59 patients who required high-flow oxygen therapy with HFNC at admission, 24 were later transitioned to MV; the remaining 35 patients recovered. Four of the 24 patients in the MV group died, and the ROX index values of these patients were 9.8, 7.3, 5.4, and 3.0, respectively. These index values indicated that the ROX index of half of the patients who died was higher than the reported cut-off values of the ROX index, which range from 2.7-5.99. The cut-off value of the ROX index 6 hours after the start of HFNC, which was used to classify the management of HFNC or MV as a physician's clinical decision, was approximately 6.1. The LIV cut-off value on chest CT between HFNC and MV was 35.5%. Using both the ROX index and LIV, the cut-off classifying HFNC or MV was obtained using the equation, LIV = 4.26 × (ROX index) + 7.89. The area under the receiver operating characteristic curve, as an evaluation metric of the classification, improved to 0.94 with a sensitivity of 0.79 and specificity of 0.91 using both the ROX index and LIV.

Conclusion

Physicians' empirical decisions associated with the choice of respiratory therapy for HFNC oxygen therapy or MV can be supported by the combination of the ROX index and the LIV index calculated from chest CT images.

Pulse oximetry for the diagnosis and management of acute respiratory distress syndrome

The diagnosis of acute respiratory distress syndrome (ARDS) traditionally requires calculation of the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2) using arterial blood, which can be costly and is not possible in many resource-limited settings. By contrast, pulse oximetry is continuously available, accurate, inexpensive, and non-invasive. Pulse oximetry-based indices, such as the ratio of pulse-oximetric oxygen saturation to FiO2 (SpO2/FiO2), have been validated in clinical studies for the diagnosis and risk stratification of patients with ARDS. Limitations of the SpO2/FiO2 ratio include reduced accuracy in poor perfusion states or above oxygen saturations of 97%, and the potential for reduced accuracy in patients with darker skin pigmentation. Application of pulse oximetry to the diagnosis and management of ARDS, including formal adoption of the SpO2/FiO2 ratio as an alternative to PaO2/FiO2 to meet the diagnostic criterion for hypoxaemia in ARDS, could facilitate increased and earlier recognition of ARDS worldwide to advance both clinical practice and research.

Intubation Timing in COVID-19 Based on ROX Index and Association With Patient Outcomes

BACKGROUND

Timing of intubation in COVID-19 is controversial. We sought to determine the association of the ROX (Respiratory rate-OXygenation) index defined as [Formula: see text] divided by [Formula: see text] divided by breathing frequency at the time of intubation with clinical outcomes.

METHODS

We conducted a retrospective cohort study of patients with COVID-19 who were intubated by using a database composed of electronic health record data from patients with COVID-19 from 62 institutions. Multivariable logistic regression was used to evaluate the impact of ROX index score on mortality. We analyzed the ROX index as a continuous variable as well as a categorical variable by using cutoffs previously described as predicting success with high-flow nasal cannula.

RESULTS

Of 1,087 subjects in the analysis group, the median age was 64 years, and more than half had diabetes; 55.2% died, 1.8% were discharged to hospice, 7.8% were discharged to home, 27.3% were discharged to another institution, and 7.8% had another disposition. Increasing age and a longer time from admission to intubation were associated with mortality. After adjusting for sex, race, age, comorbidities, and days from admission to intubation, an increasing ROX index score at the time of intubation was associated with a lower risk of death. In a logistic regression model, each increase in the ROX index score by 1 at the time of intubation was associated with an 8% reduction in odds of mortality (odds ratio 0.92, 95% CI 0.88-0.95). We also found an odds ratio for death of 0.62 (95% CI 0.47-0.81) for subjects with an ROX index score ≥ 4.88 at the time of intubation.

CONCLUSIONS

Among a cohort of subjects with COVID-19 who were ultimately intubated, a higher ROX index at the time of intubation was positively associated with survival.

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

Ability of IMPROVE and IMPROVE-DD scores to predict outcomes in patients with severe COVID-19: a prospective observational study

In this study we aimed to evaluate the ability of IMPROVE and IMPROVE-DD scores in predicting in-hospital mortality in patients with severe COVID-19. This prospective observational study included adult patients with severe COVID-19 within 12 h from admission. We recorded patients' demographic and laboratory data, Charlson comorbidity index (CCI), SpO₂ at room air, acute physiology and chronic health evaluation II (APACHE II), IMPROVE score and IMPROVE-DD score. In-hospital mortality and incidence of clinical worsening (the need for invasive mechanical ventilation, vasopressors, renal replacement therapy) were recorded. Our outcomes included the ability of the IMPROVE and IMPROVE-DD to predict in-hospital mortality and clinical worsening using the area under receiver operating characteristic curve (AUC) analysis. Multivariate analysis was used to detect independent risk factors for the study outcomes. Eighty-nine patients were available for the final analysis. The IMPROVE and IMPROVE-DD score showed the highest ability for predicting in-hospital mortality (AUC [95% confidence intervals {CI}] 0.96 [0.90–0.99] and 0.96 [0.90–0.99], respectively) in comparison to other risk stratification tools (APACHE II, CCI, SpO₂). The AUC (95% CI) for IMPROVE and IMPROVE-DD to predict clinical worsening were 0.80 (0.70–0.88) and 0.79 (0.69–0.87), respectively. Using multivariate analysis, IMPROVE-DD and SpO₂ were the only predictors for in-hospital mortality and clinical worsening. In patients with severe COVID-19, high IMPROVE and IMOROVE-DD scores showed excellent ability to predict in-hospital mortality and clinical worsening. Independent risk factors for in-hospital mortality and clinical worsening were IMPROVE-DD and SpO₂.

Deciding When to Intubate a COVID-19 Patient

Background

The SARS-CoV-2 pandemic is one of the most significant challenges for healthcare providers, particularly in the critical care setting. The timing of intubation in COVID-19 patients seems to be challenging. Therefore, we aimed to investigate how it may have a survival benefit, and we determined which clinical characteristics were associated with outcomes.

Methods

This cross-sectional study was conducted in the Imam Khomeini Hospital Complex. We randomly selected patients admitted to intensive care units and, based on intubation status, categorized them into three subgroups (early, late, and not intubated). Early intubation is defined as intubation within 48 hours of ICU admission, and late intubation is defined as intubation after 48 hours of ICU admission.

Results

Early-intubated patients were more likely to have dyspnea than late-intubated patients, and late-intubated patients had a higher mean heart rate than early-intubated patients. The neutrophil/lymphocyte ratio was significantly (P < 0.05) lower in not-intubated patients than in other patients. There was no difference in NLR between early- and late-intubated patients. Mean serum creatine phosphokinase and troponin I levels were higher in late-intubated patients than in early- and not-intubated patients. Early-intubated patients had a lower ROX index than late-intubated patients. Patients with higher scores of APACHE 2, respiratory rates, and neutrophil to lymphocyte ratio were more likely to be intubated. Increasing APACHE and SOFA scores were associated with decreased odds of survival.

Conclusions

There were no statistically significant differences in total mortality between early- and late-intubated patients. APACHE 2 scores, NLR, RR, and history of ischemic heart disease are some of the appropriate predictors of intubation. Higher respiratory rates (tachypnea) can be an indicator of early intubation. The ROX index is one of the most sensitive and capable tools for predicting intubation. Intubation status is a potent predictor of in-hospital mortality.

Silent hypoxia is not an identifiable characteristic in patients with COVID-19 infection

Background

Methods

Results

Conclusions

Noninvasive ventilation and high-flow nasal cannula in patients with acute hypoxemic respiratory failure by covid-19: a retrospective study of the feasebility, safety and outcomes

BACKGROUND

Noninvasive ventilation (NIV) and High-flow nasal cannula (HFNC) are the main forms of treatment for acute respiratory failure. This study aimed to evaluate the effect, safety, and applicability of the NIV and HFNC in patients with acute hypoxemic respiratory failure (AHRF) caused by COVID-19.

METHODS

In this retrospective study, we monitored the effect of NIV and HFNC on the SpO₂ and respiratory rate before, during, and after treatment, length of stay, rates of endotracheal intubation, and mortality in patients with AHRF caused by COVID-19. Additionally, data regarding RT-PCR from physiotherapists who were directly involved in assisting COVID-19 patients and non-COVID-19.

RESULTS

62.2% of patients were treated with HFNC. ROX index increased during and after NIV and HFNC treatment (P < 0.05). SpO₂ increased during NIV treatment (P < 0.05), but was not maintained after treatment (P = 0.17). In addition, there was no difference in the respiratory rate during or after the NIV (P = 0.95) or HFNC (P = 0.60) treatment. The mortality rate was 35.7% for NIV vs 21.4% for HFNC (P = 0.45), while the total endotracheal intubation rate was 57.1% for NIV vs 69.6% for HFNC (P = 0.49). Two adverse events occurred during treatment with NIV and eight occurred during treatment with HFNC. There was no difference in the physiotherapists who tested positive for SARS-CoV-2 directly involved in assisting COVID-19 patients and non-COVID-19 ones (P = 0.81).

CONCLUSION

The application of NIV and HFNC in the critical care unit is feasible and associated with favorable outcomes. In addition, there was no increase in the infection of physiotherapists with SARS-CoV-2.

Single-Breath Counting Test Predicts Non-Invasive Respiratory Support Requirements in Patients with COVID-19 Pneumonia

The use of non-invasive respiratory strategies (NIRS) is crucial to improve oxygenation in COVID-19 patients with hypoxemia refractory to conventional oxygen therapy. However, the absence of respiratory symptoms may delay the start of NIRS. The aim of this study was to determine whether a simple bedside test such as single-breath counting test (SBCT) can predict the need for NIRS in the 24 h following the access to Emergency Department (ED). We performed a prospective observational study on 120 patients with COVID-19 pneumonia. ROC curves were used to analyze factors which might predict NIRS requirement. We found that 36% of patients had normal respiratory rate and did not experience dyspnea at rest. 65% of study population required NIRS in the 24 h following the access to ED. NIRS-requiring group presented lower PaO₂/FiO₂ (235.09 vs. 299.02), SpO₂/FiO₂ ratio (357.83 vs. 431.07), PaCO₂ (35.12 vs. 40.08), and SBCT (24.46 vs. 30.36) and showed higher incidence of dyspnea at rest (57.7% vs. 28.6%). Furthermore, SBCT predicted NIRS requirement even in the subgroup of patients without respiratory symptoms (AUC = 0.882, cut-off = 30). SBCT might be a valuable tool for bedside assessment of respiratory function in patients with COVID-19 pneumonia and might be considered as an early clinical sign of impending respiratory deterioration.

The novel use of diaphragmatic excursion on hospital admission to predict the need for ventilatory support in patients with coronavirus disease 2019

Background

We aimed to evaluate the ability of diaphragmatic excursion at hospital admission to predict outcomes in patients with coronavirus disease-2019 (COVID-19).

Methods

In this prospective observational study, we included adult patients with severe COVID-19 admitted to a tertiary hospital. Ultrasound examination of the diaphragm was performed within 12 h of admission. Other collected data included peripheral oxygen saturation (SpO₂), respiratory rate, and computed tomography (CT) score. The outcomes included the ability of diaphragmatic excursion, respiratory rate, SpO₂, and CT score at admission to predict the need for ventilatory support (need for non-invasive or invasive ventilation) and patient mortality using the area under the receiver operating characteristic curve (AUC) analysis. Univariate and multivariable analyses about the need for ventilatory support and mortality were performed.

Results

Diaphragmatic excursion showed an excellent ability to predict the need for ventilatory support, which was the highest among respiratory rate, SpO₂, and CT score; the AUCs (95% confidence interval [CI]) was 0.96 (0.85–1.00) for the right diaphragmatic excursion and 0.94 (0.82–0.99) for the left diaphragmatic excursion. The right diaphragmatic excursion also had the highest AUC for predicting mortality in relation to respiratory rate, SpO₂, and CT score. Multivariable analysis revealed that low diaphragmatic excursion was an independent predictor of mortality with an odds ratio (95% CI) of 0.55 (0.31–0.98).

Conclusion

Diaphragmatic excursion on hospital admission can accurately predict the need for ventilatory support and mortality in patients with severe COVID-19. Low diaphragmatic excursion was an independent risk factor for in-hospital mortality.