A simple nomogram for predicting failure of non-invasive respiratory strategies in adults with COVID-19: a retrospective multicentre study

Predictors of high-flow nasal cannula failure in COVID-19 patients in a northern Peruvian hospital

OBJECTIVES

To determine predictors of high-flow nasal cannula (HFNC) failure in COVID-19 patients in a hospital in northern Peru.

METHODOLOGY

A retrospective cohort study was conducted during the months of March and May 2021. Data collection was based on a follow-up of 156 hospitalized patients with a diagnosis of COVID-19 who were users of HFNC. Epidemiological factors and clinical outcomes of treatment were analyzed from medical records. Epidemiological, analytical, and HFNC use-related characteristics were described using measures of absolute and relative frequencies, measures of central tendency, and dispersion. A multivariate Poisson regression analysis with robust variance and a 95% confidence interval was performed.

RESULTS

We found that age, SpO2/FiO2, work of breathing (WOB scale) at admission, degree of involvement, type of infiltrate on CT scan, lymphocytes, c-reactive protein, and D-dimer were significantly associated with failure of HFNC (p < 0.05). In addition, the WOB scale, PaO2/FiO2, SaO2/FiO2, and ROX index were variables that presented statistical significance (p < 0.0001). In the multivariate analysis model, a risk of failure of HFNC was determined with age > = 60 years [RRa 1.39 (1.05-1.85)] and PaO2/FiO2 score less than 100 [Rra 1.65 (0.99-2.76)].

CONCLUSIONS

Predictors to failure of HFNC are age older than 60 years and minimally significantly lower PaO2/FiO2 than 100.

Effect of noninvasive ventilation on mortality and clinical outcomes among patients with severe hypoxemic COVID-19 pneumonia after high-flow nasal oxygen failure: a multicenter retrospective French cohort with propensity score analysis

BACKGROUND

We assessed the effect of noninvasive ventilation (NIV) on mortality and length of stay after high flow nasal oxygenation (HFNO) failure among patients with severe hypoxemic COVID-19 pneumonia.

METHODS

In this multicenter, retrospective study, we enrolled COVID-19 patients admitted in intensive care unit (ICU) for severe COVID-19 pneumonia with a HFNO failure from December 2020 to January 2022. The primary outcome was to compare the 90-day mortality between patients who required a straight intubation after HFNO failure and patients who received NIV after HFNO failure. Secondary outcomes included ICU and hospital length of stay. A propensity score analysis was performed to control for confounding factors between groups. Exploratory outcomes included a subgroup analysis for 90-day mortality.

RESULTS

We included 461 patients with HFNO failure in the analysis, 233 patients in the straight intubation group and 228 in the NIV group. The 90-day mortality did not significantly differ between groups, 58/228 (25.4%) int the NIV group compared with 59/233 (25.3%) in the straight intubation group, with an adjusted hazard ratio (HR) after propensity score weighting of 0.82 [95%CI, 0.50-1.35] (p = 0.434). ICU length of stay was significantly shorter in the NIV group compared to the straight intubation group, 10.0 days [IQR, 7.0-19.8] versus 18.0 days [IQR,11.0-31.0] with a propensity score weighted HR of 1.77 [95%CI, 1.29-2.43] (p < 0.001). A subgroup analysis showed a significant increase in mortality rate for intubated patients in the NIV group with 56/122 (45.9%), compared to 59/233 (25.3%) for patients in the straight intubation group (p < 0.001).

CONCLUSIONS

In severely hypoxemic COVID-19 patients, no significant differences were observed on 90-day mortality between patients receiving straight intubation and those receiving NIV after HFNO failure. NIV strategy was associated with a significant reduction in ICU length of stay, despite an increase in mortality in the subgroup of patients finally intubated.

Develop a bone mineral density T-score distribution nomograms based on osteoporosis risk factors for middle-aged and older adults

PURPOSE

This study aimed to understand how age, health status, and lifestyle impact bone mineral density (BMD) in middle-aged and older adults, focusing on predicting osteoporosis risk.

METHODS

This study included 2836 participants aged 50-88 from the Health Improvement Program of Bone (HOPE) conducted from 2021 to 2023. We used logistic regression to make a prediction tool. Then checked its accuracy and reliability using receiver operating characteristic (ROC) and calibration curves.

RESULTS

Factors like age, body weight, prior fractures, and smoking were independently found to affect BMD T-score distribution in men. In women, age and body weight were identified as independent factors influencing BMD T-score distribution. A nomogram was created to visually illustrate these predictive relationships.

CONCLUSIONS

The nomogram proved highly accurate in identifying men aged 50 and above and postmenopausal women based on their BMD T-score distribution, improving clinical decision-making and patient care in osteoporosis evaluation and treatment.

Risk Factor Analysis and Nomogram for Early Progression of COVID-19 Pneumonia in Older Adult Patients in the Omicron Era

Background and Objective

Timely recognition of risk factors for early progression in older adult patients with COVID-19 is of great significance to the following clinical management. This study aims to analyze the risk factors and create a nomogram for early progression in older adult patients with COVID-19 in the Omicron era.

Methods

A total of 272 older adults infected with COVID-19 admitted from December 2022 to February 2023 were retrospectively recruited. Risk factor selection was determined using the logistic and the least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then created to predict early progression, followed by the internal validation and assessment of its performance through plotting the receiver operating characteristic (ROC), calibration, and decision curves.

Results

A total of 83 (30.5%) older adult patients presented an early progression on chest CT after 3-5 days of admission under standard initiate therapy. Six independent predictive factors were incorporated into the nomogram to predict the early progression, including CRP > 10 mg/L, IL-6 > 6.6 pg/mL, LDH > 245 U/L, CD4+ T-lymphocyte count <400/µL, the Activities of Daily Living (ADL) score ≤40 points, and the Mini Nutritional Assessment Scale-Short Form (MNA-SF) score ≤7 points. The area under the curve (AUC) of the nomogram in discriminating older adult patients who had risk factors in the training and validation cohort was 0.857 (95% CI 0.798, 0.916) and 0.774 (95% CI 0.667, 0.881), respectively. The calibration and decision curves demonstrated a high agreement in the predicted and observed risks, and the acceptable net benefit in predicting the early progression, respectively.

Conclusion

We created a nomogram incorporating highly available laboratory data and the Comprehensive Geriatric Assessment (CGA) findings that effectively predict early-stage progression in older adult patients with COVID-19 in the Omicron era.

Machine learning prediction of the failure of high-flow nasal oxygen therapy in patients with acute respiratory failure

Acute respiratory failure (ARF) is a prevalent and serious condition in intensive care unit (ICU), often associated with high mortality rates. High-flow nasal oxygen (HFNO) therapy has gained popularity for treating ARF in recent years. However, there is a limited understanding of the factors that predict HFNO failure in ARF patients. This study aimed to explore early indicators of HFNO failure in ARF patients, utilizing machine learning (ML) algorithms to more accurately pinpoint individuals at elevated risk of HFNO failure. Utilizing ML algorithms, we developed seven predictive models. Their performance was evaluated using various metrics, including the area under the receiver operating characteristic curve, calibration curve, and precision recall curve. The study enrolled 700 patients, with 490 in the training group and 210 in the validation group. The overall HFNO failure rate was 14.1% among the 700 patients. The ML algorithms demonstrated robust performance in our study. This research underscores the potential of ML techniques in creating clinically relevant models for predicting HFNO outcomes in ARF patients. These models could play a pivotal role in enhancing the risk management of HFNO, leading to more patient-centered and personalized care approaches.

Safety and Outcome of High-Flow Nasal Oxygen Therapy Outside ICU Setting in Hypoxemic Patients With COVID-19

OBJECTIVE

High-flow nasal oxygen (HFNO) therapy is frequently applied outside ICU setting in hypoxemic patients with COVID-19. However, safety concerns limit more widespread use. We aimed to assess the safety and clinical outcomes of initiation of HFNO therapy in COVID-19 on non-ICU wards.

DESIGN

Prospective observational multicenter pragmatic study.

SETTING

Respiratory wards and ICUs of 10 hospitals in The Netherlands.

PATIENTS

Adult patients treated with HFNO for COVID-19-associated hypoxemia between December 2020 and July 2021 were included. Patients with treatment limitations were excluded from this analysis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcomes included intubation and mortality rate, duration of hospital and ICU stay, severity of respiratory failure, and complications. Using propensity-matched analysis, we compared patients who initiated HFNO on the wards versus those in ICU. Six hundred eight patients were included, of whom 379 started HFNO on the ward and 229 in the ICU. The intubation rate in the matched cohort ( n = 214 patients) was 53% and 60% in ward and ICU starters, respectively ( p = 0.41). Mortality rates were comparable between groups (28-d [8% vs 13%], p = 0.28). ICU-free days were significantly higher in ward starters (21 vs 17 d, p < 0.001). No patient died before endotracheal intubation, and the severity of respiratory failure surrounding invasive ventilation and clinical outcomes did not differ between intubated ward and ICU starters (respiratory rate-oxygenation index 3.20 vs 3.38; Pa o2 :F io2 ratio 65 vs 64 mm Hg; prone positioning after intubation 81 vs 78%; mortality rate 17 vs 25% and ventilator-free days at 28 d 15 vs 13 d, all p values > 0.05).

CONCLUSIONS

In this large cohort of hypoxemic patients with COVID-19, initiation of HFNO outside the ICU was safe, and clinical outcomes were similar to initiation in the ICU. Furthermore, the initiation of HFNO on wards saved time in ICU without excess mortality or complicated course. Our results indicate that HFNO initiation outside ICU should be further explored in other hypoxemic diseases and clinical settings aiming to preserve ICU capacity and healthcare costs.

Adjunctive use of oral MAF is associated with no disease progression or mortality in hospitalized patients with COVID-19 pneumonia: The single-arm COral-MAF1 prospective trial

Based on a growing body of evidence that a dysregulated innate immune response mediated by monocytes/macrophages plays a key role in the pathogenesis of COVID-19, a clinical trial was conducted to investigate the therapeutic potential and safety of oral macrophage activating factor (MAF) plus standard of care (SoC) in the treatment of hospitalized patients with COVID-19 pneumonia. Ninety-seven hospitalized patients with confirmed COVID-19 pneumonia were treated with oral MAF and a vitamin D3 supplement, in combination with SoC, in a single-arm, open label, multicentre, phase II clinical trial. The primary outcome measure was a reduction in an intensive care unit transfer rate below 13% after MAF administration. At the end of the study, an additional propensity score matching (PSM) analysis was performed to compare the MAF group with a control group treated with SoC alone. Out of 97 patients treated with MAF, none needed care in the ICU and/or intubation with mechanical ventilation or died during hospitalization. Oxygen therapy was discontinued after a median of nine days of MAF treatment. The median length of viral shedding and hospital stay was 14 days and 18 days, respectively. After PSM, statistically significant differences were found in all of the in-hospital outcomes between the two groups. No mild to serious adverse events were recorded during the study. Notwithstanding the limitations of a single-arm study, which prevented definitive conclusions, a 21-day course of MAF treatment plus SoC was found to be safe and promising in the treatment of hospitalized adult patients with COVID-19 pneumonia. Further research will be needed to confirm these preliminary findings.

Novel nomograms to predict risk and prognosis in hospitalized patients with severe fever with thrombocytopenia syndrome

Background

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging and life-threatening infectious disease caused by SFTS virus. Although recent studies have reported the use of nomograms based on demographic and laboratory data to predict the prognosis of SFTS, no study has included viral load, which is an important factor that influences the prognosis, when compared with other risk factors. Therefore, this study aimed to develop a model that predicts SFTS prognosis before it reaches the critical illness stage and to compare the predictive ability of groups with and without viral load.

Methods

Two hundred patients with SFTS were enrolled between June 2018 and August 2023. Data were sourced from the first laboratory results at admission, and two nomograms for mortality risk were developed using multivariate logistic regression to identify the risk variables for poor prognosis in these patients. We calculated the area under the receiver operating characteristic curve (AUC) for the two nomograms to assess their discrimination, and predictive abilities were compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results

The multivariate logistic regression analysis identified four independent risk factors: age, bleeding manifestations, prolonged activated partial thromboplastin time, and viral load. Based on these factors, a final nomogram predicting mortality risk in patients with SFTS was constructed; in addition, a simplified nomogram was constructed excluding the viral load. The AUC [0.926, 95% confidence interval (CI): 0.882-0.970 vs. 0.882, 95% CI: 35 0.823-0.942], NRI (0.143, 95% CI, 0.036-0.285), and IDI (0.124, 95% CI, 0.061-0.186) were calculated and compared between the two models. The calibration curves of the two models showed excellent concordance, and decision curve analysis was used to quantify the net benefit at different threshold probabilities.

Conclusion

Two critical risk nomograms were developed based on the indicators for early prediction of mortality risk in patients with SFTS, and enhanced predictive accuracy was observed in the model that incorporated the viral load. The models developed will provide frontline clinicians with a convenient tool for early identification of critically ill patients and initiation of a better personalized treatment in a timely manner.

[Ventilatory management of SARS-CoV-2 acute respiratory failure]

COVID-19 pneumonia presents several particularities in its clinical presentation (cytokine storm, silent hypoxemia, thrombo-embolic risk) and may lead to a number of acute respiratory distress syndrome (ARDS) phenotypes. While the optimal oxygenation strategy in cases of hypoxemic acute respiratory failure (ARF) is still under debate, ventilatory management of COVID-19-related ARF has confirmed the efficacy of high-flow oxygen therapy and restored interest in other ventilatory approaches such as continuous positive airway pressure (CPAP) and noninvasive ventilation involving a helmet, which due to patient overflow are sometimes implemented outside of critical care units. However, further studies are still needed to determine which patients should be given which oxygenation technique, and under which conditions they require invasive mechanical ventilation, given that delayed initiation potentially burdens prognosis. During invasive mechanical ventilation, ventral decubitus and extracorporeal membrane oxygenation have become increasingly prevalent. While innovative therapies such as awake prone position or lung transplantation have likewise been developed, their indications, modalities and efficacy remain to be determined.

A visualized MAC nomogram online predicts the risk of three-month mortality in Chinese elderly aneurysmal subarachnoid hemorrhage patients undergoing endovascular coiling

OBJECTIVE

Aneurysmal subarachnoid hemorrhage (aSAH) is an aggressive disease with higher mortality rate in the elderly population. Unfortunately, the previous models for predicting clinical prognosis are still not accurate enough. Therefore, we aimed to construct and validate a visualized nomogram model to predict online the 3-month mortality in elderly aSAH patients undergoing endovascular coiling.

METHOD

We conducted a retrospective analysis of 209 elderly aSAH patients at People's Hospital of Hunan Province, China. A nomogram was developed based on multivariate logistic regression and forward stepwise regression analysis, then validated using the bootstrap validation method (n = 1000). In addition, the performance of the nomogram was evaluated by various indicators to prove its clinical value.

RESULT

Morbid pupillary reflex, age, and using a breathing machine were independent predictors of 3-month mortality. The AUC of the nomogram was 0.901 (95% CI: 0.853-0.950), and the Hosmer-Lemeshow goodness-of-fit test showed good calibration of the nomogram (p = 0.4328). Besides, the bootstrap validation method internally validated the nomogram with an area under the curve of the receiver operator characteristic (AUROC) of 0.896 (95% CI: 0.846-0.945). Decision curve analysis (DCA) and clinical impact curve (CIC) indicated the nomogram's excellent clinical utility and applicability.

CONCLUSION

An easily applied visualized nomogram model named MAC (morbid pupillary reflex-age-breathing machine) based on three accessible factors has been successfully developed. The MAC nomogram is an accurate and complementary tool to support individualized decision-making and emphasizes that patients with higher risk of mortality may require closer monitoring. Furthermore, a web-based online version of the risk calculator would greatly contribute to the spread of the model in this field.

Improved Differential Diagnosis Based on BI-RADS Descriptors and Apparent Diffusion Coefficient for Breast Lesions: A Multiparametric MRI Analysis as Compared to Kaiser Score

RATIONALE AND OBJECTIVES

To develop the nomogram utilizing the American College of Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to differentiate benign from malignant breast lesions.

MATERIALS AND METHODS

A total of 341 lesions (161 malignant and 180 benign) were included. Clinical data and imaging features were reviewed. Univariable and multivariable logistic regression analyses were performed to determine the independent variables. ADC as a continuous or classified into binary form with a cutoff value of 1.30 × 10-3 mm2/s, incorporated other independent predictors to construct two nomograms, respectively. Receiver operating curve and calibration plot was employed to test the models' discriminative ability. The diagnostic performance between the developed model and the Kaiser score (KS) was also compared.

RESULTS

In both models, high patient age, the presence of root sign, time-intensity curves (TICs) types (plateau and washout), heterogenous internal enhancement, the presence of peritumoral edema, and ADC were independently associated with malignancy. The AUCs of two multivariable models (AUC, 0.957; 95% CI: 0.929-0.976 and AUC, 0.958; 95% CI: 0.931-0.976) were significantly higher than that of the KS (AUC, 0.919, 95% CI: 0.885-0.946; both P < 0.001). At the same sensitivity of 95.7%, our models showed an increase in specificity by 5.56% (P = 0.076) and 6.11% (P = 0.035), respectively, as compared to the KS.

CONCLUSION

The models incorporating MRI features (root sign, TIC, margins, internal enhancement, and presence of edema), quantitative ADC value, and patient age showed improved diagnostic performance and might have avoided more unnecessary biopsies in comparison with the KS, although further external validation is required.

Pre- and Post-Operative Online Prediction of Outcome in Patients Undergoing Endovascular Coiling after Aneurysmal Subarachnoid Hemorrhage: Visual and Dynamic Nomograms

BACKGROUND

Aneurysmal subarachnoid hemorrhage (aSAH) causes long-term functional dependence and death. Early prediction of functional outcomes in aSAH patients with appropriate intervention strategies could lower the risk of poor prognosis. Therefore, we aimed to develop pre- and post-operative dynamic visualization nomograms to predict the 1-year functional outcomes of aSAH patients undergoing coil embolization.

METHODS

Data were obtained from 400 aSAH patients undergoing endovascular coiling admitted to the People's Hospital of Hunan Province in China (2015-2019). The key indicator was the modified Rankin Score (mRS), with 3-6 representing poor functional outcomes. Multivariate logistic regression (MLR)-based visual nomograms were developed to analyze baseline characteristics and post-operative complications. The evaluation of nomogram performance included discrimination (measured by C statistic), calibration (measured by the Hosmer-Lemeshow test and calibration curves), and clinical usefulness (measured by decision curve analysis).

RESULTS

Fifty-nine aSAH patients (14.8%) had poor outcomes. Both nomograms showed good discrimination, and the post-operative nomogram demonstrated superior discrimination to the pre-operative nomogram with a C statistic of 0.895 (95% CI: 0.844-0.945) vs. 0.801 (95% CI: 0.733-0.870). Each was well calibrated with a Hosmer-Lemeshow p-value of 0.498 vs. 0.276. Moreover, decision curve analysis showed that both nomograms were clinically useful, and the post-operative nomogram generated more net benefit than the pre-operative nomogram. Web-based online calculators have been developed to greatly improve the efficiency of clinical applications.

CONCLUSIONS

Pre- and post-operative dynamic nomograms could support pre-operative treatment decisions and post-operative management in aSAH patients, respectively. Moreover, this study indicates that integrating post-operative variables into the nomogram enhanced prediction accuracy for the poor outcome of aSAH patients.

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4.25 vs. 2.68) and its recommended treatments reduced the expected mortality from 27.82% to 25.44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13.36 hours earlier than clinicians (8.64 vs. 22 hours after the NIV treatment), granting a 21.7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes.

An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study

Background

Severe traumatic brain injury (sTBI) is extremely disabling and associated with high mortality. Early detection of patients at risk of short-term (≤14 days after injury) death and provision of timely treatment is critical. This study aimed to establish and independently validate a nomogram to estimate individualised short-term mortality for sTBI based on large-scale data from China.

Methods

The data were from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry (between Dec 22, 2014, and Aug 1, 2017; registered at ClinicalTrials.gov, NCT02210221). This analysis included information of eligible patients with diagnosed sTBI from 52 centres (2631 cases). 1808 cases from 36 centres were enrolled in the training group (used to construct the nomogram) and 823 cases from 16 centres were enrolled in the validation group. Multivariate logistic regression was used to identify independent predictors of short-term mortality and establish the nomogram. The discrimination of the nomogram was evaluated using area under the receiver operating characteristic curves (AUC) and concordance indexes (C-index), the calibration was evaluated using calibration curves and Hosmer-Lemeshow tests (H-L tests). Decision curve analysis (DCA) was used to evaluate the net benefit of the model for patients.

Findings

In the training group, multivariate logistic regression demonstrated that age (odds ratio [OR] 1.013, 95% confidence interval [CI] 1.003-1.022), Glasgow Coma Scale score (OR 33.997, 95% CI 14.657-78.856), Injury Severity Score (OR 1.020, 95% CI 1.009-1.032), abnormal pupil status (OR 1.738, 95% CI 1.178-2.565), midline shift (OR 2.266, 95% CI 1.378-3.727), and pre-hospital intubation (OR 2.059, 95% CI 1.472-2.879) were independent predictors for short-term death in patients with sTBI. A nomogram was built using the logistic regression prediction model. The AUC and C-index were 0.859 (95% CI 0.837-0.880). The calibration curve of the nomogram was close to the ideal reference line, and the H-L test p value was 0.504. DCA curve demonstrated significantly better net benefit with the model. Application of the nomogram in external validation group still showed good discrimination (AUC and C-index were 0.856, 95% CI 0.827-0.886), calibration, and clinical usefulness.

Interpretation

A nomogram was developed for predicting the occurrence of short-term (≤14 days after injury) death in patients with sTBI. This can provide clinicians with an effective and accurate tool for the early prediction and timely management of sTBI, as well as support clinical decision-making around the withdrawal of life-sustaining therapy. This nomogram is based on Chinese large-scale data and is especially relevant to low- and middle-income countries.

Funding

Shanghai Academic Research Leader (21XD1422400), Shanghai Medical and Health Development Foundation (20224Z0012).

Clinical and Epidemiological Characteristics of Patients with COVID-19 Admitted to the Intensive Care Unit: A Two-Year Retrospective Analysis

In March 2020, COVID-19 was characterized as a pandemic by the World Health Organization. Hospitalized patients affected by COVID-19 presented with severe respiratory and motor impairment, especially those who required intensive treatment and invasive mechanical ventilation, with sequelae that extended after the period of hospitalization. Thus, the aim of the current study was to verify the clinical and epidemiological characteristics of patients with COVID-19 admitted to the Intensive Care Unit in 2020 and 2021, according to age group. Methods: A retrospective cohort study. Data were collected through the “ICUs Brasileiras” between March 2020 and November 2021 for severe acute respiratory syndrome (SARS) due to COVID-19. The following were analyzed: the number of hospital admissions, days in the ICU and hospital, clinical aspects (non-invasive or invasive ventilatory support, comorbidities, frailty, SAPS 3 and SOFA severity scales, use of amines and renal support), and ICU and hospital mortality rate. Results: A total of 166,966 ICU hospital admissions were evaluated over the evaluated quarters. The main results showed a peak in the number of hospitalizations between March and May 2021, with a higher percentage of males. The peak of ICU admissions for 7 days was between March and May 2021 and 21 days between March and May 2020. In addition, higher deaths were observed in the age groups between 40 and 80 years between 2020 and 2021, with the group above 81 being the age group with the highest mortality. Mortality in the ICU of ventilated patients was higher in the age group above 70 years. Another observation was the predominance of SAPS 3 and the peak of mechanical ventilation for more than 7 days between June and August 2021. Conclusion: The clinical and epidemiological characteristics of patients with COVID-19 were influenced by age group, showing higher mortality over 81 years and over 70 years in the ICU supported by mechanical ventilation, maintained for 7 days from June to August 2021. The years 2020 and 2021 also showed differences for patients with COVID-19, with greater hospitalization between March and May 2021, especially in the ICU for 7 days, and between March and May 2020 for the 21-day period.

Lessons from COVID-19 in the management of acute respiratory failure

Accumulated evidence supports the efficacy of noninvasive respiratory support therapies in coronavirus disease 2019 (COVID-19)-related acute hypoxaemic respiratory failure, alleviating admissions to intensive care units. Noninvasive respiratory support strategies, including high-flow oxygen therapy, continuous positive airway pressure via mask or helmet and noninvasive ventilation, can be alternatives that may avoid the need for invasive ventilation. Alternating different noninvasive respiratory support therapies and introducing complementary interventions, like self-proning, may improve outcomes. Proper monitoring is warranted to ensure the efficacy of the techniques and to avoid complications while supporting transfer to the intensive care unit. This article reviews the latest evidence on noninvasive respiratory support therapies in COVID-19-related acute hypoxaemic respiratory failure.

Construction and Validation of a Nomogram Clinical Prediction Model for Predicting Osteoporosis in an Asymptomatic Elderly Population in Beijing

BACKGROUND

Based on the high prevalence and occult-onset of osteoporosis, the development of novel early screening tools was imminent. Therefore, this study attempted to construct a nomogram clinical prediction model for predicting osteoporosis.

METHODS

Asymptomatic elderly residents in the training (n = 438) and validation groups (n = 146) were recruited. BMD examinations were performed and clinical data were collected for the participants. Logistic regression analyses were performed. A logistic nomogram clinical prediction model and an online dynamic nomogram clinical prediction model were constructed. The nomogram model was validated by means of ROC curves, calibration curves, DCA curves, and clinical impact curves.

RESULTS

The nomogram clinical prediction model constructed based on gender, education level, and body weight was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. An online dynamic nomogram was constructed.

CONCLUSIONS

The nomogram clinical prediction model was easy to generalize, and could help family physicians and primary community healthcare institutions to better screen for osteoporosis in the general elderly population and achieve early detection and diagnosis of the disease.

Effect of Ward-Based High-Flow Nasal Cannula (HFNC) Oxygen Therapy on Critical Care Utilization During the COVID-19 Pandemic: A Retrospective Cohort Analysis

BACKGROUND

Hospitals expanded critical care capacity during the COVID-19 pandemic by treating COVID-19 patients with high-flow nasal cannula oxygen therapy (HFNC) in non-traditional settings, including general internal medicine (GIM) wards. The impact of this practice on intensive care unit (ICU) capacity is unknown.

OBJECTIVE

To describe how our hospital operationalized the use of HFNC on GIM wards, assess its impact on ICU capacity, and examine the characteristics and outcomes of treated patients.

DESIGN

Retrospective cohort study of all patients treated with HFNC on GIM wards at a Canadian tertiary care hospital.

PARTICIPANTS

All patients admitted with COVID-19 and treated with HFNC on GIM wards from December 28, 2020, to June 13, 2021, were included.

MAIN MEASURES

We combined administrative data on critical care occupancy daily with chart-abstracted data for included patients to establish the total number of patients receiving ICU-level care at our hospital per day. We also collected data on demographics, medical comorbidities, illness severity, COVID-19 treatments, HFNC care processes, and patient outcomes.

KEY RESULTS

We treated 124 patients with HFNC on the GIM wards (median age 66 years; 48% female). Patients were treated with HFNC for a median of 5 days (IQR 3 to 8); collectively, they received HFNC for a total of 740 hospital days, 71% of which were on GIM wards. At peak ICU capacity strain (144%), delivering HFNC on GIM wards added 20% to overall ICU capacity by managing up to 14 patients per day. Patients required a median maximal fraction of inspired oxygen of 80% (IQR 60 to 95). There were 18 deaths (15%) and 85 patients (69%) required critical care admission; of those, 40 (47%) required mechanical ventilation.

CONCLUSIONS

With appropriate training and resources, treatment of COVID-19 patients with HFNC on GIM wards appears to be a feasible strategy to increase critical care capacity.

A Nomogram for Predicting Prognosis of Advanced Schistosomiasis japonica in Dongzhi County-A Case Study

BACKGROUNDS

Advanced schistosomiasis is the late stage of schistosomiasis, seriously jeopardizing the quality of life or lifetime of infected people. This study aimed to develop a nomogram for predicting mortality of patients with advanced schistosomiasis japonica, taking Dongzhi County of China as a case study.

METHOD

Data of patients with advanced schistosomiasis japonica were collected from Dongzhi Schistosomiasis Hospital from January 2019 to July 2022. Data of patients were randomly divided into a training set and validation set with a ratio of 7:3. Candidate variables, including survival outcomes, demographics, clinical features, laboratory examinations, and ultrasound examinations, were analyzed and selected by LASSO logistic regression for the nomogram. The performance of the nomogram was assessed by concordance index (C-index), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The calibration of the nomogram was evaluated by the calibration plots, while clinical benefit was evaluated by decision curve and clinical impact curve analysis.

RESULTS

A total of 628 patients were included in the final analysis. Atrophy of the right liver, creatinine, ascites level III, N-terminal procollagen III peptide, and high-density lipoprotein were selected as parameters for the nomogram model. The C-index, sensitivity, specificity, PPV, and NPV of the nomogram were 0.97 (95% [CI]: [0.95-0.99]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]) in the training set; and 0.98 (95% [CI]: [0.94-0.99]), 0.86 (95% [CI]: [0.64-0.96]), 0.97 (95% [CI]: [0.93-0.99]), 0.79 (95% [CI]: [0.57-0.92]), 0.98 (95% [CI]: [0.94-0.99]) in the validation set, respectively. The calibration curves showed that the model fitted well between the prediction and actual observation in both the training set and validation set. The decision and the clinical impact curves showed that the nomogram had good clinical use for discriminating patients with high risk of death.

CONCLUSIONS

A nomogram was developed to predict prognosis of advanced schistosomiasis. It could guide clinical staff or policy makers to formulate intervention strategies or efficiently allocate resources against advanced schistosomiasis.

Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison

BACKGROUND

Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support.

METHODS

Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS).

RESULTS

Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores.

CONCLUSIONS

Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

Detailed Analysis of Primary Non-invasive Respiratory Support and Outcomes of Subjects With COVID-19 Acute Hypoxaemic Respiratory Failure

Background The role of non-invasive (continuous positive airway pressure (CPAP) or Non-invasive ventilation (NIV)) respiratory support (NIRS) as a primary oxygenation strategy for COVID-19 patients with acute severe hypoxic respiratory failure (AHRF), as opposed to invasive mechanical ventilation (invasive-MV), is uncertain. While NIRS may prevent complications related to invasive MV, prolonged NIRS and delays in intubation may lead to adverse outcomes. This study was conducted to assess the role of NIRS in COVID-19 hypoxemic respiratory failure and to explore the variables associated with NRIS failure. Methods This is a single-center, observational study of two distinct waves of severe COVID-19 patients admitted to the ICU. Patients initially managed with non-invasive respiratory support with laboratory-confirmed SARS-CoV-2 in acute hypoxaemic respiratory failure were included. Demographics, comorbidities, admission laboratory variables, and ICU admission scores were extracted from electronic health records. Univariate and multiple logistic regression was used to identify predictive factors for invasive mechanical ventilation. Kaplan-Meier survival curves were used to summarise survival between the ventilatory and time-to-intubation groups. Results There were 291 patients, of which 232 were managed with NIRS as an initial ventilation strategy. There was a high incidence of failure (48.7%). Admission APACHE II score, SOFA score, HACOR score, ROX index, and PaO2/FiO2 were all predictive of NIRS failure. Daily (days 1-4) HACOR scores and ROX index measurements highly predicted NIRS failure. Late NIRS failure (>24 hours) was independently associated with increased mortality (44%). Conclusion NIRS is effective as first-line therapy for COVID-19 patients with AHRF. However, failure, particularly delayed failure, is associated with significant mortality. Early prediction of NIRS failure may prevent adverse outcomes.

Clinical Outcomes of COVID-19-Induced Acute Respiratory Distress Syndrome in Patients With Three Different Respiratory Support Modalities: A Retrospective Cohort Study

Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced acute respiratory failure and acute respiratory distress syndrome (ARDS) had a considerable impact on intensive care utilization and resource optimization. Multiple modalities for respiratory support were implemented during the COVID-19 pandemic with the main concern of being able to identify those patients at high risk of rapidly progressive respiratory failure, whom the early initiation of invasive respiratory support would, in particular, affect the outcome in comparison to the noninvasive management strategy. Objectives In our cohort study, we describe demographic characteristics, respiratory support modalities, and their relation to patient outcomes. Method Patients 18 years of age and older who were admitted to a tertiary center COVID-19-dedicated medical intensive care unit (MICU) in Qatar between March 2020 and May 2020 with a confirmed diagnosis of COVID-19 pneumonia were included in this study. Patients were divided into invasive or noninvasive, and those who required invasive strategy were subdivided into the early intubation group (patients who were intubated within 72 hours of intensive care unit (ICU) admission) and the late intubation group (patients who were intubated after 72 hours from ICU admission). The primary outcome was ICU and hospital mortality, and the secondary effects were the length of stay and mortality determinants. Results A total of 686 patients were admitted to the medical intensive care unit (MICU) during the study period. There were 222 (32.4%), 131 (19.1%), and 333 (48.5%) patients in the early, late, and not intubated groups, respectively. Compared to the late intubated group, the early intubated group had a higher proportion of males. Diabetes (39.8%) was the most common comorbidity, followed by hypertension (HTN) (36%) and heart disease (9.8%). The 30-day ICU and hospital mortality were significantly higher in the late intubated group compared to the early intubated group (30.5% versus 15.8%, and 30% versus 16.2%). The median ICU and hospital stay days in the total sample were 8 (interquartile range (IQR): 5-14) and 19 (IQR: 14-25), respectively. The mean estimates of 30-day ICU survival times for early intubated, late intubated, and not intubated groups were 25.14 (95% confidence interval (CI): 23.71, 26.57), 23.35 (95% CI: 21.63, 25.07), and 29.91 (95% CI: 29.74, 30.09) respectively. Conclusions In our study, the COVID-19 ARDS patients who required early invasive ventilatory support and in whom the physiological parameter was more severe (Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores) had a better outcome than the late intubation group. Age more than 60 years old, diabetes, hypertension, chronic kidney disease (CKD), and chronic liver disease (CLD) were the main predictor of mortality in total.

Role of prognostic scores in predicting in-hospital mortality and failure of non-invasive ventilation in adults with COVID-19

We tested the prognostic performance of different scores for the identification of subjects with acute respiratory failure by COVID-19, at risk of in-hospital mortality and NIV failure. We conducted a retrospective study, in the Medical High-Dependency Unit of the University-Hospital Careggi. We included all subjects with COVID-19 and ARF requiring non-invasive ventilation (NIV) between March 2020 and January 2021. Clinical parameters, the HACOR score (Heart rate, Acidosis, Consciousness, Oxygenation, Respiratory Rate) and ROX index ((SpO2/FiO2)/respiratory rate) were collected 3 (-3) and 1 day (-1) before the NIV initiation, the first day of treatment (Day0) and after 1 (+1), 2 (+2), 5 (+5), 8 (+8) and 11 (+11) of treatment. The primary outcomes were in-hospital mortality and NIV failure. We included 135 subjects, mean age 69±13 years, 69% male. Patients, who needed mechanical ventilation, showed a higher HACOR score (Day0: 6 [5-7] vs 6 [6-7], p=.057; Day+2: 6 [6-6] vs 6 [4-6], p=.013) and a lower ROX index (Day0: 4.2±2.3 vs 5.1±2.3, p=.055; Day+2: 4.4±1.2.vs 5.5±1.3, p=.001) than those with successful NIV. An HACOR score >5 was more frequent among nonsurvivors (Day0: 82% vs 58%; Day2: 82% vs 48%, all p<0.01) and it was associated with in-hospital mortality (Day0: RR 5.88, 95%CI 2.01-17.22; Day2: RR 4.33, 95%CI 1.64-11.41) independent to age and Charlson index. In conclusion, in subjects treated with NIV for ARF caused by COVID19, respiratory parameters collected after the beginning of NIV allowed to identify those at risk of an adverse outcome. An HACOR score >5 was independently associated with increased mortality rate.

Prediction of Noninvasive Ventilation Failure in COVID-19 Patients: When Shall We Stop?

INTRODUCTION

In coronavirus disease 2019 (COVID-19), there are no tools available for the difficult task of recognizing which patients do not benefit from maintaining respiratory support, such as noninvasive ventilation (NIV). Identifying treatment failure is crucial to provide the best possible care and optimizing resources. Therefore, this study aimed to build a model that predicts NIV failure in patients who did not progress to invasive mechanical ventilation (IMV).

METHODS

This retrospective observational study included critical COVID-19 patients treated with NIV who did not progress to IMV. Patients were admitted to a Portuguese tertiary hospital between October 1, 2020, and March 31, 2021. The outcome of interest was NIV failure, defined as COVID-19-related in-hospital death. A binary logistic regression was performed, where the outcome (mortality) was the dependent variable. Using the independent variables of the logistic regression a decision-tree classification model was implemented.

RESULTS

The study sample, composed of 103 patients, had a mean age of 66.3 years (SD=14.9), of which 38.8% (40 patients) were female. Most patients (82.5%) were autonomous for basic activities of daily living. The prediction model was statistically significant with an area under the curve of 0.994 and a precision of 0.950. Higher age, a higher number of days with increases in the fraction of inspired oxygen (FiO₂), a higher number of days of maximum expiratory positive airway pressure, a lower number of days on NIV, and a lower number of days from disease onset to hospital admission were, with statistical significance, associated with increased odds of death. A decision-tree classification model was then obtained to achieve the best combination of variables to predict the outcome of interest.

CONCLUSIONS

This study presents a model to predict death in COVID-19 patients treated with NIV in patients who did not progress to IMV, based on easily applicable variables that mainly reflect patients' evolution during hospitalization. Along with the decision-tree classification model, these original findings may help clinicians define the best therapeutical approach to each patient, prioritizing life-comforting measures when adequate, and optimizing resources, which is crucial within limited or overloaded healthcare systems. Further research is needed on this subject of treatment failure, not only to understand if these results are reproducible but also, in a broader sense, helping to fill this gap in modern medicine guidelines.

Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model

Background

Noninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs).

Methods

Patients who underwent NIV after extubation in the eICU Collaborative Research Database (eICU-CRD) were included. NIV failure was defined as need for invasive ventilatory support (reintubation or tracheotomy) or death after NIV initiation. A total of 93 clinical and laboratory variables were assessed, and the recursive feature elimination algorithm was used to select key features. Hyperparameter optimization was conducted with an automated machine-learning toolkit called Neural Network Intelligence. A machine-learning model called Categorical Boosting (CatBoost) was developed and compared with nine other models. The model was then prospectively validated among patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University.

Results

Of 929 patients included in the eICU-CRD cohort, 248 (26.7%) had NIV failure. The time from extubation to NIV, age, Glasgow Coma Scale (GCS) score, heart rate, respiratory rate, mean blood pressure (MBP), saturation of pulse oxygen (SpO₂), temperature, glucose, pH, pressure of oxygen in blood (PaO₂), urine output, input volume, ventilation duration, and mean airway pressure were selected. After hyperparameter optimization, our model showed the greatest accuracy in predicting NIV failure (AUROC: 0.872 [95% CI 0.82–0.92]) among all predictive methods in an internal validation. In the prospective validation cohort, our model was also superior (AUROC: 0.846 [95% CI 0.80–0.89]). The sensitivity and specificity in the prediction group is 89% and 75%, while in the validation group they are 90% and 70%. MV duration and respiratory rate were the most important features. Additionally, we developed a web-based tool to help clinicians use our model.

Conclusions

This study developed and prospectively validated the CatBoost model, which can be used to identify patients who are at risk of NIV failure. Thus, those patients might benefit from early triage and more intensive monitoring.

Trial registration: NCT03704324. Registered 1 September 2018, https://register.clinicaltrials.gov.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02096-7.

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

Objective

Although risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).

Materials and methods

A total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.

Results

At the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.

Conclusion

In this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy.

Risk factors and a nomogram for frailty in Chinese older patients with Alzheimer's disease: A single-center cross-sectional study

This study aimed to determine the risk factors of frailty in Chinese older patients with Alzheimer's disease (AD) and then construct a nomogram for frailty in this population. A total of 205 eligible older AD patients were recruited. Patients' general demographic characteristics were collected through a self-designed questionnaire. A nomogram was constructed for frailty based on the risk factors identified from the multivariate analysis. The discrimination and calibration capabilities of this nomogram were assessed with the C-index and calibration curve, respectively. The results showed that older age, no regular exercise habit, severe cognitive decline, and low social support were identified as important risk factors of frailty in AD patients. The C-index of the nomogram was 0.884 by bootstrapping validation, and the calibration curve of the nomogram showed high coherence between the predicted and actual probabilities of frailty. In conclusion, this nomogram was validated to have favorable discrimination and calibration capabilities.

Noninvasive Respiratory Assist Devices in the Management of COVID-19-related Hypoxic Respiratory Failure: Pune ISCCM COVID-19 ARDS Study Consortium (PICASo)

Objective

To determine whether high-flow nasal oxygen (HFNO) or noninvasive ventilator (NIV) can avoid invasive mechanical ventilation (IMV) in COVID-19-related acute respiratory distress syndrome (ADRS), and the outcome predictors of these modalities.

Design

Multicenter retrospective study conducted in 12 ICUs in Pune, India.

Patients

Patients with COVID-19 pneumonia who had PaO₂/FiO₂ ratio <150 and were treated with HFNO and/or NIV.

Intervention

HFNO and/or NIV.

Measurements

The primary outcome was to assess the need of IMV. Secondary outcomes were death at Day 28 and mortality rates in different treatment groups.

Main results

Among 1,201 patients who met the inclusion criteria, 35.9% (431/1,201) were treated successfully with HFNO and/or NIV and did not require IMV. About 59.5% (714/1,201) patients needed IMV for the failure of HFNO and/or NIV. About 48.3, 61.6, and 63.6% of patients who were treated with HFNO, NIV, or both, respectively, needed IMV. The need of IMV was significantly lower in the HFNO group (p <0.001). The 28-day mortality was 44.9, 59.9, and 59.6% in the patients treated with HFNO, NIV, or both, respectively (p <0.001). On multivariate regression analysis, presence of any comorbidity, SpO₂ <90%, and presence of nonrespiratory organ dysfunction were independent and significant determinants of mortality (p <0.05).

Conclusions

During COVID-19 pandemic surge, HFNO and/or NIV could successfully avoid IMV in 35.5% individuals with PO₂/FiO₂ ratio <150. Those who needed IMV due to failure of HFNO or NIV had high (87.5%) mortality.

How to cite this article

Jog S, Zirpe K, Dixit S, Godavarthy P, Shahane M, Kadapatti K, et al. Noninvasive Respiratory Assist Devices in the Management of COVID-19-related Hypoxic Respiratory Failure: Pune ISCCM COVID-19 ARDS Study Consortium (PICASo). Indian J Crit Care Med 2022;26(7):791-797.

Coupling of right ventricular function to pulmonary circulation as an independent predictor for non invasive ventilation failure in SARSCoV 2-related acute respiratory distress syndrome

Study objectives

To assess whether echocardiography, systematically performed, could help in risk stratifying patients with acute respiratory distress syndrome (ARDS) due to SARS-CoV2 (COVID) infection for non invasive ventilation (NIV) failure.

Design

Observational single center investigation.

Setting

Intensive care unit.

Interventions

Echocardiography.

Outcome measures

NIV failure.

Main results

Seventy-five patients were included in our study. In respect to patients who did not need mechanical ventilation (NIV success), those in the NIV failure subgroup (31 patients, 41 %) were older, with more comorbidities and showed a higher SOFA score and LOS. Higher values of NTpro BNP, CRP and D-dimer were observed in the NIV failure subgroup who exhibited a higher ICU mortality rate. At echocardiographic examination, the NIV failure subgroup showed higher values of RV/LV ratio, systolic pulmonary arterial pressure (sPAP) and lower values of tricuspid annular plane systolic excursion (TAPSE)/SPAP, and PaO2/FiO2. At logistic regression analysis TAPSE/sPAP resulted an independent predictor of NIV failure. At receiving operating characteristic curve analysis, the TAPSE/SPAP cut-off of 0.575 mm/mm Hg showed a sensitivity of 97 % and a specificity of 48 %.

Conclusions

Our results documented a marked uncoupling of right ventricular function from the pulmonary circulation (as indicated by TAPSE/sPAP) in COVID-related ARDS treated with non invasive ventilation and the measurement of this parameter, performed on ICU admission, provides independent prognostic relevance for NIV failure.

Risk Stratification in Noninvasive Respiratory Support Failure: A Narrative Review

TOPIC/CLINICAL RELEVANCE

The use of noninvasive respiratory support, including noninvasive ventilation and high-flow nasal cannula therapy, has increased over the years. Failure of noninvasive respiratory support, defined as the need for invasive mechanical ventilation, increases the mortality rate.

PURPOSE

To familiarize critical care nurses with available risk stratification assessments and identify common concepts and limitations.

CONTENT COVERED

Few risk stratification assessments are available to identify patients at risk of failure of noninvasive respiratory support. Although many studies have analyzed risk, substantial variation in study design, definitions, terminology, and outcomes have led to a wide range of findings, making clinical application difficult. Further study is needed to broaden known assessments to general patient populations, determine diagnostic accuracy during critical periods, and analyze noninvasive ventilation and high-flow nasal cannula therapy. Risk stratification could allow for better implementation of preventive strategies and patient education.

CONCLUSION

Future research opportunities include improving study design for risk stratification and implementing preventive strategies for patients requiring noninvasive respiratory support. Clinically, risk stratification can provide an opportunity to share knowledge and facilitate conversations with patients and families.

Use of CPAP Failure Score to Predict the Risk of Helmet-CPAP Support Failure in COVID-19 Patients: A Retrospective Study

(1) Background: the aim of this study was to create a score to predict the incidence of CPAP failure in COVID-19 patients early. (2) Methods: in this retrospective observational study, we included all consecutive adult patients admitted between February and April 2021. The main outcome was the failure of CPAP support (intubation or death). (3) Results: two-hundred and sixty-three COVID-19 patients were managed with CPAP. The population was divided in short-CPAP (CPAP days ≤ 10; 72.6%) and long-CPAP (>10; 27.4%) groups. After balancing the entire population using a stabilized IPTW method, we applied a multivariable logistic regression analysis to identify the risk factors for CPAP failure. We used the identified covariates to create a mathematical model, the CPAP Failure Score (CPAP-FS). The multivariable logistic regression analysis identified four variables: SpO2 (OR = 0.86; p-value = 0.001), P/F ratio (OR = 0.99; p-value = 0.008), the Call Score (OR = 1.44; p-value = 0.02), and a pre-existing chronic lung disease (OR = 3.08; p-value = 0.057). The beta-coefficients obtained were used to develop the CPAP-FS, whose diagnostic ability outperformed other relevant COVID-19-related parameters (AUC = 0.87; p-value < 0.0001). We validated the CPAP-FS using a 10-fold internal cross-validation method which confirmed the observed results (AUCs 0.76−0.80; p-values < 0.0001). (4) Conclusions: the CPAP-FS can early identify COVID-19 patients who are at risk of CPAP failure.

Mechanical Ventilation for COVID-19 Patients

Non-invasive ventilation (NIV) or invasive mechanical ventilation (MV) is frequently needed in patients with acute hypoxemic respiratory failure due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. While NIV can be delivered in hospital wards and nonintensive care environments, intubated patients require intensive care unit (ICU) admission and support. Thus, the lack of ICU beds generated by the pandemic has often forced the use of NIV in severely hypoxemic patients treated outside the ICU. In this context, awake prone positioning has been widely adopted to ameliorate oxygenation during noninvasive respiratory support. Still, the incidence of NIV failure and the role of patient self-induced lung injury on hospital outcomes of COVID-19 subjects need to be elucidated. On the other hand, endotracheal intubation is indicated when gas exchange deterioration, muscular exhaustion, and/or neurological impairment ensue. Yet, the best timing for intubation in COVID-19 is still widely debated, as it is the safest use of neuromuscular blocking agents. Not differently from other types of acute respiratory distress syndrome, the aim of MV during COVID-19 is to provide adequate gas exchange while avoiding ventilator-induced lung injury. At the same time, the use of rescue therapies is advocated when standard care is unable to guarantee sufficient organ support. Nevertheless, the general shortage of health care resources experienced during SARS-CoV-2 pandemic might affect the utilization of high-cost, highly specialized, and long-term supports. In this article, we describe the state-of-the-art of NIV and MV setting and their usage for acute hypoxemic respiratory failure of COVID-19 patients.

Derivation and Validation of a Predictive Score for Respiratory Failure Worsening Leading to Secondary Intubation in COVID-19: The CERES Score

Predictive scores assessing the risk of respiratory failure in COVID-19 mostly focused on the prediction of early intubation. A combined assessment of clinical parameters and biomarkers of endotheliopathy could allow to predict late worsening of acute respiratory failure (ARF), subsequently warranting intubation in COVID-19. Retrospective single-center derivation (n = 92 subjects) and validation cohorts (n = 59 subjects), including severe COVID-19 patients with non-invasive respiratory support, were assessed for at least 48 h following intensive care unit (ICU) admission. We used stepwise regression to construct the COVID endothelial and respiratory failure (CERES) score in a derivation cohort, and secondly assessed its accuracy for the prediction of late ARF worsening, requiring intubation within 15 days following ICU admission in an independent validation cohort. Platelet count, fraction of inspired oxygen, and endocan measured on ICU admission were identified as the top three predictive variables for late ARF worsening and subsequently included in the CERES score. The area under the ROC curve of the CERES score to predict late ARF worsening was calculated in the derivation and validation cohorts at 0.834 and 0.780, respectively. The CERES score is a simple tool with good performances to predict respiratory failure worsening, leading to secondary intubation, in COVID-19 patients.

HSP90 Inhibitors Modulate SARS-CoV-2 Spike Protein Subunit 1-Induced Human Pulmonary Microvascular Endothelial Activation and Barrier Dysfunction

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused more than 5 million deaths worldwide. Multiple reports indicate that the endothelium is involved during SARS-Cov-2-related disease (COVID-19). Indeed, COVID-19 patients display increased thrombophilia with arterial and venous embolism and lung microcapillary thrombotic disease as major determinants of deaths. The pathophysiology of endothelial dysfunction in COVID-19 is not completely understood. We have investigated the role of subunit 1 of the SARS-CoV-2 spike protein (S1SP) in eliciting endothelial barrier dysfunction, characterized dose and time relationships, and tested the hypothesis that heat shock protein 90 (HSP90) inhibitors would prevent and repair such injury. S1SP activated (phosphorylated) IKBα, STAT3, and AKT and reduced the expression of intercellular junctional proteins, occludin, and VE-cadherin. HSP90 inhibitors (AT13387 and AUY-922) prevented endothelial barrier dysfunction and hyperpermeability and reduced IKBα and AKT activation. These two inhibitors also blocked S1SP-mediated barrier dysfunction and loss of VE-cadherin. These data suggest that spike protein subunit 1 can elicit, by itself, direct injury to the endothelium and suggest a role of HSP90 inhibitors in preserving endothelial functionality.

Endotracheal intubation rate is lower in critically-ill SARS-CoV-2 patients requiring high-flow nasal oxygen receiving additional face-mask noninvasive ventilation: a retrospective bicentric cohort with propensity score analysis

BACKGROUND

SARS-CoV-2 pneumonia is responsible for unprecedented numbers of acute respiratory failure requiring invasive mechanical ventilation (IMV). This work aimed to assess whether adding face-mask noninvasive ventilation (NIV) to high-flow nasal oxygen (HFNO) was associated with a reduced need for endotracheal intubation.

METHODS

This retrospective cohort study was conducted from July 2020 to January 2021 in two tertiary care intensive care units (ICUs) in Paris, France. Patients admitted for laboratory confirmed SARS-CoV-2 infection with acute hypoxemic respiratory failure requiring HFNO with or without NIV were included. The primary outcome was the rate of endotracheal intubation. Secondary outcomes included day-28 mortality, day-28 respiratory support and IMV free days, ICU and hospital length-of-stay. Sensitivity analyses with both propensity score matching and overlap weighting were used.

RESULTS

128 patients were included, 88 (69%) received HFNO alone and 40 (31%) received additional NIV. Additional NIV was associated with a reduced rate of endotracheal intubation in multivariate analysis (53 (60%) vs 15 (38%), HR=0.46 (95%CI, 0.23-0.95), p=0.04). Sensitivity analyses by propensity score matching (HR=0.45 (95%IC, 0.24-0.84), p=0.01) and overlap weighting (HR=0.52 (95% CI, 0.28-0.94), p=0.03) were consistent. Day-28 mortality was 25 (28%) in the HFNO group and 8 (20%) in the NIV group (HR=0.75 (95%CI, 0.15-3.82), p=0.72). NIV was associated with higher IMV free days (20 (0-28) vs 28 (14-28), p=0.015). All sensitivity analyses were consistent regarding secondary outcomes.

CONCLUSION

Need for endotracheal intubation was lower in critically-ill SARS-CoV-2 patients receiving face-mask noninvasive mechanical ventilation in addition to high-flow oxygen therapy.

Dihydroartemisinin attenuates pulmonary inflammation and fibrosis in rats by suppressing JAK2/STAT3 signaling

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has induced a worldwide pandemic since early 2020. COVID-19 causes pulmonary inflammation, secondary pulmonary fibrosis (PF); however, there are still no effective treatments for PF. The present study aimed to explore the inhibitory effect of dihydroartemisinin (DHA) on pulmonary inflammation and PF, and its molecular mechanism. Morphological changes and collagen deposition were analyzed using hematoxylin-eosin staining, Masson staining, and the hydroxyproline content. DHA attenuated early alveolar inflammation and later PF in a bleomycin-induced rat PF model, and inhibited the expression of interleukin (IL)-1β, IL-6, tumor necrosis factor α (TNFα), and chemokine (C-C Motif) Ligand 3 (CCL3) in model rat serum. Further molecular analysis revealed that both pulmonary inflammation and PF were associated with increased transforming growth factor-β1 (TGF-β1), Janus activated kinase 2 (JAK2), and signal transducer and activator 3(STAT3) expression in the lung tissues of model rats. DHA reduced the inflammatory response and PF in the lungs by suppressing TGF-β1, JAK2, phosphorylated (p)-JAK2, STAT3, and p-STAT3. Thus, DHA exerts therapeutic effects against bleomycin-induced pulmonary inflammation and PF by inhibiting JAK2-STAT3 activation. DHA inhibits alveolar inflammation, and attenuates lung injury and fibrosis, possibly representing a therapeutic candidate to treat PF associated with COVID-19.

Non-invasive respiratory support in the management of acute COVID-19 pneumonia: considerations for clinical practice and priorities for research

Non-invasive respiratory support (NIRS) has increasingly been used in the management of COVID-19-associated acute respiratory failure, but questions remain about the utility, safety, and outcome benefit of NIRS strategies. We identified two randomised controlled trials and 83 observational studies, compromising 13 931 patients, that examined the effects of NIRS modalities-high-flow nasal oxygen, continuous positive airway pressure, and bilevel positive airway pressure-on patients with COVID-19. Of 5120 patients who were candidates for full treatment escalation, 1880 (37%) progressed to invasive mechanical ventilation and 3658 of 4669 (78%) survived to study end. Survival was 30% among the 1050 patients for whom NIRS was the stated ceiling of treatment. The two randomised controlled trials indicate superiority of non-invasive ventilation over high-flow nasal oxygen in reducing the need for intubation. Reported complication rates were low. Overall, the studies indicate that NIRS in patients with COVID-19 is safe, improves resource utilisation, and might be associated with better outcomes. To guide clinical decision making, prospective, randomised studies are needed to address timing of intervention, optimal use of NIRS modalities-alone or in combination-and validation of tools such as oxygenation indices, response to a trial of NIRS, and inflammatory markers as predictors of treatment success.

Noninvasive respiratory support for acute respiratory failure due to COVID-19

PURPOSE OF REVIEW

Noninvasive respiratory support has been widely applied during the COVID-19 pandemic. We provide a narrative review on the benefits and possible harms of noninvasive respiratory support for COVID-19 respiratory failure.

RECENT FINDINGS

Maintenance of spontaneous breathing by means of noninvasive respiratory support in hypoxemic patients with vigorous spontaneous effort carries the risk of patient self-induced lung injury: the benefit of averting intubation in successful patients should be balanced with the harms of a worse outcome in patients who are intubated after failing a trial of noninvasive support.The risk of noninvasive treatment failure is greater in patients with the most severe oxygenation impairment (PaO2/FiO2 < 200 mmHg).High-flow nasal oxygen (HFNO) is the most widely applied intervention in COVID-19 patients with hypoxemic respiratory failure. Also, noninvasive ventilation (NIV) and continuous positive airway pressure delivered with different interfaces have been used with variable success rates. A single randomized trial showed lower need for intubation in patients receiving helmet NIV with specific settings, compared to HFNO alone.Prone positioning is recommended for moderate-to-severe acute respiratory distress syndrome patients on invasive ventilation. Awake prone position has been frequently applied in COVID-19 patients: one randomized trial showed improved oxygenation and lower intubation rate in patients receiving 6-h sessions of awake prone positioning, as compared to conventional management.

SUMMARY

Noninvasive respiratory support and awake prone position are tools possibly capable of averting endotracheal intubation in COVID-19 patients; carefully monitoring during any treatment is warranted to avoid delays in endotracheal intubation, especially in patients with PaO2/FiO2 < 200 mmHg.

Noninvasive respiratory support for COVID-19 patients: when, for whom, and how?

The significant mortality rate and prolonged ventilator days associated with invasive mechanical ventilation (IMV) in patients with severe COVID-19 have incited a debate surrounding the use of noninvasive respiratory support (NIRS) (i.e., HFNC, CPAP, NIV) as a potential treatment strategy. Central to this debate is the role of NIRS in preventing intubation in patients with mild respiratory disease and the potential beneficial effects on both patient outcome and resource utilization. However, there remains valid concern that use of NIRS may prolong time to intubation and lung protective ventilation in patients with more advanced disease, thereby worsening respiratory mechanics via self-inflicted lung injury. In addition, the risk of aerosolization with the use of NIRS has the potential to increase healthcare worker (HCW) exposure to the virus. We review the existing literature with a focus on rationale, patient selection and outcomes associated with the use of NIRS in COVID-19 and prior pandemics, as well as in patients with acute respiratory failure due to different etiologies (i.e., COPD, cardiogenic pulmonary edema, etc.) to understand the potential role of NIRS in COVID-19 patients. Based on this analysis we suggest an algorithm for NIRS in COVID-19 patients which includes indications and contraindications for use, monitoring recommendations, systems-based practices to reduce HCW exposure, and predictors of NIRS failure. We also discuss future research priorities for addressing unanswered questions regarding NIRS use in COVID-19 with the goal of improving patient outcomes.

Factors associated with mechanical ventilation in SARS-CoV-2 patients treated with high-flow nasal cannula oxygen and outcomes

Five percent of patients infected with SARS‐CoV‐2 require advanced respiratory support. The high‐flow nasal cannula oxygenotherapy (HFNCO) appears to be effective and safe to reduce the need for mechanical ventilation. However, the factors associated with HFNCO failure as well as the outcomes of patients receiving this noninvasive respiratory strategy remain unclear. Thus, we performed this study to determine factors leading to intubation of SARS‐CoV‐2 patients treated with HFNCO and patients’ outcomes. We retrospectively analyzed the medical charts of patients admitted in our ICU center for acute respiratory failure due to SARS‐CoV‐2 infection and who initially benefited from HFNCO, between September 1, 2020, and March 1, 2021. We included all adults patients who received HFNCO and compared two groups: those treated with HFNCO alone and those who failed HFNCO. Patients treated with HFNCO and secondarily limited to the use of mechanical ventilation were excluded from the analysis. Sixty‐nine patients were included, 33 were treated with HFNCO alone and 36 failed HFNCO. We found more patients with shock in the HFNCO failure group (p = 0.001). The mean IGSII score was higher in the HFNCO failure group (p < 0.001). The minimum PaO₂/FiO₂ was lower in the HFNCO failure group (p = 0.024). The length of stay in ICU was higher in the HFNCO failure group (p < 0.001). The mean duration of HFNCO before intubation was 1.77 days. Six‐week mortality was higher in the HFNCO failure group (p = 0.034). Ten patients had a complication during intubation. The HFNCO leads to reduce the intubation rate, the length of stay in ICU, and the mortality. Determining the factors associated with HFNCO failure is important to avoid complications following late intubation.

The use of HFNCO avoided the need for intubation in 47.8% of the cases in our study.

Patients with HFNCO failure have a lower minimal PaO2/FiO2, more shock, an higher IGSII score and a longer length of stay in ICU.

We found greater mortality at six weeks in the HFNCO failure group.

We found an high rate of complications following the intubation.

The Role of Noninvasive Respiratory Management in Patients with Severe COVID-19 Pneumonia

Acute hypoxemic respiratory failure is the principal cause of hospitalization, invasive mechanical ventilation and death in severe COVID-19 infection. Nearly half of intubated patients with COVID-19 eventually die. High-Flow Nasal Oxygen (HFNO) and Noninvasive Ventilation (NIV) constitute valuable tools to avert endotracheal intubation in patients with severe COVID-19 pneumonia who do not respond to conventional oxygen treatment. Sparing Intensive Care Unit beds and reducing intubation-related complications may save lives in the pandemic era. The main drawback of HFNO and/or NIV is intubation delay. Cautious selection of patients with severe hypoxemia due to COVID-19 disease, close monitoring and appropriate employment and titration of HFNO and/or NIV can increase the rate of success and eliminate the risk of intubation delay. At the same time, all precautions to protect the healthcare personnel from viral transmission should be taken. In this review, we summarize the evidence supporting the application of HFNO and NIV in severe COVID-19 hypoxemic respiratory failure, analyse the risks associated with their use and provide a path for their proper implementation.

Noninvasive respiratory support and patient self-inflicted lung injury in COVID-19: a narrative review

COVID-19 pneumonia is associated with hypoxaemic respiratory failure, ranging from mild to severe. Because of the worldwide shortage of ICU beds, a relatively high number of patients with respiratory failure are receiving prolonged noninvasive respiratory support, even when their clinical status would have required invasive mechanical ventilation. There are few experimental and clinical data reporting that vigorous breathing effort during spontaneous ventilation can worsen lung injury and cause a phenomenon that has been termed patient self-inflicted lung injury (P-SILI). The aim of this narrative review is to provide an overview of P-SILI pathophysiology and the role of noninvasive respiratory support in COVID-19 pneumonia. Respiratory mechanics, vascular compromise, viscoelastic properties, lung inhomogeneity, work of breathing, and oesophageal pressure swings are discussed. The concept of P-SILI has been widely investigated in recent years, but controversies persist regarding its mechanisms. To minimise the risk of P-SILI, intensivists should better understand its underlying pathophysiology to optimise the type of noninvasive respiratory support provided to patients with COVID-19 pneumonia, and decide on the optimal timing of intubation for these patients.

Failure of High-Flow Nasal Cannula Therapy in Pneumonia and Non-Pneumonia Sepsis Patients: A Prospective Cohort Study

Despite the increasing use of high-flow nasal cannulas (HFNCs) to treat critically ill patients, data on their effectiveness for sepsis patients remains very limited. We studied a prospective cohort of sepsis patients from the Korean Sepsis Registry (18 intensive care units (ICUs)). Patients started on HFNC therapy for hypoxemia within the first three ICU days were enrolled. HFNC failure was defined as intubation or ICU death, and the primary outcome was early HFNC failure occurring within 72 h of HFNC initiation. Of 901 patients with sepsis admitted to the ICU, 206 who received HFNC therapy were finally included (117 with pneumonia vs. 89 with non-pneumonia sepsis; median age, 71.0 (63.0–78.0) years; P_aO₂/F_iO₂ ratio, 160.2 (107.9–228.2) mm Hg; septic shock, n = 81 (39.3%)). During HFNC therapy, 72 (35.0%) patients were intubated and 51 (24.8%) died. HFNC failure developed in 95 (46.1%) patients, and among them, early failure rate was 85.3% (81/95). On multivariate analysis, an immunocompromised state (odds ratio (OR) = 2.730), use of a combination of antibiotics (OR = 0.219), and the P_aO₂/F_iO₂ ratio (OR = 0.308) were significantly associated with early HFNC failure in pneumonia sepsis patients. However, in non-pneumonia sepsis patients, lactate levels (OR = 1.532) were significantly associated with early HFNC failure. In conclusion, a high proportion of sepsis patients experience HFNC failure, usually within 72 h after therapy initiation, which emphasizes the importance of close monitoring. Furthermore, unlike in pneumonia sepsis, organ failure (i.e., lactate) might serve as a prognostic marker in non-pneumonia sepsis (i.e., type IV respiratory failure).