scholarly journals Descriptive characteristics of continuous oximetry measurement in moderate to severe COVID-19 patients

2021 ◽  
Author(s):  
Jonathan Aryeh Sobel ◽  
Jeremy Levy ◽  
Ronit Almog ◽  
Anat Reiner Benaim ◽  
Asaf Miller ◽  
...  
Keyword(s):  
Health Care ◽  
Mechanical Ventilation ◽  
High Resolution ◽  
Oxygen Saturation ◽  
Treatment Response ◽  
Vital Sign ◽  
Critical Group ◽  
Non Invasive ◽  
Critical Patients ◽  
Digital Biomarkers

Background: Non-invasive oxygen saturation (SpO2) measurement is a central vital sign that supports the management of COVID-19 patients. However, reports on SpO2 characteristics (patterns and dynamics) are scarce and none, to our knowledge, has analysed high resolution continuous SpO2 in COVID-19. Methods: SpO2 signal sampled at 1Hz and clinical data were collected from COVID-19 departments at the Rambam Health Care Campus (Haifa, Israel) between May 1st, 2020 and February 1st, 2021. Data from a total of 367 COVID-19 patients, totalling 27K hours of continuous SpO2 recording, could be retrieved, including 205 non-critical and 162 critical cases. Desaturations based on different SpO2 threshold definitions and oximetry derived digital biomarkers (OBMs) were extracted and compared across severity and support levels. Findings: An absolute SpO2 threshold at 93% was the most efficient in discriminating between critical and non-critical patients without support or under oxygen support. Under no support, the non-critical group depicted a fold change (FC) of 1,8 times more frequent desaturations compared to the critical group. However, the hypoxic burden was 1,6 times more important in critical versus non-critical patients. Other OBMs depicted significant differences, notably the percentage of time below 93% SpO2 (CT93) was the most discriminating OBM. Mechanical ventilation depicted a strong effect on SpO2 by significantly reducing the frequency (1,85 FC) and depth (1,21 FC) of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected up to several hours before the initiation of the mechanical ventilation. Interpretation: This is the first report investigating continuous SpO2 measurements in hospitalized patients affected with COVID-19. SpO2 characteristics differ between critical and non-critical patients and are impacted by the level of support. OBMs from high resolution SpO2 signal may enable to anticipate clinically relevant events, monitoring of treatment response and may be indicative of future deterioration.

scholarly journals Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: a single center pilot study

2018 ◽  
Author(s):  
Sam Ghazal ◽  
Michael Sauthier ◽  
David Brossier ◽  
Wassim Bouachir ◽  
Philippe Jouvet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Pilot Study ◽  
High Resolution ◽  
Oxygen Saturation ◽  
Predictive Model ◽  
Critically Ill Children ◽  
Learning Models ◽  
Machine Learning Models

AbstractClinicians’ experts in mechanical ventilation are not continuously at each patient’s bedside in an intensive care unit to adjust mechanical ventilation settings and to analyze the impact of ventilator settings adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict hemoglobin oxygen saturation 5 min after a ventilator setting change. Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 7.105 rows of data were obtained from 610 patients, discretized into 3 class labels. Due to data imbalance, four different data balancing process were applied and two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with accuracies of 76%, 62% and 96% for the SpO2 class “< 84%”, “85 to 91%” and “> 92%”, respectively. This pilot study using machine learning predictive model resulted in an algorithm with good accuracy. To obtain a robust algorithm, more data are needed, suggesting the need of multicenter pediatric intensive care high resolution databases.

scholarly journals Clinical characteristics of critically ill patients with COVID-19

2020 ◽  
Author(s):  
Indalecio Carboni Bisso ◽  
Iván Huespe ◽  
Carolina Lockhart ◽  
Agustín Massó ◽  
Julieta González Anaya ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Invasive Mechanical Ventilation ◽  
High Complexity ◽  
Non Invasive ◽  
Non Invasive Ventilation ◽  
Imaging Results ◽  
Critical Patients ◽  
Ventilatory Mechanics

ABSTRACTObjectiveDescribe the clinical and respiratory characteristics of critical patients with coronavirus disease 2019 (COVID-19).DesignObservational and retrospective study over 6 months.SettingIntensive care unit (ICU) of a high complexity hospital in Buenos Aires, Argentina.PatientsPatients older than 18 years with laboratory-confirmed COVID-19 by reverse transcriptase-polymerase chain reaction (RT-PCR) for SARS-CoV-2 were included in the study.Variables of interestDemographic characteristics such as sex and age, comorbidities, laboratory results, imaging results, ventilatory mechanics data, complications, and mortality were recorded.ResultsA total of 168 critically ill patients with COVID-19 were included. 66% were men with a median age of 65 years (58-75. 79.7% had at least one comorbidity. The most frequent comorbidity was arterial hypertension, affecting 52.4% of the patients. 67.9 % required invasive mechanical ventilation (MV), and no patient was treated with non-invasive ventilation. Most of the patients in MV (73.7%) required neuromuscular blockade due to severe hypoxemia. 36% of patients were ventilated in the prone position. The length of stay in the ICU was 13 days (6-24) and the mortality in the ICU was 25%.ConclusionsIn this study of critical patients infected by SARS-CoV-2 in a high-complexity hospital, the majority were comorbid elderly men, a large percentage required invasive mechanical ventilation, and ICU mortality was 25%.

Ambulatory care of non-invasive mechanical ventilation in COPD patients with global decompensated respiratory acidosis

Pneumologie ◽  
2017 ◽  
Vol 71 (S 01) ◽  
pp. S1-S125
Author(s):  
EJ Soto Hurtado ◽  
P Gutiérrez Castaño ◽  
JJ Torres ◽  
MD Jiménez Fernández ◽  
M Pérez Soriano ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Ambulatory Care ◽  
Invasive Mechanical Ventilation ◽  
Respiratory Acidosis ◽  
Non Invasive ◽  
Copd Patients

974 Non-invasive transabdominal assessment of In-Utero fetal oxygen saturation in a hypoxic lamb model

2021 ◽  
Vol 224 (2) ◽  
pp. S604
Author(s):  
Kourosh Vali ◽  
Begum Kasap ◽  
Weitai Qian ◽  
Christina M. Theodorou ◽  
Tailai Lihe ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
In Utero ◽  
Non Invasive

scholarly journals Non-invasive duo positive airway pressure ventilation versus nasal continuous positive airway pressure in preterm infants with respiratory distress syndrome: a randomized controlled trial

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Arash Malakian ◽  
Mohammad Reza Aramesh ◽  
Mina Agahin ◽  
Masoud Dehdashtian
Keyword(s):  
Mechanical Ventilation ◽  
Continuous Positive Airway Pressure ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Airway Pressure ◽  
Distress Syndrome ◽  
Positive Airway Pressure ◽  
Invasive Ventilation ◽  
Non Invasive ◽  
Non Invasive Ventilation

Abstract Background The most common cause of respiratory failure in premature infants is respiratory distress syndrome. Historically, respiratory distress syndrome has been treated by intratracheal surfactant injection followed by mechanical ventilation. In view of the risk of pulmonary injury associated with mechanical ventilation and subsequent chronic pulmonary lung disease, less invasive treatment modalities have been suggested to reduce pulmonary complications. Methods 148 neonates (with gestational age of 28 to 34 weeks) with respiratory distress syndrome admitted to Imam Khomeini Hospital in Ahwaz in 2018 were enrolled in this clinical trial study. 74 neonates were assigned to duo positive airway pressure (NDUOPAP) group and 74 neonates to nasal continuous positive airway pressure (NCPAP) group. The primary outcome in this study was failure of N-DUOPAP and NCPAP treatments within the first 72 h after birth and secondary outcomes included treatment complications. Results there was not significant difference between DUOPAP (4.1 %) and NCPAP (8.1 %) in treatment failure at the first 72 h of birth (p = 0.494), but non-invasive ventilation time was less in the DUOPAP group (p = 0.004). There were not significant differences in the frequency of patent ductus arteriosus (PDA), pneumothorax, intraventricular hemorrhage (IVH) and bronchopulmonary dysplasia (BPD), apnea and mortality between the two groups. Need for repeated doses of surfactant (p = 0.042) in the NDUOPAP group was significantly lower than that of the NCPAP group. The duration of oxygen therapy in the NDUOPAP group was significantly lower than that of the NCPAP group (p = 0.034). Also, the duration of hospitalization in the NDUOPAP group was shorter than that of the NCPAP group (p = 0.002). Conclusions In the present study, DUOPAP compared to NCPAP did not reduce the need for mechanical ventilation during the first 72 h of birth, but the duration of non-invasive ventilation and oxygen demand, the need for multiple doses of surfactant and length of stay in the DUOPAP group were less than those in the CPAP group. Trial registration IRCT20180821040847N1, Approved on 2018-09-10.

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