In order to accurately capture the respiratory muscle movement and extract the synchronization signals corresponding to the breathing phases, a comprehensive signal sensing system for sensing the movement of the respiratory muscle was developed with applying the thin-film varistor FSR402 IMS-C07A in this paper. The system integrated a sensor, a signal processing circuit, and an application program to collect, amplify and denoise electronic signals. Based on the respiratory muscle movement sensor and a STM32F107 development board, an experimental platform was designed to conduct experiments. The respiratory muscle movement data and respiratory airflow data were collected from 3 healthy adults for comparative analysis. In this paper, the results demonstrated that the method for determining respiratory phase based on the sensing the respiratory muscle movement exhibited strong real-time performance. Compared to traditional airflow-based respiratory phase detection, the proposed method showed a lead times ranging from 33 to 210 ms [(88.3 ± 47.9) ms] for expiration switched into inspiration and 17 to 222 ms [(92.9 ± 63.8) ms] for inspiration switched into expiration, respectively. When this system is applied to trigger the output of the ventilator, it will effectively improve the patient-ventilator synchrony and facilitate the ventilation treatment for patients with respiratory diseases.
Humans ; Respiratory Muscles/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Respiration ; Monitoring, Physiologic/methods* ; Adult

OBJECTIVE: To analyze the risk factors of acute spinal cord injury complicated with respiratory dysfunction, and to construct the clinical prediction model of acute spinal cord injury complicated with respiratory dysfunction. METHODS: Continuous 170 cases of acute spinal cord injury treated from April 2019 to October 2022 were retrospectively collected, and clinical data were uniformly collected. Patients were divided into respiratory dysfunction group 30 cases and non-respiratory dysfunction group 140 cases according to whether they had respiratory dysfunction during treatment. The predictive factors of acute spinal cord injury complicated with respiratory dysfunction were screened by Lasso analysis, and the risk factors of acute spinal cord injury complicated with respiratory dysfunction were screened by multivariate Logistic regression analysis. R(R4.2.1) software was used to establish a nomogram risk warning model for predicting acute spinal cord injury complicated with respiratory dysfunction, and Hosmer-Lemeshow test was used to evaluate the model fit. Finally, area under receiver operating characteristic(ROC) curve (AUC), calibration curve, and decision curve analysis(DCA) were used to evaluate the differentiation, calibration and clinical impact of the model. RESULTS: The incidence of respiratory dysfunction in 170 patients was 17.65%. Lasso regression analysis selected age, residence, marital status, smoking, hypertension, degree of paralysis, spinal cord injury plane, multiple injuries, spinal cord fracture and dislocation, and ASIA grade as the influencing factors. Multivariate Logistic regression analysis showed that age, smoking, degree of paralysis, level of spinal cord injury, spinal cord injury of fracture and dislocation, and ASIA grade were risk factors for acute spinal cord injury complicated with respiratory dysfunction. The prediction model of acute spinal cord injury complicated with respiratory dysfunction was established by Hosmer-Lemeshow test, χ²=5.830, P=0.67. The AUC value of the model was 0.912. DCA analysis showed that the net benefit value of nomogram prediction of acute spinal cord injury complicated with respiratory dysfunction was higher when threshold probability ranged from 1% to 100%. CONCLUSION This column chart can help identify the risk of acute spinal cord injury complicated with respiratory dysfunction in early clinical stage, facilitate early clinical decision-making and intervention, and has important guiding significance for optimizing clinical efficacy and improving prognosis of patients. It is expected to improve and verify this model with larger samples and multi-center in the future.
Humans ; Spinal Cord Injuries/complications* ; Nomograms ; Male ; Female ; Middle Aged ; Adult ; Retrospective Studies ; Risk Factors ; Aged ; Respiration Disorders/etiology* ; Adolescent ; Logistic Models

The intelligent oxygen management system is a software designed with various algorithms to automatically titrate inhaled oxygen concentration according to specific patterns. This system can be integrated into various ventilator devices and used during assisted ventilation processes, aiming to maintain the patient's blood oxygen saturation within a target range. This paper employs a scoping review methodology, focusing on research related to intelligent oxygen management systems in neonatal intensive care units. It reviews the fundamental principles, application platforms, and clinical outcomes of these systems, providing a theoretical basis for clinical implementation.
Humans ; Intensive Care Units, Neonatal ; Infant, Newborn ; Oxygen/administration & dosage* ; Oxygen Inhalation Therapy/methods* ; Respiration, Artificial

At present, there is no effective drug treatment for obstructive sleep apnea hypopnea syndrome (OSAHS). It is usually treated by mechanical ventilation through a ventilator. In this paper, a non-invasive bi-level breathing therapy system suitable for home scenarios is developed. The system supports single-level and bi-level positive airway pressure therapies, and introduces the function of inspiratory synchronous trigger based on flow monitoring to enhance the synchrony of patient-ventilator synchronization. The test results show that the performance indicators of the system meet expectations. Each ventilation mode can operate normally and can meet the requirements for the use of home non-invasive ventilators.
Humans ; Sleep Apnea, Obstructive/therapy* ; Equipment Design ; Noninvasive Ventilation/instrumentation* ; Respiration, Artificial

OBJECTIVES: Prolonged invasive mechanical ventilation is associated with increased risks of severe complications such as retinopathy of prematurity and bronchopulmonary dysplasia. Although neonatal intensive care unit (NICU) follow the principle of early extubation, extubation failure rates remain high, and reintubation may further increase the risk of adverse outcomes. This study aims to identify risk factors and short-term prognosis associated with first extubation failure in neonates, to provide evidence for effective clinical intervention strategies. METHODS: Clinical data of neonates who received invasive ventilation in the NICU of Children's Hospital of Nanjing Medical University from January 1, 2019, to December 31, 2021, were retrospectively collected. Neonates were divided into a successful extubation group and a failed extubation group based on whether reintubation occurred within 72 hours after the first extubation. Risk factors and short-term outcomes related to extubation failure were analyzed. RESULTS: A total of 337 infants were included, with 218 males (64.69%). Initial extubation failed in 34 (10.09%) infants. Compared with the successful extubation group, the failed extubation group had significantly lower gestational age [(31.37±5.14) weeks vs (34.44±4.07) weeks], age [2.5 (1.00, 8.25) h vs 5 (1.00, 22.00) h], birth weight [(1 818.97±1128.80) g vs (2 432.18±928.94) g], 1-minute Apgar score (6.91±1.90 vs 7.68±2.03), and the proportion of using mask oxygenation after extubation (21% vs 46%) (all P<0.05). Conversely, compared with the successful extubation group, the failed extubation group had significantly higher rates of vaginal delivery (59% vs 32%), caffeine use during mechanical ventilation (71% vs 38%), dexamethasone use at extubation (44% vs 17%), the highest positive end-expiratory pressure level within 72 hours post-extubation [6(5.00, 6.00) cmH₂O vs 5 (0.00, 6.00) cmH₂O] (1 cmH₂O=0.098 kPa), the highest FiO₂ within 72 hours post-extubation [(34.35±5.95)% vs (30.22±3.58)%], and duration of noninvasive intermittent positive pressure ventilation after extubation [0.5 (0.00, 42.00) hours vs 0 (0, 0) hours] (all P<0.05). Multivariate analysis identified gestational age <28 weeks (OR=5.570, 95% CI 1.866 to 16.430), age at NICU admission (OR=0.959, 95% CI 0.918 to 0.989), and a maximum FiO₂≥35% within 72 hours post-extubation (OR=4.541, 95% CI 1.849 to 10.980) as independent risk factors for extubation failure (all P<0.05). Additionally, the failed extubation group exhibited significantly higher incidences of necrotizing enterocolitis grade II or above, moderate-to-severe bronchopulmonary dysplasia, severe bronchopulmonary dysplasia, retinopathy of prematurity, treatment abandonment due to poor prognosis, and discharge on home oxygen therapy (all P<0.05). Total hospital length of stay and total hospitalization costs were also significantly increased in the failed extubation group (all P<0.05). CONCLUSIONS Gestational age <28 weeks, younger age at NICU admission, and FiO₂≥35% after extubation are high-risk factors for first extubation failure in neonates. Extubation failure markedly increases the risk of adverse clinical outcomes.
Humans ; Infant, Newborn ; Male ; Female ; Airway Extubation/adverse effects* ; Risk Factors ; Retrospective Studies ; Respiration, Artificial/methods* ; Intensive Care Units, Neonatal ; Prognosis ; Gestational Age ; Bronchopulmonary Dysplasia ; Infant, Premature ; Treatment Failure ; Intubation, Intratracheal

This case report outlines the treatment of an 11-year-old female who underwent adenotonsillectomy six years ago for snoring but experienced postoperative inefficacy. Her symptoms worsened two weeks before readmission, with increased snoring and sleep apnea, disabling her from lying down to sleep. She was readmitted on December 1, 2023, and diagnosed with severe obstructive sleep apnea hypopnea syndrome and hypercapnia. Automatic BiPAP alleviated her symptoms, with sleep breathing parameters normalizing during treatment. Follow-up at one month showed significant acceleration in her growth and resolution of her hypersomnolence issue.
Humans ; Female ; Child ; Hypercapnia/complications* ; Sleep Apnea, Obstructive/complications* ; Positive-Pressure Respiration ; Noninvasive Ventilation

Objective:To explore the influence of voice training combined with active breathing and circulation techniques on voice recovery following vocal cord polyp surgery. Methods:A total of 110 patients who underwent vocal cord polyp surgery at our hospital from May 2022 to November 2023 were selected and randomly divided into a control group （n=55） and a combination group （n=55） using a random number table method. During the recovery period, both groups received dietary control and aerosol treatment. The control group participated in voice training, while the combination group received active breathing and circulation techniques in addition to voice training for 2 months. Morphological changes, voice acoustic indicators （Shimmer, Jitter, Maximum Phonation Time[MPT]）, and the Voice Handicap Index （VHI） were compared between the two groups, and clinical efficacy was evaluated. Results:The combination group demonstrated higher clinical efficacy after training compared to the control group, with a statistically significant difference （P<0.05）. The proportion of incomplete closure, abnormal mucosal wave, and supraglottic compensation decreased in both groups after training （P<0.05）. However, there was no significant difference in the proportions of incomplete closure and abnormal mucosal wave between the two groups （P>0.05）. Notably, the proportion of patients with supraglottic compensation in the combination group was lower than in the control group （P<0.05）. After training, the Shimmer and Jitter values decreased in both groups, with the combination group exhibiting lower values （P<0.05）. Conversely, the MPT values increased in both groups, again with higher values in the combination group （P<0.05）. Additionally, after training, the functional, physiological, and emotional scores of the VHI decreased in both groups, with the scores in the combination group lower than those in the control group, demonstrating statistical significance （P<0.05）. Conclusion:Voice training combined with active breathing and circulation techniques has a beneficial effect on recovery following vocal cord polyp surgery. This combined approach significantly improves vocal cord morphology and acoustic indices, alleviates voice disorders, and enhances overall voice recovery.
Humans ; Vocal Cords/surgery* ; Polyps/surgery* ; Voice Training ; Male ; Female ; Voice Quality ; Laryngeal Diseases/surgery* ; Voice ; Middle Aged ; Adult ; Respiration

BACKGROUND AND OBJECTIVES

A mounting evidence links dysregulated immune response to cases of fatal pneumonia seen in COVID-19 infection. We aimed to validate the COVID-19-associated Hyperinflammatory Syndrome (cHIS) score, a novel clinical tool devised to identify those at risk for adverse outcomes, in a local population and investigate the relationship of cHIS score taken at admission and the risk of mortality and the need of mechanical ventilation.

This retrospective cohort study analyzed the sociodemographic, clinical, and laboratory data of 1,881 COVID-19 patients admitted at a tertiary hospital in Davao City, Philippines from January to December 2021. We calculated the cHIS score, composed of six clinical and laboratory criteria from admission, and used multivariate logistic regression to determine the risk of mortality and need of mechanical ventilation.

The cHIS score taken at admission, regardless of cut-off value, was a significant predictor of mortality (OR 0.979 [99% CI 0.894-1.064]) and need of mechanical ventilation (OR 0.586 [99% CI 0.4975-0.6745]). Using the Youden Index, a cut-off cHIS score of 3 or more was a better predictor of mortality (sensitivity, 88.59%; specificity, 71.72%), and a cut-off score of 2 or more was a better predictor of need of mechanical ventilation (sensitivity, 84.02%; specificity, 70.82%) than other cutoff cHIS scores.

Among COVID-19 patients, the cHIS score at admission correlated with the risk of mortality and the need of mechanical ventilation. Cutoff scores of 3 and 2 had the optimal sensitivities and specificities to predict the risk of mortality and the need of mechanical ventilation, respectively.

OBJECTIVE: To develop a method for identifying high-risk patients among septic populations requiring mechanical ventilation, and to conduct phenotypic analysis based on this method. METHODS: Data from four sources were utilized: the Medical Information Mart for Intensive Care (MIMIC-IV 2.0, MIMIC-III 1.4), the Philips eICU-Collaborative Research Database 2.0 (eICU-CRD 2.0), and the Anhui Medical University Second Affiliated Hospital dataset. The adult patients in intensive care unit (ICU) who met Sepsis-3 and received invasive mechanical ventilation (IMV) on the first day of first admission were enrolled. The MIMIC-IV dataset with the highest data integrity was divided into a training set and a test set at a 6:1 ratio, while the remaining datasets were served as validation sets. The demographic information, comorbidities, laboratory indicators, commonly used ICU scores, and treatment measures of patients were extracted. Clinical data collected within first day of ICU admission were used to calculate the sequential organ failure assessment (SOFA) score. K-means clustering was applied to cluster SOFA score components, and the sum of squared errors (SSE) and Davies-Bouldin index (DBI) were used to determine the optimal number of disease subtypes. For clustering results, normalized methods were employed to compare baseline characteristics by visualization, and Kaplan-Meier curves were used to analyze clinical outcomes across phenotypes. RESULTS: This study enrolled patients from MIMIC-IV dataset (n = 11 166), MIMIC-III dataset (n = 4 821), eICU-CRD dataset (n = 6 624), and a local dataset (n = 110), with the four datasets showing similar median ages and male proportions exceeding 50%; using 85% of the MIMIC-IV dataset as the training set, 15% as the test set, and the rest dataset as the validation set. K-means clustering based on the six-item SOFA score was performed to determine the optimal number of clusters as 3, and patients were finally classified into three phenotypes. In the training set, compared with the patients with phenotype II and phenotype III, those with phenotype I had the more severe circulatory and respiratory dysfunction, a higher proportion of vasoactive drug usage, more obvious metabolic acidosis and hypoxia, and a higher incidence of congestive heart failure. The patients with phenotype II was dominated by respiratory dysfunction with higher visceral injury. The patients with phenotype III had relatively stable organ function. The above characteristics were consistent in both the test and validation sets. Analysis of infection-related indicators showed that the patients with phenotype I had the highest SOFA score within 7 days after ICU admission, initial decreases and later increases in platelet count (PLT), and higher counts of neutrophils, lymphocytes, and monocytes as compared with those with phenotype II and phenotype III, their blood cultures had a higher positivity rates for Gram-positive bacteria, Gram-negative bacteria and fungi as compared with those with phenotype II and phenotype III. The Kaplan-Meier curve indicated that in the training, test, and validation sets, the 28-day cumulative mortality of patients with phenotype I was significantly higher than that of patients with phenotypes II and phenotype III. CONCLUSIONS Three distinct phenotypes in septic patients receiving IMV based on unsupervised machine learning is derived, among which phenotype I, characterized by cardiorespiratory failure, can be used for the early identification of high-risk patients in this population. Moreover, this population is more prone to bloodstream infections, posing a high risk and having a poor prognosis.
Humans ; Machine Learning ; Sepsis/therapy* ; Respiration, Artificial ; Intensive Care Units ; Organ Dysfunction Scores ; Male ; Female ; Middle Aged ; Adult

Avian influenza H10N3 is a type of avian influenza virus that can occasionally infect humans and cause severe pneumonia and acute respiratory distress syndrome (ARDS). On December 25, 2024, a 23-year-old obese female patient with H10N3 avian influenza complicated with severe ARDS was admitted to the Fourth People's Hospital of Nanning. The patient was transferred to our department due to "fever, cough, and shortness of breath for 13 days". Physical examination revealed moist rales in bilateral lungs. Chest imaging showed large areas of ground-glass opacity and consolidation in both lungs. Based on the patient's medical history, clinical manifestations, and laboratory findings, she was diagnosed with human infection of H10N3 avian influenza, severe pneumonia, and severe ARDS. Supported by mechanical ventilation and extracorporeal membrane oxygenation (ECMO), daily monitoring of airway peak pressure, plateau pressure (Pplat), driving pressure (ΔP), and lung compliance was performed to guide the adjustment of tidal volume (VT) and positive end-expiratory pressure (PEEP) during invasive mechanical ventilation. Medications including anti-avian influenza virus agents, antibacterial drugs, and antifungals were administered. Eventually, the patient's condition improved gradually, and she was successfully weaned from ECMO. No ventilator-induced lung injury (VILI) or multiple organ dysfunction syndrome (MODS) related to ARDS occurred during ECMO support. However, during the final stage of ventilator weaning after the restoration of spontaneous breathing, a right pneumothorax occurred. Closed thoracic drainage was performed, after which the ventilator was successfully discontinued. The patient was successfully transferred out of the intensive care unit (ICU), recovered fully, and was discharged from the hospital. In the invasive mechanical ventilation management of patients infected with H10N3 avian influenza complicated by ARDS, monitoring airway peak pressure, Pplat, ΔP, and assessing pulmonary compliance may facilitate more standardized management of such ARDS patients and help reduce VILI.
Humans ; Female ; Influenza, Human/complications* ; Respiratory Distress Syndrome/complications* ; Respiration, Artificial/methods* ; Obesity/complications* ; Young Adult ; Extracorporeal Membrane Oxygenation ; Influenza A virus