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

Kim, Eunhye; Jeon, Kyeongman; Oh, Dong Kyu; Cho, Young-Jae; Hong, Sang-Bum; Lee, Yeon Joo; Lee, Sang‐Min; Suh, Gee Young; Park, Mi-Hyeon; Lim, Chae‐Man; Park, Sunghoon

doi:10.3390/jcm10163587

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Therefore, patients with lower oxygenation index also had a higher risk of failure in HFNO. Previous studies on the prognosis of pulmonary infection induced sepsis showed that oxygenation index was an independent risk factor for predicting in-hospital mortality 31 , 32 . The results of Liu et al 33 also confirmed that oxygenation index was an independent risk factor for patients with non-invasive ventilation failure.…”

Section: Discussionmentioning

confidence: 96%

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

Wang,

Chao,

et al. 2024

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 96%

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

Wang,

Chao,

et al. 2024

Sci Rep

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“…Recent data support the use of an HFNC in this subset, especially during weaning phases from mechanical ventilation or when preventing reintubation [162,163]. However, Kim et al emphasized the need for close patient monitoring since an HNFC may fail to prevent intubation or increase survival rates [164].…”

Section: High-flow Nasal Cannulamentioning

confidence: 99%

2023 Update on Sepsis and Septic Shock in Adult Patients: Management in the Emergency Department

Guarino

Perna

Cesaro

et al. 2023

JCM

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Background: Sepsis/septic shock is a life-threatening and time-dependent condition that requires timely management to reduce mortality. This review aims to update physicians with regard to the main pillars of treatment for this insidious condition. Methods: PubMed, Scopus, and EMBASE were searched from inception with special attention paid to November 2021–January 2023. Results: The management of sepsis/septic shock is challenging and involves different pathophysiological aspects, encompassing empirical antimicrobial treatment (which is promptly administered after microbial tests), fluid (crystalloids) replacement (to be established according to fluid tolerance and fluid responsiveness), and vasoactive agents (e.g., norepinephrine (NE)), which are employed to maintain mean arterial pressure above 65 mmHg and reduce the risk of fluid overload. In cases of refractory shock, vasopressin (rather than epinephrine) should be combined with NE to reach an acceptable level of pressure control. If mechanical ventilation is indicated, the tidal volume should be reduced from 10 to 6 mL/kg. Heparin is administered to prevent venous thromboembolism, and glycemic control is recommended. The efficacy of other treatments (e.g., proton-pump inhibitors, sodium bicarbonate, etc.) is largely debated, and such treatments might be used on a case-to-case basis. Conclusions: The management of sepsis/septic shock has significantly progressed in the last few years. Improving knowledge of the main therapeutic cornerstones of this challenging condition is crucial to achieve better patient outcomes.

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Predicting High‐Flow Nasal Cannula Oxygen Therapy Failure in Patients With Acute Hypoxaemic Respiratory Failure Using Machine Learning: Model Development and External Validation

Cheng,

Wang,

Feng

et al. 2024

Journal of Clinical Nursing

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Aims and ObjectivesTo develop and validate a prediction model for high‐flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF).BackgroundAHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non‐invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction.DesignMulticentre observational study.MethodsThis study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset (n = 180). A random forest (RF)‐based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models.ResultsThe final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis.ConclusionsThis study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF.Relevance to Clinical PracticeClinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC.Reporting MethodTRIPOD checklist for prediction model studies was followed in this study.Patient or Public ContributionPatients were involved in the sample of the study.

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

Cited by 5 publications

References 38 publications

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

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

2023 Update on Sepsis and Septic Shock in Adult Patients: Management in the Emergency Department

Predicting High‐Flow Nasal Cannula Oxygen Therapy Failure in Patients With Acute Hypoxaemic Respiratory Failure Using Machine Learning: Model Development and External Validation

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