Factors for Predicting Noninvasive Ventilation Failure in Elderly Patients with Respiratory Failure

Non-invasive respiratory support in elderly hospitalized patients

INTRODUCTION

The proportion of elderly people among hospitalized patients is rapidly growing. Between 7% to 25% of ICU patients are aged 85 and over and noninvasive respiratory support is often offered to avoid the risks of invasive mechanical ventilation or in patients with a 'do-not-intubate' order. However, while noninvasive respiratory support has been extensively studied in the general population, there is limited data available on its efficacy in elderly patients with ARF.

AREAS COVERED

PubMed/Medline, Web of Science, Scopus and Embase online databases were searched for studies that assessed clinical efficacy of high flow nasal cannula, continuous positive airway pressure and noninvasive ventilation in patients ≥ 65 years old with acute de novo ARF, showing that short to mid-term benefits provided by noninvasive respiratory support in elderly patients in terms of reduction of mechanical ventilation risk and mortality are similar to younger patients, if adjusted for the severity of comorbidities and respiratory failure.

EXPERT OPINION

Noninvasive support strategies can represent an effective opportunity in elderly patients with ARF, especially in patients too frail to undergo endotracheal intubation and in whom received or decided for a 'do not intubate' order. Indeed, noninvasive support has a different impact, depending on the setting.

Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction-a retrospective analysis of the MIMIC database

Background

This study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation.

Methods

The study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm.

Result

A total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO2/FiO2 ratio, and PaO2 value as protective factors.

Conclusion

NIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients' SOFA, APSIII, GCS, PaCO2, PaO2, PH, PaO2/FiO2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.

Effect of Refined Perioperative Nursing on the Efficacy of Noninvasive Ventilation in Elderly Patients with Lung Cancer and Respiratory Failure

NIV (noninvasive ventilation) is becoming more popular as a first-line treatment for older patients with lung cancer who are experiencing acute respiratory failure. In the ICU, older age is linked to worse results with mechanical breathing. When dealing with severely sick patients, noninvasive ventilation is beneficial. Due to the risk of NIV failure and the higher mortality induced by delayed intubation, it is difficult to apply to older patients, especially those with lung cancer and respiratory insufficiency. As a result, for a successful outcome, nurse interventions should be provided to patients during noninvasive ventilation. This paper proposes the application of integrated perioperative nursing models on the elderly patients with lung cancer and respiratory failure. We have applied three nursing models: peer support nursing model, multidisciplinary cooperative nursing model, and transcultural nursing theory. The effect of the proposed nursing model on the efficacy of NIV is evaluated using the Logical Decision Tree Regression (LDTR) model. It is optimized using Iterative Fruit Fly Optimization Algorithm (IFOA). The performance of the suggested system is analysed, and it is observed that the patients showed better surgical outcomes when provided with the integrated nursing models.