Noninvasive Ventilation for Critically Ill Subjects With Acute Respiratory Failure in the Emergency Department

Machine learning predicts acute respiratory failure in pancreatitis patients: A retrospective study

PURPOSE

The purpose of the research is to design an algorithm to predict the occurrence of acute respiratory failure (ARF) in patients with acute pancreatitis (AP).

METHODS

We collected data on patients with AP in the Medical Information Mart for Intensive Care IV database. The enrolled observations were randomly divided into a 70 % training cohort and a 30 % validation cohort, and the observations in the training cohort were divided into ARF and non-ARF groups. Feature engineering was conducted using random forest (RF) and least absolute shrinkage and selection operator (LASSO) methods in the training cohort. The model building included logistic regression (LR), decision tree (DT), k-nearest neighbours (KNN), naive bayes (NB) and extreme gradient boosting (XGBoost). Parameters for model evaluation include receiver operating characteristic (ROC) curve, precision-recall curve (PRC), calibration curves, positive predictive value (PPV), negative predictive value (NPV), true positive rate (TPR), true negative rate (TNR), accuracy (ACC) and F1 score.

RESULTS

Among 4527 patients, 445 patients (9.8 %) experienced ARF. Ca, ALB, GLR, WBC, AG and BUN have been included in the prediction model as features for predicting ARF. The AUC of XGBoost were 0.86 (95 %CI 0.84-0.88) and 0.87 (95 %CI 0.84-0.90) in the training and validation cohorts. In the training cohort, XGBoost demonstrates a true positive rate (TPR) of 0.662, a true negative rate (TNR) of 0.884, a positive predictive value (PPV) of 0.380, a negative predictive value (NPV) of 0.960, an accuracy (ACC) of 0.862, and an F1 score of 0.483. In the validation cohort, XGBoost shows a TPR of 0.620, a TNR of 0.895, a PPV of 0.399, an NPV of 0.955, an ACC of 0.867, and an F1 score of 0.486.

CONCLUSION

The XGBOOST model demonstrates good discriminatory ability, which enables clinicians to ascertain the probability of developing ARF in AP patients.

Helmet CPAP in the emergency department: A narrative review

BACKGROUND

The choice of correct interface for the right patient is crucial for the success of non-invasive ventilation (NIV) therapy. Helmet CPAP is a type of interface used to deliver NIV. Helmet CPAP improves oxygenation by keeping the airway open throughout the breathing cycle with positive end-expiratory pressure (PEEP).

OBJECTIVE

This narrative review describes the technical aspects and clinical indications of helmet continuous positive airway pressure (CPAP). In addition, we explore the advantages and challenges faced using this device at the Emergency Department (ED).

DISCUSSION

Helmet CPAP is tolerable than other NIV interfaces, provides a good seal and has good airway stability. During Covid-19 pandemic, there are evidences it reduced the risk of aerosolization. The potential clinical benefit of helmet CPAP is demonstrated in acute cardiogenic pulmonary oedema (ACPO), Covid-19 pneumonia, immunocompromised patient, acute chest trauma and palliative patient. Compare to conventional oxygen therapy, helmet CPAP had been shown to reduce intubation rate and decrease mortality.

CONCLUSION

Helmet CPAP is one of the potential NIV interface in patients with acute respiratory failure presenting to the emergency department. It is better tolerated for prolonged usage, reduced intubation rate, improved respiratory parameters, and offers protection against aerosolization in infectious diseases.

Clinical Application Effect of Cluster Management in Noninvasive Ventilator Nursing Care of Patients with Severe Heart Failure

Objective

To elucidate the clinical application effect of cluster management in noninvasive ventilator nursing care of patients with severe heart failure (HF).

Methods

A total of 116 severe patients with HF who received treatment in the our hospital between October 2018 and December 2019 were included, including 50 cases (control group) treated with routine nursing and 66 cases (research group) treated with cluster management. The treatment-related indexes (mechanical ventilation time and hospitalization expenses), symptom resolution (dyspnea, insomnia, nausea, and upper abdominal pain), systolic/diastolic blood pressure (SBP/DBP), heart rate (HR), and prognosis (mortality and disability rate) were observed and compared between the two groups.

Results

Statistically shorter time of mechanical ventilation and symptom (dyspnea, insomnia, nausea, and upper abdominal pain) resolution were found in the research group compared with the control group. In addition, the research group showed significantly lower hospitalization expenses, SBP, DBP, and HR than the control group. Moreover, lower mortality and disability rates were determined in the research group, yet with no statistical significance between the two cohorts.

Conclusion

The above results indicate the remarkable clinical application effect of cluster management in noninvasive ventilator nursing of severe HF, which can enhance the treatment efficacy, blood pressure and HR of patients, and facilitate their recovery.

Seznanjenost osebja zdravstvene nege z neinvazivno mehansko ventilacijo

Uvod: Uspešno zdravljenje z neinvazivno mehansko ventilacijo predstavlja velik izziv, saj jo je mogoče učinkovito upravljati v primeru zadostne usposobljenosti vseh članov tima. Namen raziskave je bil oceniti znanje zdravstvenih delavcev, ki se srečujejo s tovrstnim zdravljenjem.Metode: Izvedena je bila kvantitativna presečna opazovalna raziskava. Vanjo je bilo vključenih 68 medicinskih sester, zaposlenih v intenzivnih enotah in na urgenci dveh bolnišnic v severovzhodni Sloveniji. Podatki so bili zbrani s pomočjo vprašalnika ter statistično analizirani in obdelani z uporabo opisne in sklepne statistike.Rezultati: V raziskavi ugotavljamo, da 76,5 % anketirancev meni, da je njihovo znanje o neinvazivni mehanski ventilaciji precej dobro. Znanje o neinvazivni mehanski ventilaciji je 85,3 % anketirancev pridobilo od sodelavcev v službi in 60,3 % od zdravnikov na oddelku. Povprečna vrednost doseženih točk, pridobljena pri vprašanjih o znanju glede uporabe neinvazivne mehanske ventilacije, je bila 23,13 (s = 4.35) od možnih 33. Med delavci, zaposlenimi v urgentnem centru in na oddelkih intenzivne enote, ne obstajajo statistično pomembne razlike v znanju o neinvazivni mehanski ventilaciji (p = 0,456).Diskusija in zaključek: Ugotovili smo, da bi anketiranci potrebovali dodatna usposabljanja s področja neinvazivne mehanske ventilacije. Smiselno bi bilo, da se na državni ravni oziroma ravni posameznih bolnišnic organizirajo izobraževanja s tega področja, na katera se povabi vse zaposlene, ki se srečujejo z neinvazivno mehansko ventilacijo.

Dynamic prediction of late noninvasive ventilation failure in intensive care unit using a time adaptive machine model

BACKGROUND

Noninvasive ventilation (NIV) failure is strongly associated with poor prognosis. Nowadays, plenty of mature studies have been proposed to predict early NIV failure (within 48 hours of NIV), however, the prediction for late NIV failure (after 48 hours of NIV) lacks sufficient research. Late NIV failure delays intubation resulting in the increasing mortality of the patients. Therefore, it is of great significance to expeditiously predict the late NIV failure. In order to dynamically predict late NIV failure, we proposed a Time Updated Light Gradient Boosting Machine (TULightGBM) model.

MATERIAL AND METHODS

In this work, 5653 patients undergoing NIV over 48 hours were extracted from the database of Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) for model construction. The TULightGBM model consists of a series of sub-models which learn clinical information from updating data within 48 hours of NIV and integrates the outputs of the sub-models by the dynamic attention mechanism to predict late NIV failure. The performance of the proposed TULightGBM model was assessed by comparison with common models of logistic regression (LR), random forest (RF), LightGBM, eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and long short-term memory (LSTM).

RESULTS

The TULightGBM model yielded prediction results at 8, 16, 24, 36, and 48 hours after the start of the NIV with dynamic AUC values of 0.8323, 0.8435, 0.8576, 0.8886, and 0.9123, respectively. Furthermore, the sensitivity, specificity, and accuracy of the TULightGBM model were 0.8207, 0.8164, and 0.8184, respectively. The proposed model achieved superior performance over other tested models.

CONCLUSIONS

The TULightGBM model is able to dynamically predict the late NIV failure with high accuracy and offer potential decision support for clinical practice.

Analysis of the clinical effect of noninvasive mechanical ventilation in AIDS patients complicated with pneumonia

OBJECTIVE

To explore the clinical application effect of noninvasive mechanical ventilation for patients with acquired immune deficiency syndrome (AIDS) complicated with pneumonia.

METHODS

A prospective study was conducted on 86 patients with AIDS complicated with pneumocystis pneumonia. The patients were randomly divided into a control group and an experimental group, both of which were treated with conventional drugs. The control group was supplemented with oxygen via a mask, and the experimental group was additionally treated with noninvasive ventilator ventilation. The changes of arterial oxygen partial pressure, oxygenation index, respiratory frequency, pulse rate, serum albumin and other indicators between the two groups before and after treatment were observed. The patient's hospitalization time, overall improvement and mortality rate were compared.

RESULTS

Compared with those before treatment, the arterial oxygen partial pressure, oxygenation index, respiratory frequency, and pulse rate of the two groups of patients were significantly improved after treatment (P<0.05). The improvement of the experimental group after treatment was more significant than that of the control group, and the difference was statistically significant (P<0.001). After treatment, the proportion of recovery rate of serum albumin in the experimental group was 81.40%, which was significantly higher than that in the control group (53.49%), and the difference was statistically significant (P<0.05). Compared with the control group, hospitalization time, treatment improvement and mortality rate in the experimental group had significant advantages and statistical significance (P<0.05).

CONCLUSION

For AIDS patients complicated with pneumonia, noninvasive mechanical ventilation had obvious treatment effects, which could significantly improve respiratory function, reduce mortality rate, and increase recovery rate. It can be considered as a therapeutic method to be included in routine treatment protocols.