Dataset Information

Predicting the risk of acute respiratory failure among asthma patients-the A2-BEST2 risk score: a retrospective study.

ABSTRACT:

Objectives

Acute respiratory failure (ARF) is a common complication of bronchial asthma (BA). ARF onset increases the risk of patient death. This study aims to develop a predictive model for ARF in BA patients during hospitalization.

Methods

This was a retrospective cohort study carried out at two large tertiary hospitals. Three models were developed using three different ways: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability.

Results

This study included 398 patients, with 298 constituting the modeling group and 100 constituting the validation group. Models A, B, and C yielded seven, seven, and eleven predictors, respectively. Finally, we chose the clinical knowledge-driven model, whose C-statistics and Brier scores were 0.862 (0.820-0.904) and 0.1320, respectively. The Hosmer-Lemeshow test revealed that this model had good calibration. The clinical knowledge-driven model demonstrated satisfactory C-statistics during external and internal validation, with values of 0.890 (0.815-0.965) and 0.854 (0.820-0.900), respectively. A risk score for ARF incidence was created: The A₂-BEST₂ Risk Score (A₂ (area of pulmonary infection, albumin), BMI, Economic condition, Smoking, and T₂(hormone initiation Time and long-term regular medication Treatment)). ARF incidence increased gradually from 1.37% (The A₂-BEST₂ Risk Score ≤ 4) to 90.32% (A₂-BEST₂ Risk Score ≥ 11.5).

Conclusion

We constructed a predictive model of seven predictors to predict ARF in BA patients. This predictor's model is simple, practical, and supported by existing clinical knowledge.

SUBMITTER: Qi Y

PROVIDER: S-EPMC10607202 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Predicting the risk of acute respiratory failure among asthma patients-the A2-BEST2 risk score: a retrospective study.

Qi Yanhong Y Zhang Jing J Lin Jiaying J Yang Jingwen J Guan Jiangan J Li Keying K Weng Jie J Wang Zhiyi Z Chen Chan C Xu Hui H

PeerJ 20231024

<h4>Objectives</h4>Acute respiratory failure (ARF) is a common complication of bronchial asthma (BA). ARF onset increases the risk of patient death. This study aims to develop a predictive model for ARF in BA patients during hospitalization.<h4>Methods</h4>This was a retrospective cohort study carried out at two large tertiary hospitals. Three models were developed using three different ways: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree mode ...[more]

PMID: 37901467

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Project description:ObjectiveTo describe the proportion and clinical characteristics of hospitalized children with acute asthma attacks complicated by respiratory failure and to analyze the risk factors.MethodsThis retrospective study analyzed hospital admissions of children and adolescents with acute asthma attacks between January 2016 and December 2021. Inclusion criteria were used to identify eligible cases, and demographic information and disease characteristics were collected. Patients were categorized into respiratory failure group and the other group based on the result of artery blood gas analysis. Multivariate logistic regression was utilized to investigate the risk factors associated with respiratory failure resulting from acute asthma attacks. The data were analyzed using SPSS 22.0, and significance was considered at P < 0.05.ResultsOur research involved 225 participants, with 18.7% diagnosed with respiratory failure. The respiratory failure group was found to be younger and have higher percentage of male, while birth weight, nationality, and type of residence did not differ between the two groups. In the respiratory failure group, a significant difference was observed in emergency hospitalization, ICU treatment, severe to critical attack, dyspnea and allergy history. The two groups did not differ in admission season, first asthma diagnosis, respiratory infection and comorbidity. The respiratory failure group exhibited a higher proportion of atopy-only asthma and a lower proportion of T2-high asthma. The eosinophil count, and eosinophil percentage were lower in the respiratory failure group, while neutrophil count was higher. Having a history of allergies (OR = 2.46, 95% CI: 1.08-5.59) and neutrophil count (OR = 1.10, 95% CI: 1.00-1.21) were the risk factors for respiratory failure in children with asthma. There also existed that the risk of respiratory failure increases with decreasing age of the children (OR = 0.85, 95% CI: 0.73-0.99).ConclusionNotably, risk factors for respiratory failure in hospitalized asthma children include age, having a history of allergies, and neutrophil count. The identification of the above factors and the implementation of timely intervention can optimize the treatment of asthma in children.

Project description:BackgroundAcute hypoxemic respiratory failure (AHRF) and acute respiratory distress syndrome (ARDS) are associated with high in-hospital mortality. However, in cohorts of ARDS patients from the 1990s, patients more commonly died from sepsis or multi-organ failure rather than refractory hypoxemia. Given increased attention to lung-protective ventilation and sepsis treatment in the past 25 years, we hypothesized that causes of death may be different among contemporary cohorts. These differences may provide clinicians with insight into targets for future therapeutic interventions.MethodsWe identified adult patients hospitalized at a single tertiary care center (2016-2017) with AHRF, defined as PaO2/FiO2 ≤ 300 while receiving invasive mechanical ventilation for > 12 h, who died during hospitalization. ARDS was adjudicated by multiple physicians using the Berlin definition. Separate abstractors blinded to ARDS status collected data on organ dysfunction and withdrawal of life support using a standardized tool. The primary cause of death was defined as the organ system that most directly contributed to death or withdrawal of life support.ResultsWe identified 385 decedents with AHRF, of whom 127 (33%) had ARDS. The most common primary causes of death were sepsis (26%), pulmonary dysfunction (22%), and neurologic dysfunction (19%). Multi-organ failure was present in 70% at time of death, most commonly due to sepsis (50% of all patients), and 70% were on significant respiratory support at the time of death. Only 2% of patients had insupportable oxygenation or ventilation. Eighty-five percent died following withdrawal of life support. Patients with ARDS more often had pulmonary dysfunction as the primary cause of death (28% vs 19%; p = 0.04) and were also more likely to die while requiring significant respiratory support (82% vs 64%; p < 0.01).ConclusionsIn this contemporary cohort of patients with AHRF, the most common primary causes of death were sepsis and pulmonary dysfunction, but few patients had insupportable oxygenation or ventilation. The vast majority of deaths occurred after withdrawal of life support. ARDS patients were more likely to have pulmonary dysfunction as the primary cause of death and die while requiring significant respiratory support compared to patients without ARDS.

Project description:AimsThis study aimed to develop and validate a simple method for predicting long-term all-cause mortality in ambulatory patients with chronic heart failure (CHF) residing in an area where Chagas disease is endemic, which will be important not only for patients living in Latin America but also to those living in developed non-endemic countries.Methods and resultsA total of 677 patients with a wide spectrum of aetiologies for left ventricular systolic dysfunction and receiving optimized evidence-based treatment for CHF were prospectively followed for approximately 11 years. We established a risk score using Cox proportional hazard regression models. After multivariable analysis, four variables were independently associated with mortality and included in the CALL Risk Score: Chagas cardiomyopathy aetiology alone [hazard ratio, 3.36; 95% confidence interval (CI), 2.61-4.33; P < 0.001], age ≥60 years (hazard ratio, 1.36; 95% CI, 1.06-1.74; P = 0.016), left anterior fascicular block (hazard ratio, 1.64; 95% CI, 1.27-2.11; P < 0.001), and left ventricular ejection fraction <40% (hazard ratio, 1.73; 95% CI, 1.30-2.28; P < 0.001). The internal validation considered the bootstrapping, a resampling technique recommended for prediction model development. Hence, we established a scoring system attributing weights according to each risk factor: 3 points for Chagas cardiomyopathy alone, 1 point for age ≥60 years, and 2 points each for left anterior fascicular block and left ventricular ejection fraction <40%. Three risk groups were identified: low risk (score ≤2 points), intermediate risk (score of 3 to 5 points), and high risk (score ≥6 points). High-risk patients had more than two-fold increase in mortality (26.9 events/100 patient-years) compared with intermediate-risk patients (10.1 events/100 patient-years) and almost seven-fold increase compared with low-risk patients (4.3 events/100 patient-years). The CALL Risk Score data sets from the development and internal validation cohorts both displayed suitable discrimination c-index of 0.689 (95% CI, 0.688-0.690; P < 0.001) and 0.687 (95% CI, 0.686-0.688; P < 0.001), respectively, and satisfactory calibration [Greenwood-Nam-D'Agostino test (8) = 7.867; P = 0.447] and [Greenwood-Nam-D'Agostino test (8) = 10.08; P = 0.273], respectively.ConclusionsThe CALL Risk Score represents a simple and validated method with a limited number of non-invasive variables that successfully predicts long-term all-cause mortality in a real-world cohort of patients with CHF. Patients with CHF stratified as high risk according to the CALL Risk Score should be monitored and aggressively managed, including those with CHF secondary to Chagas disease.

Project description:BackgroundIn the treatment of acute hypoxemic respiratory failure (AHRF) due to coronavirus 2019 (COVID-19), physicians choose respiratory management ranging from low-flow oxygen therapy to more invasive methods, depending on the severity of the patient's symptoms. Recently, the ratio of oxygen saturation (ROX) index has been proposed as a clinical indicator to support the decision for either high-flow nasal cannulation (HFNC) or mechanical ventilation (MV). However, the reported cut-off value of the ROX index ranges widely from 2.7 to 5.9. The objective of this study was to identify indices to achieve empirical physician decisions for MV initiation, providing insights to shorten the delay from HFNC to MV. We retrospectively analyzed the ROX index 6 hours after initiating HFNC and lung infiltration volume (LIV) calculated from chest computed tomography (CT) images in COVID-19 patients with AHRF.MethodsWe retrospectively analyzed the data for 59 COVID-19 patients with AHRF in our facility to determine the cut-off value of the ROX index for respiratory therapeutic decisions and the significance of radiological evaluation of pneumonia severity. The physicians chose either HFNC or MV, and the outcomes were retrospectively analyzed using the ROX index for initiating HFNC. LIV was calculated using chest CT images at admission.ResultsAmong the 59 patients who required high-flow oxygen therapy with HFNC at admission, 24 were later transitioned to MV; the remaining 35 patients recovered. Four of the 24 patients in the MV group died, and the ROX index values of these patients were 9.8, 7.3, 5.4, and 3.0, respectively. These index values indicated that the ROX index of half of the patients who died was higher than the reported cut-off values of the ROX index, which range from 2.7-5.99. The cut-off value of the ROX index 6 hours after the start of HFNC, which was used to classify the management of HFNC or MV as a physician's clinical decision, was approximately 6.1. The LIV cut-off value on chest CT between HFNC and MV was 35.5%. Using both the ROX index and LIV, the cut-off classifying HFNC or MV was obtained using the equation, LIV = 4.26 × (ROX index) + 7.89. The area under the receiver operating characteristic curve, as an evaluation metric of the classification, improved to 0.94 with a sensitivity of 0.79 and specificity of 0.91 using both the ROX index and LIV.ConclusionPhysicians' empirical decisions associated with the choice of respiratory therapy for HFNC oxygen therapy or MV can be supported by the combination of the ROX index and the LIV index calculated from chest CT images.

Project description:BackgroundAcute lower respiratory infections in children under 5 years present a real challenge for diagnosis and treatment and are the first cause of mortality for this group of age. The study aimed to describe the characteristics of infectious acute respiratory failure due to bronchiolitis, pulmonary infection or severe acute asthma related to a virus or bacteria in this population of children under 5 years old at admission to the paediatric intensive care unit (PICU), PICU management and outcomes in order to better identify the needs of these patients. Our secondary aim was to compare the characteristics and PICU management of this population (1) depending on their age (less or more than 6 months old) and (2) depending on the pulmonary imaging (absence or presence of an alveolar condensation on the chest X-ray or lung ultrasound).MethodsWe conducted a retrospective study in two PICUs in the Ile-de-France region. We included children under 5 years old hospitalised between 1 January 2017 and 31 December 2021 due to a respiratory infection complicated by acute respiratory failure.ResultsWe included 707 patients. The median age was 3 months. On arrival, patients were oxygen-dependent with a mean fraction of inspired oxygen (FiO2) of 34% and 63% required non-invasive ventilation (NIV). During hospitalisation, more than 70% required ventilatory support by NIV and 10% by tracheal intubation. 18% required volaemic expansion and 4% vasopressors. Nearly 90% of PCRs for respiratory viruses were positive, and respiratory syncytial virus (RSV) was found in almost two-thirds of cases. Streptococcus pneumoniae, Moraxella catarrhalis and Haemophilus influenzae were frequently found. Significantly, patients aged less than 6 months old needed more NIV, had less alveolar condensation, had slightly lower oxygen requirements, a less frank inflammatory syndrome and a more frequently positive PCR for respiratory viruses.ConclusionsWe highlighted similarities between patients hospitalised for lower respiratory infection in PICU in France and those in Australia or Brazil. Optimal management relies mainly on NIV, oxygen therapy with FiO2 under 40% and available antibiotics. These results lead us to believe that the implementation of NIV training and equipment could help reduce mortality due to lower respiratory infections in children worldwide.

Project description:Background: Acute heart failure (AHF) represents a leading cause of unscheduled hospital stays, frequent rehospitalisations, and mortality worldwide. The aim of our study was to develop a bedside prognostic tool, a multivariable predictive risk score, that is useful in daily practice, thus providing an early prognostic evaluation at admission and an accurate risk stratification after discharge in patients with AHF. Methods: This study is a subanalysis of the STADE HF study, which is a single-centre, prospective, randomised controlled trial enrolling 123 patients admitted to hospital for AHF. Here, 117 patients were included in the analysis, due to data exhaustivity. Regression analysis was performed to determine predictive variables for one-year mortality and/or rehospitalisation after discharge. Results: During the first year after discharge, 23 patients died. After modellisation, the variables considered to be of prognostic relevance in terms of mortality were (1) non-ischaemic aetiology of HF, (2) elevated creatinine levels at admission, (3) moderate/severe mitral regurgitation, and (4) prior HF hospitalisation. We designed a linear model based on these four independent predictive variables, and it showed a good ability to score and predict patient mortality with an AUC of 0.84 (95%CI: 0.76-0.92), thus denoting a high discriminative ability. A risk score equation was developed. During the first year after discharge, we observed as well that 41 patients died or were rehospitalised; hence, while searching for a model that could predict worsening health conditions (i.e., death and/or rehospitalisation), only two predictive variables were identified: non-ischaemic HF aetiology and previous HF hospitalisation (also included in the one-year mortality model). This second modellisation showed a more discrete discriminative ability with an AUC of 0.67 (95% C.I. 0.59-0.77). Conclusions: The proposed risk score and model, based on readily available predictive variables, are promising and useful tools to assess, respectively, the one-year mortality risk and the one-year mortality and/or rehospitalisations in patients hospitalised for AHF and to assist clinicians in the management of patients with HF aiming at improving their prognosis.

Dataset Information

Predicting the risk of acute respiratory failure among asthma patients-the A2-BEST2 risk score: a retrospective study.

Objectives

Methods

Results

Conclusion

Publications

Predicting the risk of acute respiratory failure among asthma patients-the A2-BEST2 risk score: a retrospective study.

Similar Datasets

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