ORCID Profile
0000-0003-4288-4492
Current Organisations
Third Affiliated Hospital of Sun Yat-Sen University
,
Curtin University
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Publisher: JMIR Publications Inc.
Date: 10-11-2020
DOI: 10.2196/24587
Abstract: Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252 PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117 PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135 PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments. Australian New Zealand Clinical Trials Registry ACTRN12618001521213 www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939
Publisher: JMIR Publications Inc.
Date: 25-09-2020
Abstract: apid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. he aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. articipants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. he algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252 PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117 PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135 PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. he algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments. ustralian New Zealand Clinical Trials Registry ACTRN12618001521213 www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939
Publisher: Cold Spring Harbor Laboratory
Date: 14-12-2020
DOI: 10.1101/2020.12.13.20247486
Abstract: Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) are commonly encountered in the primary care setting, though accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9-89.9%) of subjects (n=86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4-96.3%) of in iduals (n=78). Diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0-87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.
Publisher: Cold Spring Harbor Laboratory
Date: 07-07-2021
DOI: 10.1101/2021.07.06.21259221
Abstract: Diagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments. To determine the diagnostic error rate of acute childhood respiratory diseases in emergency departments. Prospective, multicenter, single-blinded, diagnostic accuracy study in two well-resourced pediatric emergency departments in a large Australian city. Between September 2016 and August 2018, a convenience s le of children aged 29 days to 12 years who presented with respiratory symptoms was enrolled. The emergency department discharge diagnoses were reported by clinicians based upon standard clinical diagnostic definitions. These diagnoses were compared against consensus diagnoses given by an expert panel of pediatric specialists using standardized disease definitions after they reviewed all medical records. For 620 participants, the positive and negative percent agreement (%, [95% CI]) of the emergency department compared with the expert panel diagnoses were generally poor: isolated upper respiratory tract disease (61.4 [51.2, 70.9], 90.9 [88.1, 93.3]), croup (75.6 [64.9, 84.4], 97.9 [96.2, 98.9]), lower respiratory tract disease (86.4 [83.1, 89.6], 92.9 [87.7, 96.4]), bronchiolitis (66.9 [58.6, 74.5], 94.3 [80.8, 99.3]), asthma/reactive airway disease (91.0 [85.8, 94.8], 93.0 [90.1, 95.3]), clinical pneumonia (62·9 [49·7, 74·8], 95·0 [92·8, 96·7]), focal (consolidative) pneumonia (54·8 [38·7, 70·2], 86.2 [79.3, 91.5]). Only 59% of chest x-rays with consolidation were correctly identified. Between 6.9% and 14.5% of children were inappropriately prescribed based on their eventual diagnosis. In well-resourced emergency departments, we have identified a previously unrecognized high diagnostic error rate for acute childhood respiratory disorders, particularly in pneumonia and bronchiolitis. These errors lead to the potential of avoidable harm and the administration of inappropriate treatment.
Publisher: Wiley
Date: 17-08-2023
DOI: 10.1111/INM.13042
Abstract: Unplanned hospital readmission rate is up to 43% in mental health settings, which is higher than in general health settings. Unplanned readmissions delay the recovery of patients with mental illness and add financial burden on families and healthcare services. There have been efforts to reduce readmissions with a particular interest in identifying patients at higher readmission risk after index admission however, the results have been inconsistent. This systematic review synthesized risk factors associated with 30‐day unplanned hospital readmissions for patients with mental illness. Eleven electronic databases were searched from 2010 to 30 September 2021 using key terms of 'mental illness', 'readmission' and 'risk factors'. Sixteen studies met the selection criteria for this review. Data were synthesized using content analysis and presented in narrative and tabular form because the extracted risk factors could not be pooled statistically due to methodological heterogeneity of the included studies. Consistently cited readmission predictors were patients with lower educational background, unemployment, previous mental illness hospital admission and more than 7 days of the index hospitalization. Results revealed the complexity of identifying unplanned hospital readmission predictors for people with mental illness. Policymakers need to specify the expected standards that written discharge summary must reach general practitioners concurrently at discharge. Hospital clinicians should ensure that discharge summary summaries are distributed to general practitioners for effective ongoing patient care and management. Having an advanced mental health nurse for patients during their transition period needs to be explored to understand how this role could ensure referrals to the general practitioner are eventuated.
Publisher: Springer Science and Business Media LLC
Date: 27-01-2023
DOI: 10.1007/S00431-023-04819-2
Abstract: The purpose of this study is to synthesize evidence on risk factors associated with newborn 31-day unplanned hospital readmissions (UHRs). A systematic review was conducted searching CINAHL, EMBASE (Ovid), and MEDLINE from January 1st 2000 to 30th June 2021. Studies examining unplanned readmissions of newborns within 31 days of discharge following the initial hospitalization at the time of their birth were included. Characteristics of the included studies examined variables and statistically significant risk factors were extracted from the inclusion studies. Extracted risk factors could not be pooled statistically due to the heterogeneity of the included studies. Data were synthesized using content analysis and presented in narrative and tabular form. Twenty-eight studies met the eligibility criteria, and 17 significant risk factors were extracted from the included studies. The most frequently cited risk factors associated with newborn readmissions were gestational age, postnatal length of stay, neonatal comorbidity, and feeding methods. The most frequently cited maternal-related risk factors which contributed to newborn readmissions were parity, race/ethnicity, and complications in pregnancy and/or perinatal period. Conclusion : This systematic review identified a complex and erse range of risk factors associated with 31-day UHR in newborn. Six of the 17 extracted risk factors were consistently cited by studies. Four factors were maternal (primiparous, mother being Asian, vaginal delivery, maternal complications), and two factors were neonatal (male infant and neonatal comorbidities). Implementation of evidence-based clinical practice guidelines for inpatient care and in idualized hospital-to-home transition plans, including transition checklists and discharge readiness assessments, are recommended to reduce newborn UHRs. What is Known: • Attempts have been made to identify risk factors associated with newborn UHRs however, the results are inconsistent. What is New: • Six consistently cited risk factors related to newborn 31-day UHRs. Four maternal factors (primiparous, mother being Asian, vaginal delivery, maternal complications) and 2 neonatal factors (male infant and neonatal comorbidities). • The importance of discharge readiness assessment, including newborn clinical fitness for discharge and parental readiness for discharge. Future research is warranted to establish standardised maternal and newborn-related variables which healthcare providers can utilize to identify newborns at greater risk of UHRs and enable comparison of research findings.
Publisher: Cold Spring Harbor Laboratory
Date: 08-09-2020
DOI: 10.1101/2020.09.05.20164731
Abstract: Rapid and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD) is problematic in acute-care settings, particularly in the presence of infective comorbidities. The aim of this study was to develop a rapid, smartphone-based algorithm for the detection of COPD, in the presence or absence of acute respiratory infection, and then evaluate diagnostic accuracy on an independent validation set. Subjects aged 40-75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis or emphysema, were recruited into the study. The algorithm analysed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. The algorithm demonstrated high percent agreement (PA) with clinical diagnosis for COPD in the total cohort (n=252, Positive PA=93.8%, Negative PA=77.0%, AUC=0.95) in subjects with pneumonia or infective exacerbations of COPD (n=117, PPA=86.7%, NPA=80.5%, AUC=0.93) and in subjects without an infective comorbidity (n=135, PPA=100.0%, NPA=74.0%, AUC=0.97.) In those who had their COPD confirmed by spirometry (n=229), PPA = 100.0% and NPA = 77.0%, AUC=0.97. The algorithm demonstrates high agreement with clinical diagnosis and rapidly detects COPD in subjects presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens and identify those at increased risk of mortality due to seasonal or other respiratory ailments.
No related grants have been discovered for Phillip Della AM.