Clinical prediction rule to predict pneumonia in adult presented with acute febrile respiratory illness

Tse, Cindy Wing-Sze; Chan, Yat Sun; Poon, Kin Ming; Lui, Chun Tat

doi:10.1016/j.ajem.2018.10.039

Cited by 3 publications

(9 citation statements)

References 22 publications

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“…These findings were in line with previous studies. [4][5][6][7][8] As shown by a previous study, 11 children with pneumonia are more prone to hypoxaemia than those suffering from acute upper respiratory tract infection. Oxygen saturation with SpO 2 of 90% or less also reflects the severity of CAP with high specificity.…”

Section: Comparison Of Accuracy Of Various Clinical Prediction Rulesmentioning

confidence: 86%

“…Tse et al developed a clinical rule in predicting CAP in the adult population. 7 Seven weighted factors were included in the AFRI rule: age ≥65 years (1 point), peak temperature within 24 h ≥40°C (2 points), fever duration ≥3 days (2 points), sore throat (−2 points), abnormal breath sounds (1 point), history of pneumonia (1 point) and SpO 2 ≤96% (1 point). At the cut-off of AFRI ≥0, the rule showed 95% sensitivity for diagnosing CAP, with a negative predictive value (NPV) of 97.2%.…”

Section: Methodsmentioning

confidence: 99%

“…These findings were consistent with those reported in other literature. 4,7,8 Abbreviations: NLR, negative likelihood ratio; NPV, negative predictive value; PAFRI, paediatric acute febrile respiratory illness; PLR, positive likelihood ratio; PPV, positive predictive value.…”

Section: Comparison Of Accuracy Of Various Clinical Prediction Rulesmentioning

confidence: 99%

“…The prediction rules tested in our study were Diehr rule, Heckerling rule, Bilkis simpler rule, acute febrile respiratory illness rule (AFRI) and paediatric acute febrile respiratory illness rule (PAFRI). [4][5][6][7][8] They were derived from correlating pneumonia diagnosed by abnormal CXR and weighing the respective clinical parameters.…”

Section: Introductionmentioning

confidence: 99%

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Comparing accuracy of clinical prediction rules to predict pneumonia in children and adolescents with acute febrile respiratory illness

Wu,

Chiu,

Tse

et al. 2024

Hong Kong j. emerg. med.

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BackgroundIt is a common challenge for emergency physicians to differentiate pneumonia from simple upper respiratory tract infections. Several clinical prediction rules exist to assist the diagnosis process and guide the clinical decisions of ordering investigations such as chest X‐ray (CXR).ObjectiveThis study aims to validate and compare the accuracy of various prediction rules in the setting of children and adolescents presenting with acute febrile respiratory illness (AFRI).MethodThis was a prospective multicentre study. Three hundred and fifty‐five patients, aged 6–18 years, were recruited. Patients with immunocompromised state or hypoxia were excluded. Pneumonia was defined as diagnosis by CXR or subsequent diagnosis of pneumonia upon re‐attendance within 7 days. Clinical rules including Diehr rule, Heckerling rule, Bilkis simpler rule, the AFRI rule, the paediatric acute febrile respiratory illness rule (PAFRI) were compared in terms of accuracy of predicting pneumonia in the recruited subjects and presented as receiver operating characteristic curves.ResultsFive patients were excluded. In the 350 patients included, 38 were diagnosed as pneumonia by CXR and 1 was subsequently diagnosed as pneumonia upon re‐attendance. The area under the receiver operating characteristic curve of Diehr rule, Heckerling rule, Bilkis simpler rule, AFRI rule and PAFRI rule were 0.703, 0.565, 0.59, 0.807 and 0.846 respectively. The PAFRI rule is superior to other prediction rules in terms of diagnostic accuracy. At the cut‐off of PAFRI ≥0, the rule has high sensitivity of 97.44% and negative predictive value of 99.09%.ConclusionAmong the rules compared, the PAFRI rule has the highest diagnostic accuracy in assisting emergency physicians to identify pneumonia among children and adolescents aged 6–18 years presenting with AFRI.

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Section: Comparison Of Accuracy Of Various Clinical Prediction Rulesmentioning

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: Comparison Of Accuracy Of Various Clinical Prediction Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing accuracy of clinical prediction rules to predict pneumonia in children and adolescents with acute febrile respiratory illness

Wu,

Chiu,

Tse

et al. 2024

Hong Kong j. emerg. med.

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“…This finding has also been observed in other pneumonia due to lung tissue damage. [ 73 ] Both lymphopenia and lymphocytosis were found in patients. Meanwhile, half of the patient had lymphopenia, while slightly more than one-tenth had Lymphocytosis.…”

Section: Discussionmentioning

confidence: 99%

Clinical manifestation, laboratory and radiology finding, treatment and outcomes of COVID-19

Nayeri

Nadali

Divani

et al. 2021

Journal of Research in Medical Sciences

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Background: Since December 2019, coronavirus (COVID-19) spread throughout the world. The high rate of infection and its unknown nature led specialists to report the condition of patients. The aim of this study is to systematically review of symptoms, laboratory and radiologic findings, treatment, and outcomes of patients with COVID-19. Materials and Methods: Databases such as PubMed, Embase, Scopus, Web of Science, Google Scholar, and Cochrane were searched. Finally, 46 articles were appropriate for the aim of the study. After quality evaluation, the necessary data were extracted and meta-analysis was performed. Results: 4858 articles were retrieved until March 30, 2020. After screening, the full-text of 46 articles was assessed. Of the reported cases, 31.7% had no comorbidities, 21.4% had high blood pressure, 70.6% had fever, and lymphopenia was reported in 55.2% of patients. For 16% bilateral patchy shadowing in radiography and for 51% ground-glass opacity was reported. Outcomes were remarkable for recover to death. Conclusion: COVID-19 leads to healthcare problems for countries. Nonspecific symptoms have made it difficult for differential diagnoses without computed tomography-scan or corona Test, but they are not available in many countries. Therefore, this systematic review can help health care staff to make decisions based on symptoms, treatments, and outcomes..

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Comparison Analysis of Prediction Model for Respiratory Diseases

Priya

Jinny

2021

Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality

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Millions of people around the world have one or many respiratory-related illnesses. Many chronic respiratory diseases like asthma, COPD, pneumonia, respiratory distress, etc. are considered to be a significant public health burden. To reduce the mortality rate, it is better to perform early prediction of respiratory disorders and treat them accordingly. To build an efficient prediction model for various types of respiratory diseases, machine learning approaches are used. The proposed methodology builds classifier model using supervised learning algorithms like random forest, decision tree, and multi-layer perceptron neural network (MLP-NN) for the detection of different respiratory diseases of ICU admitted patients. It achieves accuracy of nearly 99% by various machine learning approaches.

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Clinical prediction rule to predict pneumonia in adult presented with acute febrile respiratory illness

Cited by 3 publications

References 22 publications

Comparing accuracy of clinical prediction rules to predict pneumonia in children and adolescents with acute febrile respiratory illness

Comparing accuracy of clinical prediction rules to predict pneumonia in children and adolescents with acute febrile respiratory illness

Clinical manifestation, laboratory and radiology finding, treatment and outcomes of COVID-19

Comparison Analysis of Prediction Model for Respiratory Diseases

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