Acute Appendicitis Revealing a Diagnosis of Chronic Myelogenous Leukemia

Raza, Ahmad; Ali, Elrazi; Okar, Lina; Elaiwy, Orwa; Abdelrazek, Mohamed; Mulkandathil, Yahya; Yassin, Mohamed A

doi:10.22541/au.160473066.61421385/v1

Cited by 2 publications

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“…However, non-essential diagnostics inevitably increase the cost of care for patients and the burden on hospital systems. Additionally, there are potential risk factors, such as allergies to contrast media and radiation exposure, that cannot be ignored, particularly in areas with limited healthcare resources (14)(15)(16). According to Trentzsch et al, it is crucial to quickly determine the underlying cause in AAP and assess whether urgent or immediate surgical intervention is needed (17).…”

Section: Introductionmentioning

confidence: 99%

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Gan,

Liu,

Liu

et al. 2024

Front. Med.

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BackgroundAcute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.MethodsIn this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.ResultsEleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.ConclusionThe ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.

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Section: Introductionmentioning

confidence: 99%

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Gan,

Liu,

Liu

et al. 2024

Front. Med.

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“…Patients with CML usually present with nonspecific symptoms like fever, fatigue, and abdominal discomfort; many are discovered during routine investigations with elevated white blood cell count. Other unusual presentations of CML include priapism, abdominal pain or eye symptoms, or urologic manifestation [1, 2]. Among the different presentations and complications, myeloid sarcoma is a rare way for CML to present.…”

Section: Introductionmentioning

confidence: 99%

Myeloid Sarcoma Mimicking Dental Abscess in a Patient with Chronic Myeloid Leukemia: Diagnostic and Therapeutic Dilemma

et al. 2022

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Chronic myeloid leukemia (CML) is a hematologic malignancy that has significant improvement in its prognosis after the introduction of tyrosine kinase inhibitors. Transformation to accelerated phase or blast phase can happen. Myeloid sarcoma or chloroma is an uncommon extramedullary disease. It is very unusual for patients with CML to develop myeloid sarcoma. We report a young man with CML in the chronic phase who developed myeloid sarcoma. There were many difficulties in the diagnosis of myeloid sarcoma due to the simulation of other more common conditions like infections and other malignancies. In addition, there are treatment challenges because of lack of standardized treatment. The case shed light on this rare complication, the challenging diagnosis, and its implication in patients with CML.

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Acute Appendicitis Revealing a Diagnosis of Chronic Myelogenous Leukemia

Cited by 2 publications

References 7 publications

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Myeloid Sarcoma Mimicking Dental Abscess in a Patient with Chronic Myeloid Leukemia: Diagnostic and Therapeutic Dilemma

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