Impact of clinical information on CT diagnosis by radiologist and subsequent clinical management by physician in acute abdominal pain

Hattori, Satoshi; Yokota, Hajime; Takada, Toshihiko; Horikoshi, Takuro; Takishima, Hazuki; Mikami, Wakako; Mukai, Hiroyuki; Funakoshi, Hiraku; Naganuma, Takako; Yano, Toshiaki; Terada, Kazuhiko; Inokuchi, Ryota; Tsuchiya, Satoshi; Akutsu, Akira; Saiga, Atsushi; Kubota, Yoshihiro; Wada, Takeshi; Takada, Akiyo; Fujimoto, Hajime; Umemura, Takashi

doi:10.1007/s00330-021-07700-8

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…Based on these anecdotal data and our own experience, it is hypothesized that the clinical reasoning quality of physicians who request medical imaging has deteriorated over the years. However, establishing a good differential diagnosis based on clinical history, physical examination, and laboratory findings, is considered important to optimize the interpretation and diagnostic yield of an imaging examination [4][5][6][7]. In addition, a good differential diagnosis is necessary to avoid unnecessary imaging studies that may be costly, that may have potential side effects due to the use of ionizing radiation and contrast agents, and that may yield incidental findings.…”

Section: Introductionmentioning

confidence: 99%

Did medical doctors who order abdominal CT scans during on-call hours truly become worse at clinical reasoning? Yes, they did

et al. 2022

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Objective To investigate temporal changes in clinical reasoning quality of physicians who requested abdominal CT scans at a tertiary care center during on-call hours within a 15-year period. Methods This retrospective study included 531 patients who underwent abdominal CT at a tertiary care center during on-call hours on 36 randomly sampled unique calendar days in each of the years between 2005 and 2019. Clinical reasoning quality was expressed as a percentage (0–100%), taking into account the degree by which the differential diagnoses on the CT request form matched the CT diagnosis. Temporal changes in the quality of clinical reasoning and number of CT scans were assessed using Mann-Kendall tests. Associations between the quality of clinical reasoning with patient age and gender, requesting department, and time of CT scanning were determined with linear regression analyses. Results The median annual clinical reasoning score was 0.4% (interquartile range: 0.3 to 0.6%; range: 0.1 to 1.9%). The quality of clinical reasoning significantly decreased between 2005 and 2019 (Mann-Kendall Tau of −0.829, p < 0.001), while the number of abdominal CT scans significantly increased (Mann-Kendall tau of 0.790, p < 0.001). There was a significant association between the quality of clinical reasoning and patient age (β coefficient of 0.210, p = 0.002). The quality of clinical reasoning was not significantly associated with patient gender, requesting department, or time of CT scanning. Conclusion The clinical reasoning quality of physicians who request abdominal CT scans during on-call hours has deteriorated over time. Clinical reasoning appears to be worse in younger patients. Key Points • In patients with suspected acute abdominal pathology who are scheduled to undergo CT scanning, referring physicians generally have difficulties in making an accurate pretest (differential) diagnosis. • Clinical reasoning quality of physicians who request acute abdominal CT scans has deteriorated over the years, while the number of CT scans has shown a significant increase. • Clinical reasoning quality appears to be worse in younger patients in this setting.

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Section: Introductionmentioning

confidence: 99%

Did medical doctors who order abdominal CT scans during on-call hours truly become worse at clinical reasoning? Yes, they did

et al. 2022

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“…However, it is undeniable that laboratory testing and imaging have made an excellent contribution to the management of AAP (55). Sufficient clinical information not only facilitates the interpretation of laboratory results but also helps radiologists make accurate imaging diagnoses (56). The use of artificial intelligence (AI) in automated agricultural machinery (AAP) can be traced back to the 1970s when it was introduced by Gunn and applied to AAP diagnosis (57).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…However, there was no significant difference between the diagnostic accuracy regardless of whether the imaging investigated was preceded by other modalities. Furthermore, the readers had access to clinical information via Clinical Suite, and we do not know how many actually received this clinical information, which is known to influence the diagnostic reports [ 20 , 21 , 22 , 23 ]. Finally, it has been shown that the localization of the lesion has an influence on the diagnostic accuracy, with MRI showing superior diagnostic properties in spine lesions, whereas in non-spine lesions, the accuracy of the imaging modalities is largely comparable [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Studies have shown that if one looks at a prior negative report before looking at imaging studies, there is a greater chance of missing a significant abnormality than by looking at the imaging studies first [ 5 ]. It has also been shown that radiological diagnoses made with adequate clinical information are more accurate than those made without clinical information [ 20 , 21 , 22 , 23 ]. However, to the best of our knowledge, no studies have investigated the influence, or the sequence, of prior imaging and reports on diagnostic accuracy using biopsy as the reference standard.…”

Section: Introductionmentioning

confidence: 99%

Influence of Prior Imaging Information on Diagnostic Accuracy for Focal Skeletal Processes—A Retrospective Analysis of the Consistency between Biopsy-Verified Imaging Diagnoses

et al. 2022

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Introduction: Comparing imaging examinations with those previously obtained is considered mandatory in imaging guidelines. To our knowledge, no studies are available on neither the influence, nor the sequence, of prior imaging and reports on diagnostic accuracy using biopsy as the reference standard. Such data are important to minimize diagnostic errors and to improve the preparation of diagnostic imaging guidelines. The aim of our study was to provide such data. Materials and methods: A retrospective cohort of 216 consecutive skeletal biopsies from patients with at least 2 different imaging modalities (X-ray, CT and MRI) performed within 6 months of biopsy was identified. The diagnostic accuracy of the individual imaging modality was assessed. Finally, the possible influence of the sequence of imaging modalities was investigated. Results: No significant difference in the accuracy of the imaging modalities was shown, being preceded by another imaging modality or not. However, the sequence analyses indicate sequential biases, particularly if MRI was the first imaging modality. Conclusion: The sequence of the imaging modalities seems to influence the diagnostic accuracy against a pathology reference standard. Further studies are needed to establish evidence-based guidelines for the strategy of using previous imaging and reports to improve diagnostic accuracy.

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Impact of clinical information on CT diagnosis by radiologist and subsequent clinical management by physician in acute abdominal pain

Cited by 5 publications

References 32 publications

Did medical doctors who order abdominal CT scans during on-call hours truly become worse at clinical reasoning? Yes, they did

Did medical doctors who order abdominal CT scans during on-call hours truly become worse at clinical reasoning? Yes, they did

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

Influence of Prior Imaging Information on Diagnostic Accuracy for Focal Skeletal Processes—A Retrospective Analysis of the Consistency between Biopsy-Verified Imaging Diagnoses

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