Performance of an algorithm for diagnosing acute cholecystitis using clinical and sonographic parameters.
Purpose: Identify an algorithm using clinical and ultrasound (US) parameters with high diagnostic performance for acute cholecystitis. Methods: Consecutive emergency department (ED) patients from 4/1/2019 to 12/31/2019 were retrospectively reviewed to record non-US parameters and make US observation...
| Publicado en: | Abdominal Radiology Vol. 47; no. 2; pp. 576 - 586 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154994642&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154994642 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2022 vid: 47 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154994642 154343469 154994642 154994642 10.1007/s00261-021-03384-2 154994642 ppf: 576 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Performance of an algorithm for diagnosing acute cholecystitis using clinical and sonographic parameters. aug: au: Patel, Maitray D. Sill, Andrew P. Dahiya, Nirvikar Chen, Frederick Eversman, William G. Kriegshauser, J. Scott Young, Scott W. affil: Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, 85054, Phoenix, AZ, USA sug: subj: Algorithms Evaluation Acute Disease Diagnosis Cholecystitis Diagnosis Sensitivity and Specificity Evaluation Diagnosis, Computer Assisted Methods Physical Examination Hematologic Tests Acute Disease Ultrasonography Cholecystitis Ultrasonography Human Emergency Patients Retrospective Design Predictive Value of Tests Descriptive Statistics Male Female Adult Middle Age Aged Chronic Disease Diagnosis Cholecystitis Pathology Radiologists Comparative Studies Emergency Service Record Review Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Purpose: Identify an algorithm using clinical and ultrasound (US) parameters with high diagnostic performance for acute cholecystitis. Methods: Consecutive emergency department (ED) patients from 4/1/2019 to 12/31/2019 were retrospectively reviewed to record non-US parameters and make US observations. Outcomes were categorized as either: (1) acute cholecystitis; or (2) negative acute cholecystitis. Pivot tables identified parameter combinations either not found with acute cholecystitis or with predictive value for acute cholecystitis to establish the algorithm. US Division radiologists finalized an US report prior to ED disposition without use of the algorithm. Radiologist impression and algorithm prediction for acute cholecystitis were categorized as either (1) acute cholecystitis; (2) negative acute cholecystitis; or (3) inconclusive. Results: Three hundred and sixty-six studies on 357 patients (mean age, 51 yrs ± 20 yrs; 215 women) met the inclusion criteria. 10.9% (40/366) of US studies had acute cholecystitis, 12.6% (46/366) had pathologically identified chronic cholecystitis without acute cholecystitis, and 76.5% (280/366) were negative acute cholecystitis. Algorithm compared to radiologist diagnostic performance was as follows: (1) sensitivity: 90.0% vs. 55.0%, p < 0.001; (2) augmented sensitivity (defined as when inconclusive categorization is considered consistent with acute cholecystitis): 100% vs. 85.0%, p < 0.001; (3) specificity: 93.6% vs. 94.8%, p = 0.50; (4) diagnostic rate (opposite of inconclusive rate): 96.4% vs. 93.2%, p = 0.04; (5) adverse outcome rate: 0.0% vs. 1.6%, p undefined. Conclusion: For acute cholecystitis, an algorithm using non-binary ultrasound and clinical assessments had higher sensitivity, higher diagnostic rate, and fewer adverse outcomes, than subspecialty radiologist impressions. pubtype: Academic Journal doctype: algorithm diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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