Literature DB >> 31067292

Outcome-Based Critical Result Thresholds in the Adult Patient Population.

Eng Hooi Tan1, Zhutian Yang2, Yingda Li3, Michael P Metz4, Tze Ping Loh5,6.   

Abstract

OBJECTIVES: To derive outcome-based critical result thresholds in the adult patient population.
METHODS: We extracted deidentified laboratory results and outcomes (death or discharged) of patients 18 years and older from the Medical Information Mart for Intensive Care database. The lower and upper critical result thresholds were obtained from the nearest minimum and maximum laboratory values, which corresponded to predicted probability of death at 90%.
RESULTS: The critical value thresholds were sodium (<123, >153 mmol/L), potassium (<2.2, >6.6 mmol/L), bicarbonate (<15, >49 mmol/L), chloride (<82, >121 mmol/L), urea (>20 mmol/L), creatinine (>1,052 μmol/L), glucose (<1.5, >23.8 mmol/L), total calcium (<1.62, >2.95 mmol/L), magnesium (<0.37, >1.48 mmol/L), phosphate (<0.19, >2.52 mmol/L), pH (<7.22, >7.57), lactate (>5.0 mmol/L), hemoglobin (<4.6 g/dL), WBCs (>32 × 103/μL), prothrombin time (>90 seconds), and international normalized ratio (>10).
CONCLUSIONS: The indirect approach described in this study is a pragmatic way to obtain threshold values that are clinically and operationally meaningful. © American Society for Clinical Pathology, 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Critical reporting; Critical results; Critical value; Panic value; Postanalytical

Mesh:

Year:  2019        PMID: 31067292     DOI: 10.1093/ajcp/aqz026

Source DB:  PubMed          Journal:  Am J Clin Pathol        ISSN: 0002-9173            Impact factor:   2.493


  3 in total

1.  Critical Test Result Recall Supporting System (CTR RSS) Improves Follow-Up among Patients in the Community.

Authors:  Hsu-Tung Chang; Su-Feng Kuo; Shu-Hui Chen; Jen-Shiou Lin; Shu-Hui Lin; Chin-Fu Chang; Chih-Wen Twu; Mei-Chu Chen; Yuan-Ting Yang; Chew-Teng Kor; Ching-Hsiung Lin
Journal:  Diagnostics (Basel)       Date:  2022-05-18

2.  A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.

Authors:  Albion Dervishi
Journal:  PLoS One       Date:  2020-12-17       Impact factor: 3.240

3.  Baseline assessment of staff perception of critical value practices in government hospitals in Kuwait.

Authors:  Talal ALFadhalah; Buthaina Al Mudaf; Haya Al Tawalah; Wadha A Al Fouzan; Gheed Al Salem; Hanaa A Alghanim; Samaa Zenhom Ibrahim; Hossam Elamir; Hamad Al Kharji
Journal:  BMC Health Serv Res       Date:  2022-08-03       Impact factor: 2.908

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.