The Value of C-Reactive Protein, C-Reactive Protein Kinetics and C-reactive Protein to Albumin Ratio on Prognosis in Newly Diagnosed Indolent B-cell Non-Hodgkin Lymphoma

Zhang, Huimin; Zhou, Xiangxiang; Sun, Xiaoling; Han, Yang; Hu, Xinting; Yun, Xiaoya; Zhang, Ya; Wang, Xin

doi:10.21203/rs.3.rs-103728/v1

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…By supplying RIs, laboratories enable the clinician to make informed clinical decisions. In the example given, C-reactive protein would be highly elevated whereas ferritin could be considered "normal" [20,21]. One would most definitely require further investigation, and the other wouldn't in most cases.…”

Section: The Use Of the Reference Interval In Clinical Chemistrymentioning

confidence: 95%

The Role and Limitations of the Reference Interval Within Clinical Chemistry and Its Reliability for Disease Detection

Timbrell

2024

Br J Biomed Sci

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Reference intervals (RIs) are a range of values that are supplied alongside laboratory measurements for comparison to allow interpretation of this data. Historically, RIs were referred to as the normal range. However, the perception of what is normal can lead to confusion in clinicians and unnecessary emotional distress in patients. RIs can be acquired using several methods. Laboratories may quote published studies or derive their own using established direct or indirect methods. Alternatively, laboratories may verify RIs provided by assay manufacturers using in-house studies. RIs have several limitations that clinicians should be aware of. The statistical methodology associated with establishment of RIs means that approximately 5% of “disease free” individuals will fall outside the RI. Additionally, the higher the number of tests requested, the higher the probability that one will be abnormal, and repeat results in an individual may show regression to the mean. Completion of studies for establishment of RIs can be expensive, difficult, and time consuming. Method bias and differences in populations can greatly influence RIs and prevent them from being transferable between some laboratories. Differences in individual characteristics such as age, ethnicity, and sex can result in large variation in some analytes. Some patients, such as those whose gender differs from that which was presumed for them at birth, may require their own RIs. Alternatively, a decision will need to be made about which to use. Overall, the issue common to these factors lies within interpretation. As such, RIs can be improved with better training in their use, combined with a better understanding of influences that affect them, and more transparent communication from laboratories in how RIs were derived.

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Section: The Use Of the Reference Interval In Clinical Chemistrymentioning

confidence: 95%

The Role and Limitations of the Reference Interval Within Clinical Chemistry and Its Reliability for Disease Detection

Timbrell

2024

Br J Biomed Sci

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“…A normal CRP level can be considered as less than 0.3 mg/dL. More than 10.0 mg/dL may be the result of acute bacterial infections, viral infections, or trauma, while levels higher than 50 mg/dL are associated with bacterial infections 32 .…”

Section: C-reactive Proteinmentioning

confidence: 99%

Evaluation of machine learning pipeline for blood culture outcome prediction on prospectively collected data in Western Australian emergency department

McFadden,

Inglis,

Celenza

et al. 2024

Preprint

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Bloodstream infections (BSIs) are particularly problematic in the emergency department (ED) of hospitals, where patients often present with undifferentiated illness, and the presence of BSI is hard to detect. Identifying whether a patient needs a blood culture (BC) test performed is one component of this challenge with implications for diagnostic efficiency, stewardship, and unnecessary resource expenditure. This paper explores the validation of previously developed machine learning (ML) models for BC outcome prediction using a prospectively collected ED patient dataset. An ML pipeline containing models previously trained using complete blood count (CBC), white blood cell differential (DIFF), and cell population data (CPD) generated by Sysmex XN-2000 haematology analysers was further evaluated using prospectively collected data containing patient sample results from the ED at Sir Charles Gairdner hospital (SCGH), Perth, Western Australia. Blood samples used to produce CBC,DIFF, and CPD were obtained at the same time as BC samples. There were 64 samples from 63 unique patients. 54 of those samples were associated with negative BC results, and 10 with positive BC results. We evaluated previously developed XGBoost (XG) and Random Forest (RF) ML models for positive BC outcome prediction. The RF and XG models obtained area under the receiver operating characteristic curve scores of 0.870 and 0.841 on the ED dataset. These results provide the foundation for further validation and shadow deployments of BC outcome prediction models in clinical settings. ML models,data, and code for reproducing the results presented in this work is provided in an open-source repository.

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The Value of C-Reactive Protein, C-Reactive Protein Kinetics and C-reactive Protein to Albumin Ratio on Prognosis in Newly Diagnosed Indolent B-cell Non-Hodgkin Lymphoma

Cited by 2 publications

References 51 publications

The Role and Limitations of the Reference Interval Within Clinical Chemistry and Its Reliability for Disease Detection

The Role and Limitations of the Reference Interval Within Clinical Chemistry and Its Reliability for Disease Detection

Evaluation of machine learning pipeline for blood culture outcome prediction on prospectively collected data in Western Australian emergency department

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