Mean Corpuscular Hemoglobin Predicts Length of Hospital Stay Independent of Severity Classification in Acute Pancreatitis Patients: a Retrospective Study

Lin, Hao; Yu, Ting; Xu, Rong; Li, Xing

doi:10.21203/rs.3.rs-757394/v1

Cited by 1 publication

(1 citation statement)

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“…This indicates that MCH is impacted by factors beyond cell size in dogs with PSS. In people, MCH is an independent predictor of iron deficiency, length of hospital stay of people with acute pancreatitis, and premature discontinuation of antiplatelet therapy in people receiving cardiovascular stents indicating this hematologic parameter provides additional information to the more traditionally utilized hemoglobin and MCV (28)(29)(30).…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and classification of portosystemic shunts: a machine learning retrospective case-control study

Farhoodimoghadam,

Reagan,

Zwingenberger

2024

Front. Vet. Sci.

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Diagnosis of portosystemic shunts (PSS) in dogs often requires multiple diagnostic tests, and available clinicopathologic tests have limitations in sensitivity and specificity. The objective of this study was to train and validate a machine learning model (MLM) that can accurately predict the presence of a PSS utilizing routinely collected demographic data and clinicopathologic features. Dogs diagnosed with PSS or control dogs tested for PSS but had the condition ruled out (non-PSS) were identified. Dogs were included if a complete blood count and serum chemistry panel were available from PSS diagnostic testing. Dogs with a PSS were subcategorized as having a single intrahepatic PSS, a single extrahepatic PSS, or multiple extrahepatic PSS. An extreme gradient boosting (XGboost) MLM was trained with data from 70% of the cases, and MLM performance was determined on the test set, comprising the remaining 30% of the case data. Two MLMs were created. The first was designed to predict the presence of any PSS (PSS MLM), and the second to predict the PSS subcategory (PSS SubCat MLM). The trained PSS MLM had a sensitivity of 94.3% (95% CI 90.1–96.8%) and specificity of 90.5% (95% CI 85.32–94.0%) for dogs in the test set. The area under the receiver operator characteristic curve (AUC) was 0.976 (95% CI; 0.964–0.989). The mean corpuscular hemoglobin, lymphocyte count, and serum globulin concentration were most important in prediction classification. The PSS SubCat MLM had an accuracy of 85.7% in determining the subtype of PSS of dogs in the test set, with variable sensitivity and specificity depending on PSS subtype. These MLMs have a high accuracy for diagnosing PSS; however, the prediction of PSS subclassification is less accurate. The MLMs can be used as a screening tool to increase or decrease the index of suspicion for PSS before confirmatory diagnostics such as advanced imaging are pursued.

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Section: Discussionmentioning

confidence: 99%