Blood Count-Derived Inflammatory Markers Correlate with Lengthier Hospital Stay and Are Predictors of Pneumothorax Risk in Thoracic Trauma Patients

Vunvulea, Vlad; Melinte, Răzvan Marian; Brînzaniuc, Klara; Suciu, Bogdan Andrei; Ivănescu, Adrian Dumitru; Hălmaciu, Ioana; Incze-Bartha, Zsuzsanna; Pastorello, Ylenia; Trâmbiţaş, Cristian; Mărginean, Lucian; Kaller, Réka; Kassas, Ahmad; Hogea, Timur

doi:10.3390/diagnostics13050954

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…Moreover, derived indices such as the monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) offer valuable perspectives on post-traumatic inflammatory and immunological dynamics. These ratios, notably NLR, MLR, and PLR, stand as facile, cost-efficient prognostic tools for trauma patient assessment [35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Elevation of White Blood Cell Subtypes in Adult Trauma Patients with Stress-Induced Hyperglycemia

Rau,

Kuo,

Tsai

et al. 2023

Diagnostics

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Background: Blood immune cell subset alterations following trauma can indicate a patient′s immune–inflammatory status. This research explored the influence of stress-induced hyperglycemia (SIH) on platelet counts and white blood cell (WBC) subtypes, including the derived indices of the monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), in trauma patients. Methods: We studied 15,480 adult trauma patients admitted from 1 January 1998 to 31 December 2022. They were categorized into four groups: nondiabetic normoglycemia (NDN, n = 11,602), diabetic normoglycemia (DN, n = 1750), SIH (n = 716), and diabetic hyperglycemia (DH, n = 1412). A propensity score-matched cohort was formed after adjusting for age, sex, and comorbidities, allowing for comparing the WBC subtypes and platelet counts. Results: Patients with SIH exhibited significantly increased counts of monocytes, neutrophils, and lymphocytes in contrast to NDN patients. However, no significant rise in platelet counts was noted in the SIH group. There were no observed increases in these cell counts in either the DN or DH groups. Conclusions: Our results demonstrated that trauma patients with SIH showed significantly higher counts of monocytes, neutrophils, and lymphocytes when compared to NDN patients, whereas the DN and DH groups remained unaffected. This underscores the profound association between SIH and elevated levels of specific WBC subtypes.

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Section: Introductionmentioning

confidence: 99%

Elevation of White Blood Cell Subtypes in Adult Trauma Patients with Stress-Induced Hyperglycemia

Rau,

Kuo,

Tsai

et al. 2023

Diagnostics

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The associations of two novel inflammation indexes, systemic immune-inflammation index (SII) and system inflammation response index (SIRI), with periodontitis: evidence from NHANES 2009–2014

Luo,

Liu,

Jiao

et al. 2024

Clin Oral Invest

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Streamlining management in thoracic trauma: radiomics- and AI-based assessment of patient risks

Hefny,

Almansoori,

Smetanina

et al. 2024

Front. Surg.

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BackgroundIn blunt chest trauma, patient management is challenging because clinical guidelines miss tools for risk assessment. No clinical scale reliably measures the severity of cases and the chance of complications.AimThe objective of the study was to optimize the management of patients with blunt chest trauma by creating models prognosticating the transfer to the intensive care unit and in-hospital length of stay (LOS).MethodsThe study cohort consisted of 212 cases. We retrieved information on the cases from the hospital’s trauma registry. After segmenting the lungs with Lung CT Analyzer, we performed volumetric feature extraction with data-characterization algorithms in PyRadiomics.ResultsTo predict whether the patient will require intensive care, we used the three groups of findings: ambulance, admission, and radiomics data. When trained on the ambulance data, the models exhibited a borderline performance. The metrics improved after we retrained the models on a combination of ambulance, laboratory, radiologic, and physical examination data (81.5% vs. 94.4% Sn). Radiomics data were the top-accurate predictors (96.3% Sn). Age, vital signs, anthropometrics, and first aid time were the best-performing features collected by the ambulance service. Laboratory findings, AIS scores for the lower extremity, abdomen, head, and thorax constituted the top-rank predictors received on admission to the hospital. The original first-order kurtosis had the highest predictive value among radiomics data. Top-informative radiomics features were derived from the right hemithorax because the right lung is larger. We constructed regression models that can adequately reflect the in-hospital LOS. When trained on different groups of data, the machine-learning regression models showed similar performance (MAE/ROV≈8%). Anatomic scores for the body parts other than thorax and laboratory markers of hemorrhage had the highest predictive value. Hence, the number of injured body parts correlated with the case severity.ConclusionThe study findings can be used to optimize the management of patients with a chest blunt injury as a specific case of monotrauma. The models we built may help physicians to stratify patients by risk of worsening and overcome the limitations of existing tools for risk assessment. High-quality AI models trained on radiomics data demonstrate superior performance.

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Blood Count-Derived Inflammatory Markers Correlate with Lengthier Hospital Stay and Are Predictors of Pneumothorax Risk in Thoracic Trauma Patients

Cited by 3 publications

References 59 publications

Elevation of White Blood Cell Subtypes in Adult Trauma Patients with Stress-Induced Hyperglycemia

Elevation of White Blood Cell Subtypes in Adult Trauma Patients with Stress-Induced Hyperglycemia

The associations of two novel inflammation indexes, systemic immune-inflammation index (SII) and system inflammation response index (SIRI), with periodontitis: evidence from NHANES 2009–2014

Streamlining management in thoracic trauma: radiomics- and AI-based assessment of patient risks

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