Torna ai risultati
Scheda bibliografica · Consultazione e accesso
Artículo de revista

Optimizing healthcare resources in pyogenic liver abscess: a dual-threshold HDL-CRP model for predicting hospitalization duration across multi-cohorts

Mingzhu Tao et al · Frontiers Media S.A · 2026

Accesso aperto disponibile
Lettura rapida. Controlla i dati essenziali della risorsa e accedi al contenuto con il pulsante principale. La scheda mostra solo le informazioni necessarie per identificare, citare e aprire l’opera.
Pubblicazione seriale

A combined model of BCVA, TRAb, and NLR predicts response to intravenous methylprednisolone in dysthyroid optic neuropathy

Questa pubblicazione seriale contiene 132 contenuti correlati.

Accesso alla risorsa

Apri il contenuto dall’opzione principale o scegli un’altra fonte disponibile.

DOAJ DOAJ Articles
Entrar por DOAJ
Accesso principale

Accesso aperto disponibile

Recurso identificado como acceso abierto, sin confirmar automáticamente si es texto completo directo.
Apri risorsa

Riepilogo

Descripción general del contenido del recurso.

BackgroundProlonged hospitalization for pyogenic liver abscess (PLA) burdens patients and health systems. We examined whether admission high-density lipoprotein cholesterol (HDL-C) predicts length of stay (LOS) and whether a simple HDL–C-reactive protein (CRP) dual-threshold can flag patients at risk of prolonged stay.MethodsWe analyzed a prospective adult PLA cohort at a tertiary center (2018–2023; n = 138) and validated findings in MIMIC-IV ICU patients with liver abscess (n = 38) and in NHANES 2017–2020 (n = 9,226). Multivariable models related admission HDL-C to log-transformed LOS; model performance and calibration were assessed with internal resampling and external validation. We further evaluated a dual-threshold rule and conducted mediation analysis with CRP.ResultsLower HDL-C independently associated with longer LOS. A nomogram combining HDL-C, abscess size, and sepsis performed well (R2≈0.66; RMSE≈6.4 d) and remained directionally consistent across external datasets. A dual-threshold (HDL-C < 1.03 mmol/L and CRP > 1.0 mg/dL) identified a high-risk subgroup with greater odds of prolonged stay. CRP mediated a small proportion of the HDL-C–LOS association.ConclusionAdmission HDL-C, particularly when interpreted together with CRP, may help identify PLA patients at increased risk of prolonged hospitalization at the time of admission. Patients with low HDL-C and high CRP may warrant closer monitoring, earlier evaluation for source control or drainage, and more proactive inpatient planning. Prospective implementation studies are warranted.

Come citare

Elegí el formato que necesitás y copiá la referencia al portapapeles.

APA 7

al, M. T. E. (2026). Optimizing healthcare resources in pyogenic liver abscess: a dual-threshold HDL-CRP model for predicting hospitalization duration across multi-cohorts. https://doi.org/10.3389/fmed.2026.1708360

MLA

al, Mingzhu Tao et. "Optimizing healthcare resources in pyogenic liver abscess: a dual-threshold HDL-CRP model for predicting hospitalization duration across multi-cohorts." 2026. https://doi.org/10.3389/fmed.2026.1708360.

Chicago

al, Mingzhu Tao et. 2026. "Optimizing healthcare resources in pyogenic liver abscess: a dual-threshold HDL-CRP model for predicting hospitalization duration across multi-cohorts.". https://doi.org/10.3389/fmed.2026.1708360.

Harvard

al, M. T. E. 2026, Optimizing healthcare resources in pyogenic liver abscess: a dual-threshold HDL-CRP model for predicting hospitalization duration across multi-cohorts, Frontiers Media S.A, available at: https://doi.org/10.3389/fmed.2026.1708360 [Accessed 8 Aug. 2026].

Condividi e stampa

Salva la scheda, copia il link permanente o stampala in PDF.

Esporta riferimento

Esporta il record nei formati più comuni per usarlo con un gestore bibliografico.

Dettagli della risorsa

Informazioni bibliografiche utili per verificare che sia il materiale corretto.

Titolo
Optimizing healthcare resources in pyogenic liver abscess: a dual-threshold HDL-CRP model for predicting hospitalization duration across multi-cohorts
Autore / collaboratori
Mingzhu Tao et al
Editore
Frontiers Media S.A
Anno di pubblicazione
2026
ISSN
2296-858X
ISSN
2296-858X
Lingua
Inglés

Soggetti

Esplora risorse correlate a partire da questi soggetti.

Copiato