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Clinical risk scoring for predicting non-alcoholic fatty liver disease in metabolic syndrome patients (NAFLD-MS score).
Saokaew, Surasak; Kanchanasuwan, Shada; Apisarnthanarak, Piyaporn; Charoensak, Aphinya; Charatcharoenwitthaya, Phunchai; Phisalprapa, Pochamana; Chaiyakunapruk, Nathorn.
Afiliação
  • Saokaew S; Center of Health Outcomes Research and Therapeutic Safety (Cohorts), School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand.
  • Kanchanasuwan S; School of Pharmacy, Monash University Malaysia, Selangor, Malaysia.
  • Apisarnthanarak P; Center of Pharmaceutical Outcomes Research (CPOR), Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand.
  • Charoensak A; Clinical and Administrative Pharmacy, The University of Georgia College of Pharmacy, Athens, GA, USA.
  • Charatcharoenwitthaya P; Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Phisalprapa P; Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Chaiyakunapruk N; Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Liver Int ; 37(10): 1535-1543, 2017 10.
Article em En | MEDLINE | ID: mdl-28294515
ABSTRACT
BACKGROUND &

AIMS:

Non-alcoholic fatty liver disease (NAFLD) can progress from simple steatosis to hepatocellular carcinoma. None of tools have been developed specifically for high-risk patients. This study aimed to develop a simple risk scoring to predict NAFLD in patients with metabolic syndrome (MetS).

METHODS:

A total of 509 patients with MetS were recruited. All were diagnosed by clinicians with ultrasonography-confirmed whether they were patients with NAFLD. Patients were randomly divided into derivation (n=400) and validation (n=109) cohort. To develop the risk score, clinical risk indicators measured at the time of recruitment were built by logistic regression. Regression coefficients were transformed into item scores and added up to a total score. A risk scoring scheme was developed from clinical predictors BMI ≥25, AST/ALT ≥1, ALT ≥40, type 2 diabetes mellitus and central obesity. The scoring scheme was applied in validation cohort to test the performance.

RESULTS:

The scheme explained, by area under the receiver operating characteristic curve (AuROC), 76.8% of being NAFLD with good calibration (Hosmer-Lemeshow χ2 =4.35; P=.629). The positive likelihood ratio of NAFLD in patients with low risk (scores below 3) and high risk (scores 5 and over) were 2.32 (95% CI 1.90-2.82) and 7.77 (95% CI 2.47-24.47) respectively. When applied in validation cohort, the score showed good performance with AuROC 76.7%, and illustrated 84%, and 100% certainty in low- and high-risk groups respectively.

CONCLUSIONS:

A simple and non-invasive scoring scheme of five predictors provides good prediction indices for NAFLD in MetS patients. This scheme may help clinicians in order to take further appropriate action.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Técnicas de Apoio para a Decisão / Síndrome Metabólica / Hepatopatia Gordurosa não Alcoólica Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Técnicas de Apoio para a Decisão / Síndrome Metabólica / Hepatopatia Gordurosa não Alcoólica Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2017 Tipo de documento: Article