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    The prognostic value of HALP score and sPESI in predicting in-hospital mortality in patients with pulmonary thromboembolism
    (Oxford Univ Press, 2024) Yaman, Mahmut; Orak, Murat; Durgun, Hasan Mansur; Tekin, Veysi; Ulgut, Silan Goger; Belek, Sema; Gunel, Bercem Tugay
    Background Pulmonary thromboembolism (PTE), often arising from deep vein thrombosis, remains a high-mortality condition despite diagnostic advancements. Prognostic models like Pulmonary Embolism Severity Index (PESI) and sPESI identify low-risk groups effectively. The Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) score, reflecting nutritional status and systemic inflammation, shows prognostic value in cancers and cardiovascular diseases. This study examines the relationship between in-hospital mortality HALP score and simplified PESI (sPESI) in PTE patients. Methods This retrospective observational study included patients diagnosed with PTE in the emergency department of a tertiary medical faculty from 2018 to 2023. PTE diagnosis was confirmed via computed tomography pulmonary angiography. Data on transthoracic echocardiography, D-dimer levels, demographics, laboratory results, PESI, sPESI, and HALP scores, and in-hospital mortality were collected. Results In this study, clinical characteristics of 171 patients with PTE were analysed. The average age was 61.88 +/- 19.94 years, and 53.2% were female. Mortality was observed in 19.3% of patients. PESI and sPESI scores were significant predictors of mortality, with area under the curve values of 0.938 and 0.879, respectively. PESI score > 175.50 indicated a significantly higher mortality risk (HR = 18.208; P < .001), while sPESI >2.50 was also a strong predictor (HR = 11.840; P < .001). No significant cut-off value for HALP in predicting mortality was identified. Conclusions Our study supports the reliability of sPESI and PESI scores in predicting in-hospital mortality in PTE patients. However, the prognostic value of the HALP score requires further investigation. Our findings highlight the need for developing risk stratification models.