PubMed · 16394304
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Krishna R Polu, Myles Wolf. 2006-01-05. Clinical problem-solving. Needle in a haystack.. https://doi.org/10.1056/nejmcps051939
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PURPOSE: To develop and internally validate the PANTHEIA-SIRI prognostic model, which integrates log-transformed systemic inflammation response index (SIRI) with clinical predictors, to estimate overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (mPDAC) treated with first-line chemotherapy. METHODS: We used data from the multicenter PANTHEIA-SEOM registry. OS was defined from chemotherapy start. The model was fitted as a Weibull accelerated failure time model in the survival-analysis population with multiple imputation. Predictors were log-transformed baseline SIRI, modeled with restricted cubic splines, ECOG, tumor burden, chemotherapy regimen, and anorexia-cachexia syndrome. Internal validation used a separate, non-overlapping cohort from the same registry; the centers contributing to each cohort are listed in a supplementary annex. TRIPOD was followed. Discrimination was assessed with Harrell´s C-index and calibration with IPCW Brier scores and IPA. RESULTS: The derivation cohort comprised 672 patients with SIRI data (593 analyzed for survival) across 22 Spanish hospitals (2015-2025); 80.1% had died after a median OS of 9.9 months. The imputation-pooled derivation C-index was 0.654 (95% CI, 0.627-0.681); optimism-corrected, 0.629. Internal validation used 62 separate patients from the same registry; 96.8% had died after a median OS of 9.2 months. The validation C-index was 0.603 (95% CI, 0.518-0.687). Calibration was adequate at 6 and 12 months. CONCLUSIONS: The PANTHEIA-SIRI model provides individualized OS estimates in mPDAC with routine clinical predictors. Its open-access calculator ( https://pantheia-siri.shinyapps.io/calc/ ) may support prognostic communication, treatment-intensity selection, and supportive-care planning. Routine clinical implementation will require further validation in larger, fully independent cohorts.
Among the 350,000 maintenance dialysis patients in the USA, the mortality rate is high (20-23% per year) as is the prevalence of hepatitis C virus (HCV) infection (5-15%). An additional same number of dialysis patients in the USA may be infected with HCV but have undetectable HCV antibodies. Almost half of all deaths in dialysis patients, including HCV-infected patients, are due to cardiovascular disease. Since over two-thirds of dialysis patients die within 5 years of initiating dialysis and because markers of malnutrition-inflammation complex syndrome (MICS), rather than traditional cardiovascular risk factors, are among the strongest predictors of early death in these patients, the impact of HCV infection on nutritional status and inflammation may be a main cause of poor survival in this population. Based on data from our cross-sectional and limited longitudinal studies, we hypothesize that HCV infection confounds the association between MICS and clinical outcomes in dialysis patients and, by doing so, leads to higher short-term cardiovascular events and death. Understanding the natural history of HCV and its association with inflammation, nutrition and outcomes in dialysis patients may lead to testing more effective anti-HCV management strategies in this and other similar patient populations, providing benefits not only for HCV infection but the detrimental consequences associated with this infection. In this article, we review the link between the HCV infection and mortality in dialysis patients and compare HCV antibody to molecular methods to detect HCV infection in these individuals.
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