Background: The long-term survival of patients hospitalized with COVID-19 and the factors associated with poorer survival months after infection are not well understood. The aims of the present study were to analyze the overall mortality 10 months after admission. Methods: 762 patients with COVID-19 disease were included. Patients underwent a complete clinical evaluation, routine laboratory analysis and chest X-ray. Data collected included demographic and clinical data, such as vascular risk factors, tobacco or alcohol use, comorbidity, and institutionalization. Results: Ten-month mortality was 25.6%: 108 deaths occurred in-hospital, while 87 patients died after discharge. In-hospital mortality was independently related to NT-proBNP values > 503.5 pg/mL [OR = 4.67 (2.38–9.20)], urea > 37 mg/dL [3.21 (1.86–7.31)] and age older than 71 years [OR = 1.93 (1.05–3.54)]. NT-proBNP values > 503.5 pg/mL [OR = 5.00 (3.06–8.19)], urea > 37 mg/dL [3.51 (1.97–6.27)], cognitive impairment [OR = 1.96 (1.30–2.95), cancer [OR = 2.23 (1.36–3.68), and leukocytes > 6330/mm³ [OR = 1.64 (1.08–2.50)], were independently associated with long-term mortality. Conclusions: the risk of death remains high even months after COVID-19 infection. Overall mortality of COVID-19 patients during 10 months after hospital discharge is nearly as high as that observed during hospital admission. Comorbidities such as cancer or cognitive impairment, organ dysfunction and inflammatory reaction are independent prognostic markers of long-term mortality.

Colorectal cancer liver metastases (CRLM) remain a major therapeutic challenge, shaped by marked biological heterogeneity and unpredictable clinical outcomes. While classical Clinical Risk Scores (CRS) continue to inform surgical decision making, their static nature limits their relevance in an era defined by molecular profiling, dynamic biomarkers, and personalized therapy. Advances in genomics and liquid biopsy technologies have expanded the understanding of CRLM beyond traditional clinicopathological parameters, enabling real-time disease monitoring and more refined risk stratification. In parallel, dynamic scoring systems that incorporate evolving clinical and molecular data are redefining how risk is assessed and managed throughout the course of treatment. This review revisits classical CRS, explores emerging molecular and dynamic tools, and discusses their potential integration into clinical practice to improve patient selection, guide treatment timing, and refine the overall management of CRLM.