Background: A large amount of evidence from clinical studies has demonstrated that circulating tumor cells are strong predictors of outcomes in many cancers. However, the clinical significance of CTC enumeration in metastatic colorectal cancer is still questioned. The aim of this study was to evaluate the clinical value of CTC dynamics in mCRC patients receiving first-line treatments. Materials and methods: Serial CTC data from 218 patients were used to identify CTC trajectory patterns during the course of treatment. CTCs were evaluated at baseline, at a first-time point check and at the radiological progression of the disease. CTC dynamics were correlated with clinical endpoints. Results: Using a cut-off of ≥1 CTC/7.5 mL, four prognostic trajectories were outlined. The best prognosis was obtained for patients with no evidence of CTCs at any timepoints, with a significant difference compared to all other groups. Lower PFS and OS were recognized in group 4 (CTCs always positive) at 7 and 16 months, respectively. Conclusions: We confirmed the clinical value of CTC positivity, even with only one cell detected. CTC trajectories are better prognostic indicators than CTC enumeration at baseline. The reported prognostic groups might help to improve risk stratification, providing potential biomarkers to monitor first-line treatments.

Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. Following the PRISMA recommendations, this systematic review outlines ML models employed in the prediction and detection of NOAF in ICU patients and compares the ML-based approach with clinical-based methods. Inclusion criteria comprised randomized controlled trials (RCTs), observational studies, cohort studies, and case–control studies. A total of five articles published between November 2020 and April 2023 were identified and reviewed to extract the algorithms and performance metrics. Reviewed studies sourced 108,724 ICU admission records form databases, e.g., MIMIC. Eight prediction and detection methods were examined. Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.

This systematic review presents clinical evidence on the association of heart rate variability with outcome prediction in intracerebral and subarachnoid hemorrhages. The literature search led to the retrieval of 19 significant studies. Outcome prediction included functional outcome, cardiovascular complications, secondary brain injury, and mortality. Various aspects of heart rate recording and analysis, based on linear time and frequency domains and a non-linear entropy approach, are reviewed. Heart rate variability was consistently associated with poor functional outcome and mortality, while controversial results were found regarding the association between heart rate variability and secondary brain injury and cardiovascular complications.

Transient or persistent immunosuppression is a known risk factor for morbidity and mortality in critically ill patients. Aim of the present study is to evaluate the lymphopenia in patients admitted to the Emergency Unit of AOU Policlinico Umberto I, to investigate its prevalence at admission and the persistence during hospitalization until discharge. Possible correlations were evaluated between lymphopenia, diagnosis of admission, comorbidities and chronic treatments. In this study, 240 patients (142 men; 98 female; mean age 75.1 ± 15.1) were enrolled. Patients were divided into two groups according to the lymphocytes count at hospital admission, namely “Group A” with lymphopenia and “Group B” with values in the normal range. Moreover, the patients in group A were distinguished in relation to the regression or persistence of the lymphopenia assessed at the time of hospital discharge (Group A1: persistence; Group A2: normalization). Prevalence of lymphopenia at admission was 57%; Group A showed higher mean age and percentage of patients over 65 years of age; and none differences were observed regarding gender. Prevalence of lymphopenia at admission was 57%; Group A showed higher mean age and percentage of patients over 65 years of age; no differences were observed regarding gender. All subsets of the lymphocytes (CD4⁺, CD8⁺, NK) were equally reduced. Persistent lymphopenia was found in 19% of patients. Lymphopenia should be valued at the time of hospital admission as a factor influencing the prognosis, the management and the treatment of these patients.