A nomogram for predicting complications in patients with solid tumours and seemingly stable febrile neutropenia.

BACKGROUND: We sought to develop and externally validate a nomogram and web-based calculator to individually predict the development of serious complications in seemingly stable adult patients with solid tumours and episodes of febrile neutropenia (FN). PATIENTS AND METHODS: The data from the FINITE study (n=1133) and University of Salamanca Hospital (USH) FN registry (n=296) were used to develop and validate this tool. The main eligibility criterion was the presence of apparent clinical stability, defined as events without acute organ dysfunction, abnormal vital signs, or major infections. Discriminatory ability was measured as the concordance index and stratification into risk groups. RESULTS: The rate of infection-related complications in the FINITE and USH series was 13.4% and 18.6%, respectively. The nomogram used the following covariates: Eastern Cooperative Group (ECOG) Performance Status ⩾2, chronic obstructive pulmonary disease, chronic cardiovascular disease, mucositis of grade ⩾2 (National Cancer Institute Common Toxicity Criteria), monocytes <200/mm(3), and stress-induced hyperglycaemia. The nomogram predictions appeared to be well calibrated in both data sets (Hosmer-Lemeshow test, P>0.1). The concordance index was 0.855 and 0.831 in each series. Risk group stratification revealed a significant distinction in the proportion of complications. With a ⩾116-point cutoff, the nomogram yielded the following prognostic indices in the USH registry validation series: 66% sensitivity, 83% specificity, 3.88 positive likelihood ratio, 48% positive predictive value, and 91% negative predictive value. CONCLUSIONS: We have developed and externally validated a nomogram and web calculator to predict serious complications that can potentially impact decision-making in patients with seemingly stable FN.

Neoadjuvant Statistical Algorithm to Predict Individual Risk of Relapse in Patients with Resected Liver Metastases from Colorectal Cancer.

(1) Background: Liver metastases (LM) are the leading cause of death in colorectal cancer (CRC) patients. Despite advancements, relapse rates remain high and current prognostic nomograms lack accuracy. Our objective is to develop an interpretable neoadjuvant algorithm based on mathematical models to accurately predict individual risk, ensuring mathematical transparency and auditability. (2) Methods: We retrospectively evaluated 86 CRC patients with LM treated with neoadjuvant systemic therapy followed by complete surgical resection. A comprehensive analysis of 155 individual patient variables was performed. Logistic regression (LR) was utilized to develop the predictive model for relapse risk through significance testing and ANOVA analysis. Due to data limitations, gradient boosting machine (GBM) and synthetic data were also used. (3) Results: The model was based on data from 74 patients (12 were excluded). After a median follow-up of 58 months, 5-year relapse-free survival (RFS) rate was 33% and 5-year overall survival (OS) rate was 60.7%. Fifteen key variables were used to train the GBM model, which showed promising accuracy (0.82), sensitivity (0.59), and specificity (0.96) in predicting relapse. Similar results were obtained when external validation was performed as well. (4) Conclusions: This model offers an alternative for predicting individual relapse risk, aiding in personalized adjuvant therapy and follow-up strategies.