Trial reportCancer chemotherapy and pharmacology2022
A clinical scoring tool validated with machine learning for predicting severe hand-foot syndrome from sorafenib in hepatocellular carcinoma.
Trial report in Cancer chemotherapy and pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00699374 (A Multinational, Randomized, Open-Label, Phase 3 Study Of Sunitinib Malate Versus Sorafenib In Patients With Advanced Hepatocellular Carcinoma), which is not on this map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multinational, Randomized, Open-Label, Phase 3 Study Of Sunitinib Malate Versus Sorafenib In Patients With Advanced Hepatocellular Carcinoma
Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- Topical interventions for preventing hand-foot syndrome resulting from antineoplastic therapy: A scoping review.Revista da Escola de Enfermagem da U S P · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSorafenib is an effective therapy for advanced hepatocellular carcinoma (HCC). Hand-foot syndrome (HFS) is a serious adverse effect associated with sorafenib therapy. This study aimed to develop an updated clinical prediction tool that allows personalized prediction of HFS following sorafenib initiation.
methodsIndividual participant data from Phase III clinical trial NCT00699374 were used in Cox proportional hazard analysis of the association between pre-treatment clinicopathological data and grade ≥ 3 HFS occurring within the first 365 days of sorafenib treatment for advanced HCC. Multivariable prediction models were developed using stepwise forward inclusion and backward deletion and internally validated using a random forest machine learning approach.
resultsOf 542 patients, 116 (21%) experienced grades ≥ 3 HFS. The prediction tool was optimally defined by sex (male vs female), haemoglobin (< 130 vs ≥ 130 g/L) and bilirubin (< 10 vs 10-20 vs ≥ 20 µmol/L). The prediction tool was able to discriminate subgroups with significantly different risks of grade ≥ 3 HFS (P ≤ 0.001). The high (score = 3 +)-, intermediate (score = 2)- and low (score = 0-1)-risk subgroups had 40%, 27% and 14% probability of developing grade ≥ 3 HFS within the first 365 days of sorafenib treatment, respectively.
conclusionA clinical prediction tool defined by female sex, high haemoglobin and low bilirubin had high discrimination for predicting HFS risk. The tool may enable improved evaluation of personalized risks of HFS for patients with advanced HCC initiating sorafenib.
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Registered trials
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