Observational studyCancer medicine2021
Prevalence of frailty and prediction of mortality in Chinese cancer patients using a frailty index-based clinical algorithm-A multicentre study.
Observational study in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- Retrospective Cohort Study on the Association Between a Laboratory Test-Derived Frailty Index and Mortality in Elderly Critically Ill Patients With Chronic Kidney Disease.Health science reports · 2026Article
- Frailty and Oncology: An Increasingly Common Combination.Cancer medicine · 2026Review
- Development and validation of a risk prediction model for postoperative frailty in breast cancer patients.PeerJ · 2026Article
- [Preoperative frailty and postoperative health outcomes in postmenopausal patients undergoing gynecological cancer surgery: a retrospective observational study].Journal of Korean biological nursing science · 2025Article
- Association Between the Frailty Index Based on Laboratory Tests and All-Cause Mortality in Hospitalized Older Adults: Retrospective Cohort Study.JMIR aging · 2025Article
- Article
- Impact of Laboratory-Derived Frailty Index on Clinical Outcomes in Critical Care Patients with COPD: A Retrospective Analysis Using the MIMIC-IV Database.International journal of chronic obstructive pulmonary disease · 2025Article
- Association between a laboratory-based frailty index and mortality of critically ill patients with acute pancreatitis: a retrospective study.Frontiers in nutrition · 2025Article
- FI-Lab predicts all-cause mortality, but not successful rehabilitation, among patients aged 65 and older after hip or pelvic fracture.Frontiers in medicine · 2025Article
- Association between frailty index based on laboratory tests and all-cause mortality in critically ill patients with heart failure.ESC heart failure · 2024Observational
- The association of a frailty index derived from laboratory tests and vital signs with clinical outcomes in critical care patients with septic shock: a retrospective study based on the MIMIC-IV database.BMC infectious diseases · 2024Article
- Use of Digital Health Technology Among Older Adults With Cancer in the United States: Findings From a National Longitudinal Cohort Study (2015-2021).Journal of medical Internet research · 2023Article
- Physiological Systems in Promoting Frailty.Comprehensive Physiology · 2022Review
- The impact of frailty on breast cancer outcomes: evidence from analysis of the Nationwide Inpatient Sample, 2005-2018.American journal of cancer research · 2022Article
- Prevalence of frailty and prediction of mortality in Chinese cancer patients using a frailty index-based clinical algorithm-A multicentre study.Cancer medicine · 2021Observational
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Authors and funding
20 authors at 15 institutions in 1 country.
Funding
Abstract
purposeTo investigate the frailty status in Chinese cancer patients through establishing a novel prediction algorithm.
methodsThe percentage of frailty in various age groups, locations, and tumor types in Chinese cancer patients was investigated. The prediction capacity of frailty on mortality of Chinese cancer patients was analysed by the frailty index composing of routine laboratory data (FI-LAB) accessible from a blood test and calculated as the ratio of abnormal factors to 22 total variables. The establishment of a novel algorithm, MCP (mortality of cancer patients), to predict the 5-year mortality in Chinese cancer patients was accomplished and the algorithm's prediction capacity was tested in the training and validation sets using receiver operating characteristic (ROC) analysis.
resultsWe found that the risk of death in cancer patients can be successfully identified through FI-LAB. The univariable and multivariable Cox regression were used to evaluate the effect of frailty on death. In the 5-year follow-up, 20.6% of the 2959 participants (age = 55.8 ± 11.7 years; 43.5% female) died, while the mean FI-LAB score in baseline was 0.23 (standard deviation = 0.13; range = 0-0.73). Frailty (after adjusting for gender, age, and other confounders) directly correlated with an increased risk of death, hazard ratio of 12.67 (95% confidence interval [CI]: 7.19, 22.31), compared to those without frailty. In addition, the MCP algorithm (MCP) = 3.678 × FI-LAB + 1.575 × sex + 1.779 × first tumor node metastasis staging, presented an area under the ROC (AUC) of 0.691 (95% CI: 0.656-0.726) and 0.648 (95% CI: 0.613-0.684) in the training and validation sets, respectively.
conclusionFrailty as defined by FI-LAB was common and indicated a significant death risk in cancer patients. Our novel developed algorithm MCP had a passable prediction capacity on 5-year MCP.
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