Background: In order to ensure accurate survival estimates, population-based cancer registries must ascertain all, or nearly all, patients diagnosed with cancer in their catchment area, and obtain complete follow-up information on all deaths that occurred among registered cancer patients. In the US, linkage with state death records may not be sufficient to ascertain all deaths. Since 1979, all state vital statistics offices have reported their death certificate information to the National Death Index (NDI). Objective: This study was designed to measure the impact of linkage with the NDI on population-based relative and cancer cause-specific survival rates in the US. Methods: Central cancer registry records for patients diagnosed 1993-1995 from California, Colorado, and Idaho were linked with death certificate information (deaths 1993-2004) from their individual state vital statistics offices and with the NDI. Two databases were created: one contained incident records with deceased patients linked only to state death records and the second database contained incident records with deceased patients linked to both state death records and the NDI. Survival estimates and 95% confidence intervals from each database were compared by state and primary site category. Results: At 60 months follow-up, 42.1-48.1% of incident records linked with state death records and an additional 0.7-3.4% of records linked with the NDI. Survival point estimates from the analysis without NDI were not contained within the corresponding 95% CIs from the NDI augmented analysis for all sites combined and colorectal, pancreas, lung and bronchus, breast, prostate, non-Hodgkin lymphoma, and Kaposi sarcoma cases in all 3 states using relative survival methods. Additional combinations of state and primary site had significant survival estimate differences, which differed by method (relative versus cause-specific survival). Conclusion: To ensure accurate population-based cancer survival rates, linkage with the National Death Index to ascertain out of state and late registered deaths is a necessary process for US central cancer registries.
[Show abstract][Hide abstract] ABSTRACT: Stoichiometry and stability of antitumor ruthenium(II)-η6-p-cymene complexes of picolinic acid and its 6-methyl and 6-carboxylic acid derivatives were determined by pH-potentiometry, 1H NMR spectroscopy and UV–Vis spectrophotometry in aqueous solution in the presence or absence of coordinating chloride ions. The picolinates form exclusively mono-ligand complexes in which they can coordinate via the bidentate (O,N) mode and a chloride or a water molecule is found at the third binding site of the ruthenium(II)-η6-p-cymene moiety depending on the conditions. [Ru(η6-p-cymene)(L)(H2O/Cl)] species are predominant at physiological pH in all studied cases. Hydrolysis of the aqua complex or the chlorido/hydroxido co-ligand exchange results in the formation of the mixed-hydroxido species [Ru(η6-p-cymene)(L)(OH)] in the basic pH range. There is no indication for the decomposition of the mono-ligand complexes during 24 h in the ruthenium(II)-η6-p-cymene-picolinic acid system between pH 3 and 11; however, a slight dissociation with a low reaction rate was found in the other two systems leading to the appearance of the dinuclear trihydroxido-bridged species [Ru2(η6-p-cymene)2(OH)3]+ and free ligands at pH > 10. The replacement of the chlorido by an aqua ligand in [Ru(η6-p-cymene)(L)Cl] was also monitored and equilibrium constants for the exchange process were determined.
[Show abstract][Hide abstract] ABSTRACT: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.
A regional cancer centre in Australia.
Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data.
Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC).
The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours.
Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.
BMJ Open 03/2014; 4(3):e004007. DOI:10.1136/bmjopen-2013-004007 · 2.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Cancer-specific survival estimates rely on precise and correct data on the cause of death; however, these data can be difficult to acquire, particularly in elderly patients where comorbidity is common. Furthermore, while some deaths are directly related to cancer, others are more complex, with cancer merely contributing. Another, more precise, method is to assess the relative mortality, i.e., mortality in patients compared to the general population. The aims of this study were to describe the relative mortality in soft tissue sarcoma, and to compare the relative mortality with the cancer-specific mortality.
We included 1246 patients treated for soft tissue sarcoma and 6230 individually age- and sex-matched individuals from the general population. The relative mortality was estimated as rates, and rate ratios adjusted for comorbidity. Mortality rate ratios were computed using the Cox proportional hazard model for 0–5 years and 5–10 years, according to age, sex and level of comorbidity. The cancer-specific mortality was compared to the corresponding relative mortality.
The overall 5- and 10-year relative mortality was 32.8% and 36.0%. Patients with low-grade soft tissue sarcoma did not have increased mortality compared with the general population. Soft tissue sarcoma patients had a 4.4 times higher risk of dying within the first five years after diagnosis and a 1.6 times higher risk between five and ten years compared with the general comparison cohort. The relative mortality varied according to age, grade, stage at diagnosis, and level of comorbidity, being highest in younger patients and in patients without comorbidity. The overall 5- and 10-year cancer-specific mortality was underestimated by 1.5 and overestimated by 0.7 percentage points compared to the relative mortality, respectively. No statistical significant difference between the relative and the cancer-specific mortality was found.
The relative mortality provides an unbiased and accurate method to differentiate between cancer-specific and non-cancer-specific deaths. However, when data on the cause of death is of a sufficient quality, there is no difference between relative mortality and disease-specific mortality based on death certificates.
BMC Cancer 09/2014; 14(1):682. DOI:10.1186/1471-2407-14-682 · 3.36 Impact Factor
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