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Background: Heart failure (HF) may increase the risk of dementia via shared risk factors. Objectives: The authors investigated the incidence, types, clinical correlates, and prognostic impact of dementia in a population-based cohort of patients with index HF. Methods: The previously territory-wide database was interrogated to identify eligible patients with HF (N = 202,121) from 1995 to 2018. Clinical correlates of incident dementia and their associations with all-cause mortality were assessed using multivariable Cox/competing risk regression models where appropriate. Results: Among a total cohort aged ≥18 years with HF (mean age 75.3 ± 13.0 years, 51.3% women, median follow-up 4.1 [IQR: 1.2-10.2] years), new-onset dementia occurred in 22,145 (11.0%), with age-standardized incidence rate of 1,297 (95% CI: 1,276-1,318) per 10,000 in women and 744 (723-765) per 10,000 in men. Types of dementia were Alzheimer's disease (26.8%), vascular dementia (18.1%), and unspecified dementia (55.1%). Independent predictors of dementia included: older age (≥75 years, subdistribution hazard ratio [SHR]: 2.22), female sex (SHR: 1.31), Parkinson's disease (SHR: 1.28), peripheral vascular disease (SHR: 1.46), stroke (SHR: 1.24), anemia (SHR: 1.11), and hypertension (SHR: 1.21). The population attributable risk was highest for age ≥75 years (17.4%) and female sex (10.2%). New-onset dementia was independently associated with increased risk of all-cause mortality (adjusted SHR: 4.51; P < 0.001). Conclusions: New-onset dementia affected more than 1 in 10 patients with index HF over the follow-up, and portended a worse prognosis in these patients. Older women were at highest risk and should be targeted for screening and preventive strategies.
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BACKGROUND & AIMS: Accurate hepatocellular carcinoma (HCC) risk prediction facilitates appropriate surveillance strategy and reduces cancer mortality. We aimed to derive and validate novel machine learning models to predict HCC in a territory-wide cohort of patients with chronic viral hepatitis (CVH) using data from the Hospital Authority Data Collaboration Lab (HADCL). METHODS: This was a territory-wide, retrospective, observational, cohort study of patients with CVH in Hong Kong in 2000-2018 identified from HADCL based on viral markers, diagnosis codes, and antiviral treatment for chronic hepatitis B and/or C. The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Five popular machine learning methods, namely, logistic regression, ridge regression, AdaBoost, decision tree, and random forest, were performed and compared to find the best prediction model. RESULTS: A total of 124,006 patients with CVH with complete data were included to build the models. In the training cohort (n = 86,804; 6,821 HCC), ridge regression (area under the receiver operating characteristic curve [AUROC] 0.842), decision tree (0.952), and random forest (0.992) performed the best. In the validation cohort (n = 37,202; 2,875 HCC), ridge regression (AUROC 0.844) and random forest (0.837) maintained their accuracy, which was significantly higher than those of HCC risk scores: CU-HCC (0.672), GAG-HCC (0.745), REACH-B (0.671), PAGE-B (0.748), and REAL-B (0.712) scores. The low cut-off (0.07) of HCC ridge score (HCC-RS) achieved 90.0% sensitivity and 98.6% negative predictive value (NPV) in the validation cohort. The high cut-off (0.15) of HCC-RS achieved high specificity (90.0%) and NPV (95.6%); 31.1% of patients remained indeterminate. CONCLUSIONS: HCC-RS from the ridge regression machine learning model accurately predicted HCC in patients with CVH. These machine learning models may be developed as built-in functional keys or calculators in electronic health systems to reduce cancer mortality. LAY SUMMARY: Novel machine learning models generated accurate risk scores for hepatocellular carcinoma (HCC) in patients with chronic viral hepatitis. HCC ridge score was consistently more accurate than existing HCC risk scores. These models may be incorporated into electronic medical health systems to develop appropriate cancer surveillance strategies and reduce cancer death.
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BACKGROUND & AIMS: Antiviral treatment is known to improve survival in patients with chronic hepatitis B (CHB)-related hepatocellular carcinoma (HCC). Yet, the treatment uptake in CHB patients remains low. We aimed to report the secular trend in antiviral treatment uptake from 2007-2017, and to compare the effect of different nucleos(t)ide analogue (NA) initiation times (before vs. after HCC diagnosis) on survival. METHODS: A 3-month landmark analysis was used to compare overall survival in patients not receiving NA treatment (i.e. no NA), patients receiving NAs after their first HCC treatment (i.e. post-HCC NA), and patients receiving NAs ≤3 months before their first HCC treatment (i.e. pre-HCC NA). A propensity score-weighted Cox proportional hazards model was used to balance clinical characteristics between the 3 groups and to estimate hazard ratios (HRs). RESULTS: The uptake of antiviral treatment in HCC patients increased from 47.3% in 2007 to 98.3% in 2017. The pre-HCC NA group contributed mostly to the uptake rate, which increased from 72.7% to 96.0% in the past decade. In addition, 3,843 CHB patients (407 no NA; 2,932 pre-HCC NA; 504 post-HCC NA) with HCC, receiving at least 1 type of HCC treatment, were included in the analysis. Lack of NA treatment at the time of HCC diagnosis increased the risk of death (weighted HR 3.05; 95% CI 2.70-3.44; p <0.001). The impact of the timing of NA treatment was insignificant (weighted HR 0.90; 95% CI 0.78-1.04; p = 0.161). CONCLUSIONS: The uptake of antiviral treatment in HCC patients increased over the past decade. NA treatment, regardless of whether it was initiated before or after HCC diagnosis, improved survival. It is never too late to initiate NA treatment, even after HCC diagnosis. LAY SUMMARY: More and more patients who have hepatitis B-related liver cancer received antiviral treatment over the past decade. The timing of starting antiviral treatment, regardless of whether it was before or after liver cancer happens, does not really matter in terms of survival benefits.