The Clinical Frailty Scale is a useful tool for predicting postoperative complications following elective colon cancer surgery at the age of 80 years and above: A prospective, multicentre observational study

Aim Identification of the risks of postoperative complications may be challenging in older patients with heterogeneous physical and cognitive status. The aim of this multicentre, observational study was to identify variables that affect the outcomes of colon cancer surgery and, especially, to find tools to quantify the risks related to surgery. Method Patients aged >= 80 years with electively operated Stage I-III colon cancer were recruited. The prospectively collected data included comorbidities, results of the onco-geriatric screening tool (G8), Clinical Frailty Scale (CFS), Charlson Comorbidity Index (CCI) and Mini Nutritional Assessment-Short Form (MNA-SF), and operative and postoperative outcomes. Results A total of 161 patients (mean 84.5 years, range 80-97, 60% female) were included. History of cerebral stroke (64% vs. 37%, p = 0.02), albumin level 31-34 g/l compared with >= 35 g/l (57% vs. 32%, p = 0.007), CFS 3-4 and 5-9 compared with CFS 1-2 (49% and 47% vs. 16%, respectively) and American Society of Anesthesiologists score >3 (77% vs. 28%, P = 0.006) were related to a higher risk of complications. In multivariate logistic regression analysis CFS >= 3 (OR 6.06, 95% CI 1.88-19.5, p = 0.003) and albumin level 31-34 g/l (OR 3.88, 1.61-9.38, p = 0.003) were significantly associated with postoperative complications. Severe complications were more common in patients with chronic obstructive pulmonary disease (43% vs. 13%, p = 0.047), renal failure (25% vs. 12%, p = 0.021), albumin level 31-34 g/l (26% vs. 8%, p = 0.014) and CCI >6 (23% vs. 10%, p = 0.034). Conclusion Surgery on physically and cognitively fit aged colon cancer patients with CFS 1-2 can lead to excellent operative outcomes similar to those of younger patients. The CFS could be a useful screening tool for predicting postoperative complications.Peer reviewe

Associations of cognitive reserve and psychological resilience with cognitive functioning in subjects with cerebral white matter hyperintensities

Background and purpose Cerebral small vessel disease is characterized by progressive white matter hyperintensities (WMH) and cognitive decline. However, variability exists in how individuals maintain cognitive capabilities despite significant neuropathology. The relationships between individual cognitive reserve, psychological resilience and cognitive functioning were examined in subjects with varying degrees of WMH. Methods In the Helsinki Small Vessel Disease Study, 152 subjects (aged 65-75 years) underwent a comprehensive neuropsychological assessment, evaluation of subjective cognitive complaints and brain magnetic resonance imaging with volumetric WMH evaluation. Cognitive reserve was determined by education (years) and the modified Cognitive Reserve Scale (mCRS). Psychological resilience was evaluated with the Resilience Scale 14. Results The mCRS total score correlated significantly with years of education (r = 0.23, p < 0.01), but it was not related to age, sex or WMH volume. Together, mCRS score and education were associated with performance in a wide range of cognitive domains including processing speed, executive functions, working memory, verbal memory, visuospatial perception and verbal reasoning. Independently of education, the mCRS score had incremental predictive value on delayed verbal recall and subjective cognitive complaints. Psychological resilience was not significantly related to age, education, sex, WMH severity or cognitive test scores, but it was associated with subjective cognitive complaints. Conclusions Cognitive reserve has strong and consistent associations with cognitive functioning in subjects with WMH. Education is widely associated with objective cognitive functioning, whereas lifetime engagement in cognitively stimulating leisure activities (mCRS) has independent predictive value on memory performance and subjective cognitive complaints. Psychological resilience is strongly associated with subjective, but not objective, cognitive functioning.Peer reviewe

Midlife Insulin Resistance as a Predictor for Late-Life Cognitive Function and Cerebrovascular Lesions

Background: Type 2 diabetes (T2DM) increases the risk for Alzheimer's disease (AD) but not for AD neuropathology. The association between T2DM and AD is assumed to be mediated through vascular mechanisms. However, insulin resistance (IR), the hallmark of T2DM, has been shown to associate with AD neuropathology and cognitive decline.Objective: To evaluate if midlife IR predicts late-life cognitive performance and cerebrovascular lesions (white matter hyperintensities and total vascular burden), and whether cerebrovascular lesions and brain amyloid load are associated with cognitive functioning.Methods: This exposure-to-control follow-up study examined 60 volunteers without dementia (mean age 70.9 years) with neurocognitive testing, brain 3T-MRI and amyloid-PET imaging. The volunteers were recruited from the Finnish Health 2000 survey (n = 6062) to attend follow-up examinations in 2014-2016 according to their insulin sensitivity in 2000 and their APOE genotype. The exposure group (n = 30) had IR in 2000 and the 30 controls had normal insulin sensitivity. There were 15 APOE epsilon 4 carriers per group. Statistical analyses were performed with multivariable linear models.Results: At follow-up the IR+group performed worse on executive functions (p = 0.02) and processing speed (p = 0.007) than the IR- group. The groups did not differ in cerebrovascular lesions. No associations were found between cerebrovascular lesions and neurocognitive test scores. Brain amyloid deposition associated with slower processing speed.Conclusion: Midlife IR predicted poorer executive functions and slower processing speed, but not cerebrovascular lesions. Brain amyloid deposition was associated with slower processing speed. The association between midlife IR and late-life cognition might not be mediated through cerebrovascular lesions measured here

One-year functional outcomes of patients aged 80 years or more undergoing colonic cancer surgery: prospective, multicentre observational study

Background: Older patients are at high risk of experiencing delayed functional recovery after surgical treatment. This study aimed to identify factors that predict changes in the level of support for activities of daily living and mobility 1 year after colonic cancer surgery.Methods: This was a multicentre, observational study conforming to STROBE guidelines. The prospective data included pre-and postoperative mobility and need for support in daily activities, co-morbidities, onco-geriatric screening tool (G8), clinical frailty scale (CFS), operative data, and postoperative surgical outcomes.Results: A total of 167 patients aged 80 years or more with colonic cancer were recruited. After surgery, 30 per cent and 22 per cent of all patients had increased need for support and decreased motility. Multivariableanalysis with all patients demonstrated that preoperative support in daily activities outside the home (OR 3.23, 95 per cent c.i. 1.06 to 9.80, P = 0.039) was associated with an increased support at follow-up. A history of cognitive impairment (3.15, 1.06 to 9.34, P = 0.038) haemoglobin less than 120 g/l (7.48, 1.97 to 28.4, P = 0.003) and discharge to other medical facilities (4.72, 1.39 to 16.0, P = 0.013) were independently associated with declined mobility. With functionally independent patients, haemoglobin less than 120 g/l (8.31, 1.76 to 39.2, P = 0.008) and discharge to other medical facilities (4.38, 1.20 to 16.0, P = 0.026) were associated with declined mobility.Conclusion: Increased need for support before surgery, cognitive impairment, preoperative anaemia, and discharge to other medical facilities predicts an increased need for support or declined mobility 1 year after colonic cancer surgery. Preoperative assessment and optimization should focus on anaemia correction, nutritional status, and mobility with detailed rehabilitation plan.Greater increased need for support before surgery, cognitive impairment, preoperative anaemia, and discharge to other medical facilities predicted an increased need for support or declined mobility 1 year after colonic cancer surgery. Preoperative assessment and optimization should especially focus on anaemia correction, nutritional status, and mobility with a detailed rehabilitation plan.</p

Improved Classification of Alzheimer's Disease Data via Removal of Nuisance Variability

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making

Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making

Computer-assisted prediction of clinical progression in the earliest stages of AD

INTRODUCTION: Individuals with subjective cognitive decline (SCD) are at increased risk for clinical progression. We studied how combining different diagnostic tests can help to identify individuals who are likely to show clinical progression. METHODS: We included 674 patients with SCD (46% female, 64 ± 9 years, Mini–Mental State Examination 28 ± 2) from three memory clinic cohorts. A multivariate model based on the Disease State Index classifier incorporated the available baseline tests to predict progression to MCI or dementia over time. We developed and internally validated the model in one cohort and externally validated it in the other cohorts. RESULTS: After 2.9 ± 2.0 years, 151(22%) patients showed clinical progression. Overall performance of the classifier when combining cognitive tests, magnetic resonance imagining, and cerebrospinal fluid showed a balanced accuracy of 74.0 ± 5.5, with high negative predictive value (93.3 ± 2.8). DISCUSSION: We found that a combination of diagnostic tests helps to identify individuals at risk of progression. The classifier had particularly good accuracy in identifying patients who remained stable