Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

BACKGROUND AND AIM: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. METHODS: In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. RESULTS: In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. CONCLUSIONS: Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

Association between anemia and depression: results from NHANES 2005-2018 and mendelian randomization analyses.

BACKGROUND: The relationship between anemia and depression remains controversial. OBJECTIVE: To explore the association between anemia/hemoglobin and depression. METHODS: The data for our cross-sectional study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Weighted multivariate logistic regression was performed to examine the association between anemia/hemoglobin and depression. Inverse variance weighted (IVW), weighted-median, and MR-Egger were used in MR analyses to assess the causal relationship between anemia/hemoglobin and depression. Heterogeneity and directional pleiotropy were assessed using the Cochrane Q test and Egger-intercept test, respectively. Sensitivity analysis was conducted by the leave-one-out approach. All analyses were carried out using IBM SPSS 24.0 and R version 4.2.2. RESULTS: A total of 29,391 NHANES participants were included in this study. After adjusting for all covariates, the association between anemia/hemoglobin and depression was not significant (P < 0.05). IVW estimates revealed that broad anemia had no significant effect on the risk of depression (OR = 1.00, 95% CI = 0.99-1.01, P = 0.432). Findings of weighted median and MR-Egger were consistent with those from IVW (weighted median: OR = 1.00, 95% CI = 0.99-1.02; P = 0.547; MR-Egger: OR = 1.01, 95% CI = 0.98-1.03, P = 0.605). The results of three MR Analyses methods also showed no causal association between hemoglobin and depression. CONCLUSIONS: Our findings do not support a causal association between anemia and depression. The association between hemoglobin concentration and depression was not statistically significant either.

Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis.

PURPOSE: Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI). METHODS: The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias. RESULTS: We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95). CONCLUSION: ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005-2018.

BACKGROUND: Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations. METHODS: Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score. RESULTS: Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors. CONCLUSIONS: Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.

Association of chronic diseases with depression in the United States, NHANES 2007-2018.

Depression often coexists with many chronic diseases. However, previous studies mainly focused on the association between a single chronic disease or chronic diseases of the elderly and depression. This study included 26,177 adults aged more than 20 years old from the 2007-2018 National Health and Nutrition Examination Survey. Depression was determined by nine questions which were from the Patient Health Questionnaire. We used propensity score matching to reduce the influence of confounders between the depression and non-depression groups. A multivariate logistic regression model was used to analyze the relationship between various chronic diseases and the number of diseases and depression. The prevalence of depression in participants with chronic diseases was higher than that in participants without chronic diseases, 20.8% of participants with chronic bronchitis had depression. After matching and controlling sleep, insurance and smoking, the highest risk of depression (OR = 1.524; 95% CI: 1.162-2.001) was found in people with stroke, followed by arthritis (OR = 1.464; 95% CI: 1.275-1.681). The percentage of participants with two or more chronic diseases with depression and without depression was 68.9% and 51.9%, respectively. Participants with five or more chronic diseases had the highest risk of depression (OR = 3.653; 95% CI: 3.001-4.446). In conclusion, patients with chronic diseases are at higher risk for depression, especially those with multiple chronic diseases. This study suggested that we should pay more attention to the mental health of people with chronic diseases.

Accuracy of clustered regularly interspaced short palindromic repeats (CRISPR) to diagnose COVID-19, a meta-analysis.

OBJECTIVE: To estimate the accuracy of clustered regularly interspaced short palindromic repeats (CRISPR) in determining coronavirus disease-19 (COVID-19). METHODS: As of January 31, 2022, PubMed, Web of Science, Embase, Science Direct, Wiley and Springer Link were searched. Sensitivity, specificity, likelihood ratio (LR), diagnostic odds ratio (DOR) and area under the summary receiver-operating characteristic (AUC) curve were used to assess the accuracy of CRISPR. RESULTS: According to the inclusion criteria, 5857 patients from 54 studies were included in this meta-analysis. The pooled sensitivity, specificity and AUC were 0.98, 1.00 and 1.00, respectively. For CRISPR-associated (Cas) proteins-12, the sensitivity, specificity was 0.96, 1.00, respectively. For Cas-13, the sensitivity and specificity were 0.99 and 0.99. CONCLUSION: This meta-analysis showed that the diagnostic performance of CRISPR is close to the gold standard, and it is expected to meet the Point of care requirements in resource poor areas.

Distant metastasis and prognostic factors in patients with invasive ductal carcinoma of the breast.

OBJECTIVE: To explore the risk factors and prognostic factors of invasive ductal carcinoma (IDC) and to predict the survival of IDC patients with metastasis. METHOD: We used multivariate logistic regression to identify independent risk factors affecting metastasis in IDC patients and used Cox regression to identify independent prognostic factors affecting the overall survival of patients with metastasis. Nomogram was used to predict survival, while C-index and calibration curves were used to measure the performance of nomogram. Kaplan-Meier method was used to calculate the survival curves of patients with different independent prognostics factors and different metastatic sites, and the differences were compared by log-rank test. The data of our study were obtained from the Surveillance, Epidemiology and End Results cancer registry. RESULT: Our study included 226,094 patients with IDC. In multivariate analysis, independent risk factors of metastasis included age, race, marital status, income, geographic region, grade, T stage, N stage, subtype, surgery and radiotherapy. Independent prognostic factors included age, race, marital status, income, geographic region, grade, T stage, N stage, subtype, surgery and chemotherapy. We established a nomogram, of which the C-index was 0.701 (0.693, 0.709), with the calibration curves showing that the disease-specific survival between actual observation and prediction had a good consistency. The survival curves of different metastatic patterns were significantly different (log-rank test: χ2  = 18784, p < 0.001; χ2  = 47.1, p < 0.001; χ2  = 20, p < 0.001). CONCLUSION: The nomogram we established may provide risk assessment and survival prediction for IDC patients with metastasis, which can be used for clinical decision-making and reference.

Trends in asthma among adults in the United States, National Health and Nutrition Examination Survey 2005 to 2018.

BACKGROUND: Asthma is a common chronic disease in American adults. The prevalence of asthma has varied over time, but there are few studies on the long-term trend of asthma in American adults. OBJECTIVE: To describe the prevalence and trend of asthma in American adults from 2005 to 2018 and analyze the risk factors for asthma. METHODS: Data collection was performed from National Health and Nutrition Examination Survey 2005 to 2018. The unweighted number and weighted percentages of normal participants and patients with asthma and the trends of asthma were calculated. Weighted univariate logistic regression was used to analyze the risk factors for asthma. RESULTS: A total of 39,601 adults were included in this study. From 2005 to 2018, the overall prevalence of asthma in American adults was 8.41%, whereas that in young, middle-aged, and elderly adults was 8.30%, 8.70%, and 7.92%, respectively. The estimated prevalence of asthma in the overall adults and young adults increased with time (P for trend = .03, difference = 0.023 and P for trend = .007, difference = 0.060, respectively), and the estimated prevalence of middle-aged and elderly adults remained stable with time (P for trend = .33, difference = 0.015 and P for trend = .80, difference = -0.024, respectively). CONCLUSION: Asthma in American adults was on the rise. Female sex, non-Hispanic Blacks, individuals with low annual household income, active smokers, obese patients, patients with hypertension, patients with diabetes, and individuals with positive asthma family history were associated with a higher risk for developing asthma.

Real-time quaking-induced conversion assay is accurate for Lewy body diseases: a meta-analysis.

OBJECTIVE: Real-time quaking-induced conversion (RT-QuIC) is a novel in vitro acellular seed amplification analysis and has been widely used to detect prion diseases. Due to the similar mechanism of abnormal aggregation of α-synuclein, RT-QuIC has great potential for diagnosing Lewy body diseases. This meta-analysis was performed to evaluate the diagnostic accuracy of RT-QuIC for Lewy body diseases. METHODS: This study followed the PRISMA statement. We searched six databases for relevant studies published until February 20, 2022. Meta-analysis was conducted using RevMan 5.3, Stata 17.0, and Meta-Disc 1.4. Subgroup analyses were performed to explore sources of heterogeneity. RESULTS: A total of 16 studies were included in this study. The pooled sensitivity and specificity were 0.91 (95%CI: 0.85-0.94) and 0.95 (95%CI: 0.90-0.97), respectively. The pooled positive and negative likelihood ratios were 17.16 (95% CI: 9.16-32.14) and 0.10 (95% CI: 0.06-0.17), respectively. The pooled diagnostic odds rate and area under the summary receiver operating characteristic curve were 171.16 (95% CI: 66.64-439.62) and 0.97 (95% CI: 0.96-0.99), respectively. CONCLUSIONS: This study was the first meta-analysis on RT-QuIC for Lewy body diseases. RT-QuIC is a reliable and accurate method to diagnose Lewy body diseases.

Plasma Fluorescent Oxidation Products and Bone Mineral Density Among Male Veterans: A Cross-Sectional Study.

In vitro and vivo studies indicate that oxidative stress contributes to bone loss. Fluorescent oxidation products (FlOPs) are novel biomarkers of oxidative stress; they reflect global oxidative damage of lipids, proteins, carbohydrates, and DNA. However, whether FlOPs are associated with bone mineral density (BMD) is still unclear. In the present study, we examined the association between FlOPs and BMD among male veterans. This cross-sectional study was conducted among participants recruited from the Department of Medical Examination, The Second Hospital of Jilin University in Jilin, China. We identified male veterans who were at least 50 y old between June and October of 2019. Plasma FlOPs were measured with a fluorescent microplate reader (excitation/emission wavelength: 320/420 nm). BMD were measured by dual-energy X-ray absorptiometry (DXA). The association between FlOPs and BMD was tested by multivariable linear regression models. A total of 164 male veterans were enrolled in the study, the average age was 56.6 y. After adjusting for covariates, veterans who had FlOP levels in the highest tertile had a statistically significant lower femoral neck (ß = -0.044; p = 0.007) and total hip BMD (ß = -0.045; p = 0.020) as compared to those with FlOP levels in the lowest tertile. Similar results were found when FlOPs were treated as a continuous variable (per 1-SD increase, ß = -0.014 and p = 0.033 for femoral neck BMD; ß = -0.016 and p = 0.047 for total hip BMD). Higher FlOP levels were associated with lower BMD among male veterans.