Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study

Time-dependent cardiovascular risks following pneumonia in inpatient and outpatient settings: A register-based cohort study

Background

The elevated long-term cardiovascular disease (CVD) risks associated with pneumonia have been observed among inpatients, yet the risks associated with outpatients are less understood.

Methods

We used register-based data and a matched cohort design, including 98,354 pneumonia inpatients and 44,486 outpatients, as well as a 5-fold number of matched healthy controls. Associations between pneumonia presentation (in inpatient and outpatient settings) and long-term CVD risks were measured by rate difference and hazard ratio (HR) using Poisson and Cox regressions in a time-dependent manner.

Results

During a maximum follow-up period of 5.7 years of ischemic heart disease (IHD), heart failure (HF), and stroke were documented among pneumonia inpatients.Relative to healthy controls, pneumonia patients showed increased risks of IHD, HF, and stroke. Women and young inpatients demonstrated stronger associations of CVD with pneumonia; inpatients aged 60 years or older showed the highest excessive CVD risks.

Conclusions

Pneumonia demanding outpatient and inpatient cares are intermediate-term and long-term risk factors of incident CVDs respectively, underscoring the need to plan setting-specific and time-dependent CVD-preventive cares following pneumonia presentation.

NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment.

Risk factors for subsequent fractures in hip fracture patients: a nested case-control study

BACKGROUND

The risk factors for subsequent fractures following an initial hip fracture are not entirely understood. This study examined the clinical characteristics of hip fracture patients to identify potential risk factors associated with a higher risk of experiencing subsequent fractures.

METHODS

We conducted a nested case-control study using data from the Chinese PLA General Hospital Hip Fracture Cohort between January 2008 and March 2022. The cases were individuals who experienced subsequent fractures following an initial hip fracture. Each case was matched with up to 2 controls who did not develop subsequent fractures. Important clinical factors were compared across groups, including traditional fracture risk factors and potential risk factors (e.g., comorbidities, falls risk, physical impairment, calcium or vitamin D use, and anti-osteoporosis medications). Conditional logistic regression analyses were used to evaluate the impact of these clinical features as potential risk factors for subsequent fractures.

RESULTS

A total of 96 individuals who suffered from subsequent fractures were matched with 176 controls. The median time between the initial hip fracture and the subsequent fracture was 2.1 years. The overall proportion of patients receiving anti-osteoporosis treatment after initial hip fracture was 25.7%. In the multivariable regression analysis, living in a care facility (OR = 3.78, 95%CI: 1.53-9.34), longer hospital stays (OR = 1.05, 95%CI: 1.00-1.11), and falls after discharge (OR = 7.58, 95%CI: 3.37-17.04) were associated with higher odds of subsequent fractures.

CONCLUSIONS

This study showed that living in a care facility, longer hospital stays, and falls after discharge may be independent risk factors for repeat fractures following an initial hip fracture. These findings could be used to identify and manage patients at high risk of subsequent fractures.

Prediction of cardiovascular and renal risk among patients with apparent treatment-resistant hypertension in the United States using machine learning methods

Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment with three antihypertensives, is associated with an increased risk of developing cardiovascular and renal complications compared with controlled hypertension. We aimed to identify the characteristics of aTRH patients with an elevated risk of major adverse cardiovascular events plus (MACE+; defined as stroke, myocardial infarction, or heart failure hospitalization) and end stage renal disease (ESRD). This retrospective cohort study included aTRH patients (BP ≥140/90 mmHg and taking ≥3 antihypertensives) from the United States-based Optum® de-identified Electronic Health Record dataset and used machine learning models to identify risk factors of MACE+ or ESRD. Patients had claims for ≥3 antihypertensive classes within 30 days between January 1, 2015 and June 30, 2021, and two office BP measures recorded 1-90 days apart within 30 days to 11 months after the index regimen date. Of a total 18 797 070 patients identified with any hypertension, 71 100 patients had aTRH. During the study period (mean 25.5 months), 4944 (7.0%) patients had a MACE+ and 2403 (3.4%) developed ESRD. In total, 22 risk factors were included in the MACE+ model and 16 in the ESRD model, and most were significantly associated with study outcomes. The risk factors with the largest impact on MACE+ risk were congestive heart failure, stages 4 and 5 chronic kidney disease (CKD), age ≥80 years, and living in the Southern region of the United States. The risk factors with the largest impact on ESRD risk, other than pre-existing CKD, were anemia, congestive heart failure, and type 2 diabetes. The overall study cohort had a 5-year predicted MACE+ risk of 13.4%; this risk was increased in those in the top 50% and 25% high-risk groups (21.2% and 29.5%, respectively). The overall study cohort had a predicted 5-year risk of ESRD of 6.8%, which was increased in the top 50% and 25% high-risk groups (10.9% and 17.1%, respectively). We conclude that risk models developed in our study can reliably identify patients with aTRH at risk of MACE+ and ESRD based on information available in electronic health records; such models may be used to identify aTRH patients at high risk of adverse outcomes who may benefit from novel treatment interventions.

Recent advancement in integrating artificial intelligence and information technology with real-world data for clinical decision-making in China: A scoping review

OBJECTIVE

Striking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real-world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence-/information technology-based clinical decision support tools in China.

METHODS

PubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision-making.

RESULTS

A total of 37 peer-reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single-center electronic medical records were the mainstream data sources leveraged to inform clinical decision-making, with internal validation being predominately used for model evaluation.

CONCLUSIONS

To effectively promote the extensive use of real-world data and drive a paradigm shift in clinical decision-making in China, multidisciplinary collaboration of key stakeholders is urgently needed.

Performance evaluation of machine learning and Computer Coded Verbal Autopsy (CCVA) algorithms for cause of death determination: A comparative analysis of data from rural South Africa

Computer Coded Verbal Autopsy (CCVA) algorithms are commonly used to determine the cause of death (CoD) from questionnaire responses extracted from verbal autopsies (VAs). However, they can only operate on structured data and cannot effectively harness information from unstructured VA narratives. Machine Learning (ML) algorithms have also been applied successfully in determining the CoD from VA narratives, allowing the use of auxiliary information that CCVA algorithms cannot directly utilize. However, most ML-based studies only use responses from the structured questionnaire, and the results lack generalisability and comparability across studies. We present a comparative performance evaluation of ML methods and CCVA algorithms on South African VA narratives data, using data from Agincourt Health and Demographic Surveillance Site (HDSS) with physicians' classifications as the gold standard. The data were collected from 1993 to 2015 and have 16,338 cases. The random forest and extreme gradient boosting classifiers outperformed the other classifiers on the combined dataset, attaining accuracy of 96% respectively, with significant statistical differences in algorithmic performance (p < 0.0001). All our models attained Area Under Receiver Operating Characteristics (AUROC) of greater than 0.884. The InterVA CCVA attained 83% Cause Specific Mortality Fraction accuracy and an Overall Chance-Corrected Concordance of 0.36. We demonstrate that ML models could accurately determine the cause of death from VA narratives. Additionally, through mortality trends and pattern analysis, we discovered that in the first decade of the civil registration system in South Africa, the average life expectancy was approximately 50 years. However, in the second decade, life expectancy significantly dropped, and the population was dying at a much younger average age of 40 years, mostly from the leading HIV related causes. Interestingly, in the third decade, we see a gradual improvement in life expectancy, possibly attributed to effective health intervention programmes. Through a structure and semantic analysis of narratives where experts disagree, we also demonstrate the most frequent terms of traditional healer consultations and visits. The comparative approach also makes this study a baseline that can be used for future research enforcing generalization and comparability. Future study will entail exploring deep learning models for CoD classification.

Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation

Objective. Physical therapy is a common clinical treatment for patients with lumbar disc herniation. The study is aimed at exploring the feasibility of mathematical expression and curative effect prediction of physical therapy in patients with lumbar disc herniation using a logistic model and gradient boosting machine (GBM). Methods. A total of 142 patients with lumbar disc herniation were treated with physical therapy. The pain was evaluated by the visual analogue scale (VAS) before each treatment. The logistic model was used to conduct a global regression analysis on patients with lumbar disc herniation. The final results of the whole course of treatment were predicted by the measured values of 2-9 times of treatment. The GBM model was used to predict and analyze the curative effect of physical therapy. Results. The mathematical expression ability of the logistic regression model for patients with lumbar disc herniation undergoing physical therapy was sufficient, and the global determination coefficient was 0.721. The results would be better for more than five measurements. The AUC of GBM mode logistic regression analysis was 0.936 and 0.883, and the prediction effect is statistically significant. Conclusion. Both the logistic and GBM model can fully express the changes in patients with lumbar disc herniation during physical therapy.