Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry

An interpretable hybrid machine learning approach for predicting three-month unfavorable outcomes in patients with acute ischemic stroke

BACKGROUND

Acute ischemic stroke (AIS) is a clinical disorder caused by nontraumatic cerebrovascular disease with a high incidence, mortality, and disability rate. Most stroke survivors are left with speech and physical impairments, and emotional problems. Despite technological advances and improved treatment options, death and disability after stroke remain a major problem. Our research aims to develop interpretable hybrid machine learning (ML) models to accurately predict three-month unfavorable outcomes in patients with AIS.

METHODS

Within the framework of this analysis, the model was trained using data from 731 cases in the dataset and subsequently validated using data from both internal and external validation datasets. A total of 25 models (including ML and deep learning models) were initially employed, along with 14 evaluation metrics, and the results were subjected to cluster analysis to objectively validate the model's effectiveness and assess the similarity of evaluation metrics. For the final model evaluation, 10 metrics selected after metric screening and calibration analysis were utilized to evaluate model performance, while clinical decision analysis, cost curve analysis, and model fairness analysis were applied to assess the clinical applicability of the model. Nested cross-validation and optimal hyperparameter search were employed to determine the best hyperparameter for the ML models. The SHAP diagram is utilized to provide further visual explanations regarding the importance of features and their interaction effects, ultimately leading to the establishment of a practical AIS three-month prognostic prediction platform.

RESULTS

The frequencies of unfavorable outcomes in the internal dataset and external validation dataset were 389 / 1045 (37.2 %) and 161 / 411 (39.2 %), respectively. Through cluster analysis of the results of 14 evaluation metrics across 25 models and a comparison of clinical applicability, 12 ML models were ultimately selected for further analysis. The findings revealed that XGBoost and CatBoost performed the best. Further ensemble modeling of these two models and adjustment of decision thresholds using cost curves resulted in the final model performing as follows on the internal validation set: PRAUC of 0.856 (0.801, 0.902), ROCAUC of 0.856 (0.801, 0.901), specificity of 0.879 (0.797, 0.953), balanced accuracy of 0.840 (0.763, 0.912) and MCC of 0.678 (0.591, 0.760). Similarly, the model exhibited excellent performance on the external validation set, with a PRAUC of 0.823 (0.775, 0.872), ROCAUC of 0.842 (0.801, 0.890), specificity of 0.888 (0.822, 0.920), balanced accuracy of 0.814 (0.751, 0.869) and MCC of 0.639 (0.546, 0.721). In terms of the important features of AIS three-month outcomes, albumin ranked highest, followed by FBG, BMI, Scr, WBC, and age, while gender exhibited significant interactions with other indicators. Ultimately, based on the final ensemble model and optimal decision thresholds, a tailored short-term prognostic prediction platform for AIS patients was developed.

CONCLUSIONS

We constructed an interpretable hybrid ML model that maintained good performance on both internal and external validation datasets using the most readily accessible 30 clinical data variables, indicating its ability to accurately predict the three-month unfavorable outcomes for AIS patients. Meanwhile, our superior predictive model provides practicality for routine and more frequent initial risk assessments, making it easier to integrate into network or mobile-based telemedicine solutions.

Associations of Social, Behavioral, and Clinical Factors With Sex Differences in Stroke Recurrence and Poststroke Mortality

BACKGROUND

Few population-based studies have assessed sex differences in stroke recurrence. In addition, contributors to sex differences in recurrence and poststroke mortality, including social factors, are unclear. We investigated sex differences in these outcomes and the contribution of social, clinical, and behavioral factors to the sex differences.

METHODS

First-ever ischemic stroke cases identified from 2008 to 2019 from the population-based Brain Attack Surveillance in Corpus Christi Project in Texas were included and followed for recurrence and all-cause mortality through 2020. Sex differences in outcomes with and without adjustment for potential confounding factors, including social, behavioral, and clinical factors, were examined using Cox proportional hazard models. Factors that changed the log hazard ratio (HR) for sex by at least 10% after adjustment were identified as confounders/contributors. Final models were adjusted for all identified confounders.

RESULTS

Of 2326 participants (mean age, 68 years; 48% women; 57% Mexican American), over median follow-ups of 5.4 years for recurrence and 3.7 years for mortality, 274 recurrences and 965 deaths occurred. No significant sex differences in recurrence were noted in unadjusted (HR, 0.89 [95% CI, 0.70-1.13]), age-adjusted (HR, 0.92 [95% CI, 0.72-1.18]), or fully adjusted models (HR, 0.88 [95% CI, 0.67-1.16]). Although women had a higher crude mortality rate than men (HR, 1.22 [95% CI, 1.08-1.38]), this sex difference disappeared after age adjustment (HR, 0.91 [95% CI, 0.80-1.03]). Other factors contributing to the sex difference included education, marital status, prestroke depression, health behaviors, initial stroke severity, prestroke disability, comorbidities, atrial fibrillation, and coronary artery disease. After simultaneously adjusting for all identified confounders, women had lower poststroke mortality (HR, 0.79 [95% CI, 0.68-0.91]).

CONCLUSIONS

Sex differences in stroke recurrence were not apparent. Women had a higher unadjusted poststroke mortality rate but lower adjusted mortality than men. Social and psychosocial factors, alongside clinical factors, primarily explained the sex disparity in poststroke mortality.

An interpretable machine learning scoring tool for estimating time to recurrence readmissions in stroke patients

BACKGROUND

Stroke recurrence readmission poses an additional burden on both patients and healthcare systems. Risk stratification aims to accurately divide patients into groups to provide targeted interventions at reducing readmission. To accurately predict short and intermediate-term risks of readmission and provide information for further temporal risk stratification, we developed and validated an interpretable machine learning risk scoring tool.

METHODS

In this retrospective study, all stroke admission episodes from January 1st 2015 to December 31st 2019 were obtained from the Shanghai Health and Health Development Research Centre database, which covers medical records of all patients hospitalized in 436 medical institutes in Shanghai. The outcome was time to stroke recurrence readmission within 90 days post discharge. The Score for Stroke Recurrence Readmission Prediction (SSRRP) tool was derived via an interpretable machine learning-based system for time-to-event outcomes. SSRRP as six-variable survival score includes sequelae, length of stay, type of stroke, random plasma glucose, medical expense payment, and number of hospitalizations.

RESULTS

A total of 339,212 S admission episodes were finally included in the whole cohort. Among them, 217,393 episodes were included in the training dataset, 54,347 episodes in the internal validation dataset, and 67,472 in the temporal validation dataset. Readmission within 90 days was documented in 33922(9.97 %) episodes, with a median time to emergency readmission of 19 days (Interquartile range: 8-43). In the temporal validation dataset, the SSRRP achieved an integrated area under the curve of 0.730(95 % CI, 0.724-0.737). In addition, SSRRP demonstrated good calibration and clinical benefit rate.

CONCLUSIONS

In this retrospective cohort study, the SSRRP, a parsimonious and point-based scoring tool, was developed to predict the risk of recurrent readmission for stroke. It also provided accurate information on the time to stroke readmission, enabling further temporal risk stratification and informed clinical decision-making.

Predictors of affective disturbances and cognitive impairment following small spontaneous supratentorial intracerebral hemorrhage

BACKGROUND AND PURPOSE

Affective disturbances and cognitive impairment are common sequelae of intracerebral hemorrhage (ICH), yet predictive models for these outcomes remain limited, especially for spontaneous supratentorial ICH with small hematomas (<30 mL). The aim of this study was to investigate predictors of affective disturbances and cognitive impairment following small spontaneous supratentorial intracerebral hemorrhage.

METHODS

We retrospectively analyzed 1692 patients with spontaneous supratentorial ICH between January 2018 and December 2020 at the First Affiliated Hospital of Harbin Medical University. Of these, 1563 patients completed a median follow-up of 3.5 years. Cognitive function was evaluated using the modified Telephone Interview for Cognitive Status, and affective disturbances using the Hamilton Depression Scale and the Hamilton Anxiety Scale. Restricted cubic spline analyses were employed to examine the relationships between predictors and outcomes.

RESULTS

In this cohort, 58.5% had cognitive impairment, 52.8% reported depressive symptoms, and 39.4% exhibited anxiety symptoms. Logistic regression models using Boruta's algorithm demonstrated strong predictive capacity, with areas under the curve of 0.82 for cognitive impairment, 0.78 for depressive symptoms, and 0.73 for anxiety symptoms. Hematoma volume was significantly linked to depressive symptoms (odds ratio [OR] 1.56, 95% confidence interval [CI] 1.38-1.76) and inversely to cognitive impairment (OR 0.67, 95% CI 0.59-0.77). Uric acid levels displayed a nonlinear relationship with cognitive impairment (OR 0.70, 95% CI 0.61-0.81). Hospitalization days significantly raised the risk of both depressive (OR 1.16, 95% CI 1.03-1.30) and anxiety symptoms (OR 1.17, 95% CI 1.04-1.31).

CONCLUSIONS

The logistic regression model, enhanced by Boruta's algorithm, provides a valuable tool for predicting affective disturbances and cognitive impairment after ICH. It facilitates early identification and improves risk assessment for these neuropsychiatric outcomes in patients with small hematomas.

Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis

Background

Ischemic stroke is one of the leading causes of disability and death worldwide, with a high risk of recurrence that severely impacts the quality of life of patients. Therefore, identifying and analyzing the risk factors for recurrent ischemic stroke is crucial for the prevention and management of this disease.

Methods

A total of 114 cases of recurrent acute ischemic stroke patients admitted from July 2017 to March 2021 were selected as the observation group, and another 409 cases of initial ischemic stroke patients from the same period as the control group. The clinical data of the observation group and the control group were compared to analyze the risk factors associated with the readmission of ischemic stroke. A single-factor analysis (Model 1), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning methods (Model 2) were used to screen important variables, and a multi-factor COX Proportional Hazards Model regression stroke recurrence risk prediction model was constructed. The predictive performance of the model was evaluated by the consistency index (C-index).

Results

Multivariate COX regression analysis revealed that history of hypertension (Hazard Ratio [HR] = 2.549; 95% Confidence Interval (CI) [1.503-4.321]; P = 0.001), history of cerebral infarction (HR = 1.709; 95% CI [1.066-2.738]; P = 0.026), cerebral artery stenosis (HR = 0.534; 95% CI [0.306-0.931]; P = 0.027), carotid arteriosclerosis (HR = 1.823; 95% CI [1.137-2.924]; P = 0.013), systolic blood pressure (HR = 0.981; 95% CI [0.971-0.991]; P < 0.0001), red cell distribution width-coefficient of variation (RDW-CV) (HR = 1.251; 95% CI [1.019-1.536]; P = 0.033), mean platelet volume (MPV) (HR = 1.506; 95% CI [1.148-1.976]; P = 0.003), uric acid (UA) (HR = 0.995; 95% CI [0.991-1.000]; P = 0.049) were found significantly associated with acute ischemic stroke. The C-index of the full COX model was 0.777 (0.732~0.821), showing a good discrimination between Model 1 and Model 2.

Conclusions

History of hypertension, history of cerebral infarction, cerebral artery stenosis, carotid atherosclerosis, systolic blood pressure, UA, RDW-CV, and MPV were identified as risk factors for acute ischemic stroke recurrence. The model can be used to predict the recurrence of acute ischemic stroke.

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients

BACKGROUND

Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent stroke.

METHOD

This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Scopus, Embase, and Web of Science (WOS) were searched until January 1, 2024. The quality assessment of studies was conducted using the QUADAS-2 tool. The diagnostic meta-analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive and negative diagnostic likelihood ratio (DLR), diagnostic accuracy, diagnostic odds ratio (DOR), and area under of the curve (AUC) by the MIDAS package in STATA V.17.

RESULTS

Twelve studies, comprising 24,350 individuals, were included. The meta-analysis revealed a sensitivity of 71% (95% CI 0.64-0.78) and a specificity of 88% (95% confidence interval (CI) 0.76-0.95). Positive and negative DLR were 5.93 (95% CI 3.05-11.55) and 0.33 (95% CI 0.28-0.39), respectively. The diagnostic accuracy and DOR was 2.89 (95% CI 2.32-3.46) and 18.04 (95% CI 10.21-31.87), respectively. The summary ROC curve indicated an AUC of 0.82 (95% CI 0.78-0.85).

CONCLUSION

ML demonstrates promise in predicting recurrent strokes, with moderate to high sensitivity and specificity. However, the high heterogeneity observed underscores the need for standardized approaches and further research to enhance the reliability and generalizability of these models. ML-based recurrent stroke prediction can potentially augment clinical decision-making and improve patient outcomes by identifying high-risk patients.

Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review

In recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area being one of its most impactful fields of application. However, to be applied reliably, these models must provide users with clear, simple, and transparent explanations about the medical decision-making process. This systematic review aims to investigate the use and application of explainability in ML models used in brain disease studies. A systematic search was conducted in three major bibliographic databases, Web of Science, Scopus, and PubMed, from January 2014 to December 2023. A total of 133 relevant studies were identified and analyzed out of a total of 682 found in the initial search, in which the explainability of ML models in the medical context was studied, identifying 11 ML models and 12 explainability techniques applied in the study of 20 brain diseases.

The most efficient machine learning algorithms in stroke prediction: A systematic review

Background and Aims

Stroke is one of the most common causes of death worldwide, leading to numerous complications and significantly diminishing the quality of life for those affected. The purpose of this study is to systematically review published papers on stroke prediction using machine learning algorithms and introduce the most efficient machine learning algorithms and compare their performance. The papers have published in period from 2019 to August 2023.

Methods

The authors conducted a systematic search in PubMed, Scopus, Web of Science, and IEEE using the keywords "Artificial Intelligence," "Predictive Modeling," "Machine Learning," "Stroke," and "Cerebrovascular Accident" from 2019 to August 2023.

Results

Twenty articles were included based on the inclusion criteria. The Random Forest (RF) algorithm was introduced as the best and most efficient stroke ML algorithm in 25% of the articles (n = 5). In addition, in other articles, Support Vector Machines (SVM), Stacking and XGBOOST, DSGD, COX& GBT, ANN, NB, and RXLM algorithms were introduced as the best and most efficient ML algorithms in stroke prediction.

Conclusion

This research has shown a rapid increase in using ML algorithms to predict stroke, with significant improvements in model accuracy in recent years. However, no model has reached 100% accuracy or is entirely error-free. Variations in algorithm efficiency and accuracy stem from differences in sample sizes, datasets, and data types. Further studies should focus on consistent datasets, sample sizes, and data types for more reliable outcomes.

An Integrated Multi-omics prediction model for stroke recurrence based on Lnet transformer layer and dynamic weighting mechanism

BACKGROUND AND OBJECTIVE

Stroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data.

METHODS

We systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics.

RESULTS

We compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model.

CONCLUSION

MPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke

BACKGROUND

Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence.

METHODS

We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models.

RESULTS

Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74-0.77), 0.60 (95% CI, 0.58-0.61), and 0.71 (95% CI, 0.69-0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72-0.75), 0.59 (95% CI, 0.57-0.61), and 0.67 (95% CI, 0.66-0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (P<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors.

CONCLUSIONS

PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.

Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking

Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis

OBJECTIVE

This study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China.

METHODS

Longitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix.

RESULTS

Two physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group.

LIMITATION

This study didn't carry out external validation.

CONCLUSIONS

This study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.

Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Predicting mild cognitive impairment in older adults: A machine learning analysis of the Alzheimer's Disease Neuroimaging Initiative

AIM

Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI-related markers could predict the onset of MCI early.

METHODS

We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3 years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI-related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model.

RESULTS

The baseline age of the study sample was 74.8 years old. The best-performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI.

CONCLUSIONS

With the help of machine learning, fewer neuropsychological tests and MRI-related markers are required to accurately predict MCI within 3 years, thereby facilitating targeted intervention. Geriatr Gerontol Int 2024; 24: 96-101.

Declining hip fracture burden in Sweden 1998-2019 and consequences for projections through 2050

We aimed to estimate the absolute and age-standardized number of hip fractures in Sweden during the past two decades to produce time trends and future projections. We used nationwide register data from 1998 to 2019 and a validated algorithm to calculate the annual absolute and age-standardized number of incident hip fractures over time. The total hip fracture burden was 335,399 incident events over the 22 years, with a change from 16,180 in 1998 to 13,929 in 2019, a 14% decrease. One decade after the index hip fracture event, 80% of the patients had died, and 11% had a new hip fracture. After considering the steady growth of the older population, the decline in the age-standardized number of hip fractures from 1998 through 2019 was 29.2% (95% CI 28.1-30.2%) in women and 29.3% (95% CI 27.5-30.7%) in men. With a continued similar reduction in hip fracture incidence, we can predict that 14,800 hip fractures will occur in 2034 and 12,000 in 2050 despite doubling the oldest old (≥ 80 years). Without an algorithm, a naïve estimate of the total number of hip fractures over the study period was 539,947, with a second 10-year hip fracture risk of 35%. We note an ongoing decline in the absolute and age-standardized actual number of hip fractures in Sweden, with consequences for future projections.

A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy

BACKGROUND AND AIMS

Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach or an empty stomach. And previous studies have shown that preoperative oral carbohydrates (POCs) can improve the discomfort induced by fasting, but there are different perspectives on their safety. This study aimed to develop a convenient, accurate machine learning (ML) model to predict full stomach. And based on the model outcomes, evaluate the safety and comfort improvements of POCs in empty- and full stomach groups.

METHODS

We enrolled 1386 painless GIE patients between October 2022 and January 2023 in Nanjing First Hospital, and 1090 patients without POCs were used to construct five different ML models to identify full stomach. The metrics of discrimination and calibration validated the robustness of the models. For the best-performance model, we further interpreted it through SHapley Additive exPlanations (SHAP) and constructed a web calculator to facilitate clinical use. We evaluated the safety and comfort improvements of POCs by propensity score matching (PSM) in the two groups, respectively.

RESULTS

Random Forest (RF) model showed the greatest discrimination with the area under the receiver operating characteristic curve (AUROC) 0.837 [95% confidence interval (CI): 79.1-88.2], F1 71.5%, and best calibration with a Brier score of 15.2%. The web calculator can be visited at https://medication.shinyapps.io/RF_model/. PSM results demonstrated that POCs significantly reduced the full stomach incident in empty stomach group (p < 0.05), but no differences in full stomach group (p > 0.05). Comfort improved in both groups and was more significant in empty stomach group.

CONCLUSIONS

The developed convenient RF model predicted full stomach with high accuracy and interpretability. POCs were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may guide the patients' gastrointestinal preparation.

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable.

OBJECTIVE

We aimed to assess the value of applying machine learning in predicting the time of stroke onset.

METHODS

PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team).

RESULTS

Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86).

CONCLUSIONS

Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds.

TRIAL REGISTRATION

PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898.

Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy

Background and purpose

Corpus callosum (CC) infarction is an extremely rare subtype of cerebral ischemic stroke, however, the symptoms of cognitive impairment often fail to attract early attention of patients, which seriously affects the long-term prognosis, such as high mortality, personality changes, mood disorders, psychotic reactions, financial burden and so on. This study seeks to develop and validate models for early predicting the risk of subjective cognitive decline (SCD) after CC infarction by machine learning (ML) algorithms.

Methods

This is a prospective study that enrolled 213 (only 3.7%) CC infarction patients from a nine-year cohort comprising 8,555 patients with acute ischemic stroke. Telephone follow-up surveys were carried out for the patients with definite diagnosis of CC infarction one-year after disease onset, and SCD was identified by Behavioral Risk Factor Surveillance System (BRFSS) questionnaire. Based on the significant features selected by the least absolute shrinkage and selection operator (LASSO), seven ML models including Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naïve Bayes (GNB), Complement Naïve Bayes (CNB), and Support vector machine (SVM) were established and their predictive performances were compared by different metrics. Importantly, the SHapley Additive exPlanations (SHAP) was also utilized to examine internal behavior of the highest-performance ML classifier.

Results

The Logistic Regression (LR)-model performed better than other six ML-models in SCD predictability after the CC infarction, with the area under the receiver characteristic operator curve (AUC) of 77.1% in the validation set. Using LASSO and SHAP analysis, we found that infarction subregions of CC infarction, female, 3-month modified Rankin Scale (mRS) score, age, homocysteine, location of angiostenosis, neutrophil to lymphocyte ratio, pure CC infarction, and number of angiostenosis were the top-nine significant predictors in the order of importance for the output of LR-model. Meanwhile, we identified that infarction subregion of CC, female, 3-month mRS score and pure CC infarction were the factors which independently associated with the cognitive outcome.

Conclusion

Our study firstly demonstrated that the LR-model with 9 common variables has the best-performance to predict the risk of post-stroke SCD due to CC infarcton. Particularly, the combination of LR-model and SHAP-explainer could aid in achieving personalized risk prediction and be served as a decision-making tool for early intervention since its poor long-term outcome.

A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes

The burden of diabetic retinopathy (DR) is increasing, and the sensitive biomarkers of the disease were not enough. Studies have found that the metabolic profile, such as amino acid (AA) and acylcarnitine (AcylCN), in the early stages of DR patients might have changed, indicating the potential of metabolites to become new biomarkers. We are amid to construct a metabolite-based prediction model for DR risk. This study was conducted on type 2 diabetes (T2D) patients with or without DR. Logistic regression and extreme gradient boosting (XGBoost) prediction models were constructed using the traditional clinical features and the screening features, respectively. Assessing the predictive power of the models in terms of both discrimination and calibration, the optimal model was interpreted using the Shapley Additive exPlanations (SHAP) to quantify the effect of features on prediction. Finally, the XGBoost model incorporating AA and AcylCN variables had the best comprehensive evaluation (ROCAUC = 0.82, PRAUC = 0.44, Brier score = 0.09). C18 : 1OH lower than 0.04 μmol/L, C18 : 1 lower than 0.70 μmol/L, threonine higher than 27.0 μmol/L, and tyrosine lower than 36.0 μmol/L were associated with an increased risk of developing DR. Phenylalanine higher than 52.0 μmol/L was associated with a decreased risk of developing DR. In conclusion, our study mainly used AAs and AcylCNs to construct an interpretable XGBoost model to predict the risk of developing DR in T2D patients which is beneficial in identifying high-risk groups and preventing or delaying the onset of DR. In addition, our study proposed possible risk cut-off values for DR of C18 : 1OH, C18 : 1, threonine, tyrosine, and phenylalanine.

Association between red blood cell distribution width and ischemic stroke recurrence in patients with acute ischemic stroke: a 10-years retrospective cohort analysis

Numerous studies have reported that a higher red blood cell distribution width (RDW) level was associated with adverse outcomes in patients with the first stroke. However, no studies have examined the association between RDW and recurrent ischemic stroke. We performed a population-based cohort data analysis from 2007 to 2017. Baseline RDW was measured in 6402 first ischemic stroke participants, who were followed for about five years on average. During 62 months of median follow-up, 205 participants (3.20%) reported a recurrence (self-reported). RDW showed a nonlinear relationship with the risk of ischemic stroke recurrence. When RDW was assessed as quartiles (quartile 1, RDW<12.4; quartile 2, 12.4 to 12.8; quartile 3,12.8 to 13.3, quartile4, RDW>13.3), compared with the reference group (quartile 1), the hazard ratios (HRs) of ischemic stroke recurrence were 1.372 (95% confidence interval [CI]=0.671-2.805, P=0.386) in quartile 2, 1.835 (95% CI=1.222-2.755, P=0.003) in quartile 3, and 1.732 (95% CI=1.114-2.561, P<0.001) in quartile 4. The trend test was significant (P<0.001). When quartiles 3 and 4 were combined, the adjusted HR of ischemic stroke recurrence was 1.439 (95% CI=1.330-1.556, P<0.001) compared with the combined quartiles 1 and 2 subgroups. This study demonstrated that elevated RDW levels were positively associated with an increased risk of recurrent ischemic stroke. RDW can provide a new perspective for initial risk assessment and identify high-risk patients early. Further research is required to confirm our results.