Predictions for functional outcome and mortality in acute ischaemic stroke following successful endovascular thrombectomy.

Background: Accurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and care. Our study developed prediction models for key clinical outcomes in patients with successful reperfusion following EVT in an Australian population. Methods: The study included all patients who had ischaemic stroke with occlusion in the proximal anterior cerebral circulation and successful reperfusion post-EVT over a 7-year period. Multivariable logistic regression and Cox regression models, incorporating bootstrap and multiple imputation techniques, were used to identify predictors and develop models for key clinical outcomes: 3-month poor functional status; 30-day, 1-year and 3-year mortality; survival time. Results: A total of 978 patients were included in the analyses. Predictors associated with one or more poor outcomes include: older age (ORs for every 5-year increase: 1.22-1.40), higher premorbid functional modified Rankin Scale (ORs: 1.31-1.75), higher baseline National Institutes of Health Stroke Scale (ORs: 1.05-1.07) score, higher blood glucose (ORs: 1.08-1.19), larger core volume (ORs for every 10 mL increase: 1.10-1.22), pre-EVT thrombolytic therapy (ORs: 0.44-0.56), history of heart failure (outcome: 30-day mortality, OR=1.87), interhospital transfer (ORs: 1.42 to 1.53), non-rural/regional stroke onset (outcome: functional dependency, OR=0.64), longer onset-to-groin puncture time (outcome: 3-year mortality, OR=1.08) and atherosclerosis-caused stroke (outcome: functional dependency, OR=1.68). The models using these predictors demonstrated moderate predictive abilities (area under the receiver operating characteristic curve range: 0.752-0.796). Conclusion: Our models using real-world predictors assessed at hospital admission showed satisfactory performance in predicting poor functional outcomes and short-term and long-term mortality for patients with successful reperfusion following EVT. These can be used to inform EVT treatment provision and consent.

Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores.

OBJECTIVE: In this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years. METHODS: A cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors. RESULTS: The discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment. CONCLUSION: In our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment. Key Points • Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data. • Patient reported outcomes were more consistent predictors of function than traditional prognostic markers. • Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.

Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.

BACKGROUND: Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps. METHODS: We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18-90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV1). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I2>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052). FINDINGS: We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69-0·85]) and there was significant heterogeneity (I2 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74-0·80]) with significant degrees of heterogeneity (I2 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias. INTERPRETATION: There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility. FUNDING: None.

Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint.

Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has been the assessment of plain radiographs with scoring techniques that quantify joint damage. However, with significant improvements in therapy, current radiographic scoring systems may no longer be fit for purpose for the milder spectrum of disease seen today. We argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease. Current work in the area demonstrates the feasibility of automating scoring but is yet to take full advantage of the strengths of artificial intelligence. By fully leveraging the power of artificial intelligence, faster and more sensitive scoring could enable the ongoing development of effective treatments for patients with rheumatoid arthritis.

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.

Introduction: Machine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps. Methods: Literature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance. Results: Sixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77-0.85, AUC range: 0.68-0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70-0.81, AUC range: 0.71-0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56-0.88, AUC range: 0.55-0.88) and one DL model (AUC=0.65, 95% CI: 0.62-0.68). Conclusions: Conventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524.

Human cytomegalovirus infection is associated with stroke in women: the US National Health and Nutrition Examination Survey 1999-2004.

BACKGROUND: Increasing evidence indicated that infection factors play important roles in stroke development. Human cytomegalovirus (HCMV) infection was positively associated with atherosclerosis and hypertension which are stroke risk factors. Therefore, we aimed to explore the relationship between HCMV infection and stroke using the data of US National Health and Nutrition Examination Survey (NHANES). METHODS: We analysed data on 2844 men and 3257 women in the NHANES 1999-2004. We included participants aged 20-49 years who had valid data on HCMV infection and stroke. RESULTS: 54.1% of participants had serological evidence of HCMV infection and 0.8% of them had a previous diagnosis of stroke. There were ethnic differences in the prevalence of HCMV seropositivity (p<0.001). There was no significant association between HCMV seropositivity and stroke in men in any of the models. In women, HCMV seropositivity was associated with stroke before adjustment (OR=3.45, 95% CI 1.09 to 10.95, p=0.036). After adjusting for race/ethnicity, the association remained significant (OR=4.40, 95% CI 1.37 to 14.09, p=0.014). After further adjustment for body mass index, diabetes, hypercholesterolaemia, hypertension, alcohol consumption, smoking and physical activity, the association still existed (OR=3.58, 95% CI 1.14 to 11.25, p=0.030). The association was significant consistently in adjusted model for age (OR=3.39, 95% CI 1.08 to 10.64, p=0.037). CONCLUSIONS: We found a strong association between HCMV and stroke in women from the nationally representative population-based survey. This provide additional motivation for undertaking the difficult challenge to reduce the prevalence of stroke.

Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data.

The coronavirus disease 2019 (COVID-19) emerged by end of 2019, and became a serious public health threat globally in less than half a year. The generation interval and latent period, though both are of importance in understanding the features of COVID-19 transmission, are difficult to observe, and thus they can rarely be learnt from surveillance data empirically. In this study, we develop a likelihood framework to estimate the generation interval and incubation period simultaneously by using the contact tracing data of COVID-19 cases, and infer the pre-symptomatic transmission proportion and latent period thereafter. We estimate the mean of incubation period at 6.8 days (95 %CI: 6.2, 7.5) and SD at 4.1 days (95 %CI: 3.7, 4.8), and the mean of generation interval at 6.7 days (95 %CI: 5.4, 7.6) and SD at 1.8 days (95 %CI: 0.3, 3.8). The basic reproduction number is estimated ranging from 1.9 to 3.6, and there are 49.8 % (95 %CI: 33.3, 71.5) of the secondary COVID-19 infections likely due to pre-symptomatic transmission. Using the best estimates of model parameters, we further infer the mean latent period at 3.3 days (95 %CI: 0.2, 7.9). Our findings highlight the importance of both isolation for symptomatic cases, and for the pre-symptomatic and asymptomatic cases.

The Role of DNA Methylation in Ischemic Stroke: A Systematic Review.

Background: Knowledge about the classic risk and protective factors of ischemic stroke is accumulating, but the underlying pathogenesis has not yet been fully understood. As emerging evidence indicates that DNA methylation plays a role in the pathological process of cerebral ischemia, this study aims to summarize the evidence of the association between DNA methylation and ischemic stroke. Methods: MEDLINE, EMBASE, PubMed, and Cochrane Central Register of Controlled Trials were searched for eligible studies. The results reported by each study were summarized narratively. Results: A total of 20 studies with 7,014 individuals finally met the inclusion criteria. Three studies focused on global methylation, 11 studies on candidate-gene methylation, and six on epigenome-wide methylation analysis. Long-interspersed nuclear element 1 was found to be hypomethylated in stroke cases in two studies. Another 16 studies reported 37 genes that were differentially methylated between stroke cases and controls. Individuals with ischemic stroke were also reported to have higher acceleration in Hanuum 's epigenetic age compared to controls. Conclusion: DNA methylation might be associated with ischemic stroke and play a role in several pathological pathways. It is potentially a promising biomarker for stroke prevention, diagnosis and treatment, but the current evidence is limited by sample size and cross-sectional or retrospective design. Therefore, studies on large asymptomatic populations with the prospective design are needed to validate the current evidence, explore new pathways and identify novel risk/protective loci.

Epidemiological Parameters of COVID-19: Case Series Study.

BACKGROUND: The estimates of several key epidemiological parameters of the COVID-19 pandemic are often based on small sample sizes or are inaccurate for various reasons. OBJECTIVE: The aim of this study is to obtain more robust estimates of the incubation period, serial interval, frequency of presymptomatic transmission, and basic reproduction number (R0) of COVID-19 based on a large case series. METHODS: We systematically retrieved and screened 20,658 reports of laboratory-confirmed COVID-19 cases released by the health authorities of China, Japan, and Singapore. In addition, 9942 publications were retrieved from PubMed and China National Knowledge Infrastructure (CNKI) through April 8, 2020. To be eligible, a report had to contain individual data that allowed for accurate estimation of at least one parameter. Widely used models such as gamma distributions were fitted to the data sets and the results with the best-fitting values were presented. RESULTS: In total, 1591 cases were included for the final analysis. The mean incubation period (n=687) and mean serial interval (n=1015 pairs) were estimated to be 7.04 (SD 4.27) days and 6.49 (SD 4.90) days, respectively. In 40 cases (5.82%), the incubation period was longer than 14 days. In 32 infector-infectee pairs (3.15%), infectees' symptom onsets occurred before those of infectors. Presymptomatic transmission occurred in 129 of 296 infector-infectee pairs (43.58%). R0 was estimated to be 1.85 (95% CI 1.37-2.60). CONCLUSIONS: This study provides robust estimates of several epidemiological parameters of COVID-19. The findings support the current practice of 14-day quarantine of persons with potential exposure, but also suggest the need for additional measures. Presymptomatic transmission together with the asymptomatic transmission reported by previous studies highlight the importance of adequate testing, strict quarantine, and social distancing.

Time-dependence of NIHSS in predicting functional outcome of patients with acute ischemic stroke treated with intravenous thrombolysis.

OBJECTIVES: The National Institute of Health Stroke Scale (NIHSS) is a predictor for the prognosis of acute ischaemic stroke (AIS) and its prediction is time-dependent. We examined the performance of NIHSS at different timepoints in predicting functional outcome of patients with thrombolysed AIS. METHODS: This prospective study included 269 patients with AIS treated with recombinant tissue plasminogen activator (rt-PA). Unfavourable functional outcome was defined as modified Rankin Scale score 4-6 at 3 months after rt-PA treatment. Receiver operating characteristic curves were used to examine the predictive power of NIHSS score at admission and 2 hours/24 hours/7 days/10 days after rt-PA treatment. Youden's index was used to select the threshold of NIHSS score. Logistic regression was used to estimate the ORs of unfavourable functional outcome for patients with NIHSS score higher than the selected thresholds. RESULTS: The threshold of NIHSS score at admission was 12 (sensitivity: 0.51, specificity: 0.84) with an acceptable predictive power (area under curve [AUC] 0.74) for unfavourable functional outcome. The threshold changed to 5 at 24 hours after rt-PA treatment (sensitivity: 0.83, specificity: 0.65) and remained unchanged afterwards. The predictive power and sensitivity sequentially increased over time and peaked at 10 days after rt-PA treatment (AUC 0.92, sensitivity: 0.85, specificity: 0.80). NIHSS scores higher than the thresholds were associated with elevated risk of unfavourable functional outcome at all timepoints (all p<0.001). CONCLUSIONS: NIHSS is time-dependent in predicting AIS prognosis with increasing predictive power over time. Since patients whose NIHSS score ≥ 12 are likely to have unfavourable functional outcome with rt-PA treatment only, mechanical thrombectomy should be largely taken into consideration for these patients.

Alpha-synuclein alterations in red blood cells of peripheral blood after acute ischemic stroke.

Post-stroke induction of alpha-synuclein (AS), a neuronal protein implicated in the pathogenesis of Parkinson's disease (PD), has been demonstrated to induce secondary brain damage after cerebral ischemia. Therefore, understanding the expression and pathogenic modifications of AS is clinically meaningful for evaluating the prognosis of stroke. Here, 54 patients with acute ischemic stroke (AIS) and 55 controls were enrolled. Different forms of AS in red blood cells (RBCs), including hemoglobin-bound AS (Hb-AS), oligomeric AS (O-AS), and serine 129-phosphorylated AS (pS-AS), were measured using ELISA methods. Compared with controls, significantly increased levels of Hb-AS, O-AS, and pS-AS were observed in AIS patients. The levels of O-AS and pS-AS were both positively correlated with that of Hb-AS. However, no correlation was observed between O-AS and pS-AS. The levels of all three forms of AS were associated with increased risk of AIS diagnosis. Receiver operating characteristic (ROC) curves revealed that the three forms of AS yielded a moderate discriminative power (AUC ranging from 0.67 to 0.71 in discriminating AIS patients from controls, with varying sensitivity (0.41~0.61), specificity (0.78~0.90), PPV (0.73~0.81), and NPV (0.61~0.68)). These findings suggest that RBC AS can be a potential biomarker for evaluating AS changes in the brain of AIS patients.

Predictive role of skin rash in advanced pancreatic cancer patients treated with gemcitabine plus erlotinib: a systematic review and meta-analysis.

PURPOSE: The survival benefit from gemcitabine plus erlotinib was on average marginal for advanced pancreatic cancer (APC) patients. Skin rash developed shortly after starting treatment seemed to be associated with better efficacy and might be used to assist clinical decision-making, but the results across studies were inconsistent. Thus, we conducted a systematic review and meta-analysis. METHODS: PubMed, Embase, Cochrane Central Register of Controlled Trials, three Chinese databases, and the abstracts of important conferences were searched for eligible studies. The primary outcome was overall survival (OS), and the secondary outcomes were progression-free survival (PFS) and objective response. The random-effects model was used to pool results across studies if heterogeneity was substantial. Otherwise, the fixed-effect model was used. RESULTS: A total of 16 studies with 1,776 patients were included. Patients who developed skin rash during treatment had longer OS (8.9 vs 4.9 months, HR=0.57, 95% CI 0.50-0.64) and longer PFS (4.5 vs 2.4 months, HR=0.53, 95% CI 0.40-0.68) than those who did not. A dose- response relationship was also observed for both OS (HR=0.64 for grade-1 rash vs no rash and HR=0.46 for ≥grade-2 rash vs no rash) and PFS (HR=0.72 for grade-1 rash vs no rash and HR=0.43 for ≥grade-2 rash vs no rash). CONCLUSION: Skin rash was associated with better OS and PFS in APC patients treated with gemcitabine plus erlotinib. It might be used as a marker for efficacy to guide clinical decision-making toward a more precise and personalized treatment.