Dynamic prediction model and risk assessment chart for cardiovascular disease based on on-treatment blood pressure and baseline risk factors

The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis

BACKGROUND

Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis of smoking status ascertainment algorithms to describe the characteristics and validity of these algorithms.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We searched articles published from 1990 to 2022 in EMBASE, MEDLINE, Scopus, and Web of Science with key terms such as validity, administrative data, electronic health records, smoking, and tobacco use. The extracted information, including article characteristics, algorithm characteristics, and validity measures, was descriptively analyzed. Sources of heterogeneity in validity measures were estimated using a meta-regression model. Risk of bias (ROB) in the reviewed articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

The initial search yielded 2086 articles; 57 were selected for review and 116 algorithms were identified. Almost three-quarters (71.6%) of algorithms were based on EMR data. The algorithms were primarily constructed using diagnosis codes for smoking-related conditions, although prescription medication codes for smoking treatments were also adopted. About half of the algorithms were developed using machine-learning models. The pooled estimates of positive predictive value, sensitivity, and specificity were 0.843, 0.672, and 0.918 respectively. Algorithm sensitivity and specificity were highly variable and ranged from 3 to 100% and 36 to 100%, respectively. Model-based algorithms had significantly greater sensitivity (p = 0.006) than rule-based algorithms. Algorithms for EMR data had higher sensitivity than algorithms for administrative data (p = 0.001). The ROB was low in most of the articles (76.3%) that underwent the assessment.

CONCLUSIONS

Multiple algorithms using different data sources and methods have been proposed to ascertain smoking status in electronic health data. Many algorithms had low sensitivity and positive predictive value, but the data source influenced their validity. Algorithms based on machine-learning models for multiple linked data sources have improved validity.

The Role of HMG COA Reductase Inhibitors on the Progression of Coronary Artery Disease: Focus on Prediction Model

Currently, an integrated site-specific and patient-specific comprehensive predictive model of plaque progression in various CVD is not available. In this study, we considered medical records of 256 patients obtained within the EU H2020 SMARTool project which is carefully designed to collect the features from various domains relevant for disease which are used in everyday clinical practice. The database contains detailed information of patients with suspected CAD disease regarding the clinical status, risk factors, routine blood analyses, CAD morphology and progression and current therapy. Results showed that there was statistically significant difference of values of this parameter for the SMARTool patients with and without disease progression, measured at the follow-up, F(1,250)=33.39, p < 0.001, while the CAD Score in the follow-up is significantly different from the score measured at the initial time point, F(1,254)=76.244, p < 0.001. The significant interaction of statins is achieved with aspirin F(1,252)= 3.921, p=0.049, while interactions with other medicaments are insignificant for CAD Score. The results showed that there was no significant interaction of statins and dyslipidemia, F(1,251)=0.877, p = 0.350. Also, there was no significant interaction of statins and hypertension, F(1,245)=0.283, p=0.596. The CAD score in the baseline was significantly different among patients who were further prescribed with therapy than those who were not, and this trend remained unchanged after a given time period, i.e. those patients who were at risk had progression in addition to statins, but the combination of statins and aspirin was shown as effective in decreasing the CAD Score. The Random Forest classifier applied on 24 selected features is the most reliable among all tested ML algorithms for the prediction of CAD progress.

Dynamic prediction for clinically relevant pancreatic fistula: a novel prediction model for laparoscopic pancreaticoduodenectomy

Background

With the recent emerge of dynamic prediction model on the use of diabetes, cardiovascular diseases and renal failure, and its advantage of providing timely predicted results according to the fluctuation of the condition of the patients, we aim to develop a dynamic prediction model with its corresponding risk assessment chart for clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by combining baseline factors and postoperative time-relevant drainage fluid amylase level and C-reactive protein-to-albumin ratio.

Methods

We collected data of 251 patients undergoing LPD at West China Hospital of Sichuan University from January 2016 to April 2019. We extracted preoperative and intraoperative baseline factors and time-window of postoperative drainage fluid amylase and C-reactive protein-to-albumin ratio relevant to clinically relevant pancreatic fistula by performing univariate and multivariate analyses, developing a time-relevant logistic model with the evaluation of its discrimination ability. We also established a risk assessment chart in each time-point.

Results

The proportion of the patients who developed clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy was 7.6% (19/251); preoperative albumin and creatine levels, as well as drainage fluid amylase and C-reactive protein-to-albumin ratio on postoperative days 2, 3, and 5, were the independent risk factors for clinically relevant postoperative pancreatic fistula. The cut-off points of the prediction value of each time-relevant logistic model were 14.0% (sensitivity: 81.9%, specificity: 86.5%), 8.3% (sensitivity: 85.7%, specificity: 79.1%), and 7.4% (sensitivity: 76.9%, specificity: 85.9%) on postoperative days 2, 3, and 5, respectively, the area under the receiver operating characteristic curve was 0.866 (95% CI 0.737–0.996), 0.896 (95% CI 0.814–0.978), and 0.888 (95% CI 0.806–0.971), respectively.

Conclusions

The dynamic prediction model for clinically relevant postoperative pancreatic fistula has a good to very good discriminative ability and predictive accuracy. Patients whose predictive values were above 14.0%, 8.3%, and 7.5% on postoperative days 2, 3, and 5 would be very likely to develop clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data

Landmark model (LM) is a dynamic prediction model that uses a longitudinal biomarker in time-to-event data to make prognosis prediction. This study was designed to improve this model and to apply it to assess the cardiovascular risk in on-treatment blood pressure patients. A frailty parameter was used in LM, landmark frailty model (LFM), to account the frailty of the patients and measure the correlation between different landmarks. The proposed model was compared with LM in different scenarios respecting data missing status, sample size (100, 200, and 400), landmarks (6, 12, 24, and 48), and failure percentage (30, 50, and 100%). Bias of parameter estimation and mean square error as well as deviance statistic between models were compared. Additionally, discrimination and calibration capability as the goodness of fit of the model were evaluated using dynamic concordance index (DCI), dynamic prediction error (DPE), and dynamic relative prediction error (DRPE). The proposed model was performed on blood pressure data, obtained from systolic blood pressure intervention trial (SPRINT), in order to calculate the cardiovascular risk. Dynpred, coxme, and coxphw packages in the R.3.4.3 software were used. It was proved that our proposed model, LFM, had a better performance than LM. Parameter estimation in LFM was closer to true values in comparison to that in LM. Deviance statistic showed that there was a statistically significant difference between the two models. In the landmark numbers 6, 12, and 24, the LFM had a higher DCI over time and the three landmarks showed better performance in discrimination. Both DPE and DRPE in LFM were lower in comparison to those in LM over time. It was indicated that LFM had better calibration in comparison to its peer. Moreover, real data showed that the structure of prognostic process was predicted better in LFM than in LM. Accordingly, it is recommended to use the LFM model for assessing cardiovascular risk due to its better performance.

Prediction models for cardiovascular disease risk in the hypertensive population: a systematic review

OBJECTIVE

The aim of this study was to identify, describe, and evaluate the available cardiovascular disease risk prediction models developed or validated in the hypertensive population.

METHODS

MEDLINE and the Web of Science were searched from database inception to March 2019, and all reference lists of included articles were reviewed.

RESULTS

A total of 4766 references were screened, of which 18 articles were included in the review, presenting 17 prediction models specifically developed for hypertensive populations and 25 external validations. Among the 17 prediction models, most were constructed based on randomized trials in Europe or North America to predict the risk of fatal or nonfatal cardiovascular events. The most common predictors were classic cardiovascular risk factors such as age, diabetes, sex, smoking, and SBP. Of the 17 models, only one model was externally validated. Among the 25 external validations, C-statistics ranged from 0.58 to 0.83, 0.56 to 0.75, and 0.64 to 0.78 for models developed in the hypertensive population, the general population and other specific populations, respectively. Most of the development studies and validation studies had an overall high risk of bias according to PROBAST.

CONCLUSION

There are a certain number of cardiovascular risk prediction models in patients with hypertension. The risk of bias assessment showed several shortcomings in the methodological quality and reporting in both the development and validation studies. Most models developed in the hypertensive population have not been externally validated. Compared with models developed for the general population and other specific populations, models developed for the hypertensive population do not display a better performance when validated among patients with hypertension. Research is needed to validate and improve the existing cardiovascular disease risk prediction models in hypertensive populations rather than developing completely new models.

Insights into Systemic Disease through Retinal Imaging-Based Oculomics

Among the most noteworthy developments in ophthalmology over the last decade has been the emergence of quantifiable high-resolution imaging modalities, which are typically non-invasive, rapid and widely available. Such imaging is of unquestionable utility in the assessment of ocular disease however evidence is also mounting for its role in identifying ocular biomarkers of systemic disease, which we term oculomics. In this review, we highlight our current understanding of how retinal morphology evolves in two leading causes of global morbidity and mortality, cardiovascular disease and dementia. Population-based analyses have demonstrated the predictive value of retinal microvascular indices, as measured through fundus photography, in screening for heart attack and stroke. Similarly, the association between the structure of the neurosensory retina and prevalent neurodegenerative disease, in particular Alzheimer's disease, is now well-established. Given the growing size and complexity of emerging multimodal datasets, modern artificial intelligence techniques, such as deep learning, may provide the optimal opportunity to further characterize these associations, enhance our understanding of eye-body relationships and secure novel scalable approaches to the risk stratification of chronic complex disorders of ageing.

Sex differences and the prognosis of depressive and nondepressive patients with cardiovascular risk factors: the Japan Morning Surge-Home Blood Pressure (J-HOP) study

Depression is associated with mortality in patients with cardiovascular risk factors. The frequency and severity of depression and the association between depression and cardiovascular events have sex-specific and ethnic differences. We conducted this study to evaluate the sex-specific difference in the association between depression and cardiovascular prognosis in patients with cardiovascular risk factors. We enrolled 4025 patients (64.7 ± 10.9 years, 53% women, 47% men) with cardiovascular risk factors in the Japan Morning Surge-Home Blood Pressure study. Depressive symptoms were assessed using the Beck Depression Inventory (BDI). The follow-up period was 47 ± 24 months. The primary end points were all-cause mortality and nonfatal cardiovascular events. The BDI scores and the prevalence of depression were significantly higher in women than in men. When a BDI score of 16 was the cutoff, the primary end points in the depression group (n = 217) were significantly higher than those in the nondepression group (n = 1677) among men (adjusted hazard ratio 1.76, 95% confidence interval: 1.17, 2.64; P = 0.007). In women, the primary end points in the depression and nondepression groups were similar when BDI scores of 16, 14, and 10 were the cutoffs. In conclusion, depression defined by a BDI score ≥16 was associated with cardiovascular events in men with cardiovascular risk factors.

A genetic variant in the catechol-O-methyl transferase (COMT) gene is related to age-dependent differences in the therapeutic effect of calcium-channel blockers

Hypertension is the leading risk factor for cardiovascular disease and one of the major health concerns worldwide. Genetic factors impact both the risk for hypertension and the therapeutic effect of antihypertensive drugs. Sex- and age-specific variances in the prevalence of hypertension are partly induced by estrogen. We investigated 6 single nucleotide polymorphisms in genes encoding enzymes involved in estrogen metabolism in relation to sex- and age-specific differences in the systolic and diastolic blood pressure (SBP and DBP) outcome under the treatment of diuretics, calcium-channel blockers (CCBs), angiotensin-converting-enzyme inhibitors, and angiotensin-receptor blockers (ARBs).We included 5064 subjects (age: 40-82) from the population-based CoLaus cohort. Participants were genotyped for the catechol-O-methyltransferase gene (COMT) variants rs4680, rs737865, and rs165599; the uridine-diphospho-glucuronosyltransferase 1A gene family (UGT1A) variants rs2070959 and rs887829; and the aromatase gene (CYP19A1) variant rs10046. Binomial and linear regression analyses were performed correcting for age, sex, body mass index, smoking, diabetes, and antihypertensive therapy to test whether the variants in focus are significantly associated with BP.All investigated COMT variants were strongly associated with the effect of diuretics, CCBs, and ARBs on SBP or DBP (P < .05), showing an additive effect when occurring in combination. After Bonferroni correction the polymorphism rs4680 (ValMet) in COMT was significantly associated with lower SBP in participants treated with CCBs (P = .009) with an especially strong impact in elderly individuals (age ≥ 70) alone (Δ = -14.08 mm Hg, P = .0005).These results underline the important role of estrogens and catecholamines in hypertension and the importance of genotype dependent, age-related adjustments of calcium-channel blocker treatment.

Atherosclerotic Cardiovascular Risk in Japan

Increased global mortality is associated with atherosclerosis, which appears to be independent of race. Cardiovascular disease is one of the leading causes of mortality and morbidity in Japan. Atherosclerosis, an inflammatory disease characterized by abnormal lipid accumulation and inflammation in the arterial wall, is the main underlying cause of cardiovascular disease. Numerous cardiovascular risk scores have been developed and are used to prioritize patients' treatment needs. The predictive performance of risk scores established in Western nations needs to be examined in Japanese populations. For secondary prevention, it is imperative to control hypertension, hyperlipidemia, diabetes mellitus, smoking, and local interventions. In this review, we present a historical overview of atherosclerotic risk research and the risk factors for atherosclerosis in Japan and compare the situation in Japan with that in Western nations. In addition, we discuss relevant cardiovascular risk assessment tools in the context of clinical practice in Japan.

Prediction models for cardiovascular disease risk in the general population: systematic review

OBJECTIVE

 To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population.

DESIGN

 Systematic review.

DATA SOURCES

 Medline and Embase until June 2013.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

 Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population.

RESULTS

 9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively.

CONCLUSIONS

 There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.