Clinical Risk Factors For Prediction Research Articles

Longitudinal aortic strain, ventriculo-arterial coupling and fatty acid oxidation: novel insights into human cardiovascular aging.

Aging-induced aortic stiffness has been associated with altered fatty acid metabolism. We studied aortic stiffness using cardiac magnetic resonance (CMR)-assessed ventriculo-arterial coupling (VAC) and novel aortic (AO) global longitudinal strain (GLS) combined with targeted metabolomic profiling. Among community older adults without cardiovascular disease, VAC was calculated as aortic pulse wave velocity (PWV), a marker of arterial stiffness, divided by left ventricular (LV) GLS. AOGLS was the maximum absolute strain measured by tracking the phasic distance between brachiocephalic artery origin and aortic annulus. In 194 subjects (71 ± 8.6 years; 88 women), AOGLS (mean 5.6 ± 2.1%) was associated with PWV (R = -0.3644, p < 0.0001), LVGLS (R = 0.2756, p = 0.0001) and VAC (R = -0.3742, p <0.0001). Stiff aorta denoted by low AOGLS <4.26% (25th percentile) was associated with age (OR 1.13, 95% CI 1.04-1.24, p = 0.007), body mass index (OR 1.12, 95% CI 1.01-1.25, p = 0.03), heart rate (OR 1.04, 95% CI 1.01-1.06, p = 0.011) and metabolites of medium-chain fatty acid oxidation: C8 (OR 1.005, p = 0.026), C10 (OR 1.003, p = 0.036), C12 (OR 1.013, p = 0.028), C12:2-OH/C10:2-DC (OR 1.084, p = 0.032) and C16-OH (OR 0.82, p = 0.006). VAC was associated with changes in long-chain hydroxyl and dicarboxyl carnitines. Multivariable models that included acyl-carnitine metabolites, but not amino acids, significantly increased the discrimination over clinical risk factors for prediction of AOGLS (AUC [area-under-curve] 0.73 to 0.81, p = 0.037) and VAC (AUC 0.78 to 0.87, p = 0.0044). Low AO GLS and high VAC were associated with altered medium-chain and long-chain fatty acid oxidation, respectively, which may identify early metabolic perturbations in aging-associated aortic stiffening. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02791139.

A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral Hemorrhage.

To develop and validate a clinical-radiomics nomogram based on clinical risk factors and CT radiomics feature to predict hypertensive intracerebral hemorrhage (HICH) prognosis. A total of 195 patients with HICH treated in our hospital from January 2018 to January 2022 were retrospectively enrolled and randomly divided into two cohorts for training (n = 138) and validation (n = 57) according to the ratio of 7 : 3. All CT radiomics features were extracted from intrahematomal, perihematomal, and combined intra- and perihematomal regions by using free open-source software called 3D slicer. The least absolute shrinkage and selection operator method was used to select the optimal radiomics features, and the radiomics score (Rad-score) was calculated. The relationship between Rad-score, clinical risk factors, and the HICH prognosis was analyzed by univariate and multivariate logistic regression analyses, and the clinical-radiomics nomogram was built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the clinical-radiomics nomogram in predicting the prognosis of HICH. A total of 1702 radiomics features were extracted from the CT images of each patient for analysis. By univariate and stepwise multivariate logistic regression analyses, age, sex, RBC, serum glucose, D-dimer level, hematoma volume, and midline shift were clinical risk factors for the prognosis of HICH. Rad-score and clinical risk factors developed the clinical-radiomics nomogram. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.95, 95% confidence interval (CI), 0.92 to 0.98) and the validation cohort (AUC = 0.90, 95% CI, 0.82 to 0.98). The calibration curve indicated that the clinical-radiomics nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice. The clinical-radiomics nomogram incorporated with the radiomics features and clinical risk factors has good potential in predicting the prognosis of HICH.

Prediction of atrial fibrillation in patients with embolic stroke with undetermined source using electrocardiogram deep learning algorithm and clinical risk factors

Abstract Background Combining the artificial intelligence algorithm with the known clinical risk factors may provide enhanced accuracy for prediction of the hidden atrial fibrillation (AF) in patients with embolic stroke with undetermined source (ESUS). Purpose We aimed to develop enhanced prediction models for AF with deep learning algorithm (DLA) and clinical predictors in patients with ESUS. The DLA was created to identify the patients with paroxysmal AF based on their electrocardiograms (ECG) during sinus rhythm. Methods We analyzed the 221 patients who underwent insertable cardiac monitor (ICM) for AF detection following ESUS. The DLA was previously developed with sinus rhythm ECGs of 10,605 paroxysmal AF patients and 50,522 non-AF patients. The convolutional neural network was used for the DLA. The primary endpoint was defined as any AF episode lasting over 5 min by ICM. The atrial ectopic burden (AEB) was calculated as the percentage of the number of conducted QRS by atrial ectopy on Holter monitoring. Results AF (≥5 min) was detected by ICM in 32 patients (14.5%) during follow-up period of 15.1±8.6 months. AF patients had higher AEB (0.199% vs 0.023%, p&amp;lt;0.001), larger left atrial diameters (LAD, 41.2 mm vs 35.7 mm, p&amp;lt;0.001), and larger left atrial volume index (LAVI, 46.4 ml/m2 vs 32.3 ml/m2, p&amp;lt;0.001) than those without AF. The means of calculated probabilities of AF by DLA were higher in patients with AF than those without AF (63.8% vs 40.2%, p&amp;lt;0.001). In the receiver operating characteristic curve analysis, the areas under the curve (AUC) were the highest in DLA (0.824) followed by AEB (0.784), LAVI (0.780), and LAD (0.768). The multivariable model with AEB, LAVI, and DLA demonstrated excellent prediction accuracy for paroxysmal AF (AUC: 0.902, Figure 1) Conclusions In patients with ESUS, the DLA outperformed other clinical risk factors for prediction of AF. Combining DLA with AEB, LAD and LAVI could is a potential useful tool to predict AF in ESUS patients. Funding Acknowledgement Type of funding sources: None.

Combination of biomarker with clinical risk factors for prediction of severe acute kidney injury in critically ill patients

BackgroundAcute kidney injury (AKI) occurs commonly in the intensive care unit (ICU). Insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinase-2 (TIMP-2), known as [TIMP-2] x [IGFBP7] (NephroCheck), have been identified as novel biomarkers for the prediction of AKI risk. However, the effective use of disease biomarkers is indispensable from an appropriate clinical context. We conducted a retrospective cohort study to find risk factors and assess the performance of the combination of NephroCheck with risk factors, so as to provide feasible information for AKI prediction.MethodsAll patients who were admitted in the ICU (from June 2016 to July 2017) participated in the study. The primary outcome was the detection of severe AKI within the first 7 days after patients being admitted to the ICU. The predictors were separated into three categories: chronic risk factors, acute risk factors and biochemical indicators.ResultsThe study included 577 patients. 96 patients developed to severe AKI (16.6%) within 7 days. In addition to NephroCheck (+) (OR = 2.139, 95% CI (1.260–3.630), P = 0.005), age > 65 years (OR = 1.961, 95% CI (1.153–3.336), P = 0.013), CKD (OR = 2.573, 95% CI (1.319–5.018), P = 0.006) and PCT (+)(OR = 3.223, 95% CI (1.643–6.321), P = 0.001) were also the independent predictors of severe AKI within 7 days. Compared to NephroCheck (+) only (AUC = 0.66, 95% CI:0.60–0.72), the combination of NephroCheck (+) and risk factors (age > 65 years, CKD and PCT positive) (AUC = 0.75, 95% CI:0.70–0.81) led to a significant increase in the area under ROC curve for severe AKI prediction within 7 days.ConclusionsAlthough NephroCheck is an effective screening tool for recognizing high-risk patients, we found that combination with biomarker and risk factors (age > 65 years, CKD, procalcitonin positive) for risk assessment of AKI has the greatest significance to patients with uncertain disease trajectories.

Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation.

BackgroundFractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years.ObjectiveThe goal of this study was to develop and evaluate an algorithm for the identification of patients at risk of fracture in a subsequent 1- to 2-year period. In order to address the aforementioned limitations of current prediction tools, this approach focused on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions.MethodsUsing retrospective electronic health record data from over 1,000,000 patients, we developed Crystal Bone, an algorithm that applies machine learning techniques from natural language processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient’s future trajectory might contain a fracture event, or whether the signature of the patient’s journey is similar to that of a typical future fracture patient. A holdout set with 192,590 patients was used to validate accuracy. Experimental baseline models and human-level performance were used for comparison.ResultsThe model accurately predicted 1- to 2-year fracture risk for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC] 0.81). These algorithms outperformed the experimental baselines (AUROC 0.67) and showed meaningful improvements when compared to retrospective approximation of human-level performance by correctly identifying 9649 of 13,765 (70%) at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians.ConclusionsThese findings indicate that it is possible to use a patient’s unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the health care system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture.

Estimating regional bone mineral density-based T-scores using clinical information; tools validated for postmenopausal women in Sri Lanka

ObjectivesThis study aims to develop and validate a country specific osteoporosis risk assessing tool for Sri Lankan postmenopausal women. MethodsCommunity-dwelling postmenopausal women were enrolled to development (n = 602) and validation (n = 339) samples. Clinical risk factors (CRFs) of osteoporosis were assessed. Bone mineral densities (BMD) of femoral neck, total hip and lumbar spine were assessed by dual energy X-ray absorptiometry (DXA) scan. Radial ultrasound (US) bone scan was done. Linear regression analysis was performed in development sample considering regional BMDs as dependent and CRFs as independent variables. Regression equations were developed to estimate regional BMDs using best predictive CRFs. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) were assessed to validate the new tools. ResultsAge, body weight and US T-scores showed positive correlations with BMDs of all 3 sites. Two osteoporosis risk assessing tools (OPRATs) were developed as OPRAT-1 and OPRAT-2. Prevalence of osteoporosis, in the validation sample was 74.3%. Sensitivity were high in both tools (OPRAT-1 and OPRAT-2; 83.2% and 82.5%) while specificity were moderate (44.8% for both). PPV of OPRAT-1 and OPRAT-2 were 79.5% and 81.2%. Both tools showed moderate NPV (OPRAT-1 and OPRAT-2; 51% and 47%). ConclusionsBoth OPRAT-1 and OPRAT-2 have high performance in screening postmenopausal women in Sri Lanka for risk of osteoporosis. OPRAT-2 is more convenient and can be used in any healthcare setting with limited resources to identify women who will be benefitted by DXA. OPRAT-1 can be used if the radial US facility is available.

Proposal for a new health record to support personalized, predictive, preventative and participatory medicine.

Today's approach to patient care and the medical record that directs and documents it is largely focused on identifying and treating the patient's disease. This has resulted in a sporadic, reactive healthcare system. Shifting medicine's focus to personalized strategic health planning will require a new approach to the patient 'work-up', a new relationship between the patient and the provider and a new medical record to support it. A prospective health record should be developed to enable personalized and preventative strategies, including assessment of health risks, evaluation of current health status, tracking of disease pathogenesis, prediction of disease events and long-term planning for maintaining wellness and limiting disease. The record should utilize emerging technologies to track predictive clinical risk factors, thereby enabling preventative responses and personalized medicine.

Quantitative ultrasound and risk factor enquiry as predictors of postmenopausal osteoporosis: comparative study in primary care

The current recommendation for primary care physicians to identify women at high risk of osteoporosis relies on the assessment of clinical risk factors as a selection method for referral for...