Validation and modification of HEART score components for patients with chest pain in the emergency department

Objective This study aimed to clarify the relative prognostic value of each History, Electrocardiography, Age, Risk Factors, and Troponin (HEART) score component for major adverse cardiac events (MACE) within 3 months and validate the modified HEART (mHEART) score.Methods This study evaluated the HEART score components for patients with chest symptoms visiting the emergency department from November 19, 2018 to November 19, 2019. All components were evaluated using logistic regression analysis and the scores for HEART, mHEART, and Thrombolysis in Myocardial Infarction (TIMI) were determined using the receiver operating characteristics curve.Results The patients were divided into a derivation (809 patients) and a validation group (298 patients). In multivariate analysis, age did not show statistical significance in the detection of MACE within 3 months and the mHEART score was calculated after omitting the age component. The areas under the receiver operating characteristics curves for HEART, mHEART and TIMI scores in the prediction of MACE within 3 months were 0.88, 0.91, and 0.83, respectively, in the derivation group; and 0.88, 0.91, and 0.81, respectively, in the validation group. When the cutoff value for each scoring system was determined for the maintenance of a negative predictive value for a MACE rate >99%, the mHEART score showed the highest sensitivity, specificity, positive predictive value, and negative predictive value (97.4%, 54.2%, 23.7%, and 99.3%, respectively).Conclusion Our study showed that the mHEART score better detects short-term MACE in high-risk patients and ensures the safe disposition of low-risk patients than the HEART and TIMI scores.

Diagnostic Accuracy of Ultrasonographic Respiratory Variation in the Inferior Vena Cava, Subclavian Vein, Internal Jugular Vein, and Femoral Vein Diameter to Predict Fluid Responsiveness: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis aimed to investigate the ultrasonographic variation of the diameter of the inferior vena cava (IVC), internal jugular vein (IJV), subclavian vein (SCV), and femoral vein (FV) to predict fluid responsiveness in critically ill patients. Relevant articles were obtained by searching PubMed, EMBASE, and Cochrane databases (articles up to 21 October 2021). The number of true positives, false positives, false negatives, and true negatives for the index test to predict fluid responsiveness was collected. We used a hierarchical summary receiver operating characteristics model and bivariate model for meta-analysis. Finally, 30 studies comprising 1719 patients were included in this review. The ultrasonographic variation of the IVC showed a pooled sensitivity and specificity of 0.75 and 0.83, respectively. The area under the receiver operating characteristics curve was 0.86. In the subgroup analysis, there was no difference between patients on mechanical ventilation and those breathing spontaneously. In terms of the IJV, SCV, and FV, meta-analysis was not conducted due to the limited number of studies. The ultrasonographic measurement of the variation in diameter of the IVC has a favorable diagnostic accuracy for predicting fluid responsiveness in critically ill patients. However, there was insufficient evidence in terms of the IJV, SCV, and FV.

Prognostic Value of Platelet-Lymphocyte Ratio and High-Density Lipoprotein in Patients with Acute Myocardial Infarct

Acute Myocardial Infarct (AMI) is the main reason for mortality. Platelet to Lymphocyte Ratio (PLR) describesthrombocyte aggregation and inflammation that is linked to cardiovascular disease. High-Density Lipoprotein (HDL) is antiatherogenic.This study aims to analyze the prognostic value of PLR and HDL in patients with AMI. This study was aretrospective observational study by obtaining laboratory results from complete blood count and lipid profiles frominpatients with AMI (STEMI and NSTEMI) medical records during Mei 2019–August 2020. Receiver Operating Characteristics(ROC) analysis was done to get the PLR and HDL cut-off. Prognostic value evaluation was based on sensitivity, specificity,positive and negative predictive value, and accuracy. Results obtained were from 302 subjects with a mean age of 58.4+9.6years old, with most male patients (74.5%). Receiver operating characteristics curve analysis showed an 0.514 Area UnderCurve (AUC) for PLR with p=0.685. High-density lipoprotein ROC was 0.573 with a p=0.033 (p< 0.05), with HDL cut-off = 50.0;sensitivity 72.7%, specificity 32.3%, positive predictive value 63.3%, negative predictive value 42.0% and 57.3% accuracy.Platelet to lymphocyte ratio mean was lower in the HDL <50 group (187.9) compared to the HDL > 50 (210.8), (p=0.009).High-density lipoprotein can be concluded as a potential prognostic factor of acute myocardial infarct. The lower the HDL,the greater the risk for a poor prognosis. A big-scale prospective study should be held to clarify and confirm these findings.

Sensitivity and specificity of Sirius indices in diagnosis of keratoconus and suspect keratoconus

Aim To estimate the sensitivity and specificity of topographic and tomographic corneal parameters as determined by Sirius (CSO, Florence, Italy) in discriminating keratoconus (KC) and suspect keratoconus from normal cornea. Method In this retrospective case-series study, keratoconus screening indices were measured using Sirius tomographer. Receiver operating characteristics (ROC) curves were used to determine the test's overall predictive accuracy (area under the curve) and to identify optimal cut-off points to maximize sensitivity and specificity in differentiating keratoconus and suspect keratoconus from normal corneas. Results Receiver operating characteristics (ROC) curve analyses showed high predictive accuracy for Symmetry Index back (SIb), Keratoconus Vertex front (KVf), Symmetry Index front (SIf), Keratoconus Vertex back (KVb), Apex Keratometry (Curve-Apex) and Minimum corneal Thickness (ThkMin) to distinguish keratoconus from normal (area under the curve > 0.9, all). Symmetry Index back was identified as the best diagnostic parameter for detecting suspect keratoconus with AUC of 0.86. Highest specificity to detect keratoconus and suspect keratoconus was seen for SIb, 99.87% and 84.66%, respectively. These values were associated with optimal cut-off points of 0.46 D for keratoconus and 0.12 D for suspect keratoconus. Conclusion Sirius parameters evaluated in the study were effective to differentiate keratoconus from normal corneas. However, Symmetry Index back was the index with the highest ability to detect suspect keratoconus.

Delineation of groundwater potential zones for sustainable development and planning using analytical hierarchy process (AHP), and MIF techniques

Groundwater plays a vital role in the sustainable development of agriculture, society and economy, and it's demand is increasing due to low rainfall, especially in arid and semiarid regions. In this context, delineation of groundwater potential zones is essential for meeting the demand of different sectors. In this research, the integrated approach consisting of analytical hierarchy process (AHP), multiple influence factors (MIF) and receiver operating characteristics (ROC) was applied. The demarcation of groundwater potential zones is based on thematic maps, namely Land Use/Land Cover (LULC), Digital Elevation Model (DEM), hillshade, soil texture, slope, groundwater depth, geomorphology, Normalized Difference Vegetation Index (NDVI), and flow direction and accumulation. The pairwise comparison matrix has been created, and weights are assigned to each thematic layer. The comparative score to every factor was calculated from the overall weight of two major and minor influences. Groundwater potential zones were classified into five classes, namely very poor, poor, moderate, good and very good, which cover an area as follows: 3.33 km2, 785.84 km2, 1147.47 km2, 595.82 km2 and 302.65 km2, respectively, based on AHP method. However, the MIF groundwater potential zones map was classified into five classes: very poor, poor, moderate, good and very good areas covered 3.049 km2, 567.42 km2, 1124.50 km2 868.86 km2 and 266.67 km2, respectively. The results of MIF and AHP techniques were validated using receiver operating characteristics (ROC). The result of this research would be helpful to prepare the sustainable groundwater planning map and policy. The proposed framework has admitted to test and could be implemented in different in various regions around the world to maintain the sustainable practices.

Grip strength as a predictor of depressive symptoms among vulnerable elderly Europeans with musculoskeletal conditions

The present study aimed to investigate the grip strength (GS) discrimination capacity and cutoffs points for depressive symptoms among vulnerable elderly individuals with musculoskeletal conditions. The Survey of Health, Aging, and Retirement in Europe wave 6 was analyzed. GS was measured by a handgrip dynamometer, and EURO-D scale was used to assess depressive symptoms. GS cutoff values for depressive symptoms were calculated using the receiver operating characteristics curve. 2206 participants, mean age 74.0 (73.7–74.3), 78.8% with osteoarthritis/other rheumatism, enrolled in the study. Sensitivity varies between 0.44 (men, ≥ 85 years) and 0.82 (men, 75–84 years), and specificity varying between 0.35 (women, 75–84 years) and 0.70 (men 75–84 years). GS is associated with depressive symptoms just for women and it is not possible to use a GS cutoff point for screening depressive symptoms for vulnerable men and women with musculoskeletal conditions over the age of 65 years.

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

Bedeutung der Desorientierung bei der Delireinschätzung

ZusammenfassungDesorientierung kann ein frühes Merkmal eines Delirs sein. Für die Überwachung eines Delirs testet die im deutschsprachigen Raum weit verbreitete „Confusion Assessment Method for Intensive Care Unit“ (CAM-ICU) die Orientierung nicht, da intubierte Intensivpatienten sich nicht verbal äußern können. Die Mehrheit der Patienten auf deutschen Intensivstationen ist aber nicht beatmet, sie könnten hinsichtlich ihrer Orientiertheit befragt werden. Die vorliegende Studie untersuchte, ob sich durch das Kriterium „Desorientierung“ bei extubierten Patienten im Vergleich zur CAM-ICU divergierende Befunde ergeben und ob sich die Sensitivität der CAM-ICU durch Kombination mit dem Merkmal „Desorientierung“ („CAM-IMC“) erhöhen lassen. Insgesamt 86 gepaarte Untersuchungen fanden bei 50 extubierten Patienten statt. Ein Delir fand sich bei 19,8 % (n = 17) aller Untersuchungen. Die CAM-ICU hatte eine Sensitivität von 71 % (95%-KI: 44–90 %) und eine Spezifität von 100 % (95–100 %). Für „Desorientierung“ als alleiniges Delir-Merkmal fand sich eine Sensitivität von 77 % (50–93 %) und eine Spezifität von 93 % (89–100 %). Die CAM-IMC erreichte eine Sensitivität von 88 % (64–99 %) bei einer Spezifität von 100 % (95–100 %). Die „Receiver-Operating-Characteristics(ROC)-Analyse“ fand mit einer „area under the curve“ (AUC) von 0,941 (95%-KI: 0,851–1,000) für die CAM-IMC den höchsten Wert im Vergleich zu den anderen Delir-Tests (CAM-ICU, AUC 0,853 [0,720–0,986]; Desorientierung, AUC 0,868 [0,745–0,991]). Diese Arbeit unterstreicht die Wertigkeit des Merkmals „Desorientierung“ für Delir-Tests bei verbal kommunikationsfähigen Patienten und erklärt einige diskrepante Beurteilungen schwierig einzuschätzender Patienten in der täglichen Praxis. Die CAM-IMC scheint als Delir-Test für extubierte Patienten günstigere Eigenschaften als die CAM-ICU zu haben und sollte eingehender überprüft werden.

Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets

Background The design used to create labelled data for training prediction models from observational healthcare databases (e.g., case-control and cohort) may impact the clinical usefulness. We aim to investigate hypothetical design issues and determine how the design impacts prediction model performance. Aim To empirically investigate differences between models developed using a case-control design and a cohort design. Methods Using a US claims database, we replicated two published prediction models (dementia and type 2 diabetes) which were developed using a case-control design, and trained models for the same prediction questions using cohort designs. We validated each model on data mimicking the point in time the models would be applied in clinical practice. We calculated the models’ discrimination and calibration-in-the-large performances. Results The dementia models obtained area under the receiver operating characteristics of 0.560 and 0.897 for the case-control and cohort designs respectively. The type 2 diabetes models obtained area under the receiver operating characteristics of 0.733 and 0.727 for the case-control and cohort designs respectively. The dementia and diabetes case-control models were both poorly calibrated, whereas the dementia cohort model achieved good calibration. We show that careful construction of a case-control design can lead to comparable discriminative performance as a cohort design, but case-control designs over-represent the outcome class leading to miscalibration. Conclusions Any case-control design can be converted to a cohort design. We recommend that researchers with observational data use the less subjective and generally better calibrated cohort design when extracting labelled data. However, if a carefully constructed case-control design is used, then the model must be prospectively validated using a cohort design for fair evaluation and be recalibrated.