Simple Prediction Research Articles

A novel peptide pair-based rapid fluorescent diagnostic system for malaria Plasmodium falciparum detection

Two peptides M2.9 [(KPTAEQTESPELQSAPEN) and M2.17 (KILFNVYSPLGCTCECWV)] were designed using simple epitope prediction tools and modified against the merozoite surface antigen 2 of P. falciparum (Pf.MSP2) by 3-dimensional modeling based on binding affinity. Based on five prediction tools for hydropathy, M2.17 was selected as an appropriate capture peptide. A peptide-based fluorescence-linked immunosorbent assay (FLISA) and a peptide pair-based fluorescent immunochromatographic test strip (FICT) were developed to detect P. falciparum 3D7 (drug-sensitive) and P. falciparum K1 (multi drugs-resistant) strains. Bioinformatic analysis of two peptides demonstrated the potential binding affinity with the merozoite surface protein 2 of P. falciparum (Pf.MSP2) with a positive hydropathy value. The limit of detection (LOD) of FLISA was 10 parasites/μL and of a peptide pair-linked rapid FICT system was 5 and 200 parasites/μL for P. falciparum 3D7 and K1, respectively. Compared to commercial rapid detection systems (RDTs), a peptide pair-linked FICT system exhibited a 20-fold greater efficiency in detecting P. falciparum 3D7 and specifically discriminated another protozoan spp. A peptide pair-linked rapid diagnostic strip could be an alternative to conventional RDTs for monitoring wild-type and drug-resistant malaria parasites.

Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach

End-to-end multilingual scene text spotting aims to integrate scene text detection and recognition into a unified framework. Actually, the accuracy of text recognition largely depends on the accuracy of text detection. Due to the lackage of benchmarks with adequate and high-quality character-level annotations for multilingual scene text spotting, most of the existing methods train on the benchmarks only with word-level annotations. However, the performance of multilingual scene text spotting are not that satisfied training on the existing benchmarks, especially for those images with special layout or words out of vocabulary. In this paper, we proposed a simple YOLO-like baseline named CMSTR for character-level multilingual scene text spotting simultaneously and efficiently. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, DeepSolo not only performs well in English scenes but also masters the Chinese transcription with complex font structure and a thousand-level character classes. On the other hand, based on the extensibility of DeepSolo, we launch DeepSolo++ for multilingual text spotting, making a further step to let Transformer decoder with explicit points solo for multilingual text detection, recognition, and script identification all at once.

Imprints of the Local Bubble and Dust Complexity on Polarized Dust Emission

Using 3D dust maps and Planck polarized dust emission data, we investigate the influence of the 3D geometry of the nearby interstellar medium (ISM) on the statistics of the dust polarization on large ( 80′ ) scales. We test recent models that assume that the magnetic field probed by the polarized dust emission is preferentially tangential to the Local Bubble wall, but we do not find an imprint of the Local Bubble geometry on the dust polarization fraction. We also test the hypothesis that the complexity of the 3D dust distribution drives some of the measured variation of the dust polarization fraction. We compare sight lines with similar total column densities and find that, on average, the dust polarization fraction decreases when the dust column is substantially distributed among multiple components at different distances. Conversely, the dust polarization fraction is higher for sight lines where the dust is more concentrated in 3D space. This finding is statistically significant for the dust within 1.25 kpc, but the effect disappears if we only consider dust within 270 pc. In conclusion, we find that the extended 3D dust distribution, rather than solely the dust associated with the Local Bubble, plays a role in determining the observed dust polarization fraction at 80′. This conclusion is consistent with a simple analytical prediction and remains robust under various modifications to the analysis. These results illuminate the relationship between the 3D geometry of the ISM and tracers of the interstellar magnetic field. We discuss implications for our understanding of the polarized dust foreground to the cosmic microwave background.

Abstract P102: Reducing Testing Burden in Patients Screened for Primary Aldosteronism

Introduction: Primary aldosteronism (PA) is an increasingly recognized secondary cause of hypertension characterized by higher cardiovascular, renal, and metabolic disease rates than blood pressure-matched essential hypertensives. Current literature suggests expanding screening for PA to include the majority, if not all, hypertensive patients. This expansive screening would place considerable burden on patients and the healthcare system. A simple clinical risk prediction model that identifies patients with low risk for PA could reduce this testing burden. Methods: We evaluated 111 hypertensive patients in the Southern Illinois University Hypertension Clinic who underwent a screening test and, if positive, had subsequent confirmatory testing for PA. We characterized patients based on routine clinical measures including antihypertensive treatment intensity, blood pressure, serum electrolytes, and body size. Classification trees incorporating 96 patients without missing data points for any considered predictor variable were used to identify hypertensive screen-positive patients who have low to absent risk for PA as determined by confirmatory testing. Results: Patients with PA had higher serum aldosterone levels at screening (mean = 22.5 ng/dl ± SD = 11.6 vs. 15.2 ± 4.5, p < 0.001), higher BMI (35.8 kg/m 2 ± 9.9 vs. 29.9 ± 5.8, p < 0.001), lower potassium (3.9 mmol/L ± 0.5 vs. 4.1 ± 0.6, p = 0.03), and more intense antihypertensive drug therapy (defined by the World Health Organization defined daily dose [6.1 ± 3.2 vs. 4.4 ± 3.0, p = 0.01]) than hypertensive patients without PA. The optimal classification tree model performed relatively poorly at differentiating those with PA from those without PA (sensitivity = 0.76, specificity = 0.86, positive predictive value = 0.87, and negative predictive value = 0.73). However, the model did reveal a subset of individuals who consistently tested negative for PA. Out of 14 patients with serum aldosterone concentration < 25.5, serum potassium > 3.55, and BMI < 27, none had PA. Conclusion: We identified a set of readily available clinical variables for identifying hypertensive patients who screened positive for PA but in whom PA was subsequently proven to be absent. Applying these criteria to our screen-positive hypertensive cohort reduces confirmatory testing by approximately 15%. Study in a larger sample size and external validation of this model is needed prior to recommending it for use in clinical decision-making.

A simple prediction of the nonlinear matter power spectrum in Brans–Dicke gravity from linear theory

Brans–Dicke (BD), one of the first proposed scalar-tensor theories of gravity, effectively makes the gravitational constant of general relativity (GR) time-dependent. Constraints on the BD parameter ω serve as a benchmark for testing GR, which is recovered in the limit ω → ∞. Current small-scale astrophysical constraints ω ≳ 105 are much tighter than large-scale cosmological constraints ω ≳ 103, but the two decouple if the true theory of gravity features screening. On the largest cosmological scales, BD approximates the most general second-order scalar–tensor (Horndeski) theory, so constraints here have wider implications. These constraints will improve with upcoming large-scale structure and cosmic microwave background surveys. To constrain BD with weak gravitational lensing, one needs its nonlinear matter power spectrum PBD. By comparing the boost B = PBD/PGR from linear theory and nonlinear N-body simulations, we show that the nonlinear boost can simply be predicted from linear theory if the BD and GR universes are parameterized in a way that makes their early cosmological evolution and quasilinear power today similar. In particular, they need the same H0/√Geff(a = 0) and σ8, where Geff is the (effective) gravitational strength. Our prediction is 1% accurate for ω ≥ 100, z ≤ 3, and k ≤ 1 h/Mpc; and 2% up to k ≤ 5 h/Mpc. It also holds for GBD that do not match Newton’s constant today, so one can study GR with different gravitational constants GGR by sending ω → ∞. We provide a code that computes B with the linear Einstein-Boltzmann solver HI_CLASS and multiplies it by the nonlinear PGR from EUCLIDEMULATOR2 to predict PBD.

Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records.

Colorectal Polyps are the main source of precancerous lesions in colorectal cancer. To increase the early diagnosis of tumors and improve their screening, we aimed to develop a simple and non-invasive diagnostic prediction model for colorectal polyps based on machine learning (ML) and using accessible health examination records. We conducted a single-center observational retrospective study in China. The derivation cohort, consisting of 5426 individuals who underwent colonoscopy screening from January 2021 to January 2024, was separated for training (cohort 1) and validation (cohort 2). The variables considered in this study included demographic data, vital signs, and laboratory results recorded by health examination records. With features selected by univariate analysis and Lasso regression analysis, nine machine learning methods were utilized to develop a colorectal polyp diagnostic model. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley additive explanation method (SHAP) was used to rank the feature importance and explain the final model. 14 independent predictors were identified as the most valuable features to establish the models. The adaptive boosting machine (AdaBoost) model exhibited the best performance among the 9 ML models in cohort 1, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) of 0.632 (0.618-0.646), 0.635 (0.550-0.721), 0.674 (0.591-0.758), 0.593 (0.576-0.611), 0.673 (0.654-0.691), 0.608 (0.560-0.655) and 0.687 (0.626-0.749), respectively. The final model gave an AUC of 0.675 in cohort 2. Additionally, the precision recall (PR) curve for the AdaBoost model reached the highest AUPR of 0.648, positioning it nearest to the upper right corner. SHAP analysis provided visualized explanations, reaffirming the critical factors associated with the risk of colorectal polyps in the asymptomatic population. This study integrated the clinical and laboratory indicators with machine learning techniques to establish the predictive model for colorectal polyps, providing non-invasive, cost-effective screening strategies for asymptomatic individuals and guiding decisions for further examination and treatment.

Pan-immune-inflammation value: a new prognostic index in epithelial ovarian cancer

BackgroundEpithelial ovarian cancer (EOC) is one of the deadliest gynaecological malignancies worldwide. The aim of this retrospective study was to create a predictive scoring model based on simple immunological and inflammatory parameters to predict overall survival (OS) and progression-free survival (PFS) in patients with EOC.MethodsWe obtained 576 EOC patients and randomly assigned them to the training set (n = 405) and the validation set (n = 171) in a ratio of 7:3. We retrospectively evaluated the association between PIV and OS and PFS using a novel immunoinflammatory marker, according to the optihmal treshold of PIV, we divided the patients into two different subgroups, high PIV (PIV > 254.9) and low PIV (PIV ≤ 254.9). Pan-immune Inflammatory Value (PIV) was computed as follows: neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L). Then developed a simple score prediction model based on several independent prognostic parameters using Cox regression analysis. We used receiver operator characteristic (ROC) curves, calibration plots, and decision analysis (DCA) curves to evaluate the performance of the model. Finally, we used Kaplan-Meier curves to ensure that the model could distinguish well between low- and high-risk groups.ResultsThere was a significant difference in survival outcomes between high PIV (PIV > 310.2) and low PIV (PIV ≤ PIV310.2) (3-year survival rates of 61.34% and 76.71%, respectively); 5-year OS, 25.21% and 51.14%, respectively; 3-year PFS, 40.90% and 65.30%; 5-year PFS, 19.33% and 39.73%, respectively). Column plots of OS and PFS were constructed using independent prognostic factors. In the training module, the 3-, 5-, and 10-year AUCs for OS and PFS column charts were 0.713, 0.796, 0.839, and 0.730, 0.799, 0.826, respectively.In the validation cohort, the 3-, 5-, and 10-year AUCs for OS and PFS column charts were 0.676, 0.803, 0.685, and 0.700, respectively, 0.754, 0.727. The calibration curves showed good agreement between predicted survival and actual observations. The decision analysis curves also showed that the current model has good accuracy and clinical applicability. 3-year OS was 61.34% and 76.71%, respectively; 5-year OS was 25.21% and 51.14%, respectively; 3-year PFS was 40.90% and 65.30%, respectively; 5-year PFS was 19.33% and 39.73%, respectively.ConclusionsWe constructed and validated a PIV-based nomogram to predict OS and PFS in EOC patients, with a view to helping gynaecologists converge on oncologists in their treatment and follow-up expertise in epithelial ovarian cancer.

Performance of profile steel CFDSP composite walls under compression and bending loads

Performance of profile steel CFDSP composite walls under compression and bending loads

DPGS: Cross-cooperation guided dynamic points generation for scene text spotting

DPGS: Cross-cooperation guided dynamic points generation for scene text spotting

In vitro approach to refine bioconcentration and biotransformation predictions of organic persistent pollutants using cell lines

The application of the 3Rs (Replacement, Reduction, and Refinement) in animal experimentation has recently concentrated its efforts on utilizing cellular systems to predict toxicity in organisms. In this context, while refining the data obtained from cell lines, this study assesses their bioaccumulation potential and various methods for extrapolating the in vitro metabolization rate constant to support modelled bioaccumulation assessments for fish and their limitations. For this purpose, the concentrations of the parent compound, phenanthrene, and its major metabolites within the cells and in the medium at various exposure times were quantified. A chemical distribution model (mass balance) was applied to calculate the concentrations of the cell-bioaccessible compounds (Cfree) based on the experimentally determined concentrations. An elevated matching was observed between the in vitro bioconcentration factor (BCF) and the in vivo BCFs reported in the literature for zebrafish liver cells (ZFL). This study demonstrates the importance of further investigating in vitro biotransformation kinetics. The results obtained with the approach developed here provide valuable information to enhance current models. Additionally, it underscores the potential of cell lines as a strategy for rapid, simple, and cost-effective predictions without the need for animal experimentation.

HERMES-24 Score Derivation and Validation for Simple and Robust Outcome Prediction After Large Vessel Occlusion Treatment.

Clinicians need simple and highly predictive prognostic scores to assist practical decision-making. We aimed to develop a simple outcome prediction score applied 24 hours after anterior circulation acute ischemic stroke treatment with endovascular thrombectomy and validate it in patients treated both with and without endovascular thrombectomy. Using the HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) collaboration data set (n=1764), patients in the endovascular thrombectomy arm were divided randomly into a derivation cohort (n=430) and a validation cohort (n=441). From a set of candidate predictors, logistic regression modeling using forward variable selection was used to select a model that was both parsimonious and highly predictive for modified Rankin Scale (mRS) ≤2 at 90 days. The score was validated in validation cohort, control arm (n=893), and external validation cohorts from the ESCAPE-NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischaemic Stroke; n=1066) and INTERRSeCT (Identifying New Approaches to Optimize Thrombus Characterization for Predicting Early Recanalization and Reperfusion With IV Alteplase and Other Treatments Using Serial CT Angiography; n=614). In the derivation cohort, we selected 2 significant predictors of mRS ≤2 (National Institutes of Health Stroke Scale score at 24 hours and age [β-coefficient, 0.34 and 0.06]) and derived the HERMES-24 score: age (years)/10+National Institutes of Health Stroke Scale score at 24 hours. The HERMES-24 score was highly predictive for mRS ≤2 (c-statistic 0.907 [95% CI, 0.879-0.935]) in the derivation cohort. In the validation cohort and the control arm, the HERMES-24 score predicts mRS ≤2 (c-statistic, 0.914 [95% CI, 0.886-0.944] and 0.909 [95% CI, 0.887-0.930]). Observed provability of mRS ≤2 ranged between 3.1% and 3.4% when HERMES-24 score ≥25, while it ranged between 90.6% and 93.0% when HERMES-24 score <10 in the derivation cohort, validation cohort, and control arm. The HERMES-24 score also showed c-statistics of 0.894 and 0.889 for mRS ≤2 in the ESCAPE-NA1 and INTERRSeCT populations. The post-treatment HERMES-24 score is a simple validated score that predicts a 3-month outcome after anterior circulation large vessel occlusion stroke regardless of intervention, which helps prognostic discussion with families on day 2.

Risk factors and prediction score for new-onset diabetes mellitus after liver transplantation.

New-onset diabetes mellitus is a frequent and severe complication arising after liver transplantation (LT). We aimed to identify the risk factors for new-onset diabetes mellitus after liver transplantation (NODALT) and to develop a risk prediction score system for relevant risks. We collected and analyzed data from all recipients who underwent liver transplantation at the First Affiliated Hospital of Xi'an Jiaotong University. The OR derived from a multiple logistic regression predicting the presence of NODALT was used to calculate the risk prediction score. The performance of the risk prediction score was externally validated in patients who were from the CLTR (China Liver Transplant Registry) database. A total of 468 patients met the outlined criteria and finished the follow-up. Overall, NODALT was diagnosed in 115 (24.6%) patients. Age, preoperative impaired fasting glucose (IFG), postoperative fasting plasma glucose (FPG), and the length of hospital stay were significantly associated with the presence of NODALT. The risk prediction score includes age, preoperative IFG, postoperative FPG, and the length of hospital stay. The risk prediction score of the area under the receiver operating curve was 0.785 (95% CI: 0.724-0.846) in the experimental population and 0.782 (95% CI: 0.708-0.856) in the validation population. Age at the time of transplantation, preoperative IFG, postoperative FPG, and length of hospital stay were independent predictive factors of NODALT. The use of a simple risk prediction score can identify the patients who have the highest risk of NODALT and interventions may start early.

Incremental value of multidomain risk factors for dementia prediction: A machine learning approach

The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims to assess the incremental value of five predictor domains when added to a simple dementia risk prediction model (DRPM) for predicting incident dementia in older adults. Population-based, prospective cohort study. UK Biobank study. Individuals aged 60 or older without dementia. Fifty-five dementia-related predictors were gathered and categorized into clinical and medical history, questionnaire, cognition, polygenetic risk, and neuroimaging domains. Incident dementia (all-cause) and the subtypes, Alzheimer's disease (AD) and vascular dementia (VaD), were determined through hospital and death registries. Ensemble machine learning (ML) DRPMs were employed for prediction. The incremental values of risk predictors were assessed using the percent change in Area Under the Curve (∆AUC%) and the net reclassification index (NRI). The simple DRPM which included age, body mass index, sex, education, diabetes, hyperlipidaemia, hypertension, depression, smoking, and alcohol consumption yielded an AUC of 0.711 (± 0.008 SD). The five predictor domains exhibited varying levels of incremental value over the basic model when predicting all-cause dementia and the two subtypes. Neuroimaging markers provided the highest incremental value in predicting all-cause dementia (∆AUC% +9.6%) and AD (∆AUC% +16.5%) while clinical and medical history data performed the best at predicting VaD (∆AUC% +12.2%). Combining clinical and medical history, and questionnaire data synergistically enhanced ML DRPM performance. Combining predictors from different domains generally results in better predictive performance. Selecting predictors involves trade-offs, and while neuroimaging markers can significantly enhance predictive accuracy, they may pose challenges in terms of cost or accessibility.

The frequency of transiting planetary systems around polluted white dwarfs

ABSTRACT This paper investigates the frequency of transiting planetary systems around metal-polluted white dwarfs using high-cadence photometry from ULTRACAM and ULTRASPEC on the ground and space-based observations with TESS. Within a sample of 313 metal-polluted white dwarfs with available TESS light curves, two systems known to have irregular transits are blindly recovered by box-least-squares and Lomb–Scargle analyses, with no new detections, yielding a transit fraction of $0.8_{-0.4}^{+0.6}$ per cent. Planet detection sensitivities are determined using simulated transit injection and recovery for all light curves, producing upper limit occurrences over radii from dwarf to Kronian planets, with periods from 1 h to 27 d. The dearth of short-period, transiting planets orbiting polluted white dwarfs is consistent with engulfment during the giant phases of stellar evolution, and modestly constrains dynamical re-injection of planets to the shortest orbital periods. Based on simple predictions of transit probability, where $(R_* + R_{\rm p})/a\simeq 0.01$, the findings here are nominally consistent with a model where 100 per cent of polluted white dwarfs have circumstellar debris near the Roche limit; however, the small sample size precludes statistical confidence in this result. Single transits are also ruled out in all light curves using a search for correlated outliers, providing weak constraints on the role of Oort-like comet clouds in white dwarf pollution.

Acoustic optimization of a tee via a Helmholtz resonant cavity and noise prediction via a genetic algorithm coupled with the grey model

Acoustic optimization of a tee via a Helmholtz resonant cavity and noise prediction via a genetic algorithm coupled with the grey model

Prediction of peak overpressure from hydrogen-air cloud explosions in vented cylindrical vessels

Prediction of peak overpressure from hydrogen-air cloud explosions in vented cylindrical vessels

Amplitude‐dependent modal viscous damping for distributed stick–slip systems

AbstractAn accurate damping model serves as the foundation for reliable structural analysis. One of the primary sources of structural damping is the stick–slip mechanism caused by interfacial friction. Structural damping is usually represented by the modal damping ratio, and the modal equivalent viscous damping ratio (EVDR) of a stick–slip system is crucial in structural dynamic modeling. To date, an accurate yet simple prediction method for modal amplitude‐dependent EVDR that considers the distribution characteristics of the stick–slip mechanism remains unavailable. In the present study, the modal EVDRs of a distributed stick–slip system (DSSS) extended from a single degree‐of‐freedom stick–slip system were calculated using the modal energy equivalence method. The correlation between the stick–slip parameters and the modal damping behavior was investigated at various amplitude levels on the basis of the assumption of equal stick–slip parameters. A two‐parameter damping model represented by a single stick–slip damping model was developed to capture the amplitude dependence of the frictional EVDR. Furthermore, a practical prediction method based on the stick–slip parameters was proposed for the modal EVDR of the fundamental and higher‐order modes. The seismic analysis of two illustrative structures with nonuniform and uniform interstory stiffness demonstrated the effectiveness and applicability of the proposed method in predicting the modal EVDR of the DSSS. The proposed equivalent linear viscous damping system is suitable for the quick prediction of structural dynamic responses.

A simple prediction model for the risk of boron neutron capture therapy-induced nausea and vomiting in head and neck cancer

Head and neck cancer patients undergoing boron neutron capture therapy (BNCT) often experience BNCT-induced nausea and vomiting (BINV). This study aimed to construct a BINV risk prediction model. In this retrospective study, 237 patients were randomly divided into a training and test cohort. In the training cohort, a univariate analysis was performed to identify factors associated with BINV. Multivariate analysis was used to identify factors and calculate coefficients for the model. The Hosmer-Lemeshow test was used to assess the goodness of fit, and receiver operating characteristic curves were plotted to evaluate the accuracy of the model. For both the training and test cohort, the predictive model was used to generate the scores and calculate the sensitivity and specificity. The incidence of nausea and vomiting was 50% and 18%, respectively. Female sex, younger age, non-squamous cell carcinoma, no prior chemotherapy, and beam entry from the face/lateral region were associated with the occurrence of BINV. The prediction model showed a good fit (P=0.96) and performance (area under the curve=0.75). The sensitivity and specificity were 83% and45% for the training cohort (n=193) and 86% and 59% for the test cohort (n=44), respectively. We developed a simple model that predicts BINV. This will enable appropriate care to be implemented based on increased risk to prevent its occurrence.

Prediction of non-dipper blood pressure pattern in Chinese patients with hypertension using a nomogram model.

Non-dipper blood pressure has been shown to affect cardiovascular outcomes and cognitive function in patients with hypertension. Although some studies have explored the influencing factors of non-dipper blood pressure, there is still relatively little research on constructing a prediction model. This study aimed to develop and validate a simple and practical nomogram prediction model and explore relevant elements that could affect the dipper blood pressure relationship in patients with hypertension. A convenient sampling method was used to select 356 inpatients with hypertension who visited the Affiliated Hospital of Jining Medical College from January 2022 to September 2022. All patients were randomly assigned to the training cohort (75%, n = 267) and the validation cohort (25%, n = 89). Univariate and multivariate logistic regression were utilized to identify influencing factors. The nomogram was developed and evaluated based on the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and decision curve analyses. The optimal cutoff values for the prevalence of dipper blood pressure were estimated. The nomogram was established using six variables, including age, sex, hemoglobin (Hb), estimated glomerular filtration rate (eGFR), ejection fraction (EF), and heart rate. The AUC was 0.860 in the training cohort. The cutoff values for optimally predicting the prevalence of dipper blood pressure were 41.50 years, 151.00g/L, 117.53mL/min/1.73m2, 64.50%, and 75 beats per minute for age, Hb, eGFR, ejection fraction, and heart rate, respectively. In summary, our nomogram can be used as a simple, plausible, affordable, and widely implementable tool to predict the blood pressure pattern of Chinese patients with hypertension.

Valid and accurate simple equation to predict 3,000-m steeplechase performance.

Predict running performances is very important for athletes and trainers. Sport researchers have therefore developed certain tools to predict running performances, but only in non-obstacle races. This study aimed to develop and test the validity and accuracy of an equation for predicting 3,000-m steeplechase performance (PerfSteeple ). The official rankings of French runners for the 3,000-m track-running (Perf3000 ) and 3,000-m steeplechase events were examined. Age, height and body mass were collected. From 146 included athletes, two groups were randomly composed: one comprising 80% of the sample (n = 117) to develop a simple equation to predict PerfSteeple (i.e., development group) and the other comprising the remaining 20% (n = 29) to test the validity and accuracy of the developed prediction equation (i.e., cross-validation group). The simple prediction equation included Perf3000 and age: . No significant difference was noted between the actual and predicted performances. Predicted performances were significantly correlated with the actual ones, with a very high correlation coefficient (p < 0.001; r = 0.929). Bias and 95% limits of agreement were -5 ± 24 s, i.e., -0.8 ± 7.6%. In 95 of 100 new predictions, the difference between actual and predicted performance would be less or equal to-5 ± 24 s. The study confirms the validity and accuracy of the equation for predicting PerfSteeple . Predictions using this simple equation may be used in training and competitions for athletes and coaches. PerfSteeple = -57,165 + 1,147 X Perf3000 + 0,955 X age.