Risk Prediction Score Research Articles

Serum proteomic profiling of carotid atherosclerosis: a population outcome study

Abstract Background Circulating proteins reflecting atherosclerosis may improve cardiovascular (CV) disease prediction. We applied feature selection and unsupervised clustering on proteomic data to identify proteins associated with carotid atherosclerosis and construct a protein-based classification system for prediction of CV events. Methods 491 community-dwelling participants (mean age, 58±11 years; 51% women) underwent carotid ultrasonography and proteomic profiling (CVD II panel, Olink Proteomics). Subsequently, CV outcome was collected (median follow-up time: 10.2 years). We applied partial least squares (PLS) to identify proteins linked to carotid intima-media thickness (cIMT). Next, we assessed the association between future CV events and protein-based phenogroups derived by unsupervised clustering (Gaussian Mixture modelling) based on proteins influential in PLS. Results PLS marked 19 proteins as important, which were all associated with cIMT in multivariable-adjusted linear regression. 8 of the 19 proteins were excluded from the clustering due to high collinearity. Based on the 11 remaining proteins, the clustering algorithm subdivided the cohort into two phenogroups. Compared to the first (n=177), participants in the second phenogroup (n=314) presented: i) a more unfavourable lipid profile with higher total cholesterol and triglycerides and lower HDL cholesterol (P≤0.014); ii) higher cIMT (P=0.0020); and iii) a significantly higher risk for future CV events (multivariable-adjusted hazard ratio (95% CI) versus cluster 1: 2.05 (1.26 to 3.52); P=0.0093). The protein-based phenogrouping supplemented the ACC/AHA 10-year ASCVD risk score for prediction of a first CV event. Conclusions Phenogrouping based on a focused set of proteins related to carotid atherosclerosis identified individuals at high risk for future CV events. This approach may complement current CV risk stratification strategies.Summary of workflow and findingsCV event prediction

Improving risk stratification of recurrent myocardial infarction in a large real-world dataset using machine learning

Abstract Background Patient (pt) risk stratification for cardiovascular (CV) events is key to determine the appropriate approach to secondary prevention. Several risk scores have been developed (SCORE2, EUROASPIRE, Thrombolysis in Myocardial Infarction [TIMI] risk score for secondary prevention [TRS 2°P]); these are based on few predictors and have low discriminatory power. Machine learning methods using real-word data (RWD) from electronic health records (EHR) databases and capable of handling a multitude of commonly available variables can serve as powerful tools to improve the discriminatory power and generate efficient and clinically relevant risk scores. Purpose This study used a non-linear machine learning model to build an improved risk score for secondary CV events from a large Israeli EHR database. Methods Records of pts with a first record of atherosclerotic CV disease (ASCVD) admission or coronary procedure and available follow-up data (mean: 1052 days) from the period of 01/2005–11/2020 were considered (n=77432 [67% coronary artery disease, 21% carotid artery disease, 12% peripheral artery disease], a stratified 1:1 randomization by cause of admission yielding in training set [n=38716] and test set [n=38716]). The model’s performance was assessed by comparison with TRS 2°P score on the test set independent of the training set. Twenty-six covariates commonly used in clinical routine and widely available in EHR have been selected and a gradient boosting trees algorithm was applied to the pt data at baseline after the original event to predict recurrent myocardial infarction (MI). The ability of the model to discriminate pts was evaluated using the concordance index (c-index), which expresses how well a predicted risk score describes the observed events (1 implies perfect concordance, 0.5 implies random concordance). Results Prediction accuracy of our model showed a c-index for a future MI of 0.74 (95% confidence interval [CI]: 0.72, 0.76) which is significantly higher than 0.57 (95% CI: 0.56, 0.58) obtained with TRS 2°P. Our 2-tier risk score has successfully discriminated a low-risk group (74% of cohort) with a 3-year MI prevalence of 5% from a high-risk group (26% of cohort) that had a 5-fold higher prevalence (26.6%) (Figure 1). In comparison, the 3-tier TRS 2°P yielded an unbalanced distribution of group sizes with a disproportionate intermediate risk group (22% low, 67% intermediate, 11% of high-risk) with a limited clinical discriminatory power (7.1%, 10,2% and 15.6% of observed events, respectively) (Figure 2). Conclusions Our machine learning based risk score model created on a large set of RWD with a long follow-up seems to offer substantial gain of performance compared to the TRS 2°P. The present 2-tier model shows significant gain of accuracy and clinical discriminatory power paving the way for more individualized prevention strategies.Figure 1Figure 2

Abstract 12476: QT Prolongation Predicts All-Cause Mortality Above and Beyond a Validated Risk Score

Introduction: QT prolongation is a risk factor for life-threatening arrhythmias and sudden cardiac death. In large cohorts, QT interval was associated with all-cause mortality after adjusting for cardiovascular risk factors, but these analyses may contain residual confounding. Whether the QT interval provides prognostic information above and beyond a validated mortality risk score is unknown. Hypothesis: QT interval on ECG will independently predict mortality after adjustment for the Care Assessment Needs (CAN) score, which was validated to predict mortality nationwide at the Veterans Administration (VA) (c-index 0.86). Methods: Patients receiving primary care at the Minneapolis VA from 2012 to 2016, with at least one ECG were included in this retrospective cohort study. QT intervals were corrected for heart rate (QTc) using the Bazett’s formula. CAN score, calculated within 1-week of the ECG, was obtained from the VA Corporate Data Warehouse. Results: Of the 36,240 patients, 1526 (4.2%) had QTc ≥ 500 ms, 3249 (9.0%) had QTc 470-500 ms, and 31,465 (86.8%) had QTc < 470 ms. A total of 2,189 (6.0%) patients died at 1-year follow-up and 7898 (21.8%) died at 5-year follow-up. Mortality was higher in patients with longer QTc (Table 1). Compared to those with QTc < 470 ms, CAN-adjusted odds of 1-year and 5-year mortality were significantly higher in those with longer QTc (Table 2). C-index for CAN score predicting 1-year and 5-year mortality (0.829 and 0.826, respectively) improved marginally after adding QTc data (0.834 and 0.832, respectively; p <0.0001 for both). Conclusions: QT prolongation predicts all-cause mortality independently of a validated mortality risk prediction score.

Abstract 16327: Machine Learning-Based Prediction of Clinical Frailty Scale Based on Gait Analysis in Elderly Patients With Heart Failure

Background: Although frailty assessment is recommended for guiding treatment strategies and outcome prediction in elderly patients with heart failure (HF), most frailty scales are subjective, and the scores vary among raters. We sought to develop a machine learning-based automatic rating method/system/model of the clinical frailty scale (CFS) for patients with HF. Methods: We prospectively examined 358 elderly (≥75 years) with symptomatic chronic HF patients (mean age 82.3±5.2 years, left ventricular ejection fraction 58 [IQR 45-67]%) from six sites between January 2019 and September 2022. The CFS was evaluated based on the modified Delphi method by 10 independent trained cardiologists as the gold standard. We obtained body-tracking motion data using OpenPose ® , a deep learning-based pose estimation library, on a smartphone camera. Predicted CFS was calculated from 128 key features, including gait parameters, using the extremely randomized trees (ERT) model. To evaluate the performance of this model, we calculated Cohen’s weighted kappa and intraclass correlation coefficient (ICC) between the predicted and actual CFSs. The studied patients were divided into derivation (from a university hospital) and validation (from other five sites) cohorts. Results: In the derivation (n = 181) and validation (n = 177) cohorts, the ERT model showed sufficient agreement between the actual and predicted CFSs (kappa 0.864, ICC 0.864 and kappa 0.796, ICC 0.796, respectively) ( Figure ). During a median follow-up period of 421 (IQR 280-616) days, the higher predicted CFS was independently associated with a higher risk of all-cause death (HR 1.97, 95% CI 1.21-3.20) adjusted for the MAGGIC mortality risk prediction score, N-terminal pro-brain natriuretic peptide, and serum albumin levels. Conclusions: Machine learning-based algorithms that automatically rate the CFS are feasible, and the predicted CFS is associated with the risk of all-cause death in elderly patients with HF.

Derivation of the Screening of Nutritional Risk in Intensive Care risk prediction score: A secondary analysis of a prospective cohort study.

For patients who are critically ill, the recommended nutrition risk screening tools are the Nutrition Risk in the Critically Ill (NUTRIC) and the Nutritional Risk Screening 2002 (NRS-2002) have limitations. To develop a new screening tool, the Screening of Nutritional Risk in Intensive Care (SCREENIC score), and assess its predictive validity. A secondary analysis of a prospective cohort study was conducted. Variables from several nutritional screening and assessment tools were considered. The high nutrition risk cutoff point was defined using mNUTRIC as a reference. Predictive validity was evaluated using logistic regression and Cox regression. The study included 450 patients (64 [54-71] years, 52.2% men). The SCREENIC score comprised six questions: (1) does the patient have ≥2 comorbidities (1.3 points); (2) was the patient hospitalized for ≥2 days before intensive care unit (ICU) admission (0.9 points); (3) does the patient have sepsis (1.0 point); (4) was the patient on mechanical ventilation upon ICU admission (1.2 points); (5) is the patient aged >65 years (1.2 points); and (6) does the patient exhibit signs of moderate/severe muscle mass loss according to the physical exam (0.6 points). The high nutrition risk cutoff point was set at 4.0. SCREENIC demonstrated moderate agreement (κ = 0.564) and high accuracy (0.896 [95% CI, 0.867-0.925]) with mNUTRIC. It predicted prolonged ICU (odds ratio [OR] = 1.81 [95% CI, 1.14-2.85]) and hospital stay (OR = 2.15 [95% CI, 1.37-3.38]). The SCREENIC score comprises questions with variables that do not require nutrition history. Further evaluation of its applicability, reproducibility, and validity in guiding nutrition therapy is needed using large external cohorts.

Abstract 18140: Is Socioeconomic Status Associated With a Novel Clinical Risk Prediction Score for Sudden Cardiac Death?

Introduction: Lower socioeconomic status (SES) is associated with higher risk of sudden cardiac death (SCD), but SES has not been used for SCD risk stratification. We have recently developed a novel risk score (VFRisk) for avertable SCD (8 clinical factors, 4 ECG markers, and 1 echocardiographic marker). We evaluated the association of the clinical components of the VFRisk score with SES. Hypothesis: Higher VFRisk scores will associate with lower SES. Methods: Individuals with SCD (2002-2019), were identified from a large ongoing study in a northwest US metro area (pop ~1 million). For each SCD case, a partial VFRisk score was calculated limited to the 8 clinical components (VFRisk-clinical; diabetes, HF, stroke, atrial fibrillation, COPD, history of MI, seizure, and syncope). The VFRisk-clinical score was categorized as low (<25 th percentile), moderate (25 th -75 th ), and high (>75 th percentile). SES was defined for each SCD case by residential census tract median household income and categorized into quintiles. Results: Among 4,412 SCD cases, data for SES and VFRisk-clinical was available for 3,606 (82%) with a median VFRisk of 2.8 (min 0 - max 13.4). There was a weak negative linear correlation between median household income quintile and VFRisk-clinical score (r = -0.05, p=0.002) and mean VFRisk-clinical score was slightly lower with higher SES quintile (Figure). However, there was no significant trend in the proportion of SCD cases with a high VFRisk-clinical score (>75 th percentile) by median income quintile (p=0.13). In a logistic regression model, income quintile was not significantly associated with a high VFRisk-clinical score (p=0.60). Conclusions: No clear association was observed between the VFRisk-clinical score and SES, possibly due to involvement of SES early in the causal pathway of these clinical conditions, or due to effects of SES on risk pathways for SCD separate from the components of the VFRisk score.

Abstract 14129: Identification of Highly Correlated Sub-Networks of Cardiovascular Ageing Imaging Markers With 10-Year Predicted Risk Scores

Introduction: While Framingham 10-year risk scores (FRS) estimates cardiovascular disease (CVD) risk in older adults, few studies have benchmarked these estimates against age-related changes in cardiac structure and function, which are complex and highly inter-related. We investigated associations between cardiovascular (CV) imaging markers and FRS, vascular age, calculated regression, and determined connectivity between them in a network analysis. Methods: Participants recruited from a community-based cohort without CVD completed standardized questionnaires that collected baseline clinical factors, from which FRS, vascular age and calculated regression were derived. Cardiac structural and functional parameters were obtained via echocardiography. Results: 202 participants (46.0% women, mean age 70.2 SD8.8) were included in this analysis. CV imaging markers correlated across a spectrum with FRS, vascular age and calculated regression. Predictive features and their associations by pearson correlation (nodes containing the variables; edges denoting links) (Fig 1) show significant associations. In ageing, this network showed that E/A ratio is inversely linked to FRS, vascular age and calculated regression while LVEF has a weak association with FRS and vascular age, without direct relationship with E/A ratio. While LAVI is associated with FRS and vascular age, LAVI is associated with E/E1 and not E/A ratio nor LVMI. Multivariate regression confirmed independent associations between FRS and vascular age with E/A ratio (b=-7.03, 95% CI -10.55, -3.52, p < 0.001; b= -15.13, 95% CI -19.33, -10.93, p < 0.001) and E/E prime ratio (b= 0.33, 95% CI 0.03 - 0.63, p = 0.033; b=0.66, 95% CI 0.30 - 1.02, p < 0.001). Conclusion: This structural collation of associations depicted different sub networks of cardiovascular imaging features into context. FRS, vascular age and calculated regression were associated with imaging markers of CV ageing at various network levels.

Abstract 17060: Preliminary Assessment of Artificial Intelligence Guided Echocardiographic Screening of Cardiac Amyloidosis

Background: Cardiac amyloidosis (CA) diagnosis is often overlooked. Novel CA treatments underscore the importance of early identification, as untreated CA has a poor prognosis. EchoNet-Screening is an echocardiogram-based artificial intelligence model to identify CA; previously refined by echocardiograms from patients with known CA. The goal of our study is to prospectively evaluate the accuracy of EchoNet-Screening across a large health system. Methods: All echocardiographic studies from our cardiac quaternary care center between 2019-2021 were batch-processed by EchoNet-Screening to produce a CA risk-prediction score (CA-RPS) for each patient based on the highest quality study, which is determined by the model. 336 patients with the highest CA-RPS underwent evaluation by detailed chart review by our CA clinical cardiology team. High risk patients without an alternative diagnosis are subsequently referred for CA specialty evaluation. (ClinicalTrial.gov/NCT05139797) Results: EchoNet-Screening was performed on echocardiographic images from 58,236 patients. Preliminary retrospective assessment of 336 patients with the highest CA-RPS revealed CA in 142 patients; almost half were not known from our advanced heart failure (AHF) registry and newly identified from chart review. 62 patients are pending prospective enrollment for CA subspecialty evaluation to further characterize the performance of EchoNet-Screening. Conclusions: Preliminary results from our trial demonstrate a high accuracy of EchoNet-Screening for detection of patients with known CA, emphasized by high rates of identification of CA patients even on initial chart review. Further work is ongoing to evaluate the performance of EchoNet-Screening to bring high-risk patients to new diagnoses and eventual care.

Predicting Vaso-Occlusive Crises in Sickle Cell Disease through Digital, Longitudinal Tracking of Wearable Metrics and Patient-Reported Outcomes

Background Sickle Cell Disease (SCD) is a genetic disorder caused by an HBB gene mutation and resulting structural variant haemoglobin (HbS) that polymerises upon deoxygenation, leading to a sickled morphology of red blood cells ( Chakravorty et al., Arch Dis Child 2015). This causes acute pain episodes, known as vaso-occlusive crises (VOC), that can result in severe end-organ damage in the long-term and a shortened lifespan ( Jang et al., J Transl Med 2021). Responsible for an estimated 95% of SCD hospitalisations and as a key predictor of death ( Darbari et al., Eur J Haematol 2020, Ballas et al., Am J Hematol 2005), it is critical to find new ways to drive early detection of VOCs in order to support preventative interventions. Aims This work aimed to develop a machine learning (ML) algorithm capable of predicting the potential onset of a VOC, using physiological data captured longitudinally by a wearable smartwatch, as well as patient-reported outcomes (PROs) entered via a specialised mobile phone application. Methods Following informed patient consent, participants gained access to a mobile phone application, or “digital patient wallet”, encompassing their health data and access to a PRO entry portal ( Summers et al., HemaSphere 2023). This allowed for daily recording of PROs including EQ-5D-5L, pain, mood, and fatigue scores, alongside self-reported VOCs. Participants were also provided with a Withings ScanWatch to be worn day and night, capturing physical activity, sleep quality, and heart rate data in an automated manner. Medical record data including healthcare utilisation, pathology, and demographic data was obtained through participant completion of a Subject Access Request form. Data for a snapshot cohort of 186 patients over a 1-month period (May-June 2023) were analysed through an ensemble of ML models: gradient boosting machine, neural network, and k-means clustering. All collated metrics were fed into the model, with the exclusion of hospitalisation as an 'a posteriori' variable, to ensure predictions were based on appropriate variables and not strengthened by self-confirming factors. The resulting predicted likelihood of a VOC for each participant was calculated on each day and given as a scale from 0-100%, with a likelihood of 75% or higher deemed to be a predicted VOC. This was then assessed against the true incidence of the participants' self-reported VOCs. Results The mean age was 36 (SD 11.6) years, and most patients were female (70%). The HbSS genotype was most common (75%), followed by HbSC (16%), and HbS Beta + Thalassemia (5%). 82,804 datapoints were collated for all participants across this period, encompassing 90 different variables. 69 instances of a VOC were self-reported by patients during the period of analysis, 58 (84%) of which were accurately predicted by the algorithm. 2,366 instances of no VOC were self-recorded, giving a negative predicted value of 83% (1,958/2,366 predicted). Further breakdown by the predicted likelihood score is displayed in Figure 2, highlighting the spread of individually calculated VOC likelihood scores per day against the incidence of patient-reported VOCs. Summary/Conclusions This work provides an important translation of daily trackable metrics through a remote, digital ecosystem, to the real-world prediction of potential VOC events in SCD. The purpose of this algorithm is, at its core, to act as a pre-emptive warning system from which proactive behavioural changes or interventions can be enacted. As such, the prediction of additional potential risk scores above the 75% threshold reflects an approach of ‘over-predicting’ considered to be a preferable strategy for the algorithm, given the severe nature of VOCs and the health impacts of missing a potential event. Our ongoing aim is to further develop the algorithm and its contributing metrics against medical data, in addition to patient self-reported events, to further optimise the predictor. This includes the curation of core metrics requiring minimal patient input, in order to reduce missing datapoints across those that feed into the algorithm and thereby reduce unpredicted VOCs. Ultimately, we hope to provide an early alert system that will help patients to identify their VOCs sooner, and thus action the next steps that may prevent possible hospital admissions.

Validating and Improving Adjusted Clinical Group's Future Hospitalization and High-Cost Prediction Models for Dutch Primary Care.

The rise in health care costs, caused by older and more complex patient populations, requires Population Health Management approaches including risk stratification. With risk stratification, patients are assigned individual risk scores based on medical records. These patient stratifications focus on future high costs and expensive care utilization such as hospitalization, for which different models exist. With this study, the research team validated the accuracy of risk prediction scores for future hospitalization and high health care costs, calculated by the Adjusted Clinical Group (ACG)'s risk stratification models, using Dutch primary health care data registries. In addition, they aimed to adjust the US-based predictive models for Dutch primary care. The statistical validity of the existing models was assessed. In addition, the underlying prediction models were trained on 95,262 patients' data from de Zoetermeer region and externally validated on data of 48,780 patients from Zeist, Nijkerk, and Urk. Information on age, sex, number of general practitioner visits, International Classification of Primary Care coded information on the diagnosis and Anatomical Therapeutic Chemical Classification coded information on the prescribed medications, were incorporated in the model. C-statistics were used to validate the discriminatory ability of the models. Calibrating ability was assessed by visual inspection of calibration plots. Adjustment of the hospitalization model based on Dutch data improved C-statistics from 0.69 to 0.75, whereas adjustment of the high-cost model improved C-statistics from 0.78 to 0.85, indicating good discrimination of the models. The models also showed good calibration. In conclusion, the local adjustments of the ACG prediction models show great potential for use in Dutch primary care.

Risk factor analysis of refracture in the same cemented vertebra after percutaneous kyphoplasty for Kümmell's disease.

The objective of this study was to evaluate the factors that affect refracture in the same cemented vertebra after percutaneous kyphoplasty (PKP) for Kümmell's disease (KD) and establish a risk prediction score. A total of 2932 patients who were treated with PKP for KD between January 2019 and December 2021 were retrospectively reviewed. After inclusion and exclusion criteria were applied, 191 patients were included in the study. According to the criteria for refracture, there were 50 patients in the refracture group and 141 patients in the no-refracture group. Twenty-five factors were analyzed. Patient demographics, medical history, imaging data, surgical data, and postoperative management were reviewed. Multivariate logistic regression modeling was used to identify the independent risk factors for refracture. Receiver operating characteristic (ROC) curve analysis was used to assess and establish a risk score system and further predict the risk of refracture. In this study, 50 (26.2%) patients developed a refracture. Through univariate analysis, bone mineral density (BMD) (p < 0.001), compression rate (p = 0.007), classification (i.e., the stages determined by the compression ratios) (p < 0.001), bone cement volume (p < 0.001), volume fraction (p < 0.001), distribution pattern (p = 0.007), non-PMMA endplate contact (p < 0.001), and anti-osteoporosis therapy (p < 0.001) were found to be significant factors for post-cement vertebral refracture after PKP in patients with KD. Three independent risk factors were found to be significant for refracture: small volume fraction, low BMD, and no anti-osteoporosis therapy. One point was assigned for each factor. The incidence rates of refracture in patients with scores of 0, 1, 2, and 3 were 3.7%, 4.4%, 42.0%, and 100%, respectively. The area under the ROC curve for this risk prediction score was 0.888 (p < 0.001), indicating moderate accuracy. Volume fraction, BMD, and osteoporosis therapy are the main factors influencing the refracture of the same cemented vertebra in KD. On the basis of these factors, the risk prediction score developed in this paper can be used to forecast the incidence of refracture.

Risk Scores for Prediction of Postoperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Meta-Analysis

Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery and is associated with poor clinical outcomes. The objective of this systematic review and meta-analysis was to assess the performance of risk scores to predict POAF in cardiac surgery patients. We searched MEDLINE, Embase, and Cochrane CENTRAL for studies that developed/evaluated a POAF risk prediction model. Pairs of reviewers independently screened studies and extracted data. We pooled area under the receiver operating curves (AUCs), sensitivity and specificity, and adjusted odds ratios from multivariable regression analyses using the generic inverse variance method and random effects models. Forty-three studies (n=63,847) were included in the quantitative synthesis. Most scores were originally developed for other purposes but evaluated for predicting POAF. Pooled AUC revealed moderate POAF discrimination for the EuroSCORE II (AUC 0.59, 95% confidence interval [CI] 0.54 to 0.65), Society of Thoracic Surgeons (AUC 0.60, 95% CI 0.56 to 0.63), EuroSCORE (AUC 0.63, 95% CI 0.58 to 0.68), CHADS2 (AUC 0.66, 95% CI 0.57 to 0.75), POAF Score (AUC 0.66, 95% CI 0.63 to 0.68), HATCH (AUC 0.67, 95% CI 0.57 to 0.75), CHA2DS2-VASc (AUC 0.68, 95% CI 0.60 to 0.75) and SYNTAX scores (AUC 0.74, 95% CI 0.71 to 0.78). Pooled analyses at specific cutoffs of the CHA2DS2-VASc, CHADS2, HATCH, and POAF scores demonstrated moderate-to-high sensitivity (range 46% to 87%) and low-to-moderate specificity (range 31% to 70%) for POAF prediction. In conclusion, existing clinical risk scores offer at best moderate prediction for POAF after cardiac surgery. Better models are needed to guide POAF risk stratification in cardiac surgery patients.

Comparison between the Apfel score and the Koivuranta score in predicting the occurrence of postoperative nausea and vomiting during general anesthesia

Abstract Background: PONV is one of the side effects that often occur after general anesthesia, occurs in the first 24 hours post surgery and occurs in as many as 30-70% of hospitalized patients. PONV greatly avoided by most patients and anesthesiologists. Recent risk score for prediction of PONV has been used as a way to classify patients according to risk prediction and provide prophylaxis in accordance with this classification. For everyday clinical purposes, a simple risk score in easy to do and shows the correlation between the predicted incidence of PONV in patients hospitalized. In the clinical practice known various risk score for prediction of PONV as Apfel scores, Koivuranta scores, Sinclair scores, Palazzo scores, Gan scores, and Scholz scores varying accuracy. Methods: The research design was a cohort study with the total of 80 patients recruited. Ordinal and nominal data was analyzed using chi-square test. P value &lt; 0,05 was declared significant with 95% confidence level. Measurements taken are incidence of PONV between Apfel scores to Koivuranta scores with PONV scores. Results: Incidence of PONV in patients who carried the scoring with Apfel scores higher (80%) than Koivuranta scores, and statistically highly significant differences (p &lt; 0,01). The results said that Apfel scores are more accurate than the Koivuranta scores, indicated by results of PONV scores positive (&gt;1) on the Apfel scores 32 people (80%) and on the Koivuranta scores 12 people (30%), whereas PONV scores negative (≤ 1) on the Apfel scores 8 people (20%) and on the Koivuranta scores 28 people (70%). Conclusions: Apfel scores are more accurate than the Koivuranta scores in predicting the occurrence of PONV in patients with general anesthesia.

Risk Prediction Score for AKI in Critically Ill Septic Filipino Patients Admitted in Perpetual Succour Hospital: An Analytical Prospective Cohort Single-Center Study

Risk Prediction Score for AKI in Critically Ill Septic Filipino Patients Admitted in Perpetual Succour Hospital: An Analytical Prospective Cohort Single-Center Study

Improving the performance of the Cleveland Clinic Score for predicting acute kidney injury after cardiac surgery: a prospective multicenter cohort study.

Cardiac surgery-associated acute kidney injury (CSA-AKI) is associated with high short- and long-term mortality rates. The prediction of CSA-AKI is crucial for early detection and treatment. Current predictive models may be improved by potentially useful preoperative and intraoperative information. This multicenter prospective cohort study recruited 261 consecutive patients at high risk for developing CSA-AKI, based on a Cleveland Clinical Score (CCS) of ≥4 points from July to December 2017 in 14 hospitals in Spain and the UK. Postoperative AKI occurred in 145 (55.5%) patients. The receiver operating characteristics curve (AUC) of a base model including only the CCS was compared with models including additional preoperative and intraoperative variables such as the estimated glomerular filtration rate (eGFR) instead of plasmatic creatinine, intraoperative urine output, baseline hemoglobin, nadir hemoglobin, and glycosylated hemoglobin (HbA<inf>1c</inf>) instead of diabetes mellitus. The performance of each model for AKI was compared. The CCS alone gave an AUC of 0.67 (95% CI, 0.56-0.78) for postoperative AKI. None of the single variables added to the base model CCS improve discrimination. The AUC for postoperative AKI was improved when baseline hemoglobin, eGFR instead of plasmatic creatinine, HbA<inf>1c</inf>, and nadir hemoglobin were added to the CCS (AUC=0.77; 95% CI, 0.67-0.87; P=0.02). The addition of baseline hemoglobin, eGFR, HbA<inf>1c</inf>, and nadir intraoperative hemoglobin may be useful for improving the discrimination of the clinical predictive risk scores for AKI.

Predictive Risk Score for Postparathyroidectomy Hungry Bone Syndrome in Patients With Secondary Hyperparathyroidism.

Secondary hyperparathyroidism (SHPT) frequently affects patients with end-stage renal disease. Hungry bone syndrome (HBS) is a common complication among patients who undergo parathyroidectomy for SHPT and may cause prolonged hospitalization or require intensive care. The objective of this study is to develop a scoring system to stratify patients according to their risk of developing HBS. A retrospective cohort study was performed using the US Renal Data System (2010-2021). Univariable and multivariable logistic regression models were developed and weighted β-coefficients from the multivariable model were used to construct a risk score for the development of HBS. Positive and negative predictive values were assessed. Of 17 074 patients who underwent parathyroidectomy for SHPT, 19.4% developed HBS. Intensive care unit admission was more common in patients who developed HBS (33.5% vs 24.6%, P < .001). On multivariable logistic regression analysis, younger age, renal osteodystrophy, longer duration of dialysis, longer duration of kidney transplant, and higher Elixhauser score were significantly associated with HBS. A risk score based on these clinical factors was developed, with a total of 6 possible points. Rates of HBS ranged from 8% in patients with 0 points to 44% in patients with 6 points. The risk score had a poor positive predictive value (20.3%) but excellent negative predictive value (89.3%) for HBS. We developed a weighted risk score that effectively stratifies patients by risk for developing HBS after parathyroidectomy. This tool can be used to counsel patients and to identify patients who may not require postoperative hospitalization.

Risk Factors and Predictive Model for Mortality of Hospitalized COVID-19 Elderly Patients from a Tertiary Care Hospital in Thailand.

Background: Early detection of elderly patients with COVID-19 who are at high risk of mortality is vital for appropriate clinical decisions. We aimed to evaluate the risk factors associated with all-cause in-hospital mortality among elderly patients with COVID-19. Methods: In this retrospective study, the medical records of elderly patients aged over 60 who were hospitalized with COVID-19 at Thammasat University Hospital from 1 July to 30 September 2021 were reviewed. Multivariate logistic regression was used to identify independent predictors of mortality. The sum of weighted integers was used as a total risk score for each patient. Results: In total, 138 medical records of patients were reviewed. Four identified variables based on the odds ratio (age, respiratory rate, glomerular filtration rate and history of stroke) were assigned a weighted integer and were developed to predict mortality risk in hospitalized elderly patients. The AUROC of the scoring system were 0.9415 (95% confidence interval, 0.9033-0.9716). The optimized scoring system was developed and a risk score over 213 was considered a cut-off point for high mortality risk. Conclusions: A simple predictive risk score provides an initial assessment of mortality risk at the time of admission with a high degree of accuracy among hospitalized elderly patients with COVID-19.

STC2 is a potential biomarker of hepatocellular carcinoma with its expression being upregulated in Nrf1α-deficient cells, but downregulated in Nrf2-deficient cells

Nrf1 (encoded by Nfe2l1) and Nrf2 (encoded by Nfe2l2), as two key members of the CNC-bZIP transcription factor, exhibit significant functional differences in their pathophysiology. Our previous findings demonstrated that loss of Nrf1α (i.e., a full-length isoform of Nrf1) promotes HepG2-derived tumor growth in xenograft mice, but malgrowth of the xenograft tumor is significantly suppressed by knockout of Nrf2. To gain insights into the mechanism underlying such marked distinctions in their pathologic phenotypes, we mined transcriptome data from liver cancer in the TCGA database to establish a prognostic model and calculate predicted risk scores for each cell line. The results revealed that knockout of Nrf1α markedly increased the risk score in HepG2 cells, whereas the risk score was reduced by knockout of Nrf2. Notably, stanniocalcin 2 (STC2), a biomarker associated with liver cancer, that is upexpressed in hepatocellular carcinoma (HCC) tissues with a reduction in the overall survival ratio of those patients. We observed increased expression levels of STC2 in Nrf1α−/− cells but decreased expression in Nrf2−/− cells. These findings suggested that STC2 may play a role in mediating the distinction between Nrf1α−/− and Nrf2−/−. Such potential function of STC2 was further corroborated through a series of experiments combined with transcriptomic sequencing. The results revealed that STC2 functions as a dominant tumor-promoter, because the STC2-leading increases in clonogenicity of hepatoma cells and malgrowth of relevant xenograft tumor were almost completely abolished in STC2−/− cells. Together, these demonstrate that STC2 could be paved as a potential therapeutic target, albeit as a diagnostic marker, for HCC.

A novel risk score for venous thromboembolism in lung cancer patients: a retrospective cohort study

Background: Venous thromboembolism (VTE) is a common and potentially fatal complication in patients with lung cancer. This study aimed to develop and validate a risk score for early prediction of VTE in these patients. Methods: Four hundred and one patients with lung cancer from three pulmonology departments hospitalized between January 2011 and December 2021 were retrospectively assessed. The population was divided into two groups: a Development Group (182 patients) and a validation group (199 patients). In the development group, the risk score system was developed, via univariate and multivariate analyses, based on demographic and clinicopathological variables; it was then validated in the validation group. Results: The incidence of VTE was 26.8% in the development group. It was 25.8%, and 27.6% in the internal and external validation groups, respectively. Hemoglobin level &lt;10g/l, metastasis, histological type poorly or undifferentiated non-small cell carcinoma, and active smoking were the items of the risk score system. This score allowed proper stratification of patients with either high or low risk of VTE in the development group (c statistic =0.703). The patients in the development group were classified into 3 risk groups: low risk (scores 0-1), moderate risk (scores 2-3), and high risk (scores 4-5). When validated in the validation group, there was a moderate loss of predictive power of the score (c statistic=0.641), but the categorization of the patients by the score remained clinically useful. Conclusions: This risk score requires prospective validation studies on a nationwide scale in order to use it as a valid tool for the prevention of VTE in lung cancer.

Development of a Predictive Model for Persistent Instability Following Conservative Management of Type II Odontoid Fractures

Odontoid fractures are common cervical spine fractures; however, significant controversy exists regarding their treatment. Risk factors for failure of conservative therapy have been identified, although no predictive risk score has been developed to aid in decision-making. A retrospective review was conducted of all patients evaluated at a level 1 trauma center. Patients identified with type II odontoid fractures as classified by the D'Alonzo Classification system who were treated with external orthosis were included in analysis. Patients were considered to have failed conservative therapy if they were offered surgical intervention. A machine learning method (Risk-SLIM) was then utilized to create a risk stratification score based on risk factors to identify patients at high risk for requiring surgical intervention due to persistent instability. A total of 138 patients were identified as presenting with type II odontoid fractures that were treated conservatively; 38 patients were offered surgery for persistent instability. The Odontoid Fracture Predictive Model (OFPM) was created using a machine learning algorithm with a 5-fold cross validation area under the curve of 0.7389 (95% CI: 0.671 to 0.808). Predictive factors were found to include fracture displacement, displacement greater than 5 mm, comminution at the fracture base, and history of smoking. The probability of persistent instability was <5% with a score of 0 and 88% with a score of5. The OFPM model is a unique, quick, and accurate tool to assist in clinical decision-making in patients with type II odontoid fractures. External validation is necessary to evaluate the validity of these findings.