Risk Prediction Score Research Articles

Use of Polygenic Risk Score for Prediction of Heart Failure in Cancer Survivors

BackgroundThe risk for heart failure (HF) is increased among cancer survivors, but predicting individual HF risk is difficult. Polygenic risk scores (PRS) for HF prediction summarize the combined effects of multiple genetic variants specific to the individual. ObjectivesThe aim of this study was to compare clinical HF prediction models with PRS in both cancer and noncancer populations. MethodsCancer and HF diagnoses were identified using International Classification of Diseases-10th Revision codes. HF risk was calculated using the ARIC (Atherosclerosis Risk in Communities) HF score (ARIC-HF). The PRS for HF (PRS-HF) was calculated according to the Global Biobank Meta-analysis Initiative. The predictive performance of the ARIC-HF and PRS-HF was compared using the area under the curve (AUC) in both cancer and noncancer populations. ResultsAfter excluding 2,644 participants with HF prior to consent, 440,813 participants without cancer (mean age 57 years, 53% women) and 43,720 cancer survivors (mean age 60 years, 65% women) were identified at baseline. Both the ARIC-HF and PRS-HF were significant predictors of incident HF after adjustment for chronic kidney disease, overall health rating, and total cholesterol. The PRS-HF performed poorly in predicting HF among cancer (AUC: 0.552; 95% CI: 0.539-0.564) and noncancer (AUC: 0.561; 95% CI: 0.556-0.566) populations. However, the ARIC-HF predicted incident HF in the noncancer population (AUC: 0.804; 95% CI: 0.800-0.808) and provided acceptable performance among cancer survivors (AUC: 0.748; 95% CI: 0.737-0.758). ConclusionsThe prediction of HF on the basis of conventional risk factors using the ARIC-HF score is superior compared to the PRS, in cancer survivors, and especially among the noncancer population.

Prediction Models for HIV Infection in Infants: Analysis of Scoring Systems on Maternal, Infants, and Mode of Delivery Risk Factors.

Diagnosis for HIV in infants is hard to determine, particularly in limited- resource areas. A delay in the diagnosis of HIV-infected infants will lead to high morbidity and mortality. The purpose of this project is to construct a model of an HIV-positive infant and develop a useful and practical scoring system to estimate the likelihood of mother-to-child transmission that can be applied in the field. A cross-sectional study on 100 subjects through medical records of infants born to HIV-infected mothers was conducted at four hospitals and one community health center. Several models of risk prediction scores of HIV-infected infants were then made. Furthermore, the performed validation was performed on 20 subjects of infants born to mothers with HIV in three hospitals by comparing the scoring system and the result of the PCR RNA examination performed at the age of 6 weeks old. The risk of HIV-infected infants was higher in mothers who did not receive ARV through PMTCT programs (OR 33.6; 95% CI 4.0 to 282.2), pulmonary TB infection (OR 5.1; IK95% 1.6 to 16.0) and vaginal delivery (OR 9.2; IK95 2.2 to 38.0%). Two models can predict the occurrence of infected HIV infants effectively. Model 1 consists of maternal age, maternal ARVs, lung TB infection, gestational age, mode of delivery, and sex of the infants with sensitivity and specificity of 78.9% and 70.8% (AUC=0.817 [95% CI 0.709 to 0.926]) and likelihood ratio score of 4. Model 2 consists of ARVs to the mother, pulmonary TB infection, and mode of delivery with sensitivity and specificity of 73.7% and 86.1%; AUC value of 0.812 (95% CI 0.687 to 0.938) and likelihood ratio of 5. External Validation gave similar results to the Model 2 scoring system with PCR RNA. The prediction score of HIV-infected infants in Model 2 can be used in newborns of HIV-positive mothers as an effective and practical risk screening tool for HIV-infected infants before the gold standard examination by PCR.

Risk factors of passive suicidal ideation among outpatients in traditional medicine clinics: The case of Korean medicine in South Korea

Suicide is the leading cause of death worldwide, especially in South Korea. Individuals using Korean medicine (KM) clinics are common in this country, but KM doctor is not yet used in the country's suicide prevention policy. In this study, we investigated the prevalence and risk factors of passive suicidal ideation (SI) among outpatients at KM clinics, and attempted to develop a predictive model of SI through multivariate analysis. The Korea Health Panel Annual Data 2019, a nationally representative survey in South Korea, was analyzed. In this study, 1924 (weighted n:5,958,666) people aged 19 or older who used the KM outpatient service at least once in 2019 were set as study subjects. The authors calculated the weighted prevalence of passive SI and identified significant sociodemographic, physical, and mental factors using the Rao-Scott chi-square test and weighted logistic regression. A suicide risk scorecard was developed using the point to double the odds method to quantify individual risk, resulting in a score range of 0–100. We found high prevalence of passive SI among KM outpatients (7.5 %), and the annual prevalence of passive SI tended to be higher with increasing age (4.6 % in young adults, 6.7 % in middle-aged, and 12.4 % in old age). Also, the prevalence increased as stress worsened (2.0 % in hardly, 3.9 % in a little, 13.7 % in a lot, and 24.8 % in very much). By using multiple logistic regression analysis, SI risk prediction score model was developed, including basic livelihood security recipients, presence of chronic diseases, perceived stress, depression, and low quality of life. According to our findings, passive SI is not uncommon among KM outpatients. Based on current findings, the potential role of KMDs in the country's suicide prevention strategy can be discussed focusing on specific groups such as socioeconomically vulnerable groups, the elderly population, and those with physical illnesses.

Higher Circulating Neutrophil Counts Is Associated with Increased Risk of All-Cause Mortality and Cardiovascular Disease in Patients with Diabetic Kidney Disease.

Accumulating evidence has suggested the pathogenic roles of chronic inflammation and neutrophils in diabetic kidney disease (DKD). This study investigated the relationship between neutrophils, all-cause, and cardiovascular disease (CVD) mortality in type 2 diabetes mellitus (T2DM) patients with DKD. We used data from the National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2020 to investigate the relationship between circulating neutrophils counts, kidney function indices, all-cause, and CVD mortality in adult T2DM patients with DKD. Clinical predictive models and risk scores for long-term mortality were constructed. 44,332 patients [8034 with T2DM and 36,323 without T2DM] were included. Two thousand two hundred twenty patients had DKD, and 775 died (31.5% related to CVD) during a follow-up of 6.18 (range: 5.94-6.42) years. Higher neutrophil counts (Quartile 4, Q4) were associated with increased all-cause and CVD mortality [HR 1.73 (95% CI 1.34-2.25) and 1.81 (95% CI 1.14-2.89), respectively, p < 0.0001 and 0.01]. Neutrophil counts in Q4 showed a positive correlation with urine albumin-creatinine ratio (UACR) but a negative association with eGFR (p < 0.01 for all). Clinical predictive models incorporating neutrophil counts showed satisfactory performance in forecasting 5-year and 10-year CVD mortality-free survival (ROC AUC 0.824 and 0.842, respectively), and the nomogram-predicted survival demonstrated good concordance with observed survival. Higher levels of circulating neutrophil counts show a significant correlation with renal abnormalities and higher all-cause and CVD mortality in T2DM patients with DKD. The novel clinical predictive models and risk scores incorporating neutrophil counts may facilitate stratification and, hence, risk factor management in DKD patients.

Development of a prediction score for Barrett's esophagus in Japanese health checkup settings.

The incidence of esophageal adenocarcinoma has recently increased in Asia, including Japan. A system to identify individuals at high risk for Barrett's esophagus (BE), a pre-cancerous condition of esophageal adenocarcinoma, among the general population is needed to perform endoscopic surveillance appropriately. We therefore developed risk prediction scores for BE at health checkups in Japan. 4128 consecutive health checkup examinees were retrospectively enrolled from October 2021 to March 2022. A prediction score for BE was developed based on the linear transformation of β-regression coefficients in a multivariable regression model incorporating BE predictors. Internal validation was performed by evaluating discrimination and calibration of the prediction model. Three prediction scores corresponding to BE based on its length were developed: all lengths, ≥ 1cm, ≥ 2cm. All scores were internally validated, and the model calibration was excellent. The performance of the prediction models was better for longer BE, with a c-statistic of 0.70 for BE ≥ 2cm, than for shorter values. The prediction score for BE ≥ 2cm yielded sensitivity and specificity of 52.9% and 78.6% in high-risk subjects and 91.2% and 29.3% in intermediate- or high-risk subjects, respectively. This prediction score can potentially increase the endoscopic detection of BE by identifying potentially high-risk individuals from the general population. This is the first report on developing a prediction score for BE that may suit the Japanese population.

Risk Prediction Score for Thermal Mapping of Pharmaceutical Transport Routes in Brazil

Risk Prediction Score for Thermal Mapping of Pharmaceutical Transport Routes in Brazil

Escalating Surgical Treatment for Left Ventricular Assist Device Infection and Expected Mortality: Clinical Risk Prediction Score.

Left ventricular assist devices (LVAD) improve survival for patients with cardiac failure, but LVAD-specific infection (VSI) remains a challenge with poorly understood predictive risk factors. The indications and use of escalating medical treatment to surgical debridement and potential flap reconstruction are not well characterized. A retrospective review of consecutive patients undergoing primary LVAD implantation at a tertiary academic center was performed. The primary outcome measures were 90-day and overall mortality after VSI. Cox proportional hazards regression was used to generate a risk prediction score for mortality. Of the 760 patients undergoing primary LVAD implantation, 255 (34%) developed VSI; of these patients, 91 (36%) were managed medically, 134 (52%) with surgical debridement, and 30 (12%) with surgical debridement and flap reconstruction. One-year survival after infection was 85% with median survival of 2.40 years. Factors independently associated with increased mortality were diabetes (hazard ratio [HR] 1.44, p = 0.04), MRSA infection (HR 1.64, p = 0.03), deep space (pump pocket or outflow cannula) involvement (HR 2.26, p < 0.001), and extracorporeal membrane oxygenation after LVAD (HR 2.52, p < 0.01). Factors independently associated with decreased mortality were flap reconstruction (HR 0.49, p = 0.02) and methicillin-sensitive Staphylococcus aureus infection (HR 0.63, p = 0.03). A clinical risk prediction score was developed using these factors and showed significant differences in median survival, which was 5.67 years for low-risk (score 0 to 1), 3.62 years for intermediate-risk (score 2), and 1.48 years for high-risk (score ≥3; p < 0.001) patients. We developed a clinical risk prediction score to stratify patients with VSI. In selected cases, escalating surgical treatment was associated with increased survival. Future work is needed to determine whether early surgical debridement and flap reconstruction can alter outcomes in select cases of VSI.

Hanc score is a replicable clinical risk score for outcome prediction of Taiwanese clopidogrel non-responders after percutaneous coronary intervention

Hanc score is a replicable clinical risk score for outcome prediction of Taiwanese clopidogrel non-responders after percutaneous coronary intervention

Evaluation of risk prediction scores for adults hospitalized with COVID-19 in a highly-vaccinated population, Aotearoa New Zealand 2022

ObjectivesCOVID-19 severity prediction scores need further validation due to evolving COVID-19 illness. We evaluated existing COVID-19 risk prediction scores in Aotearoa New Zealand, including for Māori and Pacific peoples who have been inequitably affected by COVID-19. MethodsWe conducted a multicenter retrospective cohort study in adults hospitalized with COVID-19 from January to May 2022, including all Māori and Pacific patients, and every second non-Māori, non-Pacific (NMNP) patient to achieve equal analytic power by ethnic grouping. We assessed the accuracy of existing severity scores (4C Mortality, CURB-65, PRIEST, and VACO) to predict death in the hospital or within 28 days. ResultsOf 2319 patients, 582 (25.1%) identified as Māori, 914 (39.4%) as Pacific, and 862 (37.2%) as NMNP. There were 146 (6.3%, 95% confidence interval 5.4-7.4%) deaths, with a predicted probability of death higher than observed mortality for VACO (10.4%), modified PRIEST (15.1%) and 4C mortality (15.5%) scores, but lower for CURB-65 (4.5%). C-statistics (95% CI) of severity scores were: 4C mortality: Māori 0.82 (0.75, 0.88), Pacific 0.87 (0.83, 0.90), NMNP 0.90 (0.86, 0.93); CURB-65: Māori 0.83 (0.69, 0.92), Pacific 0.87 (0.82, 0.91), NMNP 0.86 (0.80, 0.91); modified PRIEST: Māori 0.85 (0.79, 0.90), Pacific 0.81 (0.76, 0.86), NMNP 0.83 (0.78, 0.87); and VACO: Māori 0.79 (0.75, 0.83), Pacific 0.71 (0.58, 0.82), NMNP 0.78 (0.73, 0.83). ConclusionsFollowing re-calibration, existing risk prediction scores accurately predicted mortality.

Exploring the prognostic landscape of oral squamous cell carcinoma through mitochondrial damage-related genes

Oral squamous cell carcinoma (OSCC), the most prevalent form of oral cancer, poses significant challenges to the medical community due to its high recurrence rate and low survival rate. Mitochondrial Damage-Related Genes (MDGs) have been closely associated with the occurrence, metastasis, and progression of OSCC. Consequently, we constructed a prognostic model for OSCC based on MDGs and identified potential mitochondrial damage-related biomarkers. Gene expression profiles and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Differential analysis was conducted to identify MDGs associated with OSCC. COX analysis was employed to screen seven prognosis-related MDGs and build a prognostic prediction model for OSCC. Cases were categorized into low-risk or high-risk groups based on the optimal risk score threshold. Kaplan-Meier (KM) analysis revealed significant survival differences (P < 0.05). Additionally, the area under the ROC curve (AUC) for patient survival at 1 year, 3 years, and 5 years were 0.687, 0.704, and 0.70, respectively, indicating a high long-term predictive accuracy of the prognostic model. To enhance predictive accuracy, age, gender, risk score, and TN staging were incorporated into a nomogram and verified using calibration curves. Risk scoring based on MDGs was identified as a potential independent prognostic biomarker. Furthermore, BID and SLC25A20 were identified as two potential independent mitochondrial damage-related prognostic biomarkers, offering new therapeutic targets for OSCC.

Hemodynamic characteristics in ruptured and unruptured intracranial aneurysms: a prospective cohort study utilizing the AneurysmFlow™ tool.

Hemodynamic factors significantly influence the onset, progression, and rupture of intracranial aneurysms (IAs). Current rupture risk prediction scores focus primarily on the clinical, anatomical and morphological aspects. This study aimed to investigate the hemodynamic characteristics differences between ruptured and unruptured IAs. Conducted from July 2021 to July 2022, this prospective cohort study involved patients with ruptured and unruptured IAs undergoing digital subtraction angiography (DSA). Hemodynamic characteristics were assessed using the AneurysmFlow™ tool. Hemodynamic, clinical, anatomical and morphological parameters were compared between ruptured and unruptured IA groups. The study included 127 patients with 135 aneurysms (67 ruptured, 68 unruptured). Complex flow patterns (type 3 and 4) were observed more frequently in ruptured aneurysms compared to unruptured aneurysms (odds ratio [OR], 5.57; 95% confidence interval [CI], 2.49-12.45; P < 0.001) in univariate analysis, and were also more common in unruptured aneurysms associated with daughter sacs features (P = 0.015). The mean aneurysm flow amplitude (MAFA) was lower in ruptured aneurysms, and associated with lower flow velocity in the parent artery related to vasospasm. MAFA in the aneurysmal dome or any additional daughter sacs was lowest compared to other regions inside the aneurysms. The technical failure rate of AneurysmFlow™ measurements was 8.5% (12 out of 139 patients). Additionally, hypertension (OR, 0.42; 95% CI, 0.30-0.54; P < 0.001), bifurcation location (AcomA/ACA/MCA/PcomA/posterior circulation) (OR, 0.17; 95% CI, 0.05-0.29; P = 0.005), and irregular shape (OR, 0.19; 95% CI, 0.05-0.35; P = 0.012) were identified as independently associated with rupture. Complex flow patterns identified on the AneurysmFlow™ tool are significantly more common in ruptured and unruptured aneurysms associated with daughter sac features. The lowest MAFA in the aneurysmal dome and daughter sacs likely indicates specific pathophysiological changes within the aneurysm wall associated with rupture incidence. Hypertension, bifurcation location, and an irregular shape are independently associated with the risk of rupture. Further multicenter studies with larger sample sizes are needed to validate these findings. ACA = anterior cerebral artery; AcomA = anterior communicating artery; IAs = intracranial aneurysms; ICA = internal carotid artery; MAFA = mean aneurysm flow amplitude; MCA = middle cerebral artery; PcomA = posterior communicating artery; RIAs = ruptured intracranial aneurysms; SAH = subarachnoid hemorrhage; UIAs = unruptured intracranial aneurysms.

Developing a risk score to inform the use of rhBMP-2 in adult spinal deformity surgery.

Recombinant human bone morphogenetic protein-2 (rhBMP-2) has not shown superior benefit overall in cost-effectiveness during adult spinal deformity (ASD) surgery. Retrospective PURPOSE: Generate a risk score for pseudarthrosis to inform the utilization of rhBMP-2, balancing costs against quality of life and complications. ASD patients with 3-year data were included. Quality of life gained was calculated from ODI to SF-6D and translated to quality-adjusted life years (QALYs). Cost was calculated using the PearlDiver database and CMS definitions for complications and comorbidities. Established weights were generated for predictive variables via logistic regression to yield a predictive risk score for pseudarthrosis that accounted for frailty, diabetes, depression, ASA grade, thoracolumbar kyphosis and three-column osteotomy use. Risk score categories, established via conditional inference tree (CIT)-derived thresholds were tested for cost-utility of rhBMP-2 usage, controlling for age, prior fusion, and baseline deformity and disability. 64% of ASD patients received rhBMP-2 (308/481). There were 17 (3.5%) patients that developed pseudarthrosis. rhBMP-2 use overall did not lower pseudarthrosis rates (OR: 0.5, [0.2-1.3]). Pseudarthrosis rates for each risk category were: No Risk (NoR) 0%; Low-Risk (LowR) 1.6%; Moderate Risk (ModR) 9.3%; High-Risk (HighR) 24.3%. Patients receiving rhBMP-2 had similar QALYs overall to those that did not (0.163 vs. 0.171, p = .65). rhBMP-2 usage had worse cost-utility in the LowR cohort (p < .001). In ModR patients, rhBMP-2 usage had equivocal cost-utility ($53,398 vs. $61,581, p = .232). In the HighR cohort, the cost-utility was reduced via rhBMP-2 usage ($98,328 vs. $211,091, p < .001). Our study shows rhBMP-2 demonstrates effective cost-utility for individuals at high risk for developing pseudarthrosis. The generated score can aid spine surgeons in the assessment of risk and enhance justification for the strategic use of rhBMP-2 in the appropriate clinical contexts. III.

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.

A clinical risk score for predicting acute kidney injury in sepsis patients receiving normal saline in Northern Thailand: a retrospective cohort study.

Normal saline is commonly used for resuscitation in sepsis patients but has a high chloride content, potentially increasing the risk of acute kidney injury (AKI). This study evaluated risk factors and developed a predictive risk score for AKI in sepsis patients treated with normal saline. This retrospective cohort study utilized the medical and electronic health records of sepsis patients who received normal saline between January 2018 and May 2020. Predictors of AKI used to construct the predictive risk score were identified through multivariate logistic regression models, with discrimination and calibration assessed using the area under the receiver operating characteristic curve (AUROC) and the expected-to-observed (E/O) ratio. Internal validation was conducted using bootstrapping techniques. AKI was reported in 211 of 735 patients (28.7%). Eight potential risk factors, including norepinephrine, the Acute Physiology and Chronic Health Evaluation II score, serum chloride, respiratory failure with invasive mechanical ventilation, nephrotoxic antimicrobial drug use, history of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers use, history of liver disease, and serum creatinine were used to create the NACl RENAL-Cr score. The model demonstrated good discrimination and calibration (AUROC, 0.79; E/O, 1). The optimal cutoff was 2.5 points, with corresponding sensitivity, specificity, positive predictive value, and negative predictive value scores of 71.6%, 72.5%, 51.2%, and 86.4%, respectively. The NACl RENAL-Cr score, consisting of eight critical variables, was used to predict AKI in sepsis patients who received normal saline. This tool can assist healthcare professionals when deciding on sepsis treatment and AKI monitoring.

Early identification of people at high risk of oral cancer-A review of existing risk prediction models.

Prediction plays a ubiquitous role in cancer care. At every stage of the illness, the patient, the physician, and the family must make numerous decisions. Utilizing epidemiological, clinical, biological, lifestyle, and genetic factors, a cancer-specific risk assessment model calculates the likelihood of developing cancer. In India, oral cancer ranks as the fourth most common cancer, affecting nearly 3,000,00 individuals annually. Because it is in the premalignant stage, oral cancer is easily detectable in the oral cavity. Prompt identification of this lesion can result in better outcomes and a higher standard of living. Advanced statistical techniques have been used to develop prediction algorithms or risk scores that identify individuals with a high risk of developing oral cancer. With the aid of these risk assessment models, specific individuals can be screened to aid in the early detection of the disease, which may result in better outcomes and lifestyle modifications. Finding the best model among the current risk models for oral cancer may be aided by a thorough examination of all these models. Finding and assessing the risk model that primary care physicians can use and easily apply in clinical practice will be made easier with a succinct and straightforward comparison of the models. This review compares the current models to determine which has the best performance metrics, which could lead to a better understanding of the advantages and disadvantages of various risk prediction models of oral cancer.

Establishing a risk score for prediction of intrapartum cesarean delivery among older women: A retrospective cohort study

ObjectiveTo determine risk factors and to develop a risk prediction score for intrapartum cesarean delivery (CD) in women over 40 years old. Study designA retrospective cohort study, in a single university-affiliated tertiary medical center. All women aged 40 years or more who planned a trial of labor between 2012 and 2022. Women who opted for an elective CD and those with non-viable fetuses were excluded. Maternal and neonatal characteristics of women who delivered vaginally were compared to those who underwent an intrapartum CD. Risk factors were examined using univariate and multivariate analysis. A score was developed to predict the need for intrapartum CD. We assessed a receiver operating characteristic curve to evaluate the performance of our model. Main outcome measureAn unplanned intrapartum cesarean section. ResultsDuring the study period, 122,583 women delivered at our center, of whom 6122 (4.9 %) aged 40 years or more attempted a trial of labor. Of them, 428 (7 %) underwent intrapartum CD. Several independent risk factors were identified, including nulliparity, regional anesthesia, induction of labor, use of antibiotics during labor, multiple gestation, previous cesarean delivery, and the presence of gestational diabetes or preeclampsia. A risk score model, employing a cut-off of 7, demonstrated successful prediction of intrapartum CD, with an area under the curve of 0.86. ConclusionThe score model for intrapartum CD can be used by caregivers to offer a more informed consultation to women aged 40 years or more deciding on the mode of delivery.

Proposal of a novel cardiovascular risk prediction score in lupus nephritis.

Patients with systemic lupus erythematosus are prone to develop cardiovascular disease (CVD), and have increased morbidity and mortality. We conducted a retrospective analysis on lupus nephritis patients to assess the occurrence and predictors of major adverse cardiovascular events (MACE). Data were collected from patients who underwent kidney biopsy between 2005 and 2020. Statistical analysis was performed to unveil correlations. 91 patients were analyzed in this period, with a mean age of 37.3 ± 12.3 years and 86% being female. The mean follow-up time was 62 ± 48 months. 15.38% of the patients underwent at least one MACE. Two patients deceased of CVD. Increased age (35.81 ± 11.14 vs 45.5 ± 15.11 years, p=0.012) entailed a higher occurrence of MACEs. Neutrophil count (5.15 ± 2.83 vs 7.3 ± 2.99 Giga/L, p=0.001) was higher, whereas diastolic blood pressure (DBP) was lower (89.51 ± 10.96 vs 78.43 ± 6.9 mmHg, p<0.001) at the time of the biopsy in patients with MACE. Age, neutrophil count, and DBP proved to be independent predictors of MACEs. We propose a new model (CANDE - Cardiovascular risk based on Age, Neutrophil count, and Diastolic blood pressure Estimation score) calculated from these variables, which predicts the probability of MACE occurrence. This study underscores the importance of actively screening for cardiovascular risks in this vulnerable patient population. Age, neutrophil count, and diastolic blood pressure have been established as independent risk factors for MACE in lupus nephritis. The CANDE score derived from these parameters may serve as a prompt, cost-effective, and easily accessible estimation tool for assessing the likelihood of major adverse cardiovascular risk. These findings emphasize the necessity for comprehensive management strategies addressing both immune dysregulation and cardiovascular risk factors in systemic lupus erythematosus to mitigate adverse outcomes.

External validation and comparison of six cardiovascular risk prediction models in the Prospective Urban Rural Epidemiology (PURE)-Colombia study.

To external validate the SCORE2, AHA/ACC Pooled Cohort Equation (PCE), Framingham Risk Score (FRS), Non-Laboratory INTERHEART Risk Score (NL-IHRS), Globorisk-LAC, and WHO prediction models and compare their discrimination and calibration capacity. Validation in individuals aged 40-69 years with at least 10 years follow-up and without baseline use of statins or cardiovascular diseases from the Prospective Urban Rural Epidemiology prospective cohort study (PURE)-Colombia. For discrimination, the C-statistic, and Receiver Operating Characteristic curves with the integrated area under the curve (AUCi) were used and compared. For calibration, the smoothed time-to-event method was used, choosing a recalibration factor based on the integrated calibration index (ICI). In the NL-IHRS, linear regressions were used. In 3,802 participants (59.1% women), baseline risk ranged from 4.8% (SCORE2 women) to 55.7% (NL-IHRS). After a mean follow-up of 13.2 years, 234 events were reported (4.8 cases per 1000 person-years). The C-statistic ranged between 0.637 (0.601-0.672) in NL-IHRS and 0.767 (0.657-0.877) in AHA/ACC PCE. Discrimination was similar between AUCi. In women, higher overprediction was observed in the Globorisk-LAC (61%) and WHO (59%). In men, higher overprediction was observed in FRS (72%) and AHA/ACC PCE (71%). Overestimations were corrected after multiplying by a factor derived from the ICI. Six prediction models had a similar discrimination capacity, supporting their use after multiplying by a correction factor. If blood tests are unavailable, NL-IHRS is a reasonable option. Our results suggest that these models could be used in other countries of Latin America after correcting the overestimations with a multiplying factor.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.

Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME. We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset. Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes. Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

30-Day Risk Score for Mortality and Stroke in Patients with Carotid Artery Stenosis Using Artificial Intelligence Based Carotid Plaque Morphology

The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology. Patients with a computed tomographic angiography head/neck between 2010 and 2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each computed tomography was used to create a three-dimensional image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with area under the receiver operating characteristic curves (AUCs). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke. We created 3 models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, peripheral arterial disease, hyperlipidemia, body mass index (BMI), chronic obstructive pulmonary disease (COPD), and history of transient ischemia attack (TIA)/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue (PVAT) volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC. Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model has a 20% chance of stroke/death. Further prospective studies are needed to validate our findings.