Browsing by Subject "Risk prediction"
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Item Developing a predictive model for perinatal morbidity among small for gestational age infants(Taylor & Francis, 2022) Blue, Nathan R.; Allhouse, Amanda A.; Grobman, William A.; Day, Robert C.; Haas, David M.; Simhan, Hyagriv N.; Parry, Samuel; Saade, George R.; Silver, Robert M.; Obstetrics and Gynecology, School of MedicineBackground: While neonates with birth weight <10th percentile are at increased risk of morbidity and mortality, most of these are constitutionally small and not at increased risk. There are no current strategies that reliably distinguish constitutionally small neonates from small neonates at the highest risk of morbidity, so additional tools for risk stratification are needed. Objective: Our objectives were to identify factors that are independently associated with perinatal morbidity among neonates with birth weight <10th percentile (small for gestational age, SGA) and to create predictive models of perinatal morbidity among SGA neonates based on the timing of information availability. Study design: This secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be, was a nested case-control study. Participants were prospectively enrolled at eight U.S. centers, with data collection occurring at three standard time points during pregnancy and again after delivery. Our analysis included neonates with birth weights <10th percentile and excluded those with major congenital malformations or suspected or confirmed aneuploidy. The primary outcome was a composite of perinatal morbidity, defined as NICU admission >48 h, NEC, sepsis, RDS, mechanical ventilation, retinopathy of prematurity, seizures, grade 3 or 4 IVH, stillbirth, or death before discharge. Cases were SGA neonates that experienced the primary outcome, and controls were SGA neonates that did not. Maternal factors for potential inclusion in predictive modeling were drawn from a broad list of variables collected as part of the NuMoM2B study, including demographic, anthropometric, clinical, ultrasound, social/behavioral, dietary, and psychological variables. Characteristics that were different in bivariate analysis between cases and controls then underwent further evaluation and refinement. Continuous and multi-category variables were assessed using multiple approaches, including as continuous variables, using standard categories (such as for BMI) as well as empirically-derived cut-points identified by receiver-operating characteristics methodology. The approach for each variable that resulted in the best performance was selected for use in modeling. After variable optimization, multivariable analysis was used to derive prediction models using factors known at mid-pregnancy (Model 1) and delivery (Model 2). Results: Of the original cohort, 865 were eligible and analyzed, with 134 (15.5%) experiencing the primary outcome. After bivariable and multivariable analysis, these variables were included in Model 1: BMI, stress level, diastolic blood pressure, narcotic use (all in 1st trimester), and uterine artery pulsatility index at 16-21 weeks. Model 2 added the following variables to Model 1: preterm delivery, preeclampsia, and suspected fetal growth restriction. When models 1 and 2 were empirically tested and compared to predicted performance to demonstrate calibration, observed morbidity rates approximately followed expected rates within deciles. Models 1 and 2 had respective areas under the receiver-operating characteristic curve of 0.72 (95% CI 0.67-0.76) and 0.84 (0.80-0.88), to predict the composite morbidity. Conclusion: Using a deeply phenotyped cohort of nulliparous women, we created two models with the moderate-good prediction of perinatal morbidity among SGA neonates.Item HOMA2-B enhances assessment of type 1 diabetes risk among TrialNet Pathway to Prevention participants(Springer, 2022) Felton, Jamie L.; Cuthbertson, David; Warnock, Megan; Lohano, Kuldeep; Meah, Farah; Wentworth, John M.; Sosenko, Jay; Evans-Molina, Carmella; Type 1 Diabetes TrialNet Study Group; Pediatrics, School of MedicineAims/hypothesis: Methods to identify individuals at highest risk for type 1 diabetes are essential for the successful implementation of disease-modifying interventions. Simple metabolic measures are needed to help stratify autoantibody-positive (Aab+) individuals who are at risk of developing type 1 diabetes. HOMA2-B is a validated mathematical tool commonly used to estimate beta cell function in type 2 diabetes using fasting glucose and insulin. The utility of HOMA2-B in association with type 1 diabetes progression has not been tested. Methods: Baseline HOMA2-B values from single-Aab+ (n = 2652; mean age, 21.1 ± 14.0 years) and multiple-Aab+ (n = 3794; mean age, 14.5 ± 11.2 years) individuals enrolled in the TrialNet Pathway to Prevention study were compared. Cox proportional hazard models were used to determine associations between HOMA2-B tertiles and time to progression to type 1 diabetes, with adjustments for age, sex, HLA status and BMI z score. Receiver operating characteristic (ROC) analysis was used to test the association of HOMA2-B with type 1 diabetes development in 1, 2, 5 and 10 years. Results: At study entry, HOMA2-B values were higher in single- compared with multiple-Aab+ Pathway to Prevention participants (91.1 ± 44.5 vs 83.9 ± 38.9; p < 0.001). Single- and multiple-Aab+ individuals in the lowest HOMA2-B tertile had a higher risk and faster rate of progression to type 1 diabetes. For progression to type 1 diabetes within 1 year, area under the ROC curve (AUC-ROC) was 0.685, 0.666 and 0.680 for all Aab+, single-Aab+ and multiple-Aab+ individuals, respectively. When correlation between HOMA2-B and type 1 diabetes risk was assessed in combination with additional factors known to influence type 1 diabetes progression (insulin sensitivity, age and HLA status), AUC-ROC was highest for the single-Aab+ group's risk of progression at 2 years (AUC-ROC 0.723 [95% CI 0.652, 0.794]). Conclusions/interpretation: These data suggest that HOMA2-B may have utility as a single-time-point measurement to stratify risk of type 1 diabetes development in Aab+ individuals.Item KCNN2 polymorphisms and cardiac tachyarrhythmias(Wolters Kluwer, 2016-07) Yu, Chih-Chieh; Chia-Ti, Tsai; Chen, Pei-Lung; Wu, Cho-Kai; Chiu, Fu-Chun; Chiang, Fu-Tien; Chen, Peng-Sheng; Chen, Chi-Ling; Lin, Lian-Yu; Juang, Jyh-Ming; Ho, Li-Ting; Lai, Ling-Ping; Yang, Wei-Shiung; Lin, Jiunn-Lee; Department of Medicine, IU School of MedicinePotassium calcium-activated channel subfamily N member 2 (KCNN2) encodes an integral membrane protein that forms small-conductance calcium-activated potassium (SK) channels. Recent studies in animal models show that SK channels are important in atrial and ventricular repolarization and arrhythmogenesis. However, the importance of SK channels in human arrhythmia remains unclear. The purpose of the present study was to test the association between genetic polymorphism of the SK2 channel and the occurrence of cardiac tachyarrhythmias in humans. We enrolled 327 Han Chinese, including 72 with clinically significant ventricular tachyarrhythmias (VTa) who had a history of aborted sudden cardiac death (SCD) or unexplained syncope, 98 with a history of atrial fibrillation (AF), and 144 normal controls. We genotyped 12 representative tag single nucleotide polymorphisms (SNPs) across a 141-kb genetic region containing the KCNN2 gene; these captured the full haplotype information. The rs13184658 and rs10076582 variants of KCNN2 were associated with VTa in both the additive and dominant models (odds ratio [OR] 2.89, 95% confidence interval [CI] = 1.505-5.545, P = 0.001; and OR 2.55, 95% CI = 1.428-4.566, P = 0.002, respectively). After adjustment for potential risk factors, the association remained significant. The population attributable risks of these 2 variants of VTa were 17.3% and 10.6%, respectively. One variant (rs13184658) showed weak but significant association with AF in a dominant model (OR 1.91, CI = 1.025-3.570], P = 0.042). There was a significant association between the KCNN2 variants and clinically significant VTa. These findings suggest an association between KCNN2 and VTa; it also appears that KCNN2 variants may be adjunctive markers for risk stratification in patients susceptible to SCD.Item New Scoring Systems for Predicting Advanced Proximal Neoplasia in Asymptomatic Adults With or Without Knowing Distal Colorectal Findings: A Prospective, Cross-sectional Study(Wolters Kluwer, 2022) Imperiale, Thomas F.; Monahan, Patrick O.; Stump, Timothy E.; Ransohoff, David F.; Medicine, School of MedicineBackground: Models estimating risk for advanced proximal colorectal neoplasia (APN) may be used to select colorectal cancer (CRC) screening test, either prior to knowing distal colorectal findings or afterward. Current models have only fair discrimination and nearly all require knowing distal findings. Objective: Derive and test risk prediction models for APN with and without distal findings. Setting: Selected endoscopy centers within central Indiana, USA. Participants: Average-risk persons undergoing first-time screening colonoscopy. Interventions: Demographics, personal and family medical history, lifestyle factors and physical measures were linked to the most advanced finding in proximal and distal colorectal segments. For both models, logistic regression identified factors independently associated with APN on a derivation set. Based on equation coefficients, points were assigned to each factor, and risk for APN was examined for each score. Scores with comparable risks were collapsed into risk categories. Both models and their scoring systems were tested on the validation set. Main outcome: APN, defined as any adenoma or sessile serrated lesion ≥1 cm, one with villous histology or high-grade dysplasia, or CRC proximal to the descending colon. Results: Among 3025 subjects in the derivation set (mean age 57.3 ± 6.5 years; 52% women), APN prevalence was 4.5%; 2859 (94.5%) had complete data on risk factors. Independently associated with APN were age, sex, cigarette smoking, cohabitation status, metabolic syndrome, non-steroidal anti-inflammatory drug use and physical activity. This model (without distal findings) was well-calibrated (P = 0.62) and had good discrimination (c-statistic = 0.73). In low-, intermediate- and high-risk groups that comprised 21, 58 and 21% of the sample, respectively, APN risks were 1.47% (95% CI, 0.67-2.77%), 3.09% (CI, 2.31-4.04%) and 11.6% (CI, 9.10-14.4%), respectively (P < 0.0001), with no proximal CRCs in the low-risk group and 2 in the intermediate-risk group. When tested in the validation set of 1455, the model retained good metrics (calibration P = 0.85; c-statistic = 0.83), with APN risks in low- (22%), intermediate- (56%) and high-risk (22%) subgroups of 0.62% (CI, 0.08-2.23%) 2.20% (CI, 1.31-3.46%) and 13.0% (CI, 9.50-17.2%), respectively (P < 0.0001). There were no proximal CRCs in the low-risk group, and two in the intermediate-risk group. The model with distal findings performed comparably, with validation set metrics of 0.18 for calibration, 0.76 for discrimination and APN risk (% sample) in low-, intermediate-, and high-risk groups of 1.1 (69%), 8.3 (22%) and 22.3% (9%). Conclusion: These models stratify large proportions of average-risk persons into clinically meaningful risk groups, and could improve screening efficiency, particularly for noncolonoscopy-based programs.Item Polysomnographic Phenotypes of Obstructive Sleep Apnea and Incident Type 2 Diabetes: Results from the DREAM Study(American Thoracic Society, 2021) Ding, Qinglan; Qin, Li; Wojeck, Brian; Inzucchi, Silvio E.; Ibrahim, Ahmad; Bravata, Dawn M.; Strohl, Kingman P.; Yaggi, Henry K.; Zinchuk, Andrey V.; Medicine, School of MedicineRationale: Obstructive sleep apnea (OSA) is associated with cardiovascular disease and incident type 2 diabetes (T2DM). Seven OSA phenotypes, labeled on the basis of their most distinguishing polysomnographic features, have been shown to be differentially associated with incident cardiovascular disease. However, little is known about the relevance of polysomnographic phenotypes for the risk of T2DM. Objectives: To assess whether polysomnographic phenotypes are associated with incident T2DM and to compare the predictive value of baseline polysomnographic phenotypes with the Apnea-Hypopnea Index (AHI) for T2DM. Methods: The study included 840 individuals without baseline diabetes from a multisite observational U.S. veteran cohort who underwent OSA evaluation between 2000 and 2004, with follow-up through 2012. The primary outcome was incident T2DM, defined as no diagnosis at baseline and a new physician diagnosis confirmed by fasting blood glucose >126 mg/dL during follow-up. Relationships between the seven polysomnographic phenotypes (1. mild, 2. periodic limb movements of sleep [PLMS], 3. non-rapid eye movement and poor sleep, 4. rapid eye movement and hypoxia, 5. hypopnea and hypoxia, 6. arousal and poor sleep, and 7. combined severe) and incident T2DM were investigated using Cox proportional hazards regression and competing risk regression models with and without adjustment for baseline covariates. Likelihood ratio tests were conducted to compare the predictive value of the phenotypes with the AHI. Results: During a median follow-up period of 61 months, 122 (14.5%) patients developed incident T2DM. After adjustment for baseline sociodemographics, fasting blood glucose, body mass index, comorbidities, and behavioral risk factors, hazard ratios among persons with "hypopnea and hypoxia" and "PLMS" phenotypes as compared with persons with "mild" phenotype were 3.18 (95% confidence interval [CI], 1.53-6.61] and 2.26 (95% CI, 1.06-4.83) for incident T2DM, respectively. Mild OSA (5 ⩽ AHI < 15) (vs. no OSA) was directly associated with incident T2DM in both unadjusted and multivariable-adjusted regression models. The addition of polysomnographic phenotypes, but not AHI, to known T2DM risk factors greatly improved the predictive value of the computed prediction model. Conclusions: Polysomnographic phenotypes "hypopnea and hypoxia" and "PLMS" independently predict risk of T2DM among a predominantly male veteran population. Polysomnographic phenotypes improved T2DM risk prediction comared with the use of AHI.Item A Review of Breast Cancer Risk Factors in Adolescents and Young Adults(MDPI, 2021-11-05) McVeigh, Una Mary; Tepper, John William; McVeigh, Terri Patricia; Pediatrics, School of MedicineCancer in adolescents and young adults (AYAs) deserves special consideration for several reasons. AYA cancers encompass paediatric malignancies that present at an older age than expected, or early-onset of cancers that are typically observed in adults. However, disease diagnosed in the AYA population is distinct to those same cancers which are diagnosed in a paediatric or older adult setting. Worse disease-free and overall survival outcomes are observed in the AYA setting, and the incidence of AYA cancers is increasing. Knowledge of an individual's underlying cancer predisposition can influence their clinical care and may facilitate early tumour surveillance strategies and cascade testing of at-risk relatives. This information can further influence reproductive decision making. In this review we discuss the risk factors contributing to AYA breast cancer, such as heritable predisposition, environmental, and lifestyle factors. We also describe a number of risk models which incorporate genetic factors that aid clinicians in quantifying an individual's lifetime risk of disease.Item Type 2 Diabetes Genetic Risk Scores Are Associated With Increased Type 2 Diabetes Risk Among African Americans by Cardiometabolic Status(Sage, 2018-01-03) Layton, Jill; Li, Xiaochen; Shen, Changyu; de Groot, Mary; Lange, Leslie; Correa, Adolfo; Wessel, Jennifer; Epidemiology, School of Public HealthThe relationship between genetic risk variants associated with glucose homeostasis and type 2 diabetes risk has yet to be fully explored in African American populations. We pooled data from 4 prospective studies including 4622 African Americans to assess whether β-cell dysfunction (BCD) and/or insulin resistance (IR) genetic variants were associated with increased type 2 diabetes risk. The BCD genetic risk score (GRS) and combined BCD/IR GRS were significantly associated with increased type 2 diabetes risk. In cardiometabolic-stratified models, the BCD and IR GRS were associated with increased type 2 diabetes risk among 5 cardiometabolic strata: 3 clinically healthy strata and 2 clinically unhealthy strata. Genetic risk scores related to BCD and IR were associated with increased risk of type 2 diabetes in African Americans. Notably, the GRSs were significant predictors of type 2 diabetes among individuals in clinically normal ranges of cardiometabolic traits.Item Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure(Elsevier, 2022) Johnson, Amber E.; Brewer, LaPrincess C.; Echols, Melvin R.; Mazimba, Sula; Shah, Rashmee U.; Breathett, Khadijah; Medicine, School of MedicinePatients with heart failure (HF) are heterogeneous with various intrapersonal and interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and social determinants of health have been implicated in unequal treatment of patients with HF. Through several methodologies, artificial intelligence (AI) can provide models in HF prediction, prognostication, and provision of care, which may help prevent unequal outcomes. This review highlights AI as a strategy to address racial inequalities in HF; discusses key AI definitions within a health equity context; describes the current uses of AI in HF, strengths and harms in using AI; and offers recommendations for future directions.