Trajectories of Weight for Length Growth for Infants During the First Year of Life

Background: Childhood obesity is a major public health problem. Studies of patterns of child growth contributing to the development of obesity are scarce, particularly in infancy. Group based trajectory analyses among infants are a novel procedure that may help characterize subgroups of infants with similar longitudinal growth profiles. Objective: To identify trajectories of weight for length growth during the first year of life. Methods: Subjects were singleton infants and their mothers (N=90 mother-infant pairs) who participated in the Pregnancy and Postpartum Observational Dietary Study. Women completed assessments throughout their infant\u27s first year of life and included sociodemographic characteristics and feeding behaviors. Infant weight for length measures from birth to 12 months were abstracted from pediatric office records. Weight for length percentiles were calculated according to the World Health Organization guidelines for infants. Group-based trajectory analysis was done to identify subgroups of infants with similar growth profiles. Results: Infants were from mother’s with average of 28 years (SD=5.2), 70.0% White, 60.0% high-school educated and 63.2% had two or more children. Over half of mothers introduced solid foods to their infants by 6 months of age (63.2%) and about one third self-reported breast feeding at 12 months post-partum (31.9%). Three growth trajectories were identified: a low and stable growth group (38.3%), a rapid growth group (35.0%) and a moderate growth group (26.7%). Maternal and feeding variables were all similar across the three infant growth trajectory groups (p\u3e0.05). Conclusion: Trajectory models suggested three patterns of infant growth. If replicated, future studies can help identify and subsequently target modifiable risk factors associated with rapid infant growth trajectories

Patient- and Hospital-level Predictors of 30-day Readmission after Acute Coronary Syndrome: A Systematic Review

Background: Readmissions following acute myocardial infarction (AMI) are costly and may be partly due to poor care. A previous systematic review examined the literature through 2007. Since then, health policy has changed and additional articles examining predictors of readmission have appeared. We sought to conduct a systematic review of the literature after 2007 regarding socio-demographic, clinical, psychosocial, and hospital level predictors of 30-day readmissions after acute coronary syndrome. Methods: A systematic search of the literature using Pubmed, OVID, ISI web of science, CINAHL, ACP and the Cochrane Library was conducted, including a quality assessment using Downs and Black criteria. Articles reporting on 30-day readmission rate and examining at least one patient-level predictor of readmission at 30 days were included; articles examining interventions to reduce readmissions were excluded. Results: Twenty-two studies were included in this review from which more than 60 predictors of 30-day readmission were identified. Age, co-morbidity, COPD, diabetes, hypertension and having had a previous AMI were all consistently associated with higher risk of readmission. However, no studies reported psychosocial factors as predictors of readmission at 30 days. Conclusion: Studies of readmission should adjust for age and co-morbidity, consistent predictors of readmission at 30-days. Patients with these risk factors for readmission should be targeted for more-intensive follow-up after discharge. Psychosocial predictors of readmission remains a relatively unexplored area of research

Hyperspectral Imaging for Burn Depth Assessment in an Animal Model

Differentiating between superficial and deep-dermal (DD) burns remains challenging. Superficial-dermal burns heal with conservative treatment; DD burns often require excision and skin grafting. Decision of surgical treatment is often delayed until burn depth is definitively identified. This study\u27s aim is to assess the ability of hyperspectral imaging (HSI) to differentiate burn depth. METHODS: Thermal injury of graded severity was generated on the dorsum of hairless mice with a heated brass rod. Perfusion and oxygenation parameters of injured skin were measured with HSI, a noninvasive method of diffuse reflectance spectroscopy, at 2 minutes, 1, 24, 48 and 72 hours after wounding. Burn depth was measured histologically in 12 mice from each burn group (n = 72) at 72 hours. RESULTS: Three levels of burn depth were verified histologically: intermediate-dermal (ID), DD, and full-thickness. At 24 hours post injury, total hemoglobin (tHb) increased by 67% and 16% in ID and DD burns, respectively. In contrast, tHb decreased to 36% of its original levels in full-thickness burns. Differences in deoxygenated and tHb among all groups were significant (P \u3c 0.001) at 24 hours post injury. CONCLUSIONS: HSI was able to differentiate among 3 discrete levels of burn injury. This is likely because of its correlation with skin perfusion: superficial burn injury causes an inflammatory response and increased perfusion to the burn site, whereas deeper burns destroy the dermal microvasculature and a decrease in perfusion follows. This study supports further investigation of HSI in early burn depth assessment

Angina Characteristics as Predictors of Trajectories of Quality of Life Following Acute Coronary Syndrome in the Transitions, Risks and Actions in Coronary Events-Center for Outcomes Research and Education cohort (TRACE-CORE)

BACKGROUND: To describe longitudinal trajectories of health-related quality of life (HRQoL) after hospitalization with an acute coronary syndrome (ACS), their associations with baseline angina characteristics, and associations with anxiety, depression, and cognitive impairment. METHODS: TRACE-CORE participants (N=1,613) completed the SF-36 during hospitalization for ACS and 1, 3, & 6 months post-discharge. Latent growth curves identified trajectories of physical and mental components of HRQOL (MCS and PCS) and sequential multiple logistic regression estimated associations between trajectories and angina characteristics. RESULTS: Participants (N=1613) had mean age 63.3 (SD 11.4) years, 33.0% female, and 78.2% non-Hispanic white. We identified 2 MCS trajectories: AVERAGE and IMPAIRED HRQoL. The majority of participants (81.0%) had AVERAGE MCS at baseline (mean MCS 53.6) and slight improvement in scores over time. A minority (19.0%) had IMPAIRED HRQoL at baseline (mean MCS 36.7) and slight improvement in scores over time. We identified 2 similar PCS trajectories with similar patterns of scores over time: AVERAGE (71.1%) and IMPAIRED (28.9%) HRQoL at baseline. Adjusting for demographics & comorbidities, patients with less severe baseline angina were more likely to have AVERAGE MCS (odds ratio [OR]/10 unit change in severity 1.1) and PCS (OR 1.1) trajectories, and similarly for less frequent angina (MCS OR 1.2; PCS OR 1.3). The associations of MCS trajectory with severity and frequency lost significance after adjusting for psychosocial factors, whereas the PCS associations remained significant [All p \u3c 0.05 unless noted]. CONCLUSIONS: About 1/3 of patients exhibited impaired 6-month HRQoL trajectories, which can be predicted by angina characteristics. Psychosocial factors may explain the prediction of mental, not physical, trajectories. Interventions to improve HRQoL after ACS should consider psychosocial factors and angina

The molecular portraits of breast tumors are conserved acress microarray platforms

Background Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. Results A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. Conclusion This study validates the breast tumor intrinsic subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

The molecular portraits of breast tumors are conserved across microarray platforms

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

Patient and Social Determinants of Health Trajectories Following Coronary Events

More than 1.2 million Americans are hospitalized annually with an acute coronary syndrome (ACS); many impaired quality of life after discharge with an ACS. This dissertation focuses on two novel aspects of patient health status (PHS) after ACS: how it can be predicted based on the socioeconomic status (SES) of the patient, and how it evolves over time. We used data from TRACE-CORE, a longitudinal prospective cohort of patients hospitalized with ACS. We measured PHS using both the SF-36 mental and physical component subscales (MCS and PCS) and the Seattle Angina Questionnaire (SAQ) health-related quality of life (HRQoL) and physical limitations subscales at the index hospitalization and at 1, 3, and 6-months post-discharge. Firstly, after adjusting for individual-level SES, we found that individuals living in the neighborhoods with the lowest neighborhood SES had significantly worse PHS. Secondly, we found that each of the components of PHS had subgroups with distinct patterns of evolution over time (trajectories). Both the PCS and the SAQ physical limitations subscale had two trajectories; one with average and one with impaired health status over time. For the HRQoL subscale of SAQ, we found three trajectories: Low, Average, and High scores. For MCS, we found four trajectories: High (consistently high scores), Low (consistently low scores), and two with average scores at baseline that either improved or worsened over time, referred to as Improving and Worsening, respectively. All PHS trajectories, except for MCS, predicted readmission and mortality during the 6 months to 1 year post-ACS discharge

Index of cardiometabolic health: a new method of measuring allostatic load using electronic health records

OBJECTIVE: We developed a measure of allostatic load from electronic medical records (EMRs), which we named Index of Cardiometabolic Health (ICMH). METHODS: Data were collected from participants\u27 EMRs and a written survey in 2005. We computed allostatic load scores using the ICMH score and two previously described approaches. RESULTS: We included 1865 employed adults who were 25-59 years old. Although the magnitude of the association was small, all methods of were predictive of SF-12 physical component subscales (all p \u3c 0.001). CONCLUSION: We found that the ICMH had similar relationships with health-related quality of life as previously reported in the literature

Developmental plasticity in thermal tolerance : ontogenetic variation, persistence, and future directions

Understanding the factors affecting thermal tolerance is crucial for predicting the impact climate change will have on ectotherms. However, the role developmental plasticity plays in allowing populations to cope with thermal extremes is poorly understood. Here, we meta‐analyse how thermal tolerance is initially and persistently impacted by early (embryonic and juvenile) thermal environments by using data from 150 experimental studies on 138 ectothermic species. Thermal tolerance only increased by 0.13°C per 1°C change in developmental temperature and substantial variation in plasticity (~36%) was the result of shared evolutionary history and species ecology. Aquatic ectotherms were more than three times as plastic as terrestrial ectotherms. Notably, embryos expressed weaker but more heterogenous plasticity than older life stages, with numerous responses appearing as non‐adaptive. While developmental temperatures did not have persistent effects on thermal tolerance overall, persistent effects were vastly under‐studied, and their direction and magnitude varied with ontogeny. Embryonic stages may represent a critical window of vulnerability to changing environments and we urge researchers to consider early life stages when assessing the climate vulnerability of ectotherms. Overall, our synthesis suggests that developmental changes in thermal tolerance rarely reach levels of perfect compensation and may provide limited benefit in changing environments