TRIM37 controls cancer-specific vulnerability to PLK4 inhibition.

Centrosomes catalyse the formation of microtubules needed to assemble the mitotic spindle apparatus1. Centrosomes themselves duplicate once per cell cycle, in a process that is controlled by the serine/threonine protein kinase PLK4 (refs. 2,3). When PLK4 is chemically inhibited, cell division proceeds without centrosome duplication, generating centrosome-less cells that exhibit delayed, acentrosomal spindle assembly4. Whether PLK4 inhibitors can be leveraged as a treatment for cancer is not yet clear. Here we show that acentrosomal spindle assembly following PLK4 inhibition depends on levels of the centrosomal ubiquitin ligase TRIM37. Low TRIM37 levels accelerate acentrosomal spindle assembly and improve proliferation following PLK4 inhibition, whereas high TRIM37 levels inhibit acentrosomal spindle assembly, leading to mitotic failure and cessation of proliferation. The Chr17q region containing the TRIM37 gene is frequently amplified in neuroblastoma and in breast cancer5-8, rendering these cancer types highly sensitive to PLK4 inhibition. We find that inactivating TRIM37 improves acentrosomal mitosis because TRIM37 prevents PLK4 from self-assembling into centrosome-independent condensates that serve as ectopic microtubule-organizing centres. By contrast, elevated TRIM37 expression inhibits acentrosomal spindle assembly through a distinct mechanism that involves degradation of the centrosomal component CEP192. Thus, TRIM37 is an essential determinant of mitotic vulnerability to PLK4 inhibition. Linkage of TRIM37 to prevalent cancer-associated genomic changes-including 17q gain in neuroblastoma and 17q23 amplification in breast cancer-may offer an opportunity to use PLK4 inhibition to trigger selective mitotic failure and provide new avenues to treatments for these cancers.

Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease.

INTRODUCTION: Acute kidney injury (AKI) is common among hospitalized patients with sickle cell disease (SCD) and contributes to increased morbidity and mortality. Early identification and management of AKI is essential to preventing poor outcomes. We aimed to predict AKI earlier in patients with SCD using a machine-learning model that utilized continuous minute-by-minute physiological data. METHODS: A total of6,278 adult SCD patient encounters were admitted to inpatient units across five regional hospitals in Memphis, TN, over 3 years, from July 2017 to December 2020. From these, 1,178 patients were selected after filtering for data availability. AKI was identified in 82 (7%) patient encounters, using the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The remaining 1,096 encounters served as controls. Features derived from five physiological data streams, heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from bedside monitors were used. An XGBoost classifier was used for classification. RESULTS: Our model accurately predicted AKI up to 12 h before onset with an area under the receiver operator curve (AUROC) of 0.91 (95% CI [0.89-0.93]) and up to 48 h before AKI with an AUROC of 0.82 (95% CI [0.80-0.83]). Patients with AKI were more likely to be female (64.6%) and have history of hypertension, pulmonary hypertension, chronic kidney disease, and pneumonia than the control group. CONCLUSION: XGBoost accurately predicted AKI as early as 12 h before onset in hospitalized SCD patients and may enable the development of innovative prevention strategies.

Associations between APOL1 genetic variants and blood pressure in African American mothers and children from a U.S. pregnancy cohort: Modification by air pollution exposures.

INTRODUCTION: Carriage of high-risk APOL1 genetic variants is associated with increased risks for kidney diseases in people of African descent. Less is known about the variants' associations with blood pressure or potential moderators. METHODS: We investigated these associations in a pregnancy cohort of 556 women and 493 children identified as African American. Participants with two APOL1 risk alleles were defined as having the high-risk genotype. Blood pressure in both populations was measured at the child's 4-6 years visit. We fit multivariate linear and Poisson regressions and further adjusted for population stratification to estimate the APOL1-blood pressure associations. We also examined the associations modified by air pollution exposures (particulate matter ≤2.5 µ m in aerodynamic diameter [PM2.5] and nitrogen dioxide) and explored other moderators such as health conditions and behaviors. RESULTS: Neither APOL1 risk alleles nor risk genotypes had a main effect on blood pressure in mothers or children. However, each 2-µg/m3 increase of four-year average PM2.5 was associated with a 16.3 (95%CI: 5.7, 26.9) mmHg higher diastolic blood pressure in mothers with the APOL1 high-risk genotype, while the estimated effect was much smaller in mothers with the low-risk genotype (i.e., 2.9 [95%CI: -3.1, 8.8] mmHg; Pinteraction = 0.01). Additionally, the associations of APOL1 risk alleles and the high-risk genotype with high blood pressure (i.e., SBP and/or DBP ≥ 90th percentile) were stronger in girls vs. boys (Pinteraction = 0.02 and 0.005, respectively). CONCLUSION: This study sheds light on the distribution of high blood pressure by APOL1 genetic variants and informs regulatory policy to protect vulnerable population subgroups.

Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study.

BACKGROUND: The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. OBJECTIVE: Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms-Reddit and Twitter-harvested from January 1, 2020, to March 1, 2022. METHODS: To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. RESULTS: Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. CONCLUSIONS: Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.

Fetal-Not Maternal-APOL1 Genotype Associated with Risk for Preeclampsia in Those with African Ancestry.

Black Americans are at increased risk for preeclampsia. Genetic variants in apolipoprotein L1 (APOL1) account for much of the increased risk for kidney disease in blacks. APOL1 is expressed in human placenta and transgenic mice expressing APOL1 develop preeclampsia. We evaluated the role of APOL1 variants in human preeclampsia. We determined maternal and fetal APOL1 genotypes in black women with preeclampsia in two populations. At Einstein Montefiore Center (EMC) Affiliated Hospitals, we studied 121 pregnancies in black women with preeclampsia. At University of Tennessee Health Science Center (UTHSC), we studied 93 pregnancies in black women with preeclampsia and 793 pregnancies without preeclampsia. We measured serum markers of preeclampsia soluble fms-like tyrosine kinase 1 (sFlt-1), placental growth factor (PlGF), and soluble endoglin (sEng). Fetal APOL1 high-risk (HR) genotype was associated with preeclampsia, with odds ratios at EMC and UTHSC of 1.84 (95% CI 1.11, 2.93) and 1.92 (95% CI 1.05, 3.49), respectively. Maternal APOL1 HR genotype was not associated with preeclampsia. Mothers with the fetal APOL1 HR genotype had more cerebral or visual disturbances (63% versus 37%, p = 0.04). In addition, fetal APOL1 HR genotype was associated with a higher sFLT-1/PlGF ratio at birth (p = 0.04). Fetal APOL1 high-risk genotype increases the risk for preeclampsia, likely by adversely affecting placental function. Further research is needed to assess whether APOL1 genetic testing can predict preeclampsia and improve pregnancy outcomes.

Joint Associations of Maternal-Fetal APOL1 Genotypes and Maternal Country of Origin With Preeclampsia Risk.

RATIONALE & OBJECTIVES: Preeclampsia, which disproportionately affects Black women, is a leading cause of preterm delivery and risk for future hypertension and chronic kidney disease (CKD). Apolipoprotein L1 (APOL1) kidney risk alleles, common among Black individuals, contribute substantially to CKD disparities. Given the strong link between preeclampsia and CKD, we investigated whether maternal and fetal APOL1 risk alleles can jointly influence preeclampsia risk, and explored potential modifiers of the association between APOL1 and preeclampsia. STUDY DESIGN: Nested case-control study. SETTING & PARTICIPANTS: 426 Black mother-infant pairs (275 African Americans and 151 Haitians) from the Boston Birth Cohort. EXPOSURE: Maternal and fetal APOL1 risk alleles. OUTCOMES: Preeclampsia. ANALYTICAL APPROACH: Logistic regression models with adjustment for demographic characteristics were applied to analyze associations between fetal and maternal APOL1 risk alleles and risk of preeclampsia and to investigate the effects of modification by maternal country of origin. RESULTS: Fetal APOL1 risk alleles tended to be associated with an increased risk of preeclampsia, which was not statistically significant in the total genotyped population. However, this association was modified by maternal country of origin (P<0.05 for interaction tests): fetal APOL1 risk alleles were significantly associated with an increased risk of preeclampsia among African Americans under recessive (odds ratio [OR], 3.6 [95% CI, 1.3-9.7]; P=0.01) and additive (OR, 1.7 [95% CI, 1.1-2.6]; P=0.01) genetic models but not in Haitian Americans. Also, maternal-fetal genotype discordance at the APOL1 locus was associated with a 2.6-fold higher risk of preeclampsia (P<0.001) in African Americans. LIMITATIONS: Limited sample size in stratified analyses; self-reported maternal country of origin; pre-pregnancy estimated glomerular filtration rate (eGFR) and proteinuria data in mothers were not collected; unmeasured confounding social and/or environmental factors; no replication study. CONCLUSIONS: This study supports the hypothesis that fetal APOL1 kidney risk alleles are associated with increased risk for preeclampsia in a recessive mode of inheritance in African Americans and suggests that maternal-fetal genotype discordance is also associated with this risk. These conclusions underscore the need to better understand maternal-fetal interaction and their genetic and environmental factors as contributors to ethnic disparities in preeclampsia.

Prenatal polyunsaturated fatty acids and child asthma: Effect modification by maternal asthma and child sex.

BACKGROUND: Findings on prenatal polyunsaturated fatty acid (PUFA) intake and child wheeze and asthma have been inconsistent. OBJECTIVE: We sought to examine associations between prenatal PUFA status and child wheeze/asthma and modifying effects of maternal asthma/atopy, child sex, and maternal race. METHODS: Analyses included 1019 mother-child dyads with omega-3 (n-3) and omega-3 (n-6) PUFAs measured in second-trimester plasma; n-6/n-3 ratios were calculated. Child wheeze/asthma outcomes ascertained at age 4 to 6 years included ever physician-diagnosed asthma, current wheeze (symptoms past 12 months), current asthma (diagnosis and medication and/or symptoms past 12 months), and current diagnosed asthma. Each PUFA indicator and outcome was analyzed in separate models using modified Poisson regression with interaction terms. RESULTS: In quartile (Q) analyses, higher n-6 PUFAs were associated with increased risk of ever (risk ratio [RR] high vs low [RR Q4 vs Q1], 1.70; 95% CI, 1.07-2.71) and current (RR Q4 vs Q1, 1.70; 95% CI, 1.07-2.71) diagnosed asthma, whereas n-3 PUFAs were associated with lower risk (RR Q4 vs Q1, 0.59; 95% CI, 0.33-1.03) of current diagnosed asthma (Ptrend < .05 for all). Higher n-6 PUFAs were associated with a higher risk of all respiratory outcomes among children born to women with asthma (Pinteraction < .05 for all outcomes). A significant 3-way interaction between child sex, maternal asthma, and n-6/n-3 PUFA indicated that male children born to women with asthma and a higher ratio had the highest risk across wheeze/asthma outcomes (Pinteraction < .05). CONCLUSIONS: Associations between prenatal PUFA status and childhood wheeze/asthma were modified by maternal history of asthma and child sex.

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable early identification and treatment and potentially reduce mortality. OBJECTIVE: The aim of this study was to test the hypothesis that machine learning physiomarkers can predict the development of organ dysfunction in a sample of adult patients with SCD admitted to intensive care units (ICUs). METHODS: We applied diverse machine learning methods, statistical methods, and data visualization techniques to develop classification models to distinguish SCD from controls. RESULTS: We studied 63 sequential SCD patients admitted to ICUs with 163 patient encounters (mean age 30.7 years, SD 9.8 years). A subset of these patient encounters, 22.7% (37/163), met the sequential organ failure assessment criteria. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast Fourier transform, energy, and continuous wavelet transform) derived from heart rate, blood pressure, and respiratory rate was identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure from patients with SCD who did not meet the criteria. A multilayer perceptron model accurately predicted organ failure up to 6 hours before onset, with an average sensitivity and specificity of 96% and 98%, respectively. CONCLUSIONS: This retrospective study demonstrated the viability of using machine learning to predict acute organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

The Impact of the Revised WIC Food Package on Maternal Nutrition During Pregnancy and Postpartum.

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) provides nutritional support for pregnant and postpartum women and young children. The typical food package provided to recipient families was revised in October 2009 to include more whole grains, fruits, vegetables, and low-fat milk. Little is known about whether these revisions improved nutrition among women during this critical period of the life course. We conducted a quasiexperimental difference-in-differences analysis, comparing WIC recipients ("treatment" group) before and after the WIC policy change, while accounting for temporal trends among nonrecipients ("control" group). We examined nutritional outcomes among a cohort of 1,454 women recruited during pregnancy in 2006-2011 in Memphis and surrounding Shelby County, Tennessee. We found improvements in several measures of dietary quality and nutrient intake during pregnancy, although these did not persist into the postpartum period. Results were robust to numerous sensitivity analyses. At a time when federal WIC funding is threatened, this study provides some of the first evidence of the benefits of recent WIC revisions among low-income women.

Maternal metabolic factors during pregnancy predict early childhood growth trajectories and obesity risk: the CANDLE Study.

BACKGROUND: We investigated the individual and additive effects of three modifiable maternal metabolic factors, including pre-pregnancy overweight/obesity, gestational weight gain (GWG), and gestational diabetes mellitus (GDM), on early childhood growth trajectories and obesity risk. METHODS: A total of 1425 mother-offspring dyads (953 black and 472 white) from a longitudinal birth cohort were included in this study. Latent class growth modeling was performed to identify the trajectories of body mass index (BMI) from birth to 4 years in children. Poisson regression models were used to examine the associations between the maternal metabolic risk factors and child BMI trajectories and obesity risk at 4 years. RESULTS: We identified three discrete BMI trajectory groups, characterized as rising-high-BMI (12.6%), moderate-BMI (61.0%), or low-BMI (26.4%) growth. Both maternal pre-pregnancy obesity (adjusted relative risk [adjRR] = 1.96; 95% confidence interval [CI]: 1.36-2.83) and excessive GWG (adjRR = 1.71, 95% CI: 1.13-2.58) were significantly associated with the rising-high-BMI trajectory, as manifested by rapid weight gain during infancy and a stable but high BMI until 4 years. All three maternal metabolic indices were significantly associated with childhood obesity at age 4 years (adjRR for pre-pregnancy obesity = 2.24, 95% CI: 1.62-3.10; adjRR for excessive GWG = 1.46, 95% CI: 1.01-2.09; and adjRR for GDM = 2.14, 95% = 1.47-3.12). In addition, risk of rising-high BMI trajectory or obesity at age 4 years was stronger among mothers with more than one metabolic risk factor. We did not observe any difference in these associations by race. CONCLUSION: Maternal pre-pregnancy obesity, excessive GWG, and GDM individually and jointly predict rapid growth and obesity at age 4 years in offspring, regardless of race. Interventions targeting maternal obesity and metabolism may prevent or slow the rate of development of childhood obesity.

The Promise of Machine Learning: When Will it be Delivered?

BACKGROUND: The real-life applications of machine learning clinical decision making is currently lagging behind its promise. One of the critics on machine learning is that it doesn't outperform more traditional statistical approaches in every problem. METHODS AND RESULTS: Authors of "Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database" presented in the current issue of the Journal of Cardiac Failure that machine learning approaches do not provide significantly higher performance when compared to more traditional statistical approaches in predicting mortality following heart transplant. In this brief report, we provide an insight on the possible reasons for why machine learning methods do not outperform more traditional approaches for every problem and every dataset. CONCLUSIONS: Most of the performance-focused critics on machine learning are because the bar is set unfairly too high for machine learning. In most cases, machine learning methods provides at least as good results as traditional statistical methods do. It is normal for machine learning models to provide similar performance with linear models if the actual underlying input-outcome relationship is linear. Moreover, machine learning methods outperforms linear statistical models when the underlying input-output relationship is not linear and if the dataset is large enough and include predictors capturing that nonlinear relationship.

Near Real-time Surveillance for Consequences of Health Policies Using Sequential Analysis.

BACKGROUND: New health policies may have intended and unintended consequences. Active surveillance of population-level data may provide initial signals of policy effects for further rigorous evaluation soon after policy implementation. OBJECTIVE: This study evaluated the utility of sequential analysis for prospectively assessing signals of health policy impacts. As a policy example, we studied the consequences of the widely publicized Food and Drug Administration's warnings cautioning that antidepressant use could increase suicidal risk in youth. METHOD: This was a retrospective, longitudinal study, modeling prospective surveillance, using the maximized sequential probability ratio test. We used historical data (2000-2010) from 11 health systems in the US Mental Health Research Network. The study cohort included adolescents (ages 10-17 y) and young adults (ages 18-29 y), who were targeted by the warnings, and adults (ages 30-64 y) as a comparison group. Outcome measures were observed and expected events of 2 possible unintended policy outcomes: psychotropic drug poisonings (as a proxy for suicide attempts) and completed suicides. RESULTS: We detected statistically significant (P<0.05) signals of excess risk for suicidal behavior in adolescents and young adults within 5-7 quarters of the warnings. The excess risk in psychotropic drug poisonings was consistent with results from a previous, more rigorous interrupted time series analysis but use of the maximized sequential probability ratio test method allows timely detection. While we also detected signals of increased risk of completed suicide in these younger age groups, on its own it should not be taken as conclusive evidence that the policy caused the signal. A statistical signal indicates the need for further scrutiny using rigorous quasi-experimental studies to investigate the possibility of a cause-and-effect relationship. CONCLUSIONS: This was a proof-of-concept study. Prospective, periodic evaluation of administrative health care data using sequential analysis can provide timely population-based signals of effects of health policies. This method may be useful to use as new policies are introduced.

Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children. DESIGN: Observational cohort study. SETTING: PICU. PATIENTS: Children age between 6 and 18 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Continuous minute-by-minute physiologic data were available for a total of 493 critically ill children admitted to a tertiary care PICU over an 8-month period, 20 of whom developed severe sepsis. Using an alert time stamp generated by an electronic screening algorithm as a reference point, we studied up to 24 prior hours of continuous physiologic data. We identified physiomarkers, including SD of heart rate, systolic and diastolic blood pressure, and symbolic transitions probabilities of those variables that discriminated severe sepsis patients from controls (all other patients admitted to the PICU who did not meet severe sepsis criteria). We used logistic regression, random forests, and deep Convolutional Neural Network methods to derive our models. Analysis was performed using data generated in two windows prior to the firing of the electronic screening algorithm, namely, 2-8 and 8-24 hours. When analyzing the physiomarkers present in the 2-8 hours analysis window, logistic regression performed with specificity of 87.4% and sensitivity of 55.0%, random forest performed with 79.6% specificity and 80.0% sensitivity, and the Convolutional Neural Network performed with 83.0% specificity and 75.0% sensitivity. When analyzing physiomarkers from the 8-24 hours window, logistic regression resulted in 77.1% specificity and 39.3% sensitivity, random forest performed with 82.3% specificity and 61.1% sensitivity, whereas the Convolutional Neural Network method achieved 81% specificity and 76% sensitivity. CONCLUSIONS: Artificial intelligence can be used to predict the onset of severe sepsis using physiomarkers in critically ill children. Further, it may detect severe sepsis as early as 8 hours prior to a real-time electronic severe sepsis screening algorithm.

Identification of Stevens-Johnson syndrome and toxic epidermal necrolysis in electronic health record databases.

BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) carry a high mortality risk. While identifying clinical and genetic risk factors for these conditions has been hindered by their rarity, large electronic health databases hold promise for identifying large numbers of cases for study, especially with the introduction in 2008 of ICD-9 codes more specific for these conditions. OBJECTIVE: The objective of this study is to estimate the validity of ICD-9 codes for ascertaining SJS/TEN in 12 collaborating research units in the USA, covering almost 60 million lives. METHODS: From the electronic databases at each site, we ascertained potential cases of SJS/TEN using ICD-9 codes. At five sites, a subset of medical records was abstracted and standardized criteria applied by board-certified dermatologists to adjudicate diagnoses. Multivariate logistic regression was used to identify factors independently associated with validated SJS/TEN cases. RESULTS: A total of 56 591 potential cases of SJS/TEN were identified. A subset of 276 charts was selected for adjudication and 39 (of the 276) were confirmed as SJS/TEN. Patients with the ICD-9 codes introduced after 2008 were more likely to be confirmed as cases (OR 3.32; 95%CI 0.82, 13.47) than those identified in earlier years. Likelihood of case status increased with length of hospitalization. Applying the probability of case status to the 56 591 potential cases, we estimated 475-875 to be valid SJS/TEN cases. CONCLUSION: Newer ICD-9 codes, along with length of hospitalization, identified patients with a high likelihood of SJS/TEN. This is important for identification of subjects for future pharmacogenomics studies.

Performance of claims-based algorithms for identifying heart failure and cardiomyopathy among patients diagnosed with breast cancer.

BACKGROUND: Cardiotoxicity is a known complication of certain breast cancer therapies, but rates come from clinical trials with design features that limit external validity. The ability to accurately identify cardiotoxicity from administrative data would enhance safety information. OBJECTIVE: To characterize the performance of clinical coding algorithms for identification of cardiac dysfunction in a cancer population. RESEARCH DESIGN: We sampled 400 charts among 6460 women diagnosed with incident breast cancer, tumor size ≥ 2 cm or node positivity, treated within 8 US health care systems between 1999 and 2007. We abstracted medical records for clinical diagnoses of heart failure (HF) and cardiomyopathy (CM) or evidence of reduced left ventricular ejection fraction. We then assessed the performance of 3 different International Classification of Diseases, 9th Edition (ICD-9)-based algorithms. RESULTS: The HF/CM coding algorithm designed a priori to balance performance characteristics provided a sensitivity of 62% (95% confidence interval, 40%-80%), specificity of 99% (range, 97% to 99%), positive predictive value (PPV) of 69% (range, 45% to 85%), and negative predictive value (NPV) of 98% (range, 96% to 99%). When applied only to incident HF/CM (ICD-9 codes and gold standard diagnosis both occurring after breast cancer diagnosis) in patients exposed to anthracycline and/or trastuzumab therapy, the PPV was 42% (range, 14% to 76%). CONCLUSIONS: Claims-based algorithms have moderate sensitivity and high specificity for identifying HF/CM among patients with invasive breast cancer. As the prevalence of HF/CM among the breast cancer population is low, ICD-9 codes have high NPV but only moderate PPV. These findings suggest a significant degree of misclassification due to HF/CM overcoding versus incomplete clinical documentation of HF/CM in the medical record.

The prevalence and trends of antiviral medication use during pregnancy in the US: a population-based study of 664,297 deliveries in 2001-2007.

To evaluate the prevalence, trends, timing and duration of exposure to antiviral medications during pregnancy within a US cohort of pregnant women and to evaluate the proportion of deliveries with a viral infection diagnosis among women given antiviral medication during pregnancy. Live-born deliveries between 2001 and 2007, to women aged 15-45 years, were included from the Medication Exposure in Pregnancy Risk Evaluation Program, a collaborative research program between the U.S. Food and Drug Administration and eleven health plans. They were evaluated for prevalence, timing, duration, and temporal trends of exposure to antiviral medications during pregnancy. We also calculated the proportion of deliveries with a viral infection diagnosis among those exposed to antiviral medications. Among 664,297 live births, the overall prevalence of antiviral exposure during pregnancy was 4 % (n = 25,155). Between 2001 and 2007, antiviral medication exposure during pregnancy doubled from 2.5 to 5 %. The most commonly used antiviral medication was acyclovir, with 3 % of the deliveries being exposed and most of the exposure occurring after the 1st trimester. Most deliveries exposed to antiviral medications were exposed for less than 30 days (2 % of all live births). Forty percent of the women delivering an infant exposed to antiviral medications had a herpes diagnosis. Our findings highlight the increased prevalence of women delivering an infant exposed to antiviral medications over time. These findings support the need for large, well-designed studies to assess the safety and effectiveness of these medications during pregnancy.

Control of cell proliferation by memories of mitosis.

Mitotic duration is tightly constrained, and extended mitosis is characteristic of problematic cells prone to chromosome missegregation and genomic instability. We show here that mitotic extension leads to the formation of p53-binding protein 1 (53BP1)-ubiquitin-specific protease 28 (USP28)-p53 protein complexes that are transmitted to, and stably retained by, daughter cells. Complexes assembled through a Polo-like kinase 1-dependent mechanism during extended mitosis and elicited a p53 response in G1 that prevented the proliferation of the progeny of cells that experienced an approximately threefold extended mitosis or successive less extended mitoses. The ability to monitor mitotic extension was lost in p53-mutant cancers and some p53-wild-type (p53-WT) cancers, consistent with classification of TP53BP1 and USP28 as tumor suppressors. Cancers retaining the ability to monitor mitotic extension exhibited sensitivity to antimitotic agents.