Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.

MOTIVATION: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION: phecodeX is available at https://github.com/PheWAS/phecodeX.

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Telehealth for Endocrinology: Experience and Effect on Utilization in the Prepandemic Era.

OBJECTIVE: Telehealth (TH) use in endocrinology was limited before the COVID-19 pandemic but will remain a major modality of care postpandemic. Reimbursement policies have been limited historically due to concerns of overutilization of visits and testing. Additionally, there is limited literature on endocrinology care delivered via TH for conditions other than diabetes. We assess real-world TH use for endocrinology in a prepandemic environment with the hypothesis that TH would not increase the utilization of total visits or related ancillary testing services compared with conventional (CVL) face-to-face office visits. METHODS: A single-institution retrospective cohort study assessing the prepandemic use of TH in endocrinology, consisting of 75 patients seen via TH and 225 patients seen in CVL visits. For most patients, TH was conducted via a clinic-to-clinic model. Outcomes measured were total endocrine visit frequency and frequency of related laboratory and radiology testing per patient, hemoglobin A1C, microalbumin, low-density lipoprotein, thyroid-stimulating hormone, thyroglobulin, and thyroid ultrasounds. RESULTS: For all endocrine visits, TH patients had a median of 0.24 (interquartile range, 0.015-0.36) visits per month. CVL patients had a median of 0.20 visits per month (interquartile range, 0.11-0.37). Total visits per month did not vary significantly between groups (P = .051). Hemoglobin A1C outcomes were equivalent and there was no increase in ancillary laboratory testing for the TH group. CONCLUSION: Our observations demonstrate that, in a prepandemic health care setting, TH visits can provide equivalent care for endocrinology patients, without increasing utilization of total visits or ancillary services.

2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group.

The 2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group was coordinated and supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health. It is designed to improve patient care and support informed decision making about asthma management in the clinical setting. This update addresses six priority topic areas as determined by the state of the science at the time of a needs assessment, and input from multiple stakeholders:A rigorous process was undertaken to develop these evidence-based guidelines. The Agency for Healthcare Research and Quality's (AHRQ) Evidence-Based Practice Centers conducted systematic reviews on these topics, which were used by the Expert Panel Working Group as a basis for developing recommendations and guidance. The Expert Panel used GRADE (Grading of Recommendations, Assessment, Development and Evaluation), an internationally accepted framework, in consultation with an experienced methodology team for determining the certainty of evidence and the direction and strength of recommendations based on the evidence. Practical implementation guidance for each recommendation incorporates findings from NHLBI-led patient, caregiver, and clinician focus groups. To assist clincians in implementing these recommendations into patient care, the new recommendations have been integrated into the existing Expert Panel Report-3 (EPR-3) asthma management step diagram format.

Patient cumulative radiation exposure-the potential for unintended consequences.

Several recent publications have sought to highlight the benefits of tracking patient radiation exposure and how it can be used to strengthen the justification and optimization processes to enhance patient safety. In some cases, authors promote the use of dose tracking to keep medical prescribers aware of the patients' cumulative exposure, presumably with a view to influencing the justification process. Inclusion of patient dose history in the justification process can be problematic for reasons that are not always well understood by medical prescribers. Our paper seeks to highlight these issues and attempts to provide some clarification. Key Points • Care must be taken in how cumulative radiation exposure is interpreted as it pertains to the justification process.

Detection of calibration drift in clinical prediction models to inform model updating.

Model calibration, critical to the success and safety of clinical prediction models, deteriorates over time in response to the dynamic nature of clinical environments. To support informed, data-driven model updating strategies, we present and evaluate a calibration drift detection system. Methods are developed for maintaining dynamic calibration curves with optimized online stochastic gradient descent and for detecting increasing miscalibration with adaptive sliding windows. These methods are generalizable to support diverse prediction models developed using a variety of learning algorithms and customizable to address the unique needs of clinical use cases. In both simulation and case studies, our system accurately detected calibration drift. When drift is detected, our system further provides actionable alerts by including information on a window of recent data that may be appropriate for model updating. Simulations showed these windows were primarily composed of data accruing after drift onset, supporting the potential utility of the windows for model updating. By promoting model updating as calibration deteriorates rather than on pre-determined schedules, implementations of our drift detection system may minimize interim periods of insufficient model accuracy and focus analytic resources on those models most in need of attention.

Maternal activity restriction to reduce preterm birth: Time to put this fallacy to bed.

Activity restriction has traditionally been recommended to pregnant women, especially high-risk patients, to reduce preterm birth. However, there is no scientific evidence that bed-rest reduces preterm birth and, in many studies, women on bed-rest had higher rates of delivering preterm. Bed-rest in pregnancy is associated with significant physiological and psychosocial sequelae and reduced neonatal birth weight and be cannot be endorsed, even in women with a short cervix. The practice of prescribing bed-rest in pregnancy is outdated and should be abandoned.

Interpretation of risk for low dose radiation exposures: inconsistencies, philosophical problems and fallacies.

Low doses of radiation are used in diagnostic tests such as X-rays or CT exams. A Linear No Threshold model (LNT) is used to assess radiation risk. According to this model, risk decreases linearly with a decreasing dose, with no lower threshold. However, there is no definitive evidence to support risk at very low doses and the model relies on extrapolation to estimate risk at doses below a few 10 s mSv. The simplicity of the LNT model can disguise the more complex questions that arise when the facts are uncertain. There is a considerable debate over how best to interpret and communicate these risks for low dose exposures. This paper criticises arguments on both sides of this debate. In discussing the case for asserting risk we argue that it is only as a scientific model that LNT should be used to make predictions that can be tested. Taking the further step of using these calculations to state how many cancers are caused by the use of X-rays requires a strong scientific model and the disputed status of the LNT model and the consequent uncertainty as to whether low dose radiation definitely causes cancer, is the major obstacle to making these types of claims. In discussing arguments which tend to dismiss risk, we identify the hidden premises needed to support these claims. Differences between a prudent model and a scientific model are considered. A 'reductio ad absurdum' objection, and fallacies of ambiguity, where 'insignificant risk' may be confused with 'inconsequential risk', are also discussed. Disagreement over the interpretation of risk has the potential to disrupt the optimum use of medical imaging technology. While communication of risk need not address the underlying philosophical complexities, advice from the radiation protection community must be rooted in an awareness of these issues if inconsistencies are to be avoided.

A method for analyzing inpatient care variability through physicians' orders.

OBJECTIVE: Administrators assess care variability through chart review or cost variability to inform care standardization efforts. Chart review is costly and cost variability is imprecise. This study explores the potential of physician orders as an alternative measure of care variability. MATERIALS & METHODS: The authors constructed an order variability metric from adult Vanderbilt University Hospital patients treated between 2013 and 2016. The study compared how well a cost variability model predicts variability in the length of stay compared to an order variability model. Both models adjusted for covariates such as severity of illness, comorbidities, and hospital transfers. RESULTS: The order variability model significantly minimized the Akaike information criterion (superior outcome) compared to the cost variability model. This result also held when excluding patients who received intensive care. CONCLUSION: Order variability can potentially typify care variability better than cost variability. Order variability is a scalable metric, calculable during the course of care.

Maintaining automated measurement of Choosing Wisely adherence across the ICD 9 to 10 transition.

BACKGROUND: It remains unclear how to incorporate terminology changes, such as the International Classification of Disease (ICD) transition from ICD-9 to ICD-10, into established automated healthcare quality metrics. OBJECTIVE: To evaluate whether general equivalence mapping (GEM) can apply ICD-9 based metrics to ICD-10 patient data. To develop and validate novel ICD-10 reference codesets. DESIGN: Retrospective analysis for eleven Choosing Wisely (CW) metrics was performed using three scripted algorithms on an institutional clinical data warehouse. ICD-10 data were compared against published ICD-9 based metric definitions using two equivalence mapping algorithms. A third algorithm implemented novel reference ICD-10 codes matching the original ICD-9 codes' intent for comparison with patient ICD-10 data. PARTICIPANTS: All adult patients seen at Vanderbilt University Medical Center, April - September 2016. MAIN MEASURES: The prevalence of eleven CW services during the six-month period. KEY RESULTS: The three algorithms found similar prevalence of avoidable CW services, with an unweighted-mean of 8.4% (range: 0.16-65%), or approximately 20,000 CW services out of 240,000 potential cases in 515,406 unique patients. The algorithms' median sensitivity was 0.80 (interquartile range: 0.75-0.95), median specificity was 0.88 (IQR: 0.77-0.94), and median Rand accuracy was 0.84 (IQR: 0.79-0.89). The attributed waste of these eleven services for the period ranged from $871,049 to $951,829 between methods. Accuracy assessment demonstrated that the GEM-based methods suffered recall losses for metrics requiring multistep mapping due to incompleteness, while novel ICD-10 metric definitions avoided these challenges. CONCLUSIONS: Comprehensive mapping enables use of legacy metrics across ICD generations, but requires computational complexity that can be avoided with novel ICD-10 based metric definitions. Variation in the dollars attributed to waste due to ICD mapping introduces ambiguity that may affect quality-based reimbursement.

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.

BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suicide risk using only routinely collected clinical data. We used a machine learning approach validated on longitudinal clinical data in adults to address this challenge in adolescents. METHODS: This is a retrospective, longitudinal cohort study. Data were collected from the Vanderbilt Synthetic Derivative from January 1998 to December 2015 and included 974 adolescents with nonfatal suicide attempts and multiple control comparisons: 496 adolescents with other self-injury (OSI), 7,059 adolescents with depressive symptoms, and 25,081 adolescent general hospital controls. Candidate predictors included diagnostic, demographic, medication, and socioeconomic factors. Outcome was determined by multiexpert review of electronic health records. Random forests were validated with optimism adjustment at multiple time points (from 1 week to 2 years). Recalibration was done via isotonic regression. Evaluation metrics included discrimination (AUC, sensitivity/specificity, precision/recall) and calibration (calibration plots, slope/intercept, Brier score). RESULTS: Computational models performed well and did not require face-to-face screening. Performance improved as suicide attempts became more imminent. Discrimination was good in comparison with OSI controls (AUC = 0.83 [0.82-0.84] at 720 days; AUC = 0.85 [0.84-0.87] at 7 days) and depressed controls (AUC = 0.87 [95% CI 0.85-0.90] at 720 days; 0.90 [0.85-0.94] at 7 days) and best in comparison with general hospital controls (AUC 0.94 [0.92-0.96] at 720 days; 0.97 [0.95-0.98] at 7 days). Random forests significantly outperformed logistic regression in every comparison. Recalibration improved performance as much as ninefold - clinical recommendations with poorly calibrated predictions can lead to decision errors. CONCLUSIONS: Machine learning on longitudinal clinical data may provide a scalable approach to broaden screening for risk of nonfatal suicide attempts in adolescents.

Psychosocial co-morbidities in Interstitial Cystitis/Bladder Pain syndrome (IC/BPS): A systematic review.

AIMS: Psychosocial factors amplify symptoms of Interstitial Cystitis (IC/BPS). While psychosocial self-management is efficacious in other pain conditions, its impact on an IC/BPS population has rarely been studied. The objective of this review is to learn the prevalence and impact of psychosocial factors on IC/BPS, assess baseline psychosocial characteristics, and offer recommendations for assessment and treatment. METHOD: Following PRISMA guidelines, primary information sources were PubMed including MEDLINE, Embase, CINAHL, and GoogleScholar. Inclusion criteria included: (i) a clearly defined cohort with IC/BPS or with Chronic Pelvic Pain Syndrome provided the IC/BPS cohort was delineated with quantitative results from the main cohort; (ii) all genders and regions; (iii) studies written in English from 1995 to April 14, 2017; (iv) quantitative report of psychosocial factors as outcome measures or at minimum as baseline characteristics. RESULTS: Thirty-four of an initial 642 articles were reviewed. Quantitative analyses demonstrate the magnitude of psychosocial difficulties in IC/BPS, which are worse than average on all measures, and fall into areas of clinical concern for 7 out of 10 measures. Meta-analyses shows mean Mental Component Score of the Short-Form 12 Health Survey (MCS) of 40.80 (SD 6.25, N = 2912), where <36 is consistent with severe psychological impairment. Averaged across studies, the population scored in the range seen in clinical depression (CES-D 19.89, SD 13.12, N = 564) and generalized anxiety disorder (HADS-A 8.15, SD 4.85, N = 465). CONCLUSION: The psychological impact of IC/BPS is pervasive and severe. Existing evidence of treatment is lacking and suggests self-management intervention may be helpful.

Discovering hidden knowledge through auditing clinical diagnostic knowledge bases.

OBJECTIVE: Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation. MATERIALS AND METHODS: Authors used unsupervised (Pearson's correlation - PC, Kendall's correlation - KC, and a heuristic algorithm - HA) methods to identify existing and discover new finding-finding interrelationships ("properties") in the INTERNIST-1/QMR KB. Authors estimated KB maintenance efficiency gains (effort reduction) of the approaches. RESULTS: The methods discovered new properties at 95% CI rates of [0.1%, 5.4%] (PC), [2.8%, 12.5%] (KC), and [5.6%, 18.8%] (HA). Estimated manual effort reduction for HA-assisted determination of new properties was approximately 50-fold. CONCLUSION: Data mining can provide an efficient supplement to ensuring the completeness of finding-finding interdependencies in diagnostic knowledge bases. Authors' findings should be applicable to other diagnostic systems that record finding frequencies within diseases (e.g., DXplain, ISABEL).

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk.

BACKGROUND: Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. OBJECTIVES: To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. MATERIALS AND METHODS: Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. RESULTS: C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862. CONCLUSIONS: Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result.

Natural history of fetal trisomy 13 after prenatal diagnosis.

There are currently limited data describing the natural history and outcome for fetal trisomy 13 diagnosed prenatally. The aim of this study was to evaluate the fetal and neonatal outcome for pregnancies with an established prenatal diagnosis of fetal trisomy 13, and a parental decision for continuation of the pregnancy. To this end, the obstetric and neonatal outcome data for such pregnancies, diagnosed at two referral Fetal Medicine Centers, were retrospectively obtained and examined. During the study period, there were 45 cases of trisomy 13 diagnosed at both units, of which 26 (56%) continued with the pregnancy to its natural outcome. There were 12 intrauterine deaths in the cohort resulting in a rate of 46.2% of intrauterine lethality. Conversely, the live birth rate was 53.8%. For infants born alive, neonatal death on day 1 of life occurred in 78.6% of cases. The overall early neonatal mortality rate was 93%. There was one infant death at 6 weeks of age and no survival noted beyond this period. These data provide reliable information for parental counseling pertaining to risk of intrauterine death when trisomy 13 is diagnosed prenatally. These data also indicate that the survival outcome is worse than that previously accepted from studies of postnatal follow up of live born infants with this diagnosis.

Prenatal detection of structural cardiac defects and presence of associated anomalies: a retrospective observational study of 1262 fetal echocardiograms.

OBJECTIVE: The aim of this study is to document the detection of fetal congenital heart defect (CHD) in relation to the following: (1) indication for referral, (2) chromosomal and (3) extracardiac abnormalities. METHOD: All fetal echocardiograms performed in our institution from 2007 to 2011 were reviewed retrospectively. Indication for referral, cardiac diagnosis based on the World Health Organization International Classification of Diseases tenth revision criteria and the presence of chromosomal and extracardiac defects were recorded. RESULTS: Of 1262 echocardiograms, 287 (22.7%) had CHD. Abnormal anatomy scan in pregnancies originally considered to be at low risk of CHD was the best indicator for detecting CHD (91.2% of positive cardiac diagnoses), compared with other indications of family history (5.6%) or maternal medical disorder (3.1%). Congenital anomalies of the cardiac septa comprised the largest category (n = 89), within which atrioventricular septal defects were the most common anomaly (n = 36). Invasive prenatal testing was performed for 126 of 287 cases, of which 44% (n = 55) had a chromosomal abnormality. Of 232 fetuses without chromosomal abnormalities, 31% had an extracardiac defect (n = 76). CONCLUSIONS: Most CHDs occur in pregnancies regarded to be at low risk, highlighting the importance of a routine midtrimester fetal anatomy scan. Frequent association of fetal CHD and chromosomal and extracardiac pathology emphasises the importance of thorough evaluation of any fetus with CHD.

The contribution of massive fetomaternal hemorrhage to antepartum stillbirth: a 25-year cross-sectional study.

INTRODUCTION: Fatal antepartum fetomaternal hemorrhage is a relatively uncommon clinical presentation, though one that appears quickly and without warning. The pathophysiology of this disease is unclear, and the incidence does not appear to be decreasing in line with overall antepartum mortality. This study was undertaken to analyse trends in antepartum fetal death from fetomaternal hemorrhage over a 25-year period in a single maternity hospital in Dublin, Ireland. MATERIAL AND METHODS: A cross-sectional study of 192 132 nonanomalous infants weighing 500 g or more, delivered in a single tertiary-referral university institution between 1987 and 2011. Data was compared using Fisher's exact test, univariate analysis, and Cuzick's test for trend. RESULTS: There was no decrease in the rate of fatal fetomaternal hemorrhage over the past 25 years (p = 0.29), despite a decline in overall antepartum deaths (p = 0.0049). Fetomaternal hemorrhage accounted for 4.1% (34/828) of antepartum stillbirths. A higher proportion of these stillbirths occurred at term gestations (74%; 25/34) compared with other causes (40%; 321/794; p = 0.0003). Female infants were statistically more likely to be involved than males [odds ratio (OR) 2.33, 95% confidence interval (CI) 1.08-5.47, p = 0.02). Multiple gestations were up to six times as likely to be affected as singleton pregnancies (OR 6.52, 95% CI 1.67-18.50, p = 0.005). CONCLUSIONS: Over the past 25 years there has been no reduction in rates of fatal fetomaternal hemorrhage. Female infants and multiple gestations remain at higher risk of antepartum death from fatal fetomaternal hemorrhage.

The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions.

BACKGROUND: Hospital readmission risk prediction remains a motivated area of investigation and operations in light of the hospital readmissions reduction program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance. OBJECTIVES: To study the effects of varying three factors of model development in the prediction of risk based on health record data: (1) reason for readmission (primary readmission diagnosis); (2) available data and data types (e.g. visit history, laboratory results, etc); (3) cohort selection. METHODS: Regularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison in cohort selection testing. Calibration by model refitting to outcome prevalence. RESULTS: Predicting readmission risk across multiple reasons for readmission resulted in ROC areas ranging from 0.92 for readmission for congestive heart failure to 0.71 for syncope and 0.68 for all-cause readmission. Visit history and laboratory tests contributed the most predictive value; contributions varied by readmission diagnosis. Cohort definition affected performance for both parametric and nonparametric algorithms. Compared to all patients, limiting the cohort to patients whose index admission and readmission diagnoses matched resulted in a decrease in average ROC from 0.78 to 0.55 (difference in ROC 0.23, p value 0.01). Calibration plots demonstrate good calibration with low mean squared error. CONCLUSION: Targeting reason for readmission in risk prediction impacted discriminatory performance. In general, laboratory data and visit history data contributed the most to prediction; data source contributions varied by reason for readmission. Cohort selection had a large impact on model performance, and these results demonstrate the difficulty of comparing results across different studies of predictive risk modeling.

Induced Pluripotent Stem Cells and CRISPR-Cas9 Innovations for Treating Alpha-1 Antitrypsin Deficiency and Glycogen Storage Diseases.

Human induced pluripotent stem cell (iPSC) and CRISPR-Cas9 gene-editing technologies have become powerful tools in disease modeling and treatment. By harnessing recent biotechnological advancements, this review aims to equip researchers and clinicians with a comprehensive and updated understanding of the evolving treatment landscape for metabolic and genetic disorders, highlighting how iPSCs provide a unique platform for detailed pathological modeling and pharmacological testing, driving forward precision medicine and drug discovery. Concurrently, CRISPR-Cas9 offers unprecedented precision in gene correction, presenting potential curative therapies that move beyond symptomatic treatment. Therefore, this review examines the transformative role of iPSC technology and CRISPR-Cas9 gene editing in addressing metabolic and genetic disorders such as alpha-1 antitrypsin deficiency (A1AD) and glycogen storage disease (GSD), which significantly impact liver and pulmonary health and pose substantial challenges in clinical management. In addition, this review discusses significant achievements alongside persistent challenges such as technical limitations, ethical concerns, and regulatory hurdles. Future directions, including innovations in gene-editing accuracy and therapeutic delivery systems, are emphasized for next-generation therapies that leverage the full potential of iPSC and CRISPR-Cas9 technologies.

Prevalence of anomalies on the routine mid-trimester ultrasound: 3172 consecutive cases by a single maternal-fetal medicine specialist.

Introduction/Purpose: The routine mid-trimester fetal anatomy ultrasound (FAS) is offered to every pregnant woman and remains critical in the detection of structural fetal anomalies. Our study aimed to determine the prevalence of abnormalities on routine FAS performed by a single operator, who is an experienced sub-specialist in maternal-fetal medicine. Methods: A retrospective analysis of all routine FAS performed a tertiary private obstetric ultrasound practice in metropolitan Sydney over a 7-year period, August 2015-July 2022. An advanced ultrasound protocol including detailed cardiac views was used in every case. Second opinion scans for suspected abnormalities were excluded. Fetal anomalies were classified into major and minor, based on the likely need for neonatal intervention. Results: Among 14,908 obstetric ultrasound examinations, routine FAS were performed on 3172 fetuses by a single operator. More than 99% of women had screened low-risk for fetal aneuploidy. Structural anomalies were identified in 5% (157/3172) of fetuses; the prevalence of major anomalies was 1% (30/3172). Almost 60% of total anomalies were either cardiac or renal. No differences were identified in anomaly rates for singletons compared with twins (5.0% vs. 4.2%; P = 0.75). The prevalence of placenta previa and vasa previa was 10% and 0.1%, respectively. Discussion: The prevalence of fetal anomalies on routine FAS by a single operator using a standardised protocol was higher in our practice (5%) than in previously published studies. Although most anomalies were minor, the rate of major abnormality was 1%. Conclusion: The routine mid-trimester FAS remains an integral component of prenatal ultrasound screening.