Patient-specific Risk Estimates Research Articles

First Trimester Preeclampsia Screening and Prevention: Perspective in Chinese Mainland

Abstract Preeclampsia (PE), a multisystem disorder in pregnancy, is one of the leading causes of perinatal morbidity and mortality that poses financial and physical burdens worldwide. Preterm PE with delivery at <37 weeks of gestation is associated with a higher risk of adverse maternal and perinatal outcomes than term PE with delivery at ≥37 weeks of gestation. A myriad of first trimester screening models have been developed to identifying women at risk of preterm PE. In fact, the Fetal Medicine Foundation (FMF) first trimester prediction model has undergone successful internal and external validation. The FMF triple test enables the estimation of patient-specific risks, using Bayes theorem to combine maternal characteristics and medical history together with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor. Establishing a quality control process for regular monitoring and to ensure data standardization, reliability, and accuracy is key to maintaining optimal screening performance. The rate of preterm PE can be reduced by 62% by using the FMF prediction model, followed by the administration of low-dose aspirin. Recent evidence has also demonstrated that metformin has the potential for preventing PE in patients at high-risk of the disorder. In this article, we will summarize the existing literature on the different screening methods, different components of risk assessment, therapeutic interventions, and clinical implementation of the first trimester screening and prevention program for PE with specific considerations for Chinese mainland.

To Remplissage or not to Remplissage Part 2: Recurrent Instability After Primary Stabilization Surgery (RIPSS) Risk Tool Assesses Preoperative Failure Rates in On-track Shoulders Undergoing Primary Arthroscopic Anterior Stabilization with or without Remplissage Augmentation

Objectives: Given the high rates of recurrent instability following the initial arthroscopic Bankart repair (ABR), it is crucial to provide recommendations for appropriate surgical interventions for patients at a higher risk of experiencing recurring instability. In Part 1 of the study, it was discovered that on-track Hill-Sachs lesions (HSL) with specific prognostic factors (i.e., younger age, increased shoulder laxity, lower DTD, and 2+ preoperative instability events) were associated with a greater risk of failure. Therefore, the objective of Part 2 was to develop a risk assessment tool that takes into account significant prognostic factors for recurrent shoulder instability following primary ABR and evaluate the role of remplissage augmentation given a patient’s risk profile. Methods: We retrospectively reviewed prospectively collected data of consecutive patients aged 14-40 who underwent either ABR (arthroscopic Bankart repair) or ABR+R (ABR with remplissage) procedures between 2013 and 2021 for anterior glenohumeral instability. Preoperative magnetic resonance imaging was used to determine the values of glenoid bone loss, Hills-Sachs Interval (HSI), glenoid track (GT), and distance-to-dislocation (DTD). On-track lesions have a distance-to-dislocation (DTD) value greater than zero. In addition, within the category of on-track lesions, there is a subset known as “near-track” lesions, with a DTD ranging from 0 to 10 mm. Capsuloligamentous laxity scores were categorized according to hyperlaxity status, defined as external rotation greater than 85 degrees and/or grade 2+ posterior and inferior load-and-shift on examination under anesthesia. Recurrent shoulder instability was defined as recurrent dislocation and/or subjective subluxation postoperatively. Patients were excluded if the indexed surgery was a revision procedure, < 2-year follow-up, or glenoid bone loss (GBL) >20%. A final multivariate survival analysis was constructed using categorical prognostic factors (i.e., patient age, “near-track” status, hyperlaxity, and 2+ preoperative instability episodes) identified in Part 1, while adjusting for GBL. Although contact athlete status was not identified as a significant predictor of recurrent shoulder instability in Part 1, the final model stratified by contact athlete status given its established importance in prior literature. Multivariate hazard ratio estimates were utilized to create a risk assessment tool and correlated with patient-specific risk estimates via post-estimation analysis. Results: One-hundred-and-fifty-five patients were included for analysis (ABR: 116 | ABR+R: 39) with an average age of 21.6 ± 6.2 years and an average follow-up of 5.1 ± 2.0 years (range: 2.0 – 8.7 yrs). Patients with near-track lesions had a three-fold higher risk of recurrent instability (Hazard Ratio [HR]: 2.5, p = 0.03) compared to on-track HSL with DTD > 10mm. Individuals with evidence of hyperlaxity exhibited a five-fold increased risk (HR: 5.4, p=0.03) relative to patients without hyperlaxity. Younger patient groups had twice the risk of recurrent shoulder instability (HR: 2.2, p=0.003) compared to older adjacent groups. Patients with two or more preoperative recurrent instability episodes faced a four-times greater risk (HR: 4.2, p=0.004) for recurrent shoulder instability compared to patients who experienced a single instability event preoperatively. Lastly, patients who underwent primary ABR only had almost 10-times greater risk of recurrent shoulder instability (HR: 9.5, p=0.002) than those who underwent primary ABR+R. The Recurrent Instability after Primary Shoulder Stabilization (RIPSS) risk assessment score considers patient age, near-track status, hyperlaxity, preoperative instability episodes, and surgical technique (ABR vs. ABR+R) as important prognostic indicators, Table 2. The RIPSS score was created with risk stratifying subgroups: Low-risk (0 – 2), Moderate-risk (3 – 6), High-risk (7 – 9), and Extreme-risk (10+). Figure 1 illustrates a strong correlation between patient RIPSS scores with patient-specific hazard ratio estimates derived from the multivariate Cox regression modeling computed in “To Remplissage or not to Remplissage Part 1”. According to RIPSS score subgroups, recurrent shoulder instability rates range from 4.3% among low-risk groups to 54.6% among extreme-risk groups, Figure 1. Conclusions: Patients with a low-risk RIPSS score without ABR+R may not benefit from remplissage augmentation. Their risk for recurrent instability would remain low despite the addition of remplissage. However, patients with a high- or extreme-risk RIPSS score prior to considering ARB+R would likely benefit from remplissage supplementation. The RIPSS score may be an effective risk assessment tool that may help identify patients who may benefit from a primary Bankart repair with remplissage augmentation. The risk assessment tool developed may optimize surgical treatment and allow physicians to determine patient risk scores with and without remplissage augmentation.

Key biomarkers in cerebral arteriovenous malformations: Updated review.

The formation of vascular networks consisting of arteries, capillaries, and veins is vital in embryogenesis. It is also crucial in adulthood for the formation of a functional vasculature. Cerebral arteriovenous malformations (CAVMs) are linked with a remarkable risk of intracerebral hemorrhage because arterial blood is directly shunted into the veins before the arterial blood pressure is dissipated. The underlying mechanisms responsible for arteriovenous malformation (AVM) growth, progression, and rupture are not fully known, yet the critical role of inflammation in AVM pathogenesis has been noted. The proinflammatory cytokines are upregulated in CAVM, which stimulates overexpression of cell adhesion molecules in endothelial cells (ECs), leading to improved leukocyte recruitment. It is well-known that metalloproteinase-9 secretion by leukocytes disrupts CAVM walls resulting in rupture. Moreover, inflammation alters the angioarchitecture of CAVMs by upregulating angiogenic factors impacting the apoptosis, migration, and proliferation of ECs. A better understanding of the molecular signature of CAVM might allow us to identify biomarkers predicting this complication, acting as a goal for further investigations that may be potentially targeted in gene therapy. The present review is focused on the numerous studies conducted on the molecular signature of CAVM and the associated hemorrhage. The association of numerous molecular signatures with a higher risk of CAVM rupture is shown through inducing proinflammatory mediators, as well as growth factors signaling, Ras-mitogen-activated protein kinase-extracellular signal-regulated kinase, and NOTCH pathways, which are accompanied by cellular level inflammation and endothelial alterations resulting in vascular wall instability. According to the studies, it is assumed that matrix metalloproteinase, interleukin-6, and vascular endothelial growth factor are the biomarkers most associated with CAVM and the rate of hemorrhage, as well as diagnostic methods, with respect to enhancing the patient-specific risk estimation and improving treatment choices.

Evaluation of Infection in Patients with Myelodysplastic Syndromes Treated with Hypomethylating Agents: A Systematic Review

BACKGROUND: Myelodysplastic syndromes (MDS) are a heterogeneous group of disorders associated with cytopenias and risk of febrile neutropenia and infection. Hypomethylating therapy, such as azacitidine and decitabine, form the backbone of outpatient chemotherapy for many patients with MDS. Current literature reports heterogeneous and variable frequency, location, and severity of infections among patients receiving this treatment. Further, the relationship between infection and neutropenia is not well established. OBJECTIVE: To facilitate evidence-informed supportive care therapy, we evaluated the incidence and severity of infection and neutropenia among patients with MDS treated with hypomethylating therapy. We also aimed to evaluate the relationship between neutropenia and infection (CRD42022339150). POPULATION: Adult patients (age ≥ 18 years) with MDS (≥80% of the study population) treated with hypomethylating agents. OUTCOMES: Our primary outcome was febrile neutropenia, as per the National Cancer Institute's Common Toxicity Criteria for Adverse Events (NCI-CTCAE). Secondary outcomes included infection of any grade, grade 3 or 4 infections, death due to infection, microbial and clinical infection subtype, frequency of grade 3 or 4 neutropenia, duration of neutropenia, and use of antimicrobial prophylaxis. METHODS: We identified randomized controlled trials (RCT) from Medline, Embase, CENTRAL, and CINAHL from inception to April 2021. RCTs were included if they evaluated hypomethylating therapy in patients with MDS and reported at least one outcome of interest. We used Freeman-Tukey transformation to calculate the weighted summary proportion using a random effects model. RESULTS: We included 21 unique trials enrolling 1380 patients. 14 trials evaluated azacitidine (n=946 patients) and 8 trials studied decitabine (n=434 patients). The median patient age was 70, and 56% were male. 10 trials (n=729 patients) reported risk by International Prognostic Scoring System, of which 5% were low risk, 30% were intermediate-1 risk, 41% were intermediate-2 risk, and 24% were high risk. 49% of patients had grade 3 or 4 neutropenia (95% confidence interval (CI) 34%-63%, n=10 trials, n=697 patients). Febrile neutropenia was reported in 22% of patients (95% confidence interval (CI) 16% to 29%; n=16 trials, n=1124 patients). Grade 3 or 4 infection was reported in 33% of patients (95% CI 0% to 67%; n=3 trials, n=294 patients). Death due to infection occurred in 7% of patients (95% CI 4%-10%; n=9 trials, n=528 patients). Reporting of infection location was inconsistent, and uninterpretable on a per-patient basis. Microbial subtype of infection, duration of neutropenia, and antimicrobial prophylaxis use were not reported. CONCLUSION: In patients with MDS treated with hypomethylating therapy, grade 3 or 4 neutropenia, febrile neutropenia, and grade 3 or 4 infection were common, yet reported rates were heterogeneous between trials. Limited information was available to define the relationship between neutropenia and infection, as well as the use of antimicrobial prophylaxis. Trials commonly reported infection events in the absence of patient-specific risk estimates. Comprehensive reporting of febrile neutropenia, infection and use of antimicrobial prophylaxis is needed to direct evidence-informed care.

A Tool to Estimate Risk of 30-day Mortality and Complications After Hip Fracture Surgery: Accurate Enough for Some but Not All Purposes? A Study From the ACS-NSQIP Database.

Surgical repair of hip fracture carries substantial short-term risks of mortality and complications. The risk-reward calculus for most patients with hip fractures favors surgical repair. However, some patients have low prefracture functioning, frailty, and/or very high risk of postoperative mortality, making the choice between surgical and nonsurgical management more difficult. The importance of high-quality informed consent and shared decision-making for frail patients with hip fracture has recently been demonstrated. A tool to accurately estimate patient-specific risks of surgery could improve these processes. With this study, we sought (1) to develop, validate, and estimate the overall accuracy (C-index) of risk prediction models for 30-day mortality and complications after hip fracture surgery; (2) to evaluate the accuracy (sensitivity, specificity, and false discovery rates) of risk prediction thresholds for identifying very high-risk patients; and (3) to implement the models in an accessible web calculator. In this comparative study, preoperative demographics, comorbidities, and preoperatively known operative variables were extracted for all 82,168 patients aged 18 years and older undergoing surgery for hip fracture in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) between 2011 and 2017. Eighty-two percent (66,994 of 82,168 ) of patients were at least 70 years old, 21% (17,007 of 82,168 ) were at least 90 years old, 70% (57,260 of 82,168 ) were female, and 79% (65,301 of 82,168 ) were White. A total of 5% (4260 of 82,168) of patients died within 30 days of surgery, and 8% (6786 of 82,168) experienced a major complication. The ACS-NSQIP database was chosen for its clinically abstracted and reliable data from more than 600 hospitals on important surgical outcomes, as well as rich characterization of preoperative demographic and clinical predictors for demographically diverse patients. Using all the preoperative variables in the ACS-NSQIP dataset, least absolute shrinkage and selection operator (LASSO) logistic regression, a type of machine learning that selects variables to optimize accuracy and parsimony, was used to develop and validate models to predict two primary outcomes: 30-day postoperative mortality and any 30-day major complications. Major complications were defined by the occurrence of ACS-NSQIP complications including: on a ventilator longer than 48 hours, intraoperative or postoperative unplanned intubation, septic shock, deep incisional surgical site infection (SSI), organ/space SSI, wound disruption, sepsis, intraoperative or postoperative myocardial infarction, intraoperative or postoperative cardiac arrest requiring cardiopulmonary resuscitation, acute renal failure needing dialysis, pulmonary embolism, stroke/cerebral vascular accident, and return to the operating room. Secondary outcomes were six clusters of complications recently developed and increasingly used for the development of surgical risk models, namely: (1) pulmonary complications, (2) infectious complications, (3) cardiac events, (4) renal complications, (5) venous thromboembolic events, and (6) neurological events. Tenfold cross-validation was used to assess overall model accuracy with C-indexes, a measure of how well models discriminate patients who experience an outcome from those who do not. Using the models, the predicted risk of outcomes for each patient were used to estimate the accuracy (sensitivity, specificity, and false discovery rates) of a wide range of predicted risk thresholds. We then implemented the prediction models into a web-accessible risk calculator. The 30-day mortality and major complication models had good to fair discrimination (C-indexes of 0.76 and 0.64, respectively) and good calibration throughout the range of predicted risk. Thresholds of predicted risk to identify patients at very high risk of 30-day mortality had high specificity but also high false discovery rates. For example, a 30-day mortality predicted risk threshold of 15% resulted in 97% specificity, meaning 97% of patients who lived longer than 30 days were below that risk threshold. However, this threshold had a false discovery rate of 78%, meaning 78% of patients above that threshold survived longer than 30 days and might have benefitted from surgery. The tool is available here: https://s-spire-clintools.shinyapps.io/hip_deploy/ . The models of mortality and complications we developed may be accurate enough for some uses, especially personalizing informed consent and shared decision-making with patient-specific risk estimates. However, the high false discovery rate suggests the models should not be used to restrict access to surgery for high-risk patients. Deciding which measures of accuracy to prioritize and what is "accurate enough" depends on the clinical question and use of the predictions. Discrimination and calibration are commonly used measures of overall model accuracy but may be poorly suited to certain clinical questions and applications. Clinically, overall accuracy may not be as important as knowing how accurate and useful specific values of predicted risk are for specific purposes.Level of Evidence Level III, therapeutic study.

Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics and medical history.

The established method of identifying a group of women at high risk of delivering a small-for-gestational-age (SGA) neonate, requiring increased surveillance, is use of risk scoring systems based on maternal demographic characteristics and medical history. Although this approach is relatively simple to perform, it does not provide patient-specific risks and has an uncertain performance in predicting SGA. Another approach to predict delivery of a SGA neonate is to use logistic regression models that combine maternal factors with first-trimester biomarkers. These models provide patient-specific risks for different prespecified cut-offs of birth-weight percentile and gestational age (GA) at delivery. First, to develop a competing-risks model for prediction of SGA based on maternal demographic characteristics and medical history, in which GA at the time of delivery and birth-weight Z-score are treated as continuous variables. Second, to compare the predictive performance of the new model for SGA neonates to that of previous methods. This was a prospective observational study in 124 443 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. The dataset was divided randomly into a training and a test dataset. The training dataset was used to develop a model for the joint distribution of GA at delivery and birth-weight Z-score from variables of maternal characteristics and medical history. This patient-specific joint Gaussian distribution of GA at delivery and birth-weight Z-score allows risk calculation for SGA defined in terms of different birth-weight percentiles and GA. The new model was then validated in the test dataset to assess performance of screening and we compared its predictive performance to that of logistic regression models for different SGA definitions. In the new model, the joint Gaussian distribution of GA at delivery and birth-weight Z-score is shifted to lower GA at delivery and birth-weight Z-score values, resulting in an increased risk for SGA, by lower maternal weight and height, black, East Asian, South Asian and mixed racial origin, medical history of chronic hypertension, diabetes mellitus and systemic lupus erythematosus and/or antiphospholipid syndrome, conception by in-vitro fertilization and smoking. In parous women, variables from the last pregnancy that increased the risk for SGA were history of pre-eclampsia or stillbirth, decreasing birth-weight Z-score and decreasing GA at delivery of the last pregnancy and interpregnancy interval < 0.5 years. In the test dataset, at a false-positive rate of 10%, the new model predicted 30.1%, 32.1%, 32.2% and 37.8% of cases of a SGA neonate with birth weight < 10th percentile delivered at < 42, < 37, < 34 and < 30 weeks' gestation, respectively, which were similar or higher than the respective values achieved by a series of logistic regression models. The calibration study demonstrated good agreement between the predicted risks and the observed incidence of SGA in both the training and test datasets. A new competing-risks model, based on maternal characteristics and medical history, provides estimation of patient-specific risks for SGA in which GA at delivery and birth-weight Z-score are treated as continuous variables. Such estimation of the a-priori risk for SGA is an essential first step in the use of Bayes' theorem to combine maternal factors with biomarkers for the continuing development of more effective methods of screening for SGA. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population

The administration of aspirin <16 weeks gestation to women who are at high risk for preeclampsia has been shown to reduce the rate of preterm preeclampsia by 65%. The traditional approach to identify such women who are at risk is based on risk factors from maternal characteristics, obstetrics, and medical history as recommended by the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. An alternative approach to screening for preeclampsia has been developed by the Fetal Medicine Foundation. This approach allows the estimation of patient-specific risks of preeclampsia that requires delivery before a specified gestational age with the use of Bayes theorem-based model. The purpose of this study was to examine the diagnostic accuracy of the Fetal Medicine Foundation Bayes theorem-based model, the American College of Obstetricians and Gynecologists, and the National Institute for Health and Care Excellence recommendations for the prediction of preterm preeclampsia at 11-13+6 weeks gestation in a large Asian population STUDY DESIGN: This was a prospective, nonintervention, multicenter study in 10,935 singleton pregnancies at 11-13+6 weeks gestation in 11 recruiting centers across 7 regions in Asia between December 2016 and June 2018. Maternal characteristics and medical, obstetric, and drug history were recorded. Mean arterial pressure and uterine artery pulsatility indices were measured according to standardized protocols. Maternal serum placental growth factor concentrations were measured by automated analyzers. The measured values of mean arterial pressure, uterine artery pulsatility index, and placental growth factor were converted into multiples of the median. The Fetal Medicine Foundation Bayes theorem-based model was used for the calculation of patient-specific risk of preeclampsia at <37 weeks gestation (preterm preeclampsia) and at any gestation (all preeclampsia) in each participant. The performance of screening for preterm preeclampsia and all preeclampsia by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and placental growth factor (triple test) was evaluated with the adjustment of aspirin use. We examined the predictive performance of the model by the use of receiver operating characteristic curve and calibration by measurements of calibration slope and calibration in the large. The detection rate of screening by the Fetal Medicine Foundation Bayes theorem-based model was compared with the model that was derived from the application of American College of Obstetricians and Gynecologists and National Institute for Health and Care Excellence recommendations. There were 224 women (2.05%) who experienced preeclampsia, which included 73 cases (0.67%) of preterm preeclampsia. In pregnancies with preterm preeclampsia, the mean multiples of the median values of mean arterial pressure and uterine artery pulsatility index were significantly higher (mean arterial pressure, 1.099 vs 1.008 [P<.001]; uterine artery pulsatility index, 1.188 vs 1.063[P=.006]), and the mean placental growth factor multiples of the median was significantly lower (0.760 vs 1.100 [P<.001]) than in women without preeclampsia. The Fetal Medicine Foundation triple test achieved detection rates of 48.2%, 64.0%, 71.8%, and 75.8% at 5%, 10%, 15%, and 20% fixed false-positive rates, respectively, for the prediction of preterm preeclampsia. These were comparable with those of previously published data from the Fetal Medicine Foundation study. Screening that used the American College of Obstetricians and Gynecologists recommendations achieved detection rate of 54.6% at 20.4% false-positive rate. The detection rate with the use of National Institute for Health and Care Excellence guideline was 26.3% at 5.5% false-positive rate. Based on a large number of women, this study has demonstrated that the Fetal Medicine Foundation Bayes theorem-based model is effective in the prediction of preterm preeclampsia in an Asian population and that this method of screening is superior to the approach recommended by American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. We have also shown that the Fetal Medicine Foundation prediction model can be implemented as part of routine prenatal care through the use of the existing infrastructure of routine prenatal care.

Prediction of postoperative myocardial infarction after suprainguinal bypass using the Vascular Quality Initiative Cardiac Risk Index

BackgroundThe Vascular Quality Initiative (VQI) Cardiac Risk Index (CRI) was developed to estimate the risk of postoperative myocardial infarction (POMI) for noncardiac vascular procedures. Whereas suprainguinal bypass carried the second highest odds of POMI, the performance of the all-procedure model declined when it was applied to the suprainguinal subset. We sought to improve the VQI CRI for application in this high-risk group undergoing open revascularization for aortoiliac occlusive disease. MethodsThe VQI Suprainguinal Bypass Registry was queried for elective procedures performed between January 2010 and March 2017. Logistic regression was used to create a model for estimating the risk of in-hospital POMI with preoperative variables including demographics, comorbidities, and planned inflow source. After adjustment for overfitting, internal validation was performed using both bootstrapping and 10-fold cross-validation methods. ResultsAmong 8157 procedures, the incidence of POMI was 3.2% (n = 258). After bootstrapping variable selection, age, graft inflow, preoperative stress test, American Society of Anesthesiologists class, indication for procedure, and coronary artery disease were chosen for inclusion as predictors in the final risk model. The final model demonstrated good discrimination (area under the curve = 0.725). On internal validation, the model discriminated well (area under the curve = 0.713), with good calibration (plot intercept = 0.0006 and slope = 1.001). Using this model, POMI risk estimates ranged from 0.6% to 30.4%. ConclusionsWhereas the incidence of POMI among all suprainguinal bypasses was 3%, model-based estimates ranged 50-fold, from 0.6% to 30.4%. This underscores the heterogeneity of this cohort and the need for patient-specific risk estimation. Although some of the strongest predictors were nonmodifiable (eg, age), the model provided specific estimates according to graft inflow and stress testing. This supraspecific VQI CRI module risk predictor may enhance preoperative counseling by influencing the decision to pursue preoperative stress testing and ultimately the type of revascularization strategy chosen.

Competing-risks model in screening for pre-eclampsia in twin pregnancy by maternal characteristics and medical history.

A survival-time regression model for gestational age at delivery with pre-eclampsia (PE) in singleton pregnancy, using maternal demographic characteristics and medical history, was reported previously. The objective of this study was to extend this model to dichorionic (DC) and monochorionic (MC) twin pregnancy. The study population included 1789 DC and 430 MC twin pregnancies and 93 297 singleton pregnancies. A survival-time model for gestational age at delivery with PE was developed from variables of maternal characteristics and medical history. The risk of PE with delivery < 37 weeks and < 42 weeks in twin pregnancies was determined and compared with that in singleton pregnancies. In singleton pregnancies comprising women of Caucasian racial origin, mean weight of 69 kg at 12 weeks' gestation, mean height of 164 cm, nulliparous, with spontaneous conception, no family history of PE and no history of diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, the mean of the Gaussian distribution of gestational age at delivery with PE was 55 weeks. In DC twins with PE, mean gestational age at delivery was shifted to the left by 8.2 (95% CI, 7.2-9.1) weeks and in MC twins it was shifted to the left by 10.0 (95% CI, 8.5-11.4) weeks. The risk of delivery with PE occurring at, or before, a specified gestational age is given by the area under the fitted distribution curve. For a reference population with the above characteristics, the estimated risk of PE < 37 weeks' gestation, assuming no other cause of delivery, was 0.6% for singletons, 9.0% for DC twins and 14.2% for MC twins; the respective values for PE < 42 weeks were 3.6%, 27.0% and 36.5%. A model based on maternal characteristics and medical history has been developed for estimation of patient-specific risks for PE in DC and MC twin pregnancy. Such estimation of the a-priori risk for PE is an essential first step in the use of Bayes' theorem to combine maternal factors with biomarkers for the continuing development of more effective methods of screening for the disease. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Development and Evaluation of the American College of Surgeons NSQIP Pediatric Surgical Risk Calculator

There is an increased desire among patients and families to be involved in the surgical decision-making process. A surgeon's ability to provide patients and families with patient-specific estimates of postoperative complications is critical for shared decision making and informed consent. Surgeons can also use patient-specific risk estimates to decide whether or not to operate and what options to offer patients. Our objective was to develop and evaluate a publicly available risk estimation tool that would cover many common pediatric surgical procedures across all specialties. American College of Surgeons NSQIP Pediatric standardized data from 67 hospitals were used to develop a risk estimation tool. Surgeons enter 18 preoperative variables (demographics, comorbidities, procedure) that are used in a logistic regression model to predict 9 postoperative outcomes. A surgeon adjustment score is also incorporated to adjust for any additional risk not accounted for in the 18 risk factors. A pediatric surgical risk calculator was developed based on 181,353 cases covering 382 CPT codes across all specialties. It had excellent discrimination for mortality (c-statistic= 0.98), morbidity (c-statistic= 0.81), and 7 additional complications (c-statistic > 0.77). The Hosmer-Lemeshow statistic and graphic representations also showed excellent calibration. The ACS NSQIP Pediatric Surgical Risk Calculator was developed using standardized and audited multi-institutional data from the ACS NSQIP Pediatric, and it provides empirically derived, patient-specific postoperative risks. It can be used as a tool in the shared decision-making process by providing clinicians, families, and patients with useful information for many of the most common operations performed on pediatric patients in the US.

Competing risks model in screening for preeclampsia by maternal characteristics and medical history

The purpose of this study was to develop a model for preeclampsia based on maternal demographic characteristics and medical history. This was a screening study of 120,492 singleton pregnancies at 11-13 weeks' gestation, including 2704 pregnancies (2.2%) that experienced preeclampsia. A survival-time model for the gestational age at delivery with preeclampsia was developed from variables of maternal characteristics and history. This approach assumes that, if the pregnancy was to continue indefinitely, all women would experience preeclampsia and that whether they do so or not before a specified gestational age depends on competition between delivery before or after development of preeclampsia. A 5-fold cross validation study was conducted to compare the performance of the new model with the National Institute for Health and Clinical Excellence (NICE) guidelines. In the new model, increased risk for preeclampsia, with a consequent shift in the Gaussian distribution of the gestational age at delivery with preeclampsia to the left, is provided by advancing maternal age, increasing weight, Afro-Caribbean and South Asian racial origin, medical history of chronic hypertension, diabetes mellitus and systemic lupus erythematosus or antiphospholipid syndrome, family history and personal history of preeclampsia, and conception by invitro fertilization. The risk for preeclampsia decreases with increasing maternal height and in parous women with no previous preeclampsia; in the latter, the protective effect, which is related inversely to the interpregnancy interval, persists beyond 15 years. At a screen-positive rate of 11%, as defined by NICE, the new model predicted 40%, 48%, and 54% of cases of total preeclampsia and preeclampsia requiring delivery at <37 and <34 weeks' gestation, respectively, which were significantly higher than the respective values of 35%, 40%, and 44% achieved by application of NICE guidelines. A new model that is based on maternal characteristics and medical history has been developed for the estimation of patient-specific risks for preeclampsia. Such estimation of the a priori risk for preeclampsia is an essential first step in the use of Bayes theorem to combine maternal factors with biomarkers for the continuing development of more effective methods of screening for the disease.

The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial.

ContextComputerized drug alerts for psychotropic drugs are expected to reduce fall-related injuries in older adults. However, physicians over-ride most alerts because they believe the benefit of the drugs exceeds the risk.ObjectiveTo determine whether computerized prescribing decision support with patient-specific risk estimates would increase physician response to psychotropic drug alerts and reduce injury risk in older people.DesignCluster randomized controlled trial of 81 family physicians and 5628 of their patients aged 65 and older who were prescribed psychotropic medication.InterventionIntervention physicians received information about patient-specific risk of injury computed at the time of each visit using statistical models of non-modifiable risk factors and psychotropic drug doses. Risk thermometers presented changes in absolute and relative risk with each change in drug treatment. Control physicians received commercial drug alerts.Main outcome measuresInjury risk at the end of follow-up based on psychotropic drug doses and non-modifiable risk factors. Electronic health records and provincial insurance administrative data were used to measure outcomes.ResultsMean patient age was 75.2 years. Baseline risk of injury was 3.94 per 100 patients per year. Intermediate-acting benzodiazepines (56.2%) were the most common psychotropic drug. Intervention physicians reviewed therapy in 83.3% of visits and modified therapy in 24.6%. The intervention reduced the risk of injury by 1.7 injuries per 1000 patients (95% CI 0.2/1000 to 3.2/1000; p=0.02). The effect of the intervention was greater for patients with higher baseline risks of injury (p<0.03).ConclusionPatient-specific risk estimates provide an effective method of reducing the risk of injury for high-risk older people.Trial registration numberclinicaltrials.gov Identifier: NCT00818285.

Mild Cognitive Impairment: Baseline and Longitudinal Structural MR Imaging Measures Improve Predictive Prognosis

To assess whether single-time-point and longitudinal volumetric magnetic resonance (MR) imaging measures provide predictive prognostic information in patients with amnestic mild cognitive impairment (MCI). This study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from all participants or the participants' legal guardians. Cross-validated discriminant analyses of MR imaging measures were performed to differentiate 164 Alzheimer disease (AD) cases from 203 healthy control cases. Separate analyses were performed by using data from MR images obtained at one time point or by combining single-time-point measures with 1-year change measures. Resulting discriminant functions were applied to 317 MCI cases to derive individual patient risk scores. Risk of conversion to AD was estimated as a continuous function of risk score percentile. Kaplan-Meier survival curves were computed for risk score quartiles. Odds ratios (ORs) for the conversion to AD were computed between the highest and lowest quartile scores. Individualized risk estimates from baseline MR examinations indicated that the 1-year risk of conversion to AD ranged from 3% to 40% (average group risk, 17%; OR, 7.2 for highest vs lowest score quartiles). Including measures of 1-year change in global and regional volumes significantly improved risk estimates (P = 001), with the risk of conversion to AD in the subsequent year ranging from 3% to 69% (average group risk, 27%; OR, 12.0 for highest vs lowest score quartiles). Relative to the risk of conversion to AD conferred by the clinical diagnosis of MCI alone, MR imaging measures yield substantially more informative patient-specific risk estimates. Such predictive prognostic information will be critical if disease-modifying therapies become available. http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101975/-/DC1.

Screening for trisomies in dichorionic twins by measurement of fetal nuchal translucency thickness according to the mixture model

To examine the distribution of fetal nuchal translucency (NT) thickness in dichorionic twins and investigate the effect of the correlation between NT measurements in each twin pair on the performance of screening for trisomies. The distribution of fetal NT for crown-rump length (CRL) was examined in 5646 dichorionic twin pregnancies, including 103 with fetal trisomies 21, 18 or 13. The correlation in fetal NT in each euploid twin pregnancy was estimated. The distribution of NT in both euploid and trisomic fetuses was consistent with the mixture model in singleton pregnancies. In the euploid pregnancies, there was a correlation in log NT measurements in each twin pair (r = 0.42, 95% CI: 0.39-0.45) and, after removal of the effect of the operator, this correlation was reduced to 0.34. Allowing for this correlation in risk assessment for trisomies had a major impact on the estimated patient-specific risk but had little effect on the overall performance of screening. In dichorionic twin pregnancies, the mixture model of distributions of NT can be applied as in singletons. In screening for trisomies, the correlation in NT measurements between the fetuses should be taken into account in the estimation of patient-specific risks.

587: Assessment of risk for birth weight < 5th percentile for gestational age by maternal characteristics with nuchal translucency and maternal serum markers

To develop and evaluate a method of patient-specific risk estimation for birth weight at <5th percentile for gestational age combining maternal characteristics with nuchal translucency and first and second trimester serum markers.

The impact of bias in MoM values on patient risk and screening performance for Down syndrome

First and second trimester screening protocols for Down syndrome rely on marker values being referred to smoothed median values to produce adjusted multiple of the median (MoM) values to standardise for factors such as assay, gestation, maternal weight, smoking status, and so on. Changes in assay components, such as reagent lot, and inappropriate use of published regression equations for smoothed medians have resulted in biases in reported MoM values that in many applications remain uncorrected. This paper investigates the impact of these biases on patient-specific risk estimates and screening performance, and concludes that a 10% bias for an individual marker can result in an increase of between 1 and 2% in the false positive rate of the programme. A simple formula is also derived that enables the impact of these biases to be determined without the need for simulation, thus making it easier to design effective statistical quality control procedures to monitor the output of screening software algorithms. To determine the impact of bias in MoM values on detection rates, false positive rates and patient-specific risks for Down syndrome. We show that bias in MoM values affects risk through a multiplicative factor, and present an approximation to estimate this factor. We then show how bias in MoM values changes the effective risk threshold in the screening test, and hence the test's performance characteristics are determined by reference to a different point on the ROC curve for that test. Our approximation is based on the assumption of equal variance covariance structure for the unaffected and T21 log MoM values. We demonstrate, using computer simulation and supportive theoretical results, that the approximation is reliable in situations encountered in practice. Applications of the approximation are also discussed in respect of establishing effective quality control rules for median MoMs. Substantial changes in patient risk estimates and overall screening performance can result from the sort of biases in marker MoM values encountered in routine practice. In particular, biases of 10% in individual median marker MoM values can produce a four-fold range of risks when using the triple test. A 10% bias in a single marker will change the false positive rates by up to 2%. The effects on the false positive rate are approximately additive and, in cases where all markers are biased towards Down syndrome, biases in all three markers for the triple test can more than double the false positive rate. Biases in marker MoM values can occur in many ways, inappropriate median values, kit lot change, drift in assay performance and operator effects. We present methods which allow the impact of these changes to be assessed in relation to patient-specific risks and the overall screening performance. This, in turn, will enable appropriate quality control procedures to be established to control the magnitude of reported marker MoM biases, or equivalently, the magnitude of biases associated with the calculation of patient-specific risks.

Prognostic value of cytotoxic T-lymphocytes and CD40 in biopsies with early renal allograft rejection

Abstract After renal transplantation, different immunological and non-immunological factors lead to long-term allograft deterioration. Acute rejection episodes are one risk factor for chronic renal allograft dysfunction (CRAD). Following the current Banff classification the histological grade in acute rejection episodes is of limited prognostic value, therefore, additional morphological surrogate markers would be helpful. We investigated the biopsies of 91 patients with early acute rejection episodes for the immunohistochemical expression of key molecules (perforin, granzyme B, TIA-1, CD40) in the T cell-mediated rejection process. Staining results were correlated to long-term allograft outcome. Patients with greater than 2% of granzyme B or greater than 25% of CD40-positive cells in the interstitial infiltrate showed significantly shorter allograft survival. Patients with a CD40-positive vascular rejection or greater than 2% of granzyme B-positive cells in the interstitial infiltrate were significantly correlated with an earlier onset of CRAD. Our findings provide potential morphological surrogate markers in biopsies with early acute rejection episodes after renal transplantation. These could become part of combined clinical and histological algorithms, allowing patient-specific risk estimation and customized therapy options to be made.

Prognostic value of cytotoxic T-lymphocytes and CD40 in biopsies with early renal allograft rejection.

After renal transplantation, different immunological and non-immunological factors lead to long-term allograft deterioration. Acute rejection episodes are one risk factor for chronic renal allograft dysfunction (CRAD). Following the current Banff classification the histological grade in acute rejection episodes is of limited prognostic value, therefore, additional morphological surrogate markers would be helpful. We investigated the biopsies of 91 patients with early acute rejection episodes for the immunohistochemical expression of key molecules (perforin, granzyme B, TIA-1, CD40) in the T cell-mediated rejection process. Staining results were correlated to long-term allograft outcome. Patients with greater than 2% of granzyme B or greater than 25% of CD40-positive cells in the interstitial infiltrate showed significantly shorter allograft survival. Patients with a CD40-positive vascular rejection or greater than 2% of granzyme B-positive cells in the interstitial infiltrate were significantly correlated with an earlier onset of CRAD. Our findings provide potential morphological surrogate markers in biopsies with early acute rejection episodes after renal transplantation. These could become part of combined clinical and histological algorithms, allowing patient-specific risk estimation and customized therapy options to be made.

The effect of temporal variation in biochemical markers of trisomy 21 across the first and second trimesters of pregnancy on the estimation of individual patient-specific risks and detection rates for Down's syndrome.

In a previous study we examined the changes in the median multiple of the median (MoM) with gestation of free beta human chorionic gonadotrophin (F beta-hCG), total human chorionic gonadotrophin (ThCG), alpha-fetoprotein (AFP) and pregnancy-associated plasma protein A (PAPP-A) in a large series of Down's syndrome pregnancies. Results showed that there was a significant temporal variation of the MoM for each marker. In this paper, we assess the impact of this temporal shift on the estimation of patient-specific risks and the detection rates (DRs) for Down's syndrome pregnancies. Individual patient-specific risks, DRs and false positive rates were estimated using statistical modelling techniques and computer simulations. The data for these simulations were the regressed mean log(10) analyte MoMs, marker standard deviations (as log(10) MoM) and correlation coefficients derived from the analysis of over 1000 cases of Down's syndrome and 150,000 unaffected pregnancies between 6 and 20 weeks of gestation reported in our previous study. Two models were compared: the classical constant median separation model, which assumes no variation in median shift with gestation (model 1), and a variable median separation model (model 2), which takes account of the changes in median shift with gestation as described in our previous study. When individual patient-specific risks calculated for various MoM values using model 1 were compared with those derived from model 2, considerable differences in risk estimates were observed for all marker combinations, particularly in the first trimester. Using a 1 in 250 cut-off risk, DRs at each gestation in the second trimester for the AFP+F beta-hCG combination were maximized at 14-17 weeks of gestation and were virtually identical at 63-65% for model 1 and model 2. A similar trend was observed for the AFP+ThCG combination, with an optimum gestational range of 15-18 weeks and DRs of 66-68%. In the first trimester, using a 1 in 250 cut-off risk, DRs were more variable with gestation for the prime marker combination of F beta-hCG+PAPP-A, varying from 73% at 8 weeks to 65% at 13 weeks with model 1 and from 75% to 66% with model 2. Risk algorithms should take into account temporal variation in marker MoMs in order to produce accurate patient-specific risks. This also helps to maximize DRs, particularly when samples are taken out with the optimal gestational range.

Midtrimester nuchal thickness and the prediction of postnatal congenital heart defect

Objective: The study was performed to determine the sensitivity of nuchal thickness measurements for the detection of congenital heart defects (CHD) and to develop an algorithm for estimating patient-specific risk of CHD. Study Design: Nuchal thickness measurements (expressed as multiples of the median) were obtained in 3003 midtrimester fetuses in which postnatal evaluation of the heart was available. The sensitivity and false-positive rate of nuchal thickness threshold values for detecting CHD were used to calculate the area under the receiver operating characteristics curve. Stepwise logistic regression analysis was used to determine the significant predictors of heart defect among nuchal thickness and epidemiologic risk factors. Individual risk of CHD was calculated on the basis of background population risk of major CHD (estimated at 4.4 of 1000) and the product of the likelihood ratios of the significant risk factors from the logistic regression. Results: There were 95 cases of confirmed CHD. Nuchal thickness was statistically significant for the prediction of CHD with an area under the curve = 0.58, P =.01. Nuchal thickness and prior child with CHD were the only significant predictors among the multiple risk factors for this disorder. Patient-specific risk estimates for CHD based on these two “markers” were calculated. It was accurate and improved the prediction of CHD, area under the curve = 0.63, P <.001, compared with nuchal thickness alone. Conclusion: Midtrimester nuchal thickness measurement significantly detected postnatally confirmed CHD in chromosomally normal fetuses. We report for the first time a method for estimating individual patient risk of CHD. (Am J Obstet Gynecol 2002;187:1250-3.)