Risk Calculator For Prediction Research Articles

Defining a Minimal Benchmark for Cardiovascular Risk Prediction Calculators in New England Electronic Health Record-Derived Cohort.

Defining a Minimal Benchmark for Cardiovascular Risk Prediction Calculators in New England Electronic Health Record-Derived Cohort.

Prediction of survival after neoadjuvant therapy in locally advanced rectal cancer - a retrospective analysis.

The aim of this retrospective analysis was to determine if the response to preoperative radio(chemo)therapy is predictive for survival among patients with locally advanced rectal cancer and may act as a potential surrogate endpoint for disease free survival and overall survival. Eight hundred seventy-eight patients from five centers were analyzed. There were 304 women and 574 men; the median age was 64.7 years. 77.6% and 22.4% of patients received neoadjuvant radiochemotherapy or short-course radiotherapy, resulting in a pathological complete response in 7.3%. T-downstaging and N-downstaging occurred in 50.5% and 37% of patients after neoadjuvant therapy. In patients with T-downstaging, the 10-year DFS and 10-year OS were 64.8% and 66.8% compared to 37.1% and 45.9% in patients without T-downstaging. N-downstaging resulted in 10-year DFS and 10-year OS in 56.2% and 62.5% compared to 47.3% and 52.3% without N-downstaging. Based on routinely evaluated clinical parameters, an absolute risk prediction calculator was generated for 5-year disease-free survival, and 5-year overall survival. T-downstaging and N-downstaging after neoadjuvant radiochemotherapy or short-course radiotherapy resulted in better DFS and OS compared to patients without response. Based on clinical parameters, 5-year DFS, and 5-year OS can be predicted using a prediction calculator.

Predicting Recurrence-Free and Overall Survival for Patients With Stage II Melanoma: The MIA Calculator.

Improvements in recurrence-free survival (RFS) were demonstrated in two recent randomized trials for patients with sentinel node (SN)-negative stage IIB or IIC melanoma receiving adjuvant systemic therapy (pembrolizumab/nivolumab). However, adverse events also occurred. Accurate individualized prognostic estimates of RFS and overall survival (OS) would allow patients to more accurately weigh the risks and benefits of adjuvant therapy. Since the current American Joint Committee on Cancer eighth edition (AJCC-8) melanoma staging system focuses on melanoma-specific survival, we developed a multivariable risk prediction calculator that provides estimates of 5- and 10-year RFS and OS for these patients. Data were extracted from the Melanoma Institute Australia (MIA) database for patients diagnosed with stage II (clinical or pathological) melanoma (n = 3,220). Survival prediction models were developed using multivariable Cox regression analyses (MIA models) and externally validated twice using data sets from the United States and the Netherlands. Each model's performance was assessed using C-statistics and calibration plots and compared with Cox models on the basis of AJCC-8 staging (stage models). The 5-year and 10-year RFS C-statistics were 0.70 and 0.73 (MIA-model) versus 0.61 and 0.60 (stage-model), respectively. For OS, the 5-year and 10-year C-statistics were 0.71 and 0.75 (MIA-model) compared with 0.62 and 0.61 (stage-model), respectively. The MIA models were well calibrated and externally validated. The MIA models offer accurate and personalized estimates of both RFS and OS in patients with stage II melanoma even in the absence of pathological staging with SN biopsy. These models were robust on external validations and may be used in everyday practice both with (ideally) and without performing SN biopsy to identify high-risk patients for further management strategies. An online tool will be available at the MIA website (Risk Prediction Tools).

Development of a novel machine learning-based predictive risk calculator for radical cystectomy.

Development of a novel machine learning-based predictive risk calculator for radical cystectomy.

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women

BackgroundPrevious work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model).MethodsData from the Black Women’s Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls.ResultsAUCs were 0.577 (95% CI 0.556–0.598) for the BWHS model and 0.584 (95% CI 0.563–0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603–0.644).ConclusionsThis combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.

Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation

BackgroundThe purpose of the study was to use Machine Learning (ML) to construct a risk calculator for patients who undergo Total Joint Arthroplasty (TJA) on the basis of New York State Statewide Planning and Research Cooperative System (SPARCS) data and externally validate the calculator on a single TJA center.MethodsSeven ML algorithms, i.e., logistic regression, adaptive boosting, gradient boosting (Xg Boost), random forest (RF) classifier, support vector machine, and single and a five-layered neural network were trained on the derivation cohort. Models were trained on 68% of data, validated on 15%, tested on 15%, and externally validated on 2% of the data from a single arthroplasty center.ResultsValidation of the models showed that the RF classifier performed best in terms of 30-d mortality AUROC (Area Under the Receiver Operating Characteristic) 0.78, 30-d readmission (AUROC 0.61) and 90-d composite complications (AUROC 0.73) amongst the test set. Additionally, Xg Boost was found to be the best predicting model for 90-d readmission and 90-d composite complications (AUC 0.73). External validation demonstrated that models achieved similar AUROCs to the test set although variation occurred in top model performance for 90-d composite complications and readmissions between our test and external validation set.ConclusionThis was the first study to investigate the use of ML to create a predictive risk calculator from state-wide data and then externally validate it with data from a single arthroplasty center. Discrimination between best performing ML models and between the test set and the external validation set are comparable.Level of EvidenceIII.

Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion: Erratum.

Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion: Erratum.

Combining sarcopenia and ASA status to inform emergency laparotomy outcomes: could it be that simple?

Risk assessment for emergency laparotomy (EL) is important for guiding decision-making and anticipating the level of perioperative care in acute clinical settings. While established tools such as the American College of Surgeons National Surgical Quality Improvement Program calculator (ACS-NSQIP), the National Emergency Laparotomy Audit Risk Prediction Calculator (NELA) and the Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity calculation (P-POSSUM) are accurate predictors for mortality, there has been increasing recognition of the benefits from including measurements for frailty in a simple and quantifiable manner. Psoas muscle to 3rd lumbar vertebra area ratio (PM:L3) measured on CT scans was proven to have a significant inverse association with 30-, 90- and 365-day mortality in EL patients. A retrospective analysis was conducted of 500 patients admitted to four Australian hospitals who underwent EL during 2016-2017, and had contemporaneous abdomino-pelvic CT scans. Radiological sarcopenia was measured as PM:L3 ratios. ASC-NSQIP, NELA and P-POSSUM were retrospectively calculated. Univariate and multivariate logistic regression modelling was used to assess these ratios and scores, as well as American Society of Anaesthesiologists (ASA) classification separated into ASA I-III and IV/V (simplified ASA), as potential predictors of 30-, 90- and 365-day mortality. PM:L3, simplified ASA, ACS-NSQIP, NELA and P-POSSUM were each statistically significant predictors of 30-day, 90-day and 365-day mortality (P < 0.001). Logistic regression models of 30-, 90- and 365-day mortality combining PM:L3 (P = 0.001) and simplified ASA (P < 0.001) exhibited AUCs of 0.838 (0.780, 0.896), 0.805 (0.751, 0.860) and 0.775 (0.729, 0.822), respectively, which were comparable to that of ACS-NSQIP and NELA. Combining the semi-physiological parameter ASA classification with PM:L3 provides a quick and simple alternative to the more complex established risk assessment scores and is superior to PM:L3 alone.

A Novel Preoperative Risk Assessment Tool to Identify Patients at Risk of Contrast-Associated Acute Kidney Injury After Endovascular Abdominal Aortic Aneurysm Repair

Contrast-associated acute kidney injury (CA-AKI) after endovascular abdominal aortic aneurysm repair (EVAR) is associated with mortality and morbidity. Risk stratification remains a vital component of preoperative evaluation. We sought to generate and validate a preprocedure CA-AKI risk stratification tool for elective EVAR patients. We queried the Blue Cross Blue Shield of Michigan Cardiovascular Consortium database for elective EVAR patients and excluded those on dialysis, with a history of renal transplant, death during procedure, and without creatinine measures. Association with CA-AKI (rise in creatinine > 0.5 mg/dL) was tested using mixed-effects logistic regression. Variables associated with CA-AKI were used to generate a predictive model via a single classification tree. The variables selected by the classification tree were then validated by fitting a mixed-effects logistic regression model into the Vascular Quality Initiative dataset. Our derivation cohort included 7,043 patients, 3.5% of whom developed CA-AKI. After multivariate analysis, age (odds ratio [OR] 1.021, 95% confidence interval [CI] 1.004-1.040), female sex (OR 1.393, CI 1.012-1.916), glomerular filtration rate (GFR) < 30 mL/min (OR 5.068, CI 3.255-7.891), current smoking (OR 1.942, CI 1.067-3.535), chronic obstructive pulmonary disease (OR 1.402, CI 1.066-1.843), maximum abdominal aortic aneurysm (AAA) diameter (OR 1.018, CI 1.006-1.029), and presence of iliac artery aneurysm (OR 1.352, CI 1.007-1.816) were associated with increased odds of CA-AKI. Our risk prediction calculator demonstrated that patients with a GFR < 30 mL/min, females, and patients with a maximum AAA diameter of > 6.9 cm are at a higher risk of CA-AKI after EVAR. Using the Vascular Quality Initiative dataset (N=62,986), we found that GFR < 30 mL/min (OR 4.668, CI 4.007-5.85), female sex (OR 1.352, CI 1.213-1.507), and maximum AAA diameter > 6.9 cm (OR 1.824, CI 1.212-1.506) were associated with an increased risk of CA-AKI after EVAR. Herein, we present a simple and novel risk assessment tool that can be usedpreoperatively to identify patients at risk of CA-AKI after EVAR. Patients with a GFR <30 mL/min, maximum AAA diameter > 6.9 cm, and females who are undergoing EVAR may be at risk for CA-AKI after EVAR. Prospective studies are needed to determine the efficacy of our model.

Design of a postpartum hemorrhage and transfusion risk calculator

BackgroundPostpartum hemorrhage is the major cause of maternal deaths due to childbirth and also responsible for maternal morbidity. ObjectivesIn this study we set out to look the incidence of postpartum hemorrhage in our population, to identify the most important risk factors for postpartum hemorrhage and thus develop a predictive risk calculator for postpartum hemorrhage and transfusion. Study designdata was taken from patients who presented vaginal delivery or cesarean section from January 1 to December 31, 2016, the variables taken into account as risk factors were as follows: Gestational age, history of chronic or gestational hypertension, preeclampsia, previous abortions, parity, previous cesarean section, placenta previa, labor time, and postpartum hemorrhage as the event of interest. An objective quantification was performed on a weight scale in grams for the estimation of bleeding, considering postpartum hemorrhage those with >500 ml in vaginal delivery and >1000 ml of blood loss in cesarean section. Subsequently, a predictive risk calculator was developed using the Naïve Bayes algorithm. ResultsA success rate of 58% was obtained in the identification of patients at high risk of hemorrhage, and 36% for transfusion, with a sensitivity of 50.7% and specificity of 64.06%, identifying as risk factors for postpartum hemorrhage gestational age between 35 and 40 weeks, hypertension and preeclampsia, previous cesarean section, duration of labor <1 h or more than 10 h, placenta previa and previous history of postpartum hemorrhage. ConclusionA postpartum hemorrhage risk calculator has been designed, which due to its improved accuracy after incorporation of data becomes a useful tool that will require a larger study population to improve its performance in clinical practice and more similar studies to validate it.

Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion.

A retrospective, case-control study. We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF). The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility. This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020. A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator. We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.

Can We Predict Atypical Ductal Hyperplasia Upstaging to Breast Cancer? External Validation of a Predictive Risk Calculator Using a Provincial Database

Can We Predict Atypical Ductal Hyperplasia Upstaging to Breast Cancer? External Validation of a Predictive Risk Calculator Using a Provincial Database

Heparin-binding protein-enhanced quick SOFA score improves mortality prediction in sepsis patients.

PurposeThe Quick Sequential Organ Failure Assessment (qSOFA) score proposed by Sepsis-3 as a sepsis screening tool has shown suboptimal accuracy. Heparin-binding protein (HBP) has been shown to identify early sepsis with high accuracy. Herein, we aim to investigate whether or not HBP improves the model performance of qSOFA.MethodsWe conducted a multicenter prospective observational study of 794 adult patients who presented to the emergency department (ED) with presumed sepsis between 2018 and 2019. For each participant, serum HBP levels were measured and the hospital course was followed. The qSOFA score was used as the comparator. The data was split into a training dataset (n = 556) and a validation dataset (n = 238). The primary endpoint was 30-day all-cause mortality.ResultsCompared with survivors, non-survivors had significantly higher serum HBP levels (median: 71.5 ng/mL vs 209.5 ng/mL, p < 0.001). Serum level of HBP weakly correlated with qSOFA class (r2 = 0.240, p < 0.001). Compared with the qSOFA model alone, the addition of admission HBP level to the qSOFA model significantly improved 30-day mortality discrimination (AUC, 0.70 vs. 0.80; P < 0.001), net reclassification improvement [26% (CI, 17–35%); P < 0.001], and integrated discrimination improvement [12% (CI, 9–14%); P < 0.001]. Addition of C-reactive protein (CRP) level or neutrophil-to-lymphocyte ratio (NLR) to qSOFA did not improve its performance. A web-based mortality risk prediction calculator was created to facilitate clinical implementation.ConclusionThis study confirms the value of combining qSOFA and HBP in predicting sepsis mortality. The web calculator provides a user-friendly tool for clinical implementation. Further validation in different patient populations is needed before widespread application of this prediction model.

Sarcopenia 'made simple' and outcomes from emergency laparotomy.

Emergency Laparotomy (EL) is recognized as high-risk surgery with high mortality. Established surgical risk assessment tools (NELA Risk Prediction Calculator, P-POSSUM, ACS-NSQIP) are accurate predictors of morbidity and mortality. However, their multicomponent complexity limits their use in practice. Sarcopenia is associated with poorer surgical outcomes. This study tests for an association between a simple measure of radiological sarcopenia and mortality in EL patients in an Australian cohort. A retrospective analysis was conducted of 500 patients admitted to four Australian hospitals who underwent EL during 2016-2017. All patients had a contemporaneous abdomino-pelvic CT scan. Radiological sarcopenia was measured as the ratio of total psoas muscle area (PM) to L3 vertebral body cross sectional area (PM:L3). Patients were followed up to 12 months. Primary outcomes were 30-, 90- and 365-day mortality. The mean 30-day mortality predictions for NELA, P-POSSUM and ACS-NSQIP were 11.36%, 17.28% and 11.30% respectively. PM:L3 ratio was associated with 30-, 90- and 365-day mortality (P < 0.001) and sex (P < 0.001) and negatively correlated with age (r=-0.4612; P < 0.001). Radiological sarcopenia had a weak negative correlation with NELA (r=-0.2737; P < 0.001), P-POSSUM (r=-0.1880; P < 0.001) and ACS-NSQIP (r=-0.2351; P < 0.001). The latter three metrics were significantly correlated (r > 0.5696; P < 0.001). Radiological sarcopenia (CT-assessed PM:L3) is a significant predictor of mortality in EL patients in Australia. The results of this study suggest that radiological sarcopenia is equivalent to established risk assessment tools. The more timely and easily accessible CT-assessed PM:L3 metric is potentially automatable and may have significant utility in clinical practice.

Extended anticoagulation for venous thromboembolism: A survey of the American Venous Forum and the European Venous Forum

ObjectiveExtended anticoagulation therapy should always be considered after standard treatment of an unprovoked episode of venous thromboembolism (VTE). It can also be considered for selected patients with provoked VTE. However, the evidence-based protocols suggested by some clinical guidelines and risk assessment tools to guide this practice are limited and ambiguous. The goal of the present survey research was to analyze current practices in applying extended anticoagulation therapy for patients with VTE among members of the American Venous Forum (AVF) and European Venous Forum (EVF). MethodsAn online survey was created by the AVF Research Committee. The survey consisted of 16 questions to identify the country of practice, specialty, experience of the participating physicians, and their clinical practice patterns in applying extended anticoagulation therapy for VTE patients. The survey was distributed via e-mail to the members of the AVF and EVF. ResultsA total of 144 practitioners, 48 AVF members (33%) and 96 EVF members (66%), participated in the survey. Most of the respondents identified themselves as vascular specialists with primary certification in vascular surgery (70%), vascular medicine or angiology (9%), and venous disease or phlebology (3%). Of the 144 respondents, 72% believed that the risk of VTE recurrence will generally overweigh the risk of bleeding for patients with unprovoked VTE. Extended anticoagulation therapy might be used by 97% of providers. Different patterns in real world clinical practice were identified. More than one half of the practitioners estimated the VTE recurrence and bleeding risk subjectively. The antithrombotic drugs most commonly used for secondary prophylaxis were rivaroxaban, apixaban, warfarin, dabigatran, and aspirin, in decreasing order of frequency. Among the reasons selected for not regularly considering extended anticoagulation therapy were the lack of specific clinical practice guidelines (24%), lack of reported evidence (9%), and absence of valid VTE and/or bleeding risk prediction calculators (8%). Twelve participants (8%) stated that extended anticoagulation therapy would not be beneficial for most patients with VTE. Ten participants (7%) indicated that prescribing extended anticoagulation therapy was outside the scope of their specialty. ConclusionsDifferent practice patterns exist regarding extending anticoagulation therapy beyond the standard treatment for patients with VTE. Major gaps in knowledge remain a serious challenge at least partially explaining the inaccuracy and inconsistency in long-term VTE management. Appropriately designed studies are needed to evaluate risk stratification tools when contemporary best medical therapy is used, accurately predict VTE recurrence and its long-term outcomes, and tailor safe and effective secondary prophylaxis.

Validation of a risk prediction calculator in Australian patients undergoing head and neck microsurgery reconstruction

The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) surgical risk calculator (SRC) is an open access calculator predicting patients' risk of postoperative complications. This study aims to assess the validity of the SRC in patients undergoing microsurgical free flap reconstruction at an Australian tertiary referral centre. This is a retrospective cohort study of 200 consecutive patients treated up to November 2020. SRC-predicted rates of postoperative complications and hospital length of stay (LOS) were compared to those observed for the ablative and reconstructive components of the procedure. The performance of the SRC was assessed using Brier scores, area under the receiver operating characteristic (ROC) curve (AUC), and the Hosmer-Lemeshow test. For both ablative and reconstructive components, the SRC discriminates well for pneumonia and urinary tract infection, and it is calibrated well for readmission and sepsis, but it does not discriminate and calibrate well for any single outcome. SRC-predicted hospital LOS and actual LOS did not correlate well for the reconstructive component, but they correlated strongly for the ablative component. The SRC is a poor predictor of postoperative complication rates and hospital LOS in patients undergoing head and neck microsurgical reconstruction.

Lung Cancer Screening: Review and 2021 Update.

Purpose of ReviewLung cancer screening with low-dose CT (LDCT) scans has been widely accepted within the last decade. Our knowledge and ability to implement screening has greatly increased because of significant research efforts and guidelines from multiple professional societies. The purpose of this review is to summarize some of the significant findings pertaining to lung cancer screening.Recent FindingsScreening with LDCT decreases lung cancer mortality in multiple studies. Use of validated risk prediction calculators can improve patient selection and screening efficiency. Shared decision making and smoking cessation counseling are essential screening components. Multidisciplinary involvement is required for the success of a screening program.SummaryLung cancer screening is complex, and implementation of a successful program requires multidisciplinary expertise. Further prospective studies are required to determine optimal patient selection, screening intervals, and strategies to maximize benefit while further decreasing harms.’

Prediction of Progression in Barrett’s Esophagus Using a Tissue Systems Pathology Test: A Pooled Analysis of International Multicenter Studies

Prediction of progression risk in Barrett's esophagus (BE) may enable personalized management. We aimed to assess the adjunct value of a tissue systems pathology test (TissueCypher) performed on paraffin-embedded biopsy tissue, when added to expert pathology review in predicting incident progression, pooling individual patient-level data from multiple international studies METHODS: Demographics, clinical features, the TissueCypher risk class/score, and progression status were analyzed. Conditional logistical regression analysis was used to develop multivariable models predicting incident progression with and without the TissueCypher risk class (low, intermediate, high). Concordance (c-) statistics were calculated and compared with likelihood ratio tests to assess predictive ability of models. A risk prediction calculator integrating clinical variables and TissueCypher risk class was also developed. Data from 552 patients with baseline no (n= 472), indefinite (n= 32), or low-grade dysplasia (n= 48) (comprising 152 incident progressors and 400 non-progressors) were analyzed. A high-risk test class independently predicted increased risk of progression to high-grade dysplasia/adenocarcinoma (odds ratio, 6.0; 95% confidence interval, 2.9-12.0), along with expert confirmed low-grade dysplasia (odds ratio, 2.9; 95% confidence interval, 1.2-7.2). Model prediction of progression with the TissueCypher risk class incorporated was significantly superior than without, in the whole cohort (c-statistic 0.75 vs 0.68; P < .0001) and the nondysplastic BE subset (c-statistic 0.72 vs 0.63; P < .0001). Sensitivity and specificity of the high risk TissueCypher class were 38% and 94%, respectively. An objective tissue systems pathology test high-risk class is a strong independent predictor of incident progression in patients with BE, substantially improving progression risk prediction over clinical variables alone. Although test specificity was high, sensitivity was modest.

629 A new predictive model for periviable infant survival

Currently available survival calculators for periviable births do not account for fetal presentation and mode of delivery. We set out to develop and validate a predictive risk calculator for infant survival among women who delivered between 22 0/7 – 25 6/7 weeks of gestation which accounts for mode of delivery and fetal presentation. We performed a population-based cohort study of all women with livebirths between 22 0/7 – 25 6/7 weeks of gestation in the United States from 2009 through 2013. The primary objective was to build a predictive model estimating the probability of infant survival at 12 months of age using antenatal factors obtained from de-identified U.S livebirth records. Multivariable logistic regression estimated the association of these factors on risk of infant survival. A publicly available online calculator was developed after external validation and calibration were performed for individual risk assessment. The eight variables selected for inclusion in the model by magnitude of influence on infant survival were birthweight, gestational age, sex, antenatal steroid exposure, resuscitation, presentation at birth, mode of delivery and multifetal gestation From 2009-2013, there were 67,260 liveborn infants in the U.S. between 22 0/7 – 25 6/7 weeks of gestation, of which 51,352 were included in the study cohort. The receiver operator characteristic (ROC) curve for the 8-variable model achieved an area under the curve of 0.800 (95% CI 0.795-0.804). External validation demonstrated a consistent measure of discrimination with an AUC of 0.799 (95% CI 0.789-0.808). This validated predictive model accounts for new variables obtainable from the medical record and discriminates between infants at increased or decreased risk of survival after periviable birth. This risk calculator found at https://stage.ob.tools/calc/elbw (username: [email protected] , password: personalizeCare) and can utilized by health care providers in counseling women with threatening periviable birth.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Age-Related Macular Degeneration: Epidemiology and Clinical Aspects.

Age-related macular degeneration (AMD) is a degenerative disease of the human retina affecting individuals over the age of 55years. This heterogeneous condition arises from a complex interplay between age, genetics, and environmental factors including smoking and diet. It is the leading cause of blindness in industrialized countries. Worldwide, the number of people with AMD is predicted to increase from 196 million in 2020 to 288 million by 2040. By this time, Asia is predicted to have the largest number of people with the disease. Distinct patterns of AMD prevalence and phenotype are seen between geographical areas that are not explained fully by disparities in population structures. AMD is classified into early, intermediate, and late stages. The early and intermediate stages, when visual symptoms are typically absent or mild, are characterized by macular deposits (drusen) and pigmentary abnormalities. Through risk prediction calculators, grading these features helps predict the risk of progression to late AMD. Late AMD is divided into neovascular and atrophic forms, though these can coexist. The defining lesions are macular neovascularization and geographic atrophy, respectively. At this stage, visual symptoms are often severe and irreversible, and can comprise profoundly decreased central vision in both eyes. For these reasons, the condition has major implications for individuals and society, as affected individuals may experience substantially decreased quality of life and independence. Recent advances in retinal imaging have led to the recognition of an expanded set of AMD phenotypes, including reticular pseudodrusen, nonexudative macular neovascularization, and subtypes of atrophy. These developments may lead to refinements in current classification systems.