Improving reporting standards for polygenic scores in risk prediction studies.

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Assessing the safety of physical rehabilitation in critically ill patients: a Delphi study.

BACKGROUND: Physical rehabilitation of critically ill patients is implemented to improve physical outcomes from an intensive care stay. However, before rehabilitation is implemented, a risk assessment is essential, based on robust safety data. To develop this information, a uniform definition of relevant adverse events is required. The assessment of cardiovascular stability is particularly relevant before physical activity as there is uncertainty over when it is safe to start rehabilitation with patients receiving vasoactive drugs. METHODS: A three-stage Delphi study was carried out to (a) define adverse events for a general ICU cohort, and (b) to define which risks should be assessed before physical rehabilitation of patients receiving vasoactive drugs. An international group of intensive care clinicians and clinician researchers took part. Former ICU patients and their family members/carers were involved in generating consensus for the definition of adverse events. Round one was an open round where participants gave their suggestions of what to include. In round two, participants rated their agreements with these suggestions using a five-point Likert scale; a 70% consensus agreement threshold was used. Round three was used to re-rate suggestions that had not reached consensus, whilst viewing anonymous feedback of participant ratings from round two. RESULTS: Twenty-four multi-professional ICU clinicians and clinician researchers from 10 countries across five continents were recruited. Average duration of ICU experience was 18 years (standard deviation 8) and 61% had publications related to ICU rehabilitation. For the adverse event definition, five former ICU patients and one patient relative were recruited. The Delphi process had a 97% response rate. Firstly, 54 adverse events reached consensus; an adverse event tool was created and informed by these events. Secondly, 50 risk factors requiring assessment before physical rehabilitation of patients receiving vasoactive drugs reached consensus. A second tool was created, informed by these suggestions. CONCLUSIONS: The adverse event tool can be used in studies of physical rehabilitation to ensure uniform measurement of safety. The risk assessment tool can be used to inform clinical practise when risk assessing when to start rehabilitation with patients receiving vasoactive drugs. Trial registration This study protocol was retrospectively registered on https://www.researchregistry.com/ (researchregistry2991).

Development and psychometric evaluation of the death risk perception scale for advanced cancer patients.

BACKGROUND: An accurate perception of death risk is a prerequisite for advanced cancer patients to make informed end-of-life care decisions. However, there is to date no suitable scale to measure death risk perception. This study was to develop and psychometrically test the death risk perception scale (DRPS) for advanced cancer patients. METHODS: Process of instrument development and psychometric evaluation were used. First, qualitative research, a literature review, brainstorming, a Delphi study, and cognitive interviews were conducted to construct a pretest scale of death risk perception. Second, a scale-based survey was administered to 479 advanced cancer patients. Item, exploratory factor, and confirmatory factor analyses were employed to optimize the scale. The Cronbach's alpha was calculated as a reliability analysis. The validity analysis included construct, convergent, discriminant, and content validity values. RESULTS: A three-dimension, 12-item scale was developed, including deliberative, affective, and experiential risk perception. The confirmatory factor analysis supported the three-factor model with satisfactory convergent and discriminant validity levels. The Cronbach's alpha coefficient for internal consistency was 0.807 and scale-level content validity index was 0.98. CONCLUSIONS: The 12-item DRPS is a reliable and valid instrument for assessing the level of death risk perception in advanced cancer patients. More studies are needed to examine its structure and robustness prior to use.

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Complementing Braden scale for pressure ulcer risk with clinical and demographic-related factors in a large cohort of hospitalized Italian patients.

AIMS: To determine the prevalence, risk, and determinants of pressure ulcer risk in a large cohort of hospitalized patients. DESIGN: A prospective cross-sectional study with data collection in January 2023. METHODS: Registered nurses collected data from 798 patients admitted to 27 health care units of an Italian hospital. The pressure ulcer risk was assessed using the Braden scale. The presence of comorbidities was collected from clinical reports. Obesity was assessed according to international indicators (Body Mass Index). The receiver operating characteristic (ROC) curve was used to estimate the sensitivity and specificity of different Braden scores for identifying participants with pressure ulcers. RESULTS: The prevalence of pressure ulcers was 9.5%, and 57.4% of the sample were at risk of developing pressure ulcers. The area under the ROC curve was 0.88. The best sensitivity and specificity were found for a Braden cutoff score of 15.5 (sensibility = 0.76; specificity = 0.85). The determinants of lower Braden scores were older age (p < 0.001), comorbidities (p < 0.001), wounds of other nature (p = 0.001), urinary incontinence (p < 0.001), fecal incontinence (p < 0.001), and urinary catheterization (p < 0.001). CONCLUSION: Several demographic factors and specific clinical indicators have been identified as determinants of the risk of developing pressure ulcers, which are easily ascertainable by healthcare providers; thus, they may routinely complement the Braden Scale in the assessment of pressure ulcer risk in order to reinforce and accelerate clinical judgment.

Development and validation of a risk prediction model for community-acquired pressure injury in a cancer population: A case-control study.

BACKGROUND: Patients with cancer are susceptible to pressure injuries, which accelerate deterioration and death. In patients with post-acute cancer, the risk of pressure injury is ignored in home or community settings. OBJECTIVE: To develop and validate a community-acquired pressure injury risk prediction model for cancer patients. METHODS: All research data were extracted from the hospital's electronic medical record system. The identification of optimal predictors is based on least absolute shrinkage and selection operator regression analysis combined with clinical judgment. The performance of the model was evaluated by drawing a receiver operating characteristic curve and calculating the area under the curve (AUC), calibration analysis and decision curve analysis. The model was used for internal and external validation, and was presented as a nomogram. RESULTS: In total, 6257 participants were recruited for this study. Age, malnutrition, chronic respiratory failure, body mass index, and activities of daily living scores were identified as the final predictors. The AUC of the model in the training and validation set was 0.87 (95 % confidence interval [CI], 0.85-0.89), 0.88 (95 % CI, 0.85-0.91), respectively. The model demonstrated acceptable calibration and clinical benefits. CONCLUSIONS: Comorbidities in patients with cancer are closely related to the etiology of pressure injury, and can be used to predict the risk of pressure injury. IMPLICATIONS FOR PRACTICE: This study provides a tool to predict the risk of pressure injury for cancer patients. This suggests that improving the respiratory function and nutritional status of cancer patients may reduce the risk of community-acquired pressure injury.

The frequency of pressure injury in level 3 intensive care units and determination of risk factors: A cross-sectional study.

AIM: This study was carried out to determine the prevalence of pressure injury and risk factors in patients hospitalized in a university hospital's level 3 intensive care unit. DESIGN: It is a descriptive, prospective, observational type study. METHOD: The sample of the study consisted of 176 patients aged 18 and over who were admitted to the intensive care units of a University Hospital for at least 24 h. Patient Information Form and Braden Risk Assessment Scale, Glasgow Coma Scale were used to collect data. IBM SPSS Statistics 20 program was used to analyze the data. RESULTS: Presence of chronic disease in the development of pressure injury (22.7%), high-risk patients according to the Glasgow Coma Scale (21%), high-risk patients according to the Braden Risk Assessment Scale (84.2%), low hemoglobin (31%), low albumin levels (32.4%) and duration of stay in the intensive care unit until the day of evaluation were found to be independent risk factors (p < 0.05). The prevalence of pressure injury was determined to be 32.4%, and the rate of pressure injury due to medical devices was 7.4%. CONCLUSION: Pressure injuries are still common in adult intensive care patients. In terms of patient safety, it is important to give more space to care standards and awareness-raising research and training to prevent pressure injuries.

Preexposure Prophylaxis to Prevent Acquisition of HIV: US Preventive Services Task Force Recommendation Statement.

Importance: An estimated 1.2 million persons in the US currently have HIV, and more than 760 000 persons have died of complications related to HIV since the first cases were reported in 1981. Although treatable, HIV is not curable and has significant health consequences. Therefore, effective strategies to prevent HIV are an important public health and clinical priority. Objective: The US Preventive Services Task Force (USPSTF) commissioned a systematic review to evaluate the benefits and harms of preexposure prophylaxis with antiretroviral therapy for the prevention of HIV acquisition, and the diagnostic accuracy of risk assessment tools to identify persons at increased risk of HIV acquisition. Population: Adolescents and adults who do not have HIV and are at increased risk of HIV. Evidence Assessment: The USPSTF concludes with high certainty that there is a substantial net benefit from the use of effective antiretroviral therapy to reduce the risk of acquisition of HIV in persons at increased risk of acquiring HIV. Recommendation: The USPSTF recommends that clinicians prescribe preexposure prophylaxis using effective antiretroviral therapy to persons at increased risk of HIV acquisition to decrease the risk of acquiring HIV. (A recommendation).

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy.

BACKGROUND AND PURPOSE: The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States. METHODS: We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011-2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA. RESULTS: After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds. CONCLUSIONS: Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients.

BACKGROUND: Due to the low cancer-detection rate in patients with PIRADS category 3 lesions, we created machine learning (ML) models to facilitate decision-making about whether to perform prostate biopsies or monitor clinical information without biopsy results. METHODS: In our retrospective, single-center study, 101 eligible patients with at least one PIRADS category 3 lesion but no higher PIRADS lesions underwent MRI/US fusion biopsies between September 2017 and June 2020. Thirty additional patients were included as the validation cohort from the next chronological period from June 2020 to October 2020. Our ML research was a supervised classification problem, with a binary output based on pathological reports of cancerous or benign tissue. The clinical inputs were age, prostate-specific antigen (PSA), prostate volume, prostate-specific antigen density (PSAD), and the number of previous biopsies. The radiology-report inputs were the number of lesions, maximum lesion diameter, lesion location, and lesion zone. We subsequently removed the inputs with low importance. Logistic Regression, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting Tree (XGBoost) were employed. From receiver operating characteristic (ROC) curves, we determined Area Under the ROC Curve (AUC), the cut-off point, and sensitivity score (recall score) to evaluate the ML-model performance. RESULTS: Twenty-four adenocarcinoma patients had a mean age of 70 ± 5.79 years, a mean PSA of 12.42 ± 6.67 ng/ml, a mean prostate volume of 46.49 ± 23.13 ml, and a mean PSAD of 0.31 ± 0.22 ng/ml2 . Seventy-seven patients with benign tissue reports had a mean age of 66.39 ± 6.66 years, a mean PSA of 11.31 ± 7.50 ng/ml, a mean prostate volume of 65.25 ± 35.88 ml, and a mean PSAD of 0.19 ± 0.13 ng/ml2 . On the validation cohort, XGBoost had the best AUC of 0.76, which considered 80% sensitivity and 72% specificity at a probability cutoff of 57%. The remaining possible ML models performed worse with lesser AUC. The worst was Naïve Bayes, with AUC of 0.50. CONCLUSIONS: ML models facilitate PIRADS 3 patient selection for MRI/US fusion biopsies. ML could optimize how we use previously known clinical risk factors to their full potential.

Comparative performance of risk prediction models for hepatitis B-related hepatocellular carcinoma in the United States.

BACKGROUND & AIMS: Guidelines recommend hepatocellular carcinoma (HCC) surveillance in patients with chronic HBV infection. Several HCC risk prediction models are available to guide surveillance decisions, but their comparative performance remains unclear. METHODS: Using a retrospective cohort of patients with HBV treated with nucleos(t)ide analogues at 130 Veterans Administration facilities between 9/1/2008 and 12/31/2018, we calculated risk scores from 10 HCC risk prediction models (REACH-B, PAGE-B, m-PAGE-B, CU-HCC, HCC-RESCUE, CAMD, APA-B, REAL-B, AASL-HCC, RWS-HCC). We estimated the models' discrimination and calibration. We calculated HCC incidence in risk categories defined by the reported cut-offs for all models. RESULTS: Of 3,101 patients with HBV (32.2% with cirrhosis), 47.0% were treated with entecavir, 40.6% tenofovir, and 12.4% received both. During a median follow-up of 4.5 years, 113 patients developed HCC at an incidence of 0.75/100 person-years. AUC values for 3-year HCC risk were the highest for RWS-HCC, APA-B, REAL-B, and AASL-HCC (all >0.80). Of these, 3 (APA-B, RWS-HCC, REAL-B) incorporated alpha-fetoprotein. AUC values for the other models ranged from 0.73 for PAGE-B to 0.79 for CAMD and HCC-RESCUE. Of the 7 models with AUC >0.75, only APA-B was poorly calibrated. In total, 10-20% of the cohort was deemed low-risk based on the published cut-offs. None of the patients in the low-risk groups defined by PAGE-B, m-PAGE-B, AASL-HCC, and REAL-B developed HCC during the study timeframe. CONCLUSION: In this national cohort of US-based patients with HBV on antiviral treatment, most models performed well in predicting HCC risk. A low-risk group, in which no cases of HCC occurred within a 3-year timeframe, was identified by several models (PAGE-B, m-PAGE-B, CAMD, AASL-HCC, REAL-B). Further studies are warranted to examine whether these patients could be excluded from HCC surveillance. LAY SUMMARY: Risk prediction models for hepatocellular carcinoma (HCC) in patients infected with hepatitis B virus (HBV) could guide HCC surveillance decisions. In this large cohort of US-based patients receiving treatment for HBV, most published models discriminated between those who did or did not develop HCC, although the RWS-HCC, REAL-B, and AASL-HCC performed the best. If confirmed in future studies, these models could help identify a low-risk subset of patients on antiviral treatment who could be excluded from HCC surveillance.

Construction and validation of the prognostic model for patients with neuroendocrine cervical carcinoma: a competing risk nomogram analysis.

PURPOSE: Neuroendocrine cervical carcinoma (NECC) is an uncommon malignancy of the female reproductive system. This study aimed to evaluate cancer-specific mortality and to construct prognostic nomograms for predicting the survival of patients with NECC. METHODS: we assembled the patients with NECC diagnosed between 2004 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Meanwhile, we identified other patients with NECC from the Wenling Maternal and Child Health Care Hospital between 2002 to 2017. Fine and Gray's test and Kaplan-Meier methods were used to evaluate cancer-specific mortality and overall survival (OS) rates, respectively. Nomograms were constructed for predicting cancer-specific survival (CSS) and OS for patients with NECC. The developed nomograms were validated both internally and externally. RESULTS: a total of 894 patients with NECC were extracted from the SEER database, then classified into the training cohort (n = 628) and the internal validation cohort (n = 266). Besides, 106 patients from the Wenling Maternal and Child Health Care Hospital served as an external validation cohort. Nomograms for predicting CSS and OS were constructed on clinical predictors. The validation of nomograms was calculated by calibration curves and concordance indexes (C-indexes). Furthermore, the developed nomograms presented higher areas under the receiver operating characteristic (ROC) curves when compared to the FIGO staging system. CONCLUSIONS: we established the first competing risk nomograms to predict the survival of patients with NECC. Such a model with high predictive accuracy could be a practical tool for clinicians.

2-[18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease.

Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.

Effect of routine first-trimester combined screening for pre-eclampsia on small-for-gestational-age birth: secondary interrupted time series analysis.

OBJECTIVE: To evaluate the impact of a first-trimester combined screening program for pre-eclampsia, based on the Fetal Medicine Foundation (FMF) algorithm, on the rate of small-for-gestational age (SGA) at birth and adverse pregnancy outcome. METHODS: This was a retrospective cohort study of data obtained from a London tertiary hospital between January 2017 and March 2019. The data were derived from a secondary analysis of the cohort evaluated in a clinical-effectiveness study on the implementation of a first-trimester screening program for pre-eclampsia. The cohort included 7720 women screened according to the UK National Institute for Health and Care Excellence (NICE) risk-based approach and 4841 women screened by the FMF multimodal approach, which combines maternal risk factors, blood pressure, pregnancy-associated plasma protein-A and uterine artery Doppler indices. The care package for the FMF-screened group included 150-mg aspirin prophylaxis, ultrasound scans at 28 and 36 weeks' gestation and scheduled delivery at 40 weeks. Outcome measures included the rates of SGA neonates at birth, admission to the neonatal unit, intrauterine demise, neonatal death and hypoxic-ischemic encephalopathy assessed by interrupted time series analysis (ITSA). RESULTS: There was no significant difference in the rates of intrauterine demise, neonatal death and hypoxic-ischemic encephalopathy between the FMF-screened and NICE-screened cohorts. ITSA showed a significant reduction in the rate of term SGA birth < 10th percentile at 21 months following implementation of the FMF screening program, with a relative effect reduction of 45.1% (P = 0.004). However, there was no significant relative effect reduction in term SGA birth < 5th or < 3rd percentile. CONCLUSIONS: First-trimester combined screening for pre-eclampsia based on the FMF algorithm accompanied by a care package including serial ultrasound scans for growth evaluation and elective birth from 40 weeks' gestation resulted in a significant 45% relative effect reduction in term SGA birth < 10th percentile but did not affect term SGA birth < 5th or < 3rd percentile. Further screening strategies to detect and improve the outcome of cases with SGA birth < 5th percentile need to be considered. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

A Simple Risk Scoring System for Predicting the Occurrence of Aspiration Pneumonia After Gastric Endoscopic Submucosal Dissection.

BACKGROUND: Aspiration pneumonia after endoscopic submucosal dissection (ESD) is rare, but can be fatal. We aimed to investigate risk factors and develop a simple risk scoring system for aspiration pneumonia. METHODS: We retrospectively reviewed medical records of 7833 patients who underwent gastric ESD for gastric neoplasm under anesthesiologist-directed sedation. Candidate risk factors were screened and assessed for significance using a least absolute shrinkage and selection operator (LASSO)-based method. Top significant factors were incorporated into a multivariable logistic regression model, whose prediction performance was compared with those of other machine learning models. The final risk scoring system was created based on the estimated odds ratios of the logistic regression model. RESULTS: The incidence of aspiration pneumonia was 1.5%. The logistic regression model showed comparable performance to the best predictive model, extreme gradient boost (area under receiver operating characteristic curve [AUROC], 0.731 vs 0.740). The estimated odds ratios were subsequently used for the development of the clinical scoring system. The final scoring system exhibited an AUROC of 0.730 in the test dataset with risk factors: age (≥70 years, 4 points), male sex (8 points), body mass index (≥27 kg/m2, 4 points), procedure time (≥80 minutes, 5 points), lesion in the lower third of the stomach (5 points), tumor size (≥10 mm, 3 points), recovery time (≥35 minutes, 4 points), and desaturation during ESD (9 points). For patients with total scores ranging between 0 and 33 points, aspiration pneumonia probabilities spanned between 0.1% and 17.9%. External validation using an additional cohort of 827 patients yielded AUROCs of 0.698 for the logistic regression model and 0.680 for the scoring system. CONCLUSIONS: Our simple risk scoring system has 8 predictors incorporating patient-, procedure-, and sedation-related factors. This system may help clinicians to stratify patients at risk of aspiration pneumonia after ESD.

Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System.

Rationale: Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score (EWS) systems and electronic health records, deterioration still goes unrecognized. Objectives: To develop and externally validate a Hospital- wide Alerting via Electronic Noticeboard (HAVEN) system to identify hospitalized patients at risk of reversible deterioration. Methods: This was a retrospective cohort study of patients 16 years of age or above admitted to four UK hospitals. The primary outcome was cardiac arrest or unplanned admission to the ICU. We used patient data (vital signs, laboratory tests, comorbidities, and frailty) from one hospital to train a machine-learning model (gradient boosting trees). We internally and externally validated the model and compared its performance with existing scoring systems (including the National EWS, laboratory-based acute physiology score, and electronic cardiac arrest risk triage score). Measurements and Main Results: We developed the HAVEN model using 230,415 patient admissions to a single hospital. We validated HAVEN on 266,295 admissions to four hospitals. HAVEN showed substantially higher discrimination (c-statistic, 0.901 [95% confidence interval, 0.898-0.903]) for the primary outcome within 24 hours of each measurement than other published scoring systems (which range from 0.700 [0.696-0.704] to 0.863 [0.860-0.865]). With a precision of 10%, HAVEN was able to identify 42% of cardiac arrests or unplanned ICU admissions with a lead time of up to 48 hours in advance, compared with 22% by the next best system. Conclusions: The HAVEN machine-learning algorithm for early identification of in-hospital deterioration significantly outperforms other published scores such as the National EWS.

Precision-Enhancing Risk Stratification Tools for Selecting Optimal Treatment Durations in Tuberculosis Clinical Trials.

Rationale: No evidence-based tools exist to enhance precision in the selection of patient-specific optimal treatment durations to study in tuberculosis clinical trials. Objectives: To develop risk stratification tools that assign patients with tuberculosis into risk groups of unfavorable outcome and inform selection of optimal treatment duration for each patient strata to study in clinical trials. Methods: Publicly available data from four phase 3 trials, each evaluating treatment duration shortening from 6 to 4 months, were used to develop parametric time-to-event models that describe unfavorable outcomes. Regimen, baseline, and on-treatment characteristics were evaluated as predictors of outcomes. Exact regression coefficients of predictors were used to assign risk groups and predict optimal treatment durations. Measurements and Main Results: The parametric model had an area under the receiver operating characteristic curve of 0.72. A six-item risk score (HIV status, smear grade, sex, cavitary disease status, body mass index, and Month 2 culture status) successfully grouped participants into low (1,060/3,791; 28%), moderate (1,740/3,791; 46%), and high (991/3,791; 26%) risk, requiring treatment durations of 4, 6, and greater than 6 months, respectively, to reach a target cure rate of 93% when receiving standard-dose rifamycin-containing regimens. With current one-duration-fits-all approaches, high-risk groups have a 3.7-fold (95% confidence interval, 2.7-5.1) and 2.4-fold (1.9-2.9) higher hazard risk of unfavorable outcomes compared with low- and moderate-risk groups, respectively. Four-month regimens were noninferior to the standard 6-month regimen in the low-risk group. Conclusions: Our model discrimination was modest but consistent with current models of unfavorable outcomes. Our results showed that stratified medicine approaches are feasible and may achieve high cure rates in all patients with tuberculosis. An interactive risk stratification tool is provided to facilitate decision-making in the regimen development pathway.

Non-invasive alloimmune risk stratification of long-term liver transplant recipients.

BACKGROUND & AIMS: Management of long-term immunosuppression following liver transplantation (LT) remains empirical. Surveillance liver biopsies in combination with transcriptional profiling could overcome this challenge by identifying recipients with active alloimmune-mediated liver damage despite normal liver tests, but this approach lacks applicability. Our aim was to investigate the utility of non-invasive tools for the stratification of stable long-term survivors of LT, according to their immunological risk and need for immunosuppression. METHODS: We conducted a cross-sectional multicentre study of 190 adult LT recipients assessed to determine their eligibility to participate in an immunosuppression withdrawal trial. Patients had stable liver allograft function and had been transplanted for non-autoimmune non-replicative viral liver disease >3 years before inclusion. We performed histological, immunogenetic and serological studies and measured the intrahepatic transcript levels of an 11-gene classifier highly specific for T cell-mediated rejection (TCMR). RESULTS: In this cohort, 35.8% of patients harboured clinically silent fibro-inflammatory liver lesions (13.7% had mild damage and 22.1% had moderate-to-severe damage). The severity of liver allograft damage was positively associated with TCMR-related transcripts, class II donor-specific antibodies (DSAs), ALT, AST, and liver stiffness measurement (LSM), and negatively correlated with serum creatinine and tacrolimus trough levels. Liver biopsies were stratified according to their TCMR transcript levels using a cut-off derived from biopsies with clinically significant TCMR. Two multivariable prediction models, integrating ALT+LSM or ALT+class II DSAs, had a high discriminative capacity for classifying patients with or without alloimmune damage. The latter model performed well in an independent cohort of 156 liver biopsies obtained from paediatric liver recipients with similar inclusion/exclusion criteria. CONCLUSION: ALT, class II DSAs and LSM are valuable tools to non-invasively identify stable LT recipients without significant underlying alloimmunity who could benefit from minimisation of immunosuppression. LAY SUMMARY: A large proportion of liver transplant patients with normal liver tests have inflammatory liver lesions, which in 17% of cases are molecularly indistinguishable from those seen at the time of rejection. ALT, class II donor-specific antibodies and liver stiffness are useful in identifying patients with this form of subclinical rejection. We propose these markers as a useful tool to help clinicians determine if the immunosuppression administered is adequate.