Ambient Mass Spectrometry and Machine Learning-Based Diagnosis System for Acute Coronary Syndrome.

Aims: The purpose of this study is to establish a novel diagnosis system in early acute coronary syndrome (ACS) using probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning (ML) and to validate the diagnostic accuracy. Methods: A total of 32 serum samples derived from 16 ACS patients and 16 control patients were analyzed by PESI-MS. The acquired mass spectrum dataset was subsequently analyzed by partial least squares (PLS) regression to find the relationship between the two groups. A support vector machine, an ML method, was applied to the dataset to construct the diagnostic algorithm. Results: Control and ACS groups were separated into the two clusters in the PLS plot, indicating ACS patients differed from the control in the profile of serum composition obtained by PESI-MS. The sensitivity, specificity, and accuracy of our diagnostic system were all 93.8%, and the area under the receiver operating characteristic curve showed 0.965 (95% CI: 0.84-1). Conclusion: The PESI-MS and ML-based diagnosis system are likely an optimal solution to assist physicians in ACS diagnosis with its remarkably predictive accuracy.

Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration.

Observing animals in the wild often poses extreme challenges, but animal-borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi-animal, long-term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar-mounted tri-axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free-ranging tag-equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free-ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F-measure up to 0.96) with discernible improvements observed with post-data-collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high-frequency models excelled in identifying fast-paced behaviours (e.g. locomotion), whereas lower-frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free-ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free-ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals.

The relationship between weight-adjusted-waist index and suicidal ideation: evidence from NHANES.

BACKGROUND: Amidst growing evidence of the intricate link between physical and mental health, this study aims to dissect the relationship between the waist-to-weight index (WWI) and suicidal ideation within a representative sample of the US population, proposing WWI as a novel metric for suicide risk assessment. METHODS: The study engaged a sample of 9500 participants in a cross-sectional design. It employed multivariate logistic and linear regression analyses to probe the association between WWI and suicidal ideation. It further examined potential nonlinear dynamics using a weighted generalized additive model alongside stratified analyses to test the relationship's consistency across diverse demographic and health variables. RESULTS: Our analysis revealed a significant positive correlation between increased WWI and heightened suicidal ideation, characterized by a nonlinear relationship that persisted in the adjusted model. Subgroup analysis sustained the association's uniformity across varied population segments. CONCLUSIONS: The study elucidates WWI's effectiveness as a predictive tool for suicidal ideation, underscoring its relevance in mental health evaluations. By highlighting the predictive value of WWI, our findings advocate for the integration of body composition considerations into mental health risk assessments, thereby broadening the scope of suicide prevention strategies.

A prospective cohort study evaluating the TRISS and TRISS-SpO2 scoring systems for assessing mortality risk in trauma study participants in India.

Background: Globally, trauma cases have significant morbidity and mortality. Hence, various scoring systems have been designed to improve the prognosis in trauma cases. Trauma and Injury Severity Score (TRISS) is one of the widely used models to predict mortality; however, it has certain limitation. We have aimed to evaluate the survival prediction of new model TRISS-oxygen saturation (SpO2) and to compare with original TRISS score in trauma study participants. Methods: This was a prospective cohort study conducted on 380 trauma study participants admitted to the surgery department from January 20, 2021, to November 28, 2021. The proposed model includes TRISS-SpO2 which replaces pulse SpO2 instead of revised trauma score in the original TRISS score. Probability of survival (Ps) was calculated for both models using coefficients derived from Walker-Duncan regression analysis analyzed from the Major Trauma Outcome Study. Receiver operating characteristic curve analysis was used to predict model performance and the accuracy was calculated. Results: The mortality rate in the present study was 30 (7.9%). The predictive accuracy of original TRISS score which calculated Ps based on respiratory rate was 97.11%, and for the proposed model of TRISS score which calculated Ps based on SpO2 was found 97.11%, and thus there is no significant difference in the performance. Conclusions: The new proposed model TRISS-SpO2 showed a good accuracy which is similar to original TRISS score. However, the new tool TRISS-SpO2 might be easier to use for robust performance in the clinical setting.

A Comprehensive Study on the Diagnostic Value of Multi-Slice Computed Tomography for Peripancreatic Infection in Elderly With Severe Acute Pancreatitis.

Background: This study investigated the diagnostic efficacy of multi-slice spiral computed tomography (MSCT) perfusion imaging in evaluating peripancreatic infection in elderly patients with severe acute pancreatitis (SAP). Methods: A retrospective analysis was conducted on the clinical data of 110 elderly SAP patients treated at our hospital from March 2018 to August 2019. The study correlated MSCT perfusion imaging characteristics with peripancreatic infection in elderly SAP patients. Additionally, receiver operating characteristic (ROC) curves were constructed to assess the diagnostic performance of MSCT perfusion imaging parameters in evaluating peripancreatic infection in elderly SAP patients. Results: The results indicated that among all 110 elderly SAP patients, the incidence rate of peripancreatic infection was 20.91%, with a mortality rate of 0.91%. MSCT perfusion imaging revealed that after peripancreatic infection in elderly SAP patients, there was a decrease in pancreatic density, local enlargement of the pancreas, blurring of the pancreatic margins, and associated ascites. Compression/narrowing/occlusion of the splenic vein was observed in 22 patients, compression/narrowing/occlusion of the superior mesenteric vein in 17 patients, thickening/thrombosis of the portal vein in 19 patients, and collateral circulation in 21 patients. Compared to elderly SAP patients without peripancreatic infection, those with the infection showed prolonged peak times, reduced peak heights, and decreased blood flow. ROC analysis indicated that the combination of the three parameters (peak time, peak height, and blood flow) had higher specificity and area under the curve (AUC) than single parameters, with no significant difference in sensitivity between the combination and single parameters. Conclusions: In conclusion, combining the three key MSCT perfusion imaging parameters (peak time, peak height, and blood flow) can significantly enhance the predictive efficacy for the risk of peripancreatic infection in elderly SAP patients.

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis.

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

Accuracy of respiratory muscle assessments to predict weaning outcomes: a systematic review and comparative meta-analysis.

BACKGROUND: Several bedside assessments are used to evaluate respiratory muscle function and to predict weaning from mechanical ventilation in patients on the intensive care unit. It remains unclear which assessments perform best in predicting weaning success. The primary aim of this systematic review and meta-analysis was to summarize and compare the accuracy of the following assessments to predict weaning success: maximal inspiratory (PImax) and expiratory pressures, diaphragm thickening fraction and excursion (DTF and DE), end-expiratory (Tdiee) and end-inspiratory (Tdiei) diaphragm thickness, airway occlusion pressure (P0.1), electrical activity of respiratory muscles, and volitional and non-volitional assessments of transdiaphragmatic and airway opening pressures. METHODS: Medline (via Pubmed), EMBASE, Web of Science, Cochrane Library and CINAHL were comprehensively searched from inception to 04/05/2023. Studies including adult mechanically ventilated patients reporting data on predictive accuracy were included. Hierarchical summary receiver operating characteristic (HSROC) models were used to estimate the SROC curves of each assessment method. Meta-regression was used to compare SROC curves. Sensitivity analyses were conducted by excluding studies with high risk of bias, as assessed with QUADAS-2. Direct comparisons were performed using studies comparing each pair of assessments within the same sample of patients. RESULTS: Ninety-four studies were identified of which 88 studies (n = 6296) reporting on either PImax, DTF, DE, Tdiee, Tdiei and P0.1 were included in the meta-analyses. The sensitivity to predict weaning success was 63% (95% CI 47-77%) for PImax, 75% (95% CI 67-82%) for DE, 77% (95% CI 61-87%) for DTF, 74% (95% CI 40-93%) for P0.1, 69% (95% CI 13-97%) for Tdiei, 37% (95% CI 13-70%) for Tdiee, at fixed 80% specificity. Accuracy of DE and DTF to predict weaning success was significantly higher when compared to PImax (p = 0.04 and p < 0.01, respectively). Sensitivity and direct comparisons analyses showed that the accuracy of DTF to predict weaning success was significantly higher when compared to DE (p < 0.01). CONCLUSIONS: DTF and DE are superior to PImax and DTF seems to have the highest accuracy among all included respiratory muscle assessments for predicting weaning success. Further studies aiming at identifying the optimal threshold of DTF to predict weaning success are warranted. TRIAL REGISTRATION: PROSPERO CRD42020209295, October 15, 2020.

Oncotype DX in Breast Cancer Management: Insights and Outcomes From the United Arab Emirates.

Introduction Breast cancer remains the most significant cancer affecting women worldwide, with an increasing incidence, especially in developing regions. The introduction of genomic tests like Oncotype DX has revolutionized personalized treatment, allowing for more tailored approaches to therapy. This study focuses on the United Arab Emirates (UAE), where breast cancer is the leading cause of cancer-related deaths among women, aiming to assess the predictive accuracy of the Oncotype DX test in categorizing patients based on recurrence risk. Materials and methods A retrospective cohort study was conducted on 95 breast cancer patients diagnosed at Tawam Hospital between 2013 and 2017 who underwent Oncotype DX testing. Data on patient demographics, tumor characteristics, treatment details, and Oncotype DX scores were collected. Survival analysis was performed using the Kaplan-Meier method, with the chi-square goodness of fit test assessing the model's adequacy. Results The cohort's age range was 27-71 years, with a mean age of 50, indicating a significant concentration of cases in the early post-menopausal period. The Oncotype DX analysis classified 55 patients (57.9%) as low risk, 29 (30.5%) as medium risk, and 11 (11.6%) as high risk of recurrence. The majority, 73 patients (76.8%), did not receive chemotherapy, highlighting the test's impact on treatment decisions. The survival analysis revealed no statistically significant difference in recurrence rates across the Oncotype DX risk categories (p = 0.268231). Conclusion The Oncotype DX test provides a valuable genomic approach to categorizing breast cancer patients by recurrence risk in the UAE. While the test influences treatment decisions, particularly the use of chemotherapy, this study did not find a significant correlation between Oncotype DX risk categories and actual recurrence events. These findings underscore the need for further research to optimize the use of genomic testing in the UAE's diverse patient population and enhance personalized treatment strategies in breast cancer management.

Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems.

In the field of engineering systems-particularly in underground drilling and green stormwater management-real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid architecture, uniquely specialized for multi-task real-time predictions. Building on advancements in attention mechanisms and sequence modeling, our model integrates the core strengths of LSTM and Transformer architectures, offering a superior alternative to traditional predictive models. Further enriched with online learning, our architecture dynamically adapts to variable operational conditions and continuously incorporates new field data. Utilizing knowledge distillation techniques, we efficiently transfer insights from larger, pretrained networks, thereby achieving high predictive accuracy without sacrificing computational resources. Rigorous experiments on sector-specific engineering datasets validate the robustness and effectiveness of our approach. Notably, our model exhibits clear advantages over existing methods in terms of predictive accuracy, real-time adaptability, and computational efficiency. This work contributes a pioneering predictive framework for targeted engineering applications, offering actionable insights into.

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.

BACKGROUND: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. RESULTS: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. CONCLUSIONS: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

The Effect of Missing Item Data on the Relative Predictive Accuracy of Correctional Risk Assessment Tools.

Missing data are pervasive in risk assessment but their impact on predictive accuracy has largely been unexplored. Common techniques for handling missing risk data include summing available items or proration; however, multiple imputation is a more defensible approach that has not been methodically tested against these simpler techniques. We compared the validity of these three missing data techniques across six conditions using STABLE-2007 (N = 4,286) and SARA-V2 (N = 455) assessments from men on community supervision in Canada. Condition 1 was the observed data (low missingness), and Conditions 2 to 6 were generated missing data conditions, whereby 1% to 50% of items per case were randomly deleted in 10% increments. Relative predictive accuracy was unaffected by missing data, and simpler techniques performed just as well as multiple imputation, but summed totals underestimated absolute risk. The current study therefore provides empirical justification for using proration when data are missing within a sample.

Predicting Facial Attractiveness from Colour Cues: A New Analytic Framework.

Various facial colour cues were identified as valid predictors of facial attractiveness, yet the conventional univariate approach has simplified the complex nature of attractiveness judgement for real human faces. Predicting attractiveness from colour cues is difficult due to the high number of candidate variables and their inherent correlations. Using datasets from Chinese subjects, this study proposed a novel analytic framework for modelling attractiveness from various colour characteristics. One hundred images of real human faces were used in experiments and an extensive set of 65 colour features were extracted. Two separate attractiveness evaluation sets of data were collected through psychophysical experiments in the UK and China as training and testing datasets, respectively. Eight multivariate regression strategies were compared for their predictive accuracy and simplicity. The proposed methodology achieved a comprehensive assessment of diverse facial colour features and their role in attractiveness judgements of real faces; improved the predictive accuracy (the best-fit model achieved an out-of-sample accuracy of 0.66 on a 7-point scale) and significantly mitigated the issue of model overfitting; and effectively simplified the model and identified the most important colour features. It can serve as a useful and repeatable analytic tool for future research on facial impression modelling using high-dimensional datasets.

Dynamic Risk Scales Degrade Over Time: Evidence for Reassessments.

Risk tools containing dynamic (potentially changeable) factors are routinely used to evaluate the recidivism risk of justice-involved individuals. Although frequent reassessments are recommended, there is little research on how the predictive accuracy of dynamic risk assessments changes over time. This study examined the extent to which predictive accuracy decreases over time for the ACUTE-2007 and the STABLE-2007 sexual recidivism risk tools. We used two independent samples of men on community supervision (NStudy 1 = 795; NStudy 2 = 4,221). For all outcomes (sexual, violent, and any recidivism [including technical violations]), reassessments improved predictive accuracy, with the largest effects found for the most recent assessment (i.e., those closest in time prior to the recidivism event). Based on these results, we recommend that ACUTE-2007 assessments occur at least every 30 days and that the STABLE-2007 assessments occur every 6 months or after significant life changes (e.g., successful completion of treatment).

Time-dependent receiver operating characteristic curve estimator for correlated right-censored time-to-event data.

In clinical trials, evaluating the accuracy of risk scores (markers) derived from prognostic models for prediction of survival outcomes is of major concern. The time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve are appealing measures to evaluate the predictive accuracy. Several estimation methods have been proposed in the context of classical right-censored data which assumes the event time of individuals are independent. In many applications, however, this may not hold true if, for example, individuals belong to clusters or experience recurrent events. Estimates may be biased if this correlated nature is not taken into account. This paper is then aimed to fill this knowledge gap to introduce a time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve estimation method for right-censored data that take the correlated nature into account. In the proposed method, the unknown status of censored subjects is imputed using conditional survival functions given the marker and frailty of the subjects. An extensive simulation study is conducted to evaluate and demonstrate the finite sample performance of the proposed method. Finally, the proposed method is illustrated using two real-world examples of lung cancer and kidney disease.

Performance of serial CT ASPECTS for predicting stroke outcomes in patients with thrombolyzed acute ischemic stroke.

Objectives: The objective of this study was to compare the sensitivity and specificity of serial ASPECTS for predicting IHM and unfavorable outcome defined by a modified Rankin Scale score ≥3 at the time of discharge from the hospital in thrombolyzed AACIS patients. Materials and Methods: This retrospective study examined thrombolyzed AACIS patients admitted at Saraburi Hospital, a regional health-care facility in Thailand. The study was conducted between January 2015 and July 2022. The comparative predictive performance of the baseline ASPECTS, 24-h ASPECTS, and change in ASPECTS for IHM and unfavorable outcome was examined using the receiver operating characteristic (ROC) curves. The optimal cutoff values were identified based on Youden's index and the nonparametric method to compare the area under the ROC curve (AuROC) among the three scales. The potential confounders adjusted by multivariable logistic regression were reported odds ratio (OR) and 95% confidence interval (CI). Results: Three hundred and forty-five patients with thrombolyzed AACIS were analyzed; the median age was 61.8 ± 15.2 years. 53.0% were male, and the median National Institutes of Health Stroke Scale score was 11 points (interquartile range: 8-17). The AuROC for predicting IHM was 0.823 for the baseline ASPECTS, 0.955 for 24-h ASPECTS, and 0.920 for the change in ASPECTS. For predicting unfavorable outcome, the AuROC was 0.744 for the baseline ASPECTS, 0.853 for 24-h ASPECTS, and 0.800 for the change in ASPECTS. After adjusting for other factors, the OR for predicting IHM was 14.38 (95% CI: 1.69-122.57) for 24-h ASPECTS and 16.7 (95% CI: 4.36-64.01) for the change in ASPECTS. Regarding unfavorable outcome, the adjusted OR was 5.58 (95% CI: 1.83-17.01) for 24-h ASPECTS and 4.85 (95% CI: 2.45-9.60) for the change in ASPECTS. Conclusion: The 24-h ASPECTS and change in ASPECTS could be more precise predictors for predicting IHM and unfavorable outcome in patients with thrombolyzed AACIS.

Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019.

BACKGROUND: The widening of group-level socioeconomic differences in body mass index (BMI) has received considerable research attention. However, the predictive power of socioeconomic position (SEP) indicators at the individual level remains uncertain, as does the potential temporal variation in their predictive value. Examining this is important given the increasing incorporation of SEP indicators into predictive algorithms and calls to reduce social inequality to tackle the obesity epidemic. We thus investigated SEP differences in BMI over three decades of the obesity epidemic in England, comparing population-wide (SEP group differences in mean BMI) and individual-level (out-of-sample prediction of individuals' BMI) approaches to understanding social inequalities. METHODS: We used repeated cross-sectional data from the Health Survey for England, 1991-2019. BMI (kg/m2) was measured objectively, and SEP was measured via educational attainment, occupational class, and neighbourhood index of deprivation. We ran random forest models for each survey year and measure of SEP adjusting for age and sex. RESULTS: The mean and variance of BMI increased within each SEP group over the study period. Mean differences in BMI by SEP group also increased: differences between lowest and highest education groups were 1.0 kg/m2 (0.4, 1.6) in 1991 and 1.3 kg/m2 (0.7, 1.8) in 2019. At the individual level, the predictive capacity of SEP was low, though increased in later years: including education in models improved predictive accuracy (mean absolute error) by 0.14% (- 0.9, 1.08) in 1991 and 1.05% (0.18, 1.82) in 2019. Similar patterns were obtained for occupational class and neighbourhood deprivation and when analysing obesity as an outcome. CONCLUSIONS: SEP has become increasingly important at the population (group difference) and individual (prediction) levels. However, predictive ability remains low, suggesting limited utility of including SEP in prediction algorithms. Assuming links are causal, abolishing SEP differences in BMI could have a large effect on population health but would neither reverse the obesity epidemic nor reduce much of the variation in BMI.

A Wolf in Sheep's Clothing: Idealisations and the aims of polygenic scores.

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics.

Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level walking.

Over the past few years, the use of computer models and simulations tailored to the patient's physiology to assist clinical decision-making has increased enormously.While several pipelines to develop personalized models exist, their adoption on a large scale is still limited due to the required niche computational skillset and the lengthy operations required. Novel toolboxes, such as STAPLE, promise to streamline and expedite the development of image-based skeletal lower limb models. STAPLE-generated models can be rapidly generated, with minimal user input, and present similar joint kinematics and kinetics compared to models developed employing the established INSIGNEO pipeline. Yet, it is unclear how much the observed discrepancies scale up and affect joint contact force predictions. In this study, we compared image-based musculoskeletal models developed (i) with the INSIGNEO pipeline and (ii) with a semi-automated pipeline that combines STAPLE and nmsBuilder, and assessed their accuracy against experimental implant data.Our results showed that both pipelines predicted similar total knee joint contact forces between one another in terms of profiles and average values, characterized by a moderately high level of agreement with the experimental data. Nonetheless, the Student t-test revealed statistically significant differences between both pipelines. Of note, the STAPLE-based pipeline required considerably less time than the INSIGNEO pipeline to generate a musculoskeletal model (i.e., 60 vs 160 min). This is likely to open up opportunities for the use of personalized musculoskeletal models in clinical practice, where time is of the essence.

Risk of adverse perinatal outcomes in pregnancies with "small" fetuses not meeting Delphi consensus criteria for fetal growth restriction.

BACKGROUND: Previous research endeavors examining the association between clinical characteristics, sonographic indices, and the risk of adverse perinatal outcomes in pregnancies complicated by fetal growth restriction have been hampered by a lack of agreement regarding its definition. In 2016, a consensus definition was reached by an international panel of experts via the Delphi procedure, but as it currently stands, this has not been endorsed by all professional organizations. OBJECTIVE: This study aimed to assess whether an independent association exists between estimated fetal weight and/or abdominal circumference of <10th percentile and adverse perinatal outcomes when consensus criteria for growth restriction are not met. STUDY DESIGN: Data were derived from a passive prospective cohort of singleton nonanomalous pregnancies at a single academic tertiary care institution (2010-2022) that fell into 3 groups: (1) consecutive fetuses that met the Delphi criteria for fetal growth restriction, (2) small-for-gestational-age fetuses that failed to meet the consensus criteria, and (3) fetuses with birthweights of 20th to 80th percentile randomly selected as an appropriately grown (appropriate-for-gestational-age) comparator group. This nested case-control study used 1:1 propensity score matching to adjust for confounders among the 3 groups: fetal growth restriction cases, small-for-gestational-age cases, and controls. Our primary outcome was a composite: perinatal demise, 5-minute Apgar score of <7, cord pH of ≤7.10, or base excess of ≥12. Pregnancy characteristics with a P value of <.2 on univariate analyses were considered for incorporation into a multivariable model along with fetal growth restriction and small-for-gestational-age to evaluate which outcomes were independently predictive of adverse perinatal outcomes. RESULTS: Overall, 2866 pregnancies met the inclusion criteria. After propensity score matching, there were 2186 matched pairs, including 511 (23%), 1093 (50%), and 582 (27%) patients in the small-for-gestational-age, appropriate-for-gestational-age, and fetal growth restriction groups, respectively. Moreover, 210 pregnancies (10%) were complicated by adverse perinatal outcomes. None of the pregnancies with small-for-gestational-age OR appropriate-for-gestational-age fetuses resulted in perinatal demise. Twenty-three of 511 patients (5%) in the small-for-gestational-age group had adverse outcomes based on 5-minute Apgar scores and/or cord gas results compared with 77 of 1093 patients (7%) in the appropriate-for-gestational-age group (odds ratio, 0.62; 95% confidence interval, 0.39-1.00). Furthermore, 110 of 582 patients (19%) with fetal growth restriction that met the consensus criteria had adverse outcomes (odds ratio, 3.08; 95% confidence interval, 2.25-4.20), including 34 patients with perinatal demise or death before discharge. Factors independently associated with increased odds of adverse outcomes included chronic hypertension, hypertensive disorders of pregnancy, and early-onset fetal growth restriction. Small-for-gestational age was not associated with the primary outcome after adjustment for 6 other factors included in a model predicting adverse perinatal outcomes. The bias-corrected bootstrapped area under the receiver operating characteristic curve for the model was 0.72 (95% confidence interval, 0.66-0.74). The bias-corrected bootstrapped area under the receiver operating characteristic curve for a 7-factor model predicting adverse perinatal outcomes was 0.72 (95% confidence interval, 0.66-0.74). CONCLUSION: This study found no evidence that fetuses with an estimated fetal weight and/or abdominal circumference of 3rd to 9th percentile that fail to meet the consensus criteria for fetal growth restriction (based on Doppler waveforms and/or growth velocity of ≥32 weeks) are at increased risk of adverse outcomes. Although the growth of these fetuses should be monitored closely to rule out evolving growth restriction, most cases are healthy constitutionally small fetuses. The management of these fetuses in the same manner as those with suspected pathologic growth restriction may result in unnecessary antenatal testing and increase the risk of iatrogenic complications resulting from preterm or early term delivery of small fetuses that are at relatively low risk of adverse perinatal outcomes.

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer.

Background: In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages. Methods: We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness. Results: We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM. Conclusions: Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods' ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.