Patient Characteristics Impacting Adherence to Serial Observation for Vestibular Schwannomas

OBJECTIVE

To examine patient characteristics that impact serial observation adherence among vestibular schwannoma (VS) patients.

STUDY DESIGN

Retrospective chart review.

SETTING

Single tertiary care center.

METHODS

We selected for VS patients from 201 to 2020 who elected for serial observation as initial management. Patients under 18, with previous management, bilateral or intralabyrinthine VS, and neurofibromatosis type 2 were excluded. Demographics, tumor characteristics, and follow-up status were extracted. Single and multiple logistic regression was used to identify patient characteristics impacting follow-up.

RESULTS

We identified 507 VS patients who chose serial observation as initial management. Most were female (56.0%), white (73.0%), and married (72.8%). The mean age was 59.3 and most had private insurance (56.4%). Median Charlson Comorbidity Index was 2.00. Mean pure tone audiometry (PTA) average was 41.7 Hz. Average tumor size was 9.04 mm. Of 507 patients, 358 (70.6%) returned for at least one follow-up. On multiple logistic regression analysis, patients with private insurance (odds ratio [OR]: 0.39, confidence interval [CI]: 0.22-0.68; P = .001), racial minority background (OR: 0.54, CI: 0.35-0.83; P = .005), worse PTA averages (OR: 0.99, CI: 0.98-1.00; P = .044), and older age at diagnosis (OR: 0.97, CI: 0.95-1.00; P = .038) were less likely to follow-up.

CONCLUSION

Private health insurance, racial minority background, worse PTA average, and older age were associated with decreased follow-up among adult VS patients electing serial observation. Patients with these characteristics may require additional support to ensure serial observation adherence.

Serum albumin and liver dysfunction mediate the associations between organophosphorus pesticide exposure and hypertension among US adults

BACKGROUND

Human health is commonly threatened by organophosphorus pesticides (OPPs) due to their widespread use and biological characteristics. However, the combined effect of mixtures of OPPs metabolites on the risk of hypertension and potential mechanism remain limited.

OBJECTIVES

To comprehensively investigate the effects between OPPs exposure on hypertension risk and explore and underlying mechanism among US general population.

METHODS

This cross-sectional study collected US adults who had available data on urine OPPs metabolites (dialkyl phosphate compounds, DAPs) from the National Health and Nutrition Examination Survey (NHANES) to assess the relationships of DAPs with hypertension risk. Survey-weighted logistic regression, restricted cubic spline (RCS), and mixed exposure analysis models [weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR)] were used to analyze individual, dose-response and combined associations between urinary DAPs metabolites and hypertension risk, respectively. Mediation analysis determined the potential intermediary role of serum albumin and liver function in the above associations.

RESULTS

Compared with the reference group, participants with the highest tertile levels of DEP, DMTP, DETP, and DMDTP experienced increased risk of hypertension by 1.21-fold (95%CI: 1.02-1.36), 1.20-fold (95%CI: 1.02-1.42), 1.19-fold (95%CI: 1.01-1.40), and 1.17-fold (95%CI: 1.03-1.43), respectively. RCS curve also showed positive exposure-response associations of individual DAPs with hypertension risk. WQS and BKMR analysis further confirmed DAP mixtures were significantly associated with increased risk of hypertension, with DEP identified as a major contributor to the combined effect. Mediation analysis indicated that serum albumin and AST/ALT ratios played crucial mediating roles in the relationships between individual and mixed urinary DAPs and the prevalence of hypertension.

CONCLUSION

Our findings provided more comprehensive and novel perspectives into the individual and combined effects of urinary OPPs matabolites on the increased risk of hypertension and the possible driving mechanism, which would be of great significance for environmental control and early prevention of hypertension.

Risk analysis of air pollutants and types of anemia: a UK Biobank prospective cohort study

Previous studies have suggested that exposure to air pollutants may be associated with specific blood indicators or anemia in certain populations. However, there is insufficient epidemiological data and prospective evidence to evaluate the relationship between environmental air pollution and specific types of anemia. We conducted a large-scale prospective cohort study based on the UK Biobank. Annual average concentrations of NO2, PM2.5, PM2.5-10, and PM10 were obtained from the ESCAPE study using the Land Use Regression (LUR) model. The association between atmospheric pollutants and different types of anemia was investigated using the Cox proportional hazards model. Furthermore, restricted cubic splines were used to explore exposure-response relationships for positive associations, followed by stratification and effect modification analyses by gender and age. After adjusting for demographic characteristics, 3-4 of the four types of air pollution were significantly associated with an increased risk of iron deficiency, vitamin B12 deficiency and folate deficiency anemia, while there was no significant association with other defined types of anemia. After full adjustment, we estimated that the hazard ratios (HRs) of iron deficiency anemia associated with each 10 μg/m3 increase in NO2, PM2.5, and PM10 were 1.04 (95%CI: 1.02, 1.07), 2.00 (95%CI: 1.71, 2.33), and 1.10 (95%CI: 1.02, 1.20) respectively. The HRs of folate deficiency anemia with each 10 μg/m3 increase in NO2, PM2.5, PM2.5-10, and PM10 were 1.25 (95%CI: 1.12, 1.40), 4.61 (95%CI: 2.03, 10.47), 2.81 (95%CI: 1.11, 7.08), and 1.99 (95%CI: 1.25, 3.15) respectively. For vitamin B12 deficiency anemia, no significant association with atmospheric pollution was found. Additionally, we estimated almost linear exposure-response curves between air pollution and anemia, and interaction analyses suggested that gender and age did not modify the association between air pollution and anemia. Our research provided reliable evidence for the association between long-term exposure to PM10, PM2.5, PM2.5-10, NO2, and several types of anemia. NO2, PM2.5, and PM10 significantly increased the risk of iron deficiency anemia and folate deficiency anemia. Additionally, we found that the smaller the PM diameter, the higher the risk, and folate deficiency anemia was more susceptible to air pollution than iron deficiency anemia. No association was observed between the four types of air pollution and hemolytic anemia, aplastic anemia, and other types of anemia. Although the mechanisms are not well understood, we emphasize the need to limit the levels of PM and NO2 in the environment to reduce the potential impact of air pollution on folate and iron deficiency anemia.

Associations of RBC counts and incidence of DVT in patients with spinal cord injury: a five year observational retrospective study

BACKGROUND

The role of red blood cell (RBC) counts as potential independent risk factors for deep vein thrombosis (DVT) in patients with spinal cord injury (SCI) remains uncertain. This study aims to clarify the associations between RBC counts and DVT incidence among this population.

METHODS

A retrospective analysis was performed on 576 patients with SCI admitted to the rehabilitation medicine department from January 1, 2017 to December 31, 2021. After exclusions, 319 patients were analyzed, among which 94 cases of DVT were identified.

RESULTS

Mode of injury, D-dimer and anticoagulant therapy were significant covariates (P < 0.05). Age, fibrinogen, D-dimer, anticoagulant therapy and American Spinal Cord Injury Association impairment scale (AIS) grades were associated with RBC counts and DVT incidence (P < 0.05). Adjusting for these factors, a 1.00 × 10^12/L increase in RBC counts correlated with a 45% decrease in DVT incidence (P = 0.042), revealing a "U" shaped relationship with a pivot at 4.56 × 10^12/L (P < 0.05).

CONCLUSION

RBC counts below 4.56 × 10^12/L serve as a protective factor against DVT, while counts above this threshold pose a risk. These findings could inform the development of DVT prevention strategies for patients with SCI, emphasizing the need for targeted monitoring and management of RBC counts.

Threshold and combined effects of heavy metals on the risk of phenotypic age acceleration among U.S. adults

Accumulation of heavy metals in the body has been shown to affect the phenotypic age (PhenoAge). However, the combined and threshold effects of blood heavy metals on the risk of PhenoAge acceleration (PhenoAgeAccel) are not well understood. A cross-sectional study was conducted using blood heavy metal data (N = 7763, age ≥18 years) from the 2015-2018 National Health and Nutrition Examination Survey. PhenoAgeAccel was calculated from actual age and nine biomarkers. Multiple regression equations were used to describe the relationship between heavy metals and PhenoAgeAccel. Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling was used to explore the relationship between the combined effects of heavy metals and PhenoAgeAccel. Threshold effect and multiple regression analyses were performed to explore the linear and nonlinear relationships between heavy metals and PhenoAgeAccel. Threshold effect analysis showed that blood mercury (Hg) concentration was linearly associated with PhenoAgeAccel. In contrast, lead (Pb), cadmium (Cd), manganese (Mn), and combined exposure were nonlinearly associated with PhenoAgeAccel. In addition, the combination of Pb, Cd, Hg, and Mn significantly affected PhenoAgeAccel. The risk of PhenoAgeAccel was increased by 207% (P < 0.0001). Meanwhile, a threshold relationship was found between blood Pb, Cd, Mn, and the occurrence of PhenoAgeAccel. Overall, our results indicate that combined exposure to heavy metals may increase the risk of PhenoAgeAccel. This study underscores the need to reduce heavy metal pollution in the environment and provides a reference threshold for future studies.

Association between per- and polyfluoroalkyl substances exposure and prevalence of chronic obstructive pulmonary disease: The mediating role of serum albumin

BACKGROUND

No study has examined the association between per- and polyfluoroalkyl substances (PFAS) exposure and chronic obstructive pulmonary disease (COPD) risk. This study aims to explore this relationship.

METHODS

This study enrolled 4541 individuals who had available data on PFAS, COPD, and covariates from NHANES 2007-2018. Serum PFAS including perfluorohexane sulfonate (PFHxS), perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS) were analyzed, because of high detective rates. Considering the skew distribution of PFAS levels, the natural logarithm-transformed PFAS (Ln-PFAS) was used. Logistic regression analysis, restricted cubic spline (RCS), and weighted quantile sum (WQS) regression were performed to explore the single, nonlinear, and mixed effects. A mediating analysis was used to evaluate the mediated effects of albumin.

RESULTS

Individuals with COPD had higher levels of PFHxS, PFNA, PFOA, and PFOS compared to those without COPD. Ln-PFNA (OR males: 1.92, 95 % CI:1.31 to 2.80, P: <0.001; OR females: 1.07, 95 % CI: 0.81 to 1.40, P: 0.636) and ln-PFOA (OR males: 2.17, 95 % CI:1.38 to 3.41, P: <0.001; OR females: 1.49, 95 % CI: 1.08 to 2.05, P: 0.016) were associated with COPD risk especially in males. The interaction between PFNA exposure and sex on COPD risk was significant (P interaction: <0.001). The RCS curve demonstrated the nonlinear relationship between the ln-PFOA (P nonlinear:0.001), ln-PFNA (P nonlinear:0.045), and COPD risk in males. WQS analysis showed mixed PFAS exposure was correlated with COPD risk in males (OR: 1.44, 95 % CI:1.18 to 1.75, P: <0.001). Albumin mediated the relationship between PFOA and COPD (mediated proportion: -17.94 %).

CONCLUSION

This study concludes PFOA and PFNA are linked to a higher COPD risk in males, and serum albumin plays a mediating role in the relationship between PFOA and COPD. Thess findings are beneficial for the prevention of COPD. Further studies are required to explore potential mechanisms.

Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience

The rapid proliferation of data across diverse fields has accentuated the importance of accurate imputation for missing values. This task is crucial for ensuring data integrity and deriving meaningful insights. In response to this challenge, we present Xputer, a novel imputation tool that adeptly integrates Non-negative Matrix Factorization (NMF) with the predictive strengths of XGBoost. One of Xputer's standout features is its versatility: it supports zero imputation, enables hyperparameter optimization through Optuna, and allows users to define the number of iterations. For enhanced user experience and accessibility, we have equipped Xputer with an intuitive Graphical User Interface (GUI) ensuring ease of handling, even for those less familiar with computational tools. In performance benchmarks, Xputer often outperforms IterativeImputer in terms of imputation accuracy. Furthermore, Xputer autonomously handles a diverse spectrum of data types, including categorical, continuous, and Boolean, eliminating the need for prior preprocessing. Given its blend of performance, flexibility, and user-friendly design, Xputer emerges as a state-of-the-art solution in the realm of data imputation.

Joint Imputation of General Data

High-dimensional complex survey data of general structures (e.g., containing continuous, binary, categorical, and ordinal variables), such as the US Department of Defense's Health-Related Behaviors Survey (HRBS), often confound procedures designed to impute any missing survey data. Imputation by fully conditional specification (FCS) is often considered the state of the art for such datasets due to its generality and flexibility. However, FCS procedures contain a theoretical flaw that is exposed by HRBS data-HRBS imputations created with FCS are shown to diverge across iterations of Markov Chain Monte Carlo. Imputation by joint modeling lacks this flaw; however, current joint modeling procedures are neither general nor flexible enough to handle HRBS data. As such, we introduce an algorithm that efficiently and flexibly applies multiple imputation by joint modeling in data of general structures. This procedure draws imputations from a latent joint multivariate normal model that underpins the generally structured data and models the latent data via a sequence of conditional linear models, the predictors of which can be specified by the user. We perform rigorous evaluations of HRBS imputations created with the new algorithm and show that they are convergent and of high quality. Lastly, simulations verify that the proposed method performs well compared to existing algorithms including FCS.

Racial differences in associations between adverse childhood experiences and physical, mental, and behavioral health

Purpose

Adverse childhood experiences (ACEs) are associated with poor adulthood health. Multiracial people have elevated mean ACEs scores and risk of several outcomes. We aimed to determine whether this group should be targeted for prevention efforts.

Methods

We analyzed three waves (1994-2009) of the National Longitudinal Study of Adolescent to Adult Health (n = 12,372) in 2023, estimating associations between four or more ACEs and physical (metabolic syndrome, hypertension, asthma), mental (anxiety, depression), and behavioral (suicidal ideation, drug use) outcomes. We estimated adjusted risk ratios for each outcome in modified Poisson models interacting race and ACEs. We used the interaction contrast to estimate race-specific excess cases per 1000 relative to Multiracial participants.

Results

Excess case estimates of asthma were smaller for White (-123 cases, 95% CI: -251, -4), Black (-141, 95% CI: -285, -6), and Asian (-169, 95% CI: -334, -7) participants compared to Multiracial participants. Black (-100, 95% CI: -189, -10), Asian (-163, 95% CI: -247, -79) and Indigenous (-144, 95% CI: -252, -42) participants had fewer excess cases of and weaker relative scale association with anxiety compared to Multiracial participants.

Conclusions

Adjusted associations with asthma and anxiety appear stronger for Multiracial people. Existing ACEs prevention strategies should be tailored to support Multiracial youth and families.

Evaluation of the Healthy Start voucher scheme on maternal vitamin use and child breastfeeding: a natural experiment using data linkage

Background

Having a good start in life during pregnancy and infancy has been shown to be important for living both a healthy life and a longer life. Despite the introduction of many policies for the early-years age group, including voucher schemes, with the aim of improving nutrition, there is limited evidence of their impact on health.

Objectives

To assess the effectiveness of the Healthy Start voucher scheme on infant, child and maternal outcomes, and to capture the lived experiences of the Healthy Start voucher scheme for low-income women.

Design

This was a natural experiment study using existing data sets, linked to routinely collected health data sets, with a nested qualitative study of low-income women and an assessment of the health economics.

Setting

Representative sample of Scottish children and UK children.

Participants

Growing Up in Scotland cohort 2 (n = 2240), respondents to the 2015 Infant Feeding Study (n = 8067) and a sample of 40 participants in the qualitative study.

Interventions

The Health Start voucher, a means-tested scheme that provides vouchers worth £3.10 per week to spend on liquid milk, formula milk, fruit and vegetables.

Main outcome measures

Infant and child outcomes - breastfeeding initiation and duration; maternal outcomes - vitamin use pre and during pregnancy.

Results

The exposed group were women receiving the Healthy Start voucher (R), with two control groups: eligible and not claiming the Healthy Start voucher (E) and nearly eligible. There was no difference in vitamin use during pregnancy for either comparison (receiving the Healthy Start voucher, 82%; eligible and not claiming the Healthy Start voucher, 86%; p = 0.10 vs. receiving the Healthy Start voucher, 87%; nearly eligible, 88%; p = 0.43) in the Growing Up in Scotland cohort. Proportions were similar for the Infant Feeding Study cohort (receiving the Healthy Start voucher, 89%; eligible and not claiming the Healthy Start voucher, 86%; p = 0.01 vs. receiving the Healthy Start voucher, 89%; nearly eligible, 87%; p = 0.01); although results were statistically significantly different, these were small effect sizes. There was no difference for either comparison in breastfeeding initiation or breastfeeding duration in months in Growing Up in Scotland, but there was a negative effect of the Healthy Start voucher in the Infant Feeding Survey. This contrast between data sets indicates that results are inconclusive for breastfeeding. The qualitative study found that despite the low monetary value the women valued the Healthy Start voucher scheme. However, the broader lives of low-income women are crucial to understand the constraints to offer a healthy diet.

Limitations

Owing to the policy being in place, it was difficult to identify appropriate control groups using existing data sources, especially in the Infant Feeding Study.

Conclusions

As the Healthy Start voucher scheme attempts to influence health behaviour, this evaluation can inform other policies aiming to change behaviour and use voucher incentives. The null effect of Healthy Start vouchers on the primary outcomes may be due to the value of the vouchers being insufficient to change the broader lives of low-income women to offer a healthy diet.

Future work

The methods developed to undertake an economic evaluation alongside a natural experiment using existing data can be used to explore the cost-effectiveness of the Healthy Start voucher scheme.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 11, No. 11. See the NIHR Journals Library website for further project information.

Factors associated with suicidal attempts in female patients with mood disorder

Aim

This study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD).

Method

The study included 396 female participants who were diagnosed with MD Diagnostic Group (F30-F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model.

Result

LASSO regression analysis showed that five factors led to the occurrence of suicidality, including BMI (β = -0.02, SE = 0.02), social dysfunction (β = 1.72, SE = 0.24), time interval between first onset and first dose (β = 0.03, SE = 0.01), polarity at onset (β = -1.13, SE = 0.25), and times of hospitalization (β = -0.11, SE = 0.06). We assessed the ability of the nomogram model to recognize suicidality, with good results (AUC = 0.76, 95% CI: 0.71-0.80). Indicating that the nomogram had a good consistency (C-index: 0.756, 95% CI: 0.750-0.758). The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks.

Conclusion

Five factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts.

Association between marijuana use and kidney stone: a cross-sectional study of NHANES 2009 to 2018

Objective: The purpose of this investigation is to determine whether regular marijuana use is related to history of kidney stones in the US population. Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018. Kidney stone and marijuana use data were collected from self-report questionnaires. Multivariate logistic regression and multiple sensitivity analyses were applied to examine the relationship between marijuana usage and kidney stones. Results: There are approximately 26.04% of the US population have admitted to using marijuana in their lifetime. Compared with none regular users, those with a higher frequency of marijuana use were more males, more non-Hispanic races, lower than high school education, overweight, no recreational activity, without diabetes mellitus, and more coronary heart disease. After adjusting for potential confounders, multivariate regression analysis demonstrated that marijuana use was inversely correlated to kidney stones in males (Odds ratio [OR] = 0.72, 95% Confidence interval [CI] = 0.54-0.97). One to seven times/week regular consumption of marijuana was associated with kidney stones in males (OR = 0.62, 95% CI = 0.43-0.89). Sensitivity analyses validated the robustness of our outcomes. Conclusion: Our findings revealed that regular marijuana male users were inversely associated with kidney stones. Marijuana use one to six times/week was inversely related to the risk of kidney stones in males. Further studies are required to explore the dose and type associations of marijuana with kidney stones.

Missing values and inconclusive results in diagnostic studies - A scoping review of methods

Most diagnostic studies exclude missing values and inconclusive results from the analysis or apply simple methods resulting in biased accuracy estimates. This may be due to the lack of availability or awareness of appropriate methods. This scoping review aimed to provide an overview of strategies to handle missing values and inconclusive results in the reference standard or index test in diagnostic accuracy studies. Conducting a systematic literature search in MEDLINE, Cochrane Library, and Web of Science, we could identify many articles proposing methods for addressing missing values in the reference standard. There are also several articles describing methods regarding missing values or inconclusive results in the index test. The latter encompass imputation, frequentist and Bayesian likelihood, model-based, and latent class methods. While methods for missing values in the reference standard are regularly applied in practice, this is not true for methods addressing missing values and inconclusive results in the index test. Our comprehensive overview and description of available methods may raise further awareness of these methods and will enhance their application. Future research is needed to compare the performance of these methods under different conditions to give valid and robust recommendations for their usage in various diagnostic accuracy research scenarios.

Associations between smoke exposure and kidney stones: results from the NHANES (2007-2018) and Mendelian randomization analysis

Purpose

It is currently controversial whether smoke exposure is associated with the risk of kidney stones. Herein, publicly available databases were combined to explore relationships with the risk of nephrolithiasis in terms of smoking status and serum cotinine concentrations.

Materials and methods

First, we conducted an observational study using data from 2007 to 2018, based on the National Health and Nutrition Examination Survey (NHANES) database. Univariate analysis, multivariate logistic regression, trend testing, restricted cubic spline (RCS), and multiple imputation (MI) were the main analytical methods of our study. Then, A Mendelian randomization (MR) analysis was performed to explore the causal relationship between serum cotinine and nephrolithiasis. Genetic instruments for serum cotinine and pooled data for kidney stones were derived from publicly available large-scale genome-wide association studies (GWAS). Inverse-variance weighting (IVW) was the primary method for our MR analysis.

Results

A total of 34,657 and 31,352 participants were included in the observational study based on smoking status and serum cotinine concentrations, respectively. Under full adjustment of covariates, current smokers had an increased risk of kidney stones compared to non-smokers [OR = 1.17 (1.04-1.31), P = 0.009, P for trend = 0.010]. Compared with serum cotinine of <0.05 ng/ml, serum cotinine levels of 0.05-2.99 ng/ml [OR = 1.15 (1.03-1.29), P = 0.013] and ≥3.00 ng/ml [OR = 1.22 (1.10-1.37), P < 0.001] were observed to have a higher risk of nephrolithiasis (P for trend < 0.001). In addition, a non-linear relationship between log2-transformed serum cotinine and the risk of nephrolithiasis was found (P for non-linearity = 0.028). Similar results were found when serum cotinine (log2 transformation) was used as a continuous variable [OR = 1.02 (1.01-1.03), P < 0.001] or complete data was used to analyze after MI. In the MR analysis, genetically predicted high serum cotinine was causally related to the high risk of nephrolithiasis [IVW: OR = 1.09 (1.00-1.19), P = 0.044].

Conclusion

Current smoking and high serum cotinine concentrations may be associated with an increased risk of kidney stones. Further research is needed to validate this relationship and explore its underlying mechanisms.

Machine Learning Models to Predict Kidney Stone Recurrence Using 24 Hour Urine Testing and Electronic Health Record-Derived Features

Objective

To assess the accuracy of machine learning models in predicting kidney stone recurrence using variables extracted from the electronic health record (EHR).

Methods

We trained three separate machine learning (ML) models (least absolute shrinkage and selection operator regression [LASSO], random forest [RF], and gradient boosted decision tree [XGBoost] to predict 2-year and 5-year symptomatic kidney stone recurrence from electronic health-record (EHR) derived features and 24H urine data (n = 1231). ML models were compared to logistic regression [LR]. A manual, retrospective review was performed to evaluate for a symptomatic stone event, defined as pain, acute kidney injury or recurrent infections attributed to a kidney stone identified in the clinic or the emergency department, or for any stone requiring surgical treatment. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified important features for each model.

Results

The 2- and 5- year symptomatic stone recurrence rates were 25% and 31%, respectively. The LASSO model performed best for symptomatic stone recurrence prediction (2-yr AUC: 0.62, 5-yr AUC: 0.63). Other models demonstrated modest overall performance at 2- and 5-years: LR (0.585, 0.618), RF (0.570, 0.608), and XGBoost (0.580, 0.621). Patient age was the only feature in the top 5 features of every model. Additionally, the LASSO model prioritized BMI and history of gout for prediction.

Conclusions

Throughout our cohorts, ML models demonstrated comparable results to that of LR, with the LASSO model outperforming all other models. Further model testing should evaluate the utility of 24H urine features in model structure.

Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov

Multivariate meta-analysis (MMA) is a powerful statistical technique that can provide more reliable and informative results than traditional univariate meta-analysis, which allows for comparisons across outcomes with increased statistical power. However, implementing appropriate statistical methods for MMA can be challenging due to the requirement of various specific tasks in data preparation. The metavcov package aims for model preparation, data visualization, and missing data solutions to provide tools for different methods that cannot be found in accessible software. It provides sufficient constructs for estimating coefficients from other well-established packages. For model preparation, users can compute both effect sizes of various types and their variance-covariance matrices, including correlation coefficients, standardized mean difference, mean difference, log odds ratio, log risk ratio, and risk difference. The package provides a tool to plot the confidence intervals for the primary studies and the overall estimates. When specific effect sizes are missing, single imputation is available in the model preparation stage; a multiple imputation method is also available for pooling the results in a statistically principled manner from models of users' choice. The package is demonstrated in two real data applications and a simulation study to assess methods for handling missing data.

A hypothetical intervention to reduce inequities in anxiety for Multiracial people: simulating an intervention on childhood adversity

Multiracial people report higher mean Adverse Childhood Experiences (ACEs) scores and prevalence of anxiety than other racial groups. Studies using statistical interactions to estimate racial differences in ACEs-anxiety associations do not show stronger associations for Multiracial people. Using data from Waves 1 (1995-97) through 4 (2008-09) of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we simulated a stochastic intervention over 1,000 resampled datasets to estimate the race-specific cases averted per 1,000 of anxiety if all racial groups had the same exposure distribution of ACEs as Whites. Simulated cases averted were greatest for the Multiracial group, (median = -4.17 cases per 1,000, 95% CI: -7.42, -1.86). The model also predicted smaller risk reductions for Black participants (-0.76, 95% CI: -1.53, -0.19). CIs around estimates for other racial groups included the null. An intervention to reduce racial disparities in exposure to ACEs could help reduce the inequitable burden of anxiety on the Multiracial population. Stochastic methods support consequentialist approaches to racial health equity, and can encourage greater dialogue between public health researchers, policymakers, and practitioners.

Racial Differences in Associations Between Adverse Childhood Experiences and Physical, Mental, and Behavioral Health

Introduction

Adverse childhood experiences (ACEs) are associated with poor adulthood health, with individuals experiencing multiple ACEs at greatest risk. Multiracial people have high mean ACEs scores and elevated risk of several outcomes, but are infrequently the focus of health equity research. This study aimed to determine whether this group should be targeted for prevention efforts.

Methods

We analyzed Waves 1 (1994-95), 3 (2001-02), and 4 (2008-09) of the National Longitudinal Study of Adolescent to Adult Health (n = 12,372) in 2023, estimating associations between four or more ACEs and physical (metabolic syndrome, hypertension, asthma), mental (anxiety, depression), and behavioral (suicidal ideation, drug use) outcomes. We estimated risk ratios for each outcome in modified Poisson models with a race × ACEs interaction, adjusted for hypothesized confounders of the ACE-outcome relationships. We used the interaction contrast to estimate excess cases per 1,000 individuals for each group relative to Multiracial participants.

Results

Excess case estimates of asthma were significantly smaller for White (-123 cases, 95% CI: -251, -4), Black (-141, 95% CI: -285, -6), and Asian (-169, 95% CI: -334, -7) participants compared to Multiracial participants. Black (-100, 95% CI: -189, -10), Asian (-163, 95% CI: -247, -79) and Indigenous (-144, 95% CI: -252, -42) participants had significantly fewer excess cases of and weaker (p < 0.001) relative scale association with anxiety compared to Multiracial participants.

Conclusions

Adjusted associations between ACEs and asthma or anxiety appear stronger for Multiracial people than other groups. ACEs are universally harmful but may contribute disproportionately to morbidity in this population.

The association of dietary intake of riboflavin and thiamine with kidney stone: a cross-sectional survey of NHANES 2007-2018

BACKGROUND

Kidney stone disease (KSD) is a common condition that affects 10% population in the United States (US). The relationship between thiamine and riboflavin intake and KSD has not been well-studied. We aimed to investigate the prevalence of KSD and the association between dietary thiamine and riboflavin intake with KSD in the US population.

METHODS

This large-scale, cross-sectional study included subjects from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. KSD and dietary intake were collected from questionnaires and 24-hour recall interviews. Logistic regression and sensitivity analyses were performed to investigate the association.

RESULTS

This study included 26,786 adult participants with a mean age of 50.12 ± 17.61 years old. The prevalence of KSD was 9.62%. After adjusting for all potential covariates, we found that higher riboflavin intake was negatively related to KSD compared with dietary intake of riboflavin < 2 mg/day in the fully-adjusted model (OR = 0.541, 95% CI = 0.368 to 0.795, P = 0.002). After stratifying by gender and age, we found that the impact of riboflavin on KSD still existed in all age subgroups (P < 0.05) but only in males (P = 0.001). No such associations were found between dietary intake of thiamine and KSD in any of the subgroups.

CONCLUSIONS

Our study suggested that a high intake of riboflavin is independently inversely associated with kidney stones, especially in male population. No association was found between dietary intake of thiamine and KSD. Further studies are needed to confirm our results and explore the causal relationships.

To Link or Synthesize? An Approach to Data Quality Comparison

Linking administrative data to produce more informative data for subsequent analysis has become an increasingly common practice. However, there might be concomitant risks of disclosing sensitive information about individuals. One practice that reduces these risks is data synthesis. In data synthesis the data are used to fit a model from which synthetic data are then generated. The synthetic data are then released to end users. There are some scenarios where an end user might have the option of using linked data, or accepting synthesized data. However, linkage and synthesis are susceptible to errors that could limit their usefulness. Here, we investigate the problem of comparing the quality of linked data to synthesized data and demonstrate through simulations how the problem might be approached. These comparisons are important when considering how an end user can be supplied with the highest quality data, and in situations where one must consider risk / utility trade-offs.

Association of Hemoglobin to Red Blood Cell Distribution Width-Standard Deviation (RDW-SD) Ratio and 3-Month Readmission in Elderly Chinese Patients with Heart Failure: A Retrospective Cohort Study

Purpose

Hemoglobin (Hb) and red blood cell distribution width-standard deviation (RDW-SD) have clinical significance in the prognosis of heart failure (HF). Little is known regarding the prognostic value of the Hb/RDW-SD ratio in patients with HF. This study sought to investigate the association between Hb/RDW-SD ratio and HF 3-month readmission in Chinese elderly patients.

Patients and Methods

The present study was a retrospective cohort study. A total of 1816 HF patients were extracted from the Chinese HF database. A generalized linear model was used to explore the association between Hb/RDW-SD and 3-month readmission in HF. The generalized additive model was used to explore the nonlinear relationship, and a two-piecewise linear regression model was used to find the inflection point. Subgroup analysis explored interactions and whether each subgroup was consistent with the primary outcome direction.

Results

Result showed Hb/RDW-SD was negatively associated with HF 3-month readmission (OR = 0.70, 95% CI: 0.55 to 0.89, P = 0.0031). A non-linear relationship was detected between Hb/RDW-SD and HF 3-month readmission with two inflection points (1.78 and 2.17). Both Hb/RDW-SD < 1.78 and Hb/RDW-SD > 2.17 showed a significant correlation between them, with corresponding effect values of (OR = 0.38, 95% CI: 0.17 to 0.87, P = 0.0209) and (OR = 0.44, 95% CI: 0.27 to 0.71, P = 0.0007), respectively.

Conclusion

Hb/RDW-SD is negatively associated with HF 3-month readmission. The relationship between Hb/RDW-SD and HF 3-month readmission is also non-linear. Both Hb/RDW-SD < 1.78 and Hb/RDW-SD > 2.17 were strong negatively associated with HF 3-month readmission.

Association between the systemic immune-inflammation index and kidney stone: A cross-sectional study of NHANES 2007-2018

Background

The incidence rate of kidney stones increased over the past decades globally, which brought medical expenditure and social burden. The systemic immune-inflammatory index (SII) was initially identified as a prognosis of multiple diseases. We performed an updated analysis on the impact of SII on kidney stones.

Methods

This compensatory cross-sectional study enrolled participants from the National Health and Nutrition Examination Survey 2007-2018. Univariate and multivariate logistic regression analyses were performed to investigate the association between SII and kidney stones.

Results

Of the 22220 participants, the mean (SD) age was 49.45 ± 17.36 years old, with a 9.87% incidence rate of kidney stones. A fully adjusted model showed that SII higher than 330 x 10⁹/L was parallel associated with kidney stones (Odds ratio [OR] = 1.282, 95% Confidence interval [CI] = 1.023 to 1.608, P = 0.034) in adults aged 20-50. However, no difference was found in the elderly subgroup. Multiple imputation analyses confirmed the robustness of our results.

Conclusions

Our findings suggested SII was positively associated with a high risk of kidney stones in US adults aged less than 50. The outcome compensated for previous studies that still needed more large-scale prospective cohorts for validation.

A Nomogram for Predicting Delayed Viral Shedding in Non-Severe SARS-CoV-2 Omicron Infection

Purpose

The Omicron variant of SARS-CoV-2 has emerged as a significant global concern, characterized by its rapid transmission and resistance to existing treatments and vaccines. However, the specific hematological and biochemical factors that may impact the clearance of Omicron variant infection remain unclear. The present study aimed to identify easily accessible laboratory markers that are associated with prolonged virus shedding in non-severe patients with COVID-19 caused by the Omicron variant.

Patients and Methods

A retrospective cohort study was conducted on 882 non-severe COVID-19 patients who were diagnosed with the Omicron variant in Shanghai between March and June 2022. The least absolute shrinkage and selection operator regression model was used for feature selection and dimensional reduction, and multivariate logistic regression analysis was performed to construct a nomogram for predicting the risk of prolonged SARS-CoV-2 RNA positivity lasting for more than 7 days. The receiver operating characteristic (ROC) curve and calibration curves were used to assess predictive discrimination and accuracy, with bootstrap validation.

Results

Patients were randomly divided into derivation (70%, n = 618) and validation (30%, n = 264) cohorts. Optimal independent markers for prolonged viral shedding time (VST) over 7 days were identified as Age, C-reactive protein (CRP), platelet count, leukocyte count, lymphocyte count, and eosinophil count. These factors were subsequently incorporated into the nomogram utilizing bootstrap validation. The area under the curve (AUC) in the derivation (0.761) and validation (0.756) cohorts indicated good discriminative ability. The calibration curve showed good agreement between the nomogram-predicted and actual patients with VST over 7 days.

Conclusion

Our study confirmed six factors associated with delayed VST in non-severe SARS-CoV-2 Omicron infection and constructed a Nomogram which may assist non-severely affected patients to better estimate the appropriate length of self-isolation and optimize their self-management strategies.

Comparing methods for handling missing covariates in meta-regression

Meta-analysts often encounter missing covariate values when estimating meta-regression models. In practice, ad hoc approaches involving data deletion have been widely used. The current study investigates the performance of different methods for handling missing covariates in meta-regression, including complete-case analysis (CCA), shifting-case analysis (SCA), multiple imputation (MI), and full information maximum likelihood (FIML), assuming missing at random mechanism. According to the simulation results, we advocate the use of MI and FIML than CCA and SCA approaches in practice. In addition, we cautiously note the challenges and potential advantages of using MI in the meta-analysis context.

Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data

Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, patient follow-ups, and overall patient care, and should therefore have underlying standardized, evidence-based recommendations. Currently, laboratory parameters and their significance are treated differently depending on expert opinions, clinical environment, and varying hospital guidelines. In our study, we aimed to demonstrate the capability of a set of algorithms to identify predictive analytes for a specific diagnosis. As an illustration of our proposed methodology, we examined the analytes associated with myocardial ischemia; it was a well-researched diagnosis and provides a substrate for comparison. We intend to present a toolset that will boost the evolution of evidence-based laboratory diagnostics and, therefore, improve patient care. Methods: The data we used consisted of preexisting, anonymized recordings from the emergency ward involving all patient cases with a measured value for troponin T. We used multiple imputation technique, orthogonal data augmentation, and Bayesian Model Averaging to create predictive models for myocardial ischemia. Each model incorporated different analytes as cofactors. In examining these models further, we could then conclude the predictive importance of each analyte in question. Results: The used algorithms extracted troponin T as a highly predictive analyte for myocardial ischemia. As this is a known relationship, we saw the predictive importance of troponin T as a proof of concept, suggesting a functioning method. Additionally, we could demonstrate the algorithm's capabilities to extract known risk factors of myocardial ischemia from the data. Conclusion: In this pilot study, we chose an assembly of algorithms to analyze the value of analytes in predicting myocardial ischemia. By providing reliable correlations between the analytes and the diagnosis of myocardial ischemia, we demonstrated the possibilities to create unbiased computational-based guidelines for laboratory diagnostics by using computational power in today's era of digitalization.

Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study

Background

There is an increasing trend of Metabolic syndrome (MetS) prevalence, which has been considered as an important contributor for cardiovascular disease (CVD), cancers and diabetes. However, there is often a long asymptomatic phase of MetS, resulting in not diagnosed and intervened so timely as needed. It would be very helpful to explore tools to predict the probability of suffering from MetS in daily life or routinely clinical practice.

Objective

To develop models that predict individuals' probability of suffering from MetS timely with high efficacy in general population.

Methods

The present study enrolled 8964 individuals aged 40-75 years without severe diseases, which was a part of the REACTION study from October 2011 to February 2012. We developed three prediction models for different scenarios in hospital (Model 1, 2) or at home (Model 3) based on LightGBM (LGBM) technique and corresponding logistic regression (LR) models were also constructed for comparison. Model 1 included variables of laboratory tests, lifestyles and anthropometric measurements while model 2 was built with components of MetS excluded based on model 1, and model 3 was constructed with blood biochemical indexes removed based on model 2. Additionally, we also investigated the strength of association between the predictive factors and MetS, as well as that between the predictors and each component of MetS.

Results

In this study, 2714 (30.3%) participants suffer from MetS accordingly. The performances of the LGBM models in predicting the probability of suffering from MetS produced good results and were presented as follows: model 1 had an area under the curve (AUC) value of 0.993 while model 2 indicated an AUC value of 0.885. Model 3 had an AUC value of 0.859, which is close to that of model 2. The AUC values of LR model 1 and 2 for the scenario in hospital and model 3 at home were 0.938, 0.839 and 0.820 respectively, which seemed lower than that of their corresponding machine learning models, respectively. In both LGBM and logistic models, gender, height and resting pulse rate (RPR) were predictors for MetS. Women had higher risk of MetS than men (OR 8.84, CI: 6.70-11.66), and each 1-cm increase in height indicated 3.8% higher risk of suffering from MetS in people over 58 years, whereas each 1- Beat Per Minute (bpm) increase in RPR showed 1.0% higher risk in individuals younger than 62 years.

Conclusion

The present study showed that the prediction models developed by machine learning demonstrated effective in evaluating the probability of suffering from MetS, and presented prominent predicting efficacies and accuracies. Additionally, we found that women showed a higher risk of MetS than men, and height in individuals over 58 years was important factor in predicting the probability of suffering from MetS while RPR was of vital importance in people aged 40-62 years.

A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction

BACKGROUND

Early risk stratification is important for patients with acute myocardial infarction (AMI). We aimed to develop a simple APACHE IV dynamic nomogram, combined with easily available clinical parameters within 24 h of admission, thus improving its predictive power to assess the risk of mortality at 28 days.

METHODS

Clinical information on AMI patients was extracted from the eICU database v2.0. A preliminary XGBoost examination of the degree of association between all variables in the database and 28-day mortality was conducted. Univariate and multivariate logistic regression analysis were used to perform screening of variables. Based on the multifactorial analysis, a dynamic nomogram predicting 28-day mortality in these patients was developed. To cope with missing data in records with missing variables, we applied the multiple imputation method. Predictive models are evaluated in three main areas, namely discrimination, calibration, and clinical validity. The discrimination is mainly represented by the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Calibration is represented by the calibration plot. Clinical validity is represented by the decision curve analysis (DCA) curve.

RESULTS

A total of 504 people were included in the study. All 504 people were used to build the predictive model, and the internal validation model used a 500-bootstrap method. Multivariate analysis showed that four variables, APACHE IV, the first sample of admission lactate, prior atrial fibrillation (AF), and gender, were included in the nomogram as independent predictors of 28-day mortality in AMI. The prediction model had an AUC of 0.819 (95%CI 0.770-0.868) whereas the internal validation model had an AUC of 0.814 (95%CI 0.765-0.860). Calibration and DCA curves indicated that the dynamic nomogram in this study were reflective of real-world conditions and could be applied clinically. The predictive model composed of these four variables outperformed a single APACHE IV in terms of NRI and IDI. The NRI was 16.4% (95% CI: 6.1-26.8%; p = 0.0019) and the IDI was 16.4% (95% CI: 6.0-26.8%; p = 0.0020). Lactate accounted for nearly half of the total NRI, which showed that lactate was the most important of the other three variables.

CONCLUSION

The prediction model constructed by APACHE IV in combination with the first sample of admission lactate, prior AF, and gender outperformed the APACHE IV scoring system alone in predicting 28-day mortality in AMI. The prediction dynamic nomogram model was published via a website app, allowing clinicians to improve the predictive efficacy of the APACHE IV score by 16.4% in less than 1 min.

A multiple phenotype imputation method for genetic diversity and core collection in Taiwanese vegetable soybean

Establishment of vegetable soybean (edamame) [Glycine max (L.) Merr.] germplasms has been highly valued in Asia and the United States owing to the increasing market demand for edamame. The idea of core collection (CC) is to shorten the breeding program so as to improve the availability of germplasm resources. However, multidimensional phenotypes typically are highly correlated and have different levels of missing rate, often failing to capture the underlying pattern of germplasms and select CC precisely. These are commonly observed on correlated samples. To overcome such scenario, we introduced the "multiple imputation" (MI) method to iteratively impute missing phenotypes for 46 morphological traits and jointly analyzed high-dimensional imputed missing phenotypes (EC impu ) to explore population structure and relatedness among 200 Taiwanese vegetable soybean accessions. An advanced maximization strategy with a heuristic algorithm and PowerCore was used to evaluate the morphological diversity among the EC impu . In total, 36 accessions (denoted as CC impu ) were efficiently selected representing high diversity and the entire coverage of the EC impu . Only 4 (8.7%) traits showed slightly significant differences between the CC impu and EC impu . Compared to the EC impu , 96% traits retained all characteristics or had a slight diversity loss in the CC impu . The CC impu exhibited a small percentage of significant mean difference (4.51%), and large coincidence rate (98.1%), variable rate (138.76%), and coverage (close to 100%), indicating the representativeness of the EC impu . We noted that the CC impu outperformed the CC raw in evaluation properties, suggesting that the multiple phenotype imputation method has the potential to deal with missing phenotypes in correlated samples efficiently and reliably without re-phenotyping accessions. Our results illustrated a significant role of imputed missing phenotypes in support of the MI-based framework for plant-breeding programs.

Assessing Alternative Imputation Strategies for Infrequently Missing Items on Multi-item Scales

Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to ad-hoc methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which ad-hoc procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts ad-hoc methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of ad-hoc procedures may be muted when missing data percentages are modest.

Is age at menopause decreasing? - The consequences of not completing the generational cohort

Background

Due to contradictory results in current research, whether age at menopause is increasing or decreasing in Western countries remains an open question, yet worth studying as later ages at menopause are likely to be related to an increased risk of breast cancer. Using data from breast cancer screening programs to study the temporal trend of age at menopause is difficult since especially younger women in the same generational cohort have often not yet reached menopause. Deleting these younger women in a breast cancer risk analyses may bias the results. The aim of this study is therefore to recover missing menopause ages as a covariate by comparing methods for handling missing data. Additionally, the study makes a contribution to understanding the evolution of age at menopause for several generations born in Portugal between 1920 and 1970.

Methods

Data from a breast cancer screening program in Portugal including 278,282 women aged 45–69 and collected between 1990 and 2010 are used to compare two approaches of imputing age at menopause: (i) a multiple imputation methodology based on a truncated distribution but ignoring the mechanism of missingness; (ii) a copula-based multiple imputation method that simultaneously handles the age at menopause and the missing mechanism. The linear predictors considered in both cases have a semiparametric additive structure accommodating linear and non-linear effects defined via splines or Markov random fields smoothers in the case of spatial variables.

Results

Both imputation methods unveiled an increasing trend of age at menopause when viewed as a function of the birth year for the youngest generation. This trend is hidden if we model only women with an observed age at menopause.

Conclusion

When studying age at menopause, missing ages must be recovered with an adequate procedure for incomplete data. Imputing these missing ages avoids excluding the younger generation cohort of the screening program in breast cancer risk analyses and hence reduces the bias stemming from this exclusion. In addition, imputing the not yet observed ages of menopause for mostly younger women is also crucial when studying the time trend of age at menopause otherwise the analysis will be biased.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-022-01658-x).

Improving outcomes for women aged 70 years or above with early breast cancer: research programme including a cluster RCT

Background

In breast cancer management, age-related practice variation is widespread, with older women having lower rates of surgery and chemotherapy than younger women, based on the premise of reduced treatment tolerance and benefit. This may contribute to inferior outcomes. There are currently no age- and fitness-stratified guidelines on which to base treatment recommendations.

Aim

We aimed to optimise treatment choice and outcomes for older women (aged ≥ 70 years) with operable breast cancer.

Objectives

Our objectives were to (1) determine the age, comorbidity, frailty, disease stage and biology thresholds for endocrine therapy alone versus surgery plus adjuvant endocrine therapy, or adjuvant chemotherapy versus no chemotherapy, for older women with breast cancer; (2) optimise survival outcomes for older women by improving the quality of treatment decision-making; (3) develop and evaluate a decision support intervention to enhance shared decision-making; and (4) determine the degree and causes of treatment variation between UK breast units.

Design

A prospective cohort study was used to determine age and fitness thresholds for treatment allocation. Mixed-methods research was used to determine the information needs of older women to develop a decision support intervention. A cluster-randomised trial was used to evaluate the impact of this decision support intervention on treatment choices and outcomes. Health economic analysis was used to evaluate the cost–benefit ratio of different treatment strategies according to age and fitness criteria. A mixed-methods study was used to determine the degree and causes of variation in treatment allocation.

Main outcome measures

The main outcome measures were enhanced age- and fitness-specific decision support leading to improved quality-of-life outcomes in older women (aged ≥ 70 years) with early breast cancer.

Results

(1) Cohort study: the study recruited 3416 UK women aged ≥ 70 years (median age 77 years). Follow-up was 52 months. (a) The surgery plus adjuvant endocrine therapy versus endocrine therapy alone comparison: 2854 out of 3416 (88%) women had oestrogen-receptor-positive breast cancer, 2354 of whom received surgery plus adjuvant endocrine therapy and 500 received endocrine therapy alone. Patients treated with endocrine therapy alone were older and frailer than patients treated with surgery plus adjuvant endocrine therapy. Unmatched overall survival and breast-cancer-specific survival were higher in the surgery plus adjuvant endocrine therapy group (overall survival: hazard ratio 0.27, 95% confidence interval 0.23 to 0.33; p < 0.001; breast-cancer-specific survival: hazard ratio 0.41, 95% confidence interval 0.29 to 0.58; p < 0.001) than in the endocrine therapy alone group. In matched analysis, surgery plus adjuvant endocrine therapy was still associated with better overall survival (hazard ratio 0.72, 95% confidence interval 0.53 to 0.98; p = 0.04) than endocrine therapy alone, but not with better breast-cancer-specific survival (hazard ratio 0.74, 95% confidence interval 0.40 to 1.37; p = 0.34) or progression-free-survival (hazard ratio 1.11, 95% confidence interval 0.55 to 2.26; p = 0.78). (b) The adjuvant chemotherapy versus no chemotherapy comparison: 2811 out of 3416 (82%) women received surgery plus adjuvant endocrine therapy, of whom 1520 (54%) had high-recurrence-risk breast cancer [grade 3, node positive, oestrogen receptor negative or human epidermal growth factor receptor-2 positive, or a high Oncotype DX® (Genomic Health, Inc., Redwood City, CA, USA) score of > 25]. In this high-risk population, there were no differences according to adjuvant chemotherapy use in overall survival or breast-cancer-specific survival after propensity matching. Adjuvant chemotherapy was associated with a lower risk of metastatic recurrence than no chemotherapy in the unmatched (adjusted hazard ratio 0.36, 95% confidence interval 0.19 to 0.68; p = 0.002) and propensity-matched patients (adjusted hazard ratio 0.43, 95% confidence interval 0.20 to 0.92; p = 0.03). Adjuvant chemotherapy improved the overall survival and breast-cancer-specific survival of patients with oestrogen-receptor-negative disease. (2) Mixed-methods research to develop a decision support intervention: an iterative process was used to develop two decision support interventions (each comprising a brief decision aid, a booklet and an online tool) specifically for older women facing treatment choices (endocrine therapy alone or surgery plus adjuvant endocrine therapy, and adjuvant chemotherapy or no chemotherapy) using several evidence sources (expert opinion, literature and patient interviews). The online tool was based on models developed using registry data from 23,842 patients and validated on an external data set of 14,526 patients. Mortality rates at 2 and 5 years differed by < 1% between predicted and observed values. (3) Cluster-randomised clinical trial of decision support tools: 46 UK breast units were randomised (intervention, n = 21; usual care, n = 25), recruiting 1339 women (intervention, n = 670; usual care, n = 669). There was no significant difference in global quality of life at 6 months post baseline (difference –0.20, 95% confidence interval –2.7 to 2.3; p = 0.90). In women offered a choice of endocrine therapy alone or surgery plus adjuvant endocrine therapy, knowledge about treatments was greater in the intervention arm than the usual care arm (94% vs. 74%; p = 0.003). Treatment choice was altered, with higher rates of endocrine therapy alone than of surgery in the intervention arm. Similarly, chemotherapy rates were lower in the intervention arm (endocrine therapy alone rate: intervention sites 21% vs. usual-care sites 15%, difference 5.5%, 95% confidence interval 1.1% to 10.0%; p = 0.02; adjuvant chemotherapy rate: intervention sites 10% vs. usual-care site 15%, difference 4.5%, 95% confidence interval 0.0% to 8.0%; p = 0.013). Survival was similar in both arms. (4) Health economic analysis: a probabilistic economic model was developed using registry and cohort study data. For most health and fitness strata, surgery plus adjuvant endocrine therapy had lower costs and returned more quality-adjusted life-years than endocrine therapy alone. However, for some women aged > 90 years, surgery plus adjuvant endocrine therapy was no longer cost-effective and generated fewer quality-adjusted life-years than endocrine therapy alone. The incremental benefit of surgery plus adjuvant endocrine therapy reduced with age and comorbidities. (5) Variation in practice: analysis of rates of surgery plus adjuvant endocrine therapy or endocrine therapy alone between the 56 breast units in the cohort study demonstrated significant variation in rates of endocrine therapy alone that persisted after adjustment for age, fitness and stage. Clinician preference was an important determinant of treatment choice.

Conclusions

This study demonstrates that, for older women with oestrogen-receptor-positive breast cancer, there is a cohort of women with a life expectancy of < 4 years for whom surgery plus adjuvant endocrine therapy may offer little benefit and simply have a negative impact on quality of life. The Age Gap decision tool may help make this shared decision. Similarly, although adjuvant chemotherapy offers little benefit and has a negative impact on quality of life for the majority of older women with oestrogen-receptor-positive breast cancer, for women with oestrogen-receptor-negative breast cancer, adjuvant chemotherapy is beneficial. The negative impacts of adjuvant chemotherapy on quality of life, although significant, are transient. This implies that, for the majority of fitter women aged ≥ 70 years, standard care should be offered.

Limitations

As with any observational study, despite detailed propensity score matching, residual bias cannot be excluded. Follow-up was at median 52 months for the cohort analysis. Longer-term follow-up will be required to validate these findings owing to the slow time course of oestrogen-receptor-positive breast cancer.

Future work

The online algorithm is now available (URL: https://agegap.shef.ac.uk/; accessed May 2022). There are plans to validate the tool and incorprate quality-of-life and 10-year survival outcomes.

Trial registration

This trial is registered as ISRCTN46099296.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 10, No. 6. See the NIHR Journals Library website for further project information.

Initiation of drug and alcohol use and personality development during adolescence

Personality traits predict both the initiation and continued usage of alcohol and drugs. Less established is if substance use is associated with subsequent changes in personality, especially during the sensitive period of adolescence. We used three approaches to disentangle selection and socialization effects to address whether substance use is associated with personality development (impulsivity, sensation-seeking, depression, self-esteem). First, we used a multi-wave longitudinal sample of adolescents ( N = 8,303) from the National Longitudinal Survey of Youth - Child and Young Adult dataset to study the first use of several substances. Second, we used propensity score weighting to equate users and abstainers on a range of background variables. Third, we investigated changes before, during, and after initiation of substances. Overall, there was unique variability and effects in personality across time for average levels, trajectories, and magnitudes of change both between users and abstainers as well as within users of specific substances. Results suggest that initiation of substance use is associated with changes in personality; the specifics of which are largely contingent upon the substance being used. Impulsivity and sensation-seeking were the traits associated with the most change while cocaine and cigarettes were the substances associated with the greatest changes.

Intention to Inform Relatives, Rates of Cascade Testing, and Preference for Patient-Mediated Communication in Families Concerned with Hereditary Breast and Ovarian Cancer and Lynch Syndrome: The Swiss CASCADE Cohort

Cascade screening for Tier 1 cancer genetic conditions is a significant public health intervention because it identifies untested relatives of individuals known to carry pathogenic variants associated with hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS). The Swiss CASCADE is a family-based, open-ended cohort, including carriers of HBOC- and LS-associated pathogenic variants and their relatives. This paper describes rates of cascade screening in relatives from HBOC- and LS- harboring families, examines carriers' preferences for communication of testing results, and describes theory-based predictors of intention to invite relatives to a cascade screening program. Information has been provided by 304 index cases and 115 relatives recruited from September 2017 to December 2021. On average, 10 relatives per index case were potentially eligible for cascade screening. Approximately 65% of respondents wanted to invite relatives to the cohort, and approximately 50% indicated a preference for patient-mediated communication of testing results, possibly with the assistance of digital technology. Intention to invite relatives was higher for first- compared to second- and third-degree relatives, but was not different between syndromes or based on relatives' gender. The family environment and carrying pathogenic variants predicts intention to invite relatives. Information helps optimize delivery of tailored genetic services.

A Survey on Multi-View Clustering

Clustering is a machine learning paradigm of dividing sample subjects into a number of groups such that subjects in the same groups are more similar to those in other groups. With advances in information acquisition technologies, samples can frequently be viewed from different angles or in different modalities, generating multi-view data. Multi-view clustering, that clusters subjects into subgroups using multi-view data, has attracted more and more attentions. Although MVC methods have been developed rapidly, there has not been enough survey to summarize and analyze the current progress. Therefore, we propose a novel taxonomy of the MVC approaches. Similar to other machine learning methods, we categorize them into generative and discriminative classes. In discriminative class, based on the way of view integration, we split it further into five groups: Common Eigenvector Matrix, Common Coefficient Matrix, Common Indicator Matrix, Direct Combination and Combination After Projection. Furthermore, we relate MVC to other topics: multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated for practitioners. Some benchmark multi-view datasets are introduced and representative MVC algorithms from each group are empirically evaluated to analyze how they perform on benchmark datasets. To promote future development of MVC approaches, we point out several open problems that may require further investigation and thorough examination.

A Survival Prediction for Acute Heart Failure Patients via Web-Based Dynamic Nomogram with Internal Validation: A Prospective Cohort Study

Purpose

The current study aimed to develop a convenient and accurate prognostic dynamic nomogram model for the risk of all-cause death in acute heart failure (AHF) patients that incorporates clinical characteristics including N-terminal pro-brain natriuretic peptide (NT-pro BNP) and growth stimulation expresses gene 2 protein (ST2).

Patients and Methods

We prospectively studied 537 consecutive AHF patients and derived a clinical prediction model. The least absolute shrinkage and selection operator regression model combined with clinical characteristics were used for dimensional reduction and feature selection. Multivariate Cox proportional hazard analysis and “Dynnom” package were used to build the dynamic nomogram for prediction of 1-,2-,and 5-year overall survival for AHF. With bootstrap validation, the time-dependent concordance index (C-index) and calibration curves were used to assess predictive discrimination and accuracy. The contributions of NT-pro BNP and ST2 to the nomogram were evaluated using integrated discrimination improvement (IDI) and net reclassification improvement (NRI), while decision curve analysis (DCA) was used to assess clinical value.

Results

Patients were randomly divided into derivation (74.9%, n=402) and validation (25.1%, n=135) cohorts. Optimal independent prognostic factors for 1-,2-, and 5-year all-cause mortality were BS-ACMR (B: NT-pro BNP; S: ST2; A: age; C: complete right bundle branch block; M: mean arterial pressure; and R: red cell distribution width >14.5%); these were incorporated into the dynamic nomogram (https://bs-acmr-nom.shinyapps.io/dynnomapp/) with bootstrap validation. The C-indexes in the derivation (0.793) and validation (0.782) cohorts were consistent with comparable performance parameters. The calibration curve showed good agreement between the nomogram-predicted and actual survival. Adding NT-pro BNP and ST2 provided a significant net benefit and improved performance over other less adequate schemes in terms of DCA of survival probability compared to those neglecting either of these two factors.

Conclusion

The study constructed a dynamic BS-ACMR nomogram, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis in AHF patients.

Albumin levels predict mortality in sepsis patients with acute kidney injury undergoing continuous renal replacement therapy: a secondary analysis based on a retrospective cohort study

BACKGROUND

Albumin (ALB) levels are negatively associated with mortality in patients with sepsis. However, among sepsis patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), there has been no similar study on the correlation between ALB levels and mortality alone. This study tested the hypothesis that ALB levels are negatively associated with mortality among such patients.

METHODS

We conducted a secondary analysis of 794 septic patients who were diagnosed with AKI and underwent CRRT in South Korea. For the Kaplan-Meier survival analysis, Cox proportional hazards models were used to study the hypotheses, with adjustments for the pertinent covariables. We also explore the possible nonlinear relationship and conducted sensitivity analyses including subgroup analyses and tests for interactions to investigate the association further. Additionally, ALB was used to construct model and we then compared the performance of ALB with that of APACHE II and SOFA in predicting mortality.

RESULTS

The ALB level was an independent prognostic factor for death at 28 and 90 days after CRRT initiation (HR = 0.75, 95% CI: 0.62-0.90, P = 0.0024 for death at 28 days and HR = 0.73, 95% CI: 0.63-0.86, P < 0.0001 for death at 90 days). A nonlinear association was not identified between ALB levels and the endpoints. Subgroup analyses and tests for interactions indicated that HCO₃ and CRP played an interactive role in the association. ROC analysis indicated ALB, SOFA and APACHE-II were separately inadequate for clinical applications.

CONCLUSION

A 1 g/dL increase in ALB levels was independently associated with a 25 and 27% decrease in the risk of death at 28 and 90 days, respectively. However, this conclusion needs to be taken with caution as this study has several limitations.

Can Industrial Restructuring Improve Urban Air Quality?—A Quasi-Experiment in Beijing during the COVID-19 Pandemic

The conflict between economic growth and environmental pollution has become a considerable bottleneck to future development throughout the world. The industrial structure may become the possible key factor in resolving the contradiction. Using the daily data of air quality from January to April in 2019 and 2020, we used the DID model to identify the effects of industrial structure on air quality by taking the COVID-19 pandemic as a quasi-experiment. The results show that, first, the impact of profit of the secondary industry on air quality is ten times higher than that of the tertiary industry. Therefore, the secondary industry is the main factor causing air pollution. Second, the effect of the reduction in the secondary industry on the improvement of air quality is better than that of the tertiary industry in Beijing. Therefore, the implementation of Beijing’s non-capital function relief policy is timely and reasonable, and the adjustment of the industrial structure is effective in the improvement of air quality. Third, PM2.5, NO2, and CO are affected by the secondary and tertiary industries, where PM2.5 is affected most seriously by the second industry. Therefore, the transformation from the secondary industry to the tertiary industry can not only solve the problem of unemployment but also relieve the haze. Fourth, the result of O3 is in opposition to other pollutants. The probable reason is that the decrease of PM2.5 would lead to an increase in the O3 concentration. Therefore, it is difficult to reduce O3 concentrationby production limitation and it is urgent to formulate scientific methods to deal with O3 pollution. Fifth, the air quality in the surrounding areas can also influence Beijing. As Hebei is a key area to undertake Beijing’s industry, the deterioration of its air quality would also bring pressure to Beijing’s atmospheric environment. Therefore, in the process of industrial adjustment, the selection of appropriate regions for undertaking industries is very essential, which is worth our further discussion.

Fermentable Oligo-, Di-, and Mono-Saccharides and Polyols (FODMAPs) Consumption and Irritable Bowel Syndrome in the French NutriNet-Santé Cohort

(1) Background: Specific foods, and more particularly, fermentable oligo-, di-, and mono-saccharides and polyols (FODMAPs) are often considered as triggers of digestive symptoms in Irritable Bowel Syndrome (IBS). Our aim was to study FODMAP consumption in controls and IBS participants in a large French population-based cohort; (2) Methods: Participants from the NutriNet-Santé cohort study completed the Rome IV and IBS-SSS questionnaire in a cross sectional study. Among them, 27,949 eligible participants had previously completed three 24-h recalls as well as anthropometrics, socio-demographical and lifestyle data. Total FODMAP intake (in g/day) was computed using a specific composition table. The association between FODMAPs and IBS was estimated through multivariable logistic regression models; (3) Results: Included participants were mainly women (75.4%) and the mean age was 43.4 ± 14.1 years. FODMAPs accounted for a mean daily intake of 19.4 ± 9.5 g/day. Overall 1295 participants (4.6%) were identified with an IBS. After adjusting for confounding factors, IBS participants had lower intakes in FODMAPs than non-IBS ones (aOR: 0.88, 95% CI: 0.82–0.95, p-value: 0.001). IBS severity was associated with more frequent low FODMAP intakes (<9 g/day); (4) Conclusions: Participants tended to consume 19 g of FODMAPs per day, but slightly less for IBS participants than for controls. In IBS participants, higher severity was associated with lower intakes.

Does the joint prevention and control regulation improve the air quality? A quasi-experiment in the Beijing economic belt during the COVID-19 pandemic

This study aims to clarify the correlation between air pollution of cities in Beijing Economic Belt from a time-varying perspective and estimate effects of joint prevention and control regulation of air pollution. The COVID-19 pandemic provides a unique opportunity. Based on daily data of air quality, we used TVP-VAR model and utilize the pandemic as a quasi-experiment to assess the policies. The results show air pollution in surrounding cities does influence Beijing's air quality, but the relationship has been weakening year by year, mainly due to industrial adjustment which have achieved progress on alleviating the path of air pollution. Therefore, it is necessary to implement joint regulation in areas with serious pollution. Specifically, the relationship between the air quality of Beijing and Zhangjiakou, Chengde, Tianjin decreased as the pandemic became worse. In contrast, there was no significant decline in Langfang and Baoding. So unlike Baoding and Langfang, industrial production increased relationships between air quality of Beijing and the other three cities, which highlights the validity of restrictions. However, restrictions implemented on Baoding and Langfang affect economic development but have little effect on Beijing's air governance. Therefore, joint regulation contributes to realizing sustainable cities, but more targeted policies should be formulated.

Factors Associated With Childhood Asthma and Wheeze in Chinese Preschool-Aged Children

This study was prepared to identify and characterize potential factors associated with childhood asthma and wheeze in Chinese preschool-aged children. A comprehensive questionnaire was designed for children aged 3–6 years and their parents or guardians in Beijing and Tangshan from September to December 2020. The least absolute shrinkage and selection operator (LASSO) model was used to identify factors in a significant association with childhood asthma and wheeze, respectively. The LASSO model was internally validated using bootstrap resampling with 100 replications. A total of 9,529 questionnaires were certified as eligible for inclusion after stringent quality control. The prevalence of doctor-diagnosed childhood asthma and parent-reported wheeze was 2.8 and 6.2%, respectively. Factors simultaneously associated with childhood asthma and wheeze were children with a history of allergic rhinitis, hay fever, eczema, initial age of using antibiotics, body mass index category, and family history of asthma. Specifically, children's vitamin D supplement duration was significantly associated with childhood asthma, whereas the association with childhood wheeze was significant for intake frequency of night meals for children and their screen time. Modeling of significant factors in nomograms had decent prediction accuracies, with C-index reaching 0.728 and 0.707 for asthma and wheeze, respectively. In addition, internal validation was good, with bootstrap C-statistic of being 0.736 for asthma and 0.708 for wheeze. Taken together, our findings indicated that the development of asthma and wheeze among preschool-aged children was probably determined by the joint contribution of multiple factors including inherited, nutritional, unhealthy lifestyles, and history of allergic disease. Further validation in other groups is necessary.

Evaluation of different approaches for missing data imputation on features associated to genomic data

Background

Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis. The data collection process can lead to different distribution, frequency, and structure of missing data points. They can be classified into four categories: Structurally Missing Data (SMD), Missing Completely At Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR). For the three later, and in the context of genomic data (especially non-coding data), we will discuss six imputation approaches using 31,245 variants collected from ClinVar and annotated with 13 genome-wide features.

Results

Random Forest and kNN algorithms showed the best performance in the evaluated dataset. Additionally, some features show robust imputation regardless of the algorithm (e.g. conservation scores phyloP7 and phyloP20), while other features show poor imputation across algorithms (e.g. PhasCons). We also developed an R package that helps to test which imputation method is the best for a particular data set.

Conclusions

We found that Random Forest and kNN are the best imputation method for genomics data, including non-coding variants. Since Random Forest is computationally more challenging, kNN remains a more realistic approach. Future work on variant prioritization thru genomic screening tests could largely profit from this methodology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00274-7.

Methods for interpolating missing data in aerobiological databases

Missing data is a common problem in scientific research. The availability of extensive environmental time series is usually laborious and difficult, and sometimes unexpected failures are not detected until samples are processed. Consequently, environmental databases frequently have some gaps with missing data in it. Applying an interpolation method before starting the data analysis can be a good solution in order to complete this missing information. Nevertheless, there are several different approaches whose accuracy should be considered and compared. In this study, data from 6 aerobiological sampling stations were used as an example of environmental data series to assess the accuracy of different interpolation methods. For that, observed daily pollen/spore concentration data series were randomly removed, interpolated by using different methods and then, compared with the observed data to measure the errors produced. Different periods, gap sizes, interpolation methods and bioaerosols were considered in order to check their influence in the interpolation accuracy. The moving mean interpolation method obtained the highest success rate as average. By using this method, a success rate of the 70% was obtained when the risk classes used in the alert systems of the pollen information platforms were taken into account. In general, errors were mostly greater when there were high oscillations in the concentrations of biotic particles during consecutive days. That is the reason why the pre-peak and peak periods showed the highest interpolation errors. The errors were also higher when gaps longer than 5 days were considered. So, for completing long periods of missing data, it would be advisable to test other methodological approaches. A new Variation Index based on the behaviour of the pollen/spore season (measurement of the variability of the concentrations every 2 consecutive days) was elaborated, which allows to estimate the potential error before the interpolation is applied.

Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence

BACKGROUND

Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).

METHODS

We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes.

RESULTS

Our findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts.

CONCLUSIONS

AI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs.

CLINICAL IMPACT

This study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.

Planned missing data designs and methods: Options for strengthening inference, increasing research efficiency and improving animal welfare in ecological and evolutionary research

Ecological and evolutionary research questions are increasingly requiring the integration of research fields along with larger data sets to address fundamental local- and global-scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns. Planned missing data design (PMDD), where data are randomly and deliberately missed during data collection, combined with missing data procedures, can be useful tools when working under greater research constraints. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and demonstrate with simulated data sets how missing data procedures work with incomplete data. PMDDs can provide researchers with a unique toolkit that can be applied during the experimental design stage. Planning and thinking about missing data early can (1) reduce research costs by allowing for the collection of less expensive measurement variables; (2) provide opportunities to distinguish predictions from alternative hypotheses by allowing more measurement variables to be collected; and (3) minimize distress caused by experimentation by reducing the reliance on invasive procedures or allowing data to be collected on fewer subjects (or less often on a given subject). PMDDs and missing data methods can even provide statistical benefits under certain situations by improving statistical power relative to a complete case design. The impacts of unplanned missing data, which can cause biases in parameter estimates and their uncertainty, can also be ameliorated using missing data procedures. PMDDs are still in their infancy. We discuss some of the difficulties in their implementation and provide tentative solutions. While PMDDs may not always be the best option, missing data procedures are becoming more sophisticated and more easily implemented and it is likely that PMDDs will be effective tools for a wide range of experimental designs, data types and problems in ecology and evolution.

Hb Levels and Sex Differences in Relation to Short-Term Outcomes in Patients With Acute Myocardial Infarction

Background: Women had worse outcomes after acute myocardial infarction (AMI), and physiologically, women had lower hemoglobin values. We examined whether there were sex-related differences in the relationship between hemoglobin levels and adverse outcomes in patients with acute myocardial infarction.

Method: We conducted a post-hoc analysis of data from the Acute Coronary Syndrome Quality Improvement in Kerala (ACS-QUIK) Study. We explored the relationship between baseline hemoglobin level and 30-days adverse outcomes by logistic regression model, generalized additive model (GAM) and two-piecewise linear regression model. We used multiple imputation, based on five replications and a chained equation approach method in the R multiple imputation procedure, to account for missing data. The primary outcome were 30-day major adverse cardiovascular events (MACEs) defined as death, reinfarction, stroke, and major bleeding. The secondary outcomes were 30-day major bleeding, 30-day stroke and 30-day cardiovascular death (CVD death).

Results: Twenty thousand, five hundred fifty-nine patients with AMI were included in our analysis. Baseline hemoglobin level was associated with major bleeding [OR: 0.74, 95%CI (0.60, 0.92) P < 0.01], CVD death [OR: 0.94, 95%CI (0.90, 0.99) P < 0.01], and MACEs [OR: 0.95, 95%CI (0.92, 0.99) P < 0.01]. There was no significant relationship between baseline hemoglobin level and stroke incidence in both men [OR: 1.02, 95%CI (0.90, 1.14) P = 0.77] and women [OR: 1.15, 95%CI (0.96, 1.37) P = 0.18]. Baseline hemoglobin level was associated with major bleeding [OR: 0.71, 95%CI (0.58, 0.85) P < 0.01] in male patients, however we did not find the same relationship in female patients [OR: 0.89, 95%CI (0.56, 1.41) P = 0.61]. GAM and two-piecewise linear regression model showed the relationships of hemoglobin level with major bleeding, CVD death, and MACEs were non-linear (non-linear P < 0.05), and the threshold value were 13, 14.8, and 14.3 g/dL for MACEs and CVD death, respectively.

Conclusion: Baseline hemoglobin level was one of the independent predictors of prognosis in South Asia patients with acute myocardial infarction. Moreover, its impact on prognosis was largely different depending on the patients' sex.

Development and internal validation of clinical prediction models for outcomes of complicated intra-abdominal infection

BACKGROUND

Complicated intra-abdominal infections (cIAIs) are associated with significant morbidity and mortality. The aim of this study was to describe the clinical characteristics of patients with cIAI in a multicentre study and to develop clinical prediction models (CPMs) to help identify patients at risk of mortality or relapse.

METHODS

A multicentre observational study was conducted from August 2016 to February 2017 in the UK. Adult patients diagnosed with cIAI were included. Multivariable logistic regression was performed to develop CPMs for mortality and cIAI relapse. The c-statistic was used to test model discrimination. Model calibration was tested using calibration slopes and calibration in the large (CITL). The CPMs were then presented as point scoring systems and validated further.

RESULTS

Overall, 417 patients from 31 surgical centres were included in the analysis. At 90 days after diagnosis, 17.3 per cent had a cIAI relapse and the mortality rate was 11.3 per cent. Predictors in the mortality model were age, cIAI aetiology, presence of a perforated viscus and source control procedure. Predictors of cIAI relapse included the presence of collections, outcome of initial management, and duration of antibiotic treatment. The c-statistic adjusted for model optimism was 0.79 (95 per cent c.i. 0.75 to 0.87) and 0.74 (0.73 to 0.85) for mortality and cIAI relapse CPMs. Adjusted calibration slopes were 0.88 (95 per cent c.i. 0.76 to 0.90) for the mortality model and 0.91 (0.88 to 0.94) for the relapse model; CITL was -0.19 (95 per cent c.i. -0.39 to -0.12) and - 0.01 (- 0.17 to -0.03) respectively.

CONCLUSION

Relapse of infection and death after complicated intra-abdominal infections are common. Clinical prediction models were developed to identify patients at increased risk of relapse or death after treatment, these now require external validation.