Introduction to multiple imputation for dealing with missing data

Socio-economic disparities in the psychosocial and economic impacts of the COVID-19 pandemic on children and young people in Australia

AIMS

While COVID-19 no longer presents a global health emergency, the indirect (non-infection) impacts of the pandemic may exacerbate health inequalities in years to come. We examined the socio-economic distribution of the impacts of the pandemic on the psychosocial and economic well-being of children, young people and their families.

METHODS

The CoRonavIruS Health Impact Survey (CRISIS) was conducted in seven existing longitudinal cohorts, together involving n = 3072 participants aged 0-19 years. Online surveys were conducted from May 2020-April 2021, during periods of low infection but stringent public health measures in Victoria, Australia. Analysed CRISIS domains included: COVID-19-related worries, mood state, economic difficulties and positive life changes. Social disadvantage of each sample was characterised by pre-pandemic maternal education, unemployment, income and neighbourhood disadvantage, avoiding cross-sectional data limitations. The proportion of children experiencing each type of pandemic impact was estimated within each cohort.

RESULTS

Substantial socio-economic disparities were observed. COVID-19-related worries were 22 percentage points higher in the most (24.2%, 95% confidence interval (CI) 19.4-29.1) versus least (2.9%, 95% CI 1-4.7) disadvantaged cohorts. Similar patterns were noted for negative mood state (19.4%, 95% CI 15-23.9 vs. 3.6%, 95% CI 1.6-5.6), economic difficulties (18.5%, 95% CI 14.2-22.9 vs. 5.7%, 95% CI 3.3-8.1) and lower levels of positive life changes (63.7%, 95% CI 58.3-69.1 vs. 83.2%, 95% CI 78.9-87.4).

CONCLUSIONS

Indirect consequences of the COVID-19 pandemic showed significant disparities across populations experiencing different socio-economic conditions. The pandemic offers a unique opportunity to rethink and redesign policies and services to achieve greater equity now and better preparedness for global crises in future.

Association Between Albuminuria and Glomerular Filtration Rate With Incident Frailty

Introduction

The association between estimated glomerular filtration rate (eGFR) and albuminuria with incident frailty in generally healthy older individuals is unclear. We investigated whether baseline eGFR or urine albumin-to-creatinine ratio (UACR) are associated with incident frailty and assessed them using 2 separate methods: a modified Fried frailty phenotype (FP), and a deficit accumulation frailty index (FI).

Methods

This was a secondary analysis of the ASPirin in Reducing Events in the Elderly (ASPREE) randomized trial cohort, which enrolled independent, healthy, older adults aged ≥65 years in Australia and the USA. Time to incident frailty was analyzed using discrete time survival analysis.

Results

In total, 16,965 participants were included using the FP, mean age was 75.0 ± 4.5 years, median baseline eGFR was 78.6 ml/min per 1.73 m2 (interquartile range [IQR]: 67.6-89.5), and the median UACR was 0.80 mg/mmol (0.50-1.50). Data to generate the FI outcomes were available for 12,272 participants. The relationships between eGFR and both incident FP and FI were nonlinear, such that an eGFR < 30 or ≥ 95 ml/min per 1.73 m2 was significantly associated with an increased risk of incident frailty. For every doubling of baseline UACR, risk of incident frailty increased by 4% using the FP (hazard ratio [HR]: 1.04, 95% CI: 1.02, 1.07) and the FI (HR: 1.04, 95% confidence interval [CI]: 1.01-1.07).

Conclusion

In older, generally healthy adults, both low and high eGFRs were associated with increased risk of incident frailty. Doubling of UACR, even at low levels, was independently associated with incident frailty.

Study on influencing factors of age-adjusted Charlson comorbidity index in patients with Alzheimer's disease based on machine learning model

Background

Alzheimer's disease (AD) is a widespread neurodegenerative disease, often accompanied by multiple comorbidities, significantly increasing the risk of death for patients. The age adjusted Charlson Comorbidity Index (aCCI) is an important clinical tool for measuring the burden of comorbidities in patients, closely related to mortality and prognosis. This study aims to use the MIMIC-V database and various regression and machine learning models to screen and validate features closely related to aCCI, providing a theoretical basis for personalized management of AD patients.

Methods

The research data is sourced from the MIMIC-V database, which contains detailed clinical information of AD patients. Multiple logistic regression, LASSO regression, random forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models were used to screen for feature factors significantly correlated with aCCI. By comparing model performance, evaluating the classification ability and prediction accuracy of each method, and ultimately selecting the best model to construct a regression model and a nomogram. The model performance is evaluated through classification accuracy, net benefit, and robustness. The feature selection results were validated by regression analysis.

Results

Multiple models have performed well in classifying aCCI patients, among which the model constructed using LASSO regression screening feature factors has the best performance, with the highest classification accuracy and net benefit. LASSO regression identified the following 11 features closely related to aCCI: age, respiratory rate, base excess, glucose, red blood cell distribution width (RDW), alkaline phosphatase (ALP), whole blood potassium, hematocrit (HCT), phosphate, creatinine, and mean corpuscular hemoglobin (MCH). The column chart constructed based on these feature factors enables intuitive prediction of patients with high aCCI probability, providing a convenient clinical tool.

Conclusion

The results of this study indicate that the features screened by LASSO regression have the best predictive performance and can significantly improve the predictive ability of aCCI related comorbidities in AD patients. The column chart constructed based on this feature factor provides theoretical guidance for personalized management and precise treatment of AD patients.

Machine learning for predicting acute myocardial infarction in patients with sepsis

Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in intensive care units. While sepsis frequently affects the cardiovascular system, distinguishing between sepsis-induced cardiomyopathy and AMI remains challenging due to overlapping biomarkers. Misdiagnosis can hinder timely treatment and increase risk of complications. This study used multidimensional clinical data and machine learning techniques to develop and validate a novel predictive model for identifying AMI in critically ill patients with sepsis. Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-IV database. Six machine learning algorithms were employed for model construction. Additionally, the machine learning-based models were compared with traditional scoring systems. Model performance was evaluated in terms of discrimination, calibration, and clinical applicability. In total, 2,103 critically ill patients with sepsis were included, 459 (21.8%) of whom experienced AMI during hospitalization. A total of 26 variables were selected for model construction. Among all models, the Gradient Boosting Classifier model demonstrated the best predictive performance in terms of discrimination, calibration, and clinical applicability. Machine learning models have the potential to serve as tools for predicting AMI in patients with sepsis. The Gradient Boosting Classifier model developed herein demonstrated promising predictive performance, supporting clinicians in identifying patients at high-risk of sepsis and implementing early interventions to reduce mortality rates.

Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database

BACKGROUND

The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict the mortality of AHF patients.

METHODS

The Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-Ⅳ database and randomly divided into a training set (n = 3,580, 70%) and a validation set (n = 1,534, 30%). The variates collected include demographic data, vital signs, comorbidities, laboratory test results, and treatment information within 24 hours of ICU admission. By using the least absolute shrinkage and selection operator (LASSO) regression model in the training set, variates that affect the in-hospital mortality of AHF patients were screened. Subsequently, in the training set, five common machine learning (ML) algorithms were applied to construct models using variates selected by LASSO to predict the in-hospital mortality of AHF patients. The predictive ability of the models was evaluated for sensitivity, specificity, accuracy, the area under the curve of receiver operating characteristics, and clinical net benefit in the validation set. To obtain a model with the best predictive ability, the predictive ability of common scoring systems was compared with the best ML model.

RESULTS

Among the 5,114 patients, in-hospital mortality was 12.5%. Comparing the area under the curve, the XGBoost model had the best predictive ability among all ML models, and the XGBoost model was chosen as the final model for its higher net benefit. Its predictive ability was superior to common scoring systems.

CONCLUSIONS

The XGBoost model can effectively predict the in-hospital mortality of AHF patients admitted to the ICU, which may assist clinicians in precise management and early intervention for patients with AHF to reduce mortality.

Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury

OBJECTIVES

This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms.

METHODS

Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP).

RESULTS

A total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774-0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis.

CONCLUSIONS

The RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.

Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach

The diagnosis of abdominal pain in emergency departments is challenging, and appendicitis is a common concern. Atypical symptoms often delay diagnosis. Although the Alvarado score aids in decision-making, its low specificity can lead to unnecessary surgeries. By leveraging machine learning, we aim to enhance diagnostic accuracy by predicting appendicitis and distinguishing it from other causes of abdominal pain in the emergency department. Data were collected from 534 patients who presented with acute abdominal pain. Patient characteristics, laboratory results, and causes of pain were recorded. Machine learning algorithms (support vector classifier, random forest classifier, gradient boosting classifier, and Gaussian naive Bayes) were used to predict the cause of pain. Model calibration was assessed using the Brier score. The mean age was 46.89 (20.3) years, with an almost equal sex distribution (49% male, 51% female). Cholecystitis was the most prevalent outcome (37.07%), followed by appendicitis (25.84%). The Gaussian naive Bayes model exhibited superior performance in terms of accuracy (95.03% 95% CI 90.44-97.83%), sensitivity (87.18% 95% CI 72.57-95.70%), and specificity (97.54% 95% CI 92.98-99.49%), while the random forest model showed a sensitivity of 79.49%, specificity of 96.72%, and accuracy of 92.55%. The gradient boosting algorithm achieved a sensitivity, specificity, and accuracy of 89.74%, 95.90%, and 94.41%, respectively. The support vector classifier demonstrated a sensitivity of 89.74%, specificity of 92.62%, and accuracy of 91.93%. The use of modern machine learning methods aids in the accurate diagnosis of appendicitis.

Six-month outcomes after traumatic brain injury in the Augmented versus Routine Approach to Giving Energy multicentre, double-blind, randomised controlled Trial (TARGET)

BACKGROUND

Critically ill patients with a traumatic brain injury (TBI) may require prolonged intensive care unit (ICU) admission and hence receive greater exposure to hospital enteral nutrition. It is unknown if augmented energy delivery with enteral nutrition during ICU admission impacts quality of life in survivors or gastrointestinal tolerance during nutrition delivery in the ICU.

OBJECTIVES

The objective of this study was to compare health-related quality of life, using the EuroQol five-dimensions five-level visual analogue scale at 6 months, in survivors who presented with a TBI and received augmented energy (1.5 kcal/ml) to those who received routine energy (1.0 kcal/ml). Secondary objectives were to explore differences in total energy and protein delivery, gastrointestinal tolerance, and mortality between groups.

METHODS

Secondary analysis of participants admitted with a TBI in the Augmented versus Routine Approach to Giving Energy Trial (TARGET) randomised controlled trial. Data are represented as n (%) or median (interquartile range).

RESULTS

Of the 3957 patients in TARGET, 231 (5.8%) were admitted after a TBI (augmented = 124; routine = 107). Patients within TARGET who were admitted with a TBI were relatively young (42 [27, 61] years) and received TARGET enteral nutrition for an extended period (9 [5, 15] days). At 6 months, EuroQol five-dimensions five-level quality-of-life scores were available for 166 TBI survivors (72% of TBI cohort randomised, augmented = 97, routine = 69). There was no evidence of a difference in quality of life (augmented = 70 [52, 90]; routine = 70 [55, 85]; median difference augmented vs routine = 0 [95% confidence interval: -5, 10]). TBI participants assigned to augmented energy received more energy with a similar protein than the routine group. Gastrointestinal tolerance was similar between groups.

CONCLUSION

While patients admitted after a TBI received enteral nutrition for an extended period, an increased exposure to augmented energy did not affect survivors' quality-of-life scores.

Machine-learning-derived online prediction models of outcomes for patients with cholelithiasis-induced acute cholangitis: development and validation in two retrospective cohorts

Background

Cholelithiasis-induced acute cholangitis (CIAC) is an acute inflammatory disease with poor prognosis. This study aimed to create machine-learning (ML) models to predict the outcomes of patients with CIAC.

Methods

In this retrospective cohort and ML study, patients who met the both diagnosis of 'cholangitis' and 'calculus of gallbladder or bile duct' according to the International Classification of Disease (ICD) 9th revision, or met the diagnosis of 'calculus of bile duct with acute cholangitis with or without obstruction' according to the ICD 10th revision during a single hospitalization were included from the Medical Information Mart for Intensive Care database, which records patient admissions to Beth Israel Deaconess Medical Center, MA, USA, spanning June 1, 2001 to November 16, 2022. Patients who were neither admitted in an emergency department nor underwent biliary drainage within 24 h after admission, had an age of less than 18, or lost over 20% of the information were excluded. Nine ML methods, including the Logistic Regression, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Adaptive Boosting, Decision Tree, Gradient Boosting Decision Tree, Gaussian Naive Bayes, Multi-Layer Perceptron, and Support Vector Machine were applied for prediction of in-hospital mortality, re-admission within 30 days after discharge, and mortality within 180 days after discharge. Patients from Zhongda Hospital affiliated to Southeast University in China between January 1, 2019 and July 30, 2023 were enrolled as an external validation set. The area under the receiver operating characteristic curve (AUROC) was the main index for model performance assessment.

Findings

A total of 1156 patients were included to construct models. We performed stratified analyses on all patients, patients admitted to the intensive care unit (ICU) and those who underwent biliary drainage during ICU treatment. 13-16 features were selected from 186 variables for model training. The XGBoost method demonstrated the most optimal predictive efficacy, as evidenced by training set AUROC of 0.996 (95% CI NaN-NaN) for in-hospital mortality, 0.886 (0.862-0.910) for re-admission within 30 days after discharge, and 0.988 (0.982-0.995) for mortality within 180 days after discharge in all patients, 0.998 (NaN-NaN), 0.933 (0.909-0.957), and 0.988 (0.983-0.993) in patients admitted to the ICU, 0.987 (0.970-0.999), 0.908 (0.873-0.942), and 0.982 (0.971-0.993) in patients underwent biliary drainage during ICU treatment, respectively. Meanwhile, in the internal validation set, the AUROC reached 0.967 (0.933-0.998) for in-hospital mortality, 0.589 (0.502-0.677) for re-admission within 30 days after discharge, and 0.857 (0.782-0.933) for mortality within 180 days after discharge in all patients, 0.963 (NaN-NaN), 0.668 (0.486-0.851), and 0.864 (0.757-0.970) in patients admitted to the ICU, 0.961 (0.922-0.997), 0.669 (0.540-0.799), and 0.828 (0.730-0.925) in patients underwent biliary drainage during ICU treatment, respectively. The AUROC values of external validation set consisting of 61 patients were 0.741 (0.725-0.763), 0.812 (0.798-0.824), and 0.848 (0.841-0.859), respectively.

Interpretation

The XGBoost models could be promising tools to predict outcomes in patients with CIAC, and had good clinical applicability. Multi-center validation with a larger sample size is warranted.

Funding

The Technological Development Program of Nanjing Healthy Commission, and Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds.

Walking against the odds: The intricate connection between spatiotemporal characteristics, kinetic and kinematic variables, cognitive stress, and passive assistive exoskeletons in senior and young adults

In this study, we investigated the combined effects of age, dual-tasking (DT) and a passive hip exoskeleton on gait patterns among senior (SA) and young adults (YA). It was hypothesized that SA will be more affected by DT and that wearing the exoskeleton will improve gait patterns for both groups during DT. Twenty-two SA and twenty-six YA performed a single task (normal walking) and DT walking at their preferred speed with an exoskeleton (EXO), without (noEXO), and a sham version (SHAM) in a randomized and balanced order. Speed, cadence, double support time (DST), step length, hip joint power, range of motion (ROM), and moments (mom), as well as DT performance, were extracted using mocap, force plates (1000 Hz), and a voice recorder. Three-way MANOVA with group × device × condition was conducted (p < .05, inferred significance). Results showed a predominantly significant main effect of group for step length, speed, DST, ROM, and mom (p ≤ .01), main effect of condition for cadence, DST, speed, and mom (p < .01) and a main effect of the device for ROMz and mom (p < .05). Age-related changes were seen by decreased walking speed and step length, independent of DT and use of exoskeleton. Wearing the EXO aided the SA group to maintain similar levels of cadence from single to DT and decreased the hip internal rotation mom by 65%. There was no difference in DT performance between groups. In conclusion, SA showed a decline in gait patterns during DT that was somewhat mitigated by wearing an EXO.

Childhood intra- and extra-familial maltreatment and later-life trajectories of depressive symptoms: evidence from China

BACKGROUND

Both late-life depression and childhood maltreatment have become major global public health issues, given their prevalence and social-economic and health consequences. However, previous studies have solely focused on the relationship of childhood maltreatment to average levels of depressive symptoms. The current study addresses this gap of knowledge by simultaneously examining the impacts of childhood intra- and extra-familial maltreatment on age trajectories of depressive symptoms in later life in the Chinese context.

METHODS

Hierarchical linear models were applied to data from the China Health and Retirement Longitudinal Study (2011-2018, N = 12,669 individuals aged 45 to 80, comprising N = 43,348 person-years). Depressive symptoms were measured by the CES-D-10 scale. Childhood intra-familial maltreatments were measured by physical abuse and emotional neglect, while extra-familial maltreatment was measured by peer bullying. All analyses were conducted separately by gender in Stata 16.

RESULTS

Childhood extrafamilial peer bullying (β = 1.628, p < 0.001), and intrafamilial physical abuse (β = 0.746, p < 0.001) and emotional neglect (β = 0.880, p < 0.001) were associated with higher later-life depressive symptoms levels in the whole sample. Peer bullying differences in depressive symptoms widened with age for both men and women. Physical abuse differences in depressive symptoms remained stable over the life course among men but increased among women. Emotional neglect differences in depressive symptoms decreased with age among men, while it increased first and then decreased among women.

CONCLUSIONS

Findings in this study suggest that childhood maltreatment is not only associated with later-life poorer mental health but contributes to increasing inequalities in mental health as people age, especially among peer-bullying victims and women.

Use of harm reduction strategies by individuals with a history of incarceration: A short report using baseline data collected from the STAMINA clinical trial

BACKGROUND

Due to the considerably heightened risk of overdose immediately following jail or prison release, expansion of harm reduction interventions for citizens returning to the community after an incarceration episode should be of utmost concern. However, there are no studies examining the adoption and use of harm reduction among this population. This short report examines the use of individual-level harm reduction strategies (HRS) among people who use drugs, comparing those who have lifetime histories of incarceration with those who do not.

METHODS

This study included baseline data (N = 274) from a larger clinical trial, which recruited participants at two syringe service programs. Fisher's exact tests examine correlations between HRS and lifetime incarceration.

RESULTS

Significant variation in HRS use were observed by demographic characteristics. Those with lifetime incarceration histories were less likely to report ensuring naloxone was available when using, using when others were present, using fentanyl test strips, using a trusted drug supplier, and using some other HRS compared to those without lifetime incarceration histories. Similarly, those with lifetime incarceration histories were also less likely to report using non-prescribed buprenorphine and methadone, which could potentially reduce overdose risk even if not explicitly used as an HRS.

CONCLUSION

The results suggest that one of the populations at highest risk of overdose is least likely to use strategies that may reduce overdose risk. Targeted efforts to increase exposure to HRS learning opportunities and access to these resources could help reduce overdose disparities for people returning to the community after incarceration.

INTERPRETABLE MACHINE LEARNING FOR PREDICTING RISK OF INVASIVE FUNGAL INFECTION IN CRITICALLY ILL PATIENTS IN THE INTENSIVE CARE UNIT: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE

ABSTRACT

The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for patients. The objective of this study was to develop a machine learning-based predictive model for invasive fungal infection in patients during their intensive care unit (ICU) stay. Retrospective data was extracted from adult patients in the MIMIC-IV database who spent a minimum of 48 h in the ICU. Feature selection was performed using LASSO regression, and the dataset was balanced using the BL-SMOTE approach. Predictive models were built using six machine learning algorithms. The Shapley additive explanation algorithm was used to assess the impact of various clinical features in the optimal model, enhancing interpretability. The study included 26,346 ICU patients, of whom 379 (1.44%) were diagnosed with invasive fungal infection. The predictive model was developed using 20 risk factors, and the dataset was balanced using the borderline-SMOTE (BL-SMOTE) algorithm. The BL-SMOTE random forest model demonstrated the highest predictive performance (area under curve = 0.88, 95% CI = 0.84-0.91). Shapley additive explanation analysis revealed that the three most influential clinical features in the BL-SMOTE random forest model were dialysis treatment, APSIII scores, and liver disease. The machine learning model provides a reliable tool for predicting the occurrence of IFI in ICU patients. The BL-SMOTE random forest model, based on 20 risk factors, exhibited superior predictive performance and can assist clinicians in early assessment of IFI occurrence in ICU patients. Importance: Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.

The prognostic value of Eastern Cooperative Oncology Group performance status on overall survival among patients with metastatic prostate cancer: a systematic review and meta-analysis

Background

There is heterogeneity in the literature regarding the strength of association between Eastern Cooperative Oncology Group performance status (ECOG PS) and mortality. We conducted a systematic review and meta-analysis of studies reporting the prognostic value of ECOG PS on overall survival (OS) in metastatic prostate cancer (mPC).

Methods

PubMed was searched from inception to March 21, 2022. A meta-analysis pooling the effect of ECOG PS categories (≥2 vs. <2, 2 vs. <2, and ≥1 vs. <1) on OS was performed separately for studies including patients with metastatic castration-resistant prostate cancer (mCRPC) and metastatic castration-sensitive prostate cancer (mCSPC) using a random-effects model. Analyses were stratified by prior chemotherapy and study type.

Results

Overall, 75 studies, comprising 32,298 patients, were included. Most studies (72/75) included patients with mCRPC. Higher ECOG PS was associated with a significant increase in mortality risk, with the highest estimate observed among patients with mCRPC with an ECOG PS of ≥2 versus <2 (hazard ratio [HR]: 2.10, 95% confidence interval [CI]: 1.87-2.37). When stratifying by study type, there was a higher risk estimate of mortality among patients with mCRPC with an ECOG PS of ≥1 versus <1 in real-world data studies (HR: 1.98, 95% CI: 1.72-2.26) compared with clinical trials (HR: 1.32, 95% CI: 1.13-1.54; p < 0.001). There were no significant differences in the HR of OS stratified by previous chemotherapy.

Conclusion

ECOG PS was a significant predictor of OS regardless of category, previous chemotherapy, and mPC population. Additional studies are needed to better characterize the effect of ECOG PS on OS in mCSPC.

On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint

BACKGROUND

Case-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis when there is also unintended missingness. Multiple imputation (MI) has become a default standard for handling unintended missingness and is typically used in combination with IPW to handle the intended missingness due to the case-control sampling. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of an MI-only approach has been investigated in the context of a case-cohort study with a time-to-event outcome, it is unclear how this approach performs with a binary outcome.

METHODS

We conducted a simulation study to assess and compare the performance of approaches using only MI, only IPW, and a combination of MI and IPW, for handling intended and unintended missingness in the case-cohort setting. We also applied the approaches to a case study.

RESULTS

Our results show that the combined approach is approximately unbiased for estimation of the exposure effect when the sample size is large, and was the least biased with small sample sizes, while MI-only and IPW-only exhibited larger biases in both sample size settings.

CONCLUSIONS

These findings suggest that a combined MI/IPW approach should be preferred to handle intended and unintended missing data in case-cohort studies with binary outcomes.

Adolescent Social Communication Through Smartphones: Linguistic Features of Internalizing Symptoms and Daily Mood

The increasing use of smartphone technology by adolescents has led to unprecedented opportunities to identify early indicators of shifting mental health. This intensive longitudinal study examined the extent to which differences in mental health and daily mood are associated with digital social communication in adolescence. In a sample of 30 adolescents (ages 11-15 years), we analyzed 22,152 messages from social media, email, and texting across one month. Lower daily mood was associated with linguistic features reflecting self-focus and reduced temporal distance. Adolescents with lower daily mood tended to send fewer positive emotion words on a daily basis, and more total words on low mood days. Adolescents with lower daily mood and higher depression symptoms tended to use more future focus words. Dynamic linguistic features of digital social communication that relate to changes in mental states may represent a novel target for passive detection of risk and early intervention in adolescence.

Multivariate assessments of activity-related skeletal changes: Interpreting Bell Beaker specialized male archery and social organization in Central Europe

OBJECTIVES

The Bell Beaker period witnessed the rise of individual inhumations with "wealthy" burial contexts containing archery-related grave goods, leading archaeologists to label the individuals in these tombs as "archers." This study looks to (1) compare the skeletons from male "archer" burials with those from male "non-archer" burials-those not having archery-related grave goods-in order to assess a possible link between burial context and physical activity, and (2) apply a biomechanics profile to evaluate whether the individuals associated with these "archer" burials practiced specialized archer activity.

MATERIALS AND METHODS

The corpus (males only) included 46 "archers" and 40 "non-archers" from Bell Beaker individual inhumations. Osteological data included measurements, scores of entheseal changes, and a diagnosis of certain pathologies. Data analyses involved visual observations, hypothesis tests, dimension reduction, and MANOVA, with approaches aimed at exploring the treatment of data missingness.

RESULTS

Measurement data revealed no differences between the two groups. Evaluations of entheseal changes found that "non-archers" had consistently more instances of bone surface modifications than "archers." Individual assessments of specialized archer occupation identified 11 possible specialized archers.

DISCUSSION

These findings indicate a possible labor differentiation represented through the presence of a probably prestigious "archer" burial context. This suggests a link between grave good presence and labor, but not between a Bell Beaker archery occupation and an "archer" burial context. Data analyses support the application of biomechanics to osteological analyses in order to assess specialized activity on the skeleton.

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

BACKGROUND

Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear.

METHODS

The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram.

RESULTS

Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram.

CONCLUSIONS

The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

Modeling road traffic fatalities in Iran's six most populous provinces, 2015-2016

BACKGROUND

Prevention of road traffic injuries (RTIs) as a critical public health issue requires coordinated efforts. We aimed to model influential factors related to traffic safety.

METHODS

In this cross-sectional study, the information from 384,614 observations recorded in Integrated Road Traffic Injury Registry System (IRTIRS) in a one-year period (March 2015-March 2016) was analyzed. All registered crashes from Tehran, Isfan, Fras, Razavi Khorasan, Khuzestan, and East Azerbaijan provinces, the six most populated provinces in Iran, were included in this study. The variables significantly associated with road traffic fatality in the uni-variate analysis were included in the multiple logistic regression.

RESULTS

According to the multiple logistic regression, thirty-two out of seventy-one different variables were identified to be significantly associated with road traffic fatality. The results showed that the crash scene significantly related factors were passenger presence(OR = 4.95, 95%CI = (4.54-5.40)), pedestrians presence(OR = 2.60, 95%CI = (1.75-3.86)), night-time crashes (OR = 1.64, 95%CI = (1.52-1.76)), rainy weather (OR = 1.32, 95%CI = (1.06-1.64)), no intersection control (OR = 1.40, 95%CI = (1.29-1.51)), double solid line(OR = 2.21, 95%CI = (1.31-3.74)), asphalt roads(OR = 1.95, 95%CI = (1.39-2.73)), nonresidential areas(OR = 2.15, 95%CI = (1.93-2.40)), vulnerable-user presence(OR = 1.70, 95%CI = (1.50-1.92)), human factor (OR = 1.13, 95%CI = (1.03-1.23)), multiple first causes (OR = 2.81, 95%CI = (2.04-3.87)), fatigue as prior cause(OR = 1.48, 95%CI = (1.27-1.72)), irregulation as direct cause(OR = 1.35, 95%CI = (1.20-1.51)), head-on collision(OR = 3.35, 95%CI = (2.85-3.93)), tourist destination(OR = 1.95, 95%CI = (1.69-2.24)), suburban areas(OR = 3.26, 95%CI = (2.65-4.01)), expressway(OR = 1.84, 95%CI = (1.59-2.13)), unpaved shoulders(OR = 1.84, 95%CI = (1.63-2.07)), unseparated roads (OR = 1.40, 95%CI = (1.26-1.56)), multiple road defects(OR = 2.00, 95%CI = (1.67-2.39)). In addition, the vehicle-connected factors were heavy vehicle (OR = 1.40, 95%CI = (1.26-1.56)), dark color (OR = 1.26, 95%CI = (1.17-1.35)), old vehicle(OR = 1.46, 95%CI = (1.27-1.67)), not personal-regional plaques(OR = 2.73, 95%CI = (2.42-3.08)), illegal maneuver(OR = 3.84, 95%CI = (2.72-5.43)). And, driver related factors were non-academic education (OR = 1.58, 95%CI = (1.33-1.88)), low income(OR = 2.48, 95%CI = (1.95-3.15)), old age (OR = 1.67, 95%CI = (1.44-1.94)), unlicensed driving(OR = 3.93, 95%CI = (2.51-6.15)), not-wearing seat belt (OR = 1.55, 95%CI = (1.44-1.67)), unconsciousness (OR = 1.67, 95%CI = (1.44-1.94)), driver misconduct(OR = 2.51, 95%CI = (2.29-2.76)).

CONCLUSION

This study reveals that driving behavior, infrastructure design, and geometric road factors must be considered to avoid fatal crashes. Our results found that the above-mentioned factors had higher odds of a deadly outcome than their counterparts. Generally, addressing risk factors and considering the odds ratios would be beneficial for policy makers and road safety stakeholders to provide support for compulsory interventions to reduce the severity of RTIs.

Effectiveness of a multiple health-behaviour-change intervention in increasing adherence to the Mediterranean Diet in adults (EIRA study): a randomized controlled hybrid trial

BACKGROUND

The present study describes the effectiveness of a complex intervention that addresses multiple lifestyles to promote healthy behaviours in increasing adherence to the Mediterranean diet (MD).  METHODS: Cluster-randomised, hybrid clinical trial controlled with two parallel groups. The study was carried out in 26 primary Spanish healthcare centres. People aged 45-75 years who presented at least two of the following criteria were included: smoker, low adherence to the MD or insufficient level of physical activity. The intervention group (IG) had three different levels of action: individual, group, and community, with the aim of acting on the behaviours related to smoking, diet and physical activity at the same time. The individual intervention included personalised recommendations and agreements on the objectives to attain. Group sessions were adapted to the context of each healthcare centre. The community intervention was focused on the social prescription of resources and activities performed in the environment of the community of each healthcare centre. Control group (CG) received brief advice given in the usual visits to the doctor's office. The primary outcome was the change, after 12 months, in the number of participants in each group with good adherence to the MD pattern. Secondary outcomes included the change in the total score of the MD adherence score (MEDAS) and the change in some cardiovascular risk factors.

RESULTS

Three thousand sixty-two participants were included (IG = 1,481, CG = 1,581). Low adherence to the MD was present in 1,384 (93.5%) participants, of whom 1,233 initiated the intervention and conducted at least one individual visit with a healthcare professional. A greater increase (13.7%; 95% CI, 9.9-17.5; p < 0.001) was obtained by IG in the number of participants who reached 9 points or more (good adherence) in the MEDAS at the final visit. Moreover, the effect attributable to the intervention obtained a greater increase (0.50 points; 95% CI, 0.35 to 0.66; p < 0.001) in IG.

CONCLUSIONS

A complex intervention modelled and carried out by primary healthcare professionals, within a real clinical healthcare context, achieved a global increase in the adherence to the MD compared to the brief advice.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03136211. Retrospectively registered on 02/05/2017 https://clinicaltrials.gov/ct2/show/NCT03136211.

[Impact of COVID-19 lockdown on self-perceived health in Chile by gender]

OBJECTIVE

To analyse the social factors associated with self-perceived health during the COVID-19 lockdown in the Chilean resident population according to gender perspective.

METHOD

Cross-sectional study conducted during the COVID-19 lockdown between May 17 and August 17, 2020 with an online survey. Self-perceived health was analysed in the population aged 18 years or older in relation to social variables. Multivariate logistic regression models were constructed to assess the association between independent variables with self-perceived health, through adjusted odds ratio (aOR). Analyses were stratified by sex (M: men; W: women).

RESULTS

5981 persons were analysed (women: 63.9%). 29.6% of women and 19.2% of men reported poor self-perceived health. In women it worsens with increasing age. Worse self-perceived health was mainly associated with lack of social support (ORa M: 2.05; ORa W: 2.34), concern about living together at home (ORa M: 1.66; ORa W: 1.38), perceived inadequate housing conditions (ORa M: 1.89; ORa W: 2.63), and disagreement with government measures (ORa M: 2.80; ORa W: 1.82). In women, it was also associated with informal work or being inactive in the labour market (ORa: 2.11). In men worse self-perceived health was associated with being self-employed (ORa: 1.65; confidence interval [CI]: 1.11-2.45) and has secondary education (ORa: 2.81; CI: 1.32-5.98).

CONCLUSIONS

The social impact of lockdown in self-perceived health is related to gender, age, care work, and socioeconomic conditions, as well as, by disagreement with the measures implemented to manage the pandemic.

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.

Sensitivity Analysis: A Method to Promote Certainty and Transparency in Nursing and Health Research

Nursing and health researchers may be presented with uncertainty regarding the utilization or legitimacy of methodological or analytic decisions. Sensitivity analyses are purposed to gain insight and certainty about the validity of research findings reported. Reporting guidelines and health research methodologists have emphasized the importance of utilizing and reporting sensitivity analyses in clinical research. However, sensitivity analyses are underreported in nursing and health research. The aim of this methodological overview is to provide an introduction to the purpose, conduct, interpretation, and reporting of sensitivity analyses, using a series of simulated and contemporary case examples.

Machine learning for the prediction of acute kidney injury in patients with sepsis

BACKGROUND

Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis.

METHODS

Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model.

RESULTS

A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models.

CONCLUSION

The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Multi-sided platforms in competitive B2B networks with varying governmental influence - a taxonomy of Port and Cargo Community System business models

Our knowledge on differences in business model characteristics of thriving and failing Multi-Sided Platforms in competitive B2B networks (B2B-MSP) and potential influences of increasing governmental involvement remains fragmented. This study develops a taxonomy to classify special B2B-MSP with varying governmental influence in the supply chain and transportation context, viz. Port and Cargo Community Systems (CS). Based on the classification of 44 international CS, we identify four archetypes using cluster analysis. The taxonomy provides practitioners with a differentiated view on the configuration options of CS business models including the involvement of governmental institutions, while the presented archetypes contribute an aggregated view of CS business models. The statistical analysis of our results provides initial explanatory approaches on CS business model dimension interdependencies, thereby laying the basis for a deeper understanding of sectoral and geographic differences of B2B-MSP and their diffusion dynamics as well as facilitating a higher contextualization of future research.

Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome

Background

In case-cohort studies a random subcohort is selected from the inception cohort and acts as the sample of controls for several outcome investigations. Analysis is conducted using only the cases and the subcohort, with inverse probability weighting (IPW) used to account for the unequal sampling probabilities resulting from the study design. Like all epidemiological studies, case-cohort studies are susceptible to missing data. Multiple imputation (MI) has become increasingly popular for addressing missing data in epidemiological studies. It is currently unclear how best to incorporate the weights from a case-cohort analysis in MI procedures used to address missing covariate data.

Method

A simulation study was conducted with missingness in two covariates, motivated by a case study within the Barwon Infant Study. MI methods considered were: using the outcome, a proxy for weights in the simple case-cohort design considered, as a predictor in the imputation model, with and without exposure and covariate interactions; imputing separately within each weight category; and using a weighted imputation model. These methods were compared to a complete case analysis (CCA) within the context of a standard IPW analysis model estimating either the risk or odds ratio. The strength of associations, missing data mechanism, proportion of observations with incomplete covariate data, and subcohort selection probability varied across the simulation scenarios. Methods were also applied to the case study.

Results

There was similar performance in terms of relative bias and precision with all MI methods across the scenarios considered, with expected improvements compared with the CCA. Slight underestimation of the standard error was seen throughout but the nominal level of coverage (95%) was generally achieved. All MI methods showed a similar increase in precision as the subcohort selection probability increased, irrespective of the scenario. A similar pattern of results was seen in the case study.

Conclusions

How weights were incorporated into the imputation model had minimal effect on the performance of MI; this may be due to case-cohort studies only having two weight categories. In this context, inclusion of the outcome in the imputation model was sufficient to account for the unequal sampling probabilities in the analysis model.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01495-4.

Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity

Protein Energy Malnutrition (PEM) has lifelong consequences on brain development and cognitive function. We studied the lifelong developmental trajectories of resting-state EEG source activity in 66 individuals with histories of Protein Energy Malnutrition (PEM) limited to the first year of life and in 83 matched classmate controls (CON) who are all participants of the 49 years longitudinal Barbados Nutrition Study (BNS). qEEGt source z-spectra measured deviation from normative values of EEG rhythmic activity sources at 5-11 years of age and 40 years later at 45-51 years of age. The PEM group showed qEEGt abnormalities in childhood, including a developmental delay in alpha rhythm maturation and an insufficient decrease in beta activity. These profiles may be correlated with accelerated cognitive decline.

Relationships among adverse childhood experience patterns, psychological resilience, self-esteem and depressive symptoms in Chinese adolescents: A serial multiple mediation model

Adverse childhood experiences (ACEs) and adolescent depression are both prevalent social problems that can increase the risk of several negative health consequences throughout life. The original definition of ACEs misdirects the focus of intervention efforts for ACEs to only family dysfunction and parenting practices. We used a broader definition of ACEs and a latent class analysis (LCA) model to examine ACE patterns, aiming to overcome the shortcomings of cumulative and single adversity approaches based on the special social context of China. The data were derived from a middle school in Huaibei City of Anhui Province in 2019 and 2020, which was a prospective study involving 1687 junior high school students. At the initial evaluation (T1), ACEs, psychological resilience, self-esteem, and depressive symptoms were assessed by the students. At Time 2 (T2), the depressive symptoms of students were assessed. LCA and mediation analyses were conducted with Mplus version 8.2. The LCA identified the following three heterogeneous ACE classes: "low adversity" (36.4%), "moderate adversity" (44.2%), and "high adversity" (19.4%). The mediation analysis showed that the ACE patterns affected depressive symptoms through the following two mediation paths only in the moderate but not in the high adversity class: self-esteem alone and a path combining psychological resilience and self-esteem. Psychological resilience separately did not mediate the association between ACE patterns and depressive symptoms. To reduce depressive symptoms, interventions for students with ACEs need to improve self-esteem through many channels.

A Modified Roger's Distance Algorithm for Mixed Quantitative-Qualitative Phenotypes to Establish a Core Collection for Taiwanese Vegetable Soybeans

Vegetable soybeans [Glycine max (L.) Merr.] have characteristics of larger seeds, less beany flavor, tender texture, and green-colored pods and seeds. Rich in nutrients, vegetable soybeans are conducive to preventing neurological disease. Due to the change of dietary habits and increasing health awareness, the demand for vegetable soybeans has increased. To conserve vegetable soybean germplasms in Taiwan, we built a core collection of vegetable soybeans, with minimum accessions, minimum redundancy, and maximum representation. Initially, a total of 213 vegetable soybean germplasms and 29 morphological traits were used to construct the core collection. After redundant accessions were removed, 200 accessions were retained as the entire collection, which was grouped into nine clusters. Here, we developed a modified Roger's distance for mixed quantitative-qualitative phenotypes to select 30 accessions (denoted as the core collection) that had a maximum pairwise genetic distance. No significant differences were observed in all phenotypic traits (p-values > 0.05) between the entire and the core collections, except plant height. Compared to the entire collection, we found that most traits retained diversities, but seven traits were slightly lost (ranged from 2 to 9%) in the core collection. The core collection demonstrated a small percentage of significant mean difference (3.45%) and a large coincidence rate (97.70%), indicating representativeness of the entire collection. Furthermore, large values in variable rate (149.80%) and coverage (92.5%) were in line with high diversity retained in the core collection. The results suggested that phenotype-based core collection can retain diversity and genetic variability of vegetable soybeans, providing a basis for further research and breeding programs.

Interaction network of immune-associated genes affecting the prognosis of patients with glioblastoma

Glioblastoma multiforme (GBM) is a common malignant tumor type of the nervous system. The purpose of the present study was to establish a regulatory network of immune-associated genes affecting the prognosis of patients with GBM. The GSE4290, GSE50161 and GSE2223 datasets from the Gene Expression Omnibus database were screened to identify common differentially expressed genes (co-DEGs). A functional enrichment analysis indicated that the co-DEGs were mainly enriched in cell communication, regulation of enzyme activity, immune response, nervous system, cytokine signaling in immune system and the AKT signaling pathway. The co-DEGs accumulated in immune response were then further investigated. For this, the intersection of those co-DEGs and currently known immune-regulatory genes was obtained and a differential expression analysis of these overlapping immune-associated genes was performed. A risk model was established using immune-regulatory genes that affect the prognosis of patients with GBM. The risk score was significantly associated with the prognosis of patients with GBM and had a significant independent predictive value. The risk model had high accuracy in predicting the prognosis of patients with GBM [area under the receiver operating characteristic curve (AUC)=0.764], which was higher than that of a previously reported model of prognosis-associated biomarkers (AUC=0.667). Furthermore, an interaction network was constructed by using immune-regulatory genes and transcription factors affecting the prognosis of patients with GBM and the University of California Santa Cruz database was used to perform a preliminary analysis of the transcription factors and immune genes of interest. The interaction network of immune-regulatory genes constructed in the present study enhances the current understanding of mechanisms associated with poor prognosis of patients with GBM. The risk score model established in the present study may be used to evaluate the prognosis of patients with GBM.

Acral lentiginous melanoma: Clinicopathologic and survival differences according to tumour location

BACKGROUND/OBJECTIVES

Acral lentiginous melanoma (ALM) is a melanoma subtype associated with atypical locations on the hands and feet and advanced disease at diagnosis. There is a limited understanding of whether the survival is similar for nail, non-nail, lower limb and upper limb ALM patients. We therefore explored clinicopathologic characteristics and melanoma-specific survival of ALM patients according to tumour location.

METHODS

A prospectively collected cohort study was performed of all primary invasive cutaneous acral lentiginous melanomas with known thickness and tumour location reviewed at a tertiary referral centre over 21 years.

RESULTS

A total of 101 ALM patients were reviewed from 1994 until 2016. The majority of cases (82/101) occurred on the feet. Hand ALMs were thicker and more likely to be ulcerated than feet ALMs (P = 0.05 and 0.02, respectively); however, survival was not statistically different between these two groups (univariate HR 0.48 P = 0.11, 95% CI, 0.20-1.17; multivariate HR 0.67 P = 0.40, 95% CI, 0.27-1.69, respectively). Non-nail ALM patients had longer survival when compared to nail ALM on univariate analysis (HR 0.40, 95% CI, 0.17 to 0.90) which was accounted for by Breslow thickness and ulceration (multivariate HR 0.56, 95% CI, 0.24 to 1.34).

CONCLUSIONS

The reduced melanoma-specific survival in nail ALM patients was likely due to their greater thickness and ulceration. Although hand ALMs are thicker and more frequently ulcerated, this is likely due to the higher proportion of nail ALMs present in this location.

Dose-Response Relationship between Western Diet and Being Overweight among Teachers in Malaysia

The rising prevalence of overweight and obesity is partly due to nutrition transition. The reported association between dietary patterns and overweight/obesity has been controversial because of inconsistent results and weak observed associations. Although it has been hypothesized that an unhealthy diet can increase obesity risk, none of the previous studies have examined the dose-response association using nonlinear dose-response analyses. This study aimed to examine the dose-response association between major dietary patterns and overweight/obesity. This was a cross-sectional study involving teachers selected through stratified multistage sampling from public schools in three Malaysian states. Dietary intake was assessed using a food frequency questionnaire, and two major dietary patterns (Western and Prudent diet) were extracted using factor analysis. Logistic regression followed by trend analysis was used to test the difference in odds of overweight and obesity in each quintile of diet score. A further analysis using restricted cubic spline models was performed to examine the dose-response associations of dietary patterns with odds of overweight/obesity. The logistic regression analysis showed that participants with the highest quintile of Western diet score were 1.4 times more likely to be overweight/obese compared to those in the lowest quintile (95% CI: 1.11, 1.83, p-trend < 0.001). The odds of overweight/obesity showed a significant increasing trend across quintiles of Western diet among both men and women (p-trend < 0.001). In the dose-response analysis, a positive linear association (P_nonlinearity = 0.6139) was observed where overweight/obesity was more likely to occur among participants with a Western diet score greater than a mean score of zero. There was an inverse trend of odds of overweight/obesity across quintiles of Prudent diet score, significant only for men (p for trend < 0.001). Linear association was found between Prudent diet score and odds of overweight/obesity among both men (P_nonlinearity = 0.6685) and women (P_nonlinearity = 0.3684) in the dose-response analysis. No threshold at the level of adherence to Prudent diet was linked to odds of overweight/obesity. Dose-response analysis indicated that women with a Western diet score greater than zero were more likely to be overweight or obese among women. In men, higher adherence to Western diet was associated with increased odds of overweight/obesity, while greater adherence to Prudent diet decreased the odds of overweight/obesity. Promoting and enhancing the consumption of Prudent diet and limit in Western diet may be used to guide the development of evidence-based diet interventions to curb overweight and obesity.

Cocoa-Rich Chocolate and Quality of Life in Postmenopausal Women: A Randomized Clinical Trial

Menopause has a negative impact on quality of life (QoL). The aim of the present study was to analyse the effect on QoL of adding 10 g per day of chocolate with a high concentration of cocoa (99%) to the habitual diet, for 6 months, in a sample of postmenopausal women. Postmenopausal women (n = 140) aged 50–64 years were randomised to either an addition of 10 g per day of cocoa-rich chocolate to their usual diet or no supplement addition. All variables were measured at baseline and after six months of intervention. QoL was evaluated using the 3-level version of EuroQol-5D (EuroQoL-5D-3L), the EuroQol Visual Analogue Scale (EQ-VAS) and the Cervantes scale. Analysis of covariance (ANCOVA) analyses adjusted for the main determinants of QoL considered in this study showed no changes in the global score of QoL evaluated with the EuroQoL-5D-3L. The intervention group showed an increase of 6.0 points (95% confidence interval (CI): 0.4, 11.7) in the EQ-VAS compared to the control group (p = 0.036). No significant changes were observed between groups in the global score of QoL nor in the dimensions and subdimensions measured with the Cervantes scale. The additional daily contribution of 10 g of cocoa-rich chocolate in postmenopausal women could have a slight impact on their perception toward their health state, although without modifying the health-related QoL or the dimensions that compose it.

Demographic and Psychosocial Variables Could Predict the Occurrence of Major Depressive Disorder, but Not the Severity of Depression in Patients with First-episode Major Depressive Disorder in China

BACKGROUND

This study aimed to investigate the association between psychosocial variables and depression severity among Chinese patients with first-episode major depressive disorder (MDD).

METHODS

This multicenter case-control study enrolled patients with first-episode MDD and healthy controls (HCs) from nine sites in China. Depressive symptoms, clinical features, and psychosocial variables were evaluated. Based on the total score of the Hamilton Rating Scale of Depression (HRSD-17), patients with MDD were classified into three subgroups of severity (mild, moderate and severe). The logistic regression analyses were conducted to investigate the independent risk factors of MDD and different severities of depression.

RESULTS

Overall, 598 MDD patients and 467 HCs were included. The proportions of patients with mild, moderate, and severe depression were 260 (29.5%), 443 (50.2%), and 179 (20.3%), respectively. The logistic regression model revealed that the demographic and psychosocial factors could explain 50.6% of the total variance of occurrence of MDD in the whole sample with HCs. However, in the subsample of MDD patients, only older age [OR=1.03 (95%CI: 1.02-1.05)], stressful social events [OR=1.04 (95%CI: 1.02-1.06)], and melancholic feature [OR=2.68 (95%CI: 1.91-3.74)] were independent risk factors for moderate and severe depression; these factors combined to explain only 10.2% of the total variance.

LIMITATIONS

Only patients with first-episode MDD were included in this study, leaving the associated factors for the severity of recurrent depression uninvestigated.

CONCLUSION

Demographic and psychosocial variables had satisfactory performance in predicting the occurrence of MDD, but showed inadequate value in predicting the depression severity of MDD patients.

A decision support tool to find the best cyclosporine dose when switching from intravenous to oral route in pediatric stem cell transplant patients

PURPOSE

Managing the pharmacokinetic variability of immunosuppressive drugs after pediatric hematopoietic stem cell transplantation (HSCT) is a clinical challenge. Thus, the aim of our study was to design and validate a decision support tool predicting the best first cyclosporine oral dose to give when switching from intravenous route.

METHODS

We used 10-years pediatric HSCT patients' dataset from 2008 to 2018. A tree-augmented naïve Bayesian network model (method belonging to artificial intelligence) was built with data from the first eight-years, and validated with data from the last two.

RESULTS

The Bayesian network model obtained showed good prediction performances, both after a 10-fold cross-validation and external validation, with respectively an AUC-ROC of 0.89 and 0.86, a percentage of misclassified patients of 28.7% and 35.2%, a true positive rate of 0.71 and 0.65, and a false positive rate of 0.12 and 0.14 respectively.

CONCLUSION

The final model allows the prediction of the most likely cyclosporine oral dose to reach the therapeutic target specified by the clinician. The clinical impact of using this model needs to be prospectively warranted. Respecting the decision support tool terms of use is necessary as well as remaining critical about the prediction by confronting it with the clinical context.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

Effectiveness of a multidisciplinary BIOPSYCHOSOCIAL intervention for non-specific SUBACUTE low back pain in a working population: a cluster randomized clinical trial

BACKGROUND

Low back pain (LBP) is a multifactorial condition with individual and societal impact that affects populations globally. Current guidelines for the treatment of LBP recommend pharmacological and non-pharmacological strategies. The aim of this study was to compare usual clinical practice with the effectiveness of a biopsychosocial multidisciplinary intervention in reducing disability, severity of pain and improving quality of life in a working population of patients with subacute (2-12 weeks), non-specific LBP.

METHODS

Longitudinal cluster randomized clinical trial conducted in 39 Primary Health Care Centres (PHCC) of Barcelona, with patients aged 18-65 years (n = 501; control group = 239; 26 PHCC, intervention group = 262; 13 PHCC). The control group received usual clinical care. The intervention group received usual clinical care plus a biopsychosocial multidisciplinary intervention, which consisted of physiotherapy, cognitive-behavioural therapy and medication. The main outcomes were changes in the Roland Morris Disability Questionnaire (RMDQ), and the minimal clinically important differences. Secondary outcomes were changes in the McGill Pain (MGPQ) and Quality of Life (SF-12) questionnaires. Assessment was conducted at baseline, 3 and 12 months. Analysis was by intention-to-treat and analyst-blinded. Multiple imputations were used.

RESULTS

Of the 501 enrolled patients, 421 (84%) provided data at 3 months, and 387 (77.2%) at 12 months. Mean age was 46.8 years (SD: 11.5) and 64.7% were women. In the adjusted analysis of the RMDQ outcome, only the intervention group showed significant changes at 3 months (- 1.33 points, p = 0.005) and at 12 months (- 1.11 points, p = 0.027), but minimal clinically important difference were detected in both groups. In the adjusted analysis of the RMDQ outcome, the intervention group improvement more than the control group at 3 months (- 1.33 points, p = 0.005) and at 12 months (- 1.11 points, p = 0.027). The intervention group presented a significant difference. Both groups presented a minimal clinically important difference, but more difference in the intervention group. The intervention group presented significant differences in the MGPQ scales of current pain intensity and VAS scores at 3 months. No statistically significant differences were found in the physical and mental domains of the SF-12.

CONCLUSIONS

A multidisciplinary biopsychosocial intervention in a working population with non-specific subacute LBP has a small positive impact on disability, and on the level of pain, mainly at short-term, but no difference on quality of life.

TRIAL REGISTRATION

ISRCTN21392091 (17 oct 2018) (Prospectively registred).

Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting

BACKGROUND

Antimalarial clinical efficacy studies for uncomplicated Plasmodium falciparum malaria frequently encounter situations in which molecular genotyping is unable to discriminate between parasitic recurrence, either new infection or recrudescence. The current WHO guideline recommends excluding these individuals with indeterminate outcomes in a complete case (CC) analysis. Data from the four artemisinin-based combination (4ABC) trial was used to compare the performance of multiple imputation (MI) and inverse probability weighting (IPW) against the standard CC analysis for dealing with indeterminate recurrences.

METHODS

3369 study participants from the multicentre study (4ABC trial) with molecularly defined parasitic recurrence treated with three artemisinin-based combination therapies were used to represent a complete dataset. A set proportion of recurrent infections (10, 30 and 45%) were reclassified as missing using two mechanisms: a completely random selection (mechanism 1); missingness weakly dependent (mechanism 2a) and strongly dependent (mechanism 2b) on treatment and transmission intensity. The performance of MI, IPW and CC approaches in estimating the Kaplan-Meier (K-M) probability of parasitic recrudescence at day 28 was then compared. In addition, the maximum likelihood estimate of the cured proportion was presented for further comparison (analytical solution). Performance measures (bias, relative bias, standard error and coverage) were reported as an average from 1000 simulation runs.

RESULTS

The CC analyses resulted in absolute underestimation of K-M probability of day 28 recrudescence by up to 1.7% and were associated with reduced precision and poor coverage across all the scenarios studied. Both MI and IPW method performed better (greater consistency and greater efficiency) compared to CC analysis. In the absence of censoring, the analytical solution provided the most consistent and accurate estimate of cured proportion compared to the CC analyses.

CONCLUSIONS

The widely used CC approach underestimates antimalarial failure; IPW and MI procedures provided efficient and consistent estimates and should be considered when reporting the results of antimalarial clinical trials, especially in areas of high transmission, where the proportion of indeterminate outcomes could be large. The analytical solution estimating the cured proportion could provide an alternative approach, in scenarios with minimal censoring due to loss to follow-up or new infections.

Does Statin Benefits Patients with Heart Failure Undergoing Percutaneous Coronary Intervention? Findings from the Melbourne Interventional Group Registry

PURPOSE

The effectiveness of statins in improving clinical outcomes among patients with heart failure (HF) undergoing percutaneous coronary intervention (PCI) is unclear. We examined the association between use of statins and clinical outcomes in patients with HF included in the Melbourne Interventional Group registry.

METHODS

Patients were followed from 30 days to 1 year post-PCI for a primary composite outcome of all-cause mortality and hospitalisation for cardiovascular (CV) causes. Secondary outcomes included major adverse cardiac events (MACE, a composite of all-cause mortality, myocardial infarction and target vessel revascularisation) and hospitalisation for CV causes. Outcomes were compared between statin-treated and non-statin-treated patients (at 30 days post-PCI) using propensity scores to balance for risk factors.

RESULTS

Among 991 patients included in the inverse probability-weighted Cox model, statin use had no significant effect on the primary composite outcome [adjusted hazard ratio (aHR), 1.03; 95% confidence interval (CI), 0.68 to 1.56; p = 0.89], nor MACE (aHR, 0.99; 95% CI, 0.54 to 1.84; p = 0.99) or hospitalisation for CV causes (HR, 1.13; 95% CI, 0.74 to 1.72; p = 0.57).

CONCLUSIONS

Our results suggest that statin therapy may confer no significant benefits in patients with HF undergoing PCI. However, prospective randomised controlled trials are needed to provide more definitive answers.

Rationale and design of TransplantLines: a prospective cohort study and biobank of solid organ transplant recipients

INTRODUCTION

In the past decades, short-term results after solid organ transplantation have markedly improved. Disappointingly, this has not been accompanied by parallel improvements in long-term outcomes after transplantation. To improve graft and recipient outcomes, identification of potentially modifiable risk factors and development of biomarkers are required. We provide the rationale and design of a large prospective cohort study of solid organ transplant recipients (TransplantLines).

METHODS AND ANALYSIS

TransplantLines is designed as a single-centre, prospective cohort study and biobank including all different types of solid organ transplant recipients as well as living organ donors. Data will be collected from transplant candidates before transplantation, during transplantation, at 3 months, 6 months, 1 year, 2 years and 5 years, and subsequently every 5 years after transplantation. Data from living organ donors will be collected before donation, during donation, at 3 months, 1 year and 5 years after donation, and subsequently every 5 years. The primary outcomes are mortality and graft failure. The secondary outcomes will be cause-specific mortality, cause-specific graft failure and rejection. The tertiary outcomes will be other health problems, including diabetes, obesity, hypertension, hypercholesterolaemia and cardiovascular disease, and disturbances that relate to quality of life, that is, physical and psychological functioning, including quality of sleep, and neurological problems such as tremor and polyneuropathy.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the relevant local ethics committee. The TransplantLines cohort study is designed to deliver pioneering insights into transplantation and donation outcomes. The study design allows comprehensive data collection on perioperative care, nutrition, social and psychological functioning, and biochemical parameters. This may provide a rationale for future intervention strategies to more individualised, patient-centred transplant care and individualisation of treatment.

TRIAL REGISTRATION NUMBER

NCT03272841.

Survival predictors in biopsy-proven giant cell arteritis: a northern Italian population-based study

Objective

To evaluate the influence of disease-related findings and treatment outcomes on survival in a population-based cohort of Northern Italian patients with GCA.

Methods

A total of 281 patients with incident temporal artery biopsy (TAB)-proven GCA, diagnosed over a 26-year period (1986–2012) and living in the Reggio Emilia area, were retrospectively evaluated. We analysed clinical, imaging and laboratory findings at diagnosis, pathological patterns of TAB, CS treatment and therapeutic outcomes, and traditional cardiovascular risk factors as factors predictive of survival.

Results

Univariate analysis showed that increased mortality was associated with large vessel involvement at diagnosis [hazard ratio (HR) 5.84], while reduced mortality was associated with female sex (HR 0.66), PMR (HR 0.54), higher haemoglobin levels (HR 0.84) at diagnosis, long-term remission (HR 0.47) and inflammation limited to adventitia or to the adventitial vasa vasorum (HR 0.48) at TAB examination. Multivariate analysis confirmed the association between increased mortality and large vessel involvement (HR 5.14) at diagnosis, between reduced mortality and PMR (HR 0.57) at diagnosis and adventitial inflammation (HR 0.31) at TAB.

Conclusion

PMR at diagnosis and inflammation limited to the adventitia at TAB appear to identify subsets of patients with more benign disease, while large vessel involvement at diagnosis is associated with reduced survival.

Domain-specific physical activity and the risk of colorectal cancer: results from the Melbourne Collaborative Cohort Study

BACKGROUND

Physical activity reduces the risk of colorectal cancer (CRC), but the relevant evidence derives primarily from self-reported recreational and occupational activity. Less is known about the contribution of other domains of physical activity, such as transport and household. We examined associations between domain-specific physical activities and CRC risk within the Melbourne Collaborative Cohort Study.

METHODS

Analyses included 23,586 participants who were free from invasive colorectal cancer and had completed the International Physical Activity Questionnaire-Long Form at follow-up 2 (2003-2007). Cox regression, with age as the time metric, was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for ordinal categories of each physical activity domain.

RESULTS

Adjusted HRs for the highest versus the lowest categories of physical activity were 0.71 (95% CI: 0.51-0.98; ptrend = 0.03) for recreational activity; 0.80 (95% CI: 0.49-1.28; ptrend = 0.38) for occupational activity; 0.90 (95% CI: 0.68-1.19; ptrend = 0.20) for transport activity; and 1.07 (95% CI: 0.82-1.40; ptrend = 0.46) for household activity.

CONCLUSIONS

Recreational activity was associated with reduced CRC risk. A non-significant, inverse association was observed for occupational activity, whereas no association was found for transport or household domains.

Longitudinal Associations of the Healthy Lifestyle Index Score With Quality of Life in People With Multiple Sclerosis: A Prospective Cohort Study

Objective: To explore the association between combined lifestyle risk factors with quality of life in people with multiple sclerosis (MS) over 2.5 years. Methods: People with MS were recruited to participate in a comprehensive online survey regarding their demographic and clinical characteristics, health-related quality of life (HRQOL), and lifestyle behaviors including physical activity, alcohol consumption, cigarette smoking, body mass index, and dietary habits measured at baseline and 2.5-year follow-up. A combined healthy lifestyle index score (HLIS) was constructed by assigning scores of 0-4 to each of the lifestyle risk factors, for which higher values indicate healthier lifestyle behavior. Multivariable linear regression modeling was used to assess whether the HLIS at baseline was associated with the physical and mental HRQOL over the study period in this sample of people with MS. Results: Of 2,466 participants with confirmed MS, 1,401 (57%) completed the follow-up. Multivariable linear regression analyses demonstrated that every 5-point increase (of a possible total of 20) in the baseline HLIS was associated with 1.7 (95% CI: 0.2-3.2) and 2.5 (95% CI: 1.0-4.0) higher scores in the change in physical and mental HRQOL components from baseline to follow-up respectively. Conclusion: Findings suggest the importance of healthy lifestyle behavior in quality of life in MS. A healthy lifestyle program focusing on these behaviors has the potential to positively influence health-related quality of life for people with MS.

Factors affecting operative time in primary total hip arthroplasty: A retrospective single hospital cohort study of 7674 cases

BACKGROUND

Total hip arthroplasty (THA) is one of the most common orthopedic procedures in developed countries, and the high volume of surgery and the socioeconomic burden of failures demand continuous optimization. Operative time has been identified as a significant independent factor influencing the clinical outcome of THA.

OBJECTIVE

The aim of this study was to analyze factors influencing the operative time for THA in a large, single-center cohort.

METHODS

A consecutive series of 7,674 cases undergoing primary THA was identified, and after multiple imputation of missing values, univariable and multivariable linear regression analyses were performed.

RESULTS

In the univariable analysis, all factors showed significant influences on operative time, while multivariable regression analysis revealed that sex, a diagnosis of hip dysplasia and small femoral component size did not reach significance. Younger age, an underlying diagnosis other than osteoarthritis or hip dysplasia, a large stem size, usage of a conventional stem rather than a short stem, a larger cup size and a cemented fixation technique, however, remained significantly influential in terms of a longer operative time.

CONCLUSIONS

This study identified risk factors for longer operative time that in turn is associated with a higher rate of periprosthetic joint infection and impaired clinical outcome. Our findings could help to refine scheduling of total hip arthroplasty procedures in times of increasing cost and efficiency pressure.

Cost-effectiveness of habit-based advice for weight control versus usual care in general practice in the Ten Top Tips (10TT) trial: economic evaluation based on a randomised controlled trial

OBJECTIVE

Ten Top Tips (10TT) is a primary care-led behavioural intervention which aims to help adults reduce and manage their weight by following 10 weight loss tips. The intervention promotes habit formation to encourage long-term behavioural changes. The aim of this study was to estimate the cost-effectiveness of 10TT in general practice from the perspective of the UK National Health Service.

DESIGN

An economic evaluation was conducted alongside an individually randomised controlled trial.

SETTING

14 general practitioner practices in England.

PARTICIPANTS

All patients were aged ≥18 years, with body mass index ≥30 kg/m². A total of 537 patients were recruited; 270 received the usual care offered by their practices and 267 received the 10TT intervention.

OUTCOMES MEASURES

Health service use and quality-adjusted life years (QALYs) were measured over 2 years. Analysis was conducted in terms of incremental net monetary benefits (NMBs), using non-parametric bootstrapping and multiple imputation.

RESULTS

Over a 2-year time horizon, the mean costs and QALYs per patient in the 10TT group were £1889 (95% CI £1522 to £2566) and 1.51 (95% CI 1.44 to 1.58). The mean costs and QALYs for usual care were £1925 (95% CI £1599 to £2251) and 1.51 (95% CI 1.45 to 1.57), respectively. This generated a mean cost difference of -£36 (95% CI -£512 to £441) and a mean QALY difference of 0.001 (95% CI -0.080 to 0.082). The incremental NMB for 10TT versus usual care was £49 (95% CI -£1709 to £1800) at a maximum willingness to pay for a QALY of £20 000. 10TT had a 52% probability of being cost-effective at this threshold.

CONCLUSIONS

Costs and QALYs for 10TT were not significantly different from usual care and therefore 10TT is as cost-effective as usual care. There was no evidence to recommend nor advice against offering 10TT to obese patients in general practices based on cost-effectiveness considerations.

TRIAL REGISTRATION NUMBER

ISRCTN16347068; Post-results.

Parent-reported prevalence and persistence of 19 common child health conditions

OBJECTIVE

To estimate prevalence and persistence of 19 common paediatric conditions from infancy to 14-15 years.

DESIGN

Population-based prospective cohort study.

SETTING

Australia.

PARTICIPANTS

Parallel cohorts assessed biennially from 2004 to 2014 from ages 0-1 and 4-5 years to 10-11 and 14-15 years, respectively, in the Longitudinal Study of Australian Children.

MAIN OUTCOME MEASURES

19 health conditions: 17 parent-reported, 2 (overweight/obesity, obesity) directly assessed. Two general measures: health status, special health care needs.

ANALYSIS

(1) prevalence estimated in 2-year age-bands and (2) persistence rates calculated at each subsequent time point for each condition among affected children.

RESULTS

10 090 children participated in Wave 1 and 6717 in all waves. From age 2, more than 60% of children were experiencing at least one health condition at any age. Distinct prevalence patterns by age-bands comprised eight conditions that steadily rose (overweight/obesity, obesity, injury, anxiety/depression, frequent headaches, abdominal pain, autism spectrum disorder, attention-deficit hyperactivity disorder). Six conditions fell with age (eczema, sleep problems, day-wetting, soiling, constipation, recurrent tonsillitis), three remained stable (asthma, diabetes, epilepsy) and two peaked in mid-childhood (dental decay, recurrent ear infections). Conditions were more likely to persist if present for 2 years; persistence was especially high for obesity beyond 6-7 (91.3%-95.1% persisting at 14-15).

CONCLUSIONS

Beyond infancy, most Australian children are experiencing at least one ongoing health condition at any given time. This study's age-specific estimates of prevalence and persistence should assist families and clinicians to plan care. Conditions showing little resolution (obesity, asthma, attention-deficit hyperactivity disorder) require long-term planning and management.