The rise of multiple imputation: a review of the reporting and implementation of the method in medical research

Estimating the Prevalence of Injection Drug Use Among Acute Hepatitis C Cases From a National Surveillance System: Application of Random Forest-Based Multiple Imputation

BACKGROUND

Injection drug use (IDU) is a major contributor to the syndemic of viral hepatitis, human immunodeficiency virus, and drug overdose. However, information on IDU is frequently missing in national viral hepatitis surveillance data, which limits our understanding of the full extent of IDU-associated infections. Multiple imputation by chained equations (MICE) has become a popular approach to address missing data, but its application for IDU imputation is less studied.

METHODS

Using the 2019-2021 National Notifiable Diseases Surveillance System acute hepatitis C case data and publicly available county-level measures, we evaluated listwise deletion (LD) and 3 models imputing missing IDU data through MICE: parametric logistic regression, semi-parametric predictive mean matching (PMM), and nonparametric random forest (RF) (both standard RF [sRF] and fast implementation of RF [fRF]).

RESULTS

The estimated IDU prevalence among acute hepatitis C cases increased from 63.5% by LD to 65.1% by logistic regression, 66.9% by PMM, 76.0% by sRF, and 85.1% by fRF. Evaluation studies showed that RF-based MICE imputation, especially fRF, has the highest accuracy (as measured by smallest raw bias, percent bias, and root mean square error) and highest efficiency (as measured by smallest 95% confidence interval width) compared to LD and other models. Sensitivity analyses indicated that fRF remained robust when data were missing not at random.

CONCLUSION

Our analysis suggested that RF-based MICE imputation, especially fRF, could be a valuable approach for addressing missing IDU data in the context of population-based surveillance systems like National Notifiable Diseases Surveillance System. The inclusion of imputed IDU data may enhance the effectiveness of future surveillance and prevention efforts for the IDU-driven syndemic.

Addressing missing outcome data in randomised controlled trials: A methodological scoping review

BACKGROUND

Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate.

OBJECTIVE

To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR).

STUDY DESIGN AND SETTING

We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design.

RESULTS

From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption.

CONCLUSION

This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.

Development of a prognostic nomogram for patients underwent extracorporeal circulation auxiliary to open cardiac surgery on hospital mortality: a retrospective cohort study

Background

Extracorporeal circulation auxiliary to open cardiac surgery (ECAOCS) is one of the most complex surgical procedures and carries a very high risk of death. We developed a nomogram from a retrospective study to predict the risk of death during patient hospitalization.

Methods

All clinical data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We extracted clinical variables for the first 24 hours after admission to the intensive care unit (ICU) in a total of 880 patients who underwent ECAOCS. All patients were randomly divided into training and validation cohort in a ratio of 7:3. All variables included in the study were subjected to univariate logistic regression analysis. In order to prevent overfitting and to address the problem of severe covariance, all factors with P<0.05 in the univariate logistic regression analysis were analyzed using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was developed based on the factors output from the LASSO regression and a nomogram was plotted. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) was calculated in training and validation cohort. Finally, the evaluation of the model was performed by calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) and decision curve analysis (DCA) was performed.

Results

Indicators included in the nomogram were anion gap (AG), central venous pressure (CVP), glucose, creatinine (Cr), prothrombin time (PT), activated partial thromboplastin time (APTT), bicarbonate ion (HCO3 -), cerebrovascular disease (CVD), peripheral vascular disease (PVD), and acute myocardial infarction (AMI).

Conclusions

Our study developed a model for predicting postoperative hospital mortality in patients underwent ECAOCS by incorporating AG, CVP, glucose, Cr, APTT, HCO3 -, CVD, AMI, and PVD from the first 24 hours after admission to the ICU.

Keywords

Extracorporeal circulation; cardiac surgery; intensive care; nomogram; prediction model.

Effect of elevated depressive symptoms during adolescence on health-related quality of life in young adulthood-a six-year cohort study with repeated exposure measurements

Objectives

Depression is a major contributor of young people's burden of disease. In this study we aim to estimate the effect of elevated depressive symptoms on physical health-related quality of life.

Design

We used self-reported information from the prospective BELLA cohort study, which included adolescents selected from the general population in Germany. The baseline assessment (2003-2006) and the 1-, 2-, and 6-year follow-up waves provide the data basis.

Participants

The baseline study population consisted of 1,460 adolescents between the ages of 12 and 17 who, according to their caregivers, did not suffer from depression.

Variables

The primary outcome, as measured by the physical component score (PCS) of the SF-36 at a 6-year follow-up (range: 0-100), is physical health-related quality of life. The exposure of interest is depressive symptoms, as measured by the Center for Epidemiological Studies Depression Scale for Children (CES-DC) at baseline, 1-year follow-up and 2-year follow-ups (range: 0-60). We dichotomized the exposure into subthreshold (≤15) and elevated depressive symptoms (>15). For the main analyses we considered a cumulative index for elevated depressive symptoms across the three time points (range: 0-3). Considered confounders are sex, age, socioeconomic status, migrant background, social support, anxiety symptoms, physical activity, chronic diseases, and sleeping problems.

Statistical methods

We used multiple imputation to account for missing values. Within each imputed dataset, we applied inverse probability weighting (IPW) to estimate the effect of the cumulative index for elevated depressive symptoms at baseline, 1- and 2-year follow-up on physical health-related quality of life at 6-year follow-up. We derived 95% confidence intervals by bootstrapping.

Results

After adjusting with IPW, the effect of the cumulative index per one unit increase of elevated depressive symptoms on the physical component score was -1.71 (95% CI: -3.51 to -0.04). The adjusted effect estimates of single exposure of elevated depressive symptoms on physical health-related quality of life were -0.83 (95% CI: -3.69 to 1.87) at baseline, -2.96 (95% CI: -4.94 to -0.52) at 1-year follow-up and -1.32 (95% CI: -3.85 to 1.15) at 2-year follow-up.

Conclusion

Findings suggest that elevated depressive symptoms during adolescence decrease physical health-related quality of life in young adulthood.

Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis

Background: Disease-modifying antirheumatic drugs (bDMARDs) have shown efficacy in treating Rheumatoid Arthritis (RA). Predicting treatment outcomes for RA is crucial as approximately 30% of patients do not respond to bDMARDs and only half achieve a sustained response. This study aims to leverage machine learning to predict both initial response at 6 months and sustained response at 12 months using baseline clinical data. Methods: Baseline clinical data were collected from 154 RA patients treated at the University Hospital in Erlangen, Germany. Five machine learning models were compared: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), Support Vector Machines (SVM), and Random Forest. Nested cross-validation was employed to ensure robustness and avoid overfitting, integrating hyperparameter tuning within its process. Results: XGBoost achieved the highest accuracy for predicting initial response (AUC-ROC of 0.91), while AdaBoost was the most effective for sustained response (AUC-ROC of 0.84). Key predictors included the Disease Activity Score-28 using erythrocyte sedimentation rate (DAS28-ESR), with higher scores at baseline associated with lower response chances at 6 and 12 months. Shapley additive explanations (SHAP) identified the most important baseline features and visualized their directional effects on treatment response and sustained response. Conclusions: These findings can enhance RA treatment plans and support clinical decision-making, ultimately improving patient outcomes by predicting response before starting medication.

Malignant tumor increases the risk of all causes in-hospital mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database

BACKGROUND

Ischemic stroke (IS) and malignant tumor (MT) have high morbidity and mortality rates worldwide, and several associations exist between them. This study aimed to determine the effect of MT on hospital mortality in patients with IS.

METHODS

Based on their MT status, participants with IS in the Medical Information Mart for Intensive Care IV (MIMIC-IV) were divided into two groups. The primary outcome was in-hospital all causes mortality. Kaplan-Meier survival analysis was performed to evaluate the intergroup in-hospital mortality, and three Cox regression models were used to determine the association between MT and in-hospital mortality.

RESULTS

A total of 1605 participants (749 males and 856 females) were included in the study. The mean age was 72.030 ± 15.463 years. Of these, 257 (16%) patients died in the hospital. Kaplan-Meier analysis showed that the MT group had a significantly lower possibility of in-hospital survival than the non-MT group. In the unadjusted model, in-hospital mortality among MT patients had a higher odds ratio (OR) of 1.905 (95% CI, 1.320-2.748; P < 0.001) than the non-MT group. After adjusting for basic information, vital signs, and laboratory data, MT was also associated with increased in-hospital mortality (OR = 1.844, 95% CI: 1.255-2.708; P = 0.002).

CONCLUSIONS

Among the patients with IS, the risk of all causes in-hospital mortality was higher for MT than for patients non-MT. This finding can assist clinicians in more accurately assessing prognosis and making informed treatment decisions.

Association between Dietary Patterns during Pregnancy and Children's Neurodevelopment: A Birth Cohort Study

BACKGROUND

Research studies have showed that maternal diet may influence fetal neurodevelopment, but most studies have only assessed single nutrients or food groups.

OBJECTIVE

To investigate the impact of maternal prenatal dietary patterns during pregnancy on child neurodevelopment.

METHODS

Study participants were obtained from the China National Birth Cohort. The Ages and Stages Questionnaire, Third Edition, was used to assess children's neurodevelopment at 36 months old. Maternal antenatal dietary data were collected over three trimesters using food frequency questionnaires. Five distinct maternal dietary patterns throughout pregnancy were identified by principal component analysis, namely protein- and micronutrient-rich dietary patterns, low-iron dietary patterns, pasta as the staple food dietary patterns, iron-rich dietary patterns, tubers, fruits, and baked food dietary patterns. Group-based trajectory modeling was performed for dietary patterns present in all three periods. Multiple linear regression models were used for statistical analysis.

RESULTS

Children of mothers who followed a high protein- and micronutrient-rich dietary pattern trajectory during pregnancy presented better neurodevelopment, including higher gross motor and problem-solving scores. Furthermore, it was observed that children born of women with low-iron dietary patterns had poorer neurodevelopment. In detail, children born to mothers with a low-iron dietary pattern during the first trimester had lower problem-solving scores, while to those who were exposed to a low-iron dietary pattern in the second and third trimesters had lower gross motor scores. Additionally, children with mothers who had a low-iron dietary pattern in the third trimester had lower communication scores.

CONCLUSIONS

A nutrition-balanced protein- and micronutrient-rich dietary pattern and adequate iron dietary pattern for mothers throughout pregnancy may be beneficial to children's neurodevelopment.

Use of Sequential Hot-Deck Imputation for Missing Health Care Systems Data for Population Health Research

Electronic medical record (EMR) data present many opportunities for population health research. The use of EMR data for population risk models can be impeded by the high proportion of missingness in key patient variables. Common approaches like complete case analysis and multiple imputation may not be appropriate for some population health initiatives that require a single, complete analytic data set. In this study, we demonstrate a sequential hot-deck imputation (HDI) procedure to address missingness in a set of cardiometabolic measures in an EMR data set. We assessed the performance of sequential HDI within the individual variables and a commonly used composite risk score. A data set of cardiometabolic measures based on EMR data from 2 large urban hospitals was used to create a benchmark data set with simulated missingness. Sequential HDI was applied, and the resulting data were used to calculate atherosclerotic cardiovascular disease risk scores. The performance of the imputation approach was assessed using a set of metrics to evaluate the distribution and validity of the imputed data. Of the 567,841 patients, 65% had at least 1 missing cardiometabolic measure. Sequential HDI resulted in the distribution of variables and risk scores that reflected those in the simulated data while retaining correlation. When stratified by age and sex, risk scores were plausible and captured patterns expected in the general population. The use of sequential HDI was shown to be a suitable approach to multivariate missingness in EMR data. Sequential HDI could benefit population health research by providing a straightforward, computationally nonintensive approach to missing EMR data that results in a single analytic data set.

Spectrum of prevalent cardiovascular diseases in urban Port-au-Prince, Haiti: a population-based cross-sectional study

Background

Eighty percent of global cardiovascular disease (CVD) is projected to occur in low- and middle -income countries (LMICs), yet local epidemiological data are scarce. We provide the first population-based, adjudicated CVD prevalence estimates in Port-au-Prince, Haiti to describe the spectrum of heart disease and investigate associated risk factors.

Methods

Demographic, medical history, clinical, imaging and laboratory data were collected among adults recruited using multistage random sampling from 2019 to 2021. Prevalent CVD (heart failure, stroke, ischemic disease) were adjudicated using epidemiological criteria similar to international cohorts. Multivariable Poisson regressions assessed relationships between risk factors and prevalent CVD.

Findings

Among 3003 participants, median age was 40 years, 58.1% were female, 70.2% reported income <1 USD/day, and all identified as Black Haitian. CVD age-adjusted prevalence was 14.7% (95% CI 13.3%, 16.5%), including heart failure (11.9% [95% CI 10.5%, 13.5%]), stroke (2.4% [95% CI 1.9%, 3.3%]), angina (2.1% [95% CI 1.6%, 2.9%]), myocardial infarction (1.0% [95% CI 0.6%, 1.8%]), and transient ischemic attack (0.4% [95% CI 0.2%, 1.0%]). Among participants with heart failure, median age was 57 years and 68.5% of cases were among women. The most common subtype was heart failure with preserved ejection fraction (80.4%). Heart failure was associated with hypertension, obesity, chronic kidney disease, depression, and stress.

Interpretation

Early-onset heart failure prevalence is alarmingly high in urban Haiti and challenge modelling assumptions that ischemic heart disease and stroke dominate CVDs in LMICs. These data underscore the importance of local population-based epidemiologic data within LMICs to expedite the selection and implementation of evidence-based cardiovascular health policies targeting each country's spectrum of heart disease.

Funding

This study was funded by NIH grants R01HL143788, D43TW011972, and K24HL163393, clinicaltrials.govNCT03892265.

Combining artificial neural networks and a marginal structural model to predict the progression from depression to Alzheimer's disease

Introduction

Decades of research in population health have established depression as a likely precursor to Alzheimer's disease. A combination of causal estimates and machine learning methods in artificial intelligence could identify internal and external mediating mechanisms that contribute to the likelihood of progression from depression to Alzheimer's disease.

Methods

We developed an integrated predictive model, combining the marginal structural model and an artificial intelligence predictive model, distinguishing between patients likely to progress from depressive states to Alzheimer's disease better than each model alone.

Results

The integrated predictive model achieved substantial clinical relevance when using the area under the curve measure. It performed better than the traditional statistical method or a single artificial intelligence method alone.

Discussion

The integrated predictive model could form a part of a clinical screening tool that identifies patients who are likely to progress from depression to Alzheimer's disease for early behavioral health interventions. Given the high costs of treating Alzheimer's disease, our model could serve as a cost-effective intervention for the early detection of depression before it progresses to Alzheimer's disease.

Impact of Postoperative Radiotherapy on the Prognosis of Early-Stage (pT1-2N0M0) Oral Tongue Squamous Cell Carcinoma

PURPOSE

To identify subgroups of patients with early-stage (pT1-2N0M0) oral tongue squamous cell carcinoma (OTSCC) who may benefit from postoperative radiotherapy (PORT).

PATIENTS AND METHODS

This retrospective cohort study included 528 patients diagnosed between October 2009 and December 2021. Clinicopathological characteristics and treatments with or without PORT were analyzed for their impact on outcomes.

RESULTS

Among 528 patients who underwent radical surgery (median age, 62 years [IQR, 52-69]), 145 (27.5%) also underwent PORT. Multivariate analyses revealed that PORT was associated with improved survival outcomes, whereas moderate-to-poor differentiation, perineural infiltration (PNI), lymphovascular invasion (LVI), and increasing depth of invasion (DOI) were associated with poorer survival outcomes. For patients with moderate-to-poor differentiation, the surgery + PORT group showed improved outcomes compared with the surgery-alone group. After propensity score matching, the results were as follows: overall survival (OS), 97% versus 69%, P = .003; disease-free survival (DFS), 88% versus 50%, P = .001. After excluding cases with PNI/LVI, the differences persisted: OS, 97% versus 82%, P = .040; DFS, 87% versus 64%, P = .012. Similar survival benefits were observed in 104 patients with PNI and/or LVI (OS, 81% v 58%; P = .022; DFS, 76% v 47%; P = .002). In subgroups with DOI >5 mm or close margins, PORT contributed to improved DFS (80% v 64%; P = .006; 92% v 66%; P = .049) but did not significantly affect OS.

CONCLUSION

Patients with moderately-to-poorly differentiated pT1-2N0M0 OTSCC benefited from PORT. Our study provided evidence that patients with PNI and/or LVI who underwent PORT had improved survival. PORT also offered DFS benefit among patients with DOI >5 mm.

Farit NA. The Efficacy of Elonide Nasal Corticosteroids in Managing Allergic Rhinitis: A Randomized, Double-Blinded Trial

Background: Mometasone furoate nasal spray is efficacious in relieving allergic rhinitis symptoms. The objectives of this study were, firstly, to compare the efficacy of Elonide to Nasonex® and a placebo and secondly, to investigate the side effects of Elonide. Method: This was a prospective, single-centered, double blinded, randomized, placebo-controlled, non-inferiority trial. A total of 163 participants from the Otorhinolaryngology Clinic, Hospital Canselor Tuanku Muhriz (HCTM), were randomized into three treatment groups receiving Elonide (n = 56), Nasonex® (n = 54), and placebo (n = 53) nasal sprays using an online randomizer (Random.org). Treatment was administered for 4 weeks. The primary outcome measure was the Total Nasal Resistance (TNR), and the secondary outcomes were the Visual Analogue Score (VAS) and the Rhinoconjunctivitis Quality of Life Questionnaire (RQOLQ) score. Side effects were recorded. Results: There were significant improvements for all groups from baseline. The Elonide group had the greatest mean difference for all primary and secondary outcomes compared to Nasonex® and the placebo (0.77 ± 2.44 vs. 0.35 ± 1.16, p = 1.00 vs. 0.17 ± 0.82, p = 0.01). Elonide is non-inferior to Nasonex (p = 1.00) and superior to the placebo (p < 0.05). The highest side effects reported were for Nasonex (n = 14, 26%), followed by the placebo (n = 8, 16%) and Elonide (n = 6, 12%); headaches (n = 9, 17%) and sore throat (n = 9, 17%) were the most common. Conclusions: Elonide has similar efficacy to Nasonex® when compared to a placebo in the treatment of AR in adults. Elonide is safe and tolerable, with fewer side effects and no adverse side effects.

Association between albumin-corrected anion gap and in-hospital mortality of intensive care patients with trauma: A retrospective study based on MIMIC-Ⅲ and Ⅳ databases

BACKGROUND

To investigate the correlation between albumin-corrected anion gap(ACAG) within the first 24 hours of admission and in-hospital mortality in trauma patients in intensive care unit(ICU).

MATERIALS AND METHODS

We utilized the MIMIC-Ⅲ and MIMIC-Ⅳ databases to examine trauma patients admitted to the ICU. The relationship between ACAG and in-hospital mortality in trauma patients was analyzed using Receiver Operating Characteristic(ROC) curve, Kaplan-Meier (K-M) survival curve, and Cox regression model. Propensity score matching (PSM) and subgroup analysis were conducted to enhance stability and reliability of the findings. Mortality at 30-day and 90-day served as secondary outcomes.

RESULTS

The study enrolled a total of 1038 patients. The AUC for ACAG (0.701, 95%CI: 0.652-0.749) was notably higher than that for anion gap and albumin. The Log-rank test revealed that the optimal cut-off point of ACAG for predicting in-hospital mortality was determined to be 20.375mmol/L. The multivariate Cox regression analysis demonstrated an independent association between high ACAG level and a higher risk of in-hospital mortality (HR = 3.128, 95% CI: 1.615-6.059). After PSM analysis, a matched cohort consisting of 291 subjects was obtained. We found no signifcant interaction in most stratas. Finally, The in-hospital, 30-day, and 90-day survival rates in the high ACAG group exhibited a statistically decrease compared to those in the low ACAG group both pre- and post-matching.

CONCLUSION

The elevated level of ACAG was found to be independently associated with increased in-hospital mortality among trauma patients in the ICU.

Cost-effectiveness of prehabilitation of elderly frail or pre-frail patients prior to elective surgery (PRAEP-GO) versus usual care - Protocol for a health economic evaluation alongside a randomized controlled trial

BACKGROUND

Prehabilitation aims to improve patients' functional capacity before surgery to reduce perioperative complications, promote recovery and decrease probability of disability. The planned economic evaluation is performed alongside a large German multi-centre pragmatic, two-arm parallel-group, randomized controlled trial on prehabilitation for frail elderly patients before elective surgery compared to standard care (PRAEP-GO RCT). The aim is to determine the cost-effectiveness and cost-utility of prehabilitation for frail elderly before an elective surgery.

METHODS

The planned health economic evaluation comprises cost-effectiveness, and cost-utility analyses. Analyses are conducted in the German context from different perspectives including the payer perspective, i.e. the statutory health insurance, the societal perspective and the health care provider perspective. Data on outcomes and costs, are collected alongside the ongoing PRAEP-GO RCT. The trial population includes frail or pre-frail patients aged ≥70 years with planned elective surgery. The intervention consists of frailty screening (Fried phenotype), a shared decision-making conference determining modality (physiotherapy and unsupervised physical exercises, nutrition counselling, etc.) and setting (inpatient, day care, outpatient etc.) of a 3-week individual multimodal prehabilitation prior to surgery. The control group receives standard preoperative care. Costs include the intervention costs, the costs of the index hospital stay for surgery, and health care resources consumed during a 12-month follow-up. Clinical effectiveness outcomes included in the economic evaluation are the level of care dependency, the degree of disability as measured by the WHO Disability Assessment Schedule 2.0 (WHODAS 2.0), quality-adjusted life years (QALY) derived from the EQ-5D-5L and the German utility set, and complications occurring during the index hospital stay. Each adopted perspective considers different types of costs and outcomes as outlined in the protocol. All analyses will feature Intention-To-Treat analysis. To explore methodological and parametric uncertainties, we will conduct probabilistic and deterministic sensitivity analyses. Subgroup analyses will be performed as secondary analyses.

DISCUSSION

The health economic evaluation will provide insights into the cost-effectiveness of prehabilitation in older frail populations, informing decision-making processes and contributing to the evidence base in this field. Potential limitation includes a highly heterogeneous trial population.

TRIAL REGISTRATION

PRAEP-GO RCT: NCT04418271; economic evaluation: OSF ( https://osf.io/ecm74 ).

Mindfulness intervention effect on endometriosis-related pain dimensions and its mediator role on stress and vitality: a path analysis approach

Endometriosis-related pain is supposedly mainly responsible for generating psychological stress and deteriorating the quality of life. However, the interaction between these factors has not been investigated, considering its multidimensional nature and through the path of effects of psychosocial approaches. The present study aims to investigate the effect of a brief mindfulness-based intervention (bMBI) on pain dimensions and its mediator role on psychological stress and QoL-Vitality improvement. A secondary analysis of a pilot randomized controlled trial using a series of parallel and serial mediators was carried out. The results showed that bMBI improves the sensory (B = -6.09 [-9.81, -2.52], β = -0.42) and affective (B = -3.40 [-5.02, -1.80], β = -0.47) pain. The bMBI effect on psychological stress reduction was mediated by these changes in sensory (B = -2.81 [-6.06, -0.41], β = -0.21) and affective (B = -1.97 [-5.07, -0.17], β = -0.15) pain. Serial sensory pain and psychological stress reduction (B = 2.27 [0.11, 5.81], β = -0.09) mediated the bMBI effect on quality of life vitality. Meditation training promotes additional improvement in affective and sensory pain characteristics through which psychological stress is reduced. The sensory pain dimension must be positively impacted in combination with psychological stress for the bMBI improves women's vitality. Adding a psychosocial intervention like meditation training to the standard treatment plan may be required for some women to achieve the needed changes to restore well-being.

Potential causal links of long-term air pollution with lung cancer incidence: From the perspectives of mortality and hospital admission in a large cohort study in southern China

Evidence on the potential causal links of long-term air pollution exposure with lung cancer incidence (reflected by mortality and hospital admission) was limited, especially based on large cohorts. We examined the relationship between lung cancer and long-term exposure to particulate matter (PM, including PM2.5 , PM10 and PM10-2.5 ) and nitrogen dioxide (NO2 ) among a large cohort of general Chinese adults using causal inference approaches. The study included 575 592 participants who were followed up for an average of 8.2 years. The yearly exposure of PM and NO2 was estimated through satellite-based random forest approaches and the ordinary kriging method, respectively. Marginal structural Cox models were used to examine hazard ratios (HRs) of mortality and hospital admission due to lung cancer following air pollution exposure, adjusting for potential confounders. The HRs of mortality due to lung cancer were 1.042 (95% confidence interval [CI]: 1.033-1.052), 1.032 (95% CI:1.024-1.041) and 1.052 (95% CI:1.041-1.063) for each 1 μg/m3 increase in PM2.5 , PM10 and NO2 , respectively. In addition, we observed statistically significant effects of PMs on hospital admission due to lung cancer. The HRs (95%CI) were 1.110 (1.027-1.201), 1.067 (1.020-1.115) and 1.079 (1.010-1.153) for every 1 μg/m3 increase in PM2.5 , PM10 , PM10-2.5 , respectively. Furthermore, we found larger effect estimates among the elderly and those who exercised more frequently. We provided the most comprehensive evidence of the potential causal links between two outcomes of lung cancer and long-term air pollution exposure. Relevant policies should be developed, with special attention to protecting the vulnerable groups of the population.

Imputing missing sleep data from wearables with neural networks in real-world settings

Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.

Linking maternal disrupted interaction and infant limbic volumes: The role of infant cortisol output

Despite a large animal literature documenting the role of low maternal nurturance and elevated glucocorticoid production on offspring limbic development, these pathways have not yet been assessed during human infancy. Informed by animal models, the present study examined whether 1) maternal disrupted interaction is related to infant cortisol levels, 2) infant cortisol levels are associated with infant limbic volumes, and 3) infant cortisol levels mediate associations between maternal disrupted interaction and infant limbic volumes. Participants included 57 mother-infant dyads. Infant saliva was measured at one time point before and two time points after the Still-Face Paradigm (SFP) at age 4 months. Five aspects of maternal disrupted interaction were coded during the SFP reunion episode. Between 4 and 25 months (M age = 11.74 months, SD = 6.12), under natural sleep, infants completed an MRI. Amygdala and hippocampal volumes were calculated via automated segmentation. Results indicated that 1) maternal disrupted interaction, and specifically disoriented interaction, with the infant was associated with higher infant salivary cortisol (AUCg) levels during the SFP, 2) higher infant AUCg was related to enlarged bilateral amygdala and hippocampal volumes, and 3) infant AUCg mediated the relation between maternal disrupted interaction and infant amygdala and hippocampal volumes. Findings are consistent with controlled animal studies and provide evidence of a link between increased cortisol levels and enlarged limbic volumes in human infants. Results further suggest that established interventions to decrease maternal disrupted interaction could impact both infant cortisol levels and infant limbic volumes.

The Effectiveness of the StaySafe Intervention Using a Paradigm for Predicting Missing Outcome Data

Sample attrition is a confounding issue in the analysis of data collected in follow-up studies. The present study uses a regression procedure that includes a propensity score as a predictor in estimating imputed data. The utility of the procedure was addressed by comparing results from this augmented data with those from the original data. Data were from a randomized controlled study testing the utility of a tablet-based intervention designed to improve decision-making with respect to health risk behaviors. Outcomes included self-reported testing for HIV, STD, and hepatitis. Two samples were used (163 in community facilities and 348 in residential facilities). Seventy-eight in the community sample and 238 in the residential sample completed follow-up surveys. Propensity scores based on a stepwise logistic regression were used to make the calibration sample and the missing data sample as close as possible. Multilevel analysis was performed for each outcome and multiple imputation compared estimated mean differences for the augmented and original analyses. The model imputing missing data was effective for the three outcomes and increased power. Least square mean differences between augmented and original data appeared to be essentially the same for most of the outcomes. This protocol has been registered with https://www.clinicaltrials.gov/(NCT02777086).

Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse

BACKGROUND

Data loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make better use of valuable medical data.

METHODS

In this paper, five popular imputation methods including mean imputation, expectation-maximization (EM) imputation, K-nearest neighbors (KNN) imputation, denoising autoencoders (DAE) and generative adversarial imputation nets (GAIN) are employed on an incomplete clinical data with 28,274 cases for vaginal prolapse prediction. A comprehensive comparison study for the performance of these methods has been conducted through certain classification criteria. It is shown that the prediction accuracy can be greatly improved by using the imputed data, especially by GAIN. To find out the important risk factors to this disease among a large number of candidate features, three variable selection methods: the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the broken adaptive ridge (BAR) are implemented in logistic regression for feature selection on the imputed datasets. In pursuit of our primary objective, which is accurate diagnosis, we employed diagnostic accuracy (classification accuracy) as a pivotal metric to assess both imputation and feature selection techniques. This assessment encompassed seven classifiers (logistic regression (LR) classifier, random forest (RF) classifier, support machine classifier (SVC), extreme gradient boosting (XGBoost) , LASSO classifier, SCAD classifier and Elastic Net classifier)enhancing the comprehensiveness of our evaluation.

RESULTS

The proposed framework imputation-variable selection-prediction is quite suitable to the collected vaginal prolapse datasets. It is observed that the original dataset is well imputed by GAIN first, and then 9 most significant features were selected using BAR from the original 67 features in GAIN imputed dataset, with only negligible loss in model prediction. BAR is superior to the other two variable selection methods in our tests.

CONCLUDES

Overall, combining the imputation, classification and variable selection, we achieve good interpretability while maintaining high accuracy in computer-aided medical diagnosis.

Cognitive-affective changes mediate the mindfulness-based intervention effect on endometriosis-related pain and mental health: A path analysis approach

BACKGROUND

There is poor knowledge about the therapeutic mechanisms of the conservative interventions for endometriosis. We hypothesized that the effects of a brief mindfulness-based intervention (bMBI) on pelvic pain intensity (PPI), pain unpleasantness (PU) and quality of life mental health (QoL-MH) are mediated by direct and indirect paths of changes in pain catastrophizing (PC), positive affect (PA) and negative affect (NA).

METHODS

A secondary analysis of a pilot randomized controlled trial of women with endometriosis, assigned to standard medical treatment (n = 32) and standard medical treatment plus bMBI (n = 31). We tested a series of parallel and serial mediators (PC, PA and NA) of the relationship between bMBI and outcomes (PPI, PU and QoL-MH).

RESULTS

The bMBI group demonstrated improvement in PA (Cohen's f2  = 0.12 [0.01, 0.36]), decreases in NA (Cohen's f2  = 0.06 [0.00, 0.24]) and PC (Cohen's f2  = 0.16 [0.02, 0.42]). The PC reduction mediated the effect of the bMBI on PPI and PU directly; however, the PC effect through PA increase mediated the PU marginally but not PPI changes. bMBI effect on Qol-MH was mediated directly by PA and NA. The PC improved Qol-MH through PA increase and Pain decrease but not via NA.

CONCLUSIONS

Our findings showed that bMBI impacts pain through changes in pain-related cognitive-affective factors. bMBI can improve QoL-MH by multiple pathways, including but not limited to pain reduction, highlighting the independent potential of improvement in affect to restore mental health in endometriosis.

SIGNIFICANCE

Brief mindfulness-based intervention improves endometriosis pain through pain-related cognitive-affective factors and quality of life mental health via pain and affect changes unrelated to pain.

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.

The relationship between admission hyperglycaemia and urinary tract infections in geriatric patients with hip fractures

PURPOSE

This study investigated the association of admission hyperglycaemia with catheter-associated urinary tract infections (CAUTIs) and catheter-unassociated urinary tract infections (CUUTIs) in elderly patients with hip fractures.

METHODS

In an observational cohort study of elderly patients with hip fractures, glucose values were collected within 24 h of admission. Urinary tract infections were classified as CAUTIs and CUUTIs. Multivariate logistic regression analysis and propensity score matching obtained adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for urinary tract infections. Subgroup analyses were further explored to investigate the relationship between admission hyperglycemia and urinary tract infections.

RESULTS

The study included 1279 elderly patients with hip fractures, 298 (23.3%) of whom had urinary tract infections upon hospitalization (including 182 CAUTIs and 116 CUUTIs). Propensity score matching indicated that patients with glucose levels exceeding 10.00 mmol/L had significantly higher odds of developing CAUTIs (OR 3.10, 95% CI 1.65-5.82) than those with glucose levels between 4.00-6.09 mmol/L. It is worth noting that patients with blood glucose levels greater than 10.00 mmol/L have a higher susceptibility for CUUTIs (OR 4.42, 95% CI 2.09-9.33) than CAUTIs. The subgroup analyses observed significant interactions between diabetes and CAUTIs (p for interaction = 0.01) and between bedridden time and CUUTIs (p for interaction = 0.04).

CONCLUSIONS

Elderly hip fracture patients with admission hyperglycaemia have an independent association with CAUTIs and CUUTIs. The association is stronger with CUUTIs and necessitates clinician intervention if blood glucose levels at admission exceed 10 mmol/L.

Predictors of transitioning to adult mental health services and associated costs: a cross-country comparison

BACKGROUND

Young people are at risk of falling through the care gap after leaving child and adolescent mental health services (CAMHS) despite an ongoing need for mental health support. Currently, little is known about the predictors of transitioning to adult mental health services (AMHS), and associated healthcare and societal costs as young people cross the transition boundary.

OBJECTIVE

To conduct a secondary data analysis exploring predictors of transitioning or falling through the gap and associated costs.

METHODS

Data were used from a longitudinal study, which followed young people from seven European countries for 2 years after reaching their CAMHS boundary. Predictors of transitioning (including sociodemographic and clinical variables) and longitudinal resource use were compared for 488 young people who transitioned to AMHS versus those who fell through the gap.

FINDINGS

Young people were more likely to transition to AMHS if they were severely ill. Those from Italy, the Netherlands and the UK were more likely to fall through the gap than transition to AMHS. Healthcare costs fell for all young people over the study, with a sharper decrease for those who fell through the gap.

CONCLUSIONS

Total healthcare costs fell for all participants, indicating that the intensity of mental health support reduces for all young people as they cross the CAMHS boundary, regardless of clinical need.

CLINICAL IMPLICATIONS

It is important that alternative forms of mental health support are available for young people who do not meet the AMHS care threshold but still have mental health needs after leaving CAMHS.

Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point.

Effects of a culturally adapted counselling service for low-income ethnic minorities experiencing mental distress: a pragmatic randomised clinical trial

BACKGROUND

Culturally competent early mental health interventions for ethnic minorities (EMs) with no formal diagnoses are needed.

OBJECTIVES

To determine whether 8-12 weeks culturally adapted counselling (CAC) is better than waiting (waitlist (WL) group) to reduce depressive and anxiety symptoms and stress levels among EMs with elevated mental distress.

METHODS

Hong Kong EMs with mild and above-mild mental distress were randomly assigned to CAC or WL in this pragmatic, randomised, WL-controlled trial. The CAC group received the intervention after randomisation and the WL group received the intervention after 8-12 weeks (T1). The prespecified primary outcomes were depressive and anxiety symptoms and stress levels measured by the Depression, Anxiety and Stress subscales of the Depression, Anxiety and Stress Scale (DASS-D, DASS-A and DASS-S, respectively) at postintervention (T1, 8-12 weeks).

FINDINGS

A total of 120 participants were randomly assigned to either CAC (n=60) or WL (n=60), of whom 110 provided primary outcome data. At T1, CAC led to significantly lower depressive and anxiety symptom severity and stress levels compared with waiting, with unstandardised regression coefficients of -8.91 DASS-D points (95% CI -12.57 to -5.25; d=-0.90),-6.33 DASS-A points (95% CI -9.81 to -2.86; d=-0.68) and -8.60 DASS-S points (95% CI -12.14 to -5.06; d=-0.90).

CONCLUSIONS

CAC clinically outperformed WL for mild and above-mild levels of mental distress in EMs.

CLINICAL IMPLICATIONS

Making CAC routinely available for EMs in community settings can reduce healthcare burden.

TRIAL REGISTRATION NUMBER

NCT04811170.

Handling missing covariate data in clinical studies in haematology

Missing data are frequently encountered across studies in clinical haematology. Failure to handle these missing values in an appropriate manner can complicate the interpretation of a study's findings, as estimates presented may be biased and/or imprecise. In the present work, we first provide an overview of current methods for handling missing covariate data, along with their advantages and disadvantages. Furthermore, a systematic review is presented, exploring both contemporary reporting of missing values in major haematological journals, and the methods used for handling them. A principal finding was that the method of handling missing data was explicitly specified in a minority of articles (in 76 out of 195 articles reporting missing values, 39%). Among these, complete case analysis and the missing indicator method were the most common approaches to dealing with missing values, with more complex methods such as multiple imputation being extremely rare (in 7 out of 195 articles). An example analysis (with associated code) is also provided using hematopoietic stem cell transplantation data, illustrating the different approaches to handling missing values. We conclude with various recommendations regarding the reporting and handling of missing values for future studies in clinical haematology.

Increasing efficiency of SVMp+ for handling missing values in healthcare prediction

Missing data presents a challenge for machine learning applications specifically when utilizing electronic health records to develop clinical decision support systems. The lack of these values is due in part to the complex nature of clinical data in which the content is personalized to each patient. Several methods have been developed to handle this issue, such as imputation or complete case analysis, but their limitations restrict the solidity of findings. However, recent studies have explored how using some features as fully available privileged information can increase model performance including in SVM. Building on this insight, we propose a computationally efficient kernel SVM-based framework (l2-SVMp+) that leverages partially available privileged information to guide model construction. Our experiments validated the superiority of l2-SVMp+ over common approaches for handling missingness and previous implementations of SVMp+ in both digit recognition, disease classification and patient readmission prediction tasks. The performance improves as the percentage of available privileged information increases. Our results showcase the capability of l2-SVMp+ to handle incomplete but important features in real-world medical applications, surpassing traditional SVMs that lack privileged information. Additionally, l2-SVMp+ achieves comparable or superior model performance compared to imputed privileged features.

How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion

Psychological researchers often use standard linear regression to identify relevant predictors of an outcome of interest, but challenges emerge with incomplete data and growing numbers of candidate predictors. Regularization methods like the LASSO can reduce the risk of overfitting, increase model interpretability, and improve prediction in future samples; however, handling missing data when using regularization-based variable selection methods is complicated. Using listwise deletion or an ad hoc imputation strategy to deal with missing data when using regularization methods can lead to loss of precision, substantial bias, and a reduction in predictive ability. In this tutorial, we describe three approaches for fitting a LASSO when using multiple imputation to handle missing data and illustrate how to implement these approaches in practice with an applied example. We discuss implications of each approach and describe additional research that would help solidify recommendations for best practices. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Frequency of Consuming Breakfast Meals and After-Dinner Snacks Is not Associated with Postmenopausal Breast Cancer Risk: Women's Health Initiative Observational Study

BACKGROUND

There has been little investigation into how the timing of meals and eating occasions associates with postmenopausal breast cancer risk.

OBJECTIVE

We examined the association between the frequency of consuming breakfast meals and after-dinner snacks with the risk for postmenopausal breast cancer.

METHODS

A prospective analysis of 74,825 postmenopausal women aged 49 to 81 y from the Women's Health Initiative Observational Study cohort. Breakfast and after-dinner snack intake were assessed at year 1 examination. Risk for invasive and in situ breast cancer diagnosed before 28 February 2020 was modeled with multivariable Cox proportional hazards regression models according to breakfast and after-dinner snack consumption frequencies. The models were adjusted for age, self-identified race/ethnicity, education, income, physical activity, smoking, alcohol intake, diet quality score (Healthy Eating Index 2015), energy intake, diabetic status, hormone therapy, and BMI.

RESULTS

During the follow-up period, 5313 participants were diagnosed with invasive breast cancer and 1197 participants with in situ breast cancer. Compared with participants who did not eat breakfast, those with daily breakfast consumption was not associated with invasive breast cancer (HR: 1.04; 95% CI: 0.9, 1.19) nor in situ (HR: 1.25; 95% CI: 0.91, 1.74) breast cancer. There were monotonic higher point estimates of in situ breast cancer for each higher category of breakfast intake from 0 to 7 times per week (P-trend = 0.04, Wald test). Compared with consumption of daily after-dinner snacks, avoidance of after-dinner snacks was not associated with invasive breast cancer (HR: 0.97; 95% CI: 0.87, 1.08) nor in situ (HR: 1.12; 95% CI: 0.89, 1.42) breast cancer.

CONCLUSIONS

There was no association between intake frequency of breakfast meals or after-dinner snack habits and with risk of breast cancer in postmenopausal women.

Sex Differences in Survival from Neuroendocrine Neoplasia in England 2012–2018: A Retrospective, Population-Based Study

Pre-clinical studies have suggested sex hormone signalling pathways may influence tumorigenesis in neuroendocrine neoplasia (NEN). We conducted a retrospective, population-based study to compare overall survival (OS) between males and females with NEN. A total of 14,834 cases of NEN diagnosed between 2012 and 2018, recorded in England’s National Cancer Registry and Analysis Service (NCRAS), were analysed. The primary outcome was OS with 5 years maximum follow-up. Multivariable analysis, restricted mean survival time and mediation analysis were performed. Appendiceal, pulmonary and early-stage NEN were most commonly diagnosed in females; stomach, pancreatic, small intestinal, colonic, rectal and later-stage NEN were more often diagnosed in males. Females displayed increased survival irrespective of the stage, morphology or level of deprivation. On average, they survived 3.62 (95% CI 1.73–5.90) to 10.26 (6.6–14.45) months longer than males; this was statistically significant in NEN of the lung, pancreas, rectum and stomach (p < 0.001). The stage mediated improved survival in stomach, lung, and pancreatic NEN but not in rectal NEN. The reasons underlying these differences are not yet understood. Overall, females diagnosed with NEN tend to survive longer than males, and the stage at presentation only partially explains this. Future research, as well as prognostication and treatment, should consider sex as an important factor.

Comparison of supraclavicular surgery plus radiotherapy versus radiotherapy alone in breast cancer patients with synchronous ipsilateral supraclavicular lymph node metastasis: a multicenter retrospective study

PURPOSE

To evaluate and compare the outcomes of supraclavicular lymph node dissection plus radiotherapy (RT) and RT alone for patients with synchronous ipsilateral supraclavicular lymph node metastasis.

METHODS

In all, 293 patients with synchronous ipsilateral supraclavicular lymph node metastasis across three centers were included. Of these, 85 (29.0%) received supraclavicular lymph node dissection plus RT (Surgery + RT) and 208 (71.0%) received RT alone. All patients received preoperative systemic therapy followed by mastectomy or lumpectomy and axillary dissection. Supraclavicular recurrence-free survival (SCRFS), locoregional recurrence-free survival (LRRFS), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS) were evaluated by using the Kaplan-Meier method and multivariate Cox models. Multiple imputation was used for missing data.

RESULTS

The median follow-up duration of the RT and Surgery + RT groups were 53.7 and 63.5 months, respectively. For the RT and Surgery + RT groups, the 5-year SCRFS rates were 91.7% vs. 85.5% (P=0.522), LRRFS rates were 79.1% vs. 73.1% (P=0.412), DMFS rates were 60.4 vs. 58.8% (P=0.708), DFS rates were 57.6% vs. 49.7% (P=0.291), and OS rates were 71.9% vs. 62.2% (P=0.272), respectively. There was no significant effect on any outcome when comparing Surgery +RT versus RT alone in the multivariate analysis. Based on four risk factors of DFS, patients were classified into three risk groups: the intermediate- and high-risk groups had significantly lower survival outcomes than the low-risk group. Surgery +RT did not improve outcomes of any risk group compared with RT alone.

CONCLUSIONS

Patients with synchronous ipsilateral supraclavicular lymph node metastasis may not benefit from supraclavicular lymph node dissection. Distant metastasis remained the major failure pattern, especially for intermediate- and high-risk groups.

Risk prediction of hepatobiliary and pancreatic cancers in elderly Chinese: The Dongfeng-Tongji cohort

AIM

Hepatobiliary and pancreatic (HBP) cancers are among the deadliest malignancies. The objective of the study is to build cost-effective models to identify high-risk individuals for early diagnosis and substantially to reduce the burden of HBP cancers.

METHODS

Based on the prospective Dongfeng-Tongji cohort with ∼6 years follow-up, we identified 162 incident cases of hepatocellular carcinoma (HCC), 53 of biliary tract cancer (BTC), and 58 of pancreatic cancer (PC). We matched three controls to each case by age, sex, and hospital. We applied conditional logistic regression to identify predictive clinical variables, from which we constructed clinical risk scores (CRSs). We evaluated the utility of CRSs in stratifying high-risk individuals by 10-fold cross-validation.

RESULTS

Among 50 variables we screened, 6 were independent predictors of HCC, with the top ones being hepatitis (OR = 8.51, 95% CI (3.83, 18.9)), plateletcrit (OR = 0.57, 95% CI (0.42, 0.78)), and alanine aminotransferase (OR = 2.06, 95% CI (1.39, 3.06)). Gallstone (OR = 2.70, 95% CI (1.17, 6.24)) and direct bilirubin (OR = 1.58, 95% CI (1.08, 2.31)) were predictive of BTC, while hyperlipidemia (OR = 2.56, 95% CI (1.12, 5.82)) and fasting blood glucose (OR = 2.00, 95% CI (1.26, 3.15)) were predictive of PC. The CRSs achieved AUCs of 0.784 for HCC, 0.648 for BTC, and 0.666 for PC, respectively. When applying to the full cohort with age and sex included as predictors, the AUCs were increased to 0.818, 0.704, and 0.699, respectively.

CONCLUSIONS

Disease history and routine clinical variables are predictive of incident HBP cancers in elderly Chinese.

The Personal Suicide Stigma Questionnaire (PSSQ): Relation to Self-Esteem, Well-Being, and Help-Seeking

Two studies are reported that extend the evidence base for use of the Personal Stigma of Suicide Questionnaire (PSSQ). In the first study (N = 117), the Rosenberg Self-Esteem Scale, the WHO-5 measure of well-being, as well as measures of suicidality were examined in relation to the PSSQ. A self-selected sub-sample (N = 30) completed the PSSQ after an interval of two months. In line with the stigma internalization model, when demographic variables and suicidality were accounted for, the PSSQ self-blame subscale was the most significant predictor of self-esteem. As for well-being, the rejection subscale was involved as well as self-blame. The retest stability of the PSSQ for the sub-sample was 0.85 and coefficient alpha for the total sample was 0.95, indicating both good stability and internal consistency for the scale. In the second study (N = 140), PSSQ was studied in relation to intention to seek help from four sources in the case of suicidal ideation. The strongest relationship with PSSQ was with intention not to seek help from anyone (r = 0.35). When other variables were included in the prediction of help-seeking from a general medical practitioner, family or friends, or from nobody, the only significant PSSQ correlate was minimization. For help-seeking from a psychologist or psychiatrist, the most significant predictor was judged helpfulness of prior contact with them. The results from these studies strengthen previous findings of the construct validity of the PSSQ and point to its utility in understanding barriers to help-seeking among those experiencing suicidality.

Real-World Data Validation of NAPOLI-1 Nomogram for the Prediction of Overall Survival in Metastatic Pancreatic Cancer

BACKGROUND

The nomogram derived from the pivotal phase III NAPOLI-1 study demonstrated a significant ability to predict median overall survival (OS) in gemcitabine-refractory metastatic pancreatic ductal adenocarcinoma (PDAC) treated with liposomal irinotecan plus fluorouracil and leucovorin (nal-IRI+5-FU/LV). However, the NAPOLI-1 nomogram has not been validated in a real-world setting and therefore the applicability of the NAPOLI-1 nomogram in daily practice remains unknown. This study aims to evaluate the NAPOLI-1 nomogram in a multicenter real-world cohort.

METHODS

The NAPOLI-1 nomogram was applied to a previously established cohort of metastatic PDAC patients treated with nal-IRI+5-FU/LV in nine participating centers in Taiwan. Patients were divided into three risk groups according to the NAPOLI-1 nomogram. The survival impact of relative dose intensity at 6 weeks (RDI at 6 weeks) in different risk groups was also investigated.

RESULTS

Of the 473 included patients, the median OSs of patients classified as low (n = 156), medium (n = 186), and high (n = 131) risk were 10.9, 6.3, and 4.3 months, respectively (p < 0.0001). The survival impact of RDI at 6 weeks remained significant after stratification by risk groups, adjustment with Cox regression, inverse probability weighting, or propensity score matching.

CONCLUSIONS

Our results support the usefulness of the NAPOLI-1 nomogram for risk stratification in gemcitabine-refractory metastatic PDAC treated with nal-IRI+5-FU/LV in daily practice. We further showed that the RDI at 6 weeks is an independent prognostic factor beyond the NAPOLI-1 nomogram.

Physical fitness throughout chemotherapy in children with acute lymphoblastic leukaemia and lymphoma

Acute lymphoblastic leukaemia/lymphoma (ALL/LBL) and its treatment interfere with normal physical functioning. However, it remains unclear how physical fitness (PF) is affected throughout treatment for ALL/LBL. Sixty-two patients (2.1 to 18.3 years) treated for ALL/LBL underwent four physical tests at nine timepoints from baseline up to 6 months post-treatment. We assessed muscle strength of the quadriceps and tibialis anterior, standing broad jump test (SBJ) for functional mobility and six-minute walk test (6MWT) for endurance. One-sample t-tests were used to compare our results to the norm at each timepoint. Norm-referenced Z-scores were predicted based on time, risk group and age at diagnosis, using linear mixed models. Quadriceps strength, SBJ and 6MWT scores were significantly lower than norm values at all timepoints from diagnosis up to 6 months after maintenance therapy. Significant decreases over time were encountered for quadriceps strength and SBJ, mainly occurring after induction therapy (F = 3.568, p < 0.001 and F = 2.699, p = 0.008, respectively). Age at diagnosis was a significant predictor for tibialis anterior strength (F = 5.266, p = 0.025), SBJ (F = 70.422, p < 0.001) and 6MWT (F = 15.890, p < 0.001) performances, with lower results in adolescents at all timepoints. Six months after treatment, quadriceps strength, 6MWT and SBJ scores remained below expected levels.

CONCLUSION

The decreased quadriceps strength, functional mobility and endurance at all timepoints, with a large deterioration following induction therapy, suggest the need for early interventions, specifically in the adolescent population. The continued low results 6 months after therapy emphasise the importance of long-term rehabilitation.

WHAT IS KNOWN

•Acute lymphoblastic leukaemia is the most common type of cancer among children, with increasing survival rates due to therapeutic improvements. •Acute lymphoblastic leukaemia/lymphoma and its treatment can cause muscle weakness, neuromuscular toxicity and a decreased cardiopulmonary fitness. Together with physical inactivity, this can result in a decreased physical fitness.

WHAT IS NEW

•Quadriceps strength, functional mobility and endurance are decreased during treatment for acute lymphoblastic leukaemia/lymphoma. The lowest measurements are observed after induction therapy, suggesting the need for early interventions. •We observed continued lower results for quadriceps strength, functional mobility and endurance at the end of treatment, up to 6 months after therapy, supporting the need for long-term rehabilitation.

Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review

INTRODUCTION

Observational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing data as missing completely at random, missing at random (MAR) or missing not at random does not allow for a clear assessment of missingness assumptions when missingness arises in more than one variable. This presents challenges for selecting an analytic approach and determining when a sensitivity analysis under plausible alternative missing data assumptions is required. This is particularly pertinent with multiple imputation (MI), which is often justified by assuming data are MAR. The objective of this scoping review is to examine the use of MI in observational studies that address causal questions, with a focus on if and how (a) missingness assumptions are expressed and assessed, (b) missingness assumptions are used to justify the choice of a complete case analysis and/or MI for handling missing data and (c) sensitivity analyses under alternative plausible assumptions about the missingness mechanism are conducted.

METHODS AND ANALYSIS

We will review observational studies that aim to answer causal questions and use MI, published between January 2019 and December 2021 in five top general epidemiology journals. Studies will be identified using a full text search for the term 'multiple imputation' and then assessed for eligibility. Information extracted will include details about the study characteristics, missing data, missingness assumptions and MI implementation. Data will be summarised using descriptive statistics.

ETHICS AND DISSEMINATION

Ethics approval is not required for this review because data will be collected only from published studies. The results will be disseminated through a peer reviewed publication and conference presentations.

TRIAL REGISTRATION NUMBER

This protocol is registered on figshare (https://doi.org/10.6084/m9.figshare.20010497.v1).

Ensemble averaging for categorical variables: Validation study of imputing lost data in 24-h recorded postures of inpatients

Acceleration sensors are widely used in consumer wearable devices and smartphones. Postures estimated from recorded accelerations are commonly used as features indicating the activities of patients in medical studies. However, recording for over 24 h is more likely to result in data losses than recording for a few hours, especially when consumer-grade wearable devices are used. Here, to impute postures over a period of 24 h, we propose an imputation method that uses ensemble averaging. This method outputs a time series of postures over 24 h with less lost data by calculating the ratios of postures taken at the same time of day during several measurement-session days. Whereas conventional imputation methods are based on approaches with groups of subjects having multiple variables, the proposed method imputes the lost data variables individually and does not require other variables except posture. We validated the method on 306 measurement data from 99 stroke inpatients in a hospital rehabilitation ward. First, to classify postures from acceleration data measured by a wearable sensor placed on the patient's trunk, we preliminary estimated possible thresholds for classifying postures as 'reclining' and 'sitting or standing' by investigating the valleys in the histogram of occurrences of trunk angles during a long-term recording. Next, the imputations of the proposed method were validated. The proposed method significantly reduced the missing data rate from 5.76% to 0.21%, outperforming a conventional method.

Aziz NA, Abd. Aziz A. Augmenting ECG Data with Multiple Filters for a Better Emotion Recognition System

A physiological-based emotion recognition system (ERS) with a unimodal approach such as an electrocardiogram (ECG) is not as popular compared to a multimodal approach. However, a single modality has the advantage of lower development and computational cost. Therefore, this study focuses on a unimodal ECG-based ERS. The ECG-based ERS has the potential to become the next mass-adopted consumer application due to the wide availability of wearable and mobile ECG devices in the market. Currently, ECG-inclusive affective datasets are limited, and many of the existing datasets have small sample sizes. Hence, ECG-based ERS studies are stunted by the lack of quality data. A novel multi-filtering augmentation technique is proposed here to increase the sample size of the ECG data. This technique augments the ECG signals by cleaning the data in different ways. Three small ECG datasets labelled according to emotion state are used in this study. The benefit of the proposed augmentation techniques is measured using the classification accuracy of five machine learning algorithms; k-nearest neighbours (KNN), support vector machine, decision tree, random forest and multilayer perceptron. The results show that with the proposed technique, there is a significant improvement in performance for all the datasets and classifiers. KNN classifier improved the most with the augmented data and the reported classification accuracies of over 90%.

Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies

Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional forms with different degrees of missingness. However, when variables with missing data contain skip patterns (i.e. questions not applicable to some survey participants are thus skipped), implementation of MI may not be straightforward. In this research, we compare two approaches for MI when skip-pattern covariates with missing values exist. One approach imputes missing values in the skip-pattern variables only among applicable subjects (denoted as imputation among applicable cases (IAAC)). The second approach imputes skip-pattern covariates among all subjects while using different recoding methods on the skip-pattern variables (denoted as imputation with recoded non-applicable cases (IWRNC)). A simulation study is conducted to compare these methods. Both approaches are applied to the 2015 and 2016 Research and Development Survey data from the National Center for Health Statistics.

Quality of observational studies of clinical interventions: a meta-epidemiological review

BACKGROUND

This meta-epidemiological study aimed to assess methodological quality of a sample of contemporary non-randomised clinical studies of clinical interventions.

METHODS

This was a cross-sectional study of observational studies published between January 1, 2012 and December 31, 2018. Studies were identified in PubMed using search terms 'association', 'observational,' 'non-randomised' 'comparative effectiveness' within titles or abstracts. Each study was appraised against 35 quality criteria by two authors independently, with each criterion rated fully, partially or not satisfied. These quality criteria were grouped into 6 categories: justification for observational design (n = 2); minimisation of bias in study design and data collection (n = 11); use of appropriate methods to create comparable groups (n = 6); appropriate adjustment of observed effects (n = 5); validation of observed effects (n = 9); and authors interpretations (n = 2).

RESULTS

Of 50 unique studies, 49 (98%) were published in two US general medical journals. No study fully satisfied all applicable criteria; the mean (+/-SD) proportion of applicable criteria fully satisfied across all studies was 72% (+/- 10%). The categories of quality criteria demonstrating the lowest proportions of fully satisfied criteria were measures used to adjust observed effects (criteria 20, 23, 24) and validate observed effects (criteria 25, 27, 33). Criteria associated with ≤50% of full satisfaction across studies, where applicable, comprised: imputation methods to account for missing data (50%); justification for not performing an RCT (42%); interaction analyses in identifying independent prognostic factors potentially influencing intervention effects (42%); use of statistical correction to minimise type 1 error in multiple outcome analyses (33%); clinically significant effect sizes (30%); residual bias analyses for unmeasured or unknown confounders (14%); and falsification tests for residual confounding (8%). The proportions of fully satisfied criteria did not change over time.

CONCLUSIONS

Recently published observational studies fail to fully satisfy more than one in four quality criteria. Criteria that were not or only partially satisfied were identified which serve as remediable targets for researchers and journal editors.

Effectiveness of a pharmacist-delivered primary care telemedicine intervention to increase access to pharmacotherapy and specialty treatment for alcohol use problems: Protocol for the alcohol telemedicine consult cluster-randomized pragmatic trial

BACKGROUND

Alcohol use problems are associated with serious medical, mental health and socio-economic consequences. Yet even when patients are identified in healthcare settings, most do not receive treatment, and use of pharmacotherapy is rare. This study will test the effectiveness of the Alcohol Telemedicine Consult (ATC) Service, a novel, personalized telehealth intervention approach for primary care patients with alcohol use problems.

METHODS

This cluster-randomized pragmatic trial, supplemented by qualitative interviews, will include adults with a primary care visit between 9/10/21-3/10/23 from 16 primary care clinics at two large urban medical centers within Kaiser Permanente Northern California, a large, integrated healthcare system. Clinics are randomized to the ATC Service (intervention), including alcohol pharmacotherapy and SBIRT (screening, MI (Motivational Interviewing)-based brief intervention and referral to addiction treatment) delivered by clinical pharmacists, or the Usual Care (UC) arm that provides systematic alcohol SBIRT. Primary outcomes include a comparison of the ATC and UC arms on 1) implementation outcomes (alcohol pharmacotherapy prescription rates, specialty addiction treatment referrals); and 2) patient outcomes (medication fills, addiction treatment initiation, alcohol use, healthcare services utilization) over 1.5 years. A general modeling approach will consider clustering of patients/providers, and a random effects model will account for intra-class correlations across patients within providers and across clinics. Qualitative interviews with providers will examine barriers and facilitators to implementation.

DISCUSSION

The ATC study examines the effectiveness of a pharmacist-provided telehealth intervention that combines pharmacotherapy and MI-based consultation. If effective, the ATC study could affect treatment models across the spectrum of alcohol use problems.

CLINICAL TRIALS REGISTRATION

This study has been registered on ClinicalTrials.gov (NCT05252221).

Individual and organisational factors in the psychosocial work environment are associated with home care staffs' job strain: a Swedish cross-sectional study

BACKGROUND

Home care staff (HCS) provide essential service to enable older adults to age in place. However, unreasonable demands in the work environment to deliver a safe, effective service with high quality has a negative impact on the individual employee's well-being and the care provided to the older adults. The psychosocial work environment is associated with employees´ well-being, although, knowledge regarding which individual and organisational factors that contribute to job strain for HCS is limited. These factors need to be identified to develop targeted interventions and create sustainable work situations for HCS. This study aimed to explore how HCS´s perceived job strain is associated with, and to what extent can be explained by, individual and organisational factors of the psychosocial work environment and psychosomatic health.

METHOD

An explorative cross-sectional questionnaire survey design was used in a large Swedish county. Five home care agencies with a total of 481 HCS were asked to respond to a questionnaire regarding their perceived level of job strain (Strain in Dementia Care Scale), psychosocial work environment (QPS_Nordic³⁴⁺), and psychosomatic health (Satisfaction with Work Questionnaire). Multiple linear regression (MLR) analyses were conducted to explore the association between job strain and individual and organisational factors.

RESULTS

In total, 226 (46%) HCS responded to the questionnaire. Both individual and organisational factors were significant predictors of job strain and explained a variance ranging between 39 to 51% (p = 0.001). The organisational factor job demand and the individual factor feeling worried and restless was most frequently represented in these MRL models. A higher job strain was also associated with adverse outcomes regarding leadership, organisational culture and climate, and control at work.

CONCLUSION

This study indicates that there is an intertwined complexity of individual and organisational factors that are associated with the HCS´s perception of job strain. Implementation of new multidimensional work strategies, such as a reablement approach, could support the development of efficient strategies for HCS and reduce the level of job strain. Policy changes for the provision of home care are also needed to support the development of a sustainable and healthy psychosocial work environment.

Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation

BACKGROUND

The occurrence and timing of mycobacterial culture conversion is used as a proxy for tuberculosis treatment response. When researchers serially sample sputum during tuberculosis studies, contamination or missed visits leads to missing data points. Traditionally, this is managed by ignoring missing data or simple carry-forward techniques. Statistically advanced multiple imputation methods potentially decrease bias and retain sample size and statistical power.

METHODS

We analyzed data from 261 participants who provided weekly sputa for the first 12 weeks of tuberculosis treatment. We compared methods for handling missing data points in a longitudinal study with a time-to-event outcome. Our primary outcome was time to culture conversion, defined as two consecutive weeks with no Mycobacterium tuberculosis growth. Methods used to address missing data included: 1) available case analysis, 2) last observation carried forward, and 3) multiple imputation by fully conditional specification. For each method, we calculated the proportion culture converted and used survival analysis to estimate Kaplan-Meier curves, hazard ratios, and restricted mean survival times. We compared methods based on point estimates, confidence intervals, and conclusions to specific research questions.

RESULTS

The three missing data methods lead to differences in the number of participants achieving conversion; 78 (32.8%) participants converted with available case analysis, 154 (64.7%) converted with last observation carried forward, and 184 (77.1%) converted with multiple imputation. Multiple imputation resulted in smaller point estimates than simple approaches with narrower confidence intervals. The adjusted hazard ratio for smear negative participants was 3.4 (95% CI 2.3, 5.1) using multiple imputation compared to 5.2 (95% CI 3.1, 8.7) using last observation carried forward and 5.0 (95% CI 2.4, 10.6) using available case analysis.

CONCLUSION

We showed that accounting for missing sputum data through multiple imputation, a statistically valid approach under certain conditions, can lead to different conclusions than naïve methods. Careful consideration for how to handle missing data must be taken and be pre-specified prior to analysis. We used data from a TB study to demonstrate these concepts, however, the methods we described are broadly applicable to longitudinal missing data. We provide valuable statistical guidance and code for researchers to appropriately handle missing data in longitudinal studies.

A nomogram for predicting the risk of mortality in patients with acute pancreatitis and Gram-negative bacilli infection

OBJECTIVE

Gram-negative bacilli (GNB) are common pathogens of infection in severe acute pancreatitis (SAP), and their occurrence increases the mortality of SAP. Early identification of SAP severity and prognosis is of great significance to SAP treatment. This study explored risk factors for mortality in patients with SAP and GNB infection and established a model for early prediction of the risk of death in GNB-infected SAP patients.

METHODS

Patients diagnosed with SAP from January 1, 2016, to March 31, 2022, were included, and their baseline clinical characteristics were collected. Univariate logistic regression analysis was performed to screen for death related variables, and concurrently, a Boruta analysis was performed to identify potentially important clinical features associated with mortality. The intersection of the two results was taken for further multivariate logistic regression analysis. A logistic regression model was constructed according to the independent risk factor of death and then visualized with a nomogram. The performance of the model was further validated in the training and validation cohort.

RESULTS

A total of 151 patients with SAP developed GNB infections. Univariate logistic regression analysis identified 11 variables associated with mortality. The Boruta analysis identified 11 clinical features, and 4 out of 9 clinical variables: platelet counts (odds ratio [OR] 0.99, 95% confidence interval [CI] 0.99-1.00; p = 0.007), hemoglobin (OR 0.96, 95% CI 0.92-1; p = 0.037), septic shock (OR 6.33, 95% CI 1.12-43.47; p = 0.044), and carbapenem resistance (OR 7.99, 95% CI 1.66-52.37; p = 0.016), shared by both analyses were further selected as independent risk factors by multivariate logistic regression analysis. A nomogram was used to visualize the model. The model demonstrated good performance in both training and validation cohorts with recognition sensitivity and specificity of 96% and 80% in the training cohort and 92.8% and 75% in the validation cohort, respectively.

CONCLUSION

The nomogram can accurately predict the mortality risk of patients with SAP and GNB infection. The clinical application of this model allows early identification of the severity and prognosis for patients with SAP and GNB infection and identification of patients requiring urgent management thus allowing rationalization of treatment options and improvements in clinical outcomes.

Disparities in Lung-Protective Ventilation in the United States

Background The objective of our study was to determine whether disparities exist in the use of lung-protective ventilation for critically ill mechanically ventilated patients in the United States based on gender, race/ethnicity, or insurance status. Methods This was a secondary data analysis of a prospective multicenter cohort study conducted from 2010 to 2012. The outcome of interest was the proportion of patients receiving tidal volume > 8 mL/kg predicted body weight (PBW). Results There were 1,595 patients in our primary analysis (710 women, 885 men). Women were more likely to receive tidal volumes > 8 mL/kg PBW than men (odds ratio [OR] = 3.42, 95% confidence interval [CI] = 2.67-4.40), a finding largely but not completely explained by gender differences in height. The underinsured were significantly more likely to receive tidal volume > 8 mL/kg PBW than the insured in multivariable analysis (OR = 1.54, 95% CI = 1.16-2.04). The prescription of > 8 mL/kg PBW tidal volume did not differ by racial or ethnic categories. Conclusions In this prospective nationwide cohort of critically ill mechanically ventilated patients, women and the underinsured were less likely than their comparators to receive lung-protective ventilation, with no apparent differences based on race/ethnicity alone.

Analyzing Longitudinally Collected Viral Load Measurements in Youth With Perinatally Acquired HIV Infection: Problems and Possible Remedies

Human immunodeficiency virus (HIV) viral load (VL) is an important quantitative marker of disease progression and treatment response in people living with HIV infection, including children with perinatally acquired HIV. Measures of VL are often used to predict different outcomes of interest in this population, such as HIV-associated neurocognitive disorder. One popular approach to summarizing historical viral burden is the area under a time-VL curve (AUC). However, alternative historical VL summaries (HVS) may better answer the research question of interest. In this article, we discuss and contrast the AUC with alternative HVS, including the time-averaged AUC, duration of viremia, percentage of time with suppressed VL, peak VL, and age at peak VL. Using data on youth with perinatally acquired HIV infection from the Pediatric HIV/AIDS Cohort Study Adolescent Master Protocol, we show that HVS and their associations with full-scale intelligence quotient depend on when the VLs were measured. When VL measurements are incomplete, as can be the case in observational studies, analysis results may be subject to selection bias. To alleviate bias, we detail an imputation strategy, and we present a simulation study demonstrating that unbiased estimation of a historical VL summary is possible with a correctly specified imputation model.