bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study
Authors: Turner Simon L. , Forbes Andrew B. , Karahalios Amalia , Taljaard Monica , McKenzie Joanne E. 
Published: August 2021

AbstractBackgroundInterrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. However, examination of the performance of statistical methods for this design has received relatively little attention.MethodsWe simulated continuous data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of lag-1 autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation.ResultsAll methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation.ConclusionsFrom the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.
Predicting postoperative surgical site infection with administrative data: a random forests algorithm
Authors: Petrosyan Yelena , Thavorn Kednapa , Smith Glenys , Maclure Malcolm , Preston Roanne , van Walravan Carl , Forster Alan J. 
Published: August 2021

Abstract
Background
Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms.

Methods
All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets.

Results
Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90–0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402).

Conclusion
We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.
Evaluating the impact of covariate lookback times on performance of patient-level prediction models
Authors: Hardin Jill , Reps Jenna M. 
Published: August 2021

Abstract
Background
The goal of our study is to examine the impact of the lookback length when engineering features to use in developing predictive models using observational healthcare data. Using a longer lookback for feature engineering gives more insight about patients but increases the issue of left-censoring.

Methods
We used five US observational databases to develop patient-level prediction models. A target cohort of subjects with hypertensive drug exposures and outcome cohorts of subjects with acute (stroke and gastrointestinal bleeding) and chronic outcomes (diabetes and chronic kidney disease) were developed. Candidate predictors that exist on or prior to the target index date were derived within the following lookback periods: 14, 30, 90, 180, 365, 730, and all days prior to index were evaluated. We predicted the risk of outcomes occurring 1 day until 365 days after index. Ten lasso logistic models for each lookback period were generated to create a distribution of area under the curve (AUC) metrics to evaluate the discriminative performance of the models. Calibration intercept and slope were also calculated. Impact on external validation performance was investigated across five databases.

Results
The maximum differences in AUCs for the models developed using different lookback periods within a database was < 0.04 for diabetes (in MDCR AUC of 0.593 with 14-day lookback vs. AUC of 0.631 with all-time lookback) and 0.012 for renal impairment (in MDCR AUC of 0.675 with 30-day lookback vs. AUC of 0.687 with 365-day lookback ). For the acute outcomes, the max difference in AUC across lookbacks within a database was 0.015 (in MDCD AUC of 0.767 with 14-day lookback vs. AUC 0.782 with 365-day lookback) for stroke and < 0.03 for gastrointestinal bleeding (in CCAE AUC of 0.631 with 14-day lookback vs. AUC of 0.660 with 730-day lookback).

Conclusions
In general the choice of covariate lookback had only a small impact on discrimination and calibration, with a short lookback (< 180 days) occasionally decreasing discrimination. Based on the results, if training a logistic regression model for prediction then using covariates with a 365 day lookback appear to be a good tradeoff between performance and interpretation.
Recruitment principles and strategies for supportive care research in pediatric oncology
Authors: Bradford Natalie , Cashion Christine , Condon Paula , Rumble Shelley , Bowers Alison 
Published: August 2021

Abstract
Background
Variations in clinical practice contribute to negative outcomes for children with cancer. Research in this area is imperative to standardise practice, yet such research is challenging to undertake, and a significant proportion of studies fail. A common reason for failure is poor recruitment, yet little information is available to support researchers and clinicians planning such research.

Methods
Our primary aim was to describe the recruitment strategies and outcomes in a tertiary children’s hospital across multiple observational supportive care studies. Secondary aims were to establish principles to improve both recruitment strategies and the reporting of recruitment. We undertook a retrospective descriptive analysis of the recruitment logs and data from three studies in pediatric oncology. The mean time to recruit one participant was calculated. Common reasons for not approaching eligible participants and reasons potential participants declined are described.

Results
Of the 235 potential candidates across all studies, 186 (79%) were approached and of these 125 (67%) provided consent, with 117 (63%) completing baseline measures. We estimated recruitment per participant required an average 98 min of experienced research nurse time. Four factors are described that influence recruitment and six principles are outlined to maximise recruitment and the generalisability of research findings.

Conclusions
We highlight the recruitment experiences across three different projects in children’s cancer supportive care research and provide a roadmap for other researchers planning to undertake clinical research in pediatrics.
Impact of a non-constant baseline hazard on detection of time-dependent treatment effects: a simulation study
Authors: Jachno Kim , Heritier Stephane , Wolfe Rory 
Published: August 2021

AbstractBackgroundNon-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard.MethodsA simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes.ResultsIn the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels.ConclusionsOur results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.
How are reasons for encounter associated with influenza-like illness and acute respiratory infection diagnoses and interventions? A cohort study in eight Italian general practice populations
Authors: Buono Nicola , Harris Michael , Farinaro Carmine , Petrazzuoli Ferdinando , Cavicchi Angelo , D’Addio Filippo , Scelsa Amedeo , Mirra Baldassarre , Napolitano Enrico , Soler Jean K. 
Published: August 2021

Abstract
Background
Influenza-like illness (ILI) and Acute Respiratory Infections (ARI) are a considerable health problem in Europe. Most diagnoses are made by family physicians (FPs) and based on symptoms and clinical signs rather than on diagnostic testing. The International Classification of Primary Care (ICPC) advocates that FPs record patients’ ‘Reasons for Encounters’ (RfEs) as they are presented to them.
This study analyses the association of patients’ RfEs with FPs’ diagnoses of ILI and ARI diagnoses and FPs’ management of those patients.

Methods
Cohort study of practice populations. Over a 4-month period during the winter season 2013–14, eight FPs recorded ILI and ARI patients’ RfEs and how they were managed. FPs recorded details of their patients using the ICPC format, collecting data in an Episode of Care (EoC) structure.

Results
There were 688 patients diagnosed as having ILI; between them they presented with a total of 2,153 RfEs, most commonly fever (79.7%), cough (59.7%) and pain (33.0%).
The 848 patients with ARI presented with a total of 1,647 RfEs, most commonly cough (50.4%), throat symptoms (25.9%) and fever (19.9%). For patients with ILI, 37.0% of actions were related to medication for respiratory symptoms; this figure was 38.4% for patients with ARI. FPs referred six patients to specialists or hospitals (0.39% of all patients diagnosed with ILI and ARI).

Conclusions
In this study of patients with ILI and ARI, less than half received a prescription from their FPs, and the illnesses were mainly managed in primary care, with few patients’ needing referral. The ICPC classification allowed a standardised data collection system, providing documentary evidence of the management of those diseases.
Heparin-binding protein is significantly increased in acute pancreatitis
Authors: Sjöbeck Martina , Sternby Hanna , Herwald Heiko , Thorlacius Henrik , Regnér Sara 
Published: August 2021

Abstract
Background
Most patients with acute pancreatitis (AP) experience mild, self-limiting disease with little or no need for hospital care. However, 20–25% of patients develop a more severe and potentially life-threatening condition with progressive systemic inflammatory response syndrome (SIRS) and multiorgan failure, resulting in high morbidity and mortality rates. Predicting disease severity at an early stage is important, as immediate supportive care has been demonstrated to reduce the incidence of SIRS and organ failure, improving patient outcome. Several studies have demonstrated elevated levels of heparin-binding protein (HBP) in patients with sepsis and septic shock, and HBP is believed to play a part in endothelial dysfunction leading to vascular leakage. As HBP levels increase prior to other known biomarkers, HBP has emerged as a promising early predictor of severe sepsis with organ dysfunction.

Methods
Patients admitted to Skåne University Hospital in Malmö between 2010 and 2013 fulfilling the criteria for AP were identified in the emergency department and prospectively enrolled in this study. The primary outcome was measured levels of HBP upon hospital admission in patients with confirmed AP. Correlations among HBP concentrations, disease severity and fluid balance were considered secondary endpoints. The correlation between HBP levels and fluid balance were analysed using Pearson correlation, and the ability of HBP to predict moderately severe/severe AP was assessed using a receiver operating characteristic (ROC) curve.

Results
The overall median HBP level in this study was 529 (307–898) ng/ml. There were no significant group differences in HBP levels based on AP severity. Fluid balance differed significantly between patients with mild versus moderately severe and severe pancreatitis, but we found no correlation between HBP concentration and fluid balance.

Conclusions
HBP levels are dramatically increased in patients with AP, and these levels far exceed those previously reported in other conditions. In this study, we did not observe any significant correlation between HBP levels and disease severity or the need for intravenous fluid. Additional studies on HBP are needed to further explore the role of HBP in the pathogenesis of AP and its possible clinical implications.
The intersectional impact of sex and social factors on subjective health: analysis of the Canadian longitudinal study on aging (CLSA)
Authors: Vafaei Afshin , Yu Janelle , Phillips Susan P. 
Published: August 2021

Abstract
Background
Self-rated health (SRH) is a widely validated measure of the general health of older adults. Our aim was to understand what factors shape individual perceptions of health and, in particular, whether those perceptions vary for men and women and across social locations.

Methods
We used data from the Canadian Longitudinal Study on Aging (CLSA) of community-dwelling adults aged 45 to 85. SRH was measured via a standard single question. Multiple Poisson regression identified individual, behavioural, and social factors related to SRH. Intersections between sex, education, wealth, and rural/urban status, and individual and joint cluster effects on SRH were quantified using multilevel models.

Results
After adjustment for relevant confounders, women were 43% less likely to report poor SRH. The strongest cluster effect was for groupings by wealth (21%). When wealth clusters were subdivided by sex or education the overall effect on SRH reduced to 15%. The largest variation in SRH (13.6%) was observed for intersections of sex, wealth, and rural/urban status. In contrast, interactions between sex and social factors were not significant, demonstrating that the complex interplay of sex and social location was only revealed when intersectional methods were employed.

Conclusions
Sex and social factors affected older adults’ perceptions of health in complex ways that only became apparent when multilevel analyses were carried out. Utilizing intersectionality analysis is a novel and nuanced approach for disentangling explanations for subjective health outcomes.
Short daytime napping reduces the risk of cognitive decline in community-dwelling older adults: a 5-year longitudinal study
Authors: Kitamura Kaori , Watanabe Yumi , Nakamura Kazutoshi , Takano Chikako , Hayashi Naomi , Sato Hisami , Someya Toshiyuki 
Published: August 2021

Abstract
Background
Beneficial effects of napping on cognition have been suggested in cross-sectional studies. This study aimed to clarify longitudinal associations between cognitive decline and sleep characteristics, particularly daytime napping, over a 5-year period in older adults.

Methods
Study participants were 389 community-dwelling individuals aged ≥65 years living in Ojiya City, Niigata, Japan. Baseline and follow-up examinations were conducted in 2011–2013 and 2016–2018, respectively. Trained nurses visited and interviewed participants to collect the following information at baseline and follow-up: demographic characteristics, disease history, lifestyle habits including bedtime, sleeping hours, and daytime nap duration, and cognitive function. The assessment of cognitive function was performed using the revised Hasegawa’s dementia scale (HDS-R), with cognitive decline defined as a change in the HDS-R of ≤ − 3 over 5 years. Odds ratios (ORs) for cognitive decline were calculated using multiple logistic regression analysis.

Results
Mean age of participants was 74.6 years (SD 6.4), and the cumulative incidence of cognitive decline was 106/389 (27.3%). The adjusted OR for 1–29 min daytime napping was significantly lower compared to that for no napping (OR = 0.47, 95%CI: 0.23–0.96). Earlier bedtime was associated with cognitive decline (adjusted P for trend = 0.0480).

Conclusion
Short daytime napping (< 30 min) reduces the risk of cognitive decline over 5 years for community-dwelling older people. A future study will be necessary to confirm the effect of short napping on the reduction of risk for clinically diagnosed dementia.
Vaccine preferences driving vaccine-decision making of different target groups: a systematic review of choice-based experiments
Authors: Diks Marilyn Emma , Hiligsmann Mickael , van der Putten Ingeborg Maria 
Published: August 2021

Abstract
Background
Choice-based experiments have been increasingly used to elicit preferences for vaccines and vaccination programs. This study aims to systematically identify and examine choice-based experiments assessing (differences in) vaccine preferences of vaccinees, representatives and health advisors.

Methods
Five electronic databases were searched on choice-based conjoint analysis studies or discrete choice experiments capturing vaccine preferences of children, adolescents, parents, adults and healthcare professionals for attributes of vaccines or vaccine settings up to September 2020. Data was extracted using a standardized form covering all important aspects of choice experiments. A quality assessment was used to assess the validity of studies. Attributes were categorized into outcome, process, cost and other. The importance of attributes was assessed by the frequency of reporting and statistical significance. Results were compared between high-quality studies and lower-quality studies.

Results
A total of 42 studies were included, with the majority conducted in high-income countries after 2010 (resp. n = 34 and n = 37). Preferences of representatives were studied in nearly half of the studies (47.6%), followed by vaccinees (35.7%) and health advisors (9.5%). Sixteen high-quality studies passed the quality assessment. Outcome- and cost- related attributes such as vaccine effectiveness, vaccine risk, cost and protection duration were most often statistically significant across both target groups, with vaccine effectiveness being the most important. Risks associated with vaccination, such as side effects, were more often statistically significant in studies targeting vaccinees, while cost-related attributes were more often statistically significant in studies of representatives. Process-related attributes such as vaccine accessibility and time were least important across both target groups.

Conclusion
To our knowledge, this is the first systematic review in which vaccine preferences of different target groups were assessed and compared. The same attributes were most important for vaccine decisions of vaccinees and representatives, with only minor differences in level of evidence for vaccine risk and cost. Future research on vaccine preferences of health advisors and/or among target groups in low-resource settings would give insight into the generalizability of current findings.
Streptococcus oralis MitraClip endocarditis following a dental procedure: a case report
Authors: Papamanoli Aikaterini , Rahman Tahmid , Kalogeropoulos Andreas P. , Lobo Zeena , Diggs Paul , Hamik Anne , Psevdos George 
Published: August 2021

Abstract
Background
Transcatheter edge-to-edge mitral valve repair using the MitraClip device is increasingly used for high surgical risk patients with severe mitral regurgitation (MR). Previous guidelines for infective endocarditis prophylaxis prior to dental procedures focused on high-risk patients, but without explicit recommendation for MitraClip recipients. We believe this could be the first reported case to identify Streptococcus oralis as the causative organism.

Case presentation
An 87-year-old male with severe MR treated with two MitraClip devices three months prior to index admission, presented with worsening malaise and intermittent chills on a background of multiple comorbid conditions. The patient had dental work a month prior to presentation, including a root canal procedure, without antibiotic prophylaxis. Vitals were significant for fever and hypotension. On physical examination, there was a holosystolic murmur at the apex radiating to the axilla, bilateral pitting edema in the lower extremities, and elevated jugular venous pulsation. A transthoracic echocardiogram showed severe MR with a possible echodensity on the mitral valve, prompting a transesophageal echocardiogram, which demonstrated a pedunculated, mobile mass on the posterior leaflet of the mitral valve. Five blood cultures grew gram positive cocci in pairs and chains, later identified as Streptococcus oralis, with minimum inhibitory concentration to penicillin 0.06 mg/L. Initial empiric antibiotics were switched to ceftriaxone 2 gr daily and subsequent blood cultures remained negative. However, the patient developed pulmonary edema and worsening hemodynamic instability requiring vasopressors. As surgical risk for re-operation was considered prohibitive, the decision was made to continue medical management and comfort-directed care. The patient died a week later.

Conclusions
Despite low incidence, infective endocarditis should be included in the differential among MitraClip recipients. The explicit inclusion of this growing patient population in the group requiring prophylaxis prior to dental procedures in the 2020 ACC/AHA valvular heart disease guidelines is an important step forward.