Missing data: the impact of what is not there

Cognition, mental health and quality of life amongst siblings of preterm born children: A systematic review

AIM

Globally, 1 in 10 babies are born preterm. Families with preterm born infants may suffer strains related to the presence of a preterm child. To date, most evidence focuses on the outcome of children born preterm and of their parents. Our objective was to investigate the evidence on the impact of having a preterm born sibling on cognitive function, mental health and quality of life of term-born siblings and critically appraise the evidence.

METHODS

We searched five electronic databases, Google Scholar and reference lists. Two reviewers independently conducted screening, data extraction and critical appraisal.

RESULTS

We retrieved 9121 articles. After duplicates, titles, abstract and full text review, seven studies met the inclusion criteria. One study reported higher anxiety and depression scores on index cases in the term born comparison group, compared to the index cases in the preterm born sibling group. Another study reported more feelings of reduced parental attention, and more interpersonal problems in the preterm born sibling group, than the comparison group.

CONCLUSIONS

Although two studies reported a difference in outcomes between index cases in preterm born sibling groups and comparison groups, the scarce evidence did not allow us to delineate an effect or lack of it.

Handling missing data in research

Missing data are an inevitable part of research and lead to a decrease in the size of the analyzable population, and biased and imprecise estimates. In this article, we discuss the types of missing data, methods to handle missing data and suggest ways in which missing data can be minimized.

smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies

Objectives

Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions.

Materials and methods

We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR.

Results

smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data.

Conclusions

The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.

Clinical Subtypes of Neutrophilic Asthma: A Cluster Analysis From Australasian Severe Asthma Network

BACKGROUND

Clinical heterogeneity may exist within asthma subtypes defined by inflammatory markers. However, the heterogeneity of neutrophilic asthma (NA) remains largely unexplored.

OBJECTIVE

To explore potential clusters and the stability of NA.

METHODS

Participants with NA from the Australasian Severe Asthma Network underwent a multidimensional assessment. They were then asked to participate in a 12-month longitudinal cohort study. We explored potential clusters using a hierarchical cluster analysis and validated the differential future risk of asthma exacerbations in the identified clusters. A decision tree analysis was developed to predict cluster assignments. Finally, the stability of prespecified clusters was examined within 1 month.

RESULTS

Three clusters were identified in 149 patients with NA. Cluster 1 (n = 99; 66.4%) was characterized by female-predominant nonsmokers with well-controlled NA, cluster 2 (n = 16; 10.7%) by individuals with comorbid anxiety/depressive symptoms with poorly controlled NA, and cluster 3 by older male smokers with late-onset NA. Cluster 2 had a greater proportion of participants with severe exacerbations (P = .005), hospitalization (P = .010), and unscheduled visits (P = .013) and a higher number of emergency room visits (P = .039) than that of the other two clusters. The decision tree assigned 92.6% of participants correctly. Most participants (87.5%; n = 7) in cluster 2 had a stable NA phenotype, whereas participants of clusters 1 and 3 had variable phenotypes.

CONCLUSIONS

We identified three clinical clusters of NA, in which cluster 2 represents an uncontrolled and stable NA subtype with an elevated risk of exacerbations. These findings have clinical implications for the management of NA.

Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients

PURPOSE

Many studies on cancer patients investigate the impact of treatment on health-related quality of life (QoL). Typically, QoL is measured longitudinally, at baseline and at predefined timepoints thereafter. The question is whether, at a given timepoint, patients who return their questionnaire (available cases, AC) have a different QoL than those who do not return their questionnaire (non-AC).

METHODS

We employed augmented inverse probability weighting (AIPW) to estimate the average QoL of non-AC in two studies on advanced-stage cancer patients. The AIPW estimator assumed data to be missing at random (MAR) and used machine learning (ML)-based methods to estimate answering probabilities of individuals at given timepoints as well as their reported QoL, as a function of auxiliary variables. These auxiliary variables were selected by medical oncologists based on domain expertise. We aggregated results both by timepoint and by time until death and compared AIPW estimates to the AC averages. Additionally, we used a pattern mixture model (PMM) to check sensitivity of our AIPW estimates against violation of the MAR assumption.

RESULTS

Our study included 1927 patients with advanced pancreatic and 797 patients with advanced breast cancer. The AIPW estimate for average QoL of non-AC was below the average QoL of AC when aggregated by timepoint. The difference vanished when aggregated by time until death. PMM estimates were below AIPW estimates.

CONCLUSIONS

Our results indicate that non-AC have a lower average QoL than AC. However, estimates for QoL of non-AC are subject to unverifiable assumptions about the missingness mechanism.

Disparities in Healthcare and HBV Vaccination by Smoking Status: Findings from the National Health and Nutrition Examination Survey (NHANES) 2017-2018

Cigarette smokers face greater challenges in accessing healthcare compared with non-smokers. In the US, approximately 2.2 million individuals are chronically infected with hepatitis B virus (HBV). I used data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 to investigate the association between smoking status (current, former, and never smoker) and different health outcomes, including healthcare accessibility, HBV vaccination, general health condition, and health insurance. Multivariable logistic regressions were used to analyze healthcare disparity by smoking status. I found that current smokers had 40% higher odds (AOR = 1.4, 95% CI: 1.1, 1.8) of lacking routine healthcare access compared with non-smokers. Regardless of smoking status, I observed a high rate of HBV non-vaccination among all participants. Specifically, 64% of current smokers, 67% of former smokers, and 57% of non-smokers had not received a single dose of HBV immunization. My study sheds light on the persisting gaps in healthcare access, particularly for smokers, and the urgent need to promote awareness and vaccination against hepatitis B.

From free-text electronic health records to structured cohorts: Onconum, an innovative methodology for real-world data mining in breast cancer

PURPOSE

A considerable amount of valuable information is present in electronic health records (EHRs) however it remains inaccessible because it is embedded into unstructured narrative documents that cannot be easily analyzed. We wanted to develop and evaluate a methodology able to extract and structure information from electronic health records in breast cancer.

METHODS

We developed a software platform called Onconum (ClinicalTrials.gov Identifier: NCT02810093) which uses a hybrid method relying on machine learning approaches and rule-based lexical methods. It is based on natural language processing techniques that allows a targeted analysis of free-text medical data related to breast cancer, independently of any pre-existing dictionary, in a French context (available in N files). We then evaluated it on a validation cohort called Senometry.

FINDINGS

Senometry cohort included 9,599 patients with breast cancer (both invasive and in situ), treated between 2000 and 2017 in the breast cancer unit of Strasbourg University Hospitals. Extraction rates ranged from 45 to 100%, depending on the type of each parameter. Precision of extracted information was 68%-94% compared to a structured cohort, and 89%-98% compared to manually structured databases and it retrieved more rare occurrences compared to another database search engine (+17%).

INTERPRETATION

This innovative method can accurately structure relevant medical information embedded in EHRs in the context of breast cancer. Missing data handling is the main limitation of this method however multiple sources can be incorporated to reduce this limit. Nevertheless, this methodology does not need neither pre-existing dictionaries nor manually annotated corpora. It can therefore be easily implemented in non-English-speaking countries and in other diseases outside breast cancer, and it allows prospective inclusion of new patients.

Effect of extracurricular tutoring on adolescent students cognitive ability: A propensity score matching analysis

In recent years, there has been a substantial increase in the number of adolescent students attending extracurricular tutoring. However, extracurricular tutoring, being a distinct form of education, may have varying effects on cognitive capabilities compared to conventional education. Accordingly, the purpose of this paper is to conduct a thorough examination of the effects of extracurricular tutoring on cognitive abilities among adolescent students. The study utilized national data from the China Family Panel Study 2018 to explore the relationship between involvement in extracurricular tutoring among students and cognitive abilities. The study included 2567 valid respondents. A binary logistic regression model was built to determine the factors associated with tutoring involvement while controlling for 19 individual, family, and school factors. Furthermore, a propensity score-matched analysis was conducted in order to mitigate potential bias by using confounding variables identified in the previous step. The study results show that participation in extracurricular tutoring can significantly increase the level of cognitive ability of adolescent students, with adjustments made for age, gender, ethnicity, number of family members, net family income per capita, education and training expenditure in the past years, change of residence for enrollment, change of domicile address for enrollment, locality of the current school, class size, hold a position as a class cadre, average daily study time on weekdays, average study time per day during weekends. The findings imply that the government should provide tutorial subsidies to disadvantaged groups of adolescent students, allocate educational resources equitably, and invest more in education resources in less developed regions to foster fair and healthy development of education and improve the cognitive abilities of young students in the long-term.

Association between the choice of the conditioning regimen and outcomes of allogeneic hematopoietic cell transplantation for myelofibrosis

Allogeneic hematopoietic cell transplantation (allo-HCT) remains the only curative treatment for myelofibrosis. However, the optimal conditioning regimen either with reduced-intensity conditioning (RIC) or myeloablative conditioning (MAC) is not well known. Using the Center for International Blood and Marrow Transplant Research database, we identified adults aged ≥18 years with myelofibrosis undergoing allo-HCT between 2008-2019 and analyzed the outcomes separately in the RIC and MAC cohorts based on the conditioning regimens used. Among 872 eligible patients, 493 underwent allo-HCT using RIC (fludarabine/ busulfan n=166, fludarabine/melphalan n=327) and 379 using MAC (fludarabine/busulfan n=247, busulfan/cyclophosphamide n=132). In multivariable analysis with RIC, fludarabine/melphalan was associated with inferior overall survival (hazard ratio [HR]=1.80; 95% confidenec interval [CI]: 1.15-2.81; P=0.009), higher early non-relapse mortality (HR=1.81; 95% CI: 1.12-2.91; P=0.01) and higher acute graft-versus-host disease (GvHD) (grade 2-4 HR=1.45; 95% CI: 1.03-2.03; P=0.03; grade 3-4 HR=2.21; 95%CI: 1.28-3.83; P=0.004) compared to fludarabine/busulfan. In the MAC setting, busulfan/cyclophosphamide was associated with a higher acute GvHD (grade 2-4 HR=2.33; 95% CI: 1.67-3.25; P<0.001; grade 3-4 HR=2.31; 95% CI: 1.52-3.52; P<0.001) and inferior GvHD-free relapse-free survival (GRFS) (HR=1.94; 95% CI: 1.49-2.53; P<0.001) as compared to fludarabine/busulfan. Hence, our study suggests that fludarabine/busulfan is associated with better outcomes in RIC (better overall survival, lower early non-relapse mortality, lower acute GvHD) and MAC (lower acute GvHD and better GRFS) in myelofibrosis.

Nurses' and physicians' reported difficulties and enablers to recognising and reporting child abuse in Swiss paediatric emergency and paediatric surgery departments - an observational study

BACKGROUND

Under-detection and under-reporting of child abuse remains a considerable challenge in paediatric care, with a high number of cases missed each year in Switzerland and abroad. Published data regarding the obstacles and facilitators of detecting and reporting child maltreatment among paediatric nursing and medical staff in the paediatric emergency department (PED) are scarce. Despite the existence of international guidelines, the measures taken to counteract the incomplete detection of harm done to children in paediatric care are insufficient.

AIM

We sought to examine up-to-date obstacles and enablers for detecting and reporting child abuse among nursing and medical staff in PED and paediatric surgery departments in Switzerland.

METHODS

We surveyed 421 nurses and physicians working in PEDs and on paediatric surgical wards in six large Swiss paediatric hospitals using an online questionnaire between February 1, 2017, and August 31, 2017.

RESULTS

The survey was returned by 261/421 (62.0%) respondents (complete n = 200, 76.6%; incomplete n = 61, 23.3%) with a preponderance of nurses (n = 150/261; 57.5%), 106/261 (40.6%) physicians, and 1/261 (0.4%) psychologists (n = 4/261; 1.5% missing profession). The stated obstacles to reporting child abuse were uncertainty about the diagnosis (n = 58/80; 72.5%), feeling unaccountable for notification (n = 28/80; 35%), uncertainty of whether reporting has any consequences (n = 5/80; 6.25%), lack of time (n = 4/80; 5%), forgetting to report (n = 2/80; 2.5%), and parental protection (n = 2/80; 2.5%) (unspecific answer, n = 4/80; 5%, multiple answers were possible, therefore items don not sum up to 100%). Even though most (n = 249/261 95.4%) respondents had previously been confronted with child abuse at/outside work, only 185/245 (75.5%) reported cases; significantly fewer nursing (n = 100/143, 69.9%) than medical staff (n = 83/99, 83.8%) (p = 0.013). Furthermore, significantly more nursing (n = 27/33; 81.8%) than medical staff (n = 6/33; 18.2%) (p = 0.005) reported a discrepancy between the number of suspected and reported cases (total 33/245 (13.5%). An overwhelming amount of participants were strongly interested in mandatory child abuse training (n= 226/242, 93.4%) and in the availability of standardised patient questionnaires and documentation forms (n = 185/243, 76.1%).

CONCLUSION

In line with previous studies, insufficient knowledge about and lack of confidence in detecting the signs and symptoms of child abuse were the principal obstacles to reporting maltreatment. To finally address this unacceptable gap in child abuse detection, we recommend the implementation of mandatory child protection education in all countries where no such education has been implemented in addition to the introduction of cognitive aid tools and validated screening tools to increase child abuse detection rates and ultimately prevent further harm to children.

Bayesian inference of physicochemical quality elements of tropical lagoon Nokoué (Benin)

In view of the very strong degradation of aquatic ecosystems, it is urgent to set up monitoring systems that are best able to report on the effects of the stresses they undergo. This is particularly true in developing countries, where specific and relevant quality standards and funding for monitoring programs are lacking. The objective of this study was to make a relevant and objective choice of physicochemical parameters informative of the main stressors occurring on African lakes and to identify their alteration thresholds. Based on statistical analyses of the relationship between several driving forces and the physicochemical parameters of the Nokoué lagoon, relevant physicochemical parameters were selected for its monitoring. An innovative method based on Bayesian statistical modeling was used. Eleven physicochemical parameters were selected for their response to at least one stressor and their threshold quality standards also established: Total Phosphorus (<4.5mg/L), Orthophosphates (<0.2mg/L), Nitrates (<0.5 mg/L), TKN (<1.85 mg/L), Dry Organic Matter (<5 mg/L), Dissolved Oxygen (>4 mg/L), BOD (<11.6 mg/L), Salinity (7.6 ‰), Water Temperature (<28.7 °C), pH (>6.2), and Transparency (>0.9 m). According to the System for the Evaluation of Coastal Water Quality, these thresholds correspond to "good to medium" suitability classes, except for total phosphorus. One of the original features of this study is the use of the bounds of the credibility interval of the fixed-effect coefficients as local weathering standards for the characterization of the physicochemical status of this anthropized African ecosystem.

The Association between Nursing Skill Mix and Patient Outcomes in a Mental Health Setting: An Observational Feasibility Study

Higher levels of educational preparation for nurses are associated with lower mortality rates in both medical and surgical wards. In mental health inpatient wards, few studies have examined whether specialist mental health nurse training has any impact on patient outcomes. The aim of this retrospective observational study was to establish the feasibility of extracting and linking nurse education and inpatient outcome data from hospital administrative sources to inform the design of future mental health nursing skill mix studies. Study participants were people experiencing mental ill-health and admitted to psychiatric inpatient care for at least 24 h. The exposure was the ratio of mental health nurses to comprehensive nurses for each patient for each day of their admission. The outcome was readmission for psychiatric inpatient care within 12 months of discharge from the index admission. Confounders were patient demographic (age, gender) and clinical characteristics (diagnosis, legal status, community follow-up). Forty-four patients included in the study were inpatients for a total of 595 days. The median hospital stay was 12 days (IQR = 7-17). In total, 11 (25%) patients were readmitted. In the readmitted and not readmitted groups, the median skill mix ratio was 5 (IQR = 5-7) and 5 (1-6), respectively. It was feasible to extract and code patient and nurse data from hospital databases and link them together. However, a substantial amount of manual post hoc recoding was required to enable us to calculate the exposure (mental health to comprehensive nurse ratio) in a precise way. It may be realistic to automate our methodology in an appropriately powered mental health nursing skill mix study. Australian and New Zealand clinical trial registry: ACTRN12619001337167p.

Use of statistical methods among acupuncture randomized controlled trials was far from satisfactory

OBJECTIVES

To examine whether appropriate statistical methods were used in acupuncture randomized controlled trials (RCTs).

STUDY DESIGN AND SETTING

We searched PubMed to identify acupuncture RCTs with continuous outcome as primary outcome published in the core clinical journals and complementary and alternative medicine (CAM) journals between January 2010 and December 2019 (10 years). We compared statistical characteristics of included trials published in core clinical journals and CAM journals.

RESULTS

We included 262 RCTs, including 46 published in core clinical journals and 216 in CAM journals. Of included RCTs, only 132 (50.4%) clearly predefined the primary outcome, 72 (27.5%) specified the use of intention to treat or modified intention to treat population for primary analysis. In the 167 trials reported missing participant data (MPD), 118 (70.7%) used suboptimal methods (e.g., complete case analysis) for dealing with MPD; 11 (6.6%) conducted sensitivity analysis regarding MPD. Among the 161 trials with repeated measures design, only 21 (13.0%) used advanced statistical models (e.g., mixed-effects models) for handling repeated-measure data in the primary analysis. In the 72 trials involving multiple acupuncturists, only 4 (5.6%) adjusted acupuncturist variable or considered the clustering by acupuncturist in analysis. Trials in core clinical journals were more likely to predefine primary outcome (78.3% vs. 44.4%, P < 0.001), use multiple imputations for handling MPD (40% vs. 1.5%, P < 0.001), and use statistically advanced methods for assessing treatment effect at a single time point (26.1% vs. 2%, P = 0.001).

CONCLUSION

The use of statistical methods among acupuncture RCTs is far from satisfactory. Our findings highlighted the need for researchers to carefully use the optimal statistical methods and for journal editors to strengthen the use of statistical methods.

Diabetes management behaviors associated with depression in the U.S

BACKGROUND

There is a lack of nationally representative evidence from the U.S. investigating the relationships between depression and diabetes management behaviors. Our study aimed to assess the associations between diabetes management behaviors and depression status, and to compare U.S. population-level percentages of diabetes management behaviors among patients with and without depression.

METHODS

A cross-sectional study was conducted using population-based survey data to assess patient-reported variables retrospectively. We used the Behavioral Risk Factor Surveillance System (BRFSS) data and included states in the U.S. that continuously adopted the diabetes optional modules in 2013, 2015, 2017, and 2019. We included U.S. adults (≥ 18 years old) with self-reported diabetes in our analysis. Main outcomes were diabetes management behaviors (i.e., self-check for blood glucose and feet sores/irritation, regular diabetes clinical visit, HbA1c check, professional feet check, and dilated eye examination) and lifestyle behaviors (i.e., exercise, smoking, and alcohol consumption).

RESULTS

Among the 74,011 respondents with diabetes, patients with depression had a higher likelihood of performing routine HbA1c checks (adjusted odds ratio (AOR) = 1.12; 95% CI 1.01-1.23) but had a lower likelihood to perform regular self-check for blood glucose (AOR = 0.91; 95% CI 0.84-0.99), receive professional feet checks (AOR = 0.87; 95% CI 0.79-0.95), and receive a dilated eye examination (AOR = 0.89; 95% CI 0.82-0.98). For lifestyle behaviors, patients with depression were more likely to smoke (No smoking (AOR) = 0.65; 95% CI = 0.59-0.72) and less likely to engage in sufficient exercise time (AOR = 0.69; 95% CI 0.63-0.75). There were no significant associations between depression and other behaviors, including self-check for feet sores/irritation (AOR = 0.99; 95% CI 0.92-1.08), regular diabetes clinical visit (AOR = 1.03, 95% CI 0.94-1.13), and alcohol consumption (AOR = 1.01, 95% CI 0.92-1.10).

CONCLUSIONS

The association between depression status and diabetes management behaviors varied. People with depression were positively associated with HbA1c checks. However, less uptake of other behaviors may indicate the needs for improvement in diabetes management.

Occupational therapy lifestyle intervention added to multidisciplinary treatment for adults living with chronic pain: a feasibility study

OBJECTIVES

To evaluate the feasibility and outcomes of an occupational therapy lifestyle intervention for adults living with chronic pain.

DESIGN

This one-group pre-post interventional study investigated the feasibility and outcomes of the Redesign Your Everyday Activities and Lifestyle with Occupational Therapy (REVEAL(OT)) intervention targeting meaningful activities and lifestyle.

SETTINGS

The occupational therapist-led intervention was added to standard multidisciplinary chronic pain treatment at a Danish pain centre.

PARTICIPANTS

Of the 40 adult participants aged 18-64 (mean 46.6±10.9 years old, 85% females, chronic pain duration ≥3 months), there were 31 completers.

INTERVENTION

Three feasibility rounds were carried out in 2019-2021. The intervention focused on meaningful activities, healthy eating habits and daily physical activity. Methods of didactical presentations, group discussions, personal reflection and experiential learning were used in the intervention composed both of individual and group sessions.

OUTCOMES

Primary outcomes were predefined research progression criteria evaluated by the red-amber-green method. Secondary outcomes measured pre-post changes in health-related quality of life and occupational performance and satisfaction.

RESULTS

The study demonstrated satisfactory programme adherence (77.5%), patients' self-perceived relevance (97%), timing and mode of delivery (97%) and assessment procedure acceptance (95%). No adverse events causing discontinuation occurred. Recruitment rate (n=5.7 monthly), retention (77.5%) and the fidelity of delivery (83.3%) needed improvement. We observed no improvement in health-related quality of life (mean=0.04, 95% CI -0.03 to 0.12) but positive change in occupational performance (mean=1.80, 95% CI 1.25 to 2.35) and satisfaction (mean=1.95, 95% CI 1.06 to 2.84). The participants reached the minimal clinically important difference for occupational performance (≥3.0 points in 13.8%) and satisfaction (≥3.2 points in 24.0%).

CONCLUSIONS

The REVEAL(OT) intervention was feasible to deliver and beneficial for the participants' occupational performance and satisfaction. The interventions' recruitment, retention and delivery strategies need optimisation in a future definitive trial.

TRIAL REGISTRATION NUMBER

NCT03903900.

How to assess applicability and methodological quality of comparative studies of operative interventions in orthopedic trauma surgery

PURPOSE

It is challenging to generate and subsequently implement high-quality evidence in surgical practice. A first step would be to grade the strengths and weaknesses of surgical evidence and appraise risk of bias and applicability. Here, we described items that are common to different risk-of-bias tools. We explained how these could be used to assess comparative operative intervention studies in orthopedic trauma surgery, and how these relate to applicability of results.

METHODS

We extracted information from the Cochrane risk-of-bias-2 (RoB-2) tool, Risk Of Bias In Non-randomised Studies-of Interventions tool (ROBINS-I), and Methodological Index for Non-Randomized Studies (MINORS) criteria and derived a concisely formulated set of items with signaling questions tailored to operative interventions in orthopedic trauma surgery.

RESULTS

The established set contained nine items: population, intervention, comparator, outcome, confounding, missing data and selection bias, intervention status, outcome assessment, and pre-specification of analysis. Each item can be assessed using signaling questions and was explained using good practice examples of operative intervention studies in orthopedic trauma surgery.

CONCLUSION

The set of items will be useful to form a first judgment on studies, for example when including them in a systematic review. Existing risk of bias tools can be used for further evaluation of methodological quality. Additionally, the proposed set of items and signaling questions might be a helpful starting point for peer reviewers and clinical readers.

Relapse and Disease-Free Survival in Patients With Myelodysplastic Syndrome Undergoing Allogeneic Hematopoietic Cell Transplantation Using Older Matched Sibling Donors vs Younger Matched Unrelated Donors

IMPORTANCE

Matched sibling donors (MSDs) are preferred for allogeneic hematopoietic cell transplantation (allo-HCT) in myelodysplastic syndrome even if they are older. However, whether older MSDs or younger human leukocyte antigen-matched unrelated donors (MUDs) are associated with better outcomes remains unclear.

OBJECTIVE

To investigate whether allo-HCT for myelodysplastic syndrome using younger MUDs would be associated with improved disease-free survival and less relapse compared with older MSDs.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study assessed data reported to the Center for International Blood and Marrow Transplant Research database from 1761 adults 50 years or older with myelodysplastic syndrome who underwent allo-HCT using an older MSD or younger MUD between January 1, 2011, and December 31, 2017, with a median follow-up of 48 months. Data analysis was performed from January 8, 2019, to December 30, 2020.

INTERVENTIONS/EXPOSURES

Allo-HCT from an older MSD (donor age ≥50 years) or a younger MUD (donor age ≤35 years).

MAIN OUTCOMES AND MEASURES

The primary outcome was disease-free survival. Secondary outcomes were overall survival, relapse, nonrelapse mortality, acute graft-vs-host disease (GVHD), chronic GVHD, and GVHD-free relapse-free survival.

RESULTS

Of 1761 patients (1162 [66%] male; median [range] age, 64.9 [50.2-77.6] years in the MSD cohort and 66.5 [50.4-80.9] years in MUD cohort), 646 underwent allo-HCT with an older MSD and 1115 with a younger MUD. In multivariable analysis, the rate of disease-free survival was significantly lower in allo-HCTs with older MSDs compared with younger MUDs (hazard ratio [HR], 1.17; 95% CI, 1.02-1.34; P = .02), whereas the difference in overall survival rate of allo-HCT with younger MUDs vs older MSDs was not statistically significant (HR, 1.13; 95% CI, 0.98-1.29; P = .07). Allo-HCT with older MSDs was associated with significantly higher relapse (HR, 1.62; 95% CI, 1.32-1.97; P < .001), lower nonrelapse mortality (HR, 0.76; 95% CI, 0.59-0.96; P = .02), lower acute GVHD (HR, 0.52; 95% CI, 0.42-0.65; P < .001), chronic GVHD (HR, 0.77; 95% CI, 0.64-0.92; P = .005), and a lower rate of GVHD-free relapse-free survival beyond 12 months after allo-HCT (HR, 1.42; 95% CI, 1.02-1.98; P = .04).

CONCLUSIONS AND RELEVANCE

This cohort study found higher disease-free survival and lower relapse for allo-HCT in myelodysplastic syndrome using younger MUDs compared with older MSDs. The risk of nonrelapse mortality and GVHD was lower with older MSDs. These results suggest that the use of younger MUDs should be considered in the donor selection algorithm for myelodysplastic syndrome, in which it is pivotal to minimize relapse given limited treatment options for managing relapsed disease.

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

Objectives

Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalance and missing data, which are two common issues in medical data. The current study’s main goal was to compare the performance of six machine learning (ML) methods for predicting hospital readmission in HF patients.

Methods

In this retrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in Farshchian Heart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM), least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predict hospital readmission. These methods’ performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Two imputation methods were also used to deal with missing data.

Results

Of the 1,856 HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracy in the range of 0.57–0.60, while RF performed the best, with the highest accuracy (range, 0.90–0.91). Other ML methods showed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance of the SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the median imputation method.

Conclusions

This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

Determinants of non-response in a longitudinal study of participants in the Women and Alcohol in Gothenburg project

Longitudinal assessment is useful for tracking patterns of alcohol use over time. Non-response is a common feature of longitudinal design and can bias estimates of alcohol use if there exist systematic differences between respondents and non-respondents. We investigated whether alcohol use, health status, and sociodemographic characteristics were determinants of non-response in a longitudinal cohort of women in the general population. We used data from a stratified, random sample of 479 women born in 1925, 1935, 1945, 1955, 1965, and 829 women born in 1970 and 1975, who were initially selected as participants in the Women and Alcohol in Gothenburg project. Results from multivariable logistic regression revealed that problematic alcohol use, depression, poor self-rated physical health, and basic education were associated with increased odds of non-response among women born in 1925, 1935, 1945, 1955, and 1965. Among women born between 1970 and 1975, older age and being unmarried increased the odds of non-response at follow-up. Surprisingly, problematic alcohol use and poor health were not associated with non-response in these younger birth cohorts. This study finding suggests that approaches to improve future survey response rates need to consider factors of greatest relevance to birth year and age.

A simplified primary aldosteronism surgical outcome score is a useful prediction model when target organ damage is unknown - Retrospective cohort study

Background

Cure of hypertension after adrenalectomy for primary aldosteronism is no certainty and therefore preoperative patient counseling is essential. The Primary Aldosteronism Surgical Outcome (PASO) Score is a useful prediction model with an area under the curve (AUC) of 0.839. The PASO Score includes ‘Target Organ Damage’ (TOD) (i.e., left ventricular hypertrophy and/or microalbuminuria), which is often unavailable during preoperative counseling and might therefore limit its use in clinical practice. We hypothesized that the PASO score would still be useful if TOD is unknown at time of counseling. Therefore, we aimed to examine the predictive performance of the simplified PASO Score, without taking TOD into account.

Materials and methods

In this retrospective cohort study, patients who underwent unilateral adrenalectomy between 2010 and 2016 in 16 medical centers from North America, Europe and Australia were included. TOD was unknown in our database and therefore assigned as absent. Patients were classified as complete, partial or absent clinical success using the PASO consensus criteria.

Results

A total of 380 (73.9%) patients were eligible for analysis. Complete, partial and absent clinical success were observed in 29.5%, 55.8% and 14.7% of patients, respectively. The simplified PASO Score had an AUC of 0.730 (95% confidence interval 0.674–0.785) in our total cohort.

Conclusion

Without taking TOD into account, the simplified PASO Score had a lower predictive value as compared to the original derivation cohort. Ideally, the complete PASO Score should be used, but when data on TOD are not readily available, the simplified PASO Score is a useful and reasonable alternative.

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We aimed to examine the predictive performance of the PASO Score, without taking ‘target organ damage’ (TOD) into account.

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This simplified PASO Score had a lower predictive value as compared to the PASO Score in the original derivation cohort.

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The simplified PASO Score increases the applicability of the model and is reasonable for clinicians to use in daily practice.

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Ideally, the complete PASO Score should be used, but the simplified PASO Score is a useful and reasonable alternative.