Multiple imputation with missing data indicators

Nutrition and movement to improve quality of life in patients with knee osteoarthritis: the NUMOQUA study protocol for a randomised controlled trial

BACKGROUND

Osteoarthritis (OA) has long been considered as a degenerative disease of cartilage tissue resulting from bodily wear and tear. However, there is accumulating evidence that inflammation plays a key role in the pathogenesis of OA. In knee OA, the most common form of OA, exercise therapy as an effective component of early treatment addresses functional deficits, pain and inflammation. Since inflammation is critical for the development and progress of OA, anti-inflammatory therapies must be combined strategically. In the course of the NUMOQUA project, an anti-inflammatory therapeutic diet named 'Austrian Osteoarthritis Cuisine' was developed. It is based on the framework of the New Nordic Diet combined with the food-based dietary guidelines of Austria, the guidelines for OA, the Austrian food culture and the principles of a sustainable diet. The present study examines the implementation of the 'Austrian OA Cuisine' combined with the evidence-based training programme GLA:D® (Good Life with osteoArthritis in Denmark) in Austrian patients with knee OA and the effects on quality of life, nutritional and inflammatory status, as well as oxidative stress parameters.

METHODS

A total of 60 participants aged 50 to 75 with knee OA will be included and randomly assigned either to the intervention group or the control group. All participants will undergo the GLA:D® programme in the first 6 weeks. Additionally, the intervention group will receive nutritional group training and individual nutritional counselling on the 'Austrian OA Cuisine' over 9 months. The control group will receive general information about a healthy lifestyle. Measurements at baseline and at 4 follow-up dates include nutritional, inflammatory and oxidative stress markers. Furthermore, anthropometric, behavioural and clinical data will be obtained. The recruitment process lasted from autumn 2022 to January 2024, followed by the intervention until October 2024.

DISCUSSION

The prevalence of OA is expected to increase in the future due to ongoing demographic changes and rising obesity rates. The expected results will provide important evidence on whether this interdisciplinary therapeutic approach could be a new, cost-effective and sustainable strategy to address the disease process of OA without negative side effects.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05955300. Date of registration: 23rd of October 2023.

Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets

BACKGROUND

Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk.

METHODS

The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC).

RESULTS

The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p<0.05.

CONCLUSION

KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

The effect of limited healthcare access on poor outcomes among hospitalized COVID-19 patients in Honduras: A single center cohort study

Background

The COVID-19 pandemic has had a severe impact on the Latin American subcontinent, particularly in areas with limited hospital resources and a restricted Intensive Care Unit (ICU) capacity. This study aimed to provide a comprehensive description of the clinical characteristics, outcomes, and factors associated with survival of COVID-19 hospitalized patients in Honduras.

Research question

What were the characteristics and outcomes of COVID-19 patients in a large referral center in Honduras?

Study design and methods

This study employed a retrospective cohort design conducted in a single center in San Pedro Sula, Honduras, between October 2020 to March 2021. All hospitalized cases of confirmed COVID-19 during this timeframe were included in the analysis. Univariable and multivariable survival analysis were performed using Kaplan-Meier curves and Cox proportional hazards model aiming to identify factors associated with decreased 30 day in-hospital survival, using a priori-selected factors.

Results

A total of 929 confirmed cases were identified in this cohort, with males accounting for 55.4 % of cases. The case fatality rate among the hospitalized patients was found to be 50.1 % corresponding to 466 deaths. Patients with comorbidities such as hypertension, diabetes, obesity, chronic kidney disease, chronic obstructive pulmonary disease and cardiovascular disease had a higher likelihood of mortality. Additionally, non-survivors had a significantly longer time from illness onset to hospital admission compared to survivors (8.2 days vs 4.7 days). Among the cohort, 306 patients (32.9 %) met criteria for ICU admission. However, due to limited capacity, only 60 patients (19·6 %) were admitted to the ICU. Importantly, patients that were unable to receive level-appropriate care had lower likelihood of survival compared to those who received level-appropriate care (hazard ratio: 1.84).

Interpretation

This study represents, the largest investigation of in-hospital COVID-19 cases in Honduras and Central America. The findings highlight a substantial case fatality rate among hospitalized patients. In this study, patients who couldn't receive level-appropriate care (ICU admission) had a significantly lower likelihood of survival when compared to those who did. These results underscore the significant impact of healthcare access during the pandemic, particularly in low- and middle-income countries.

Independent effect of the triglyceride-glucose index on all-cause mortality in critically ill patients with chronic obstructive pulmonary disease and asthma: A retrospective cohort study

BACKGROUND

The triglyceride-glucose (TyG) index serves as a reliable proxy for insulin resistance (IR). IR has been linked to heightened incidence, prevalence, or severity of chronic obstructive pulmonary disease (COPD) and asthma. Prior research indicates that critically ill patients are prone to developing IR. Nevertheless, few studies have delved into the correlation between IR and all-cause mortality in critically ill patients with COPD and asthma. Therefore, the aim of this study is to explore the association between the TyG index and all-cause mortality in patients with COPD and asthma, with the goal of assessing the impact of IR on the prognosis of this patient population.

METHODS

This is a retrospective study, and all data are from the Medical Information Mart for Intensive Care IV (MIMIC-IV) critical care database. This study included 684 ICU patients with COPD and asthma and divided them into quartiles based on TyG index levels. The primary outcomes of this study were all-cause mortality during follow-up, encompassing mortality at 30 days, 90 days, and 180 days. The Kaplan-Meier analysis was used to compare all-cause mortality among the above four groups. Cox proportional hazards analyses were performed to examine the association between TyG index and all-cause mortality in critically ill patients with COPD and asthma. Restricted cubic spline analysis was used to assess potential nonlinear association between the TyG index and the primary outcome.

RESULTS

A total of 684 patients (53.9% female) were included. The 90-days all-cause mortality rate and 180-days all-cause mortality were 11.7% and 12.3%, respectively. Kaplan-Meier analysis revealed a significant association between the TyG index and both 90-days all-cause mortality (log-rank p = .039) and 180-days all-cause mortality (log-rank p = .017). Cox proportional hazards analysis revealed a significant association between the TyG index and 90-days all-cause mortality in both the unadjusted model (HR, 1.30 [95% CI 1.08-1.57] p = .005) and the model adjusted for age, gender, and diabetes (HR, 1.38 [95% CI 1.15-1.67] p < .001). Similarly, the TyG index was associated with 180-days all-cause mortality in the unadjusted model (HR, 1.30 [95% CI 1.09-1.56] p = .004) and the model adjusted for age, sex, and diabetes (HR, 1.38 [95% CI 1.15-1.66] p < .001). The restricted cubic splines (RCS) regression model indicated a significant nonlinear association between the TyG index and both 90-days and 180-days all-cause mortality. Specifically, TyG index >4.8 was associated with an increased risk of mortality at both 90 days and 180 days.

CONCLUSIONS

In summary, our results extend the utility of the TyG index to critically ill patients with COPD and asthma. Our study shows that the TyG index is a potential predictor of all-cause mortality in critically ill patients with COPD and asthma. In addition, in patients with a TyG index exceeding 4.8, there was a heightened risk of mortality. Measuring the TyG index may help with risk stratification and prognosis prediction in critically ill patients with COPD and asthma. Further prospective studies are needed to confirm our findings.

Feasibility of a mobile health app-based self-management program for Chinese patients with breast cancer receiving chemotherapy: A randomized controlled pilot study

Objective

There are currently an increasing number of mobile health (mHealth) programs offered to patients with breast cancer undergoing chemotherapy, but their rate of adherence to app usage has remained low. This study aimed to examine the feasibility of an mHealth app-based program such as the adherence rate of app usage and determine the preliminary effects on self-efficacy, quality of life, symptom burden and healthcare utilization in these patients.

Methods

We conducted a randomized controlled pilot trial. Ninety-six participants were randomly allocated into either an intervention group or a control group (routine care plus a placebo app). The intervention group engaged in a 6-week self-regulation activity and received nurse-led social support via the app. The intention-to-treat principle was adopted. The generalized estimating equation was utilized to analyze the between-group, within-group and interaction effectiveness of this program.

Results

Totally 96 participants were enrolled from 16 May to 23 August 2022. The average rate of adherence to app usage increased from 4.8% at week 3 to 51.2% at week 6. There was a statistically significant reduction in the physiological efficacy scores of the intervention (p < .001) and control groups (p < .001) at week 6, compared with the baseline. At week 6, the intervention group reported a significantly lower symptom burden (p = .042) and significantly better physical well-being than the control group (p = .024).

Conclusions

It is feasible to perform an mHealth app-based self-management program for patients with breast cancer receiving chemotherapy. Nurses can utilize this program to facilitate patient self-management of symptoms during chemotherapy.

Registration

Clinicaltrials.gov, https://clinicaltrials.gov, (NCT05192525).

Development and internal validation of a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas

Aim

To develop and internally validate a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas (PPGLs).

Methods

Clinical data of patients with PPGLs who presented to Peking Union Medical College Hospital from 2013 to 2022 and underwent genetic testing were retrospectively collected. Variables were screened by backward stepwise and clinical significance and were used to construct multivariable logistic models in 50 newly generated datasets after the multiple imputation. Bootstrapping was used for internal validation. A corresponding nomogram was generated based on the model. Sensitivity analyses were also performed.

Results

A total of 556 patients with PPGLs were included, of which 99 had a germline SDHB mutation. The prediction model revealed that younger age of onset [Odds ratio (OR): 0.93, 95% CI: 0.91-0.95], synchronous metastasis (OR: 6.43, 95% CI: 2.62-15.80), multiple lesion (OR: 0.22, 95% CI: 0.09-0.54), retroperitoneal origin (OR: 5.72, 95% CI: 3.13-10.47), negative 131I-meta-iodobenzylguanidine (MIBG) (OR: 0.34, 95% CI: 0.15-0.73), positive octreotide scintigraphy (OR: 3.24, 95% CI: 1.25-8.43), elevated 24h urinary dopamine (DA) (OR: 1.72, 95% CI: 0.93-3.17), NE secretory type (OR: 2.83, 95% CI: 1.22- 6.59), normal secretory function (OR: 3.04, 95% CI: 1.04-8.85) and larger tumor size (OR: 1.09, 95% CI: 0.99-1.20) were predictors of SDHB mutations in PPGLs, and showed good and stable predictive performance with a mean area under the ROC curve (AUC) of 0.865 and coefficient of variation of 2.2%.

Conclusions

This study provided a novel and useful tool for predicting SDHB mutations by integrating easily obtained clinical data. It may help clinicians select suitable genetic testing methods and make appropriate clinical decisions for these high-risk patients.

PD-L1 expression in recurrent or metastatic head and neck squamous cell carcinoma in China (EXCEED study): a multicentre retrospective study

AIMS

Programmed death-ligand 1 (PD-L1) is known to be highly expressed in various malignancies, including head and neck squamous cell carcinoma (HNSCC). We aimed to determine the prevalence of PD-L1 expression in recurrent or metastatic HNSCC (R/M HNSCC) among Chinese patients.

METHODS

This multicentre, retrospective analysis of data from six centres in China included patients with R/M HNSCC treated from 9 August 2021 to 28 February 2022. PD-L1 expression in tumour tissue was assessed and represented using a combined positive score (CPS). The χ2 and Cochran-Mantel-Haenszel χ2 tests were used to compare the prevalence of different PD-L1 expression statuses according to related co-variables.

RESULTS

For all 402 examined patients with R/M HNSCC, 168 cases (41.8%) had PD-L1 expression with a CPS ≥20, and 337 cases (83.8%) had PD-L1 expression with a CPS ≥1. Between the PD-L1 CPS ≥20 group and PD-L1 CPS <20 group, statistically significant differences were observed for variables of sex (p<0.001), smoking habit (p=0.0138 for non-smokers vs current smokers) and primary tumour site (p<0.001 for hypopharynx vs oral cavity and p=0.0304 for larynx vs oral cavity, respectively).

CONCLUSION

PD-L1 with CPS ≥20 was expressed in about 41.8% of cases with R/M HNSCC among Chinese patients, and PD-L1 expression was significantly associated with sex, smoking history and primary tumour site. Our findings regarding the variables related to PD-L1 expression level provide insight for clinical practice and a solid basis for future research on immunotherapy in HNSCC.

TRIAL REGISTRATION NUMBER

ISRCTN10570964.

Associations between activities of daily living, leisure activities and subjective well-being in Chinese older adults

OBJECTIVES

The study aimed to explore the internal pathway of the association between activities of daily living and subjective well-being among Chinese older adults using a large and representative sample.

BACKGROUND

There is still a lack of systematic investigation into the association between activities of daily living and subjective well-being among Chinese older adults.

MATERIALS AND METHODS

The study included 10 578 participants from the Chinese Longitudinal Healthy Longevity Survey. We applied multiple linear regression analysis and Spearman correlation analysis to examine the association between activities of daily living and subjective well-being and PROCESS macro to determine the mediating effect of leisure activities on the relationship between them.

RESULTS

The mean total score of subjective well-being was 30.5 ± 4.4. The prevalence of basic activities of daily living and instrumental activities of daily living limitations was 18.0% and 60.8%, respectively. Correlation analysis showed that activities of daily living score were negatively correlated with subjective well-being. Leisure activities were positively correlated with subjective well-being. The effect of instrumental activities of daily living on subjective well-being was partially mediated by leisure activities and leisure activities completely mediated the association between basic activities of daily living and subjective well-being.

CONCLUSIONS

The effect of instrumental activities of daily living on subjective well-being was partially mediated by leisure activities and leisure activities completely mediated the association between basic activities of daily living and subjective well-being.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Accounting for nonmonotone missing data using inverse probability weighting

Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge. In this work we describe and illustrate these estimators, and examine performance in simulation and in an applied example estimating the effect of anemia on spontaneous preterm birth in the Zambia Preterm Birth Prevention Study. We compare performance with multiple imputation (MI) and focus on the setting of an observational study where inverse probability of treatment weights are used to address confounding. In simulation, weighting was less statistically efficient at the smallest sample size and lowest exposure prevalence examined (n = 1500, 15% respectively) but in other scenarios statistical performance of weighting and MI was similar. Weighting had improved computational efficiency taking, on average, 0.4 and 0.05 times the time for MI in R and SAS, respectively. UMLE was easy to implement in commonly used software and convergence failure occurred just twice in >200 000 simulated cohorts making implementation of CBE unnecessary. In conclusion, weighting is an alternative to MI for nonmonotone missingness, though MI performed as well as or better in terms of bias and statistical efficiency. Weighting's superior computational efficiency may be preferred with large sample sizes or when using resampling algorithms. As validity of weighting and MI rely on correct specification of different models, both approaches could be implemented to check agreement of results.

Racial and socioeconomic disparities in glioblastoma outcomes: A single-center, retrospective cohort study

BACKGROUND

Glioblastoma (GBM) is the most common malignant primary brain tumor. Emerging reports have suggested that racial and socioeconomic disparities influence the outcomes of patients with GBM. No studies to date have investigated these disparities controlling for isocitrate dehydrogenase (IDH) mutation and O-6-methylguanine-DNA methyltransferase (MGMT) status.

METHODS

Adult patients with GBM were retrospectively reviewed at a single institution from 2008 to 2019. Univariable and multivariable complete survival analyses were performed. A Cox proportional hazards model was used to assess the effect of race and socioeconomic status controlling for a priori selected variables with known relevance to survival.

RESULTS

In total, 995 patients met inclusion criteria. Of these, 117 patients (11.7%) were African American (AA). The median overall survival for the entire cohort was 14.23 months. In the multivariable model, AA patients had better survival compared with White patients (hazard ratio [HR], 0.37; 95% confidence interval [CI], 0.2-0.69). The observed survival difference was significant in both a complete case analysis model and a multiple imputations model accounting for missing molecular data and controlling for treatment and socioeconomic status. AA patients with low income (HR, 2.17; 95% CI, 1.04-4.50), public insurance (HR, 2.25; 95% CI, 1.04-4.87), or no insurance (HR, 15.63; 95% CI, 2.72-89.67) had worse survival compared with White patients with low income, public insurance, or no insurance, respectively.

CONCLUSIONS

Significant racial and socioeconomic disparities were identified after controlling for treatment, GBM genetic profile, and other variables associated with survival. Overall, AA patients demonstrated better survival. These findings may suggest the possibility of a protective genetic advantage in AA patients.

PLAIN LANGUAGE SUMMARY

To best personalize treatment for and understand the causes of glioblastoma, racial and socioeconomic influences must be examined. The authors report their experience at the O'Neal Comprehensive Cancer Center in the deep south. In this report, contemporary molecular diagnostic data are included. The authors conclude that there are significant racial and socioeconomic disparities that influence glioblastoma outcome and that African American patients do better.

Multiple imputation for non-monotone missing not at random data using the no self-censoring model

Although approaches for handling missing data from longitudinal studies are well-developed when the patterns of missingness are monotone, fewer methods are available for non-monotone missingness. Moreover, the conventional missing at random assumption-a natural benchmark for monotone missingness-does not model realistic beliefs about the non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random mechanisms. The "no self-censoring" model is such a mechanism and assumes the probability an outcome variable is missing is independent of its value when conditioning on all other possibly missing outcome variables and their missingness indicators. As an alternative to "weighting" methods that become computationally demanding with increasing number of outcome variables, we propose a multiple imputation approach under no self-censoring. We focus on the case of binary outcomes and present results of simulation and asymptotic studies to investigate the performance of the proposed imputation approach. We describe a related approach to sensitivity analysis to departure from no self-censoring. We discuss the relationship between missing at random and no self-censoring and prove that one is not a special case of the other. Finally, we discuss extensions to non-binary data settings. The proposed methods are illustrated with application to a substance use disorder clinical trial.

Normal Workflow and Key Strategies for Data Cleaning Toward Real-World Data: Viewpoint

With the rapid development of science, technology, and engineering, large amounts of data have been generated in many fields in the past 20 years. In the process of medical research, data are constantly generated, and large amounts of real-world data form a "data disaster." Effective data analysis and mining are based on data availability and high data quality. The premise of high data quality is the need to clean the data. Data cleaning is the process of detecting and correcting "dirty data," which is the basis of data analysis and management. Moreover, data cleaning is a common technology for improving data quality. However, the current literature on real-world research provides little guidance on how to efficiently and ethically set up and perform data cleaning. To address this issue, we proposed a data cleaning framework for real-world research, focusing on the 3 most common types of dirty data (duplicate, missing, and outlier data), and a normal workflow for data cleaning to serve as a reference for the application of such technologies in future studies. We also provided relevant suggestions for common problems in data cleaning.

Association of serum uric acid with hepatic steatosis detected by controlled attenuation parameter in the United States population

BACKGROUND

The relationship between serum uric acid (SUA) and nonalcoholic fatty liver disease (NAFLD) has been previously reported. Controlled attenuation parameter (CAP) has better diagnostic performance than ultrasonography for assessing hepatic steatosis. The association of SUA with hepatic steatosis detected by CAP is worth further study.

METHODS

The US population aged 20 years or older from the National Health and Nutrition Examination Survey (NHANES) was assessed. Hepatic steatosis was evaluated by the controlled attenuation parameter (CAP). NAFLD status was defined as CAP values of 268 dB/m without hepatitis B or C virus infection or considerable alcohol consumption. Multiple imputations were performed to fill in the missing covariate values. Linear regression, logistic regression, and smooth curve fitting were used to examine the association.

RESULTS

In total, 3919 individuals participated in this study. There was a positive association between SUA (µmol/L) and CAP (β = 0.14, 95% CI: 0.12-0.17, P < 0.01). After stratification by sex, a significant relationship between SUA and CAP existed in both males (β = 0.12, 95% CI: 0.09-0.16, P < 0.01) and females (β = 0.17, 95% CI: 0.14-0.20, P < 0.01) after multiple imputation. The inflection points of the threshold effect of SUA on CAP were 487.7 µmol/L in males and 386.6 µmol/L in females. There was a positive association between SUA (mg/dL) and NAFLD (OR = 1.30, 95% CI: 1.23-1.37, P < 0.01). After stratification by race, positive relationships were also observed. Meanwhile, a positive relationship existed between hyperuricemia and NAFLD (OR = 1.94, 95% CI: 1.64-2.30, P < 0.01). The positive relationship was more significant in females than in males (P for interaction < 0.01).

CONCLUSIONS

There was a positive association between SUA and CAP, as well as between SUA and NAFLD. Subgroup studies stratified by sex and ethnicity demonstrated that the effects were consistent.

The Relationship Between Prognostic Nutritional Indexes and the Clinical Outcomes of Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Purpose

Nutritional status is related to the clinical outcomes of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). The aim of this study was to investigate the association between nutritional status, measured by the prognostic nutritional index (PNI), and adverse hospitalization outcomes in patients with AECOPD.

Methods

Consecutive AECOPD patients admitted to the First Affiliated Hospital of Sun Yat-sen University between January 1, 2015 to October 31, 2021 were enrolled. We collected the clinical characteristics and laboratory data of patients. Multivariable logistic regression models were developed to assess the relationship between the baseline PNI and adverse hospitalization outcomes. A generalized additive model (GAM) was used to identify any non-linear relationship. In addition, we performed a subgroup analysis to tested the robustness of the results.

Results

A total of 385 AECOPD patients were involved in this retrospective cohort study. Based on the tertiles of PNI, patients in the lower tertiles of PNI showed more worse outcome incidence (30 [23.6%] versus 17 [13.2%] versus 8 [6.2%]; p < 0.001). Multivariable logistic regression analysis revealed that the PNI were independently associated with adverse hospitalization outcomes after adjustment for confounding factors (Odds ratio [OR] = 0.94, 95% CI: 0.91 to 0.97, P < 0.0001). After adjusting for confounders, smooth curve fitting showed a saturation effect, suggesting that the relationship between the PNI and adverse hospitalization outcomes was nonlinear. Two-piecewise linear regression model suggested that the incidence of adverse hospitalization outcomes significantly decreased with PNI level up to the inflection point (PNI = 42), and PNI was not associated with adverse hospitalization outcome after that point.

Conclusion

Decreased PNI levels at admission were determined to be associated with adverse hospitalization outcomes in patients with AECOPD. The results obtained in this study may potentially assist clinicians optimize risk evaluations and clinical management processes.

Effect of early vasopressin combined with norepinephrine on short-term mortality in septic shock: A retrospective study based on the MIMIC-IV database

BACKGROUND

Septic shock is a leading cause of death in intensive care units (ICUs), with short-term mortality rates of 35-40%. Vasopressin (AVP) is a second-line vasoactive agent for septic shock, and recent studies suggest that early AVP use can be beneficial. However, differences between early initiation of AVP combined with norepinephrine (NE) and nonearly AVP with NE are unclear. A retrospective cohort research was designed to explore the effects of early AVP initiation versus nonearly AVP initiation.

METHODS

This retrospective single-center cohort study included adult patients with septic shock from the MIMIC (Medical Information Mart for Intensive Care)-IV database. According to whether AVP was used early in the ICU (intensive care unit), patients were assigned to the early- (within 6 h of septic shock onset) and non-early-AVP (at least 6 h after septic shock onset) groups. The primary outcome was 28-day mortality. The secondary outcomes were ICU and hospital mortality, the numbers of vasopressor-free and ventilation-free days at 28 days, ICU length of stay (LOS), hospital LOS, sequential organ failure assessment (SOFA) score on days 2 and 3, and renal replacement therapy (RRT) use on days 2 and 3. Univariate and multivariate cox proportional-hazards regression, propensity-score matching were used to analyze the differences between the groups.

RESULTS

The study included 531 patients with septic shock: 331 (62.5%) in the early-AVP group and 200 (37.5%) in the non-early-AVP group. For 1:1 matching, 158 patients in the early-AVP group were matched with the same number of patients with nonearly AVP. Regarding the primary outcome, there was no significant difference between the early- and non-early-AVP groups in 28-day mortality (hazard ratio [HR] = 0.91, 95% confidence interval [CI] = 0.68-1.24). For the secondary outcomes, there were no differences between the early- and non-early-AVP groups in ICU mortality (HR = 0.95, 95% CI = 0.67-1.35), hospital mortality (HR = 0.95, 95% CI = 0.69-1.31), the numbers of vasopressor-free and ventilation-free days at 28 days, ICU LOS, hospital LOS, SOFA score on days 2 and 3, and RRT use on days 2 and 3.

CONCLUSIONS

There was no difference in short-term mortality between early AVP combined with NE and nonearly AVP with NE in septic shock.

Dapagliflozin reduces risk of heart failure rehospitalization in diabetic acute myocardial infarction patients: a propensity score-matched analysis

OBJECTIVE

The aim of this study was to investigate the effect of dapagliflozin (DAPA) on the rate of heart failure rehospitalization in patients with acute myocardial infarction (AMI) and type 2 diabetes mellitus (T2DM).

METHODS

AMI patients with T2DM from CZ-AMI registry between January 2017 and January 2021 were enrolled in this study. Patients were stratified into DAPA users and non-DAPA users. The primary outcome was the incidence of heart failure rehospitalization. Kaplan-Meier analysis and Cox regressions were performed to evaluate the prognostic significance of DAPA. Propensity score matching (PSM) was performed to minimize the bias of confounding factors and facilitate the comparability between groups. The enrolled patients were matched with a propensity score of 1:1.

RESULTS

A total of 961 patients were included, and 132 (13.74%) heart failure rehospitalizations occurred during a median follow-up of 540 days. In the Kaplan-Meier analysis, DAPA users had a statistically significantly lower rate of heart failure rehospitalization than non-DAPA users (p < 0.0001). Multivariate Cox analysis showed that DAPA was an independent protective factor for heart failure rehospitalization risk after discharge (HR = 0.498, 95% CI = 0.296 ~ 0.831, p = 0.001). After 1:1 propensity score matching, survival analysis showed a lower cumulative risk of heart failure rehospitalization in DAPA users than in non-DAPA users (p = 0.0007). In-hospital and continued use of DAPA remained significantly associated with a reduced risk of heart failure rehospitalization (HR = 0.417, 95% CI = 0.417 ~ 0.838, p = 0.001). Results were consistent across sensitivity and subgroup analyses.

CONCLUSION

In patients with diabetic AMI, in-hospital and continued use of DAPA after discharge were associated with a significant lower risk of heart failure rehospitalization.

Unmet healthcare needs predict frailty onset in the middle-aged and older population in China: A prospective cohort analysis

Objectives

Older populations have a relatively high prevalence of unmet healthcare needs, which can result in poor health status. Moreover, in the coming century, frailty is expected to become one of the most serious global public health challenges. However, there is a lack of clear evidence proving an association between unmet healthcare needs and frailty. This study aimed to assess whether unmet healthcare needs predict the onset of frailty in China.

Methods

The association between frailty and unmet healthcare needs was explored by analyzing data from the China Health and Retirement Longitudinal Study (CHARLS) using random-effects logistic regression and Cox regression with time-varying exposure.

Results

At baseline, 7,719 respondents were included in the analysis. Random-effects logistic regression shows that unmet outpatient healthcare needs were associated with increased risk of both contemporaneous (adjusted OR [aOR], 1.17; 95% CI, 1.02-1.35) and lagged (aOR, 1.24; 95% CI, 1.05-1.45) frailty, as were unmet inpatient needs (contemporaneous: aOR, 1.28; 95% CI, 1.00-1.64; lagged: aOR, 1.55; 95% CI, 1.17-2.06). For respondents not classified as frail at baseline (n = 5,392), Cox regression with time-varying exposure shows significant associations of both unmet outpatient needs (adjusted HR, 1.23; 95% CI, 1.05-1.44) and unmet inpatient needs (adjusted HR, 1.48; 95% CI, 1.11-1.99) with increased risk of developing frailty.

Conclusions

Reducing unmet healthcare needs would be a valuable intervention to decrease frailty risk and promote healthy aging in middle-aged and older populations. It is urgent and essential that the equity and accessibility of the medical insurance and health delivery systems be strengthened.

Instrumental activities of daily living trajectories and risk of mild cognitive impairment among Chinese older adults: results of the Chinese longitudinal healthy longevity survey, 2002-2018

Background

The association between the instrumental activities of daily living (IADL) score and the risk of initial cognitive function impairment is inconclusive. We aimed to identify distinctive IADL trajectories and examine their relationship with the onset of mild cognitive impairment (MCI) among Chinese older people.

Methods

The study used six-wave longitudinal data from the Chinese Longitudinal Healthy Longevity Survey conducted between 2002 and 2018. It included a total of 11,044 Chinese people aged 65 years or older. A group-based trajectory model was used to identify distinctive trajectories of the IADL score, and the Cox proportional hazards model was used to explore the hazard ratio of various trajectories at the onset of MCI. Interaction analysis was used to analyze individual modification between the IADL trajectories and the onset of MCI. Finally, we adopted four types of sensitivity analysis to verify the robustness of the results.

Results

During a median follow-up of 16 years, the incidence of MCI was 6.29 cases per 1,000 person-years (95% confidence interval [CI] 5.92-6.68). Three distinct IADL trajectory groups were identified: a low-risk IADL group (41.4%), an IADL group with increasing risk (28.5%), and a high-risk IADL group (30.4%). Using the Cox proportional hazards model after adjusting for covariates, we found that compared with the low risk IADL group, the hazard ratio of the IADL group with increasing risk was 4.49 (95% CI = 3.82-5.28), whereas that of the high-risk IADL group was 2.52 (95% CI 2.08-3.05). Treating the IADL group with increasing risk as the reference, the hazard ratio for the high-risk IADL group was 0.56 (95% CI 0.48-0.66). Interaction analyses showed that age and residence were significant moderators (P for interaction <0.05).

Conclusion

A group-based trajectory model was developed to classify older people into three distinct trajectory groups of the IADL score. The IADL group with increasing risk had a greater risk of MCI than the high-risk IADL group. In the IADL group with increasing risk, city residents of ≥80 years were the most likely to develop MCI.

Bidirectional relationship between nocturnal subjective sleep duration and cognitive performance in Chinese over 45 years old: a national representative longitudinal study

Introduction

Previous studies have inconsistent associations between changes in sleep duration and cognitive function and have not separated interindividual effects from intraindividual effects. This study aimed to examine the bidirectional associations between subjective night sleep duration and cognitive function while differing intraindividual and interindividual effects.

Methods

A national representative sample was obtained from China Health and Retirement Longitudinal Study during 2011–2018. Night sleep duration and potential confounders were assessed and collected by questionnaires. Cognition was assessed in three categories (orientation, executive function, and memory), and z scores were used for global cognitive performance. The random intercept cross-lagged panel model was used to examine the temporal associations during 2011–2018.

Results

Across 9404 participants over 45, at interindividual level, moderate (β= -0.014) and long sleepers (β= -0.06) had positive association between sleep duration and cognitive decline after adjusted covariates, but short sleepers had negative associations between sleep duration and cognitive decline (β = 0.87). At intraindividual level, prolonged sleep duration predicted better cognition for short (β= -0.021 at wave2-3; β= -0.04 at wave3-4) and moderate (β= -0.017 at wave3-4) sleepers.

Conclusion

For short sleepers, longer subjective nocturnal sleep duration predicted better cognitive performance; but moderate and long sleepers showed opposite results—short and moderate sleepers with prolonged subjective nocturnal sleep duration would have later cognitive decline. Our findings tentatively suggested that an increased subjective night sleep duration and subjective long sleep duration could be regarded as useful tools for identifying middle- and old adults at higher risks of progressing to cognitive decline.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03468-8.

High Molecular and Cytogenetic Risk in Myelofibrosis Does Not Benefit From Higher Intensity Conditioning Before Hematopoietic Cell Transplantation: An International Collaborative Analysis

There is no direct evidence to recommend specific conditioning intensities in myelofibrosis undergoing allogeneic hematopoietic cell transplantation, especially in the molecular era. We aimed to compare outcomes of reduced intensity (RIC) or myeloablative conditioning (MAC) transplantation in myelofibrosis with molecular information. The study included 645 genetically annotated patients (with at least driver mutation status available), of whom 414 received RIC and 231 patients received MAC. The median follow-up time from transplantation was 6.0 years for RIC and 9.4 years for MAC. The 6-year overall survival rates for RIC and MAC were 63% (95% confidence interval [CI], 58%-68%) and 59% (95% CI, 52%-66%; P = 0.34) and progression-free survival was 52% (95% CI, 47%-57%) and 52% (95% CI, 45%-59%; P = 0.64). The 2-year cumulative incidence of nonrelapse mortality was 26% (95% CI, 21%-31%) for RIC and 29% (95% CI, 23%-34%) for MAC (P = 0.51). In terms of progression/relapse, the 2-year cumulative incidence was 10% (95% CI, 5%-19%) for RIC and 9% (95% CI, 4%-14%) for MAC (P = 0.46). Higher intensity conditioning did not seem to improve outcomes for higher-risk disease, according to mutational, cytogenetic, and clinical profile. In contrast, patients with reduced performance status, matched unrelated donors, and ASXL1 mutations appeared to benefit from RIC in terms of overall survival.

Association of Red Cell Index and Adverse Hospitalization Outcomes in Chronic Obstructive Pulmonary Disease Patients with Acute Exacerbation: A Retrospective Cohort Study

Purpose

Previous studies have shown that the red cell index (RCI) can be considered as a simple and useful method to evaluate respiratory function. However, at present its association with adverse hospitalization outcomes in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not fully understood. Our study aimed to examine the relationship between adverse hospitalization outcomes and RCI among AECOPD patients.

Patients and Methods

We performed a retrospective analysis of consecutive patients from January 2015 to October 2021. In this study, RCI was the independent variable, measured at baseline, and adverse hospitalization outcome was the dependent variable. According to the RCI median (RCI=2.221), we divided 377 patients into two roughly equal groups (188 and 189, respectively). Next, the association between RCI and adverse hospitalization outcomes was explored using multivariable logistic regression models. To identify any non-linear relationship, a generalized additive model (GAM) was employed.

Results

With a total of 377 patients with AECOPD, we divided them into two roughly equal groups to compare the clinical factors and RCI levels. The patients in the higher RCI group showed poorer outcome incidence (18 [9.57%] vs 31 [16.40%]; p = 0.049). After accounting for potential confounders, the results showed that RCI was positively associated with adverse hospitalization outcomes (odds ratio [OR] = 1.15, 95% CI: 1.01-1.32). In addition, a non-linear relationship was detected between RCI and adverse hospitalization outcomes, which had an inflection point of 3.2. There were odds ratios and confidence intervals of 0.8 (0.7-1.0) and 1.3 (1.2-1.4) on the left and right sides of the inflection point, respectively.

Conclusion

The RCI and adverse hospitalization outcomes exhibited a non-linear relationship in the AECOPD patients. RCI is strongly positively correlated with adverse hospitalization outcomes when it was greater than 3.2.

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases

Background

Predictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.

Methods

Patients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.

Results

A total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.

Conclusion

Machine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.

The association between Chinese Visceral Adipose Index and coronary heart disease: A cohort study in China

BACKGROUND AND AIMS

The purpose of this study is to explore the relationship between Chinese visceral adipose index (CVAI) and the risk of coronary heart disease (CHD) in Chinese through a large cohort study.

METHODS AND RESULTS

This study included 42,165 adults who were without CHD at baseline and who completed at least one annual follow-up between 2009 and 2016. We used the Cox proportional hazards model to estimate Hazard Ratios (HRs) and 95% Confidence Intervals (CIs) for the association between CVAI and risk of CHD. During the median follow-up of 3.36 years (154,808 person years), 520 participants developed CHD, including 374 males and 146 females. Compared with the first quartile of CVAI, the risk of CHD was significantly increased in the fourth quartile of CVAI in multivariate model (HR [95% CI]: 9.92 [5.45, 18.04], P < 0.001). Sensitivity analysis by excluding incident CHD developed in the first two years of follow-up reinforced our results. Gender stratification analyses showed that the relationship between CVAI and CHD risk was higher in males than that in females. The restricted cubic spline showed a non-linear dose-response relationship between CVAI and CHD risk. In addition, CVAI was associated with CHD risk in the subgroups of participants without T2DM, without hypertension, and without fatty liver.

CONCLUSION

CVAI was significantly associated with the risk of CHD. Individuals should keep CVAI at normal level to prevent CHD.

Risk factors associated with severe disease in respiratory syncytial virus infected children under 5 years of age

OBJECTIVE

To evaluate risk factors for severe disease in children under 59 months of age hospitalized with respiratory syncytial virus (RSV) infection.

STUDY DESIGN

We prospectively enrolled 1,096 cases of laboratory confirmed RSV infection during three consecutive RSV seasons in 2015-2018. Potential risk factors for severe disease were retrieved through patient questionnaires and linkage to national health registries. Need for respiratory support (invasive ventilation, bi-level positive airway pressure, or continuous positive airway pressure), and length of stay exceeding 72 h were used as measures of disease severity. Associations were investigated using multivariable logistic regression analyses. Multiple imputation was used to avoid bias and inference induced by missing data.

RESULTS

Risk factors associated with a need for respiratory support included age younger than 3 months of age [aOR: 6.73 (95% CI 2.71-16.7)], having siblings [aOR: 1.65 (95% CI 1.05-2.59)] and comorbidity [aOR: 2.40 (95% CI 1.35-4.24)]. The length of hospital stay >72 h was significantly associated with being younger than 3 months of age [aOR: 3.52 (95% CI 1.65-7.54)], having siblings [aOR: 1.45 (95% CI 1.01-2.08)], and comorbidity [aOR: 2.18 (95% CI 1.31-3.61)]. Sub-group analysis of children younger than 6 months of age confirmed the association between both young age and having siblings and the need for respiratory support.

CONCLUSION

In a large cohort of children <59 months hospitalized with RSV infection, young age, comorbidity, and having siblings were associated with more severe disease.

On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. In an illustrative simulation study, it is shown that the Mislevy-Wu model provides unbiased model parameters. Moreover, the simulation replicates the finding from various simulation studies from the literature that scoring missing item responses as wrong provides biased estimates if the latent ignorability assumption holds in the data-generating model. However, if missing item responses are generated such that they can only be generated from incorrect item responses, applying an item response model that relies on latent ignorability results in biased estimates. The Mislevy-Wu model guarantees unbiased parameter estimates if the more general Mislevy-Wu model holds in the data-generating model. In addition, this article uses the PISA 2018 mathematics dataset as a case study to investigate the consequences of different missing data treatments on country means and country standard deviations. Obtained country means and country standard deviations can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, in the discussion section, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.