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Trial design and participants | handedness | This study was a double-blinded, randomized, sham-controlled trial with stratification by age, sex, and handedness score (Edinburgh Handedness Inventory, [EHI]) [Written informed consent was obtained from all volunteers along with demographic information, handedness, and their experience with the PPT.This study was approved by the relevant ethics committee (Approval Number: 21–85) and registered in the University Hospital Medical Information Network Clinical Trial Registry in Japan (UMIN000046868). Additionally, this study conformed to Consolidated Standards of Reporting Trials (CONSORT) guidelines. All experiments were conducted in accordance with the Declaration of Helsinki. | PMC9951449 | |
Experimental procedure | ’ adverse effects, pain | The experimental procedure consisted of three phases: pre-assessment, assessment, and post-assessment (Fig. Schematic representation of the experimental procedure and design. In the assessment phase, all participants performed the two PPT subtests (the left-handed peg and the assembly task, see the A 10-min rest period separated each assessment phase. Participants were informed that they would receive “two different intensities of forehead stimulation” and were blinded to the stimulation mode. A schematic illustration of active and sham stimulation is shown in Fig. After stimulation, during the post-assessment phase, the success of blinding was measured by asking participants to guess whether they had received active or sham tDCS to justify its effectiveness on early-phase dexterity acquisition. Additionally, subjective discomfort (pain) was measured using a sensation questionnaire to identify participants’ adverse effects, safety, and tolerability [ | PMC9951449 | |
Two subtests of PPT | The PPT is widely used as a hand function test in therapy, rehabilitation, and treatment to evaluate dexterity performance. It assesses dexterity of precision grip using two subtests [The left-handed peg task required participants to insert as many pegs into the holes as possible within 60 s, and the score is the number of pegs inserted correctly. In the assembly task, participants were required to use their left hand to insert the peg and add two washers and a collar in a certain order within 60 s. Compared to the simple peg task, higher cognitive demand is required to sequentially assemble four parts with different shapes.The score of the assembly operation task is the total number of parts from the completed assemblies and uncompleted assemblies. Each of these tasks was repeatedly conducted 4 times in each assessment session. The data in the first trial was discarded from the analysis in consideration of the possibility of a large variation in task performance and greater improvement from the first trial to second trial (Fig. | PMC9951449 | ||
Transcranial direct current stimulation | CORTEX | Stimulation was delivered using a DC-STIMULATOR PLUS (NeuroConn GmbH, Ilmenau, Germany) through a pair of 0.9% saline-soaked 5 × 7 cm electrodes, resulting in a current density of 0.057 mA/cmLeft DLPFC (F3) and orbitofrontal cortex (Fp2) electrode placement (international 10/20 system). The left image shows the electrode configurations with the anode (red) over F3 and cathode (blue) over Fp2. The right image shows the underlying cortical electric field on different directions of the brain map. The horizontal color bar indicates the electric field magnitude expressed in norm E (V/m). | PMC9951449 | |
Statistical analyses | Baseline characteristics and the assessment of blinding and sensation questionnaire were analyzed using a t-test or chi-square test.For the number of completions in each PPT subtest, a 2 × 3 mixed-design analysis of variance (ANOVA), with group (active tDCS or sham) as the between-participants factor and time (baseline, online, and offline sessions) as the within- participant factor and effect sizes calculated as partial eta squared ( | PMC9951449 | ||
Results | PMC9951449 | |||
Comparison of normalized PPT task performance | Table Effects of tDCS stimulation on the normalized Z-scores of the two PPT subtests. The results of 2 × 2 × 2 mixed-design analysis of variance of the normalized Z-scores with group as the between-participant factor and time and task type as the within-participant factors. The displayed points show the individual Z-scores. Error bars indicate the 95% confidence interval; the bottom and top of each box, the 25th and 75th percentiles; and the line and square inside the box, the 50th percentile (median) and the mean, respectively. ** = p < 0.001. | PMC9951449 | ||
Relationship between the PPT task performance at baseline and changes in the PPT task performance in each evaluation period | There was no significant correlation between the PPT task performance at baseline and changes in each task performance in online and offline sessions in the active tDCS groups (all, p > 0.08) (Fig. Scatterplots for relationship between PPT task performance at baseline and online and offline session changes. Left, scatterplots of each of the two tasks (simple peg task and assembly task) in the online session. Right, scatterplots of each of the two tasks in offline assessment. The straight and curved lines indicate the mean and 95% confidence interval, respectively. | PMC9951449 | ||
Discussion | upper-limb motor learning | CORTEX | This prospective, hypothesis-driven, double-blind, randomized controlled trial demonstrated that tDCS over the left DLPFC significantly enhanced early-phase upper-limb motor learning in both the left-handed simple peg and assembly tasks, which was the result anticipated by our hypothesis. Importantly, this study fulfilled the suggested criteria in a recent consensus and critical position article on tDCS [In the offline assessment, the post-hoc test revealed that active tDCS significantly improved learning in the assembly task but did not have an effect in the simple peg task. A meta-analysis reported that tDCS over the DLPFC significantly improved offline working memory performance in healthy cohorts; however, no effect was observed in online performance [Regarding the effect size, our results of two PPT tasks were far above the size reported for anodal tDCS on the primary motor cortex comparing sham tDCS reported in a previous meta-analysis in upper limb dexterity motor learning (0.04 (95% CI 0.01–0.07) [In addition, normalized Z-scores were calculated to elucidate whether tDCS elicited a larger learning gain in tasks with a high cognitive demand. Larger learning gain in the active tDCS group was observed in the assembly task compared to that in the simple peg task at both online and offline sessions, as hypothesized. Successful completion of the assembly task requires higher cognitive demand, action planning [In this study, there was no significant relationship between baseline performance and tDCS-induced learning gain in either of the two tasks. Previous studies [ | PMC9951449 |
Limitations | handedness, upper-limb motor learning | There are some limitations to this study. First, only single-session tDCS in the initial stage of upper-limb motor learning was evaluated. The tDCS-induced gain in motor learning of the non-dominant hand should be evaluated in long-term continuous training, rather than in a single efficacy test. Moreover, retention of acquired skills should be evaluated. This could further enhance clinical application of tDCS to individuals obliged to change their handedness. Second, the learning effect due to repetition was shown in two PPT tasks. This is an unavoidable problem in the early phase of motor learning when rapid performance gains were obtained. Nevertheless, we found the effectiveness of tDCS beyond learning effects in the task with high cognitive demand, although the potential tDCS effects may have been underestimated. Third, physiological evidence underlying the behavioral data is lacking. Functional neuroimaging can provide more robust evidence and better understanding of the impact of tDCS on motor learning. Finally, precise electrode placement based on each individual’s brain structure could not be performed. A recent study evaluating the precision of electrode placement in electroencephalography based on the international 10/20 system referring to structural magnetic resonance imaging (MRI) data revealed the variation in electrode location in Montreal Neurological Institute coordinates [ | PMC9951449 | |
Conclusions | This study demonstrated that tDCS significantly improved early-phase manual dexterity skill acquisition regardless of baseline performance level, particularly in tasks requiring high cognitive demand. tDCS on the left DLPFC can potentially accelerate early-phase manual dexterity skill acquisition and contribute to further understanding of the underlying neurophysiological mechanisms in the left DLPFC during this process. | PMC9951449 | ||
Acknowledgements | Not applicable. | PMC9951449 | ||
Author contributions | HT | AW conceptualized and designed the experiment, acquired data, and participated in the formal analysis, visualization, writing of the original draft, and writing review and editing. DS worked on the conceptualization, project administration, experiment design, methodology, validation, data acquisition, formal analysis, interpretation of results, visualization, writing -of the original draft, writing—reviewing, editing, and supervision. HN worked on the methodology, experiment design, writing, reviewing, and editing. YT worked on randomization and data acquisition. HM worked on the experiment design and data acquisition. KS worked on the experiment design, writing, reviewing, and editing. KF worked on the formal analysis, writing, reviewing, and editing. HT worked on the experiment design, writing, reviewing, and editing. SY worked on resources. SS conceptualized and designed the experiment, and worked on writing, reviewing, and editing. All authors read and approved the final manuscript. | PMC9951449 | |
Funding | This research did not receive any specific Grant from funding agencies in the public, commercial, or not-for-profit sectors. | PMC9951449 | ||
Availability of data and materials | The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. | PMC9951449 | ||
Declarations | PMC9951449 | |||
Ethics approval and consent to participate | This study was approved by the relevant ethics committee (Approval Number: 21–85) and registered in the University Hospital Medical Information Network Clinical Trial Registry in Japan (UMIN000046868). Written informed consent was obtained from all participants. | PMC9951449 | ||
Consent for publication | Not applicable. | PMC9951449 | ||
Competing interests | The authors declare no conflicts of interest. | PMC9951449 | ||
References | PMC9951449 | |||
Background | To compare the accuracy of dental implant placement using a novel dental implant robotic system (THETA) and a dynamic navigation system (Yizhimei) by a | PMC10052843 | ||
Methods | 10 partially edentulous jaws models were included in this study, and 20 sites were randomly assigned into two groups: the dental implant robotic system (THETA) group and a dynamic navigation system (Yizhimei) group. 20 implants were placed in the defects according to each manufacturer’s protocol respectively. The implant platform, apex and angle deviations were measured by fusion of the preoperative design and the actual postoperative cone-beam computed tomography (CBCT) using 3D Slicer software. Data were analyzed by | PMC10052843 | ||
Results | angulation | A total of 20 implants were placed in 10 phantoms. The comparison deviation of implant platform, apex and angulation in THETA group were 0.58 ± 0.31 mm, 0.69 ± 0.28 mm, and 1.08 ± 0.66 | PMC10052843 | |
Conclusion | The implant positioning accuracy of the robotic system, especially the angular deviation was superior to that of the dynamic navigation system, suggesting that the THETA robotic system could be a promising tool in dental implant surgery in the future. Further clinical studies are needed to evaluate the current results. | PMC10052843 | ||
Keywords | PMC10052843 | |||
Background | SURGICAL COMPLICATIONS | The accuracy of implant placement including its position, angle, and depth within the jawbone is essential and would affect its long-term stability, survival and success rate [Computer-assisted dynamic navigation allows tracking the position of the drilling needle during the implant procedure, avoiding damage to adjacent important anatomical tissue and preventing surgical complications [The novel THETA robotic dental implant system, developed by Hangzhou Jianjia Robot Co. LTD, is a semi-automatic system, which could conduct positioning, drilling and implant placement according to control the integrated button (line setting button, teaching button) with an optical navigation system. All wrist joints of UR-3e manipulator can rotate 360 degrees, and the end joints can rotate infinitely. With force sensors, UR-3e manipulator can cooperate well with users in the same space through force position coupling control and handle high-precision tasks.At present, there are CBCT image errors, registration errors, positioning and marking device printing errors, and visual system errors in both dynamic navigation system and robotic dental implant system. Few studies comparing the accuracy of the two implant systems. The accuracy of the implant-assisted technique is evaluated mainly through model experiments and clinical trials. In this study, a phantom experimental design in vitro was selected to compare the accuracy of a dental implant robotic system with a dynamic navigation system. | PMC10052843 | |
Materials and methods | PMC10052843 | |||
The experimental operational procedure | tooth | 10 partially edentulous models containing 20 tooth missing sites were included in this study, and randomly divided into two groups, the tooth position, bone density and implant information were shown in Table
Detail of experimental groups4543Nobel PCC RP 4.3 × 11.5 mmNobel PCC NP 3.5 × 8 mm4543Nobel PCC RP 4.3 × 11.5 mmNobel PCC NP 3.5 × 8 mm1221Nobel PCC NP 3.5 × 8 mmNobel PCC RP 4.3 × 10 mm1221Nobel PCC NP 3.5 × 8 mmNobel PCC RP 4.3 × 10 mm4546Nobel PCC RP 4.3 × 10 mmNobel PCC RP 4.3 × 11.5 mm4546Nobel PCC RP 4.3 × 10 mmNobel PCC RP 4.3 × 11.5 mm3637Nobel PCC RP 4.3 × 10 mmNobel PCC RP 4.3 × 11.5 mm3637Nobel PCC RP 4.3 × 10 mmNobel PCC RP 4.3 × 11.5 mm1416Nobel PCC RP 4.3 × 10 mmNobel PCC RP 5.0 × 10 mm1416Nobel PCC RP 4.3 × 10 mmNobel PCC RP 5.0 × 10 mm
Experimental operation procedure with robotic system and dynamic navigation system | PMC10052843 | |
THETA robotic-assisted dental implant surgery procedure | The THETA robotic dental implant system (Hangzhou Jianjia Robot Company, Hangzhou, China) is an integrated implant surgical robot and could complete dental implant surgery, which is composed of hardware including a mechanical arm, a binocular camera, an industrial control computer, an integrating platform, and an operation tool. The handpiece can be attached to the manipulator (Fig. A Jianjia U-shape silicone tube (Hangzhou Jianjia Robot Company, Hangzhou, China) was mounted on the edentulous area of the model with silicone impression material (DMG, Hamburg, Germany). The models were scanned with cone-beam computed tomography (CBCT) (Planmeca ProMax, Planmeca Oy, Helsinki, Finland), all scans were performed at 80 kV and 6.0 mA for 15 s (voxel size: 0.15 mm; grayscale: 15 bits; focal spot: 0.5 mm; and field of view: 12 × 9 cm), then imported into surgical planning software (Cycad DHC-DI, version: V2, Hangzhou Jianjia Robot Company, Hangzhou, China). Implants (NobelParallel CC) were placed virtually in each model. The implant procedure was designed according to the planning software. The experimental operational procedure was shown in Fig.
Experimental operation procedure with THETA dental implant robotic system | PMC10052843 | ||
Yizhimei dynamic navigation-assisted implant surgery procedure | tooth | CAVITY | The Yizhimei computer-assisted dynamic navigation system (DHC-D12, Digital-health Care Co., Ltd., Suzhou, China) consists of a cart (including host, display, infrared tracking and positioning device, dynamic navigation system software), registration device, fixture device, reference board, and implant handpiece with locator and accessories. In addition, medical data including image progression and cast model scanning, preoperative planning, head models, and software for planning, calibration, and control are necessary to form the entire simulation system for surgery.A U-tube (Digital-health Care Co., Ltd., Suzhou, China) was mounted on the edentulous model with silicone impression material. The models were scanned with CBCT and imported into surgical planning software (Yizhimei, Digital-health Care Co., Ltd., Suzhou, China). Enter the dynamic navigation implant system for preoperative planning, enter the real-time navigation interface, select the implant handpiece and reference plate, the reference plate is fixed on the adjacent tooth or the opposite side of the same jaw. Six zirconia pits on the U-tube were identified by the navigation implant mobile drill, the point-to-point registration was performed by calculating the distance between the position and the reference point, to identify and locate the surgical area. Remove the U-tube after registration. According to the guidance of the dynamic navigation system, the planting cavity was prepared and the implants were implanted. During the operation, the surgical operator can selectively observe the surgical approach and various parameters of the surgical area from all directions in time. According to the instructions of the software, the implant site, angle and depth can be dynamically adjusted to ensure that the implant results conform to the design plan. Postoperative CBCT was performed. The architecture of the dynamic implant surgery was shown in Fig.
Model implantation using dynamic implant surgery. (a) U-tube mounted on maxilla with impression material; (b)The registration process: short drill clicks any 6 small ball pits on the registration device to complete the registration information collection; (c) U-tube registration; (d) Schematic diagram of dynamic navigation system; (e) Implant insertion assisted with dynamic implant surgery; the handpiece and mandibular were tracked with stereo infrared light camera; Precise operation under the control of implantation point, angle and depth | PMC10052843 |
Accuracy analysis | After the implant was placed, postoperative CBCT was performed using the same parameters. The accuracy was analyzed by a open-source software 3D Slicer (Version 4.13, Harvard, Boston, USA,
Fusion and calibration of preoperative and postoperative images by 3D Slicer
Illustration of deviations (platform, apex and angulation) of implants between pre-operation design and actual post-operation placement | PMC10052843 | ||
Statistical analysis | The statistical analysis was conducted using SPSS 17.0 (IBM Corp, Armonk, NY, USA). All data were presented as mean, maximal/minimal value (max./min.), standardized deviation (SD) and 95% confidence interval (95% CI). The normality distribution of the data was evaluated using the Shapiro-Wilk test. The intra-group correlation coefficient (ICC) was used to evaluate the consistency of the same examiners measured at an interval of 24 h. The data conforming to normal distribution were expressed in the form of “x ± s”, and the differences were analyzed by independent sample t-test. The data that did not conform to normal distribution were analyzed using Mann-Whitney | PMC10052843 | ||
Results | angulation | In total, ten implants were placed on the partially edentulous model using the THETA robotic system and the Yizhimei dynamic navigation system, respectively. The deviations in the platform and apex points and angle of the two groups were summarized in Tables
Implant deviations using THETA robotic system
Implant deviations using Yizhimei dynamic navigation system
Implant deviations between THETA robotic system and Yizhimei dynamic system. Deviations of platform (a), apex (b) and angulation (c) | PMC10052843 | |
Discussion | angulation, tooth | The long-term success and survival of dental implants require precise positioning of the implant on the basis of the restoration. Computer-assisted systems of different technological approaches are currently used in clinical practice to improve the accuracy of implant placement, mainly including static surgical templateguided surgery, computer-assisted dynamic navigation, and robotic assisted dental surgical systems [Dental implant surgery robots have evolved rapidly in recent years, integrating a computerized surgical planning platform, a visual surgical tracking platform and a robotic operating platform [Previous studies have shown comparing with conventional implant surgery, static and dynamic navigation systems both have higher surgical precision. Block et al. [In this study, implant accuracy was evaluated using dynamic navigation surgery and a robotic system for implant surgery on a partially missing tooth model, and both systems showed excellent accuracy. The mean deviations between planned and postoperative implant position with THETA robotic system were 0.58 mm at platform, 0.69 mm at apex and 1.08° of angulation. In the group of Yizhimei dynamic navigation system, the deviations at platform, apex, and angulation were 0.73 mm, 0.86 mm and 2.32°, respectively. The high accuracy of our study was not difficult to predict since it was an in vitro study. The placement of the implants in the model does not involve the real oral environment, changes in the position of the patient’s head or tongue movements. In addition, the repetition of the operation under the same conditions reduces the operator’s random manipulation error.In a dynamic navigation system, the drill and implant were controlled by the surgeon’s hand without any mechanical guidance instruments [The length of the implant may significantly impact the precision of implant placement, particularly at the implant platform and apex. A template-guided implant placement study revealed that implant insertion with a length of 8 to 9 mm resulted in significantly higher precision than insertion with lengths of 10 to 11 mm and 12 to 13 mm, although angle deviation was unaffected by implant length [The limitation of this study is referring to the relatively small sample size. Nicchio N. also utilized small sample sizes in their research [The findings of this study have provided valuable insights into the comparative efficacy of the dental implant robotic system (THETA) and dynamic navigation system (Yizhimei) in clinical practice. Notably, the THETA system is distinguished by its use of a mechanical arm, while Yizhimei relies on a free-hand approach that demands a higher level of skill and experience from the practitioner. The study reveals that the angular deviation observed in the robotic system was superior to that of the dynamic navigation system, indicating that the fixed-line movement of THETA’s mechanical arm offers distinct advantages in dental implant surgery. These findings suggest that the THETA robotic system may hold significant promise as a tool for enhancing clinical outcomes in dental implantology, although additional research such as a prospective randomized study is necessary to further evaluate the accuracy of dental implant robots and their influencing factors. To date, dental robotics has made great progress, but it is still far from perfect yet. The intelligence of dental robotics is generally limited, and the operation is mainly assisted by doctors, with relatively simple functionality, the structure is usually complex and the volume is large, more widespread clinical application of this technology is expected in dentistry in the near future as the dental robotic systems hardware and software mature. | PMC10052843 | |
Acknowledgements | Not applicable. | PMC10052843 | ||
Author contributions | Jianping Chen, Xiaolei Bai, Yude Ding, Liheng Shen, Xin Sun, Ruijue Cao, Fan Yang, Linhong Wang, —performed the experimental and analytical part.Jianping Chen, Xiaolei Bai, and Yude Ding did the statistical analysis.Jianping Chen, Liheng Shen, Linhong Wang, and Fan Yang—wrote the main manuscript.Jianping Chen, Liheng Shen, Xin Sun, Ruijue Cao, and Linhong Wang-prepared figures.All authors reviewed the manuscript. | PMC10052843 | ||
Funding | This study was supported by Zhejiang Provincial Medical Science and Technology Planning Project (No. 2020KY010, 2021KY568). | PMC10052843 | ||
Data availability | All data are calculated by the software itself. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. | PMC10052843 | ||
Declarations | PMC10052843 | |||
Ethics approval and consent to participate | This article does not report on any studies with human participants or animals performed by any of the authors. | PMC10052843 | ||
Consent for publication | Not applicable. | PMC10052843 | ||
Competing interests | The authors declare that they have no competing interests. | PMC10052843 | ||
References | PMC10052843 | |||
Objective | death, heart disease, CHD, disability, chronic disease | CHRONIC DISEASE, CORONARY ARTERY DISEASE, HEART DISEASE, CARDIOVASCULAR DISEASES | Edited by: Pål Aukrust, Oslo University Hospital, NorwayReviewed by: Wenyue Cao, Huazhong University of Science and Technology, China; Tuva Dahl, Oslo University Hospital, Norway†These authors have contributed equally to this workCoronary heart disease (CHD) is one of the major cardiovascular diseases, a common chronic disease in the elderly and a major cause of disability and death in the world. Currently, intensive care unit (ICU) patients have a high probability of concomitant coronary artery disease, and the mortality of this category of patients in the ICU is receiving increasing attention. Therefore, the aim of this study was to verify whether the composite inflammatory indicators are significantly associated with ICU mortality in ICU patients with CHD and to develop a simple personalized prediction model. | PMC10682191 |
Method | REGRESSION | 7115 patients from the Multi-Parameter Intelligent Monitoring in Intensive Care Database IV were randomly assigned to the training cohort (n = 5692) and internal validation cohort (n = 1423), and 701 patients from the eICU Collaborative Research Database served as the external validation cohort. The association between various inflammatory indicators and ICU mortality was determined by multivariate Logistic regression analysis and Cox proportional hazards model. Subsequently, a novel predictive model for mortality in ICU patients with CHD was developed in the training cohort and performance was evaluated in the internal and external validation cohorts. | PMC10682191 | |
Results | CHD | REGRESSION | Various inflammatory indicators were demonstrated to be significantly associated with ICU mortality, 30-day ICU mortality, and 90-day ICU mortality in ICU patients with CHD by Logistic regression analysis and Cox proportional hazards model. The area under the curve of the novel predictive model for ICU mortality in ICU patients with CHD was 0.885 for the internal validation cohort and 0.726 for the external validation cohort. The calibration curve showed that the predicted probabilities of the model matched the actual observed probabilities. Furthermore, the decision curve analysis showed that the novel prediction model had a high net clinical benefit. | PMC10682191 |
Conclusion | CHD | In ICU patients with CHD, various inflammatory indicators were independent risk factors for ICU mortality. We constructed a novel predictive model of ICU mortality risk in ICU patients with CHD that had great potential to guide clinical decision-making. | PMC10682191 | |
Introduction | ST-segment elevation, death, myocardial infarction, inflammation, CHD, chronic disease, non-ST-segment elevation, disability, diabetes | CARDIOVASCULAR DISEASE, CARDIOVASCULAR DISEASES, CORONARY HEART DISEASE, INFLAMMATION, DISEASES, DIABETES, CHRONIC DISEASE, BLOOD, HIGH BLOOD PRESSURE, DYSLIPIDEMIA | Coronary heart disease (CHD), one of the major cardiovascular diseases, is a common chronic disease in the elderly and is the leading cause of disability and death in the world (The main known risk factors for the diagnosis or prognosis of CHD include dyslipidemia, high blood pressure, diabetes, and smoking (Blood test is widely used as a simple and inexpensive test for various diseases. Previous studies have shown the predictive value of inflammatory indicators for all-cause mortality in cardiovascular disease. For example, in patients with non-ST-segment elevation myocardial infarction and ST-segment elevation myocardial infarction, the platelet-lymphocyte ratio (PLR) ratio is an independent predictor for mortality (The aims of our study were: (1) determining the association of several inflammatory indicators with all-cause ICU mortality in ICU patients with CHD, including SII, SIRI, NLR, PLR, neutrophil to lymphocyte platelet ratio (NLPR), aggregate index of systemic inflammation (AISI), and RDW; (2) constructing a novel model based on these indicators and severity score to predict ICU mortality in ICU patients with CHD. | PMC10682191 |
Methods | PMC10682191 | |||
Sources of data | RECRUITMENT | Our study data were obtained from a publicly accessible Multi-Parameter Intelligent Monitoring in Intensive Care Database IV (MIMIC IV, version 2.0, recruitment during 2012 to 2019) as detailed in previous publications ( | PMC10682191 | |
Study participants | organ failure, CHD | DISEASES, CORONARY HEART DISEASE | Our study included all adult patients with CHD admitted to the ICU according to International Classification of Diseases (ICD) version 9 or 10 (We included all patients who contained any ICD codes related to coronary artery). The exclusion criteria were as follows (1) records of multiple ICU admissions other than the first ICU admission; (2) records of ICU stays of less than 24 hours; (3) records of repeated multiple hospitalizations; (4) exclusion of records of missing neutrophils, lymphocytes, monocytes, platelets, and RDW. Finally, a total of 7115 patients were extracted from MIMIC IV for initial analysis and model construction, and 701 patients were extracted from EICU for external validation (details shown in Flow chart of patient screening. CHD, coronary heart disease; EICU, eICU Collaborative Research Database; ICU, intensive care unit; MIMIC IV, Multi-Parameter Intelligent Monitoring in Intensive Care Database IV; SOFA, sequential organ failure assessment. | PMC10682191 |
Extraction of variables and study outcomes | We extracted the following data: demographic characteristics, vital signs, comorbidities, severity scores on admission to the ICU, laboratory results (within the first day of admission to the ICU), interventions, and medications. For variables measured multiple times, we used the first value. Our primary outcomes: all-cause mortality during ICU admission (ICU mortality); Secondary outcome: all-cause 30-day mortality after ICU admission (30-day ICU mortality) and all-cause 90-day mortality after ICU admission (90-day ICU mortality). | PMC10682191 | ||
Definition | SII was defined as platelet × neutrophil/lymphocyte. SIRI was defined as neutrophil × monocyte/lymphocyte. NLP was defined as neutrophil/lymphocyte. PLR was defined as platelet/lymphocyte. NLPR was defined as neutrophil/(lymphocyte × platelet). AISI was defined as neutrophils × monocytes × platelets/lymphocytes. | PMC10682191 | ||
Statistical analysis | malignant cancer, organ failure, CHD, deaths, dyslipidemia | ACUTE KIDNEY FAILURE, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, CONGESTIVE HEART FAILURE, ACUTE RESPIRATORY FAILURE, REGRESSION, DYSLIPIDEMIA | For continuous variables, normality was tested using the Shapiro-Wilk test. And depending on the type and distribution of the variable, the normal distribution was expressed as mean and standard deviation (SD) and differences between groups were assessed using the t-test, and the non-normal distribution was expressed as median and interquartile range (IQR) and differences between groups were assessed using the Kruskal-Wallis test. For categorical variables, expressed as counts and percentages, differences between groups were assessed using the ChiWe analyzed the cumulative rate of all-cause mortality in CHD patients within 90 days of ICU admission using Kaplan-Meier survival analysis to compare the cumulative distribution of deaths among patients in four-score subgroups for each indicator at admission. We also used the restricted cubic spline function (RCS) to explore the nonlinear relationship between these metrics and the study outcomes when used as continuous variables.To further assess the independent associations between the indicators and the primary endpoints, we used Logistic regression model and Cox proportional hazards model, and we used different models to adjust for potential confounders. Model 1: crude analysis without adjusting for any confounders; Model 2: adjustments including age, male, and race; Model 3: additional adjusting for confounders (heart rate, respiratory rate, saturation of peripheral oxygen, sequential organ failure assessment (SOFA), acute exacerbation of chronic obstructive pulmonary disease, congestive heart failure, malignant cancer, dyslipidemia, acute respiratory failure, acute kidney failure, potassium, aniongap, blood urea nitrogen, glucose, serum creatinine, hematocrit, hemoglobin, mean corpuscular volume, red blood cell, dialysis, vasopressor, invasive mechanical ventilation, coronary artery bypass graft, angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, antiplatelet, statin, non-vitamin K antagonist oral anticoagulant, and Vitamin K antagonists) based on Model 2. Once the indicators were grouped based on quartiles, we chose G1 as the reference group and calculated the adjusted odds ratio (OR) or hazard ratio (HR) of the primary endpoints for the other groups in comparison to the reference group.Given the importance of mortality risk management in ICU patients with CHD, then we used these inflammatory indictors in conjunction with the widely utilized SOFA to construct a novel predictive model of ICU mortality in ICU patients with CHD. We first divided these inflammatory indicators into elevated value and non-elevated value groups based on their respective third quartiles. Subsequently, we divided the entire cohort into a training cohort and an internal validation cohort on a 8:2 basis. In the training corhort, we used univariate Logistic regression analyses followed by stepwise forward multivariate Logistic regression analyses to select the variables used to construct the novel predictive model, computed correlation coefficient and variance inflation factor (VIF) to detect covariance of the variables in the model, and used the Hosmer-Lemeshow test to assess the fit of the logistic regression models. Then, the area under the curve (AUC) of receiver operating characteristic (ROC) curves, plotting of calibration curves, decision curve analysis (DCA) (compared with SOFA) were used in both the internal and external validation cohorts, in addition, we computed the integrated discrimination improvement (IDI) to validate the variability of the predictive performance of the new model between the novel model and SOFA. Furthermore, we transformed the novel model obtained from the training cohort to nomogram and interactive network dynamic nomogram.We performed all statistical processing using SPSS (version 29), Stata (version 17), and R (version 4.2.3). In all analyses, a two-tailed | PMC10682191 |
Results | PMC10682191 | |||
Comparison of ICU survival and death in ICU patients with CHD | death, CHF, ARF, organ failure, CHD | CHF, SYSTEMIC INFLAMMATORY RESPONSE, ARF, INFLAMMATION, ACUTE KIDNEY FAILURE, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, CONGESTIVE HEART FAILURE, ACUTE RESPIRATORY FAILURE, ATRIAL FIBRILLATION | In our study, 7115 ICU patients with CHD were included and the median (IQR) age of all patients was 71.43 (63.08 - 79.60), 68.9% patients were male and 69.7% patients were White. We divided all patients into two groups based on ICU survival and death (details shown in Baseline characteristics of ICU patients with CHD.ACEI, angiotensin-converting enzyme inhibitors; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; AF, atrial fibrillation; AISI, aggregate index of systemic inflammation; AKF, acute kidney failure; ARB, angiotensin receptor blocker; ARF, acute respiratory failure; BMI, body mass index; BUN, blood urea nitrogen; CABG, coronary artery bypass graft; CHF, congestive heart failure; IMV, invasive mechanical ventilation; MCH, mean corpsular hemoglobin; MCV, mean corpuscular volume; NLPR, neutrophil to lymphocyte platelet ratio; NLR, neutrophil-lymphocyte ratio; NOAC, non-vitamin K antagonist oral anticoagulant; PCI, percutaneous coronary intervention; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SII, systemic inflammatory index; SIRI, systemic inflammatory response index; SOFA, sequential organ failure assessment; SpO | PMC10682191 |
Association between inflammatory indicators and ICU mortality in ICU patients with CHD | malignant cancer, organ failure, inflammation, dyslipidemia | SYSTEMIC INFLAMMATORY RESPONSE, INFLAMMATION, ACUTE KIDNEY FAILURE, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, CONGESTIVE HEART FAILURE, ACUTE RESPIRATORY FAILURE, DYSLIPIDEMIA | Based on The association of each inflammatory indicator with ICU mortality in ICU patients with CHD.Model 1: unadjusted; Model 2: adjusted for age, male, race; Model 3: adjusted for age, male, race, heart rate, respiratory rate, saturation of peripheral oxygen, sequential organ failure assessment score, acute exacerbation of chronic obstructive pulmonary disease, congestive heart failure, malignant cancer, dyslipidemia, acute respiratory failure, acute kidney failure, potassium, aniongap, blood urea nitrogen, glucose, serum creatinine, hematocrit, hemoglobin, mean corpuscular volume, red blood cell, dialysis, vasopressor, invasive mechanical ventilation, coronary artery bypass graft, angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, antiplatelet, statin, non-vitamin K antagonist oral anticoagulant, and Vitamin K antagonists.RDW categries: Q1 (x < 13.20), Q2 (13.20 ≤ x < 14.00), Q3 (14.00 ≤ x < 15.50), Q4 (x ≥ 15.50); SII categries: Q1 (x < 548.96), Q2 (548.96 ≤ x < 1022.74),Q3 (1022.74 ≤ x < 2167.45), Q4 (x ≥ 2167.45); SIRI categries: Q1 (x < 1.41), Q2 (1.41 ≤ x < 3.17), Q3 (3.17 ≤ x < 6.99), Q4 (x ≥ 6.99); NLR categries: Q1 (x < 3.93), Q2 (3.93 ≤ x < 6.35), Q3 (6.35 ≤ x < 11.38), Q4 (x ≥ 11.35); PLR categries: Q1 (x < 66.67), Q2 (66.67 ≤ x < 113.48), Q3 (113.48 ≤ x < 217.92), Q4 (x ≥ 217.92); NLPR categries: Q1 (x < 0.02), Q2 (0.03 ≤ x < 0.04), Q3 (0.04 ≤ x < 0.68), Q4 (x ≥ 0.68); AISI categries: Q1 (x < 196.08), Q2 (196.08 ≤ x < 518.40), Q3 (518.40 ≤ x < 1349.60), Q4 (x ≥ 1349.60).AISI, aggregate index of systemic inflammation; CI, confidence interval; NLPR, neutrophil to lymphocyte platelet ratio; NLR, neutrophil-lymphocyte ratio; OR, odds ratio; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SII, systemic inflammatory index; SIRI, systemic inflammatory response index.Restricted cubic spline function between inflammation indicators (SII, SIRI, NLR, PLR, NLPR, AISI, RDW) and ICU mortality | PMC10682191 |
Association between inflammatory indicators and 30-day ICU mortality in ICU patients with CHD | malignant cancer, organ failure, dyslipidemia | SYSTEMIC INFLAMMATORY RESPONSE, INFLAMMATION, ACUTE KIDNEY FAILURE, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, CONGESTIVE HEART FAILURE, ACUTE RESPIRATORY FAILURE, DYSLIPIDEMIA | Based on The association of each inflammatory indicator with 30-day ICU mortality in ICU patients with CHD.Model 1: unadjusted; Model 2: adjusted for age, male, race; Model 3: adjusted for age, male, race, heart rate, respiratory rate, saturation of peripheral oxygen, sequential organ failure assessment score, acute exacerbation of chronic obstructive pulmonary disease, congestive heart failure, malignant cancer, dyslipidemia, acute respiratory failure, acute kidney failure, potassium, aniongap, blood urea nitrogen, glucose, serum creatinine, hematocrit, hemoglobin, mean corpuscular volume, red blood cell, dialysis, vasopressor, invasive mechanical ventilation, coronary artery bypass graft, angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, antiplatelet, statin, non-vitamin K antagonist oral anticoagulant, and Vitamin K antagonists.RDW categries: Q1 (x < 13.20), Q2 (13.20 ≤ x < 14.00), Q3 (14.00 ≤ x < 15.50), Q4 (x ≥ 15.50); SII categries: Q1 (x < 548.96), Q2 (548.96 ≤ x < 1022.74),Q3 (1022.74 ≤ x < 2167.45), Q4 (x ≥ 2167.45); SIRI categries: Q1 (x < 1.41), Q2 (1.41 ≤ x < 3.17), Q3 (3.17 ≤ x < 6.99), Q4 (x ≥ 6.99); NLR categries: Q1 (x < 3.93), Q2 (3.93 ≤ x < 6.35), Q3 (6.35 ≤ x < 11.38), Q4 (x ≥ 11.35); PLR categries: Q1 (x < 66.67), Q2 (66.67 ≤ x < 113.48), Q3 (113.48 ≤ x < 217.92), Q4 (x ≥ 217.92); NLPR categries: Q1 (x < 0.02), Q2 (0.03 ≤ x < 0.04), Q3 (0.04 ≤ x < 0.68), Q4 (x ≥ 0.68); AISI categries: Q1 (x < 196.08), Q2 (196.08 ≤ x < 518.40), Q3 (518.40 ≤ x < 1349.60), Q4 (x ≥ 1349.60).AISI, aggregate index of systemic inflammation; CI, confidence interval; HR, hazard ratio; NLPR, neutrophil to lymphocyte platelet ratio; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SII, systemic inflammatory index; SIRI, systemic inflammatory response index. | PMC10682191 |
Association between inflammatory indicators and 90-day ICU mortality in ICU patients with CHD | malignant cancer, organ failure, dyslipidemia | SYSTEMIC INFLAMMATORY RESPONSE, INFLAMMATION, ACUTE KIDNEY FAILURE, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, CONGESTIVE HEART FAILURE, ACUTE RESPIRATORY FAILURE, DYSLIPIDEMIA | Based on The association of each inflammatory indicator with 90-day ICU mortality in ICU patients with CHD.Model 1: unadjusted; Model 2: adjusted for age, male, race; Model 3: adjusted for age, male, race, heart rate, respiratory rate, saturation of peripheral oxygen, sequential organ failure assessment score, acute exacerbation of chronic obstructive pulmonary disease, congestive heart failure, malignant cancer, dyslipidemia, acute respiratory failure, acute kidney failure, potassium, aniongap, blood urea nitrogen, glucose, serum creatinine, hematocrit, hemoglobin, mean corpuscular volume, red blood cell, dialysis, vasopressor, invasive mechanical ventilation, coronary artery bypass graft, angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, antiplatelet, statin, non-vitamin K antagonist oral anticoagulant, and Vitamin K antagonists.RDW categries: Q1 (x < 13.20), Q2 (13.20 ≤ x < 14.00), Q3 (14.00 ≤ x < 15.50), Q4 (x ≥ 15.50); SII categries: Q1 (x < 548.96), Q2 (548.96 ≤ x < 1022.74),Q3 (1022.74 ≤ x < 2167.45), Q4 (x ≥ 2167.45); SIRI categries: Q1 (x < 1.41), Q2 (1.41 ≤ x < 3.17), Q3 (3.17 ≤ x < 6.99), Q4 (x ≥ 6.99); NLR categries: Q1 (x < 3.93), Q2 (3.93 ≤ x < 6.35), Q3 (6.35 ≤ x < 11.38), Q4 (x ≥ 11.35); PLR categries: Q1 (x < 66.67), Q2 (66.67 ≤ x < 113.48), Q3 (113.48 ≤ x < 217.92), Q4 (x ≥ 217.92); NLPR categries: Q1 (x < 0.02), Q2 (0.03 ≤ x < 0.04), Q3 (0.04 ≤ x < 0.68), Q4 (x ≥ 0.68); AISI categries: Q1 (x < 196.08), Q2 (196.08 ≤ x < 518.40), Q3 (518.40 ≤ x < 1349.60), Q4 (x ≥ 1349.60).AISI, aggregate index of systemic inflammation; CI, confidence interval; HR, hazard ratio; NLPR, neutrophil to lymphocyte platelet ratio; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SII, systemic inflammatory index; SIRI, systemic inflammatory response index. | PMC10682191 |
Cumulative outcomes based on quartile groups of inflammation indicators | inflammation, deaths, CHD | INFLAMMATION, CORONARY HEART DISEASE | To further explore the association of inflammation indicators with ICU deaths, we plotted Kaplan-Meier cumulative curves for the study outcomes. According to Kaplan-Mill survival analysis of cumulative all-cause mortality in CHD patients within 90 Days of ICU admission. CHD, coronary heart disease; ICU, intensive care unit. | PMC10682191 |
Development of the novel predictive model | organ failure | SYSTEMIC INFLAMMATORY RESPONSE, INFLAMMATION, REGRESSION | Given that these inflammatory indicators were independent risk factors for ICU mortality in ICU patients with CHD, our aim was to construct a novel model to predict ICU mortality in ICU patients with CHD using these inflammatory indicators combined with the SOFA score. We divided patients from MIMIC into a training cohort (n = 5692) and an internal validation cohort (n = 1423) in an 8:2 ratio. Univariate and multifactorial analyses about the association between variables and ICU mortality.Elevated SII, SII ≥ 2167.45; SIRI, SIRI ≥ 6.99; NLR, NLR ≥ 11.34; PLR, PLR ≥ 217.92; NLPR, NLPR ≥ 0.068; AISI, AISI ≥ 1349.60; RDW, RDW ≥ 15.5.AISI, aggregate index of systemic inflammation; CI, confidence interval; NLPR, neutrophil to lymphocyte platelet ratio; NLR, neutrophil-lymphocyte ratio; OR, odds ratio; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SII, systemic inflammatory index; SIRI, systemic inflammatory response index.Then, based on the results of univariate logistic regression, these variables were subjected to multivariate stepwise forward logistic regression (Correlation coefficients and variance inflation factors of the variables in the model. AISI, aggregate index of systemic inflammation; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SOFA, sequential organ failure assessment.Nomogram for the novel model. AISI, aggregate index of systemic inflammation; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SOFA, sequential organ failure assessment. | PMC10682191 |
Validation of the novel predictive model | Our external validation cohort was derived from the EICU database, based on the same inclusion and exclusion criteria, the SOFA value was calculated, and excluded individuals with missing data, finally 701 patients were obtained. According to ROC, calibration curves, and DCA in the internal validation cohort | PMC10682191 | ||
Clinical application of the novel predictive model | organ failure | INFLAMMATION | To validate the performance of our model for clinical applications, we plotted DCA curves and compared our model to SOFA scores. In both the internal validation set and the external validation set, our model-guided medical interventions provide excellent net benefits and outperform the SOFA score, the details of which are depicted in User interface for a novel model-based online prediction tool. AISI, aggregate index of systemic inflammation; PLR, platelet-lymphocyte ratio; RDW, red blood cell volume distribution width; SOFA, sequential organ failure assessment. | PMC10682191 |
Discussion | anaemia, death, inflammation, RDW, infection, CHD, atherosclerosis, tissue damage | ANAEMIA, INFLAMMATION, LYSIS, INFECTION, CORONARY ATHEROSCLEROSIS, INFLAMMATION OR INFECTION, ATHEROSCLEROSIS | This was the first study to assess the relationship between the composite inflammation indicators and mortality in ICU patients with CHD. Most inflammation indicators in our study were significantly associated with ICU mortality in the retrospective MIMIC IV database of ICU patients with CHD. Demonstrating the significant promise of these inflammation indicators for mortality risk management in ICU patients with CHD.The basic mechanism of CHD is abnormal lipid metabolism leading to coronary atherosclerosis (Neutrophils, the most abundant subtype of leukocytes in the blood, are critical in mediating inflammation. Neutrophils cause smooth muscle cell lysis and death, which has been shown to cause tissue damage and inflammation in advanced stages of atherosclerosis (In our study, RDW was the prognostic independent factor with the most pronounced correlation among the inflammatory indicators studied above. It has been shown that RDW affects the poor prognosis of CHD patients, but the underlying causes remain unclear. Although anaemia is a known risk factor for mortality, we adjusted for haemoglobin in Model 3 and, therefore, still consider RDW to be a risk factor for mortality separate from anaemia. We hypothesized two main causes for the higher risk mortality of CHD patients with high RDW: (a) RDW is associated with a variety of inflammatory markers, e.g., interleukin-6 (Our novel predictive model provides an accurate risk assessment and helps ICU physicians identify which ICU patients with CHD are at high mortality risk. And allows ICU healthcare teams to improve the prognosis of high mortality risk patients through more frequent monitoring, more urgent interventions, and stricter medication management at the early stage. In addition, earlier identification of high mortality risk patients can facilitate more informed discussions about the patient’s condition and prognosis between ICU physicians and the patient’s families. Overall, our model has a potentially important role in mortality risk management of ICU patients with CHD, helping to improve the accuracy of clinical decision-making and the quality of healthcare. However, the validity and feasibility of the model have only been confirmed in the datasets. Despite the strong clinical utility of the nomogram, further validation and adjustment is needed in the actual ICU clinical setting.Some limitations of our study should be noted. First, this was a retrospective study in which retrospective bias is unavoidable, and thus more rigorous prospective studies would be required in the future. Second, previously mentioned traditional inflammatory indicators of IL-6 and CRP are important in mortality risk of CHD. We tried to extract them but due to the limitations of MIMIC IV, the percentage of missing values for CRP was about 98% and no record of IL-6 was retrieved. Further studies are still needed to prove whether they will have an impact on our results. Third, our study data were from the database of the United States and were overwhelmingly of White race, so applicability to ICU patients from other countries or other races requires further validation. Fourth, despite we adjusted for virous potential confounders, there may still be some important factors that were missed, and these may have a non-negligible impact on our inflammatory indicators. For example, neutrophil counts are usually elevated when there is an active infection or inflammation in the patient’s body, while lymphocyte counts may be decreased, thus affecting our inflammatory indicators. However, due to MIMIC’s limitations, it is hard to definitively determine the patient’s infection condition. Fifth, although our novel model demonstrated promising predictive performance in the internal and external validation cohort form MIMIC IV and EICU, however scalability to other hospitals remains an issue as the model’s performance in the external validation cohort is weaker than the internal validation cohort. Therefore, we also need a larger external validation cohort to validate the performance of our model. Despite these limitations, our study shows that the new model we constructed is remarkably promising and deserves further exploration in future clinical work and research. | PMC10682191 |
Conclusion | inflammation, death | INFLAMMATION | Our study revealed that inflammation indicators SII, SIRI, NLR, PLR, NLPR, AISI, and RDW were significantly associated with ICU mortality. Furthermore, we constructed a novel predictive model by combining some of these indicators with SOFA to predict the risk of ICU death in ICU patients with CHD, which has a remarkable potential to guide clinical decision-making and prognostic management. | PMC10682191 |
Data availability statement | Publicly available datasets were analyzed in this study. This data can be found here: MIMIC IV, EICU. | PMC10682191 | ||
Ethics statement | Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements. | PMC10682191 | ||
Author contributions | MM | YCheng: Investigation, Methodology, Writing – original draft, Writing – review & editing. YChen: Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing. MM: Formal Analysis, Software, Writing – original draft. RW: Formal Analysis, Software, Writing – original draft. JZ: Software, Writing – original draft. QH: Methodology, Project administration, Resources, Supervision, Writing – review & editing. | PMC10682191 | |
Acknowledgments | We appreciate all of the investigators and subjects who took part in this study. | PMC10682191 | ||
Conflict of interest | The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. | PMC10682191 | ||
Publisher’s note | All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | PMC10682191 | ||
References | PMC10682191 | |||
Introduction | overweight | Chronic stress and overweight are of major public health relevance and there is a growing body of evidence that suggests that these two phenomena are connectedOn the one hand, being overweight is associated with higher psychological stressOn the other hand, psychological stress is associated with overweightFirstly, the concept of Emotional eating tends to co-occur with a second stress-related eating style: As a third relevant stress-related eating style, (3) Complicating matters, it is especially unhealthy energy-dense, highly palatable foods, high in sugar and fat, which are often eaten in response to stressWhile comprehensive lifestyle intervention programs, including our ownTo our knowledge, there are no studies that have prospectively examined the mediating role of stress-eating on weight change in behavioural interventions. Few however, have examined the role of stress eating on weight change and therefore indicate that there may be a mediating effect | PMC9977731 | |
Research gap | obesity | OBESITY | As described, a reduction of stress-eating is a promising target in the endeavours to combat obesity. To date, however, no studies have prospectively investigated the role of stress-related eating behaviour on the weight reduction effect of comprehensive healthy lifestyle intervention programs using standardized and validated instruments and assessing key variables of eating behaviour. Moreover, there are no evidence-based interventions to adequately consider the complexity of the interactions of stress, stress-eating and overweight. Holistic lifestyle approaches seem suitable to consider these interactions. As stress and obesity are highly prevalent in society today, innovative interventions should be conducted on a community-scale to improve the combined challenge of stress-related eating and obesity. | PMC9977731 |
Methods | PMC9977731 | |||
Study aim and hypotheses | weight reduction | DISEASES | We examined the effects of the holistic Healthy Lifestyle Community Programme (HLCP, cohort 1) on changes in stress-related eating behaviour, represented by emotional, external and restrained eatingWe hypothesized that weight change in participants with a low level of perceived stress would be more pronounced than in those with a high level of perceived stress.We further hypothesized, that levels of emotional and external eating behaviour would be reduced and levels of restrained eating behaviour would be increased in participants of the intervention group (IG) after 8 weeks compared to baseline and compared to participants of the control group (CG).Moreover, we hypothesized that changes in stress-eating would be positively correlated with weight change. A primary report on the effect of the HLCP regarding weight reduction and the metabolic risk profile of non-communicable diseases (NCDs) in the long-term is reported elsewhere | PMC9977731 |
Study design | SECONDARY | This report is based on a secondary analysis after 8 weeks, based on a non-randomized, controlled intervention trial with a duration of 24 months | PMC9977731 | |
Study population | ’ | The sample size was calculated for the primary outcome of weight reductionParticipants of the intervention group and control group were recruited in two separate small municipalities (‘intervention municipality’ and ‘control municipality’) to keep the participants of the control group unaware of the lifestyle recommendations given to the intervention group. The complex real-world approach of our study, required involvement of local stakeholders within the ‘intervention municipality’As with all lifestyle interventions, blinding of participants or instructors to group allocation was not possible (as described previouslyParticipants ≥ 18 years who were capable of understanding the study content were included. The study was conducted in accordance with the Declaration of Helsinki and was approved by the ethics committee of the Westphalia-Lippe Medical Association and the Muenster University (Muenster, Germany; reference: 2017-105-f-S; approved 5 April 2017). All participants provided written informed consent. | PMC9977731 | |
Participants’ flow diagram | A participants’ flow diagram shows the study process from enrolment to analysis in Fig. CONSORT structure participants’ flow diagram; participants categorized as “lost to follow-up” withdraw from the study with the given reason. In the IG, information is given on how many participants discontinued the intervention (e.g. dropped out) and why. | PMC9977731 | ||
Data assessment | PMC9977731 | |||
Health check-ups | Baseline data were collected in April and October 2017 in intervention group and control group, respectively (see | PMC9977731 | ||
Anthropometric parameters | Body weight was determined by calibrated body scales, body height by self-report and BMI (kg/m | PMC9977731 | ||
Stress-related eating behaviour | Participants answered the German version of the Dutch Eating Behaviour Questionnaire (DEBQ) | PMC9977731 | ||
Perceived stress level | In order to assess the psychosocial stress status, participants completed the German version of the Perceived Stress Scale-10 (PSS-10) | PMC9977731 | ||
Further parameters | In the main analysis of the study | PMC9977731 | ||
Lifestyle intervention | The intervention was led by the study team and cooperating health care providers (e. g. local practitioners) and has been described in detail previouslyAfter the coaching sessions, the intensive phase continued with of 14 consecutive seminars (twice per week for 2 h each) with a strong emphasis on the potential of behaviour change, perception of internal signals (e.g. appetite, hunger, frustration, stress or eating with pleasure) and community support as well as improvement of self-efficacy and relapse prevention. The four main topics of lifestyle change included a healthy, predominantly plant-based diet, stress regulation, physical activity, and social healthAfter the intensive phase of 8 weeks, a subsequent 22-months-alumni-phase with a less intensive intervention followed. Here, participants joined monthly meetings (2 h each) in which contents of the intensive phase were refreshed and group support was strengthened. Notably, results of this follow-up phase are not part of this sub-study. | PMC9977731 | ||
Control group (CG) | Participants of the control group did not receive any intervention. For ethical reasons, they were informed about their health check-up results and were offered to participate in a subsequent HLCP after completion of the study. | PMC9977731 | ||
Statistics | overweight | REGRESSION, REGRESSION | Continuous variables are presented as mean ± standard deviation (SD), categorical variables as frequencies and valid percent. Normality was tested using the Shapiro–Wilk test and judged by histograms. All data were analysed in accordance with the predefined study plan. All available data were analysed. Missing data were not imputed.To compare intervention group and control group, independent t-test was used for normally distributed continuous variables (e.g. changes of eating behaviour scores) and Mann–Whitney Subgroups were formed to separately analyse the weight reduction effects of the intervention in participants who were overweight and normal-weight and in participants with high and low stress levels.Multiple linear regression modeling (MLR) was used to explore the effect of the intervention on the change of stress eating dimensions, adjusting for sex and the baseline value of the respective variable. Variables found to be associated with change of DEBQ and PSS-10 parameters were also added as covariates (in addition to the group variable) to the multiple regression model using a forward–backward selection approach. Regression models that were statistically relevant (p ≤ 0.05), with the highest corrected Rp-values < 0.05 are considered significant, and are to be understood as exploratoryStatistical analyses were performed using IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.
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Ethics approval and consent to participate | The study was conducted in accordance with the Declaration of Helsinki and the study protocol was reviewed and approved by the ethics committee of the Westphalia-Lippe Medical Association and the Muenster University (Muenster, Germany; approval number 2017-105-f-S; approved April 5th 2017). All participants provided written informed consent.
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Results | PMC9977731 | |||
Changes of weight, stress level and stress-eating after 8 weeks | overweight, P., weight reduction | As described beforeChange of perceived stress and stress-related eating behaviour after 8 weeks in participants with normal weight and overweight of the intervention and control group.Significant values are in bold.Normal weight (BMI < 25 kg/mIn the intervention group, but not in the control group (p > 0.158), participants with a low stress level and who were overweight (n = 45) lost more weight (− 2.0 ± 2.1 kg) than those with a low stress level who were of normal-weight (n = 18; − 0.9 ± 1.1 kg; p = 0.016). There was no difference in weight reduction within the intervention group between participants with high and low stress levels (p > 0.202). Notably, the change of weight was neither associated with a change of perceived stress in all participants (IG: correlation coefficient [CC] = − 0.034; p = 0.765; CG: CC = − 0.178; p = 0.307) nor in subgroups of participants with overweight (IG: CC = 0.044; p = 0.742; CG: CC = − 1.112; p = 0.657) or normal-weight (IG: − 0.044; p = 0.853; CG: CC = − 0.207; p = 0.425).We did, however, observe a significant change of all scores of stress-related eating behaviour, i.e. a decrease of emotional and external eating and an increase of restrained eating in the intervention group (for all p < 0.001), but not in the control group, resulting in relevant between-group differences (p < 0.05; Fig. Change of stress-related eating behaviour in the intervention group (IG) and control group (CG); Changes of stress-eating scores: emotional eating: IG = − 3.5 ± 5.4 points (P); CG = − 0.05 ± 3.4 P; external eating: IG = − 2.0 ± 3.8 P; CG = − 0.6 ± 2.9 P; restrained eating: IG = 2.7 ± 5.0 P; CG = 1.0 ± 4.3 P. legend: **As correlation analyses revealed, the weight reduction in the intervention group was not associated with a change of emotional (p = 0.537) or restrained eating (p = 0.335). However, weight change was negatively correlated with external eating (RCorrelation of the change of external eating and weight change in participants of the intervention group (n = 78) after 8 weeks; Spearman correlation coefficient (CC) = 0.285; p = 0.014. | PMC9977731 | |
Multiple linear regression modelling (MLR) | weight gain, weight loss | When adjusting for the baseline value of the respective variables, MLR revealed no significant impact of the HLCP on emotional (corrected [corr.] RHowever, the changes of the three stress-related eating styles were not predictive for weight loss in the intervention group (emotional eating: p = 0.500; external eating: p = 0.071; restrained eating: p = 0.101). In the control group, weight gain was predicted by an increase of emotional eating (corr. RThe change of perceived stress did not qualify as a predictor for weight change (p > 0.754). | PMC9977731 | |
Discussion | weight gain, overweight, weight reduction, chronic disease | REGRESSION, RECRUITMENT, CHRONIC DISEASE | The Healthy Lifestyle Community Program (HLCP, cohort 1) resulted in a change of stress-related eating behaviour after 8 weeks, which was measured by emotional and external eating, which decreased, and restraint eating, which increased. This is in line with our hypotheses before the onset of the study. Although, multiple linear regression modelling did not confirm a significant impact of the HLCP on emotional and external eating behaviour, its impact on restrained eating was significant. Moreover, we found that a greater weight reduction was associated with a smaller change of external eating scores in the intervention group, especially in participants who were overweight.In the control group, an increase of emotional eating was predictive for weight gain.In our sample, a higher BMI was linked to higher levels of emotional and external eating behaviour. Scores of emotional and external eating decreased significantly in the intervention group compared to baseline and more than in the control group. As these two eating styles are related to overeating in stressful situations and to overweightHere, a more intuitive and mindful eating behaviour may contribute to the reduction of stress-eatingAnd yet, although we observed a reduction of emotional and external eating as well as weightMoreover, the mediating effect of changes in stress-eating on weight change should be clarified in mediation models to give insights in the complex interaction of stress-related eating behaviour and weight reduction in the context of comprehensive lifestyle interventions.Notably, the intervention group started with significantly higher levels of emotional and external eating behaviour than the control group. This might have influenced the results, as adjusting for baseline values did not identify the HLCP to be a significant predictor of stress-eating changes. Further research with more comparable groups may give valuable insights into the effect of the HLCP on emotional and external eating behaviour and their impact on weight change.Importantly, the role of restrained eating behaviour as a dimension of stress-eating needs to be further explored. It is generally agreed, that cognitive control of food intake is necessary to successfully reduce weight, and results from the German Weight Control Registry underline, that weight-loss maintenance is associated with higher dietary restraintMoreover, rigid restraint may lead to the above-mentioned vicious cycle, but flexible restraint was shown to not promote weight gainIn accordance with other studiesSome limitations of the study have to be mentioned. First, recruiting from the general population with regard to participation in the Healthy Lifestyle Community Program (HLCP, cohort 1) may have contributed to the considerable higher baseline levels of emotional and external eating, BMI and perceived stress in the intervention group (IG). Despite efforts to enrol comparable participants in both groups, it seems possible that recruitment of participants in the intervention group was influenced by the fact that they had a greater need for interventions to improve weight, eating behaviour, and their general chronic disease risk profile. Second, the mostly female sample might limit the generalizability of these findings. Third, in statistical analysis, subgroups (i.e. normal and overweight as well as high and low stress level) were built retrospectively and were not considered in the calculation of the sample size. Future studies examining the effect of the HLCP on stress-related eating should aim for a replication in a larger sample. | PMC9977731 |
Conclusions | obesity, overweight, weight loss | OBESITY | Our study is the first one to prospectively investigate the role of stress-related eating behaviour on the weight reduction effect of comprehensive healthy lifestyle intervention programs using standardized and validated instruments and assessing key variables of eating behaviour. Our data confirm the assumption that overweight is associated with higher levels of perceived stress as well as emotional and external eating and suggest that the Healthy Lifestyle Community Program (HLCP, cohort 1) may reduce the same, and increases dietary restraint. The impact of these changes on weight loss have to be further explored. Our findings underline the need to consider stress-associated eating behaviour in holistic weight loss interventions to account for the complex association of chronic stress, overweight, and obesity. | PMC9977731 |
Acknowledgements | We would like to thank the study participants, the local stakeholders involved, e.g. city administration, medical practitioners, clubs and volunteers, Matthias Borowski for his statistical advice, our student helpers and the entire research group, especially Sarah Husain, Christian Koeder, Alwine Kraatz, Ragna-Marie Kranz and Nora Schoch, who took part in the investigation. | PMC9977731 | ||
Author contributions | C.A.: conceptualization, methodology, validation, formal analysis, investigation, participant management, data assessment and curation, writing: original draft review and editing of the manuscript, visualization, project administration, funding acquisition. K.H.: methodology, medical advice, writing: review and editing, supervision. R.G.: methodology, medical advice, writing: review and editing, supervision. H.E.: conceptualization, methodology, investigation, writing: review and editing, project administration, supervision, funding acquisition. All authors read and approved the final manuscript. | PMC9977731 | ||
Funding | Diabetes | DIABETES | Open Access funding enabled and organized by Projekt DEAL. This work was conducted as part of the project “KRAKE” and was funded by EUREGIO within the INTERREG V A programme Deutschland-Nederland (ANBest INTERREG DE-NL) with the following grant number: 203018. Additionally, the German Diabetes Foundation (DDS) funded the analysis of cortisol with the following Grant Number: 400/04/17. We acknowledge support from the Open Access Publication Fund of the University of Muenster. | PMC9977731 |
Data availability | The data are available from the corresponding author (CA) upon reasonable request. | PMC9977731 | ||
Competing interests | The authors declare no competing interests. | PMC9977731 | ||
References | PMC9977731 |
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