Piecewise Linear Regression Research Articles

Association of Alcohol Consumption With CD4 Recovery After Antiretroviral Therapy Initiation in St. Petersburg, Russia.

Delayed CD4 recovery after initiating antiretroviral therapy (ART) is a novel potential mechanism by which alcohol consumption leads to increased morbidity and mortality in people with HIV. We hypothesized that alcohol consumption at ART initiation is associated with slower CD4 recovery. We retrospectively analyzed 2 pooled longitudinal alcohol/HIV cohorts (2014-2019) in St. Petersburg, Russia. Eligible participants initiated the first ART during parent studies; had alcohol consumption assessed by the blood biomarker, phosphatidylethanol (PEth), at the last research visit before ART initiation; and had ≥1 CD4 count measurement before and after initiating ART. Participants were stratified by low, moderate, and high PEth (<8, 8-80, and >80 ng/mL, respectively). We used random-effects piecewise linear regression models to estimate CD4 recovery, defined as CD4 count change per 30 days after ART initiation, by the alcohol group. Of 60 eligible participants, median age was 34 years and 28% were female. The median pre-ART PEth in the low, moderate, and high PEth groups were <8, 23, and 232 ng/mL, respectively. After starting ART, the CD4 count increased by 13.60 cells/mm3/mo (95% CI: 0.33 to 26.87) with low PEth, 0.93 cells/mm3/mo (95% CI: -6.18 to 8.04) with moderate PEth, and 2.33 cells/mm3/mo (95% CI: -3.44 to 8.09) with high PEth. Among Russians with HIV, we observed faster CD4 recovery after ART initiation in those with low alcohol consumption compared with those with moderate and high alcohol consumption, as assessed by PEth. This analysis provides further evidence for the possible value of alcohol reduction interventions for people with HIV who are initiating ART.

Reference equations using segmented regressions for impulse oscillometry in healthy subjects aged 2.7-90 years.

Published reference equations for impulse oscillometry (IOS) usually encompass a specific age group but not the entire lifespan. This may lead to discordant predicted values when two or more non-coincident equations can be applied to the same person, or when a person moves from one equation to the next non-convergent equation as he or she gets older. Thus, our aim was to provide a single reference equation for each IOS variable that could be applied from infancy to old age. This was an ambispective cross-sectional study in healthy nonsmokers, most of whom lived in Mexico City, who underwent IOS according to international standards. A multivariate piecewise linear regression, also known as segmented regression, was used to obtain reference equations for each IOS variable. In a population of 830 subjects (54.0% female) aged 2.7 to 90 years (54.8% children ≤12 years), segmented regression estimated two breakpoints for age in almost all IOS variables, except for R5-R20 in which only one breakpoint was detected. With this approach, multivariate regressions including sex, age, height and body mass index as independent variables were constructed, and coefficients for calculating predicted value, lower and upper limits of normal, percentage of predicted and z-score were obtained. Our study provides IOS reference equations that include the major determinants of lung function, i.e. sex, age, height and body mass index, that can be easily implemented for subjects of almost any age.

Association between triglyceride-glucose index and bone mineral density in US adults: a cross sectional study

ObjectiveDisorders in glucose and lipid metabolism have been shown to exert an influence on bone metabolism. The TyG index, which combines measures of glucose and triglycerides, provides insights into the overall metabolic status. However, the investigation of concurrent disturbances in glucose and lipid metabolism and their specific implications for bone metabolism remains limited in the existing research literature. This study aimed to explore the correlation between the TyG index and bone mineral density (BMD) in US adults.MethodsIn the National Health and Nutrition Examination Survey (NHANES), subjects were classified based on the TyG index into four groups (< 7.97, 7.97–8.39, 8.39–8.85, > 8.86). Linear regression analysis was conducted to determine the β value and 95% confidence interval (CI). Four multivariable models were constructed. Restricted cubic spline analyses and piecewise linear regression were employed to identify the association between the BMD and TyG index. An analysis of subgroups was also conducted in this study.ResultsSignificant variations in related characteristics were found among the US adult population, who were distributed into four groups based on the quartiles of the TyG index. A negative correlation between the TyG index and lumbar spine BMD was observed. In the multi-adjusted models, compared to Q1 of the TyG index, the β for Q4 of the TyG index for lumbar spine BMD was [β = − 0.008, 95% CI (− 0.017, 0)] in US adults. The association between the TyG index and lumbar spine BMD was found to be nonlinear (all nonlinear p < 0.001), with a threshold value based on restricted cubic spline analyses. Above the threshold point, the β for lumbar spine BMD was − 0.042 (95% CI, − 0.059, − 0.024). Below the threshold points, no significant difference was observed (p > 0.05). No significant interactions were observed among subgroups based on age, gender, presence of diabetes, BMI, and use of antidiabetic and antihyperlipidemic agents. Similar patterns of association were observed in total and subtotal bone density.ConclusionsThis study identified a nonlinear association between the TyG index and BMD in the US population. Furthermore, an increased level of the TyG index may indicate a higher risk of osteoporosis among US adults. These findings highlight the importance of considering glucose and lipid metabolism disturbances in understanding bone health and the potential for developing preventive strategies for osteoporosis.

Drought thresholds that impact vegetation reveal the divergent responses of vegetation growth to drought across China.

Identifying droughts and accurately evaluating drought impacts on vegetation growth are crucial to understanding the terrestrial carbon balance across China. However, few studies have identified the critical drought thresholds that impact China's vegetation growth, leading to large uncertainty in assessing the ecological consequences of droughts. In this study, we utilize gridded surface soil moisture data and satellite-observed normalized difference vegetation index (NDVI) to assess vegetation response to droughts in China during 2001-2018. Based on the nonlinear relationship between changing drought stress and the coincident anomalies of NDVI during the growing season, we derive the spatial patterns of satellite-based drought thresholds (T SM ) that impact vegetation growth in China via a framework for detecting drought thresholds combining the methods of feature extraction, coincidence analysis, and piecewise linear regression. The T SM values represent percentile-based drought threshold levels, with smaller T SM values corresponding to more negative anomalies of soil moisture. On average, T SM is at the 8.7th percentile and detectable in 64.4% of China's vegetated lands, with lower values in North China and Jianghan Plain and higher values in the Inner Mongolia Plateau. Furthermore, T SM for forests is commonly lower than that for grasslands. We also find that agricultural irrigation modifies the drought thresholds for croplands in the Sichuan Basin. For future projections, Earth System Models predict that more regions in China will face an increasing risk for ecological drought, and the Hexi Corridor-Hetao Plain and Shandong Peninsula will become hotspots of ecological drought. This study has important implications for accurately evaluating the impacts of drought on vegetation growth in China and provides a scientific reference for the effective ecomanagement of China's terrestrial ecosystems.

Establishment and comparison of piecewise linear regression models to measure thyroid volume by 2D and 3D ultrasound.

Compared thyroid volumes measured by 2-D and 3-D US with those of resected specimens and proposed new models to improve measurement accuracy. This study included 80 patients who underwent total thyroidectomy. One 2D_model and one 3D_model were developed using piecewise linear regression analysis. The accuracy of these models was compared using an ellipsoid model (2-D_US value × 0.5), 3-D_US value, and Ying's model [1.76 + (2-D_US value × 0.38)]. The new 2D_model was: V=2.66 + (0.71 * X1) - (1.51 * X2). In this model, if 2-D_US value <= 228.39, X1 = 2-D_US value and X2 = 0; otherwise, X1 = 2-D_US value and X2 = 2-D_US value - 228.39. The 3D_model was: V= 2.90 + (1.08 * X1) + (2.43 * X2). In this model, if 3-D_US value <= 102.06, X1 = 3-D_US value and X2 = 0; otherwise, X1 = 3-D_US value and X2 = 3-D_US value - 102.06. The accuracy of the new models was higher than that of the 3-D_US value, the ellipsoid model, and Ying's model (P<0.05). The models established are more accurate than the traditional ones and can accurately measure thyroid volume.

"Surrounded, detached": the relationship between defensive peripersonal space and personality.

Personality shapes the cognitive, affective, and behavioral interactions between individuals and the environment. Defensive peripersonal space (DPPS) is the projected interface between the body and the world with a protective function for the body. Previous studies suggest that DPPS displays inter-individual variability that is associated with psychiatric symptoms, such as anxiety. However, DPPS may share a link with personality traits. Fifty-five healthy participants were assessed with the Personality Inventory for DSM-5 (PID-5)-Adult to evaluate personality dimensions. Subjects underwent the Hand Blink Reflex (HBR) task that estimates the DPPS limits by assessing the modulation of blink intensity in response to the median nerve stimulation. Data of the HBR was analyzed with Bayesian multilevel models, while the relationship between DPPS and personality traits was explored using network analysis. HBR was best modeled using a piecewise linear regression model, with two distinct slope parameters for electromyographic data. Network analyzes showed a positive correlation between the proximal slope and detachment personality trait, suggesting that individuals with higher scores in the detachment trait had an increased modulation of HBR, resulting in a larger extension of the DPPS. Features of the detachment personality trait include avoidance of interpersonal experiences, restricted affectivity, and suspiciousness, which affect interpersonal functioning. We suggest that DPPS may represent a characteristic feature of maladaptive personality traits, thus constitute a biomarker or a target for rehabilitative interventions.

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.

The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation. We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs. SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges. The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.

Research on Multirelational Entity Modeling based on Knowledge Graph Representation Learning

Background: A research concern revolves around as to what can make the representation of entities and relationships fully integrate the structural information of the knowledge atlas to solve the entity modeling capability in complex relationships. World knowledge can be organized into a structured knowledge network by mining entity and relationship information in real texts. In order to apply the rich structured information in the knowledge map to downstream applications, it is particularly important to express and learn the knowledge map. In the knowledge atlas with expanding scale and more diversified knowledge sources, there are many types of relationships with complex types. The frequency of a single relationship in all triples is further reduced, which increases the difficulty of relational reasoning. Thus, this study aimed to improve the accuracy of relational reasoning and entity reasoning in complex relational models. Methods: For the multi-relational knowledge map, CTransR based on the TransE model and TransR model adopts the idea of piecewise linear regression to cluster the potential relationships between head and tail entities, and establishes a vector representation for each cluster separately, so that the same relationship represented by different clusters still has a certain degree of similarity. Results: The CTransR model carried out knowledge reasoning experiments in the open dataset, and achieved good performance. Conclusion: The CTransR model is highly effective and progressive for complex relationships. In this experiment, we have evaluated the model, including link prediction, triad classification, and text relationship extraction. The results show that the CTransR model has achieved significant improvement.

PSDefoPAT—Persistent Scatterer Deformation Pattern Analysis Tool

Persistent Scatterer Deformation Pattern Analysis Tool, for short PSDefoPAT, was designed to assign each measuring point of an advanced DInSAR data set a best-fitting time series model based on its displacement time series. In this paper, we will outline the operating principles of the tool. The periodic and trend components of a time series model are separately determined based on hypothesis tests. The periodic component is fitted as a sine function, and for the trend component, linear, quadratic, and piecewise linear regression models are considered. Additionally, the tool assesses the goodness-of-fit for each model in the form of the adjusted coefficient of determination Radj2 value. The tool works fully automatically and thus facilitates the analysis of large data sets, which are becoming more available to the public due to services such as the European Ground Motion Service. Additionally, we demonstrate the capabilities of PSDefoPAT using four case studies characterized by different deformation mechanisms, various extents of active deformation area, and varying density of measuring points. In all cases, we successfully reveal information on the temporal behavior of the deformation not apparent in the typically presented mean deformation velocity maps.

Research on dynamic evaluation method of individual scientific impact under multiple key nodes

Introduction. The purpose of this paper is to research an evaluation method for the development trend of the scientific impact of individual scientists before and after different key nodes in scientific careers. Method. This paper focuses on scientists at universities in Shanghai who obtained their first key programmes from the National Natural Science Foundation of China (NSFC) from 2011 to 2015. A two-node piecewise linear regression is used to divide the scientists’ individual academic trajectories. The Boston Consulting Group matrix (BCG-M) model is used to propose four types of talent. Analysis. The pr(y)-index is applied to evaluate the scientists’ impact. Several characteristics of the trajectory of the impact of individual scientists are defined by the change in the pr(y)-index growth rate. Results. The scientific impact of most scientists (66% and 62%) increased after they first obtained NSFC funding or their first key programme, respectively. The pr(y)-index of a 5-year time window is more sensitive to judge the of influence on scientific career. Conclusion. The two-node piecewise linear regression model successfully divided the academic trajectories of individual scientists into three stages。NSFC funding promotes academic influence. The talents are divided into star talent, focus talent, question talent and taurus talent.

Determination of Grid-Wise Monsoon Onset and Its Spatial Analysis for India (1901–2019)

Monsoon onset in India has always been a topic of interest for the research fraternity and various stakeholders. This study aimed to determine the monsoon onset date at the grid point scale, to obtain the trend of monsoon onset, and to unravel the spatial distribution of monsoon onset during the period 1901–2019 (especially in different climate modes). Based on observed cumulative rainfall, the piecewise linear regression model (PLRM), which employs least-squares principles, finds changepoints that signify the beginning of the monsoon season with the onset of monsoon. In this study, monsoon onset is examined with respect to several climate modes to evaluate their impact on monsoon onset. Monsoon onset is delayed in El Niño and drought years due to strong negative anomalies that are revealed by a spatial examination of monsoon onset. However, because of local atmospheric circulation impacts, there are outliers. The study also reveals areas with notable monotonic tendencies in monsoon onset, suggesting future changes in onset dates. These areas need more sophisticated frameworks for developing mitigation strategies since they should be viewed as susceptible. The comparison of the PLRM outcomes with objective methods reveals a strong correlation, confirming the accuracy of the PLRM method. Overall, the PLRM has been shown to be a useful tool for predicting the start of the monsoon on fine spatial scales and may be used in conjunction with regional climate models to anticipate the start of the monsoon in various regions of India. The results of this study could have a significant impact on regional planning and policy initiatives for sustainable development.

Real-World Presentation and Prognostic Effect of Allogeneic Blood Transfusion during the Intensive Induction Phase in Pediatric Acute Lymphoblastic Leukemia.

Purpose: To determine associations between allogeneic blood transfusion (ABT) during the intensive induction phase of therapy and prognostic effect in a real-world cohort of pediatric patients with acute lymphoblastic leukemia (ALL). Methods: A total of 749 pediatric patients who were diagnosed with ALL were enrolled in this study by using a single-center retrospective cohort study method from February 2008 to May 2022. Results: Among the ABT patients, 711 (94.9%) children were transfused with packed red blood cells (PRBCs), 434 (57.9%) with single-donor platelets (SDPs), and 196 (26.2%) with fresh frozen plasma (FFP). Our multivariate analysis demonstrated that FFP transfusion was the unique independent factor that affected both relapse-free survival (RFS) and overall survival (OS). The transfusion of FFP was significantly associated with higher age (p < 0.001), being more likely to receive SCCLG-ALL-2016 protocol (p < 0.001), higher proportion of more than 25 blood product transfusions, more PRBC transfusion (p < 0.001), and higher D33-MRD-positive rates (p = 0.013). Generalized additive models and threshold effect analysis using piece-wise linear regression were applied to identify the cut-off value of 25 mL/kg for average FFP transfusion. K-M survival analysis further confirmed that average FFP transfusion > 25 mL/kg was an independent adverse indicator of inferior outcome in terms of RFS (p = 0.027) and OS (p = 0.033). Conclusions: In blood products, only FFP supplement is closely related to the prognosis of childhood ALL. During the intensive induction phase, the indications of FFP transfusion should be strictly grasped, and the total amount of FFP should be controlled and kept below 25 mL/kg.

Spatiotemporal Dynamics of the Cropland Area and Its Response to Increasing Regional Extreme Weather Events in the Farming-Pastoral Ecotone of Northern China during 1992–2020

The cropland area is closely related to food production. Previously, more focuses were paid on impacts of extreme events on food production, but less on cropland dynamics. This study used the piecewise linear regression, the correlation analysis, and the ridge regression to explore the spatiotemporal dynamics of cropland and its drivers in three time periods (1992–2020, 1992–2010, and 2010–2020) at the Farming-Pastoral Ecotone of Northern China (FPEN). Specifically, 13 driving factors were considered from the perspectives of extreme events, environmental conditions, socioeconomic development, urban sprawl, and ecological construction. Results showed that the cropland area increased significantly at a rate of 333.5 km2/a during 1992–2020 and was spatially clustered in the eastern FPEN. The impact extent and size of each driving factor on the cropland trend presented large spatiotemporal differences, but ecological construction had, overall, the greatest impact on cropland area changes, followed by urban sprawl. In comparison, extreme low temperature had the smallest. Since the 2010s, areas with urban sprawl being the dominant factor in cropland dynamics have increased by 41.9%, but it is still less than the ecological construction impact. Furthermore, this study found that extreme event effects on the cropland area trend evidently increased. Particularly, extreme high temperature displayed the highest increase (~99.4%). Cropland area changes dominated by extreme temperature events in 2010–2020 increased by nearly six times compared to those in 1992–2010. These results suggest that policy and planning makers should caution increasing impacts of regional extreme weather events on cropland area changes.

A new time-varying coefficient regression approach for analyzing infectious disease data

Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.

Relationship between serum iron and blood eosinophil counts in asthmatic adults: data from NHANES 2011-2018.

So far, quite a few studies have revealed that systemic iron levels are related to asthmatic inflammatory reactions. And most studies have focused on the correlation between systemic iron levels and asthma, with inconsistent findings. Yet, few studies have investigated the connection between serum iron and blood eosinophil counts. Hence, we have explored the connection between serum iron and blood eosinophil counts in asthmatics by utilizing data from NHANES. A total of 2549 individuals were included in our study after screening NHANES participants from 2011 to 2018. The linear regression model and XGBoost model were used to discuss the potential connection. Linear or nonlinear association was further confirmed by the generalized additive model and the piecewise linear regression model. And we also performed stratified analyses to figure out specific populations. In the multivariable linear regression models, we discovered that serum iron levels were inversely related to blood eosinophil counts in asthmatic adults. Simultaneously, we found that for every unit increase in serum iron (umol/L), blood eosinophil counts reduced by 1.41/uL in model 3, which adjusted for all variables excluding the analyzed variables. Furthermore, the XGBoost model of machine learning was applied to assess the relative importance of chosen variables, and it was determined that vitamin C intake, age, vitamin B12 intake, iron intake, and serum iron were the five most important variables on blood eosinophil counts. And the generalized additive model and piecewise linear regression model further verify this linear and inverse association. Our investigation discovered that the linear and inverse association of serum iron with blood eosinophil counts in asthmatic adults, indicating that serum iron might be related to changes in the immunological state of asthmatics. Our work offers some new thoughts for next research on asthma management and therapy. Ultimately, we hope that more individuals become aware of the role of iron in the onset, development, and treatment of asthma.

Learning curve of tibial cortex transverse transport: a cumulative sum analysis

ObjectiveThis study aimed to describe the learning curve of surgeons performing tibial cortex transverse transport (TTT) and explore its safety and effectiveness during the initial stages of surgeon’s learning.MethodsThe clinical data of patients with diabetic foot ulcers classified as Wagner grade ≥ 2, who underwent TTT at our hospital from January 2020 to July 2021, were included in this retrospective analysis. The same physician performed all procedures. Patients were numbered according to the chronological order of their surgery dates. The cumulative sum and piecewise linear regression were used to evaluate the surgeon's learning curve, identify the cut-off point, and divide the patients into learning and mastery groups. A minimum follow-up period of 3 months was ensured for all patients. Baseline data, perioperative parameters, complications, and efficacy evaluation indicators were recorded and compared between the two groups.ResultsSixty patients were included in this study based on the inclusion and exclusion criteria. After completing 20 TTT surgeries, the surgeon reached the cut-off point of the learning curve. Compared to the learning group, the mastery group demonstrated a significant reduction in the average duration of the surgical procedure (34.88 min vs. 54.20 min, P < 0.05) along with a notable decrease in intraoperative fluoroscopy (9.75 times vs. 16.9 times, P < 0.05) frequency, while no significant difference was found regarding intraoperative blood loss (P = 0.318). Of the patients, seven (11.7%) experienced complications, with three (15%) and four cases (10%) occurring during the learning phase and the mastery phase, respectively. The postoperative ulcer area was significantly reduced, and the overall healing rate was 94.8%. Significant improvements were observed in postoperative VAS, ABI, and WIFI classification (P < 0.05). There were no significant differences in the occurrence of complications or efficacy indicators between the learning and mastery groups (P > 0.05).ConclusionSurgeons can master TTT after completing approximately 20 procedures. TTT is easy, secure, and highly efficient for treating foot ulcers. Furthermore, TTT’s application by surgeons can achieve almost consistent clinical outcomes in the initial implementation stages, comparable to the mastery phase.

Generalizing Reservoir Operations Using a Piecewise Classification and Regression Approach

AbstractInflow anomalies at varying temporal scales, seasonally varying storage mandates, and multipurpose allocation requirements contribute to reservoir operational decisions. The difficulty of capturing these constraints across many basins in a generalized framework has limited the accuracy of streamflow estimates in land‐surface models for locations downstream of reservoirs. We develop a Piecewise Linear Regression Tree to learn generalized daily operating policies from 76 reservoirs from four major basins across the coterminous US. Reservoir characteristics, such as residence time and maximum storage, and daily state variables, such as storage and inflow, are used to group similar observations across all reservoirs. Linear regression equations are then fit between daily state variables and release for each group. We recommend two models—Model 1 (M1) that performs the best when simulating untrained records but is complex and Model 2 (M2) that is nearly as performant as M1 but more parsimonious. The simulated release median root mean squared error is 49.7% (53.2%) of mean daily release with a median Nash‐Sutcliffe efficiency of 0.62 (0.52) for M1 (M2). Long‐term residence time is shown to be useful in grouping similar operating reservoirs. Release from low residence time reservoirs can be mostly described using inflow‐based variables. Operations at higher residence time reservoirs are more related to previous release variables or storage variables, depending on the current inflow. The ability of the models presented to capture operational dynamics of many types of reservoirs indicates their potential to be used for untrained and limited data reservoirs.

Consumer Wearables Define a Normative Recovery Trajectory Following the Modified Nuss Procedure

IntroductionThe modified Nuss procedure is an elective procedure associated with a lengthy recovery, uncontrolled pain, and risk of infrequent, yet life-threatening complications. The absence of objective measures of normative postoperative recovery creates uncertainty about the postdischarge period, which remains highly dependent on the patients' and their caregivers’ expectations and management of recovery. We aimed to describe an objective-normative, physical activity recovery trajectory after the modified Nuss procedure, using step counts from the Fitbit. MethodsThis observational study enrolled children ≤18 y with pectus excavatum who underwent the modified Nuss procedure from 2021 to 2022. The Fitbit was worn for 21 postoperative days. Postdischarge outcomes and health-care utilization were evaluated. For patients without postoperative complications, piecewise linear regression analysis was conducted to generate a normative recovery trajectory model of daily step counts. ResultsOf 80 patients enrolled, 66 (86%) met eligibility criteria (mean age, 15.1 ± 1.3 y; 89.4% male, 62.1% non-Hispanic White). The mean number of telephone and electronic message encounters regarding concerns with the patient's recovery within 30 d postoperatively was 2.1 (standard deviation = 2.7). Ten patients (15.2%) returned to the emergency department (ED) within the 30-d postoperative period. Seven patients (10.6%) presented to the ED one time, and three patients (4.5%) presented to the ED twice. Thirty-day readmission rate was four patients (6.0%). Piecewise regression model showed that patients without complications steadily increased their daily step count on each postoperative day and plateaued on day 18. ConclusionsWe have developed a normative recovery trajectory following the modified Nuss procedure using step count data collected by a consumer wearable device. This offers the potential to inform preoperative patient expectations and reduce avoidable health-care utilization through informed preoperative counseling, thus laying the ground work for the use of consumer wearable devices as a postdischarge remote monitoring tool.

Association between the Weight-Adjusted Waist Index and Serum Uric Acid: A Cross-Sectional Study.

Serum uric acid (SUA) was closely related to body metabolism. This study aimed to investigate the relationship between the adult weight-adjusted waist index (WWI) and SUA. In the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2020, 6494 eligible participants aged ≥20 were included. The multivariate logistic regression model was used to test the correlation between WWI and SUA. At the same time, subgroup analysis was carried out by using multivariate logistic regression according to age, sex, and race. Then, the fitting smooth curve was applied to solve the association between WWI and SUA. Finally, the recursive algorithm was used to calculate the inflection point in the nonlinear relationship, and the two-stage piecewise linear regression model was used to analyze the relationship between WWI and SUA on both sides of the inflection point. In all the 6494 participants, through the fully adjusted model, this study found that there was a positive correlation between WWI and SUA (β = 5.64; 95% CI: 2.62 and 8.66). In addition, this positive correlation still had certain statistical significance in the subgroup analysis stratified by sex, age, and race. Our research team found a significant positive correlation between the WWI and SUA in females, but the correlation was not significant in males. We also found a small inverted U-shaped curve between the WWI and SUA in men when we stratified the sex subgroups. The small inflection point was determined to be 11.5 cm/√ kg. In racial subgroup analysis, we also found a U-shaped relationship between the WWI and SUA in non-Hispanic White and other race/ethnicity (the inflection point was 11.08 cm/√ kg and 12.14 cm/√ kg, respectively). This study showed that the WWI was a newly developed and new predictor of centripetal obesity independent of body weight and there was a positive correlation between the WWI and SUA.

Antecedents and performance implications of stakeholder understanding in green product innovation

When developing green product innovations, firms are often confronted with a complex system of interdependent stakeholders and their interests. This study focuses on the holistic understanding of stakeholder interests and introduces the organizational capability of stakeholder understanding, defined as the degree to which a firm understands its stakeholder system. In particular, this study empirically examined the antecedents and consequences of stakeholder understanding in a green product innovation context. Through a two-stage survey approach, data were collected from 181 innovation professionals working in United States based manufacturing firms. Using hierarchical regression analysis, hypotheses about antecedents and consequences of stakeholder understanding are tested. Results show that stakeholder understanding is higher in firms with high innovation orientation, and lower in environments with high complexity. Results also show that stakeholder understanding has positive effects on three types of firm performance: green performance, market performance, and firm innovativeness. In addition, results of piecewise linear regression reveal that only for firms that exceed the median level of green orientation there is a positive relationship between green orientation and stakeholder understanding. The study's implications are that stakeholder understanding is a relevant capability for firms that aim to develop better and greener product innovations. To obtain stakeholder understanding, firms should strive for innovation and excel in green orientation, because only for truly green firms, green orientation helps to develop stakeholder understanding.