scholarly journals Vitreoretinal surgeons’ experience and time interval from pars-plana vitrectomy to cataract extraction

2021 ◽  
Vol 14 (1) ◽  
pp. 120-126
Author(s):  
Yan-Xin Xu ◽  
◽  
Jian-Bing Li ◽  
Huan-Huan Cheng ◽  
Min Hou ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Pars Plana Vitrectomy ◽  
Linear Regression Analysis ◽  
Cataract Extraction ◽  
Time Interval ◽  
Case Volume ◽  
Time Intervals ◽  
Multivariable Linear Regression Analysis ◽  
Pars Plana

AIM: To identify the association of the vitreoretinal surgeons’ experience with the time interval between pars-plana vitrectomy (PPV) and cataract extraction (CE). METHODS: Eyes with prior PPV and following CE were included in this retrospective cohort study. The years of practice and the annual case volume were used to describe the surgeons’ experience. Multivariable linear regression analysis was used to investigate the relationship between surgeons’ experience and the time interval adjusted for the patients age, gender, intraocular tamponade, and case complexity. RESULTS: Of 132 430 eyes, 1445 eyes were included in this study. In multivariable linear regression analysis, cases performed by surgeons with >20 practice years had longer time intervals compared with surgeons with <10 practice years after adjusted for other variables (β=0.329, 95%CI: 0.113 to 0.549, P=0.003). No difference in time interval was detected for comparing the lowest with the highest volume groups (β=0.089, 95%CI: -0.164 to 0.343, P=0.343). The surgeons’ practice years were not directly with the volume. For complicated surgery, the higher-practice-year surgeons had longer time interval than lower-practice-year surgeons. CONCLUSION: The time intervals from PPV to CE is longer in higher-practice-year surgeons. The surgeons’ practice years may have a greater effect on the time interval than annual case volume in high-complexity cases. Matching the complexity of vitreoretinal diseases with the surgeons’ practice year should be considered.

scholarly journals Effect of Number of Clinics and Panel Size on Patient Continuity for Medical Residents

2009 ◽  
Vol 1 (2) ◽  
pp. 310-315 ◽  
Author(s):  
Maureen D. Francis ◽  
Whitney E. Zahnd ◽  
Andrew Varney ◽  
Steven L. Scaife ◽  
Mark L. Francis
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Medical Residents ◽  
Panel Size ◽  
Medical Education Program ◽  
Internal Medicine Residency Training ◽  
Multivariable Linear Regression Analysis ◽  
Linear Regression Modeling ◽  
Attending Physician

Abstract Background Accreditation Council for Graduate Medical Education program requirements for internal medicine residency training include a longitudinal, continuity experience with a panel of patients. Objective To determine whether the number of resident clinics, the resident panel size, and the supervising attending physician affect patient continuity. To determine the number of clinics and the panel size necessary to maximize patient continuity. Design We used linear regression modeling to assess the effect of number of attended clinics, the panel size, and the attending physician on patient continuity. Participants Forty medicine residents in an academic medicine clinic. Measurements Percent patient continuity by the usual provider of care method. Results Unadjusted linear regression analysis showed that patient continuity increased 2.3% ± 0.7% for each additional clinic per 9 weeks or 0.4% ± 0.1% for each additional clinic per year (P  =  .003). Conversely, patient continuity decreased 0.7% ± 0.4% for every additional 10 patients in the panel (P  =  .04). When simultaneously controlling for number of clinics, panel size, and attending physician, multivariable linear regression analysis showed that patient continuity increased 3.3% ± 0.5% for each additional clinic per 9 weeks or 0.6% ± 0.1% for each additional clinic per year (P &lt; .001). Conversely, patient continuity decreased 2.2% ± 0.4% for every additional 10 patients in the panel (P &lt; .001). Thus, residents who actually attend at least 1 clinic per week with a panel size less than 106 patients can achieve 50% patient continuity. Interestingly, the attending physician accounted for most of the variability in patient continuity (51%). Conclusions Patient continuity for residents significantly increased with increasing numbers of clinics and decreasing panel size and was significantly influenced by the attending physician.

scholarly journals Nd:YAG Capsulotomy after Phacoemulsification in Vitrectomized Eyes: Effects of Pars Plana Vitrectomy on Posterior Capsule Opacification

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jong Hwa Jun ◽  
Kwang Soo Kim ◽  
Sung Dong Chang
Keyword(s):  
Cataract Surgery ◽  
Medical Records ◽  
Pars Plana Vitrectomy ◽  
Posterior Capsule Opacification ◽  
Posterior Capsule ◽  
Time Interval ◽  
Time Intervals ◽  
Nd:Yag Laser Capsulotomy ◽  
Pars Plana ◽  
Hydrophobic Acrylic

To compare the progression of posterior capsule opacification (PCO) in patients who required Nd:YAG laser capsulotomy following either combined cataract surgery with pars plana vitrectomy (PPV; C-CV), sequential cataract surgery after PPV (S-CV), or cataract surgery alone (CA). The medical records of 321 patients (408 eyes) who underwent Nd:YAG capsulotomy were retrospectively evaluated. The CA group had a significantly longer time interval from cataract surgery to capsulotomy than that of both the CV group(P=0.006)and the S-CV(P=0.013)and C-CV(P=0.042)subgroups when age-matched comparisons were used. CV patients who implanted a hydrophobic acrylic IOL had shorter time intervals than those of CA patients(P=0.028). CV patients had larger hazard of earlier capsulotomy than CA patients (hazard ratio (HR) = 1.337; 95% confidence interval (CI) 1.100–1.625;P=0.004). C-CV and S-CV patients both had larger hazard than CA patients in earlier capsulotomy (HR=1.304; 95%CI=1.007–1.688;P=0.044,HR=1.361; 95%  CI=1.084–1.709;P=0.008, resp.). PCO progresses more rapidly in patients undergoing combined or sequential cataract surgery and PPV than in patients undergoing CA.

Using the health belief model to analyse nurses’ perception towards their behaviours for keeping surgical instruments moist

2020 ◽  
Author(s):  
Yanhua Chen ◽  
Juan Hu ◽  
Yan Huang ◽  
Liangying Yi ◽  
Ruixue Hu
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Health Belief Model ◽  
Linear Regression Analysis ◽  
Health Belief ◽  
Surgical Instruments ◽  
Length Of Service ◽  
Multivariable Linear Regression Analysis ◽  
Belief Model ◽  
The Health Belief Model

Abstract Background Because of conflicts with work schedule of central sterile supply department (CSSD), surgical instruments might not be immediately cleaned or sterilized by CSSD staff members after use. If surgical instruments are not kept appropriately moist, tarnish or rusting may occur on the instruments, which will not only affect cleaning quality, but also shorten the normal service life of the instruments. Nurses’ perception towards their behaviours for keeping surgical instruments moist has been rarely studied. We aimed to use the health belief model to analyse nurses’ perception towards their behaviours for keeping surgical instruments moist. Methods The survey which utilised a questionnaire regarding respondent’s general information and a self-designed nurses’ perception-behaviour scale for keeping surgical instruments moist was conducted with 360 nurses from the West China Second University Hospital, Sichuan University. Data was gathered with cluster sampling, and analysed in SPSS20.0. T-test, variance analysis and multivariable linear regression analysis were performed. Results Total score of nurses’ perception-behaviour scale for keeping surgical instruments moist was 139.93 ± 15.145, among which mean score for perceived severity, perceived susceptibility, perceived benefits, perceived barriers, and self-efficacy was 4.49 ± 0.57, 4.62 ± 0.48, 4.57 ± 0.52, 3.47 ± 0.94 and 4.16 ± 0.66, respectively. Single factor analysis showed that score of nurses’ perception-behaviour scale for keeping surgical instruments moist varied with age, length of service, and job title, with a statistically significant difference (P < 0.05). Multivariable linear regression analysis showed that length of service was the main factor affecting nurses’ perception towards their behaviours for keeping surgical instruments moist. Conclusion Nurses should be offered intensive training on keeping surgical instruments moist due to their inadequate perception on it. However, an increase in knowledge does not necessarily bring behavior change. The nurses’ change in health beliefs and behaviours must be based upon developing the right attitude. As a result, they could realise the benefits of keeping surgical instruments moist, identify the barriers, and finally obtain the solutions.

scholarly journals Influence of transfusions, hemodialysis and extracorporeal life support on hyperferritinemia in critically ill patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254345
Author(s):  
Cornelia Knaak ◽  
Friederike S. Schuster ◽  
Peter Nyvlt ◽  
Patrick Heeren ◽  
Claudia Spies ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Critically Ill ◽  
Sofa Score ◽  
Critically Ill Patients ◽  
Life Support ◽  
Extracorporeal Life Support ◽  
Linear Regression Analysis ◽  
Iron Storage ◽  
Multivariable Linear Regression Analysis

Background Ferritin is the major iron storage protein and an acute phase reactant. Hyperferritinemia is frequently seen in the critically ill where it has been hypothesized that not only underlying conditions but also factors such as transfusions, hemodialysis and extracorporeal life support (ECLS) lead to hyperferritinemia. This study aims to investigate the influence of transfusions, hemodialysis, and ECLS on hyperferritinemia in a multidisciplinary ICU cohort. Methods This is a post-hoc analysis of a retrospective observational study including patients aged ≥ 18 years who were admitted to at least one adult ICU between January 2006 and August 2018 with hyperferritinemia ≥ 500 μg/L and of ≥ 14 days between two ICU ferritin measurements. Patients with hemophagocytic lymphohistiocytosis (HLH) were excluded. To identify the influence of transfusions, hemodialysis, and ECLS on ferritin change, multivariable linear regression analysis with ferritin change between two measurements as dependent variable was performed. Results A total of 268 patients was analyzed. Median duration between measurements was 36 days (22–57). Over all patients, ferritin significantly increased between the first and last measurement (p = 0.006). Multivariable linear regression analysis showed no effect of transfusions, hemodialysis, or ECLS on ferritin change. Changes in aspartate aminotransferase (ASAT) and sequential organ failure assessment (SOFA) score were identified as influencing factors on ferritin change [unstandardized regression coefficient (B) = 2.6; (95% confidence interval (CI) 1.9, 3.3); p < 0.001 and B = 376.5; (95% CI 113.8, 639.1); p = 0.005, respectively]. Using the same model for subgroups of SOFA score, we found SOFA platelet count to be associated with ferritin change [B = 1729.3; (95% CI 466.8, 2991.9); p = 0.007]. No association of ferritin change and in-hospital mortality was seen in multivariable analysis. Conclusions The present study demonstrates that transfusions, hemodialysis, and ECLS had no influence on ferritin increases in critically ill patients. Hyperferritinemia appears to be less the result of iatrogenic influences in the ICU thereby underscoring its unskewed diagnostic value. Trial registration The study was registered with www.ClinicalTrials.gov (NCT02854943) on August 1, 2016.

scholarly journals Patient Characteristics and Medical Provider Factors Are Associated With Length of Stay in Total Hip Arthroplasty Patients: a Retrospective Chinese Cohort Study

2020 ◽  
Author(s):  
Hui Zeng ◽  
Guoqing Li ◽  
Fei Yu ◽  
Jian Weng ◽  
Ao Xiong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Total Hip Arthroplasty ◽  
Linear Regression ◽  
Length Of Stay ◽  
Hip Arthroplasty ◽  
Linear Regression Analysis ◽  
Patient Characteristics ◽  
Medical Provider ◽  
Multivariable Linear Regression Analysis ◽  
The Relationship

Abstract Background Total hip arthroplasty (THA) is a successful treatment in the improvement of quality of life. Diagnosis-related groups (DRGs) payment has a significant impact on the hospital market in China and length of stay (LOS) is one of its crucial manifestations. Patient characteristics and medical provider factors can affect LOS but the relationship is uncertain. We intent to explore the relationship between patient characteristics and medical provider factors and LOS of primary THA patients. Methods We reviewed the database containing 461 patients who underwent primary THA between January 2014 to January 2019 and regressed the LOS against a variety of perioperative factors. A multivariable linear regression model was performed to assess the difference. Results For parts of patient characteristics, multivariable linear regression analysis revealed that comorbidities, pre-operation albumin < 30 g/L, and pre-operation CRP ≥ 5 mg/L were all significantly associated with LOS (p < 0.05). For parts of medical provider factors, multivariable linear regression analysis revealed that date of surgery, urinary catheter, and incision drainage were all significantly associated with LOS (p < 0.05). Conclusions Patient characteristics and medical provider factors are associated with LOS of THA patients. Evaluation and identification of risk factors are beneficial in patients' education, perioperative discussion and surgery decisions in the different primary THA patient populations.

Abstract P269: Circulating Fatty Acids, Determinants and Use as Biomarkers for Dietary Intake: The CoDAM and Hoorn Study

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Anne J Wanders ◽  
Sabine E De Hoon ◽  
Marjan Alssema ◽  
Edith J Feskens ◽  
Geertruida J Van Woudenbergh ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Regression Analysis ◽  
Waist Circumference ◽  
Linear Regression ◽  
Dietary Intake ◽  
Alcohol Intake ◽  
Linear Regression Analysis ◽  
Epa And Dha ◽  
Health Factors ◽  
Multivariable Linear Regression Analysis

Objective: To increase the understanding of circulating fatty acids (FA) as biomarkers of FA intake, we investigated (1) determinants of circulating proportions of linoleic acid (LA), alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA); and (2) the effect of demographic, lifestyle, and health factors on the relation between circulating and self-reported intake of LA, ALA, EPA, and DHA. Hypothesis: We hypothesized that demographic, lifestyle, and health factors influence the relation between circulating and self-reported intake of FA. Methods: Cross-sectional analysis in combined data from the CoDAM (n=472) and Hoorn study (n=708). Dietary FA intakes (% of total FA) were calculated from a validated 79-item semi-quantitative food frequency questionnaire. Fasting total fatty acids (% of total FA) in plasma (CoDAM) or serum (Hoorn) were measured by gas liquid chromatography. The variation in circulating proportions of FA explained by demographic, lifestyle and health factors was calculated by multivariable linear regression analysis. Correlation coefficients between circulating proportions of FA and self-reported FA intakes were calculated by standardized multivariable linear regression analysis adjusted for demographic, lifestyle, and health factors. To assess the influence of specific factors on correlations, stratified analyses were performed and interactions were calculated. Results: Self-reported intakes were the primary determinants of circulating proportions of LA (partial R2: 7%), ALA (2%), EPA (9%) and DHA (16%). Standardized regression coefficients between circulating and self-reported FA were: LA β=0.280 (95% Confidence Interval: 0.227-0.333), ALA β=0.130 (0.071-0.188), EPA β=0.338 (0.281-0.395), and DHA β=0.450 (0.397-0.503). Other determinants of circulating FA were the use of lipid lowering drugs, waist circumference and sex for LA; prevalence of type 2 diabetes mellitus, age, sex, and alcohol intake for EPA; and age for DHA. The correlation between circulating and dietary LA was stronger among people with a lower vs higher waist circumference and higher vs lower alcohol intake (interaction: p<0.05). In women, the correlation between circulating and dietary EPA and DHA was weaker than in men, and the correlation between circulating and dietary DHA was higher with higher alcohol intake. Underreporting of energy intake did not affect the correlations. Conclusion: Self-reported intake of FA is the primary, but not the only determinant of circulating proportions of LA, ALA, EPA and DHA. This analysis indicates that demographic, lifestyle, and health characteristics may influence the relation between circulating proportions and self-reported intake of FA. Improved understanding is needed of factors determining circulating FA and the implication for their use as biomarkers of dietary intake in different subgroups.

Analysis of Autoregressive Energy Models of a Research House

2015 ◽  
Author(s):  
Nelson Fumo ◽  
Daniel C. Lackey ◽  
Sara McCaslin
Keyword(s):  
Regression Analysis ◽  
Energy Consumption ◽  
Linear Regression ◽  
Dynamic Behavior ◽  
Statistical Models ◽  
Linear Regression Analysis ◽  
Measured Data ◽  
Time Interval ◽  
Common Time ◽  
Lag Variables

Energy consumption from buildings is a major component of the overall energy consumption by end-use sectors in industrialized countries. In the United States of America (USA), the residential sector alone accounts for half of the combined residential and commercial energy consumption. Therefore, efforts toward energy consumption modeling based on statistical and engineering models are in continuous development. Statistical approaches need measured data but not buildings characteristics; engineering approaches need building characteristics but not data, at least when a calibrated model is the goal. Among the statistical models, the linear regression analysis has shown promising results because of its reasonable accuracy and relatively simple implementation when compared to other methods. In addition, when observed or measured data is available, statistical models are a good option to avoid the burden associated with engineering approaches. However, the dynamic behavior of buildings suggests that models accounting for dynamic effects may lead to more effective regression models, which is not possible with standard linear regression analysis. Utilizing lag variables is one method of autoregression that can model the dynamic behavior of energy consumption. The purpose of using lag variables is to account for the thermal energy stored/release from the mass of the building, which affects the response of HVAC equipment to changes in outdoor or weather parameters. In this study, energy consumption and outdoor temperature data from a research house are used to develop autoregressive models of energy consumption during the cooling season with lag variables to account for the dynamics of the house. Models with no lag variable, one lag variable, and two lag variables are compared. To investigate the effect of the time interval on the quality of the models, data intervals of 5 minutes, 15 minutes, and one hour are used to generate the models. The 5 minutes time interval is used because that is the resolution of the acquired data; the 15 minutes time interval is used because it is a common time interval in electric smart meters; and one hour time interval is used because it is the common time interval for energy simulation in buildings. The primary results shows that the use of lag variables greatly improves the accuracy of the models, but a time interval of 5 minutes is too small to avoid the dependence of the energy consumption on operating parameters. All mathematical models and their quality parameters are presented, along with supporting graphical representation as a visual aid to comparing models.

Quantitative analysis of the effect of institutional case volume on complications after surgical clipping of unruptured aneurysms

2017 ◽  
Vol 127 (6) ◽  
pp. 1297-1306 ◽  
Author(s):  
Lorenzo Rinaldo ◽  
Brandon A. McCutcheon ◽  
Meghan E. Murphy ◽  
Daniel L. Shepherd ◽  
Patrick R. Maloney ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Stroke Rate ◽  
Case Volume ◽  
Surgical Clipping ◽  
Cart Analysis ◽  
Different Types ◽  
Relationship Of ◽  
The Relationship

OBJECTIVEThe mechanism by which greater institutional case volume translates into improved outcomes after surgical clipping of unruptured intracranial aneurysms (UIAs) is not well established. The authors thus aimed to assess the effect of case volume on the rate of various types of complications after clipping of UIAs.METHODSUsing information on the outcomes of inpatient admissions for surgical clipping of UIAs collected within a national database, the relationship of institutional case volume to the incidence of different types of complications after clipping was investigated. Complications were subdivided into different categories, which included all complications, ischemic stroke, intracerebral hemorrhage, medical complications, infectious complications, complications related to anesthesia, and wound complications. The relationship of case volume to different types of complications was assessed using linear regression analysis. The relationships between case volume and overall complication and stroke rates were fit with both linear and quadratic equations. The numerical cutoff for institutional case volume above and below which the authors found the greatest differences in mean overall complication and stroke rate was determined using classification and regression tree (CART) analysis.RESULTSBetween October 2012 and September 2015, 125 health care institutions reported patient outcomes from a total of 6040 cases of clipping of UIAs. On linear regression analysis, increasing case volume was negatively correlated to both overall complications (r2 = 0.046, p = 0.0234) and stroke (r2 = 0.029, p = 0.0557) rate, although the relationship of case volume to the complication (r2 = 0.092) and stroke (r2 = 0.067) rate was better fit with a quadratic equation. On CART analysis, the cutoff for the case number that yielded the greatest difference in overall complications and stroke rate between higher- or lower-volume centers was 6 cases/year and 3 cases/year, respectively.CONCLUSIONSAlthough the authors confirm that increasing case volume is associated with reduced complications after clipping of UIAs, their results suggest that the relationship between case volume and complications is not necessarily linear. Moreover, these results indicate that the effect of case volume on outcome is most evident between very-low-volume centers relative to centers with a medium-to-high volume.

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