scholarly journals 182. increasing Odds of Resistance for Subsequent Urinary e. Coli Isolates

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S220-S220
Author(s):  
Lauren Frisbie ◽  
Scott Weissman ◽  
Hema Kapoor ◽  
Marisa A D’Angeli ◽  
Ann Salm ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Bacterial Species ◽  
Healthcare Outcomes ◽  
Data Set ◽  
Susceptibility Data ◽  
Commercial Laboratory ◽  
Resistance Patterns ◽  
Amoxicillin Clavulanate

Abstract Background Annual cumulative antibiograms are routinely used by clinicians to guide selection of empirical antibiotic therapies. CLSI guidelines recommend that these antibiograms to analyze data yearly, include only final, verified results, include bacterial species with > 30 isolates and to include only the first isolate for each species/patient instance per analysis period. Handling multiple isolates from individual patients in cumulative antibiograms is a controversial topic within the antimicrobial stewardship community. Current practice favors removing subsequent isolates, thereby discarding data reflecting impact of selective antibiotic pressure on resistance patterns in recurring urinary tract infection (UTI). In this study we analyzed a five-year data set of deidentified outpatient antibiotic results from a commercial laboratory to determine whether there were significant differences in resistance patterns between first and subsequent isolates from the same patient. Methods The 5-year antibiotic susceptibility data was restricted to urinary Escherichia coli (EC) isolates. Patient occurrence(s) of urinary EC were categorized by frequency: 1st occurrence, 2nd occurrence, 3rd occurrence, and 4th or greater occurrence. A logistic regression analysis using a binary outcome for resistance and independent variable of patient isolate occurrence was run for amoxicillin-clavulanate, ampicillin, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, nitrofurantoin, and trimethoprim-sulfa. Results From a logistic regression analysis, we estimate that for each occurrence in the data, an isolate’s odds of resistance were higher for every increase in a patient’s number of occurrences in the data for all antibiotics reported with p values < 0.0001. Table 1: Odds ratios (OR) of resistance for each subsequent urinary EC isolate occurrence over 5 years Conclusion Our findings suggest that individuals with higher numbers of urinary EC occurrences have more resistant EC than the first EC occurrence, with effects that vary by antibiotic class. Although traditional antibiograms include only the first occurrence of urinary EC from a single patient, this approach may underestimate levels of reservoir resistance in a community. Such an underestimation likely impacts efficacy of empiric therapeutic choice, healthcare outcomes, and cost. Disclosures All Authors: No reported disclosures

Development and External Validation of Diagnostic Model for Coronary Microvascular Obstruction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li ◽  
Shuzheng Lyu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Microvascular Obstruction ◽  
External Validation ◽  
Diagnostic Model ◽  
Total Occlusion ◽  
Validation Data ◽  
Data Set ◽  
Acute Stemi

BACKGROUND Prevention of coronary microvascular obstruction /no-reflow phenomenon(CMVO/NR) is a crucial step in improving prognosis of patients with acute ST segment elevation myocardial infarction (STEMI )during primary percutaneous coronary intervention (PPCI). OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of CMVO/NR in patients with acute STEMI underwent PPCI. METHODS Design: Multivariate logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1232 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: Totally 1301 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: CMVO/NR during PPCI. We used logistic regression analysis to analyze the risk factors of CMVO/NR in the development data set. We developed a diagnostic model of CMVO/NR and constructed a nomogram.We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS A total of 147 out of 1,232 participants (11.9%) presented CMVO/NR in the development dataset.The strongest predictors of CMVO/NR were age, periprocedural bradycardia, using thrombus aspiration devices during procedure and total occlusion of culprit vessel. Logistic regression analysis showed that the differences between two group with and without CMVO/NR in age( odds ratios (OR)1.031; 95% confidence interval(CI), 1.015 ~1.048 ; P <.001), periprocedural bradycardia (OR 2.151;95% CI,1.472~ 3.143 ; P <.001) , total occlusion of the culprit vessel (OR 1.842;95% CI, 1.095~ 3.1 ; P =.021) , and using thrombus aspirationdevices during procedure (OR 1.631; 95% CI, 1.029~ 2.584 ; P =.037).We developed a diagnostic model of CMVO/NR. The area under the receiver operating characteristic curve (AUC) was .6833±.023. We constructed a nomogram. CMVO/NR occurred in 120 out of 1,301 participants (9.2%) in the validation data set. The AUC was .6547±.025. Discrimination, calibration, and DCA were satisfactory. Date of approved by ethic committee:16 May 2019. Date of data collection start: 1 June 2019. Numbers recruited as of submission of the manuscript:2,533. CONCLUSIONS We developed and externally validated a diagnostic model of CMVO/NR during PPCI. CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform on 16 May 2019. Registration number: ChiCTR1900023213. http://www.chictr.org.cn/edit.aspx?pid=39057&htm=4.

scholarly journals Bayesian Logistic Regression of Soybean Sclerotinia Stem Rot Prevalence in the U.S. North-Central Region: Accounting for Uncertainty in Parameter Estimation

2003 ◽  
Vol 93 (6) ◽  
pp. 758-764 ◽  
Author(s):  
A. L. Mila ◽  
X. B. Yang ◽  
A. L. Carriquiry
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Central Region ◽  
The United States ◽  
North Central Region ◽  
Sclerotinia Stem Rot ◽  
Data Set ◽  
North Central ◽  
Bayesian Methodology

Bayesian ideas have recently gained considerable ground in several scientific fields mainly due to the rapid progress in computing resources. Nevertheless, in plant epidemiology, Bayesian methodology is not yet commonly discussed or applied. Results of a logistic regression analysis of a 4-year data set collected between 1995 and 1998 on soybean Sclerotinia stem rot (SSR) prevalence in the north-central region of the United States were reexamined with Bayesian methodology. The objective of this study was to use Bayesian methodology to explore the level of uncertainty associated with the parameter estimates derived from the logistic regression analysis of SSR prevalence. Our results suggest that the 4-year data set used in the logistic regression analysis of SSR prevalence in the north-central region of the United States may not be informative enough to produce reliable estimates of the effect of some explanatory variables on SSR prevalence. Such confident estimations are necessary for deriving robust conclusions and high quality predictions.

scholarly journals Links between soil composition and podoconiosis occurrence and prevalence in Cameroon

2020 ◽  
Author(s):  
Harriet Gislam ◽  
Niall Burnside ◽  
Matthew Brolly ◽  
Kebede Deribe ◽  
Gail Davey ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
High Resolution ◽  
Logistic Regression Analysis ◽  
Health Research ◽  
Potassium Feldspar ◽  
Data Set ◽  
Prevalence Data ◽  
Soil Variables ◽  
Proximal Analysis

&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Podoconiosis, a form of non-filarial elephantiasis, is a geochemical disease associated with individuals exposed to red clay soil from alkalic volcanic rock. It is estimated that globally 4 million people suffer from the disease, though the exact causal agent is unknown. This study is the first analysis in Cameroon to compare high resolution ground-sampled geochemical soil variables and remote sensing data in relation to podoconiosis prevalence and occurrence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Aim: &lt;/strong&gt;To investigate the associations of soil mineralogical and element variables in relation to podoconiosis prevalence and occurrence in Cameroon.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this study, exploratory statistical and spatial data analysis were conducted on soil and spatial epidemiology data associated with podoconiosis. The studied soil data was comprised of 194 samples from an area of 65 by 45 km, containing 19 minerals and 55 elements. Initial proximal analysis included a spatial join between the prevalence data points and the closest ground-sampled soil variables. In addition, the soil variables were interpolated to create a continuous surface. At each prevalence data point, soil values from the interpolated surfaces were extracted. Correlation and logistic regression analysis were carried out on both the proximal analysis data set and the interpolated soil variables. The interpolated soil variables were also analysed using principal component analysis, to identify any patterns or clusters, regarding podoconiosis occurrence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Bivariate analysis of the proximal and interpolated data set identified several statistically significant soil variables associated with podoconiosis. Correlation analysis identified several soil variables with a statistically significant positive Spearman rho value in relation to podoconiosis prevalence. Logistic regression analysis identified several statistically significant soil variables with odds ratio values greater than 1, with respect to the podoconiosis occurrence data. The significant variables included barium, beryllium, potassium, sodium, rubidium, strontium, thallium, potassium feldspar, mica and quartz. Barium, beryllium, potassium, sodium, quartz, mica and potassium feldspar have been previously identified in the literature in relation to podoconiosis occurrence. The PCA biplots showed no definite groupings of soil compositions with respect to podoconiosis occurrence. However, the envelope of the 95% confidence ellipse, representing prevalence data with at least one case of podoconiosis, does begin to separate as the soil variables suggested to be associated with podoconiosis occurrence increase and reach maximal values.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The findings suggest that the key minerals and elements identified in this study may play a role in the pathogenesis of podoconiosis or could be disease covariates. These significant results have led to ongoing research within this project to examine the utilisation of medium and high-resolution hyperspectral methods to identify if podoconiosis-associated soil variables, such as quartz, are detectable remotely. Data can then be used to predict areas at risk using multivariate machine learning techniques theorising a link between prevalence, presence and combinations of multiple soil related variables.&lt;/p&gt;&lt;p&gt;This study is supported by the National Institute for Health Research (NIHR) Global Health Research Unit on NTDs at BSMS (16/136/29). The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care.&lt;/p&gt;

Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hospital Mortality ◽  
Logistic Regression Analysis ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
Development Data

BACKGROUND Coronary heart disease, including ST-segment elevation myocardial infarction (STEMI), remains the main cause of death. OBJECTIVE The objective of our research was to develop and externally validate a diagnostic model of in-hospital mortality in acute STEMI patients. METHODS We performed multiple logistic regression analysis on a cohort of hospitalized acute STEMI patients. Participants: From January 2002 to December 2011, a total of 2,183 inpatients with acute STEMI were admitted for development.The external validation data set of this model comes from 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019.We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set.We developed a diagnostic model of in-hospital mortality and constructed a nomogram. We evaluated the predictive performance of the diagnostic model in the validation data set by examining the measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS In the development data set, 61 of the 2,183 participants (2.8%) experienced in-hospital mortality. The strongest predictors of in-hospital mortality were advanced age and high Killip classification. Logistic regression analysis showed the difference between the two groups with and without in-hospital mortality (odds ratio [OR] 1.058, 95% CI 1.029-1.088; P <.001), Killip III (OR 8.249, 95% CI 3.502-19.433; P <.001) and Killip IV (OR 39.234, 95% CI 18.178-84.679; P <.001). We had developed a diagnostic model of in-hospital mortality. The area under the receiver operating characteristic curve (AUC) was 0.9126 (SD 0.0166, 95% CI 0.88015-0.94504). We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 of 7,485 participants(1.7%) in the validation data set. The AUC was 0 .9305(SD 0.0113, 95% CI 0. 90827-0. 95264). CONCLUSIONS We had developed and externally validated a diagnostic model of in-hospital mortality in acute STEMI patients. It was found that the discrimination, calibration and DCA of this model were satisfactory. CLINICALTRIAL ChiCTR.org ChiCTR1900027129; http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

Predicting Timber Harvests on Private Forests in Southwest Wisconsin

1987 ◽  
Vol 4 (3) ◽  
pp. 152-154 ◽  
Author(s):  
John C. Bliss ◽  
Mark J. Grassl
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Model Development ◽  
Timber Harvest ◽  
Private Forests ◽  
Data Set ◽  
River Watershed ◽  
Harvest Data ◽  
Timber Harvests

Abstract A procedure for predicting timber harvest occurrence on nonindustrial private forests correctly predicted harvests on 87% of the properties used for model development, and 71% of the properties in a separately collected data set. The procedure requires limited, easily obtained data and little computation, yet it approximates logistic regression analysis. The study utilized historical timber harvest data on 84 properties in the Kickapoo River watershed of Vernon County, Wisconsin. Tract size and farm status was significantly related to timber harvest occurrence on the properties studied; owner residency was not. The procedure can help field foresters identify potential users of timber harvest assistance. North. J. Appl. For. 4:152-154, Sept. 1987.

ANALISIS HUBUNGAN SARANA, PENGETAHUAN, DAN SIKAP BIDAN PRAKTIK MANDIRI DENGAN KEPATUHAN MENJALANKAN STANDAR PELAYANAN ANTENATAL CARE DI KOTA PADANGSIDIMPUAN

2019 ◽  
Vol 2 (1) ◽  
pp. 27-33
Author(s):  
Megawati Sinambela ◽  
Evi Erianty Hasibuan
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Antenatal Care ◽  
Pregnant Women ◽  
Logistic Regression Analysis ◽  
Care Service ◽  
Multivariate Logistic Regression Analysis ◽  
Bivariate Analysis ◽  
Cross Sectional ◽  
The North

Antenatal care is a service provided to pregnant women to monitor, support maternal health and detect mothers whether normal or problematic pregnant women. According to the WHO, globally more than 70% of maternal deaths are caused by complications of pregnancy and childbirth such as hemorrhage, hypertension, sepsis, and abortion. Based on data obtained from the profile of the North Sumatra provincial health office in 2017, in the city of Padangsidimpuan in 2017 the coverage of ANC visits reached (76.58%) and had not reached the target in accordance with the 2017 Provincial Health Office strategy plan (95%). This type of research was an observational analytic study with a cross sectional design. The population in this study were independent practice midwives who were in the Padangsidimpuan, the sample in this study amounted to 102 respondents. The technique of collecting data used questionnaires and data analysis used univariate, bivariate and multivariate analysis with logistic regression analysis. Based on bivariate analysis showed that there was a relationship between facilities, knowledge and attitudes of independent midwives with compliance with the standards of antenatal care services with a value of p <0.05. The results of the study with multivariate logistic regression analysis showed that the factors associated with the compliance of independent midwives in carrying out antenatal care service standards were attitudes with values (p = 0.026).

scholarly journals Efficacy of flurbiprofen axetil for preventing postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Atsushi Kotera
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Postoperative Pain ◽  
Logistic Regression Analysis ◽  
Pain Control ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Postoperative Pain Control ◽  
Flurbiprofen Axetil ◽  
Postanesthetic Shivering

Abstract Background Postanesthetic shivering is an unpleasant adverse event in surgical patients. A nonsteroidal anti-inflammatory drug has been reported to be useful in preventing postanesthetic shivering in several previous studies. The aim of this study was to evaluate the efficacy of flurbiprofen axetil being a prodrug of a nonsteroidal anti-inflammatory drug for preventing postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries. Method This study is a retrospective observational study. I collected data from patients undergoing gynecologic laparotomy surgeries performed between October 1, 2019, and September 30, 2020, at Kumamoto City Hospital. All the patients were managed with general anesthesia with or without epidural analgesia. The administration of intravenous 50 mg flurbiprofen axetil for postoperative pain control at the end of the surgery was left to the individual anesthesiologist. The patients were divided into two groups: those who had received intravenous flurbiprofen axetil (flurbiprofen group) and those who had not received intravenous flurbiprofen axetil (non-flurbiprofen group), and I compared the frequency of postanesthetic shivering between the two groups. Additionally, the factors presumably associated with postanesthetic shivering were collected from the medical charts. Intergroup differences were assessed with the χ2 test with Yates’ correlation for continuity category variables. The Student’s t test was used to test for differences in continuous variables. Furthermore, a multivariate logistic regression analysis was performed to elucidate the relationship between the administration of flurbiprofen axetil and the incidence of PAS. Results I retrospectively examined the cases of 141 patients aged 49 ± 13 (range 21-84) years old. The overall postanesthetic shivering rate was 21.3% (30 of the 141 patients). The frequency of postanesthetic shivering in the flurbiprofen group (n = 31) was 6.5%, which was significantly lower than that in the non-flurbiprofen group (n = 110), 25.5% (p value = 0.022). A multivariate logistic regression analysis showed that administration of flurbiprofen axetil was independently associated with a reduced incidence of postanesthetic shivering (odds ratio 0.12; 95% confidence interval, 0.02-0.66, p value = 0.015). Conclusions My result suggests that intraoperative 50 mg flurbiprofen axetil administration for postoperative pain control is useful to prevent postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries.

scholarly journals Prior Admissions as a Risk Factor for Readmission in Patients Surgically Treated for Femur Fractures: Implications for a Potential Hip Fracture Bundle

2021 ◽  
Vol 12 ◽  
pp. 215145932199616
Author(s):  
Robert Erlichman ◽  
Nicholas Kolodychuk ◽  
Joseph N. Gabra ◽  
Harshitha Dudipala ◽  
Brook Maxhimer ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hip Fracture ◽  
Logistic Regression Analysis ◽  
Hip Fractures ◽  
Surgical Procedure ◽  
Hospital Admissions ◽  
Fracture Care ◽  
Increased Risk ◽  
Payment Models

Introduction: Hip fractures are a significant economic burden to our healthcare system. As there have been efforts made to create an alternative payment model for hip fracture care, it will be imperative to risk-stratify reimbursement for these medically comorbid patients. We hypothesized that patients readmitted to the hospital within 90 days would be more likely to have a recent previous hospital admission, prior to their injury. Patients with a recent prior admission could therefore be considered higher risk for readmission and increased cost. Methods: A retrospective chart review identified 598 patients who underwent surgical fixation of a hip or femur fracture. Data on readmissions within 90 days of surgical procedure and previous admissions in the year prior to injury resulting in surgical procedure were collected. Logistic regression analysis was used to determine if recent prior admission had increased risk of 90-day readmission. A subgroup analysis of geriatric hip fractures and of readmitted patients were also performed. Results: Having a prior admission within one year was significantly associated (p < 0.0001) for 90-day readmission. Specifically, logistic regression analysis revealed that a prior admission was significantly associated with 90-day readmission with an odds ratio of 7.2 (95% CI: 4.8-10.9). Discussion: This patient population has a high rate of prior hospital admissions, and these prior admissions were predictive of 90-day readmission. Alternative payment models that include penalties for readmissions or fail to apply robust risk stratification may unjustly penalize hospital systems which care for more medically complex patients. Conclusions: Hip fracture patients with a recent prior admission to the hospital are at an increased risk for 90-day readmission. This information should be considered as alternative payment models are developed for hip fracture care.

scholarly journals Study on the relationship of depression, anxiety, lifestyle and eating habits with the severity of reflux esophagitis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rongxin Wang ◽  
Jing Wang ◽  
Shuiqing Hu
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Reflux Esophagitis ◽  
Eating Habits ◽  
Positive Correlation ◽  
Relationship Of ◽  
The Relationship

Abstract Background The etiology of reflux esophagitis (RE) is multi-factorial. This study analyzed the relationship of depression, anxiety, lifestyle and eating habits with RE and its severity and further explored the impact of anxiety and depression on patients’ symptoms and quality of life. Methods From September 2016 to February 2018, a total of 689 subjects at Xuanwu Hospital Capital Medical University participated in this survey. They were divided into the RE group (patients diagnosed with RE on gastroscopy, n = 361) and the control group (healthy individuals without heartburn, regurgitation and other gastrointestinal symptoms, n = 328). The survey included general demographic information, lifestyle habits, eating habits, comorbidities, current medications, the gastroesophageal reflux disease (GERD) questionnaire (GerdQ), the Patient Health Questionnaire-9 depression scale and the General Anxiety Disorder-7 anxiety scale. Results The mean age and sex ratio of the two groups were similar. Multivariate logistic regression analysis identified the following factors as related to the onset of RE (p < 0.05): low education level; drinking strong tea; preferences for sweets, noodles and acidic foods; sleeping on a low pillow; overeating; a short interval between dinner and sleep; anxiety; depression; constipation; history of hypertension; and use of oral calcium channel blockers. Ordinal logistic regression analysis revealed a positive correlation between sleeping on a low pillow and RE severity (p = 0.025). Depression had a positive correlation with the severity of symptoms (rs = 0.375, p < 0.001) and patients’ quality of life (rs = 0.306, p < 0.001), whereas anxiety showed no such association. Conclusions Many lifestyle factors and eating habits were correlated with the onset of RE. Notably, sleeping on a low pillow was positively correlated with RE severity, and depression was positively related to the severity of symptoms and patients’ quality of life.

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