Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair

Background: Ankle fractures in patients with diabetes mellitus have long been recognized as a challenge to practicing clinicians. Ankle fracture patients with diabetes may experience prolonged healing, higher risk of hardware failure, an increased risk of wound dehiscence and infection, and higher pain scores pre- and postoperatively, compared to patients without diabetes. However, the duration of opioid use among this patient cohort has not been previously evaluated. The purpose of this study is to retrospectively compare the time span of opioid utilization between ankle fracture patients with and without diabetes mellitus. Methods: We conducted a retrospective cohort study using our institution’s TriNetX database. A total of 640 ankle fracture patients were included in the analysis, of whom 73 had diabetes. All dates of opioid use for each patient were extracted from the data set, including the first and last date of opioid prescription. Descriptive analysis and logistic regression models were employed to explore the differences in opioid use between patients with and without diabetes after ankle fracture repair. A 2-tailed P value of .05 was set as the threshold for statistical significance. Results: Logistic regression models revealed that patients with diabetes are less likely to stop using opioids within 90 days, or within 180 days, after repair compared to patients without diabetes. Female sex, neuropathy, and prefracture opioid use are also associated with prolonged opioid use after ankle fracture repair. Conclusion: In our study cohort, ankle fracture patients with diabetes were more likely to require prolonged opioid use after fracture repair. Level of Evidence: Level III, prognostic.

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Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

10.21203/rs.3.rs-109665/v2 ◽

2021 ◽

Author(s):

Wenqian Lu ◽

Mingjuan Luo ◽

Xiangnan Fang ◽

Rong Zhang ◽

Mengyang Tang ◽

...

Keyword(s):

Diabetes Mellitus ◽

Logistic Regression ◽

Gestational Diabetes ◽

Pregnant Women ◽

Regression Models ◽

Second Trimester ◽

Third Trimester ◽

Logistic Regression Models ◽

The Third ◽

Clinical Indices

Abstract Background: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.Methods: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

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Predicting Fusarium Head Blight Epidemics with Boosted Regression Trees

Phytopathology ◽

10.1094/phyto-10-13-0273-r ◽

2014 ◽

Vol 104 (7) ◽

pp. 702-714 ◽

Cited By ~ 22

Author(s):

D. A. Shah ◽

E. D. De Wolf ◽

P. A. Paul ◽

L. V. Madden

Keyword(s):

Logistic Regression ◽

Relative Humidity ◽

Fusarium Head Blight ◽

Regression Models ◽

Boosted Regression Trees ◽

Misclassification Rate ◽

Head Blight ◽

Data Set ◽

Logistic Regression Models ◽

Daily Relative Humidity

Predicting major Fusarium head blight (FHB) epidemics allows for the judicious use of fungicides in suppressing disease development. Our objectives were to investigate the utility of boosted regression trees (BRTs) for predictive modeling of FHB epidemics in the United States, and to compare the predictive performances of the BRT models with those of logistic regression models we had developed previously. The data included 527 FHB observations from 15 states over 26 years. BRTs were fit to a training data set of 369 FHB observations, in which FHB epidemics were classified as either major (severity ≥ 10%) or non-major (severity < 10%), linked to a predictor matrix consisting of 350 weather-based variables and categorical variables for wheat type (spring or winter), presence or absence of corn residue, and cultivar resistance. Predictive performance was estimated on a test (holdout) data set consisting of the remaining 158 observations. BRTs had a misclassification rate of 0.23 on the test data, which was 31% lower than the average misclassification rate over 15 logistic regression models we had presented earlier. The strongest predictors were generally one of mean daily relative humidity, mean daily temperature, and the number of hours in which the temperature was between 9 and 30°C and relative humidity ≥ 90% simultaneously. Moreover, the predicted risk of major epidemics increased substantially when mean daily relative humidity rose above 70%, which is a lower threshold than previously modeled for most plant pathosystems. BRTs led to novel insights into the weather–epidemic relationship.

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Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

Nutrition & Metabolism ◽

10.1186/s12986-021-00606-8 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Wenqian Lu ◽

Mingjuan Luo ◽

Xiangnan Fang ◽

Rong Zhang ◽

Shanshan Li ◽

...

Keyword(s):

Diabetes Mellitus ◽

Logistic Regression ◽

Gestational Diabetes ◽

Pregnant Women ◽

Regression Models ◽

Second Trimester ◽

Third Trimester ◽

Logistic Regression Models ◽

The Third ◽

Clinical Indices

Abstract Background Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. Methods Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

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Diabetes mellitus as a risk factor for poor early outcomes in patients hospitalized with COVID-19

10.2337/figshare.12766001 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jacqueline Seiglie ◽

Jesse Platt ◽

Sara Jane Cromer ◽

Bridget Bunda ◽

Andrea S. Foulkes ◽

...

Keyword(s):

Diabetes Mellitus ◽

Mechanical Ventilation ◽

Logistic Regression ◽

Risk Factor ◽

Regression Models ◽

Massachusetts General Hospital ◽

Hospitalized Patients ◽

Patient Centered ◽

Logistic Regression Models ◽

Icu Admission

OBJECTIVE Diabetes mellitus and obesity are highly prevalent among hospitalized patients with COVID-19, but little is known about their contributions to early COVID-19 outcomes. We tested the hypothesis that diabetes is a risk factor for poor early outcomes, after adjustment for obesity, among a cohort of patients hospitalized with COVID-19. RESEARCH DESIGN AND METHODS We used data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry of patients hospitalized with COVID-19 between March 11, 2020 and April 30, 2020. Primary outcomes were admission to the intensive care unit (ICU), need for mechanical ventilation, and death within 14 days of presentation to care. Logistic regression models were adjusted for demographic characteristics, obesity, and relevant comorbidities. RESULTS Among 450 patients, 178 (39.6%) had diabetes, mostly type 2 diabetes. A higher proportion of patients with diabetes were admitted to the ICU (42.1% vs. 29.8%, p=0.007), required mechanical ventilation (37.1% vs. 23.2%, p=0.001), and died (15.9% vs. 7.9%, p=0.009), compared with patients without diabetes. In multivariable logistic regression models, diabetes was associated with greater odds of ICU admission (OR 1.59 [95% CI 1.01-2.52]), mechanical ventilation (1.97 [1.21-3.20]), and death (2.02 [1.01-4.03]) at 14-days. Obesity was associated with higher odds of ICU admission (2.16 [1.20-3.88]) and mechanical ventilation (2.13 [1.14-4.00]) but not with death. CONCLUSIONS Among hospitalized patients with COVID-19, diabetes was associated with poor early outcomes, after adjusting for obesity. These findings can help inform patient-centered care decision making for people with diabetes at risk of COVID-19.

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Can the implementation of the Opioid Start Talking form impact the amount of opioid use in patients after ankle fracture repair?

Journal of Opioid Management ◽

10.5055/jom.2021.0673 ◽

2021 ◽

Vol 17 (5) ◽

pp. 397-404

Author(s):

Benjamin Best, DO ◽

Alan Afsari, MD ◽

Rajan Sharma, DO ◽

James T. Layson, DO ◽

Marek Denisiuk, DO

Keyword(s):

Ankle Fracture ◽

Final Analysis ◽

Fracture Repair ◽

Ankle Fractures ◽

P Value ◽

Prescription Database ◽

Opioid Epidemic ◽

Opioid Use ◽

Opioid Prescription ◽

Significant Difference

Objective: As part of 2018 legislation aimed at fighting the opioid epidemic, the Michigan Department of Health and Human Services (MDHHS) published the “Opioid Start Talking” (OST) Form on June 1, 2018. We examined if the implementation of the OST form led to an identifiable decrease in patient opioid use. Specifically, we examined the opioid prescription quantities in patients who sustained ankle fractures that required open reduction internal fixation (ORIF).Design: Retrospective. Hospital medical records and Michigan Automated Prescription Database (MAPS) were analyzed for similar ankle fracture patients operated on by two surgeons prior to and after the initiation of the OST form. Records allowed us to track opioid filling through MAPS for 120 days after surgery in two groups: preimplementation (PRE) and post-implementation (POST) OST groups. The gathered data were analyzed by the investigators along with a staff statistician.Setting: Single-institution orthopedic practice.Patients, participants: Seventy eight patientsMain outcome measure: Average morphine milligram equivalent (MME) per patient encounter.Results: Seventy eight patients were included in the final analysis after applying the exclusion criteria. There were 38 patients in the pre-OST form period and 40 in the post-OST form period groups. The pre-OST and post-OST groups were well matched between the two surgeons. There was no evidence of a statistically significant difference found in the median MME between patients from the pre-period group to the post-period group (median 59 vs 50, P = 0.61). In regard to the injury pattern, the bimalleolar MME median was 50 (38 = 25th percentile, 67 = 75th percentile; min-max 0-175) and the trimalleolar median MME was 63 (39 =25% percentile, 81 = 75th percentile; min-max 0-249) with a P value of 0.20.Conclusions: Overall, the administration of the OST form to patients with ankle fractures did not result in a decrease in MMEs prescribed within 120 days of surgery. Although it is a start in the battle against the opioid epidemic, further evaluation of the effectiveness of the OST form is necessary.

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Predictive Factors for Pneumonia Onset After Cardiac Surgery in Rio de Janeiro, Brazil

Infection Control and Hospital Epidemiology ◽

10.1086/513119 ◽

2007 ◽

Vol 28 (4) ◽

pp. 382-388 ◽

Cited By ~ 5

Author(s):

Marisa Santos ◽

José Ueleres Braga ◽

Renato Vieira Gomes ◽

Guilherme L. Werneck

Keyword(s):

Logistic Regression ◽

Cardiac Surgery ◽

Predictive Models ◽

Rio De Janeiro ◽

Regression Models ◽

Tertiary Care ◽

Classification And Regression Tree ◽

Data Set ◽

Logistic Regression Models ◽

Cart Analysis

Objective.To develop a predictive system for the occurrence of nosocomial pneumonia in patients who had cardiac surgery performed.Design.Retrospective cohort study.Setting.Two cardiologic tertiary care hospitals in Rio de Janeiro, Brazil.Patients.Between June 2000 and August 2002, there were 1,158 consecutive patients who had complex heart surgery performed. Patients older than 18 years who survived the first 48 postoperative hours were included in the study. The occurrence of pneumonia was diagnosed through active surveillance by an infectious diseases specialist according to the following criteria: the presence of new infiltrate on a radiograph in association with purulent sputum and either fever or leukocytosis until day 10 after cardiac surgery. Predictive models were built on the basis of logistic regression analysis and classification and regression tree (CART) analysis. The original data set was divided randomly into 2 parts, one used to construct the models (ie, “test sample”) and the other used for validation (ie, “validation sample”).Results.The area under the receiver–operating characteristic (ROC) curve was 69% for the logistic regression model and 76% for the CART model. Considering a probability greater than 7% to be predictive of pneumonia for both models, sensitivity was higher for the logistic regression models, compared with the CART models (64% vs 56%). However, the CART models had a higher specificity (92% vs 70%) and global accuracy (90% vs 70%) than the logistic regression models. Both models showed good performance, based on the 2-graph ROC, considering that 84.6% and 84.3% of the predictions obtained by regression and CART analyses were regarded as valid.Conclusion.Although our findings are preliminary, the predictive models we created showed fairly good specificity and fair sensitivity.

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Diabetes mellitus as a risk factor for poor early outcomes in patients hospitalized with COVID-19

10.2337/figshare.12766001.v1 ◽

2020 ◽

Author(s):

Jacqueline Seiglie ◽

Jesse Platt ◽

Sara Jane Cromer ◽

Bridget Bunda ◽

Andrea S. Foulkes ◽

...

Keyword(s):

Diabetes Mellitus ◽

Mechanical Ventilation ◽

Logistic Regression ◽

Risk Factor ◽

Regression Models ◽

Massachusetts General Hospital ◽

Hospitalized Patients ◽

Patient Centered ◽

Logistic Regression Models ◽

Icu Admission

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Admission ECG changes predict short term-mortality after acute myocardial infarction less reliable in patients with diabetes

Scientific Reports ◽

10.1038/s41598-021-85674-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Timo Schmitz ◽

Christian Thilo ◽

Jakob Linseisen ◽

Margit Heier ◽

Annette Peters ◽

...

Keyword(s):

Myocardial Infarction ◽

Logistic Regression ◽

Regression Models ◽

Case Fatality ◽

Ecg Changes ◽

Day Case ◽

Short Term ◽

Logistic Regression Models ◽

Patients With Diabetes ◽

Short Term Mortality

AbstractPrior studies examined association between short-term mortality and certain changes in the admission ECG in acute myocardial infarction (AMI). Nevertheless, little is known about possible differences between patients with diabetes and without diabetes in this regard. So the aim of the study was to investigate the association between 28-day case fatality according to certain ECG changes comparing AMI cases with and without diabetes from the general population. From 2000 until 2017 a total of 9756 AMI cases was prospectively recorded in the study Area of Augsburg, Germany. Each case was assigned to one of the following groups according to admission ECG: ‘ST-elevation’, ‘ST-depression’, ‘only T-negativity’, ‘predominantly bundle branch block’, ‘unspecific changes’ and ‘normal ECG’ (the last two were put together for regression analyses). Multivariable adjusted logistic regression models were calculated to compare 28-day case-fatality between the ECG groups for the total sample and separately for diabetes and non-diabetes cases. For the non-diabetes group, the parsimonious logistic regression model revealed significantly better 28-day-outcome for the ‘normal ECG / unspecific changes’ group (OR: 0.47 [0.29–0.76]) compared to the reference group (STEMI). Contrary, in AMI cases with diabetes the category ‘normal ECG / unspecific changes’ was not significantly associated with lower short-term mortality (OR: 0.87 [0.49–1.54]). Neither of the other ECG groups was significantly associated with 28-day-mortality in the parsimonious logistic regression models. Consequently, the absence of AMI-typical changes in the admission ECG predicts favorable short-term mortality only in non-diabetic cases, but not so in patients with diabetes.

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Some Evidence On The Secondary Market Trading Of Syndicated Loans

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v8i5.717 ◽

2010 ◽

Vol 8 (5) ◽

Author(s):

Peter J. Nigro ◽

Jonathan D. Jones ◽

Murat Aydogdu

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Stock Exchange ◽

Secondary Market ◽

Syndicated Loans ◽

Leveraged Buyouts ◽

Font Size ◽

Data Set ◽

Logistic Regression Models ◽

Normal Text

An important recent development in U. S. capital markets is the tremendous growth in the secondary market trading of syndicated loans. This paper uses a unique trading data set for syndicated loans over the period 1997 to 2003 to empirically investigate two major issues. First, we present a statistical overview of the recent growth in the secondary market trading of syndicated loans. Second, we examine the determinants of which syndicated loans are most likely to be traded in the secondary market using binomial logistic regression models. We find that syndicated loans that are larger, have longer maturities, are underwritten by larger syndicates, and are used for debt repayment, takeovers, and leveraged buyouts are more likely to be traded. Lender reputation plays an important role as well, with loans originated by very active lenders more likely to be traded.  We also find that syndicated loans made to borrowers with only senior debt ratings are more likely to be traded in the secondary market than loans made to borrowers with both a debt rating and equity that trades in a stock exchange. This result most likely reflects the growing demand of institutional investors for the higher yields of levered and highly levered syndicated loans made to riskier opaque borrowers with less available market information.

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Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization

Wellcome Open Research ◽

10.12688/wellcomeopenres.17148.1 ◽

2021 ◽

Vol 6 ◽

pp. 248

Author(s):

Paul Mwaniki ◽

Timothy Kamanu ◽

Samuel Akech ◽

Dustin Dunsmuir ◽

J. Mark Ansermino ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Deep Learning ◽

Pulse Oximeter ◽

Regression Models ◽

Data Sets ◽

Learning Models ◽

Data Set ◽

Logistic Regression Models ◽

End To End

Background: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained machine learning models to predict paediatric hospitalization given raw photoplethysmography (PPG) signals obtained from a pulse oximeter. We trained self-supervised learning (SSL) for automatic feature extraction from PPG signals and assessed the utility of SSL in initializing end-to-end deep learning models trained on a small labelled data set with the aim of predicting paediatric hospitalization.Methods: We compared logistic regression models fitted using features extracted using SSL with end-to-end deep learning models initialized either randomly or using weights from the SSL model. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results: The SSL model trained on both labelled and unlabelled PPG signals produced features that were more predictive of hospitalization compared to the SSL model trained on labelled PPG only (AUC of logistic regression model: 0.78 vs 0.74). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions: This study shows that SSL can improve the classification of PPG signals by either extracting features required by logistic regression models or initializing end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets.

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