Acute Medical Ward for better care coordination of patients admitted with infection – evidence from a tertiary hospital in Singapore

2017 ◽  
Vol 16 (4) ◽  
pp. 170-176
Author(s):  
Tharmmambal Balakrishnan ◽  
◽  
Pek Siang Edmund Teo ◽  
Wan Tin Lim ◽  
Xiao Hui Xin ◽  
...  
Keyword(s):  
Regression Models ◽  
Linear Models ◽  
Tertiary Hospital ◽  
General Ward ◽  
Medical Ward ◽  
South East Asia ◽  
Multivariate Models ◽  
Readmission Rates ◽  
Logistic Regression Models ◽  
Acute Admissions

Coordination and consolidation of care provided in acute care hospitals need reconfiguration and reorganization to meet the demand of large number of acute admissions. We report on the effectiveness of an Acute Medical Ward AMW (AMW) receiving cases that were suspected to have infection related diagnosis on admission by Emergency Department (ED), addressing this in a large tertiary hospital in South East Asia. Mean Length of Stay (LOS) was compared using Gamma Generalized Linear Models with Log-link while odds of readmissions and mortality were compared using logistic regression models. The LOS (mean: 5.8 days, SD: 9.1 days) of all patients admitted to AMW was similar to discharge diagnosis-matched general ward (GW) patients admitted before AMW implementation, readmission rates were lower (15-day: 5.3%, 30-day: 8.1%). Bivariate and multivariate models revealed that mean LOS after AMW implementation was not significantly different from before AMW implementation (Ratio: 0.99, p=0.473). Our AMW had reduced readmission rates for patients with infection but has not made an overall impact on the LOS and readmission rates for the epartment as a whole.

scholarly journals Associations of Plasma 3-Methylhistidine with Frailty Status in French Cohorts of the FRAILOMIC Initiative

2019 ◽  
Vol 8 (7) ◽  
pp. 1010 ◽  
Author(s):  
Bastian Kochlik ◽  
Wolfgang Stuetz ◽  
Karine Pérès ◽  
Catherine Féart ◽  
Jesper Tegner ◽  
...  
Keyword(s):  
Regression Models ◽  
Linear Models ◽  
Estimated Glomerular Filtration Rate ◽  
Community Dwelling ◽  
Functional Tests ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Frailty Status ◽  
Imaging Parameters ◽  
Frailty Criteria

Frailty and sarcopenia are characterized by a loss of muscle mass and functionality and are diagnosed mainly by functional tests and imaging parameters. However, more muscle specific biomarkers are needed to improve frailty diagnosis. Plasma 3-methylhistidine (3-MH), as well as the 3-MH-to-creatinine (3-MH/Crea) and 3-MH-to-estimated glomerular filtration rate (3-MH/eGFR) ratios might support the diagnosis of frailty. Therefore, we investigated the cross-sectional associations between plasma 3-MH, 3-MH/Crea and 3-MH/eGFR with the frailty status of community-dwelling individuals (>65 years). 360 participants from two French cohorts of the FRAILOMIC initiative were classified into robust, pre-frail and frail according to Fried’s frailty criteria. General linear models as well as bivariate and multiple linear and logistic regression models, which were adjusted for several confounders, were applied to determine associations between biomarkers and frailty status. The present study consisted of 37.8% robust, 43.1% pre-frail and 19.2% frail participants. Frail participants had significantly higher plasma 3-MH, 3-MH/Crea and 3-MH/eGFR ratios than robust individuals, and these biomarkers were positively associated with frailty status. Additionally, the likelihood to be frail was significantly higher for every increase in 3-MH (1.31-fold) and 3-MH/GFR (1.35-fold) quintile after adjusting for confounders. We conclude that 3-MH, 3-MH/Crea and 3-MH/eGFR in plasma might be potential biomarkers to identify frail individuals or those at higher risk to be frail, and we assume that there might be biomarker thresholds to identify these individuals. However, further, especially longitudinal studies are needed.

scholarly journals Weight Smoothing for Generalized Linear Models Using a Laplace Prior

2016 ◽  
Vol 32 (2) ◽  
pp. 507-539 ◽  
Author(s):  
Xi Xia ◽  
Michael R. Elliott
Keyword(s):  
Mean Square Error ◽  
Regression Models ◽  
Linear Models ◽  
Hierarchical Bayesian Model ◽  
Mean Square ◽  
Sampled Data ◽  
Logistic Regression Models ◽  
Inclusion Probabilities ◽  
Bias Variance ◽  
Laplace Priors

Abstract When analyzing data sampled with unequal inclusion probabilities, correlations between the probability of selection and the sampled data can induce bias if the inclusion probabilities are ignored in the analysis. Weights equal to the inverse of the probability of inclusion are commonly used to correct possible bias. When weights are uncorrelated with the descriptive or model estimators of interest, highly disproportional sample designs resulting in large weights can introduce unnecessary variability, leading to an overall larger mean square error compared to unweighted methods. We describe an approach we term ‘weight smoothing’ that models the interactions between the weights and the estimators as random effects, reducing the root mean square error (RMSE) by shrinking interactions toward zero when such shrinkage is allowed by the data. This article adapts a flexible Laplace prior distribution for the hierarchical Bayesian model to gain a more robust bias-variance tradeoff than previous approaches using normal priors. Simulation and application suggest that under a linear model setting, weight-smoothing models with Laplace priors yield robust results when weighting is necessary, and provide considerable reduction in RMSE otherwise. In logistic regression models, estimates using weight-smoothing models with Laplace priors are robust, but with less gain in efficiency than in linear regression settings.

A Prospective Validation of the IOTA Logistic Regression Models (LR1 and LR2) in Comparison to Subjective Pattern Recognition for the Diagnosis of Ovarian Cancer

2013 ◽  
Vol 23 (9) ◽  
pp. 1583-1589 ◽  
Author(s):  
Natalie Nunes ◽  
Gareth Ambler ◽  
Wee-Liak Hoo ◽  
Joel Naftalin ◽  
Xulin Foo ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Models ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Tertiary Hospital ◽  
Ovarian Tumour ◽  
Logistic Regression Models ◽  
Operative Findings

ObjectivesThis study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer.Methods and MaterialsThis was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared.ResultsThe AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92–0.97] and 0.93 (95% CI, 0.90–0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84–24.6) and a LR−ve of 0.049 (95% CI, 0.022–0.107). The corresponding LR+ve and LR−ve for LR1 were 3.33 (95% CI, 2.85–3.55) and 0.03 (95% CI, 0.01–0.10), and for LR2 were 3.58 (95% CI, 2.77–4.63) and 0.052 (95% CI, 0.022–0.123). The accuracy of PR was 0.942 (95% CI, 0.908–0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781–0.870) for LR1 and 0.836 (95% CI, 0.788–0.872) for LR2 (P < 0.001).ConclusionsThe AUC of the IOTA LR1 and LR2 were similar in nonexpert’s hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.

scholarly journals Association betweenHelicobacter pyloriinfection and tumor markers: an observational retrospective study

BMJ Open ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. e022374
Author(s):  
Mei-Yan Xu ◽  
Bing Cao ◽  
Yan Chen ◽  
Natalie Musial ◽  
Shuai Wang ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Regression Models ◽  
Linear Models ◽  
Tumour Markers ◽  
Cancer Antigen ◽  
Generalised Linear Models ◽  
Logistic Regression Models ◽  
H Pylori ◽  
The Relationship ◽  
Beijing China

ObjectiveHelicobacter pyloriinfection is a major cause of several cancers such as gastric, pancreatic and lung. The relationship betweenH. pyloriand tumour markers continues to remain unclear. The primary goal of this study is to clarify the associations betweenH. pyloriinfection and six tumour markers (ie, carcinoembryonic antigen (CEA), cancer antigen (CA) 153, CA199, CA724, CA125 and alpha-fetoprotein (AFP)). The secondary goal is to provide understanding for further research aboutH. pyloriinfection and gastrointestinal cancer.DesignObservational retrospective study.SettingThe study was performed in Beijing, China, where enrolled subjects had all passed health examinations during the period of 2012–2016. Subjects were categorised intoH. pylori(+) andH. pylori(–) group according to their infection status and the measured six biomarkers. We used logistic regression models and generalised linear models to explore the associations betweenH. pyloriinfection and six tumour markers (ie, CEA, CA153, CA199, CA724, CA125 and AFP).ParticipantsA total of 14 689 subjects were included and 6493 (44.2%) subjects were infected byH. pylori. The subjects had a mean age (1SD) of 45 (18) years. There were 4530 (31.0%) female subjects.ResultsAfter adjusting for the confounding factors, infections withH. pyloriwere found to be significantly associated with abnormal ratios in CEA, AFP and CA724 ofH. pylori(+) toH. pylori(–) groups. Significant positive correlation was found betweenH. pyloriinfection and CEA values (adjusted β=0.056; 95% CI 0.005 to 0.107; p=0.033).ConclusionsIn this observational retrospective study, we observed theH. pyloriinfections in a Chinese population and found higher CEA level inH. pylori-infected subjects and abnormal ratios in CEA, AFP and CA724 in infected subjects to uninfected subjects. These findings may provide a basis for future exploration withH. pyloriand tumour markers.

scholarly journals Influenza Illness and Partial Vaccination in the First Two Years of Life

Vaccines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 676
Author(s):  
Abram L. Wagner ◽  
Lionel Gresh ◽  
Nery Sanchez ◽  
Guillermina Kuan ◽  
John Kubale ◽  
...  
Keyword(s):  
Regression Models ◽  
Linear Models ◽  
Signs And Symptoms ◽  
Vaccination Schedule ◽  
Middle Income ◽  
Future Studies ◽  
Logistic Regression Models ◽  
Middle Income Countries ◽  
Duration Of Illness ◽  
Influenza Illness

More information about influenza in low- and middle-income countries could guide the establishment of pediatric influenza vaccine programs. This study (1) characterizes the burden of influenza in infants, and (2) compares signs and symptoms by prior influenza vaccination or influenza illness. Newborns from Managua, Nicaragua, were followed for two years. Data came from primary medical appointments, PCR testing, and parents’ daily symptom diaries. Logistic regression models estimated associations between preceding vaccination or illness and influenza incidence. Linear models compared duration of illness by prior vaccination or influenza illness. Among 833 infants, 31% had PCR-positive influenza, and 28% were vaccinated against influenza. Four (<0.5%) were fully vaccinated. Overall, influenza incidence was 21.0 (95% confidence interval (CI): 18.8, 23.2) per 100 person-years. Incidence was lower among those with prior influenza compared with those without preceding illness or vaccination (OR: 0.64, 95% CI: 0.44, 0.94). Partially vaccinated children had 1 day less fever than those without prior illness or vaccination (p = 0.049). A large proportion of children <2 years in Nicaragua contract influenza. Illness was attenuated for those partially vaccinated. Since few children were fully vaccinated, future studies will need to consider the effectiveness of a two-dose vaccination schedule.

scholarly journals Significant Risk Factors Associated With Stunting for Children Under the Age of 5-years in Malawi: the Application of Proportional Odds Model Using DHS Datasets.

2021 ◽  
Author(s):  
Mzwakhe Magagula ◽  
Shaun Ramroop ◽  
Faustin Habyarimana
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Models ◽  
Linear Models ◽  
Medical Condition ◽  
Significant Risk ◽  
Proportional Odds ◽  
Data Set ◽  
Logistic Regression Models ◽  
Factors Associated

Abstract BackgroundChild malnutrition is perhaps the one of the main medical condition influencing general human wellbeing, mainly in non-industrial nations. The improvement of legitimate evaluations of malnutrition is one of the difficulties encountered by policymakers in numerous countries worldwide. In this manner, the current study was embraced with the essential goal of evaluating and determining all potential determinants of childhood malnutrition in Malawi, using the Demographic and Health Survey (DHS) data 2015/16. The study seeks to reveal some of the significant factors that are perpetuating the incidence of malnutrition in children of Malawi. It also designed to offer deeper insights on how the probability of being diagnosed with this medical condition (malnutrition) evolves across the different levels of the found significant factors.Methods The proportional odds (PO) model was the best model to utilize, motivated by the design of the current study's data set. The PO model is an alternative to conceptualize how the ordinal designed data can be sequentially into dichotomous groups without losing the ordinal nature of response variables. The model is an extension of logistic regression models with two outcomes, it is one of the best models to deal with ordinal response variable comprising of more than two categories. The PO model, as well as the logistic regression models are common classes of generalised linear models (GLMs) mostly used to model association between dependent variable and independent variables. ResultsThe observations derived from fitting the PO model on the Malawi DHS data to investigate risk factors associated with malnutrition (stunting) suggested that: the age of the child; birth type (singleton/multiple births), parents' level of education, household's type of resident; mother's age at the time of birth, mother's BMI, incident of diarrhoea in the last two weeks before the survey, are the most significant independent risk factors of malnutrition (stunting). ConclusionsAll the aforementioned risk factors are controllable, and they can be improved through intervention strategies. The policies that undergird the country are required to counteract this condition, as the majority of the risk factors need the coherent actions of several governing authorities.

scholarly journals A bean-free diet increases the risk of all-cause mortality among Taiwanese women: the role of the metabolic syndrome

2011 ◽  
Vol 15 (4) ◽  
pp. 663-672 ◽  
Author(s):  
Wan-Chi Chang ◽  
Mark L Wahlqvist ◽  
Hsing-Yi Chang ◽  
Chih-Cheng Hsu ◽  
Meei-Shyuan Lee ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Regression Models ◽  
Linear Models ◽  
Cox Regression ◽  
Disease Risk ◽  
Logistic Regression Models ◽  
The Metabolic Syndrome ◽  
Taiwanese Women ◽  
All Cause Mortality

AbstractObjectiveTo evaluate the associations with chronic disease risk and mortality of the consequences of bean-free diets in Taiwanese adults with regard to gender.DesignA sub-sample of the National Health Interview Survey (NHIS) in 2001 agreed to physical examination in the subsequent year. This group then took part in the Taiwanese Survey of Hyperglycaemia, Hyperlipidaemia and Hypertension (TwSHHH) in 2002.SettingIndividual records were linked to the eventual death files from 2002 to 2008.SubjectsUp to the end of 2008, a total of 2820 men and 2950 women were tracked by death registry over the 6·8 years of follow-up.ResultsAmong 38 077 person-years, an average follow-up 6·5 years, 225 all-cause deaths were identified. Generalized linear models showed beans to be favourable for metabolic syndrome (other than for fasting glucose) in men; in women, beans were favourable for waist circumference and HbA1c. Cumulative logistic regression models for the effect of a bean-free diet on metabolic syndrome scores according to the Taiwanese-modified National Cholesterol Education Program–Adult Treatment Panel III (NCEP-tw) gave adjusted odds ratios of 1·83 in men and 1·45 in women. Cox regression models for the bean-free diet showed an increased hazard ratio for all-cause mortality among women (1·98, 95 % CI 1·03, 3·81) but not men (1·28, 95 % CI 0·76, 2·16).ConclusionsA bean-free diet may play a role in developing the metabolic syndrome in both genders, and is a significant predictor of all-cause mortality in Taiwanese women but not men.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

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