Prediction of Hospital Mortality Rates by Admission Laboratory Tests

Abstract Background: The aim of this study was to explore whether electronically retrieved laboratory data can predict mortality in internal medicine departments in a regional hospital. Methods: All 10 308 patients hospitalized in internal medicine departments over a 1-year period were included in the cohort. Nearly all patients had a complete blood count and basic clinical chemistries on admission. We used logistic regression analysis to predict the 573 deaths (5.6%), including all variables that added significantly to the model. Results: Eight laboratory variables and age significantly and independently contributed to a logistic regression model (area under the ROC curve, 88.7%). The odds ratio for the final model per quartile of risk was 6.44 (95% confidence interval, 5.42–7.64), whereas for age alone, the odds ratio per quartile was 2.01 (95% confidence interval, 1.84–2.19). Conclusions: A logistic regression model including only age and electronically retrieved laboratory data highly predicted mortality in internal medicine departments in a regional hospital, suggesting that age and routine admission laboratory tests might be used to ensure a fair comparison when using mortality monitoring for hospital quality control.

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Association of dyslipidaemia with osteoporosis in postmenopausal women

Journal of International Medical Research ◽

10.1177/0300060521999555 ◽

2021 ◽

Vol 49 (3) ◽

pp. 030006052199955

Author(s):

Lingnan Zhang ◽

Qilong Liu ◽

Xianshang Zeng ◽

Wenshan Gao ◽

Yanan Niu ◽

...

Keyword(s):

Logistic Regression ◽

Confidence Interval ◽

Regression Model ◽

Postmenopausal Women ◽

Odds Ratio ◽

Logistic Regression Model ◽

Density Lipoprotein ◽

Binary Logistic Regression ◽

Control Group ◽

Binary Logistic Regression Model

Objective To assess the association of dyslipidaemia with osteoporosis in postmenopausal women. Methods Data from 160 postmenopausal women with newly diagnosed osteoporosis (osteoporosis group) and 156 healthy controls (control group) were retrospectively reviewed from 2016 to 2020. The primary outcomes were laboratory values assessed by a multivariate binary logistic regression model. Results Factors that greatly increased the risk of being in the osteoporosis group included high low-density lipoprotein (LDL) and low high-density lipoprotein (HDL) levels. The osteoporosis group had lower HDL and higher LDL levels than the control group. A multivariate binary logistic regression model showed that lower HDL and higher LDL levels were the only variables that were significantly associated with osteoporosis (odds ratio 1.86, 95% confidence interval: 3.66–4.25 and odds ratio 1.47, 95% confidence interval: 1.25–2.74, respectively). Conclusion Low HDL and high LDL levels may be associated with the occurrence of osteoporosis in postmenopausal women.

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The F423Y Mutation in thepfmdr2Gene and Mutations N51I, C59R, and S108N in thepfdhfrGene Are Independently Associated with Pyrimethamine Resistance in Plasmodium falciparum Isolates

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.05618-11 ◽

2012 ◽

Vol 56 (5) ◽

pp. 2750-2752 ◽

Cited By ~ 11

Author(s):

Sébastien Briolant ◽

Hervé Bogreau ◽

Marine Gil ◽

Housem Bouchiba ◽

Eric Baret ◽

...

Keyword(s):

Plasmodium Falciparum ◽

Logistic Regression ◽

Confidence Interval ◽

Regression Model ◽

Odds Ratio ◽

Logistic Regression Model ◽

Content Type

ABSTRACTScreening forin vitrosusceptibility to pyrimethamine and sequencing of thepfmdr2andpfdhfrgenes were performed in 140Plasmodium falciparumisolates. The risk ofin vitroresistance to pyrimethamine was analyzed with a logistic regression model. The mutation F423Y inpfmdr2(odds ratio [OR] = 2.12 [confidence interval {CI}, 1.02 to 4.59];P= 0.0489) and the mutation N51I, C59R, or S108N inpfdhfr(OR = 42.34 [CI, 5.52 to 324.61];P= 0.0003) were independently associated within vitroresistance to pyrimethamine.

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Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17134852 ◽

2020 ◽

Vol 17 (13) ◽

pp. 4852

Author(s):

Pouya Gholizadeh ◽

Behzad Esmaeili

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Odds Ratio ◽

Logistic Regression Model ◽

The Body ◽

Occupational Accidents ◽

Multivariate Logistic Regression Model ◽

Ratio Analysis ◽

Accident Scenarios ◽

Significant Factors

The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a “vehicle” than when it is a “tool, instrument, or equipment”. After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in “fencing, installing lights, signs, etc.” tasks in “alteration and rehabilitation” projects where the source of injury was “parts and materials”. The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.

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MO532WHICH UNHEALTHY LIFE-BEHAVIOR IS THE MOST IMPACT FOR THE PRESENCE OF PROTEINURIA?

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab087.0052 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Jun Muratsu ◽

Masahiko Hara ◽

Atsuyuki Morishima ◽

Katsuhiko Sakaguchi ◽

Takashi Fujimoto

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Odds Ratio ◽

Logistic Regression Model ◽

Multivariable Logistic Regression Model ◽

Current Smoking ◽

Exercise Habits ◽

Multivariable Logistic Regression ◽

Skipping Breakfast ◽

Life Behavior

Abstract Background and Aims Unhealthy life-behaviors such as dietary habits, lack of exercise, drinking large amount of alcohol and smoking cause obesity, hypertension, diabetes, dyslipidemia, cardiovascular disease (CVD). These are also closely associated with chronic kidney disease (CKD). CKD is characterized by proteinuria and low glomerular filtration rate (GFR). Independent of GFR, proteinuria is an important predictor of ESKD. Few studies have assessed which is the most clinical impact among the unhealthy life-behaviors: skipping breakfast, snacking, late-night dinner, smoking, heavy alcohol intake and lack of exercise habits for proteinuria in normal renal function patients. Method This cross-sectional study included 29,780 normal renal function patients: eGFR≥60 mL/min/1.73 m2 and no history of kidney diseases who underwent health checkup at the Physical Checkup Center of Sumitomo Hospital. The endpoint of this investigation is defined as dipstick proteinuria of≥ 1+. To assess the association of life-behaviors and the presence of proteinuria, their odds ratios were calculated in adjusted univariable and multivariable logistic regression model. Multivariable logistic regression model was performed by not selected items, the same with univariable model. We would like to investigate the most impact unhealthy life-behavior for the proteinuria. Results Among 29,780 total study subjects (male: 60.3%; mean age: 49±11 years), 1,118 (3.75%) subjects were shown as urinary protein above 1+. The presence of unhealthy dietary life-behaviors: skipping breakfast, snacking and late-night dinner was 5,293 (17.3%), 3,899 (13.1%) and 11,231 (37.7%), respectively. About sleeping duration, the population of <6 hours, 6-8 hours and >8 hours were 12,027 (40.4%), 17,236 (57.9%) and 517 (1.7%). The population of exercise habits: over 3 days/weeks, 1-2 days/weeks and none were 5,138 (17.3%), 9,375 (31.5%) and 15,237 (51.3%), suggesting half of them did not have exercise habits. About smoking habits, the population of current smoking, past smoking and never smoking were shown 6,445 (21.6%), 8,459 (28.4%) and 14,876 (50.0%). In addition, about alcohol amount per day, the population of over 60g, 40-60g, 20-40g and 0-20g were 1,840 (6.18%), 4,504 (15.1%), 6,727 (22.6%) and 16,709 (56.1%). To investigate the impact of life-behavior for proteinuria, we obtained odds ratio of adjusted multivariable logistic regression model. In multivariable regression, among the life-behavior: skipping breakfast, current smoking, alcohol amount (ethanol over 60 g/day), none of exercise habits and snacking were strongly associated with the prevalence of proteinuria (skipping breakfast, adjusted odds ratio 1.45 [1.26, 1.68]; current smoking, 1.35 [1.14, 1.59]; alcohol amount (ethanol over 60 g/day), 1.35 [1.08, 1.69]; none of exercise habits, 1.29 [1.07, 1.57]; snacking, 1.23 [1.04, 1.46]). In addition, among the history of medical history, diabetes mellitus, hypertension and dyslipidemia were significantly associated with the prevalence of proteinuria (diabetes mellitus, adjusted odds ratio 2.39 [1.93, 2.96]; hypertension, 1.83 [1.53, 2.17]; 1.22 [1.03, 1.45]). Conclusion Among the unhealthy life-behaviors, skipping breakfast is the most impact factor for the presence of proteinuria.

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Antibodies to SARS-CoV-2 protect against re-infection during outbreaks in care homes, September and October 2020

Eurosurveillance ◽

10.2807/1560-7917.es.2021.26.5.2100092 ◽

2021 ◽

Vol 26 (5) ◽

Author(s):

Anna Jeffery-Smith ◽

Nalini Iyanger ◽

Sarah V Williams ◽

J Yimmy Chow ◽

Felicity Aiano ◽

...

Keyword(s):

Logistic Regression ◽

Confidence Interval ◽

Regression Model ◽

Logistic Regression Model ◽

Care Homes ◽

Rt Pcr ◽

Prior Infection ◽

Risk Ratios

Two London care homes experienced a second COVID-19 outbreak, with 29/209 (13.9%) SARS-CoV-2 RT-PCR-positive cases (16/103 residents, 13/106 staff). In those with prior SARS-CoV-2 exposure, 1/88 (1.1%) individuals (antibody positive: 87; RT-PCR-positive: 1) became PCR-positive compared with 22/73 (30.1%) with confirmed seronegative status. After four months protection offered by prior infection against re-infection was 96.2% (95% confidence interval (CI): 72.7–99.5%) using risk ratios from comparison of proportions and 96.1% (95% CI: 78.8–99.3%) using a penalised logistic regression model.

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Palifermin In High Dose Chemotherapy and Autologous Stem Cell Transplantation: Reduction of Infections and of Resources Utilization with No Increase in Overall Cost

Blood ◽

10.1182/blood.v118.21.4568.4568 ◽

2011 ◽

Vol 118 (21) ◽

pp. 4568-4568

Author(s):

Giuseppe Milone ◽

Massimo Poidomani ◽

Salvatore Leotta ◽

Valeria Pinto ◽

Andrea Spadaro ◽

...

Keyword(s):

Logistic Regression ◽

Stem Cell ◽

Regression Model ◽

Odds Ratio ◽

Logistic Regression Model ◽

High Dose Chemotherapy ◽

High Dose ◽

Multiple Logistic Regression ◽

Conditioning Therapy ◽

Severe Mucositis

Abstract Abstract 4568 Forty patients affected by various haematological malignancies who underwent high dose chemotherapy and autologous stem cell transplant (ASCT) were treated with Palifermin, this group was compared to 80 control subjects not assuming this drug. Controls were selected on the basis of being matched for length of neutropenia after ASCT and for Diagnosis. Primary end point was occurrence of “infections not CVC-related” (FUO/pneumonia/gram-negative bacteremia) and secondary end point was the “severe oral mucositis”. Patients and controls resulted to be comparable for many pre-transplant and transplant features. Patients treated with Palifermin displayed a lower rate of “infections not CVC-related” when compared to controls (25% versus 50%, P=0.01) and the protective effect of Palifermin on this outcome remained significant also in a multiple logistic regression model [odds ratio: 0.34, 95% CI: 0.12–0.92, P=0.03] adjusting for a series of potential confounders. The odds ratio of severe mucositis was 30% lower in Palifermin treated patients than in controls (odds ratio: 0.70, 95% CI: 0.33–1.51) but this difference did not attain the statistical significance (P=NS). A stratified analysis by conditioning therapy showed that in patients who received Palifermin after BEAM/BU-CY conditioning, the proportion of patients experiencing severe mucositis was significantly lower than that observed in controls (14% versus 56%, P=0.008) while no such difference was found in those who underwent HD-PAM conditioning (65% versus 56%, P=NS). This result indicates that the conditioning therapy modifies the effect of Palifermin on severe mucositis, a finding fully confirmed in a multiple logistic regression model (P for the effect modification=0.018). Palifermin treated patients had also lower severe gastrointestinal toxicity (12% versus 65%, P<0.001), lower morphine utilization (12% versus 40%, P<0.001), lower total parental nutrition (10% versus 71%, P<0.001) and lower PLT and RBC transfusions (P=0.04) when compared to controls. Average economical costs related to the sum of some resources (inpatient stay, TPN, systemic antifungal treatment and blood products transfusions) were lower in Palifermin treated patients than in controls (11.985 EUROs versus 15.717 EUROs, P=0.002) so that the economical saving in Palifermin treated patients (about 3.700 EUROs) fully compensated for the cost of this drug. Remarkably, in patients treated by “BEAM/BU-CY” conditioning therapy and receiving Palifermin, the overall costs (sum of the above mentioned resources and including also cost of the study drug) was lower in Palifermin treated group in respect to group of patients not treated. In fact, in BEAM/BU-CY stratum overall cost in group treated by Palifermin was 15990±2789 EUROs versus 19276±10946 EUROs in group treated by the same conditioning but not receiving Palifermin (P=0.11). In conclusion this controlled study shows that Palifermin, after ASCT using conditioning not containing TBI, significantly reduces rate of “infections not related to CVC” and ameliorates several indicators of resource consumption, without economical overburden. Disclosures: No relevant conflicts of interest to declare.

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A simulation study for evaluating the performance of clustering measures in multilevel logistic regression

BMC Medical Research Methodology ◽

10.1186/s12874-021-01417-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Nicholas Siame Adam ◽

Halima S. Twabi ◽

Samuel O.M. Manda

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Correlation Coefficient ◽

Odds Ratio ◽

Cluster Size ◽

Logistic Regression Model ◽

Multilevel Logistic Regression ◽

Logistic Regression Models ◽

Childhood Anemia ◽

Cluster Level

Abstract Background Multilevel logistic regression models are widely used in health sciences research to account for clustering in multilevel data when estimating effects on subject binary outcomes of individual-level and cluster-level covariates. Several measures for quantifying between-cluster heterogeneity have been proposed. This study compared the performance of between-cluster variance based heterogeneity measures (the Intra-class Correlation Coefficient (ICC) and the Median Odds Ratio (MOR)), and cluster-level covariate based heterogeneity measures (the 80% Interval Odds Ratio (IOR-80) and the Sorting Out Index (SOI)). Methods We used several simulation datasets of a two-level logistic regression model to assess the performance of the four clustering measures for a multilevel logistic regression model. We also empirically compared the four measures of cluster variation with an analysis of childhood anemia to investigate the importance of unexplained heterogeneity between communities and community geographic type (rural vs urban) effect in Malawi. Results Our findings showed that the estimates of SOI and ICC were generally unbiased with at least 10 clusters and a cluster size of at least 20. On the other hand, estimates of MOR and IOR-80 were less accurate with 50 or fewer clusters regardless of the cluster size. The performance of the four clustering measures improved with increased clusters and cluster size at all cluster variances. In the analysis of childhood anemia, the estimate of the between-community variance was 0.455, and the effect of community geographic type (rural vs urban) had an odds ratio (OR)=1.21 (95% CI: 0.97, 1.52). The resulting estimates of ICC, MOR, IOR-80 and SOI were 0.122 (indicative of low homogeneity of childhood anemia in the same community); 1.898 (indicative of large unexplained heterogeneity); 0.345-3.978 and 56.7% (implying that the between community heterogeneity was more significant in explaining the variations in childhood anemia than the estimated effect of community geographic type (rural vs urban)), respectively. Conclusion At least 300 clusters with sizes of at least 50 would be adequate to estimate the strength of clustering in multilevel logistic regression with negligible bias. We recommend using the SOI to assess unexplained heterogeneity between clusters when the interest also involves the effect of cluster-level covariates, otherwise, the usual intra-cluster correlation coefficient would suffice in multilevel logistic regression analyses.

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Statistical methods for testing X chromosome variant associations: application to sex-specific characteristics of bipolar disorder

Biology of Sex Differences ◽

10.1186/s13293-019-0272-4 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

William A. Jons ◽

Colin L. Colby ◽

Susan L. McElroy ◽

Mark A. Frye ◽

Joanna M. Biernacka ◽

...

Keyword(s):

Bipolar Disorder ◽

Logistic Regression ◽

Sex Differences ◽

Regression Model ◽

Statistical Methods ◽

X Chromosome ◽

Odds Ratio ◽

Logistic Regression Model ◽

Association Studies ◽

Robust Approach

Abstract Background Bipolar disorder (BD) affects both sexes, but important sex differences exist with respect to its symptoms and comorbidities. For example, rapid cycling (RC) is more prevalent in females, and alcohol use disorder (AUD) is more prevalent in males. We hypothesize that X chromosome variants may be associated with sex-specific characteristics of BD. Few studies have explored the role of the X chromosome in BD, which is complicated by X chromosome inactivation (XCI). This process achieves “dosage compensation” for many X chromosome genes by silencing one of the two copies in females, and most statistical methods either ignore that XCI occurs or falsely assume that one copy is inactivated at all loci. We introduce new statistical methods that do not make these assumptions. Methods We investigated this hypothesis in 1001 BD patients from the Genetic Association Information Network (GAIN) and 957 BD patients from the Mayo Clinic Bipolar Disorder Biobank. We examined the association of over 14,000 X chromosome single nucleotide polymorphisms (SNPs) with sex-associated BD traits using two statistical approaches that account for whether a SNP may be undergoing or escaping XCI. In the “XCI-informed approach,” we fit a sex-adjusted logistic regression model assuming additive genetic effects where we coded the SNP either assuming one copy is expressed or two copies are expressed based on prior knowledge about which regions are inactivated. In the “XCI-robust approach,” we fit a logistic regression model with sex, SNP, and SNP-sex interaction effects that is flexible to whether the region is inactivated or escaping XCI. Results Using the “XCI-informed approach,” which considers only the main effect of SNP and does not allow the SNP effect to differ by sex, no significant associations were identified for any of the phenotypes. Using the “XCI-robust approach,” intergenic SNP rs5932307 was associated with BD (P = 8.3 × 10−8), with a stronger effect in females (odds ratio in males (ORM) = 1.13, odds ratio in females for a change of two allele copies (ORW2) = 3.86). Conclusion X chromosome association studies should employ methods which account for its unique biology. Future work is needed to validate the identified associations with BD, to formally assess the performance of both approaches under different true genetic architectures, and to apply these approaches to study sex differences in other conditions.

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