Predicting drowning from sea and weather forecasts: development and validation of a model on surf beaches of southwestern France

ObjectiveTo predict the coast-wide risk of drowning along the surf beaches of Gironde, southwestern France.MethodsData on rescues and drownings were collected from the Medical Emergency Center of Gironde (SAMU 33). Seasonality, holidays, weekends, weather and metocean conditions were considered potentially predictive. Logistic regression models were fitted with data from 2011 to 2013 and used to predict 2015–2017 events employing weather and ocean forecasts.ResultsAir temperature, wave parameters, seasonality and holidays were associated with drownings. Prospective validation was performed on 617 days, covering 232 events (rescues and drownings) reported on 104 different days. The area under the curve (AUC) of the daily risk prediction model (combined with 3-day forecasts) was 0.82 (95% CI 0.79 to 0.86). The AUC of the 3-hour step model was 0.85 (95% CI 0.81 to 0.88).ConclusionsDrowning events along the Gironde surf coast can be anticipated up to 3 days in advance. Preventative messages and rescue preparations could be increased as the forecast risk increased, especially during the off-peak season, when the number of available rescuers is low.

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Predicting drowning from sea and weather forecasts: development and validation of a model on surf beaches of southwestern France

10.1101/721142 ◽

2019 ◽

Author(s):

Éric Tellier ◽

Bruno Simonnet ◽

Cédric Gil-Jardiné ◽

Marion Bailhache ◽

Bruno Castelle ◽

...

Keyword(s):

Regression Models ◽

Area Under The Curve ◽

Temperature Wave ◽

Medical Emergency ◽

Emergency Center ◽

Logistic Regression Models ◽

Weather Forecasts ◽

Step Model ◽

Daily Risk ◽

Development And Validation

AbstractObjectiveTo predict the risk of drowning along the surf beaches of Gironde, southwestern France.MethodsData on rescues and drownings were collected from the Medical Emergency Center of Gironde (SAMU 33). Seasonality, holidays, weekends, weather, and sea conditions were considered potentially predictive. Logistic regression models were fitted with data from 2011–2013 and used to predict 2015–2017 events employing weather and ocean forecasts.ResultsAir temperature, wave parameters, seasonality, and holidays were associated with drownings. Prospective validation was performed on 617 days, covering 232 events (rescues and drownings) reported on 104 different days. The area under the curve (AUC) of the daily risk prediction model (combined with 3-day forecasts) was 0.82 [95% confidence interval (95% CI) 0.79−0.86]. The AUC of the 3-hour step model was 0.85 (95% CI 0.81−0.88).ConclusionsDrowning events along the Gironde surf coast can be anticipated up to 3 days in advance.Preventative messages and rescue preparations could be increased as the forecast risk increased, especially during the off-peak season, when the number of available rescuers is low.

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A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

Archives of Clinical Neuropsychology ◽

10.1093/arclin/acaa068.140 ◽

2020 ◽

Vol 35 (6) ◽

pp. 933-933

Author(s):

Rolin S ◽

Kitchen Andren K ◽

Mullen C ◽

Kurniadi N ◽

Davis J

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Regression Models ◽

Logistic Regression Model ◽

Area Under The Curve ◽

Anxiety And Depression ◽

Average Score ◽

Characteristic Analysis ◽

Logistic Regression Models ◽

Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (>4) and MMPI-2-RF ANX (>64 T), and BDI-II (>13) and MMPI-2-RF RC2 (>64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

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Exploring grip strength as a predictor of depression in middle-aged and older adults

Scientific Reports ◽

10.1038/s41598-021-95566-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Adilson Marques ◽

Duarte Henriques-Neto ◽

Miguel Peralta ◽

Priscila Marconcin ◽

Élvio R. Gouveia ◽

...

Keyword(s):

Older Adults ◽

Depressive Symptoms ◽

Grip Strength ◽

Regression Models ◽

Area Under The Curve ◽

Age Group ◽

Middle Aged ◽

Logistic Regression Models ◽

Depression Risk ◽

Group Data

AbstractGrip strength (GS) is an indicator of health and vulnerability and inversely associated with depressive symptoms. The aim of this study was to explore GS discrimination capacity for depression; and possible GS cut-off values for depression by sex and age group. Data from 2011 and 2015 on 20,598 (10,416 women) middle-aged and older adults from 14 European countries was analysed. GS was assessed by dynamometer, and depressive symptoms using the EURO-D scale. GS cut-off values for depression were calculated and logistic regression models were used to quantify the odds of having depression in 2011 and in 2015 according to being bellow or above the cut-off value. GS had a weak discriminant capacity for depression, with the area under the curve varying between 0.54 and 0.60 (p < 0.001). Sensitivity varied between 0.57 and 0.74; specificity varied between 0.46 and 0.66. GS cut-off values for discriminating depression were 43.5 kg for men and 29.5 kg for women aged 50–64 years, 39.5 kg for men and 22.5 kg for women aged ≥ 65 years. Having GS above the cut-off represents significant lower odds of depression in 2011 and 4 years later, in 2015. Healthcare practitioners and epidemiologic researchers may consider the low GS cut-off values to screen for potential depression risk. However, due to its weak discriminant values these cut-offs should not be used to identify depression.

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P112: Predicting patient admission from the emergency department using triage administrative data

CJEM ◽

10.1017/cem.2017.314 ◽

2017 ◽

Vol 19 (S1) ◽

pp. S116

Author(s):

D.W. Savage ◽

B. Weaver ◽

D. Wood

Keyword(s):

Emergency Department ◽

Logistic Regression ◽

Acute Care ◽

Administrative Data ◽

Statistical Models ◽

Regression Models ◽

Patient Flow ◽

Area Under The Curve ◽

Patient Admission ◽

Logistic Regression Models

Introduction: Emergency department (ED) over-crowding and increased wait times are a growing problem. Many interventions have been proposed to decrease patient length of stay and increase patient flow. Early disposition planning is one method to accomplish this goal. In this study we developed statistical models to predict patient admission based on ED administrative data. The objective of this study was to predict patient admission early in the visit with goal of preparation of the acute care bed and other resources. Methods: Retrospective administrative ED data from the Thunder Bay Regional Health Sciences Centre was obtained for the period May 2014 to April 2015. Data were divided into training and testing groups with 80% of data used to train the statistical models. Logistic regression models were developed using administrative variables (i.e., age, sex, mode of arrival, and triage level). Model accuracy was evaluated using sensitivity, specificity, and area under the curve measures. To predict hourly bed requirements, the probability of admission was summed to calculate a pooled bed requirement estimate. The estimated hourly bed requirement was then compared to the historical hourly demand. Results: The logistic regression models had a sensitivity of 23%, specificity of 97%, and an area under the curve of 0.78. Although, admission prediction for a particular individual was satisfactory, the hourly pooled probabilities showed better results. The predicted hourly bed requirements were close to historical demand for beds when compared. Conclusion: I have shown that the number of acute care beds required on an hourly basis can be predicted using triage administrative data. Early admission bed planning would allow resources to be managed more effectively. In addition, during periods of hospital over capacity, managers would be able to prioritize transfers and discharges based on early estimates of ED demand for beds.

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

International Journal of Gynecological Cancer ◽

10.1097/igc.0b013e3182a6171a ◽

2013 ◽

Vol 23 (9) ◽

pp. 1583-1589 ◽

Cited By ~ 8

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.

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Development and Validation of a Risk Prediction Model for Acute Kidney Injury After the First Course of Cisplatin

Journal of Clinical Oncology ◽

10.1200/jco.2017.75.7161 ◽

2018 ◽

Vol 36 (7) ◽

pp. 682-688 ◽

Cited By ~ 39

Author(s):

Shveta S. Motwani ◽

Gearoid M. McMahon ◽

Benjamin D. Humphreys ◽

Ann H. Partridge ◽

Sushrut S. Waikar ◽

...

Keyword(s):

Acute Kidney Injury ◽

Serum Albumin ◽

Regression Models ◽

Primary Outcome ◽

Estimated Glomerular Filtration Rate ◽

Demographic Data ◽

Kidney Injury ◽

Logistic Regression Models ◽

History Of ◽

Development And Validation

Purpose Cisplatin-associated acute kidney injury (C-AKI) is common. We sought to develop and validate a predictive model for C-AKI after the first course of cisplatin. Methods Clinical and demographic data were collected on patients who received cisplatin between 2000 and 2016 at two cancer centers. C-AKI was defined as a 0.3 mg/dL rise in serum creatinine within 14 days of receiving cisplatin. Using multivariable logistic regression models with C-AKI as the primary outcome, we created a scoring model from the development cohort (DC) and tested it in the validation cohort (VC). Results C-AKI occurred in 13.6% of 2,118 patients in the DC and in 11.6% of 2,363 patients in the VC. Factors significantly associated with C-AKI included age 61 to 70 years (odds ratio [OR], 1.64 [95% CI, 1.21 to 2.23]; P = .001) and 71 to 90 years (OR, 2.97 [95% CI, 2.06 to 4.28]; P < .001) compared with ≤ 60 years; cisplatin dose 101 to 150 mg (OR, 1.58 [95% CI, 1.14 to 2.19]; P = .007) and > 150 mg (OR, 3.73 [95% CI, 2.68 to 5.20]; P < .001) compared with ≤ 100 mg; a history of hypertension (OR, 2.10 [95% CI, 1.54 to 2.72]; P < .001) compared with no hypertension; and serum albumin 2.0 to 3.5 g/dL (OR, 2.21 [95% CI, 1.62 to 3.03]; P < .001) compared with > 3.5 g/dL. The baseline estimated glomerular filtration rate was not significantly associated with the risk of C-AKI. The c-statistics of the score-based model in the DC and the VC were 0.72 (95% CI, 0.69 to 0.75) and 0.70 (95% CI, 0.67 to 0.73), respectively. Scores of 0, 3.5, and 8.5 were associated with a probability of C-AKI of 0.03 (95% CI, 0.03 to 0.05), 0.12 (95% CI, 0.11 to 0.14), and 0.51 (95% CI, 0.43 to 0.60), respectively. Conclusion A score-based model created by using the patient’s age, cisplatin dose, hypertension, and serum albumin is predictive of C-AKI.

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Development and validation of two composite aging measures using clinical biomarkers in the Chinese population

Innovation in Aging ◽

10.1093/geroni/igaa057.3274 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 887-887

Author(s):

Zuyun Liu

Keyword(s):

Mahalanobis Distance ◽

Chinese Population ◽

Regression Models ◽

Logistic Regression Models ◽

Clinical Biomarkers ◽

Physiological Dysregulation ◽

And Gender ◽

Cox Proportional Hazard Regression ◽

One Year ◽

Development And Validation

Abstract Quantifying aging is crucial for addressing aging and related issues. This study aimed to: 1) develop two composite aging measures in the Chinese population using two recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and 2) validate the two measures by examining their associations with mortality and disease counts. Based on data from the China Nutrition and Health Survey 2009 wave (N=8,119, aged 20-79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 routine clinical biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N=9,304, aged 45-99 years, 53.4% women). We found that both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per one-year, HR=1.14, 95%CI=1.08, 1.19; PD, per one-SD, HR=1.50, 95%CI=1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The two aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although four biomarkers were not available. In summary, we successfully developed and validated two composite aging measures‒‒KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging related diseases in China.

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197 The Relative Odds of Sustaining a Sport-Related Concussion: A Study of 12,320 Student-Athletes

Neurosurgery ◽

10.1093/neuros/nyx417.197 ◽

2017 ◽

Vol 64 (CN_suppl_1) ◽

pp. 253-253

Author(s):

Benjamin L Brett ◽

Andrew W Kuhn ◽

Aaron M Yengo-Kahn ◽

Gary Solomon ◽

Scott L Zuckerman

Keyword(s):

Risk Score ◽

Regression Models ◽

Student Athletes ◽

Area Under The Curve ◽

Collegiate Athletes ◽

Multivariable Model ◽

Logistic Regression Models ◽

Small Effect Size ◽

History Of

Abstract INTRODUCTION Accurately quantifying the risk of sport-related concussion (SRC) can prove valuable in the management of student-athletes. Our objective was to develop and validate an aggregate risk score based on biopsychosocial factors to predict the odds of sustaining a SRC. METHODS An ambispective study was undertaken of 12,320 middle school, high school and collegiate athletes. Neurocognitive testing was completed at preseason (baseline) and post-SRC. Multiple univariate and multivariable logistic regression models were used to determine which pre-injury variables accurately predicted the occurrence of SRC. The score was validated utilizing bootstrapping resampling. RESULTS >Five variables maintained significance in the multivariable model, with corresponding risk score points: SRC history (21), prior headache treatment (6), contact sport (5), youth level of play (7), and history of ADHD/LD (2). Six groups were formed based on the differentiation of the probability of SRC. Classification of odds of SRC by these categories produced an area under the curve (AUC) of 0.71 (95% CI 0.69−0.72, P < 0.001). The scoring system was a significant predictor of SRC, X2 = 1112.75, P < 0.001, df = 7, although with small effect size. CONCLUSION An aggregate score was developed and internally validated to empirically assess factors associated with increased odds of sustaining a SRC. This summative score can be used as an adjunct to better conceptualize the odds of concussion for student-athletes. However, it is important to note that several other factors were not accounted for in the model and must be considered in the assessment of SRC risk.

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Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies

The Journals of Gerontology Series A ◽

10.1093/gerona/glaa238 ◽

2020 ◽

Author(s):

Zuyun Liu

Keyword(s):

Mahalanobis Distance ◽

Prospective Cohort ◽

Chinese Population ◽

Regression Models ◽

Logistic Regression Models ◽

Clinical Biomarkers ◽

Physiological Dysregulation ◽

And Gender ◽

Cox Proportional Hazard Regression ◽

Development And Validation

Abstract Background This study aimed to: (i) develop 2 composite aging measures in the Chinese population using 2 recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and (ii) validate the 2 measures by examining their associations with mortality and disease counts. Methods Based on data from the China Nutrition and Health Survey (CHNS) 2009 wave (N = 8119, aged 20–79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N = 9304, aged 45–99 years, 53.4% women). Results Both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per 1-year, hazard ratio [HR] = 1.14, 95% confidence interval [CI] = 1.08, 1.19; PD, per 1-SD, HR = 1.50, 95% CI = 1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The 2 aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although 4 biomarkers were not available. Conclusions We successfully developed and validated 2 composite aging measures—KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging-related diseases in China.

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Role of inflammatory biomarkers in the prediction of ICU admission and mortality in patients with COVID-19

Medical Research Archives ◽

10.18103/mra.v8i12.2307 ◽

2020 ◽

Vol 8 (12) ◽

Author(s):

Qamar Ahmad ◽

Sarah DePerrior ◽

Sunita Dodani ◽

Joshua Edwards ◽

Paul Marik

Keyword(s):

Regression Models ◽

Area Under The Curve ◽

Inflammatory Biomarkers ◽

Logistic Regression Models ◽

Icu Admission ◽

Composite Scores ◽

Design And Methods ◽

Improved Accuracy ◽

D Dimer

Background: Inflammatory cytokines have been implicated in the pathophysiology and prognosis of severe COVID-19. Inflammatory biomarkers may guide the clinician in making treatment decisions as well as in the allocation of resources. Objective: This study aimed to assess how levels of inflammatory biomarkers predict disease severity and mortality in patients with COVID-19 by testing a predictive scoring model developed by Zhou et al and further refining the model in a population of patients hospitalized with COVID-19. Study Design and Methods: This retrospective study included patients with COVID-19 admitted to ten Virginia hospitals from January 1, 2020, to June 15, 2020. Inflammatory markers including CRP, D-Dimer, ferritin, N/L ratio, and procalcitonin were studied and logistic regression models were applied to ascertain the risk of ICU admission and mortality with elevated markers. Results: Data from a total of 701 patients were analyzed. In bivariate tests age, CRP, D-Dimer, and N/L ratio were associated with in-hospital mortality as well as admission to the ICU. Procalcitonin was associated with admission to the ICU but not mortality. Males and African Americans were more likely to require ICU-level care. In final models, age and CRP were significantly associated with mortality (OR 1.06, 95% CI 1.04-1.08, and OR 1.06, 95% CI 1.03-1.10 respectively) as well as ICU admissions (OR 1.02, 95% CI 1.01-1.03 and OR 1.03, 95% CI 1.01-1.06 respectively). The previously established composite scores of CRP, D-Dimer, and N/L ratio were predictive of mortality (Area under the curve [AUC] 0.69 for multiplicative score) as well as ICU admissions (AUC 0.61 for multiplicative score). However, improved accuracy was obtained with age and CRP for predicting mortality (AUC 0.77) and ICU admission (AUC 0.62). Conclusions: CRP and age appear to be the strongest predictors for ICU admission and mortality compared to D-Dimer, Ferritin, Procalcitonin, and N/L ratio in patients with COVID-19.

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