How Do Avalanche Dogs (and Their Handlers) Cope with Physical Exercise? Heart Rate Changes during Endurance in a Snowy Environment

This study aimed to assess the heart rate (HR) responses of avalanche SAR dogs and handlers under working field conditions. Thirteen SAR units (dogs and handlers) performed an exercise (Endurance) consisting of approximately 5.5 km of rough tracks through deep snow, at an altitude of 1991–2250 m.a.s.l. The exercise was repeated twice for each of the two different tracks. Both handlers and dogs were equipped with a global positioning satellite/heart rate (GPS/HR) system (Polar®). Multivariable models were used to evaluate the effects of environmental (i.e., gradient, altitude, track, and time) and intrinsic (i.e., speed, repetition, and breed) factors on changes from baseline HR (Δ%HR). The dog’s Δ%HR was greater in the flat and uphill compared with downhill, and increased progressively as the speed increased (p < 0.001). Moreover, it rose at altitudes above 2100 m.a.s.l. and peaked after 30 min of the Endurance activity (p < 0.01). These findings indicated that HR monitors could be a valuable tool to contribute to the evaluation of avalanche dogs’ fitness in their real working environment. In contrast, the lack of correlation between the dogs’ and handlers’ HR changes suggests that handlers might not perceive the physical conditions of their dog in real-time. Thus, implementing protocols to monitor avalanche SAR dogs’ fitness using a GPS/HR monitoring system could help handlers to tailor the training and workload and to detect the risk factors for physical distress of working dogs.

Sex Differences in the Association Between Midlife Cardiovascular Conditions or Risk Factors With Midlife Cognitive Decline

Background and Objectives:The prevalence of mid-life cardiovascular conditions and risk factors are higher in men than women. Associations between mid-life cardiovascular conditions or risk factors and mid-life cognitive decline has been reported, but few studies have assessed sex differences in these associations.Methods:We included 1,857 participants enrolled in the population-based Mayo Clinic Study of Aging who were aged 50-69 years at baseline. Participants were evaluated every 15 months by a coordinator, neurologic evaluation, and neuropsychological testing. The neuropsychological testing used nine tests to calculate global cognitive and domain-specific (memory, language, executive function, and visuospatial skills) z-scores. Nurse abstractors reviewed participant medical records to determine the presence of cardiovascular conditions (coronary heart disease, arrhythmias, congestive heart failure) and risk factors (hypertension, diabetes, dyslipidemia, obesity, ever smoking). Linear mixed-effect models evaluated the association between baseline cardiovascular conditions or risk factors and global and domain-specific cognitive decline. Multivariable models adjusted for demographics, APOE genotype, depression, and other medical conditions. Interactions between sex and each cardiovascular condition or risk factor were examined, and results were stratified by sex.Results:Overall, 1,465 (70.3%) participants had at least one cardiovascular condition or risk factor; the proportion of men was higher than women (767 (83.4%) vs 698 (74.5%), p<0.0001). Cross-sectionally, coronary heart disease and ever smoking were associated with a lower visuospatial z-score in multivariable models. Longitudinally, several cardiovascular conditions and risk factors were associated with declines in global and/or domain-specific z scores, but not visuospatial z-scores. Most cardiovascular conditions were more strongly associated with cognition among women: coronary heart disease, and other cardiovascular conditions were associated with global cognition decline only in women (all p<0.05). Additionally, diabetes, dyslipidemia, and coronary heart disease were associated with language z-score decline only in women (all p<0.05). However, congestive heart failure was associated with language z-score decline only in men (all p<0.05).Conclusions:Mid-life cardiovascular conditions and risk factors are associated with mid-life cognitive decline. Moreover, specific cardiovascular conditions and risk factors have stronger associations with cognition decline in mid-life for women than men despite the higher prevalence of those conditions in men.

Impact of Pre-Treatment NLR and Other Hematologic Biomarkers on the Outcomes of Early-Stage Non-Small-Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy

Introduction: We evaluated the association of pre-treatment immunologic biomarkers on the outcomes of early-stage non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). Materials and methods: In this retrospective study, all newly diagnosed early-stage NSCLC treated with SBRT between January 2010 and December 2017 were screened and included for further analysis. The pre-treatment neutrophil-lymphocyte ratio (NLR), monocyte lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated by Kaplan–Meier. Multivariable models were constructed to determine the impact of different biomarkers and the Akaike information criterion (AIC), index of adequacy, and scaled Brier scores were calculated. Results: A total of 72 patients were identified and 61 were included in final analysis. The median neutrophil count at baseline was 5.4 × 109/L (IQR: 4.17–7.05 × 109/L). Median lymphocyte count was 1.63 × 109/L (IQR: 1.29–2.10 × 109/L), median monocyte count was 0.65 × 109/L (IQR: 0.54–0.83 × 109/L), median platelet count was 260.0 × 109/L (IQR: 211.0–302.0 × 109/L). The median NLR was 3.42 (IQR: 2.38–5.04), median MLR was 0.39 (IQR: 0.31–0.53), and median PLR was 156.4 (IQR: 117.2–197.5). On multivariable regression a higher NLR was associated with worse OS (p = 0.01; HR-1.26; 95% CI 1.04–1.53). The delta AIC between the two multivariable models was 3.4, suggesting a moderate impact of NLR on OS. On multivariable analysis, higher NLR was associated with poor RFS (p = 0.001; NLR^1 HR 0.36; 0.17–0.78; NLR^2 HR-1.16; 95% CI 1.06–1.26) with a nonlinear relationship. The delta AIC between the two multivariable models was 16.2, suggesting a strong impact of NLR on RFS. In our cohort, MLR and PLR were not associated with RFS or OS in multivariable models. Conclusions: Our study suggests NLR, as a biomarker of systemic inflammation, is an independent prognostic factor for OS and RFS. The nonlinear relationship with RFS may indicate a suitable immunological environment is needed for optimal SBRT action and tumoricidal mechanisms. These findings require further validation in independent cohorts.

Multivariable Models Based on Baseline Imaging Features and Clinicopathological Characteristics to Predict Breast Pathologic Response after Neoadjuvant Chemotherapy in Patients with Breast Cancer

Introduction Currently, the accurate evaluation and prediction of response to neoadjuvant chemotherapy (NAC) remains a great challenge. We developed several multivariate models based on baseline imaging features and clinicopathological characteristics to predict the breast pathologic complete response (pCR). Methods We retrospectively collected clinicopathological and imaging data of patients who received NAC and subsequent surgery for breast cancer at our hospital from 2014 June till 2020 September. We used mammography, ultrasound and magnetic resonance imaging (MRI) to investigate the breast tumors at baseline. Results A total of 308 patients were included and 111 patients achieved pCR. The HER2 status and Ki-67 index were significant factors for pCR on univariate analysis and in all multivariate models. Among the prediction models in this study, the ultrasound-MRI model performed the best, producing an area under curve of 0.801 (95%CI=0.749-0.852), a sensitivity of 0.797 and a specificity of 0.676. Conclusion Among the multivariable models constructed in this study, the ultrasound plus MRI model performed the best in predicting the probability of pCR after NAC. Further validation is required before it is generalized.

Gender Modifies the Association of Cognition With Age-Related Hearing Impairment in the Health and Retirement Study

Introduction: Despite growing recognition of hearing loss as a risk factor for late life cognitive disorders, sex and gender analysis of this association has been limited. Elucidating this is one means to advocate for holistic medicine by considering the psychosocial attributes of people. With a composite Gender Score (GS), we aimed to assess this among aging participants (50+) from the 2016 Health and Retirement Study (HRS) cohort.Methods: The GS was derived from gender-related variables in HRS by factor analyses and logistic regression, ranging from 0 (toward masculinity) to 100 (toward femininity). GS tertiles were also used to indicate three gender types (GS tertile 1: lower GS indicates masculinity; GS tertile 2: middle GS indicates androgyny; GS tertile 3: higher GS indicates femininity). Univariate followed by multiple logistic regressions were used to estimate the Odds Ratio (OR) and 95% confidence intervals (CI) of cognitive impairment (assessed by adapted Telephone Interview for Cognitive Status) from hearing acuity, as well as to explore the interactions of sex and gender with hearing acuity. The risk of cognitive impairment among hearing-impaired participants was assessed using multivariable models including sex and gender as exposure variables.Results: Five variables (taking risks, loneliness, housework, drinking, and depression) were retained to compute the GS for each participant. The distribution of GS between sexes partly overlapped. After adjusting for confounding factors, the OR for cognitive impairment associated with hearing impairment was significantly higher (OR = 1.65, 95% CI: 1.26, 2.15), and this association was not modified by female sex (OR = 0.77, 95% CI: 0.46, 1.27), but by androgynous gender (OR = 0.44, 95% CI: 0.24, 0.81). In the multivariable models for participants with hearing impairment, androgynous and feminine gender, as opposed to female sex, was associated with lower odds of cognitive impairment (OR of GS tertile 2 = 0.59, 95% CI: 0.41, 0.84; OR of GS tertile 3 = 0.60, 95% CI: 0.41, 0.87; OR of female sex = 0.78, 95% CI: 0.57, 1.08).Conclusions: Hearing impairment was associated with cognitive impairment among older people, and this association may be attenuated by a more feminine GS.

Prediction of Early Mortality with Non-Intensive Acute Myeloid Leukemia (AML) Therapies: Analysis of 1336 Patients from MRC/NCRI and SWOG

Background: Therapeutic resistance and treatment tolerance vary greatly in patients with AML, likely due to the advanced age and genetic diversity in pharmacokinetics of those affected. Undoubtedly, tools to accurately predict outcomes of individual therapies for patients could inform decision-making and improve response rates. To this end, several scoring systems have been developed aimed at identifying patients at high risk of poor outcome after intensive chemotherapy. Similar tools for use after non-intensive therapies are currently not available. As such therapies are increasingly effective and more widely utilized we sought to develop tools to predict early death and survival for patients treated with non-intensive therapies. Patients and Methods: We developed prediction models for all-cause death by day 28, 42, 56, and 100 from enrollment using data from 796 patients enrolled on MRC/NCRI trial LI-1, which we then validated in a cohort of 540 patients treated on SWOG trials S0432, S0703, and S1612. Treatments included: Low dose Ara-C (LDAC) alone, sapacitabine alone and LDAC in combination with vosaroxin, tosedostat or ganetespib (MRC/NCRI); Azacytidine (AZA) alone, tipifarnib alone, and AZA in combination with mylotarg, midostaurin, and nivolumab (SWOG). The following covariates were available in the MRC/NCRI and SWOG cohorts to build multivariable logistic regression models (quantitative unless specified otherwise): age, performance status (PS; 0-1 vs. 2-4), secondary AML (vs. de novo AML or high-risk myelodysplastic syndrome), white blood cell and platelet counts, and percentage of bone marrow blasts - all assessed at enrollment. The regression coefficients from the model fit in the MRC/NCRI cohort were used to derive a score and applied to each patient in the SWOG cohort. The models' prognostic accuracies were assessed using the area under the receiver operating characteristic curve (AUC). For the MRC/NCRI cohort, additional covariates were available: cytogenetic risk (per Grimwade 2011), FLT3-ITD, and NPM1 mutation and patient-reported outcomes using the QLQ-C30 instrument. Logistic regression models with these covariates were fit and optimism-corrected AUC estimated to assess prognostic performance for early death. Results: Both patient cohorts were largely composed of older individuals (median age of 75 [range: 60-91] for MRC/NCRI and 77 [60-94] for SWOG, respectively. A substantial subset in each had a PS of 2-4 (MRC/NCRI: 20%; SWOG: 37%) and/or secondary AML (MRC/NCRI: 26%; SWOG: 41%). Overall, the ability to predict early death either by day 28, 42, 56, or 100 was limited in the MRC/NCRI cohort. Subscales of the QLQ-C30 had univariate AUC=0.67, the highest among all covariates evaluated. Multivariable models with just clinical covariates had optimism-corrected AUCs ranging from 0.63-0.65; adding cytogenetic risk and FLT3-ITD and NPM1 mutation status led optimism-corrected AUCs ranging from 0.64-0.66; addition of two QLQ-C30 subscales (fatigue and appetite loss) led to optimism-corrected AUCs ranging from 0.66-0.69. The SWOG cohort did not collect QLQ-C30 or mutational data on all patients and only the clinical multivariable models could be evaluated. The models had a similar performance in the SWOG cohort with AUCs ranging from 0.65-0.68. Conclusion: Our ability to predict early death in older patients treated with lower intensity AML therapies is limited with routinely available clinical variables. Inclusion of cytogenetic risk, FLT3-ITD, and NPM1 mutation status minimally improved the prognostic accuracy as did some of the QLQ-C30 subscales. Our data highlight the difficulties in predicting outcomes with non-intensive AML therapy with routinely available baseline clinical information. Improving the clinical utility of these models may require more complete characterization of patient comorbidities (including frailty index, cognitive function, renal and hepatic function, comorbidity scores) or additional PRO measures since some QLQ-C30 subscales had the strongest univariate signals. Support: NIH/NCI grants CA180888 and CA180819; Blood Cancer UK grant 13041 and Cardiff University. Figure 1 Figure 1. Disclosures Assouline: Novartis: Honoraria, Research Funding; Amgen: Current equity holder in publicly-traded company, Research Funding; Gilead: Speakers Bureau; Johnson&Johnson: Current equity holder in publicly-traded company; Jewish General Hospital, Montreal, Quebec: Current Employment; Eli Lilly: Research Funding; Roche/Genentech: Research Funding; Takeda: Research Funding; BeiGene: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Erba: AbbVie Inc; Agios Pharmaceuticals Inc; Astellas; Bristol Myers Squibb; Celgene, a Bristol Myers Squibb company; Daiichi Sankyo Inc; Genentech, a member of the Roche Group; GlycoMimetics Inc; Incyte Corporation; Jazz Pharmaceuticals Inc; Kura Oncology; Nov: Other: Advisory Committee; AbbVie Inc: Other: Independent review committee; AbbVie Inc; Agios Pharmaceuticals Inc; Bristol Myers Squibb; Celgene, a Bristol Myers Squibb company; Incyte Corporation; Jazz Pharmaceuticals Inc; Novartis: Speakers Bureau; AbbVie Inc; Agios Pharmaceuticals Inc; ALX Oncology; Amgen Inc; Daiichi Sankyo Inc; FORMA Therapeutics; Forty Seven Inc; Gilead Sciences Inc; GlycoMimetics Inc; ImmunoGen Inc; Jazz Pharmaceuticals Inc; MacroGenics Inc; Novartis; PTC Therapeutics: Research Funding. Walter: Jazz: Research Funding; Pfizer: Consultancy, Research Funding; Selvita: Research Funding; Amphivena: Consultancy, Other: ownership interests; Agios: Consultancy; Astellas: Consultancy; BMS: Consultancy; Genentech: Consultancy; Janssen: Consultancy; Kite: Consultancy; Macrogenics: Consultancy, Research Funding; Immunogen: Research Funding; Celgene: Consultancy, Research Funding; Aptevo: Consultancy, Research Funding; Amgen: Research Funding. Othus: Daiichi Sankyo: Consultancy; Celgene: Other: Data safety monitoring board; Merck: Consultancy; Biosight: Consultancy; Glycomimetics: Other: Data safety monitoring board.

The Assessment of a Multifactorial Score for the Adaptability Evaluation of Six Poultry Genotypes to the Organic System

This study aimed to develop an adaptability score (AS) for chicken strains, which includes behavioral, plumage conditions, and body lesion indicators through a multifactorial approach. A total of 600 male chickens from 6 poultry genotypes—Ranger Classic (R1), Ranger Gold (R2), Rowan Ranger (R3), Hubbard Red JA (A), CY Gen 5 × JA87 (CY), and M22 × JA87 (M)—were reared under organic conditions, fed ad libitum, and individually weighed weekly to calculate the daily weight gain (DWG). The behavioral observations consisted of the explorative attitude (EA), recorded at 21 days, and the behavioral patterns (BPs) recorded the week before the slaughter. The AS was established by a principal component analysis, and the AS of these genotypes was compared. Moreover, the effect of DWG and genotype on the AS was evaluated by univariable and multivariable regression models. Although the DWG and genotype were strictly dependent, genotype was the most important factor affecting the AS. In fact, its effect was significant both in univariable (p < 0.001) and multivariable models (p < 0.001). Conversely, the DWG was significant only in the univariable and lost significance when the effect of genotype was introduced in the model.

Prognostic value of layer-specific global longitudinal strain in patients undergoing coronary artery bypass grafting

Background Recent improvements in speckle tracking echocardiography have made sectionalized quantification of layer-specific global longitudinal strain (GLS) possible. Prior studies have reported prognostic value of GLS in several cardiac diseases, however, the use of layer-specific strain has not been investigated in patients undergoing coronary artery bypass grafting (CABG). Purpose To determine the prognostic value of layer-specific GLS for predicting all-cause mortality after CABG. Methods In this retrospective cohort study, consecutive patients undergoing isolated CABG between 2006 and 2011 were included. The patients were followed through nation-wide registries for the endpoint of all-cause mortality. Multivariable Cox regression models adjusted for clinical and echocardiographic baseline characteristics were used to assess the association between layer-specific GLS and all-cause mortality. Cumulative survival was stratified by clinical age and gender-dependent cut-off values for the layer-specific GLS, which was obtained from a large healthy population study. Results Of 641 patients included (mean age 67 years, 84% male), 70 (10.9%) died during follow-up (median 3.8 years [IQR: 2.7; 4.9 years]). Patients who died during follow-up were significantly older (71 years vs. 67 years, P = &lt;0.001) and had a lower LVEF (46% vs. 51% P = &lt;0.001). Endocardial GLS (GLSendo) (−14.2% vs. −16.3%, P&lt;0.001), whole wall GLS (−12.1% vs. −13.9%, P&lt;0.001), and epicardial GLS (GLSepi) (−10.6% vs. −12.2%, P&lt;0.001) were all reduced in patients who died during follow-up, and patients with GLS below cut-off had a more than two-fold increased risk of all-cause mortality (Figure 1). The risk of dying increased linearly with decreasing absolute GLS for all layers (p&lt;0.0002 for all layers), (Figure 2). In multivariable models, all layer-specific strain parameters remained significantly associated with all-cause mortality; GLSepi: HR=1.14 (1.05–1.23), p=0.002; GLS: HR=1.12 (1.04–1.20), p=0.002; GLSendo: HR=1.09 (1.03–1.16), p=0.003, per 1% absolute decrease. However, only GLSepi remained significantly associated with mortality when also adjusting for echocardiographic parameters (GLSepi: HR=1.12 (1.00–1.25), p=0.049, per 1% absolute decrease) and separately also after adjusting for the EuroScore II (GLSepi: HR=1.09 (1.00–1.18), p=0.043, per 1% absolute decrease). Conclusion Layer-specific GLS is an independent prognosticator of all-cause mortality after CABG. In multivariable models, GLSepi provided significant prognostic value after adjusting for echocardiographic parameters and EuroScore II. FUNDunding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Research grant from Herlev & Gentofte University Hospital's internal research funds. Figure 1. Kaplan-Meier survival estimates Figure 2. Incidence rate of all-cause mortality

Skills for Dynamic Models

The chapter “Skills for Dynamic Models” provides worked examples of the dynamic models presented in Part 1, both in spreadsheets and in R. It also covers some of the mathematical steps used in model analysis. In most cases, instructions are given for both spreadsheets and R. However, when some activities are far easier to do in a programming environment than in spreadsheets, only the instructions for R are shown. The chapter starts out by discussing the skills needed for structured population models, including setting up age structure and creating cobweb plots. Next, it reviews the skills needed for multivariable models, including calculating isoclines and Jacobian matrices. Finally, it introduces the concept of Monte Carlo methods and provides guidance on how to implement them.