Predation on Common Wall Lizards: Survival Probabilities of Melanic Individuals

For estimation of predation plasticine models of prey animals are often used, because the soft material preserves imprints left by predators. We assumed that melanic common wall lizards (Podarcis muralis) disappear by selective predation faster than cryptic individuals and habitat features have important role in this process. We studied the survival probabilities of cryptic and melanic colored plasticine common wall lizard models in habitats with different background coloration on selected places near the city of Pécs (south Hungary), where melanic common wall lizards had been observed earlier. Contrary to our expectations the daily survival rates of melanic plasticine common wall lizards were somewhat higher in all three locations (sandstone quarry, stone wall, coal pit) than those of the cryptic ones, but these differences were not significant. Predators were mostly mammals, which left more marks on plasticine models than birds, but we could not show a preference of the body parts of prey. We concluded that rare occurrence of melanic common wall lizards in habitats near the city of Pécs is not due to predation pressure.

O-OGC01 Development and validation of multivariate prediction model of long-term survival after oesophagectomy in patients with oesophageal cancer

Background Long-term survival after oesophagectomy remains poor, with recurrence a feared common outcome. Prediction tools can help clinicians identify high-risk patients and optimise treatment decisions based on their prognostic factors. This study developed and evaluated a prediction model to predict long-term survival and time-to-recurrence following surgery for oesophageal cancer. Methods Patients who underwent curative surgery between June 2009-2015 from the European iNvestigation of SUrveillance After Resection for Esophageal Cancer study were included. Prediction models were developed for overall survival (OS) and disease-free survival (DFS) using Cox proportional hazards (CPH) and Random Survival Forest (RSF). Model performance was evaluated using discrimination (time-dependent area under the curve (tAUC)) and calibration (visual comparison of predicted and observed survival probabilities). Results This study included 4719 patients with an OS of 47.7% and DFS of 48.4% at 5 years. Sixteen variables were included in the final model. CPH and RSF demonstrated good discrimination with a tAUC of 78.2% (95% CI 77.4-79.1%) and 77.1% (95% CI 76.1-78.1%) for OS and a tAUC of 79.4% (95% CI 78.5-80.2%) and 78.6% (95% CI 77.5-79.5%) respectively for DFS at 5 years. CPH showed good agreement between predicted and observed probabilities in all quintiles. RSF showed good agreement for patients with survival probabilities between 20-80% and moderate agreement in the <20% and >80% quintile groups. Conclusions This study demonstrated the ability of a statistical model to accurately predict long-term survival and time-to-recurrence after surgery for oesophageal cancer, with CPH and RSF models showing good discrimination and calibration. Identification of patient groups at risk of recurrence and poor long-term survival can improve patient outcomes by enhancing selection of treatment methods and surveillance strategies. Future work evaluating prediction-based decisions against standard decision-making is required to improve understanding of the clinical utility derived from prognostic model use.

Optimising Batting Partnership Strategy in the First Innings of a Limited Overs Cricket Match

<p>In cricket, the better an individual batsman or batting partnership performs, the more likely the team is to win. Quantifying batting performance is therefore fundamental to help with in-game decisions, to optimise team performance and maximise chances of winning. Several within-game metrics exist to summarise individual batting performances in cricket. However, these metrics summarise individual performance and do not account for partnership performance. An expectation of how likely a batting partnership is to survive each ball within an innings can enable more effective partnership strategies to optimise a team’s final total. The primary objective of this research was to optimise batting partnership strategy by formulating several predictive models to calculate the probability of a batting partnership being dismissed in the first innings of a limited overs cricket match. The narrowed focus also reduced confounding factors, such as match state. More importantly, the results are of practical significance and provide new insight into how an innings evolves. The model structures were expected to reveal strategies for optimally setting a total score for the opposition to chase. In the first innings of a limited overs cricket match, there is little information available at the commencement and during the innings to guide the team in accumulating a winning total score. The secondary objective of this research was to validate the final models to ensure they were appropriately estimating the ball-by-ball survival probabilities of each batsman, in order to determine the most effective partnership combinations. The research hypothesised that the more effective a batting partnership is at occupying the crease, the more runs they will score at an appropriate rate and the more likely the team is to win the match, by setting a defendable total. Data were split into subsets based on the batting position or wicket. Cox proportional hazard models and ridge regression techniques were implemented to consider the potential effect of eight batting partnership performance predictor variables on the ball-by-ball probability of a batting partnership facing the next ball without being dismissed. The Area Under the Curve (AUC) was implemented as a performance measure used to rank the batting partnerships. Based on One-Day International (ODI) games played between 26th December 2013 and 14th February 2016, the model for opening batting partnerships ranked Pakistani’s A Ali and S Aslam as the optimal opening batting partnership. This method of calculating batting partnership rankings is also positively correlated with typical measures of success: average runs scored, proportion of team runs scored and winning. These findings support the research hypothesis. South African’s, HM Amla and AB de Villiers are ranked as the optimal partnership at wicket two. As at 28th February 2016, these batsmen were rated 6th equal and 2nd in the world respectively. More importantly, these results show that this pair enable South Africa to maximise South Africa’s chances of winning, by setting a total in an optimal manner. New Zealand captain, Kane Williamson, is suggested as the optimal batsman to bat in position three regardless of which opener is dismissed. Reviewing New Zealand’s loss against Australia on 4th December 2016, indicates a suboptimal order was used with JDS Neesham and BJ Watling batting at four and five respectively. Given the circumstances, C Munro and C de Grandhomme were quantified as a more optimal order. The results indicate that for opening batsmen, better team results are obtained when consecutive dot balls are minimised. For top order and middle order batsmen, this criteria is relaxed with the emphasis on their contribution to the team. Additionally, for middle order batsmen, minimising the occasions where 2 runs or less are scored within 4 deliveries is important. In order to validate the final models, each one was applied to the corresponding Indian Premier League (IPL) 2016 data. These models were used to generate survival probabilities for IPL batting partnerships. The probabilities were then plotted against survival probabilities for ODI batting partnerships at the same wicket. The AUC was calculated as a metric to determine which models generated survival probabilities characterising the largest difference between IPL partnerships and ODI partnerships. All models were validated by successfully demonstrating the ability of these models to distinguish between higher survival probabilities for ODI partnerships compared with IPL partnerships at the same wicket. This research has successfully determined ball-by-ball survival probabilities for individual batsmen and batting partnerships in limited overs cricket games. Additionally, the work has provided a rigorous quantitative framework for optimising team performance.</p>

Optimising Batting Partnership Strategy in the First Innings of a Limited Overs Cricket Match

<p>In cricket, the better an individual batsman or batting partnership performs, the more likely the team is to win. Quantifying batting performance is therefore fundamental to help with in-game decisions, to optimise team performance and maximise chances of winning. Several within-game metrics exist to summarise individual batting performances in cricket. However, these metrics summarise individual performance and do not account for partnership performance. An expectation of how likely a batting partnership is to survive each ball within an innings can enable more effective partnership strategies to optimise a team’s final total. The primary objective of this research was to optimise batting partnership strategy by formulating several predictive models to calculate the probability of a batting partnership being dismissed in the first innings of a limited overs cricket match. The narrowed focus also reduced confounding factors, such as match state. More importantly, the results are of practical significance and provide new insight into how an innings evolves. The model structures were expected to reveal strategies for optimally setting a total score for the opposition to chase. In the first innings of a limited overs cricket match, there is little information available at the commencement and during the innings to guide the team in accumulating a winning total score. The secondary objective of this research was to validate the final models to ensure they were appropriately estimating the ball-by-ball survival probabilities of each batsman, in order to determine the most effective partnership combinations. The research hypothesised that the more effective a batting partnership is at occupying the crease, the more runs they will score at an appropriate rate and the more likely the team is to win the match, by setting a defendable total. Data were split into subsets based on the batting position or wicket. Cox proportional hazard models and ridge regression techniques were implemented to consider the potential effect of eight batting partnership performance predictor variables on the ball-by-ball probability of a batting partnership facing the next ball without being dismissed. The Area Under the Curve (AUC) was implemented as a performance measure used to rank the batting partnerships. Based on One-Day International (ODI) games played between 26th December 2013 and 14th February 2016, the model for opening batting partnerships ranked Pakistani’s A Ali and S Aslam as the optimal opening batting partnership. This method of calculating batting partnership rankings is also positively correlated with typical measures of success: average runs scored, proportion of team runs scored and winning. These findings support the research hypothesis. South African’s, HM Amla and AB de Villiers are ranked as the optimal partnership at wicket two. As at 28th February 2016, these batsmen were rated 6th equal and 2nd in the world respectively. More importantly, these results show that this pair enable South Africa to maximise South Africa’s chances of winning, by setting a total in an optimal manner. New Zealand captain, Kane Williamson, is suggested as the optimal batsman to bat in position three regardless of which opener is dismissed. Reviewing New Zealand’s loss against Australia on 4th December 2016, indicates a suboptimal order was used with JDS Neesham and BJ Watling batting at four and five respectively. Given the circumstances, C Munro and C de Grandhomme were quantified as a more optimal order. The results indicate that for opening batsmen, better team results are obtained when consecutive dot balls are minimised. For top order and middle order batsmen, this criteria is relaxed with the emphasis on their contribution to the team. Additionally, for middle order batsmen, minimising the occasions where 2 runs or less are scored within 4 deliveries is important. In order to validate the final models, each one was applied to the corresponding Indian Premier League (IPL) 2016 data. These models were used to generate survival probabilities for IPL batting partnerships. The probabilities were then plotted against survival probabilities for ODI batting partnerships at the same wicket. The AUC was calculated as a metric to determine which models generated survival probabilities characterising the largest difference between IPL partnerships and ODI partnerships. All models were validated by successfully demonstrating the ability of these models to distinguish between higher survival probabilities for ODI partnerships compared with IPL partnerships at the same wicket. This research has successfully determined ball-by-ball survival probabilities for individual batsmen and batting partnerships in limited overs cricket games. Additionally, the work has provided a rigorous quantitative framework for optimising team performance.</p>

Independently validated sex-specific nomograms for predicting survival in patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825

Background/purpose Glioblastoma (GBM) is the most common primary malignant brain tumor. Sex has been shown to be an important prognostic factor for GBM. The purpose of this study was to develop and independently validate sex-specific nomograms for estimation of individualized GBM survival probabilities using data from 2 independent NRG Oncology clinical trials. Methods This analysis included information on 752 (NRG/RTOG 0525) and 599 (NRG/RTOG 0825) patients with newly diagnosed GBM. The Cox proportional hazard models by sex were developed using NRG/RTOG 0525 and significant variables were identified using a backward selection procedure. The final selected models by sex were then independently validated using NRG/RTOG 0825. Results Final nomograms were built by sex. Age at diagnosis, KPS, MGMT promoter methylation and location of tumor were common significant predictors of survival for both sexes. For both sexes, tumors in the frontal lobes had significantly better survival than tumors of multiple sites. Extent of resection, and use of corticosteroids were significant predictors of survival for males. Conclusions A sex specific nomogram that assesses individualized survival probabilities (6-, 12- and 24-months) for patients with GBM could be more useful than estimation of overall survival as there are factors that differ between males and females. A user friendly online application can be found here—https://npatilshinyappcalculator.shinyapps.io/SexDifferencesInGBM/.

Fine-scale plasticity in nest placement can compensate for poor quality junipers as nesting trees for gray vireos

Understanding the interactions between behavior and habitat characteristics can have important implications for species of conservation concern. Gray vireos (Vireo vicinior) are one example of a species of conservation concern that is understudied in terms of nest survival probabilities and the habitat characteristics that influence them. Our objective was to determine if habitat features such as juniper density, juniper foliage density, or tree height influence nest survival probabilities, and if gray vireo nest placement can mitigate habitat risks. Based on previous work, we expected daily nest survival probabilities to be associated with nest height and surrounding vegetation. We monitored 89 nests in central New Mexico from 2016–2018 to estimate daily nest survival probabilities. We compared variation in nest placement, nest tree characteristics, and surrounding vegetation between failed and successful nests using logistic exposure models and Akaike Information Criteria. Daily and cumulative nest survival probability were 0.983 (95% CI [0.973–0.989]) and 0.575 (95% CI [0.444–0.702]), respectively. Top models predicting nest survival included a negative interaction between nest-tree foliage density and the distance of the nest from the edge of the nesting tree. This suggests that gray vireos can mitigate risks associated with low nest concealment by nesting closer to the interior of the nesting tree.

Acute and Post-Acute COVID-19 Severity and Mortality in Patients with Hematologic Malignancies: A Population-Based Registry Study

Introduction: The severity of acute clinical outcomes and mortality in hematologic malignancy (HM) patients infected by SARS-CoV-2 was exhaustively documented in the first weeks of the pandemic. A consistent increased mortality compared to non-cancer patients was observed across studies. In this study we aimed to estimate survival in COVID-19 HM patients by type of malignancy, to describe acute and post-acute clinical outcomes, and to compare outcomes in early and later pandemic periods. Methods: In this population-based registry study sponsored by the Madrid Society of Hematology (Asociación Madrileña de Hematología y Hemoterapia), we collected de-identified data on clinical characteristics, treatment and acute and post-acute outcomes in adult patients with hematologic malignancies and confirmed SARS-CoV-2 infection within the Madrid region of Spain. Our case series included all eligible patients admitted to 26 regional health service hospitals and 5 private healthcare centers between February 28, 2020 and February 18, 2021 with a coverage of 98% on a population of 6.6 million inhabitants. The study outcomes were all-cause mortality, severity of disease (WHO), oxygen support, ICU admission, and follow-up symptoms and signs and complications. Survival probabilities were estimated with the actuarial method and reported overall and stratified by type of malignancy and for two study periods (early cohort,-COVID-19 diagnosis from February 28 to 31 May, 2020, and later cohort, up to February 18, 2021). Results: Of the 1408 patients reported to the HEMATO-MADRID COVID-19 registry, 1166 were included in the present analyses; 839 (72%) had a lymphoid malignancy, including 325 (28%) with non-Hodgkin lymphoma, 50 (4%) with Hodgkin lymphoma and 263 (23%) with multiple myeloma; and 327 (28%) had a myeloid malignancy, including 115 (10%) with myelodysplastic syndrome, 92 (8%) with acute myeloid leukemia (AML) and 87 (7%) with Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms. Overall COVID-19 clinical severity was classified as critical in 19% of patients, severe in 36%, moderate in 22%, and mild in 22%; 10% were admitted to an ICU; 8% were on mechanical ventilation and 19% on noninvasive ventilation. Mild disease increased between early and later period from 15% to 38% of patients; severe disease decreased from 42% to 24%, p&lt;0.001. COVID-19 treatment with steroids increased from 38% to 59%, p&lt;0.001. At follow-up, 22% reported persistent symptoms related to COVID-19 at 2 months, 16% at 4 months and 14% at 6 months. 381 of 1166 (33%) patients died. Overall 30-day survival was 68%; 2 and 3-month overall survival probabilities were 56% and 53%, respectively. Survival was more favorable for patients with myeloproliferative neoplasms (82%, 69% and 65% at 30-days, 2 and 3 months, respectively) than for those with lymphoid malignancies (68%, 56% and 54%) or myelodysplastic syndrome/acute myeloid leukemia (61%, 51%, 46%), p=001. 285 (37%) patients died in the early period vs 96 (24%) in the later, p&lt;0.001, but median (interquartile range) follow-up time was much higher in the early vs later, 45 (20-116) days vs. 26 (11-86), respectively. Overall survival was not different between periods, p=0.5 (hazard ratio [95%C], 0.93 [0.73-1.17]). In the later cohort, 30 and 60-day survival probabilities were 71% and 56% vs. 67% and 56% in the early cohort Conclusions. A population-based registry in Spain provided strong evidence that although COVID-19 severity decreased over year 1 of the pandemic, mortality remained high, and survival was stable over time in the group of patients with hematological malignancy infected by SARS-Coc-2. A relevant proportion of the infected patients (1 in 6) referred persistent symptoms attributable to COVID-19. The improved clinical management of severe COVID-19 in non-cancer patients that followed the dissemination of evidence-based recommendations did not translate in more favorable survival in patients with hematological malignancies. Research is needed to address the specific characteristics and improve the clinical management of this vulnerable population. Disclosures Martinez-Lopez: Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Incyte: Consultancy, Research Funding, Speakers Bureau; Roche: Consultancy, Research Funding, Speakers Bureau; Astellas: Research Funding, Speakers Bureau. Jiménez-Yuste: Pfizer: Consultancy, Honoraria, Research Funding; Grifols: Consultancy, Honoraria, Research Funding; CSL Behring: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; NovoNordisk: Consultancy, Honoraria, Research Funding; BioMarin: Consultancy; Sobi: Consultancy, Honoraria, Research Funding; Octapharma: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding. Kwon: Gilead: Honoraria.

Population-Based Analysis of Hispanic Vs Non-Hispanic Patients Diagnosed with Hodgkin Lymphoma in Texas and Florida

Introduction: Classical Hodgkin lymphoma (cHL) accounts for about 90% of cases of HL. (Medicine PMID 26107683) Within cHL, there are 4 main histologic subtypes; the incidence of cHL varies based on age, race/ethnicity, geography, socioeconomic factors, Epstein Barr virus status, and the prevalence of HIV/AIDS. (Adv HematologyPMID 21197477) Considerable disparities exist in the incidence and survival rates between Hispanic (H) and non-Hispanic (NH) populations with cHL. (Ann Oncol PMID 22241896) Between 2013-2017, the incidence rate of cHL in Florida (FL) was 457 per 100,00, and in Texas (TX), it was 408 per 100,000. (North American Association of Central Cancer Registries, 2020) Our study aims to determine demographics, treatment outcomes, and survival outcomes of H and NH patients diagnosed with cHL in TX and FL. Methods: This is a retrospective study of a cohort of patients diagnosed with lymphoma (Hodgkin and Non-Hodgkin) from the Texas Cancer Registry (TCR) and the Florida Cancer Data System (FCDS) between 2006-2017. The third edition of the International Classification of Diseases for Oncology (ICD-O-3) was used to identify patient with cHL. Variables include gender, race, ethnicity, birthplace, occupation, dates at diagnosis and death, primary payer at diagnosis, subtype of lymphoma, stage, type of treatment, poverty index, and vitality status. The significance of variation in the distribution of categorical outcomes with ethnicity (H, NH) was assessed with Fisher's Exact tests or Pearson's Chi-square tests as appropriate; age was assessed with T-tests or Wilcoxon tests as appropriate. Survival time was measured in years from date of primary diagnosis to the date of death. Survival distributions were described with Kaplan-Meier curves and significance of variation in median survival with ethnicity was assessed with log rank testing. At risk tables were computed based on the Kaplan-Meier estimate of the survival curve. All statistical testing was two-sided with a significance level of 5%. Corrections for multiple testing were not applied. Results: There were 6152 (1266 H, 4886 NH) patients in FL and 6241(1736 H, 4505 NH) patients in TX identified with cHL. In FL, the median age at diagnosis was 44.8 years (y) for H vs 48.3y for NH (p &lt; 0.001) while in TX, there was no statistically significant difference (45.8y H, 44.9y NH, p = 0.102). In FL, there was no statistically significant difference among females (44% H, 46% NH, p = 0.136) and males (56% H, 53% NH, p = 0.136) while there was one in TX among females (43% H, 45% NH, p = 0.048) and males (58% H, 55% NH, p = 0.048). In FL, the majority of H (40.5%) and NH (36.6%) were in the 10-19.9% poverty index (p&lt;0.001). In TX, the majority of H (51.2%) were in the 20-100% poverty index while the majority of NH (32.2%) were in the 10-19.9% index (p&lt;0.001). In FL, 10.7% H and 6.4% NH were without insurance at time of diagnosis (p&lt;0.001) while in TX, 23.8% H and 11.7% NH were in that position (p&lt;0.001). The most common stage of diagnosis was stage III/IV with 37.8% H vs 34.2% NH in FL (p&lt;0.001) and 44.1% H vs 34.6% NH in TX (p&lt;0001). In FL, median survival time was 10.6y H vs 11.4y NH, while in TX, it was 10.3y H vs 10.4y NH. In FL, the survival probabilities at years 2, 5, and 10 were 0.858, 0.774, and 0.550 for H vs 0.808, 0.696, and 0.545 for NH, respectively. In TX, the survival probabilities at years 2, 5, and 10 were 0.758, 0.674, and 0.522 for H vs 0.828, 0.743, and 0.579 for NH, respectively. The survival probability at years 2, 5, and 10 were higher for H compared to NH in FL (p = 0.0018), however the survival probability at years 2, 5, and 10 were lower for H compared to NH in TX (p &lt; 0.0001). Conclusion: Our study of patients diagnosed with cHL demonstrated several statistically significant differences among H and NH patients in both states. Importantly, H patients in TX had a statistically significant lower survival probability at 2, 5, and 10y compared to NH patients. A reason for this could be the more significant number of uninsured H as compared to NH patients. Conversely, H patients in FL had a statistically significant higher survival probability at 2 and 5y compared to NH patients, partly explained by the lower median age of diagnosis of H patients compared to NH patients. There is a need for further analysis that could help explain the disparities among the different ethnicities. Figure 1 Figure 1. Disclosures Diaz Duque: Astra Zeneca: Research Funding; Epizyme: Consultancy; Morphosys: Speakers Bureau; Incyte: Consultancy; ADCT: Consultancy; Hutchinson Pharmaceuticals: Research Funding.

Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning

Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,928 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2,723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performances of weighted concordance index 0.95 and precision-recall area under the curve 0.71 were measured on the test set. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.