Individual Survival Probability Research Articles

An independently validated survival nomogram for lower-grade glioma.

Gliomas are the most common primary malignant brain tumor. Diffuse low-grade and intermediate-grade gliomas, which together compose the lower-grade gliomas (LGGs; World Health Organization [WHO] grades II and III), present a therapeutic challenge to physicians due to the heterogeneity of their clinical behavior. Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a survival nomogram for patients with newly diagnosed LGG. Data were obtained for newly diagnosed LGG patients from The Cancer Genome Atlas (TCGA) and the Ohio Brain Tumor Study (OBTS) with the following variables: tumor grade (II or III), age at diagnosis, sex, Karnofsky performance status (KPS), and molecular subtype (IDH mutant with 1p/19q codeletion [IDHmut-codel], IDH mutant without 1p/19q codeletion, and IDH wild-type). Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using TCGA data and independently validated using the OBTS data. Models were internally validated using 10-fold cross-validation and externally validated with calibration curves. A final nomogram was validated for newly diagnosed LGG. Factors that increased the probability of survival included grade II tumor, younger age at diagnosis, having a high KPS, and the IDHmut-codel molecular subtype. A nomogram that calculates individualized survival probabilities for patients with newly diagnosed LGG could be useful to health care providers for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free online software for implementing this nomogram is provided: https://hgittleman.shinyapps.io/LGG_Nomogram_H_Gittleman/. 1. A survival nomogram for lower-grade glioma patients has been developed and externally validated.2. Free online software for implementing this nomogram is provided allowing for ease of use by practicing health care providers.

Prognostic Value Of Preoperative Systemic Inflammatory Biomarkers In Patients With Gallbladder Cancer And The Establishment Of A Nomogram.

Background and aimPreoperative systemic inflammatory biomarkers, including neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR) have been developed to predict patient outcome in several types of carcinomas. The aim of this study was to investigate the potential prognostic value of NLR, dNLR, PLR, and LMR, and establish a prognostic nomogram in postoperative GBC patients who underwent radical cholecystectomy.Methods169 GBC patients were retrospectively enrolled in the present study. ROC curve analysis was used to determine the optimal cut-off values of systemic inflammatory biomarkers. The prognostic value of those biomarkers was investigated according to the Kaplan-Meier method and Cox regression model. A relevant prognostic nomogram was established.ResultsResults showed that NLR, dNLR, PLR, and LMR were significantly associated with overall survival (OS); whereas, NLR and LMR were retained as independent indicators. Based on these independent predictors including tumor differentiation, T stage, N stage, CEA, NLR, and LMR, a nomogram was generated with an accuracy of 0.801.ConclusionBased on our findings, the predictive nomogram could accurately predict individualized survival probability of postoperative GBC patients, and might support clinicians in treatment optimization and clinical decision-making.

Association Between Tumor Size Kinetics and Survival in Patients With Urothelial Carcinoma Treated With Atezolizumab: Implication for Patient Follow-Up.

We characterized the association between tumor size kinetics and survival in patients with advanced urothelial carcinoma treated with atezolizumab (anti-programmed death-ligand 1, Tecentriq) using a joint model. The model, developed on data from 309 patients of a phase II clinical trial, identified the time-to-tumor growth and the instantaneous changes in tumor size as the best on-treatment predictors of survival. On the validation dataset containing data from 457 patients from a phase III study, the model predicted individual survival probability using 3-month or 6-month tumor size follow-up data with an area under the receptor-occupancy curve between 0.75 and 0.84, as compared with values comprised between 0.62 and 0.75 when the model included only information available at treatment initiation. Including tumor size kinetics in a relevant statistical framework improves the prediction of survival probability during immunotherapy treatment and may be useful to identify most-at-risk patients in "real-time."

Prediction of overall survival following colorectal cancer surgery in elderly patients.

BACKGROUNDWith advanced age and chronic illness, the life expectancy of a patient with colorectal cancer (CRC) becomes less dependent on the malignant disease and more on their pre-morbid condition. Justifying major surgery for these elderly patients can be challenging. An accurate tool demonstrating post-operative survival probability would be useful for surgeons and their patients.AIMTo integrate clinically significant prognostic factors relevant to elective colorectal surgery in the elderly into a validated pre-operative scoring system.METHODSIn this retrospective cohort study, patients aged 70 and above who underwent surgery for CRC at Singapore General Hospital between 1 January 2005 and 31 December 2012 were identified from a prospectively maintained database. Patients with evidence of metastatic disease, and those who underwent emergency surgery or had surgery for benign colorectal conditions were excluded from the analysis. The primary outcome was overall 3-year overall survival (OS) following surgery. A multivariate model predicting survival was derived and validated against an equivalent external surgical cohort from Kyungpook National University Chilgok Hospital, South Korea. Statistical analyses were performed using Stata/MP Version 15.1.RESULTSA total of 1267 patients were identified for analysis. The median post-operative length of stay was 8 [interquartile range (IQR) 6-12] d and median follow-up duration was 47 (IQR 19-75) mo. Median OS was 78 (IQR 65-85) mo. Following multivariate analysis, the factors significant for predicting overall mortality were serum albumin < 35 g/dL, serum carcinoembryonic antigen ≥ 20 µg/L, T stage 3 or 4, moderate tumor cell differentiation or worse, mucinous histology, rectal tumors, and pre-existing chronic obstructive lung disease. Advanced age alone was not found to be significant. The Korean cohort consisted of 910 patients. The Singapore cohort exhibited a poorer OS, likely due to a higher proportion of advanced cancers. Despite the clinicopathologic differences, there was successful validation of the model following recalibration. An interactive online calculator was designed to facilitate post-operative survival prediction, available at http://bit.ly/sgh_crc. The main limitation of the study was selection bias, as patients who had undergone surgery would have tended to be physiologically fitter.CONCLUSIONThis novel scoring system generates an individualized survival probability following colorectal resection and can assist in the decision-making process. Validation with an external population strengthens the generalizability of this model.

An independently validated nomogram for isocitrate dehydrogenase-wild-type glioblastoma patient survival.

BackgroundIn 2016, the World Health Organization reclassified the definition of glioblastoma (GBM), dividing these tumors into isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant GBM, where the vast majority of GBMs are IDH-wild-type. Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a nomogram for IDH-wild-type patients with newly diagnosed GBM.MethodsData were obtained from newly diagnosed GBM patients from the Ohio Brain Tumor Study (OBTS) and the University of California San Francisco (UCSF) for diagnosis years 2007–2017 with the following variables: age at diagnosis, sex, extent of resection, concurrent radiation/temozolomide (TMZ) status, Karnofsky Performance Status (KPS), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and IDH mutation status. Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the OBTS data and independently validated using the UCSF data. Models were internally validated using 10-fold cross-validation and externally validated by plotting calibration curves.ResultsA final nomogram was validated for IDH-wild-type newly diagnosed GBM. Factors that increased the probability of survival included younger age at diagnosis, female sex, having gross total resection, having concurrent radiation/TMZ, having a high KPS, and having MGMT methylation.ConclusionsA nomogram that calculates individualized survival probabilities for IDH-wild-type patients with newly diagnosed GBM could be useful to physicians for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: https://gcioffi.shinyapps.io/Nomogram_For_IDH_Wildtype_GBM_H_Gittleman/.

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer.

Importance:Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials.Objective:The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort.Design:1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots.Setting:General community.Main outcome:A nomogram model was developed to predict overall survival in NSCLC patients.Results:We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e−11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e−6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e−11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access.Conclusions:This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.

The Peregrine population study in the French Jura mountains 1964–2016: use of occupancy modeling to estimate population size and analyze site persistence and colonization rates

AbstractWe summarize key results of the first 53 years of one of the longest-running avian population studies in the world, on the Peregrine Falcon(Falco peregrinus),in the French Jura mountains (12,714 km2), launched in 1964. A total of 449 cliff sites in 338 potential Peregrine territories were surveyed: 287 (85%) of these territories were occupied by an adult pair at least once, while in 51 (15%) we never detected an adult pair. Most sites were visited several times during a breeding season to survey occupancy and later fecundity, but the proportion of sites visited was highly variable over the years. We highlight the power of the Bayesian implementation of site-occupancy models (MacKenzieet al.2002, 2003) to analyze data from raptor population studies: to correct population size estimates for sites not visited in a given year and for the biasing effects of preferential sampling (when better sites are more likely to be checked). In addition, these models allow estimation and modeling of the site-level persistence and colonization rates, which can provide important clues about drivers of population dynamics, even without individually marking any birds. Changes in the dynamics rates may serve as early-warning signals for subsequent population declines.Since 1964, the observed number of adult pairs varied between 17 in 1972 and 196 in 2008, but the proportion of sites visited increased from 43% in 1964 to 80–90% after 2002. Hence, this raw population total must be an underestimate. We found strong evidence for preferential sampling in our study. Correcting for this, we estimated 56 pairs in 1964, after which the population dropped to a minimum of 18 in 1972, but then recovered rapidly, leveling off somewhat around 1995 and reaching a maximum of 200–210 adult pairs during 2000–2012. This was then followed by a decline to 170–190 pairs. In any one year, the raw counts underestimated the true population size by 5–39% (mean 11%), due to sites not being visited (this correction ignores imperfect detection though). Site persistence rates declined from 78% to less than 60% during 1967–1972, and then increased rapidly to over 90% during 1980–1990, suggesting that once pesticide effects vanished, individual survival probability increased rapidly and as a consequence also site persistence. Since the 1990s, persistence has declined slowly, which may indicate decreasing adult survival. In contrast, colonization rates increased steadily from about 3% in the early years to maxima of 46–49% during 1994–2001, but declined thereafter and currently reach about 33%. Taller cliffs had greater persistence and colonization rates than medium or small cliffs.Both the decline in colonization and in persistence rates during the last 15 years may reflect density-dependence, predation by the expanding European Eagle Owl(Bubo bubo)population, human persecution or any as yet unknown factors. Importantly, we note that both persistence and colonization rates began to decline many years before the recent population decline became apparent. Thus, analysis of population studies using dynamic occupancy models can provide early-warning signals for future population declines. Our study demonstrates the benefits of modern analytical methods that can correct for several key deficiencies in probably all raptor population studies: incomplete coverage of sites and imperfect detection (though we only dealt with the former here). Occupancy models, possibly accounting for preferential sampling, appear to represent the logical analytical framework for abundance in raptor population studies.

Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty.

Estimating eco-epidemiological parameters in free-ranging populations can be challenging. As known individuals may be undetected during a field session, or their health status uncertain, the collected data are typically “imperfect”. Multi-event capture-mark-recapture (MECMR) models constitute a substantial methodological advance by accounting for such imperfect data. In these models, animals can be “undetected” or “detected” at each time step. Detected animals can be assigned an infection state, such as “susceptible” (S), “infected” (I), or “recovered” (R), or an “unknown” (U) state, when for instance no biological sample could be collected. There may be heterogeneity in the assignment of infection states, depending on the manifestation of the disease in the host or the diagnostic method. For example, if obtaining the samples needed to prove viral infection in a detected animal is difficult, this can result in a low chance of assigning the I state. Currently, it is unknown how much uncertainty MECMR models can tolerate to provide reliable estimates of eco-epidemiological parameters and whether these parameters are sensitive to heterogeneity in the assignment of infection states. We used simulations to assess how estimates of the survival probability of individuals in different infection states and the probabilities of infection and recovery responded to (1) increasing infection state uncertainty (i.e., the proportion of U) from 20 to 90%, and (2) heterogeneity in the probability of assigning infection states. We simulated data, mimicking a highly virulent disease, and used SIR-MECMR models to quantify bias and precision. For most parameter estimates, bias increased and precision decreased gradually with state uncertainty. The probabilities of survival of I and R individuals and of detection of R individuals were very robust to increasing state uncertainty. In contrast, the probabilities of survival and detection of S individuals, and the infection and recovery probabilities showed high biases and low precisions when state uncertainty was >50%, particularly when the assignment of the S state was reduced. Considering this specific disease scenario, SIR-MECMR models are globally robust to state uncertainty and heterogeneity in state assignment, but the previously mentioned parameter estimates should be carefully interpreted if the proportion of U is high.

Prognostic value of systemic inflammatory markers and development of a nomogram in breast cancer.

Systemic inflammatory markers derived from peripheral blood cell, such as the neutrophil-lymphocyte ratio (NLR), derived neutrophil-lymphocyte ratio (dNLR), platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR), have been demonstrated as prognostic markers in several types of malignancy. Here, we investigated and compared the association between systemic inflammatory markers and survival and developed a prognostic nomogram in breast cancer patients. We reviewed the clinical and pathological records of 661 patients diagnosed with invasive breast carcinoma between 1993 and 2011. The NLR, dNLR, PLR and LMR in the immediate preoperative period were assessed. We analyzed the relationship between these inflammatory markers and clinicopathologic variables, disease-specific survival (DSS), and disease-free survival (DFS) in patients. A nomogram was developed to predict 3- and 5-year DSS for breast cancer. In the univariate analysis, high NLR, dNLR, PLR and low LMR were all significantly associated with poor DSS and DFS. In the multivariate analysis, only the PLR (HR 3.226, 95% CI 1.768–5.885 for DSS and HR 1.824, 95% CI 1.824–6.321 for DFS) was still identified as an independent predictor of outcomes. A subgroup analysis revealed that the PLR was the sole independent marker predicting poor DSS in patients with lymph node metastasis (HR 2.294, 95% CI 1.102–4.777) and with luminal subtype (HR 4.039, 95% CI 1.905–8.562). The proposed nomogram, which includes the PLR, shows good accuracy in predicting DSS with a concordance index of 0.82. PLR is an indicator of systemic inflammation as a part of the host immune response. As an independent prognostic factor, an elevated preoperative PLR is superior to the NLR, dNLR, and LMR in predicting clinical outcomes in patients with breast cancer. Moreover, the nomogram incorporating the PLR could accurately predict individualized survival probability in breast cancer.

Cyber–Physical Correlation Effects in Defense Games for Large Discrete Infrastructures

In certain critical infrastructures, correlations between cyber and physical components can be exploited to launch strategic attacks, so that disruptions to one component may affect others and possibly the entire infrastructure. Such correlations must be explicitly taken into account in ensuring the survival of the infrastructure. For large discrete infrastructures characterized by the number of cyber and physical components, we characterize the cyber–physical interactions at two levels: (i) the cyber–physical failure correlation function specifies the conditional survival probability of the cyber sub-infrastructure given that of the physical sub-infrastructure (both specified by their marginal probabilities), and (ii) individual survival probabilities of both sub-infrastructures are characterized by first-order differential conditions expressed in terms of their multiplier functions. We formulate an abstract problem of ensuring the survival probability of a cyber–physical infrastructure with discrete components as a game between the provider and attacker, whose utility functions are composed of infrastructure survival probability terms and cost terms, both expressed in terms of the number of components attacked and reinforced. We derive Nash equilibrium conditions and sensitivity functions that highlight the dependence of infrastructure survival probability on cost terms, correlation functions, multiplier functions, and sub-infrastructure survival probabilities. We apply these analytical results to characterize the defense postures of simplified models of metro systems, cloud computing infrastructures, and smart power grids.

Brood rearing has an immediate survival cost for female Chinese Grouse Tetrastes sewerzowi

Reproductive activities can incur various costs to breeding individuals in birds. One cost is that reproduction decreases survival probabilities of attendant individuals, which may have a major effect on population demography. During brood rearing, adults of precocial species usually make extensive movements to lead their young to sites with adequate food resources and dense cover. However, few studies have evaluated the effects of brood movement on attendant precocial adults. In this study, we tracked female Chinese Grouse during brood-rearing periods using radiotelemetry at Lianhuashan Nature Reserve, Gansu, China, during 2010–2012, to evaluate the effects of brood rearing and movement distances on females’ survival probabilities using known fate models in program MARK. All 41 females attempted to breed, and 30 females successfully hatched at least one chick; 11 failed during the incubation period. Although females with broods moved more extensively than females without broods, movement distances did not influence survival probabilities of attendant females. Seven females with broods were killed by predators, resulting in a lower survival probability (0.958 ± 0.016 weekly survival and 0.679 ± 0.099 through the 9-weeks brood-rearing periods) than for females without broods, which all survived the brood-rearing periods. Our results agree with the prediction of life history theory, which assumes that cost must correlate with reproduction. The extensive movement patterns found in this study might reflect a lack of suitable brood-rearing habitat near nest sites, which might be detrimental to chick survival and influence the persistence of this population.

Defense Strategies for Asymmetric Networked Systems with Discrete Components.

We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i) the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii) the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models.

A nomogram for predicting survival in patients with breast cancer brain metastasis.

Brain metastasis (BM) is common in patients with breast cancer. Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM). The present study was designed to develop and evaluate a prognostic model for patients with newly diagnosed BCBM. Based on the clinical data of patients with BCBM treated in the Affiliated Hospital of Academy of Military Medical Sciences (Beijing, China) between 2002 and 2014, a nomogram was developed to predict survival using proportional hazards regression analysis. The model was validated internally by bootstrapping, and the concordance index (c-index) was calculated. A calibration curve and c-index were used to evaluate discriminatory and predictive ability, in order to compare the nomogram with widely used models, including recursive partitioning analysis (RPA), graded prognostic assessment (GPA) and breast-graded prognostic assessment (Breast-GPA). A total of 411 patients with BCBM were included in the development of this predictive model. The median overall survival time was 14.1 months. Statistically significant predictors for patient survival included biological subtype, Karnofsky performance score, leptomeningeal metastasis, extracranial metastasis, the number of brain metastases and disease-free survival. A nomogram for predicting 1- and 2-year overall survival rates was constructed, which exhibited good accuracy in predicting overall survival with a concordance index of 0.735. This model outperformed RPA, GPA and Breast-GPA, based on the comparisons of the c-indexes. The nomogram constructed based on a multiple factor analysis was able to more accurately predict the individual survival probability of patients with BCBM, compared with existing models.

Nomogram prediction of overall survival for patients with non-small-cell lung cancer incorporating pretreatment peripheral blood markers.

The objective of this study is to build a novel prognostic nomogram in non-small-cell lung cancer (NSCLC) incorporating pre-treatment peripheral blood markers beyond known pathoclinical predictors. We analysed 7158 patients with NSCLC diagnosed between 1 January 1997 and 31 December 2012 from a single institution with a uniform medical record and routine follow-up information. Besides common clinicopathological factors, we investigated the prognostic value of the neutrophil to lymphocyte ratio, monocytes and haemoglobin level in peripheral blood before treatment. Patients were randomly assigned to training (4772 patients, 66.7%) or validation cohorts (2386 patients, 33.3%). Cox proportional hazards models determined the effects of multiple factors on overall survival (OS). A nomogram was developed to predict median survival and 1-, 3-, 5- and 10-year OS for NSCLC. The performance of the nomogram was assessed by a concordance index and calibration curve. In the training cohort, the multivariate Cox model identified the neutrophil to lymphocyte ratio, monocytes and haemoglobin level before treatment as significant prognostic factors for OS independent of patient age, gender, smoking history of intensity and cessation, performance status, disease stage, tumour cell type and differentiation grade and therapies. All the significant prognostic variables were incorporated into a nomogram. In the validation cohort, the nomogram showed notable accuracy in predicting OS, with a concordance index of 0.81, and was well calibrated for predictions of OS. The proposed nomogram incorporating peripheral blood markers and known prognostic factors could accurately predict individualized survival probability of patients with NSCLC. It could be used in treatment planning and stratification in clinical trials.

High annual survival in infected wildlife populations may veil a persistent extinction risk from disease

AbstractHost response to emerging pathogens is variable, causing uncertainty about population‐level impacts and challenging effective disease management. White‐nose syndrome (WNS) has caused catastrophic declines in some bat species, while others appear less impacted. Developing predictive models based on observed survival patterns can generate testable hypotheses about mechanisms driving population dynamics and contribute to the development of targeted approaches to disease management. We conducted a mark–recapture study of federally endangered Indiana bats (Myotis sodalis) during 2011–2016. Annual survival decreased from 0.78 (95% CI: 0.59, 0.89) and 0.79 (95% CI: 0.70, 0.86) for females and males, respectively, in 2011 to 0.74 (95% CI: 0.33, 0.94) and 0.75 (95% CI: 0.53, 0.89) for females and males, respectively, in 2015. We then modeled two explanatory mechanisms potentially driving the observed patterns: (1) phased exposure to disease through the spatial spread of the pathogen within the hibernaculum; and (2) cumulative mortality risk from iterative yearly WNS infection. Under a phased exposure scenario, models suggest that infected individuals have an average survival probability of 0.68, and disease prevalence is predicted to reach 100% within 9 yr of disease emergence. Under the cumulative mortality risk hypothesis, survival probability of individuals decreases with each infection cycle. In either case, infected populations are predicted to stabilize at a negative growth rate. Results suggest that Indiana bats tolerate a pathogen load prior to onset of infection, leading to a less pronounced population decline than for other susceptible species. However, the long‐term risk of WNS to Indiana bats may be more severe than current population trends suggest. To inform current conservation management, we performed a vital rate sensitivity analysis, which suggested that modest increases in survival (4–5%) through targeted intervention may return declining populations to stability (λ = 1.0). Demographic modeling approaches coupled with continued population monitoring can highlight important differences in disease response, and ultimately extinction risk, in host species allowing conservation practitioners to tailor intervention actions so that they will be most effective.

A Nomogram for Predicting Survival in Patients With Breast Cancer Brain Metastasis

Brain metastasis (BM) is common in patients with breast cancer. Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM). The present study was designed to develop and evaluate a prognostic model for patients with newly diagnosed BCBM. Based on the clinical data of patients with BCBM treated in the Affiliated Hospital of Academy of Military Medical Sciences (Beijing, China) between 2002 and 2014, a nomogram was developed to predict survival using proportional hazards regression analysis. The model was validated internally by bootstrapping, and the concordance index (c-index) was calculated. A calibration curve and c-index were used to evaluate discriminatory and predictive ability, in order to compare the nomogram with widely used models, including recursive partitioning analysis (RPA), graded prognostic assessment (GPA) and breast-graded prognostic assessment (Breast-GPA). A total of 411 patients with BCBM were included in the development of this predictive model. The median overall survival time was 14.1 months. Statistically significant predictors for patient survival included biological subtype, Karnofsky performance score, leptomeningeal metastasis, extracranial metastasis, the number of brain metastases and disease-free survival. A nomogram for predicting 1- and 2-year overall survival rates was constructed, which exhibited good accuracy in predicting overall survival with a concordance index of 0.735. This model outperformed RPA, GPA and Breast-GPA, based on the comparisons of the c-indexes. The nomogram constructed based on a multiple factor analysis was able to more accurately predict the individual survival probability of patients with BCBM, compared with existing models.

A Novel Derivation Predicting Survival After Primary Tumor Resection in Stage IV Colorectal Cancer: Validation of a Prognostic Scoring Model and an Online Calculator to Provide Individualized Survival Estimation.

A prognostic scoring model has been devised previously to predict survival following primary tumor resection in patients with metastatic colorectal cancer and unresectable metastases. This has yet to be validated. The main objectives of this study are to validate the proposed prognostic scoring model and create an interactive online calculator to estimate an individual's survival after primary tumor resection. Clinical data and survival outcomes of patients were extracted from a prospectively maintained database. Patients were categorized into good, moderate, or poor survivor groups based on the previously proposed scoring algorithm. Discrimination was assessed and recalibration was performed, with the recalibrated model implemented as an interactive Web application to provide individualized survival probability. This study was conducted at a tertiary referral center. The study included 324 consecutive patients with metastatic colorectal carcinoma and unresectable metastases who underwent primary tumor resection between January 2008 and December 2013. The primary outcome measured was overall survival. Three hundred twenty-four patients were included in the study. Median survival in the good, moderate, and poor prognostic groups was 56.8, 25.7, and 19.9 months (log rank test, p = 0.003). The κ statistic was 0.638 and RD was 0.101. Significant differences in survival were found between the moderate and good prognostic groups (HR, 2.79; 95% CI, 1.51-5.15; p = 0.001) and between poor and good prognostic groups (HR, 4.12; 95% CI, 1.98-8.55; p < 0.001). The model was implemented as an interactive online calculator to provide individualized survival estimation after primary tumor resection (http://bit.ly/Stage4PrognosticScore). Selection bias and single-center data preclude the generalizability of the proposed model. Information regarding the severity or likelihood of developing symptoms from the primary tumor were also not accounted for in the prognostic scoring model proposed. The prognostic scoring model provides good prognostic stratification of survival after primary tumor resection and may be a useful tool to predict survival after primary tumor resection. See Video Abstract at http://links.lww.com/DCR/A330.

Nomograms predicting survival of patients with unresectable or metastatic gastric cancer who receive combination cytotoxic chemotherapy as first-line treatment

Some clinicopathological variables are known to influence the survival of patients with advanced gastric cancer. A comprehensive model based on these factors is needed for prediction of an individual's survival and appropriate patient counseling. A nomogram for predicting 1-year survival in patients with advanced gastric cancer in the palliative chemotherapy setting was developed using clinicopathological data from 949 patients with unresectable or metastatic gastric cancer who had received first-line doublet cytotoxic chemotherapy from 2001 to 2006 at the National Cancer Center, Korea (Baseline Nomogram). For 836 patients whose initial response to chemotherapy is known, another nomogram (ChemoResponse-based Nomogram) was constructed using the response to chemotherapy as additional variable. Nomogram performance in terms of discrimination and calibration ability was evaluated using the C statistic and Hosmer-Lemeshow-type χ 2 statistics. Two different nomograms were developed and subjected to internal validation. The baseline nomogram incorporated 13 baseline clinicopathological variables, whereas the chemoresponse-based nomogram was composed of 11 variables including initial response to chemotherapy. Internal validation revealed good performance of the two nomograms in discrimination: C statistics=0.656 (95% confidence interval, 0.628-0.673) for the baseline and 0.718 (95% confidence interval, 0.694-0.741) for the chemoresponse-based nomogram, which showed significantly better discrimination performance than the baseline nomogram (Z statistics=3.74, p<0.01). This study suggests that individual 1-year survival probability of patients receiving first-line doublet cytotoxic chemotherapy for advanced gastric cancer can be reliably predicted by a nomogram-based method incorporating clinicopathological variables and initial response to chemotherapy.

Survival phenotype, selfish individual versus Darwinian phenotype

Consider and infinitely large asexual population without mutations and direct interactions. The activities of an individual determine the fecundity and the survival probability of individuals, moreover each activity takes time. We view this population model as a simple combination of life history and optimal foraging models. The phenotypes are given by probability distributions on these activities. We concentrate on the following phenotypes defined by optimization of different objective functions: selfish individual (maximizes the average offspring number during life span), survival phenotype (maximizes the probability of non-extinction of descendants) and Darwinian phenotype (maximizes the phenotypic growth rate). We find that the objective functions above can achieve their maximum at different activity distributions, in general. We find that the objective functions above can achieve their maximum at different activity distributions, in general. The novelty of our work is that we let natural selection act on the different objective functions. Using the classical Darwinian reasoning, we show that in our selection model the Darwinian phenotype outperforms all other phenotypes.

Review of 1,000 consecutive extracorporeal membrane oxygenation runs as a quality initiative

Extracorporeal membrane oxygenation is a resource-intensive mode of life-support potentially applicable when conventional therapies fail. Given the initial success of extracorporeal membrane oxygenation to support neonates and infants in the 1980s, indications have expanded to include adolescents, adults, and selected moribund patients during cardiopulmonary resuscitation. This single-institution analysis was conducted to evaluate programmatic growth, outcomes, and risk for death despite extracorporeal membrane oxygenation across all ages and diseases. Beginning in 1989, we registered prospectively all extracorporeal membrane oxygenation patient data with the Extracorporeal Life Support Organization. We queried this registry for our institution-specific data to compare the parameter of "discharge alive" between age groups (neonatal, pediatric, adult), disease groups (respiratory, cardiac, cardiopulmonary resuscitation), and modes of extracorporeal membrane oxygenation (veno-venous; veno-arterial). Extracorporeal membrane oxygenation-specific complications (mechanical, hemorrhagic, neurologic, renal, cardiovascular, pulmonary, infectious, metabolic) were analyzed similarly. Descriptive statistics, Kaplan-Meier, and linear regression analyses were conducted. After 1,052 extracorporeal membrane oxygenation runs, indications have expanded to include adults, to supplement cardiopulmonary resuscitation, to support hemodialysis in neonates and plasmapheresis in children, and to bridge all age patients to heart and lung transplant. Overall survival to discharge was 52% and was better for respiratory diseases (P<.001). Probability of individual survival decreased to <50% if pre-extracorporeal membrane oxygenation mechanical ventilation exceeded respectively 123hours for cardiac, 166hours for cardiopulmonary resuscitation, and 183hours for respiratory diseases (P=.013). Complications occurred most commonly among cardiac and cardiopulmonary resuscitation runs (P<.001), the veno-arterial mode (P<.001), and in adults (P=.044). Our extracorporeal membrane oxygenation program, an Extracorporeal Life Support Organization-designated Center of Excellence, has experienced substantial growth in volume and indications, including increasing age and disease severity. Considering the entire cohort, pre-extracorporeal membrane oxygenation ventilation exceeding 7days was associated with an increased probability of death.