Changes In Survival Probabilities Research Articles

Are trauma centers penalized for improved prehospital resuscitation?: The effect of prehospital transfusion on arrival vitals and predicted mortality.

Prediction models for survival in trauma rely on arrival vital signs to generate survival probabilities. Hospitals are benchmarked on expected and observed outcomes. Prehospital blood (PB) transfusion has been shown to improve mortality, which may affect survival prediction modeling. We hypothesize that the use of PB increases the predicted survival derived from probability models compared with non-blood-based resuscitation. All adult trauma patients presenting to a level 1 trauma center requiring emergency release blood transfusion from January 2017 to December 2021 were reviewed. Patients were grouped into those receiving PB or those who did not (no PB). Prehospital Trauma and Injury Severity Score (TRISS) and shock index were compared with those at presentation to hospital. Univariate and multivariate regressions were performed to identify factors associated with changes in survival probability at presentation. In total, 2117 patients were reviewed (PB, 1,011; no PB, 1,106). Patients receiving PB were younger (35 vs. 40 years, p < 0.001), more likely to have blunt mechanism (71% vs. 65%, p = 0.002), and more severely injured (Injury Severity Score, 27 vs. 25; p < 0.001) and had higher rates of prehospital hypotension (44% vs. 19%, p < 0.001) and shock index (1.10 vs. 0.87, p < 0.001). Upon arrival, PB patients had lower rates of ED hypotension (34% vs. 39%, p = 0.01), and significant improvements in arrival TRISS scores (+0.09 vs. -0.02, p < 0.001) and shock index (+0.10 vs. -0.07, p < 0.001) compared with prehospital. On multivariate analysis, PB was associated with a threefold increase in unexpected survivors (odds ratio, 3.28; 95% confidence interval, 2.23-4.60). The use of PB was associated with improved probability of survival and an increase in unexpected survivors. Applying TRISS and shock index at hospital arrival does not account for en route hemostatic resuscitation, causing patients to arrive with improved vitals despite severity of injury. Caution should be used when implementing survival probability calculations using arrival vitals in centers with prehospital transfusion capability. Retrospective Comparative Study Without Negative Criteria; Level III.

Health accidents and wealth decline in old age

This paper explores the impact of a health shock and changes in survival probability on the savings and portfolio choices of older individuals. Using a theoretical framework featuring a portfolio choice that incorporates imperfect annuity markets, we analyse how elderly individuals, whose survival probability has been altered by a health shock, allocate their resources.A difference-in-differences approach complements the theoretical approach by taking into account the effect of age and cohort, and controlling for selection bias related to health events at older ages. Our analysis utilizes a panel of 5570 observations from the Survey of Health, Aging, and Retirement in Europe (Shavelle et al., 2019;2017). Both theoretical and empirical findings converge, indicating that experiencing a health accident such as a stroke or heart attack leads to a decrease in safe savings. Consequently, investing in annuities becomes crucial in enabling individuals to mitigate the consequences of poor health in aging economies.

Constructing an individualized surveillance framework for nasopharyngeal carcinoma based on a dynamic risk-adapted approach

Background and purposeThis study aims to evaluate the dynamic survival and recurrence hazard of nasopharyngeal carcinoma(NPC) patients after definitive chemoradiotherapy utilizing conditional survival(CS) analysis, and to propose a personalized surveillance strategy at different clinical stages. Materials and MethodsNon-metastatic NPC patients who received curative chemotherapy between June 2005 and December 2011 were included. The Kaplan-Meier method was used to calculate the CS rate. ResultsA total of 1616 patients were analyzed. With the prolongation of survival time, both conditional locoregional recurrence free survival and distant metastatic free survival increased gradually. Changing pattern of annual recurrence risk over time varied among different clinical stages. The annual locoregional recurrence(LRR) risk in stage I-II was always less than 2%, while in stage III-IVa, it was greater than 2% for the first three years and decreased to below 2% only after the third year. The annual distant metastases (DM) risk was always less than 2% in stage I, but higher than 2% in stage II for the first 3 years (2.5–3.8%). For those with stage III-IVa, the annual DM risk retained at a high level(>5%), and only decreased to < 5% after the third year. Based on the dynamic changes in survival probability over time, we established a surveillance plan with different follow-up intensities and frequencies for different clinical stages. ConclusionThe annual risk of LRR and DM decrease over time. Our individual surveillance model will provide critical prognostic information to optimize clinical decision-making, and promote to formulate surveillance counseling and help with resources allocation.

Surgical volume threshold to improve 3-year survival in designated cancer care hospitals in 2004-2012 in Japan.

In Japan, cancer care hospitals designated by the national government have a surgical volume requirement of 400 annually, which is not necessarily defined based on patient outcomes. This study aimed to estimate surgical volume thresholds that ensure optimal 3‐year survival for three periods. In total, 186 965 patients who had undergone surgery for solid cancers in 66 designated cancer care hospitals in Osaka between 2004 and 2012 were examined using data from a population‐based cancer registry. These hospitals were categorized by the annual surgical volume of each 50 surgeries (eg, 0‐49, 50‐99, and so on). Using multivariable Cox proportional hazard regression, we estimated the adjusted 3‐year survival probability per surgical volume category for 2004‐2006, 2007‐2009, and 2010‐2012. Using the joinpoint regression model that computes inflection points in a linear relationship, we estimated the points at which the trend of the association between surgical volume and survival probability changes, defining them as surgical volume thresholds. The adjusted 3‐year survival ranges were 71.7%‐90.0%, 68.2%‐90.0%, and 79.2%‐90.3% in 2004‐2006, 2007‐2009, and 2010‐2012, respectively. The surgical volume thresholds were identified at 100‐149 in 2004‐2006 and 2007‐2009 and 200‐249 in 2010‐2012. The extents of change in the adjusted 3‐year survival probability per increase of 50 surgical volumes were +4.00%, +6.88%, and +1.79% points until the threshold and +0.41%, +0.30%, and +0.11% points after the threshold in 2004‐2006, 2007‐2009, and 2010‐2012, respectively. The existing surgical volume requirements met our estimated thresholds. Surgical volume thresholds based on the association with patient survival may be used as a reference to validate the surgical volume requirement.

Bio-P-16 - Peripheral T-cell lymphoma: molecular profiling distinguishes subclasses, recognizes the tumor architecture and identifies prognostic markers

<h3>Introduction:</h3> Peripheral T-cell lymphomas (PTCLs) are aggressive tumours with unfavourable prognosis, with around 30% OS after 5 years. Histological and molecular studies have revealed a striking degree of heterogeneity, with three major PTCL subtypes defined. Consistent diagnosis and prognostication are still difficult to achieve, because of the difficulty of reproducing results, and the dearth of clinically applicable prognostic biological markers. <h3>Methods:</h3> We have analyzed a series of 105 PTCL cases (66 angioimmunoblastic T-cell lymphoma, AITL; 21 PTCL-not otherwise specified, PTCL-NOS; and 18 PTCL-with T follicular helper phenotype, PTCL-TFH) patients using a customized NanoString platform that includes 208 genes associated with T-cell differentiation, oncogenes and tumor suppressor genes, deregulated pathways and stromal cell subpopulations. Specifically, the platform includes genes expressed by the multiple cell types present in PTCL specimens, together with normal T-cell populations. These are used to try and enable the deconvolution of the T-cell lymphoma microenvironment, and thereby develop an integrated perspective on the cell composition of PTCL tumor specimens. <h3>Results:</h3> A comparative analysis of the various histological types of PTCL (AITL, PTCL-NOS and PTCL-TFH) showed specific sets of genes to be associated with each of the diagnoses, including TFH markers, cytotoxic markers and genes whose expression was a surrogate for specific cellular subpopulations, including follicular dendritic cells, mast cells and genes belonging to specific cell-survival pathways (NF-κB). Unsupervised analysis of the expression of the genes here studied revealed clusters of co-regulated genes that identified the main cell components of the tumor. The analysis did not reveal any differences in survival probability associated with the histological sub-classification, but did identify specific genes and gene sets whose expression was associated with changes in survival probability for each of the PTCL subtypes, independently of the clinical variables included in the International Prognostic Index (IPI). These included a B-cell gene set in cases of AITL, the expression of proliferation markers in PTCL-NOS, and the expression of cytotoxic markers in cases with a diagnosis of PTCL-TFH. For each PTCL lymphoma type, a multivariate analysis identified genes that allow the series of cases to be stratified into different risk groups. This was validated for AITL in an independent series of 54 additional cases. <h3>Conclusions:</h3> In summary, our study supports the current division of PTCL into these three categories (AITL, PTCL-NOS and PTCL-TFH), identifies gene sets potentially useful for this classification and recognizes the expression of B-cell genes as an IPI-independent prognostic factor for AITL subtype.

How can we better inform our patients about post-heart transplantation survival? A conditional survival analysis.

Conditional survival (CS) is a dynamic method of survival analysis that provides an estimate of how an individual's future survival probability changes based on time post-transplant, individual characteristics, and post-transplant events. This study sought to provide post-transplant CS probabilities for heart transplant recipients based on different prognostic variables and provide a discussion tool for the providers and the patients. Adult heart transplant recipients from January 1, 2004, through October 18, 2018, were identified in the UNOS registry. CS probabilities were calculated using data from Kaplan-Meier survival estimates. CS probability exceeded actuarial survival probability at all times post-transplant. Women had similar short-term, but greater long-term CS than men at all times post-transplant (10-year CS 1.8-11.5% greater [95% CI 1.2-12.9]). Patients with ECMO or a surgical BiVAD had decreased survival at the time of transplant, but their CS was indistinguishable from all others by 1-year post-transplant. Rejection and infection requiring hospitalization during the first year were associated with a persistently decreased CS probability. In this study, we report differential conditional survival outcomes based on time, patient characteristics, and clinical events post-transplant, providing a dynamic assessment of survival. The survival probabilities will better inform patients and clinicians of future outcomes.

Conditional Survival and Annual Hazard Estimates of Classical Hodgkin Lymphoma.

BackgroundIn the present study, we have tried to understand how the level of risk and survival probability changes over time for patients with classical Hodgkin’s lymphoma by employing conditional survival and annual hazard as dynamic estimates of prognosis and survival.MethodsThis retrospective study reviewed the clinical data of patients with newly diagnosed classical Hodgkin’s lymphoma admitted to Peking University Cancer Hospital between January 1, 2008, and December 31, 2017. Conditional survival and annual hazard rate were defined as the survival probability and yearly event rate, respectively, assuming that patients have survived for a defined time.ResultsA total of 384 patients were included (median age, 32 years; range, 6–77 years), of which 218 (56.8%) patients had early-stage disease. The median follow-up time was 41.3 months. The 5-year conditional overall survival (COS) rates remained favorable and showed an increase from 89% at treatment to 94% at year 5, while the 5-year conditional failure-free survival (CFFS) rate increased from 70% at treatment to 96% at year 5. The annual hazard of failure decreased from over 15% at diagnosis to less than 5% after 3 years. Early-stage patients had constantly lower annual estimates for hazard of death (range, 0–3.0%) and failure (range, 0–14.3%). However, the hazard of failure in advanced-stage patients decreased from 24.2% at diagnosis to below 8% after 3 years, whereas the hazard of death was always at relatively low levels. Patients with a high IPS risk score (≥3) had significantly lower COS and CFFS during the first 4 years. Patients who received the BEACOPP regimen had better 5-year COS and 5-year CFFS than those who received the ABVD regimen.ConclusionThe survival probability increased and hazard of failure decreased over time.

Outcome Prediction in CLL Patients Based on a Disease-Specific Oligonucleotide Microarray.

Chronic Lymphocytic Leukemia (CLL) is characterized by a extreme variability in the clinical course; some patients survive only a few months whereas others have stable disease for more than 10 years. Individual outcome prediction in CLL is still a challenge, in spite of the existence of multiple newly described prognostic factors: cytogenetic alterations, mutational IgVH status, and expression of surrogate markers such as ZAP-70. The accuracy of outcome prediction in CLL may improve thanks to the inclusion of new additional biological variables identified by high-throughput molecular techniques. This more reliable risk-stratification of patients could allow to assign a risk-adapted individualized therapy. With this aim, we have analysed with oligonucleotide microarrays three different series of CLL patients in a three-step process.In a first-step, RNA extracted from tumour samples of a series of 26 CLL patients, was hybridised using commercial 22K oligonucleotide chips (Agilent Technologies). A total of 135 genes whose expression was associated with changes in survival probability were identified. An additional review of published data yielded a total number of 497 genes whose expression has been related with outcome in CLL. A CLL specific chip was then built in a second-step including, besides these 632 genes, control genes until a total of 1023 genes. This comprehensive CLL-chip was then hybridised with RNA extracted from peripheral-blood samples in a new series of consecutively diagnosed 100 CLL patients. Cox's univariate analysis was performed to look for molecular variables associated with Progression Free Survival (PFS) as endpoint. FDR significance levels were also used when appropriate, for adjusting the associated p-value. Expression of 94 genes was significantly associated with PFS. Profile patterns of the 94 resulting genes were clustered, and allow to identify 12 clusters. Based on biological function, we selected a representing gene of each cluster, to perform a multivariate Cox's analysis. This approach generated a model of 4 genes, implicated in BCR signalling, signal transduction and transcription regulation, that distinguishes four quartiles of CLL patients with PFS at 5 years of 95%, 70%, 44% and 8% respectively.These results have been validated in a new independent series of 60 consecutively diagnosed cases of CLL. If confirmed in additional studies, the availability of this CLL-specific chip could allow a more accurate prediction of the clinical course in CLL patients, this giving support to a stratification of patients into different risk-groups

Variation in insurance-mortality relationship amid macroeconomic shifts: a study of SEER female-specific cancer patients in USA

ObjectivesHealth insurance availability and affordability are vital elements in diagnosis and treatment of patients with cancer and thus constitute clinical significance as well. Although past studies have explored the disparity in mortality figures for patients with different insurance statuses, this population-based study is pioneering in analyzing the changes in cancer mortality risks over time amid macroeconomic shifts. Study designThe study uses Surveillance Epidemiology and End Results (SEER) data of 424,889 non-elderly patients with breast, cervical, ovarian, and uterine cancer diagnosed during 2007–2010 and 2011–2015. MethodsIn addition to discussing incidence figures and insurance patterns, the study uses Kaplan-Meier and Cox's proportional hazard models to examine the changes in survival probability and mortality risks for insurance-stratified patients with female-specific cancer across the two time periods. ResultsPatients without insurance have an increased risk of mortality over time relative to insured patients. Moreover, uninsured patients face this heightened risk more than Medicaid patients. DiscussionDespite public policy measures as well as advancements in diagnostic facilities and treatment technology, the increased relative mortality of patients without insurance limits the long-term affordability of cancer treatment for economically vulnerable patients in comparison with insured patients.

Root suckering promotes recruitment in two temperate rainforest trees with contrasting shade tolerance

The regeneration niche differentiation helps to explain plant coexistence and thus biodiversity. The study of the regeneration niche has been traditionally based on sexual recruitment, while overlooking clonal growth. Root suckering offers a successful alternative for local dispersal under suboptimal conditions for sexual reproduction. For light-limited forests, we hypothesized that: 1) root suckering would increase the regeneration niche towards high-light conditions in shade-tolerant trees and towards dark conditions in light-demanding species; 2) contrasting responses of survival and growth to light availability would explain niche differentiation of both suckers and saplings; and 3) distinct responsiveness to light among species and recruit-types would reflect differences in functional traits. We tested these hypotheses with two evergreen tree species that coexist in the temperate rainforest of southern South America: Embothrium coccineum (light-demanding) and Eucryphia cordifolia (shade-tolerant). We measured the light availability in two study plots above each recruit and along transects established in the understory. Niche selection, niche differentiation and changes in survival probability with light were inferred from the analysis of the light frequency distributions. We evaluated the effect of light on the relative volumetric change in stems over a 1-year period. Functional traits of leaves, stems, and crowns were measured in suckers and saplings growing under similar light conditions; these traits were then compared among size classes, recruit-types and species. Root suckering was the prevalent reproduction mode of both studied species, extending the light niche towards open microenvironments only during the earliest ontogenetic stages. The poor structural strength of the leaves and wood of small Eucryphia saplings explains its underuse of open microsites. Neither photosynthetic assimilation nor carbon subsidy can sustain Embothrium suckers at the shadiest microsites. Suckering proved to increase the persistence of Embothrium until advanced stages of forest succession, facilitating its coexistence with the late-successional Eucryphia. Our study emphasizes that clonal growth is essential to understand the dynamics of temperate rainforests.

Relationship between per capita births of Cook Inlet belugas and summer salmon runs: age‐structured population modeling

AbstractAnthropogenic disturbances may alter a population's conservation status if the ability of individuals to survive and breed is affected. We used an adaptation of the Heligman‐Pollard model to estimate survival at age of Cook Inlet belugas (CIB; Delphinapterus leucas), an endangered population in south‐central Alaska. We developed an age‐structured Leslie matrix model, based on the life history parameters survival and fecundity probability, to evaluate the sensitivity of population size and growth of CIB, to variation in estimate values of Chinook and coho salmon abundance in the Deshka River, a major tributary of the Susitna River. Birth effect (eb) was regressed against Chinook and coho salmon levels for the year of, the year before, and two years before a beluga calf birth. The effect of a range of modifications of salmon availability was illustrated in CIB with a series of simulations. The maximum annual population growths (λ) were set at 1.036 (3.6%). Ranges of CIB survival and fecundity probabilities indicated small changes in survival probabilities have a greater impact on population growth than similar changes in birth probability. As either survival (es) or fecundity (eb) was reduced, the annual growth declined, with either es = 0.961 or eb = 0.388, causing a decreased annual growth of −0.4%. Regressions of Chinook salmon for the year of, the year before, and two years before a birth were all significant at the 5% level as was coho in the year of and year prior to birth. The mechanism model with the best fit was the sum of Chinook and coho in the year of birth and year prior to birth. Simulations showed that if salmon runs remained at their current levels, the CIB population would likely continue its current slow decline and per capita births would continue to be low. The results from this study suggest reproductive success in CIB is tied to salmon abundance in the Deshka River. Current management practices should consider this when setting research priorities, designing new studies, and developing management actions to achieve CIB population recovery targets.

Stochastic modeling of variability in survival behavior of Bacillus simplex spore population during isothermal inactivation at the single cell level using a Monte Carlo simulation

The control of bacterial reduction is important to maintain food safety during thermal processing. The goal of this study was to illustrate and describe variability in bacterial population behavior during thermal processing as a probability distribution based on individual cell heterogeneity regarding heat resistance. Toward this end, we performed a Monte Carlo simulation via computer, and compared and validated the simulated estimations with observed values. Weibullian fitted parameters were estimated from the kinetic survival data of Bacillus simplex during thermal treatment at 94 °C. The variability in reductions of bacterial sporular populations was illustrated using Monte Carlo simulation based on the Weibull distribution of the parameters. In particular, variabilities in viable spore counts and survival probability of the B. simplex spore population were simulated in various replicates. We also experimentally determined the changes in survival probability and distributions of survival spore counts; notably, these were successfully predicted by the Monte Carlo simulation based on the kinetic parameters. The kinetic parameter-based Monte Carlo simulation could thus successfully illustrate bacterial population behavior variability during thermal processing as a probability distribution. The simulation approach may contribute to improving food quality through risk-based processing designs and enhance risk assessment model accuracy.

Advantages of a multi-state approach in surgical research: how intermediate events and risk factor profile affect the prognosis of a patient with locally advanced rectal cancer

BackgroundStandard survival analysis fails to give insight into what happens to a patient after a first outcome event (like first relapse of a disease). Multi-state models are a useful tool for analyzing survival data when different treatments and results (intermediate events) can occur. Aim of this study was to implement a multi-state model on data of patients with rectal cancer to illustrate the advantages of multi-state analysis in comparison to standard survival analysis.MethodsWe re-analyzed data from the RCT FOGT-2 study by using a multi-state model. Based on the results we defined a high and low risk reference patient. Using dynamic prediction, we estimated how the survival probability changes as more information about the clinical history of the patient becomes available.ResultsA patient with stage UICC IIIc (vs UICC II) has a higher risk to develop distant metastasis (DM) or both DM and local recurrence (LR) if he/she discontinues chemotherapy within 6 months or between 6 and 12 months, as well as after the completion of 12 months CTx with HR 3.55 (p = 0.026), 5.33 (p = 0.001) and 3.37 (p < 0.001), respectively. He/she also has a higher risk to die after the development of DM (HR 1.72, p = 0.023). Anterior resection vs. abdominoperineal amputation means 63% risk reduction to develop DM or both DM and LR (HR 0.37, p = 0.003) after discontinuation of chemotherapy between 6 and 12 months. After development of LR, a woman has a 4.62 times higher risk to die (p = 0.006). A high risk reference patient has an estimated 43% 5-year survival probability at start of CTx, whereas for a low risk patient this is 79%. After the development of DM 1 year later, the high risk patient has an estimated 5-year survival probability of 11% and the low risk patient one of 21%.ConclusionsMulti-state models help to gain additional insight into the complex events after start of treatment. Dynamic prediction shows how survival probabilities change by progression of the clinical history.

Consequences of the genetic threshold model for observing partial migration under climate change scenarios.

Migration is a widespread phenomenon across the animal kingdom as a response to seasonality in environmental conditions. Partially migratory populations are populations that consist of both migratory and residential individuals. Such populations are very common, yet their stability has long been debated. The inheritance of migratory activity is currently best described by the threshold model of quantitative genetics. The inclusion of such a genetic threshold model for migratory behavior leads to a stable zone in time and space of partially migratory populations under a wide range of demographic parameter values, when assuming stable environmental conditions and unlimited genetic diversity. Migratory species are expected to be particularly sensitive to global warming, as arrival at the breeding grounds might be increasingly mistimed as a result of the uncoupling of long‐used cues and actual environmental conditions, with decreasing reproduction as a consequence. Here, we investigate the consequences for migratory behavior and the stability of partially migratory populations under five climate change scenarios and the assumption of a genetic threshold value for migratory behavior in an individual‐based model. The results show a spatially and temporally stable zone of partially migratory populations after different lengths of time in all scenarios. In the scenarios in which the species expands its range from a particular set of starting populations, the genetic diversity and location at initialization determine the species’ colonization speed across the zone of partial migration and therefore across the entire landscape. Abruptly changing environmental conditions after model initialization never caused a qualitative change in phenotype distributions, or complete extinction. This suggests that climate change‐induced shifts in species’ ranges as well as changes in survival probabilities and reproductive success can be met with flexibility in migratory behavior at the species level, which will reduce the risk of extinction.

Conditional survival analysis of hepatocellular carcinoma patients treated with radiofrequency ablation.

Survival estimates are commonly reported as survival from the first observation, but future survival probability changes based on the survival time already accumulated after therapy, otherwise known as conditional survival (CS). The aim of the study was to describe CS according to different prognostic variables in hepatocellular carcinoma (HCC) patients treated with radiofrequency ablation (RFA). Data on 125 very early/early HCC patients treated with RFA between 1999 and 2007 were analyzed. Actuarial survival estimates were computed by means of Kaplan-Meier method and compared by log-rank test. The 5-year CS was calculated with stratification by several predictors for patients who had already survived up to 5 years from diagnosis. Median overall survival (OS) was 72 months (95% confidence interval [CI], 58-86). Age, Child-Pugh (CP), α-fetoprotein (AFP), Cancer of the Liver Italian Program (CLIP) score and type of recurrence (early vs late) were significant predictors of OS. The 5-year CS rates of the entire study cohort assessed at 1, 2, 3 and 5 years from the treatment were 49%, 48%, 30% and 34%, respectively. Subgroup analysis confirmed age and CP as significant predictors of CS at all time points, while the CS of subgroups stratified by AFP and CLIP did not differ significantly from the 3rd year after RFA onward, as more advanced patients had probably escaped early recurrence. CS analysis showed that the impact of different variables influencing OS is not linear over time after RFA. Information derived from the study can improve the current management of HCC patients.

Intra-annual patterns in adult band-tailed pigeon survival estimates

Context The band-tailed pigeon (Patagioenas fasciata) is a migratory species occurring in western North America with low recruitment potential and populations that have declined an average of 2.4% per year since the 1960s. Investigations into band-tailed pigeon demographic rates date back to the early 1900s, and existing annual survival rate estimates were derived in the 1970s using band return data. Aims The primary purpose of the paper was to demonstrate that the apparent paradox between band-tailed pigeon population dynamics (long-term steady decline) and breeding season survival rates (very high) can be explained by changes in survival probability during the remainder of the year. Methods We trapped Pacific coast band-tailed pigeons during two separate periods: we equipped pigeons with very high frequency (VHF) radio-transmitters in 1999–2000 (1999 = 20; 2000 = 34); and outfitted pigeons with solar powered platform transmitting terminal (PTT) transmitters in 2006–08 (n = 20). We used known fate models to estimate annual survival rates and seasonal survival variation among four periods based on an annual behavioural cycle based on phenological events (nesting, autumn migration, winter and spring migrations). We used model averaged parameter estimates to account for model selection uncertainty. Key results Neither body condition nor sex were associated with variation in band-tailed pigeon survival rates. Weekly survival during the nesting season did not differ significantly between VHF-marked (0.996; CI = 0.984–0.999) and PTT-marked pigeons (0.998; CI = 0.990–1.00). Model averaged annual survival of PTT-marked pigeons was 0.682 (95% CI = 0.426–0.861) and was similar to annual survival estimated in previous studies using band return data. Survival probability was lowest during both migration periods and highest during the nesting period. Conclusions Our survival estimates are consistent with those of prior studies and suggest that mortality risk is greatest during migration. Weekly survival probability during winter was nearly the same as during the nesting season; however, winter was the longest period and survival throughout winter was lower than other seasons. Implications We present the first inter-seasonal analysis of survival probability of the Pacific coast race of band-tailed pigeons and illustrate important temporal patterns that may influence future species management including harvest strategies and disease monitoring.

Survival in infection-related acute-on-chronic liver failure is defined by extrahepatic organ failures

Infections worsen survival in cirrhosis; however, simple predictors of survival in infection-related acute-on-chronic liver failure (I-ACLF) derived from multicenter studies are required in order to improve prognostication and resource allocation. Using the North American Consortium for Study of End-stage Liver Disease (NACSELD) database, data from 18 centers were collected for survival analysis of prospectively enrolled cirrhosis patients hospitalized with an infection. We defined organ failures as 1) shock, 2) grade III/IV hepatic encephalopathy (HE), 3) need for dialysis and mechanical ventilation. Determinants of survival with these organ failures were analyzed. In all, 507 patients were included (55 years, 52% hepatitis C virus [HCV], 15.8% nosocomial infection, 96% Child score ≥ 7) and 30-day evaluations were available in 453 patients. Urinary tract infection (UTI) (28.5%), and spontaneous bacterial peritonitis (SBP) (22.5%) were the most prevalent infections. During hospitalization, 55.7% developed HE, 17.6% shock, 15.1% required renal replacement, and 15.8% needed ventilation; 23% died within 30 days and 21.6% developed second infections. Admitted patients developed none (38.4%), one (37.3%), two (10.4%), three (10%), or four (4%) organ failures. The 30-day survival worsened with a higher number of extrahepatic organ failures, none (92%), one (72.6%), two (51.3%), three (36%), and all four (23%). I-ACLF was defined as ≥ 2 organ failures given the significant change in survival probability associated at this cutoff. Baseline independent predictors for development of ACLF were nosocomial infections, Model for Endstage Liver Disease (MELD) score, low mean arterial pressure (MAP), and non-SBP infections. Independent predictors of poor 30-day survival were I-ACLF, second infections, and admission values of high MELD, low MAP, high white blood count, and low albumin. Using multicenter study data in hospitalized decompensated infected cirrhosis patients, I-ACLF defined by the presence of two or more organ failures using simple definitions is predictive of poor survival.

Dynamic prediction of hedge fund survival in crisis-prone financial markets

This study focuses on dynamic changes in survival probabilities over the lifetimes of hedge funds. To model such probabilities, a mixed Cox proportional hazards (CPH) model-specifically, a survival/hazard model with time-varying covariates and fixed covariates- is employed. Resulting dynamic survival probabilities show that the mixed CPH model provides significantly higher accuracy in predicting hedge fund failure than other models in the literature, including fixed covariate CPH models and discrete logit models. Our results are useful to investors and regulators of hedge funds in crisis-prone financial markets.

Resource-Based Patient Prioritization in Mass-Casualty Incidents

The most widely used standard for mass-casualty triage, START, relies on a fixed-priority ordering among different classes of patients, and does not explicitly consider resource limitations or the changes in survival probabilities with respect to time. We construct a fluid model of patient triage in a mass-casualty incident that incorporates these factors and characterize its optimal policy. We use this characterization to obtain useful insights about the type of simple policies that have a good chance to perform well in practice, and we demonstrate how one could develop such a policy. Using a realistic simulation model and data from emergency medicine literature, we show that the policy we developed based on our fluid formulation outperforms START in all scenarios considered, sometimes substantially.

The Impact of Malaria Eradication on Fertility

The malaria eradication campaign that started in Sri Lanka in the late 1940s virtually eliminated malaria transmission on the island. I use the preeradication differences in malaria endemicity within Sri Lanka to identify the effect of malaria eradication on fertility and child survival. Malaria eradication increased the number of live births through increasing age-specific fertility and causing an earlier first birth. The effect of malaria on the transition time to higher-order births is inconclusive. Malaria could directly or indirectly affect survival probabilities of live births. I exploit the particular epidemiology of malaria that causes more severe sequelae during an initial pregnancy. I find differential changes in survival probabilities by birth order that are most likely due to the direct in utero effects of malaria. The increase in population growth after malaria eradication reconciles the contradictory findings in the macroeconomic and microeconomic literatures: the increased productivity and education from malaria eradication will only appear in aggregate measures like GDP per capita after a delay because of the initial increase in the population size.