Derivation and validation of a predictive mortality model of in-hospital patients with Acinetobacter baumannii nosocomial infection or colonization.

PURPOSE: Acinetobacter baumannii (Ab) is a Gram-negative opportunistic bacterium responsible for nosocomial infections or colonizations. It is considered one of the most alarming pathogens due to its multi-drug resistance and due to its mortality rate, ranging from 34 to 44,5% of hospitalized patients. The aim of the work is to create a predictive mortality model for hospitalized patient with Ab infection or colonization. METHODS: A cohort of 140 sequentially hospitalized patients were randomized into a training cohort (TC) (100 patients) and a validation cohort (VC) (40 patients). Statistical bivariate analysis was performed to identify variables discriminating surviving patients from deceased ones in the TC, considering both admission time (T0) and infection detection time (T1) parameters. A custom logistic regression model was created and compared with models obtained from the "status" variable alone (Ab colonization/infection), SAPS II, and APACHE II scores. ROC curves were built to identify the best cut-off for each model. RESULTS: Ab infection status, use of penicillin within 90 days prior to ward admission, acidosis, Glasgow Coma Scale, blood pressure, hemoglobin and use of NIV entered the logistic regression model. Our model was confirmed to have a better sensitivity (63%), specificity (85%) and accuracy (80%) than the other models. CONCLUSION: Our predictive mortality model demonstrated to be a reliable and feasible model to predict mortality in Ab infected/colonized hospitalized patients.

Early-death weeks associated with COVID-19: a comparison among France, the UK and the USA.

BACKGROUND: Years of life lost (YLL) is recently used as a more insightful indicator to assess the mortality impact of COVID-19. However, this indicator still has methodological limits. This study aims to propose an alternative approach and new index, early-death weeks. METHODS: The natural mortality and social mortality laws were employed to support two essential assumptions: the sequential and translational early-mortality patterns of COVID-19. This approach was then used with the data related to COVID-19 to calculate early-death weeks associated with COVID-19 in France, the UK and the USA. RESULTS: As of week 20 of 2021, the rate of the total number of early-death weeks per the population of the USA is nearly two times compared to that of France and the UK, with 0.004% to 0.0021 and 0.0023%, respectively. The average numbers of early-death weeks after converting to units of years are 1.2, 1.0 and 1.3 years in France, the UK and the USA, respectively. CONCLUSIONS: The new approach is significantly different from death counts, excess deaths and YLL. The early-death week index provides more insights into COVID-19 and can be applied promptly at any time as well as anywhere once excess deaths have occurred.

Effect of biomass burning on premature mortality associated with long-term exposure to PM2.5 in Equatorial Asia.

The health burden from exposure to ambient fine particulates (PM2.5) in Equatorial Asia is substantially affected by the peatland fires in Indonesia, but the long-term health effect of the fires on local inhabitants is unclear. In this study, PM2.5-associated excess mortality in Equatorial Asia over the past 30 years (1990-2019) was estimated and then the health effect of biomass burning was identified. The PM2.5-related death in Equatorial Asia almost tripled from 113 (95% confidence interval, 100-125) thousand in 1990 to 337 (300-373) thousand in 2019, with a rate of increase of 6.4 (6.2-6.9) thousand/yr. The intense biomass burning between 1990 and 2019 was estimated to have induced 317 (282-348) thousand excess deaths in the study regions, with excess deaths mainly occurring in the El Niño years, such as in 1997, 2006, 2015 and 2019. Although the remote sensing data and emission inventories both reveal that the effective control measures have reduced biomass burning intensity in Equatorial Asia (especially in Sumatra and Borneo), the corresponding health benefit has been offset by variations in demographic factors, i.e., population and age structure. Over the same period, fossil fuel emissions continued to increase rapidly. Thus, more stringent and ambitious policies are required to reduce the health burden from biomass burning and anthropogenic emissions simultaneously to maximize the health benefits from government measures and policies.

Evaluation and Validation of Four Scoring Systems: the APACHE IV, SAPS III, MPM0 II, and ICMM in Critically Ill Cancer Patients.

BACKGROUND AND AIMS: To evaluate and validate four severity-of-illness scores, acute physiology and chronic health evaluation IV (APACHE IV), simplified acute physiology score III (SAPS III), mortality probability models II at 0 hours (MPM0 II), and ICU cancer mortality model (ICMM), in a prospective cohort of critically ill cancer patients. MATERIALS AND METHODS: Single-center, prospective observational study performed in a 14-bedded combined medical-surgical ICU of a tertiary care cancer center of India, from July 2014 to November 2015. Score performance was judged by discrimination and calibration, using the area under receiver-operating characteristics (ROC) curve and Hosmer-Lemeshow goodness-of-fit test, respectively. RESULTS: A total of 431 patients were included in the study. Intensive care unit (ICU) and hospital mortality were 37.4% and 41.1%, respectively. The area under ROC curve for APACHE IV, SAPS III, MPM0 II, and ICMM were 0.73, 0.70, 0.67, and 0.67, respectively. Calibration as calculated by Hosmer-Lemeshow analysis type C statistics for APACHE IV, SAPS III, MPM0 II, and ICMM shows good calibration with Chi-square values of 5.32, 9.285, 9.873, and 9.855 and p values of 0.723, 0.319, 0.274, and 0.275, respectively. CONCLUSION: All the four models had moderate discrimination and good calibration. However, none of the mortality prediction models could accurately discriminate between survivors and nonsurvivors in our patients. HOW TO CITE THIS ARTICLE: Siddiqui SS, Narkhede AM, Kulkarni AP, Prabu NR, Chaudhari HK, Divatia JV, et al. Evaluation and Validation of Four Scoring Systems: the APACHE IV, SAPS III, MPM0 II, and ICMM in Critically Ill Cancer Patients. Indian J Crit Care Med 2020;24(4):263-269.

A multifactorial severity score for left congenital diaphragmatic hernia in a high-risk population using fetal magnetic resonance imaging.

BACKGROUND: Adverse outcomes for infants born with left congenital diaphragmatic hernia (CDH) have been correlated with fetal imaging findings. OBJECTIVE: We sought to corroborate these correlations in a high-risk cohort and describe a predictive mortality algorithm combining multiple imaging biomarkers for use in prenatal counseling. MATERIALS AND METHODS: We reviewed fetal MRI examinations at our institution from 2004 to 2016 demonstrating left-side CDH. MRI findings, hospital course and outcomes were recorded and analyzed using bivariate and multivariable analysis. We generated a receiver operating curve (ROC) to determine a cut-off relation for mortality. Finally, we created a predictive mortality calculator. RESULTS: Of 41 fetuses included in this high-risk cohort, 41% survived. Per bivariate analysis, observed-to-expected total fetal lung volume (P=0.007), intrathoracic position of the stomach (P=0.049), and extracorporeal membrane oxygenation (ECMO) requirement (P<0.001) were significantly associated with infant mortality. Youden J statistic optimized the ROC for mortality at 24% observed-to-expected total fetal lung volume (sensitivity 64%, specificity 82%, area under the curve 0.72). On multivariable analysis, observed-to-expected total fetal lung volume ± 24% was predictive of mortality (adjusted odds ratio, 95% confidence interval: 0.09 [0.02, 0.55]; P=0.008). We derived a novel mortality prediction calculator from this analysis. CONCLUSION: In this high-risk cohort, decreased observed-to-expected total fetal lung volume and stomach herniation were significantly associated with mortality. The novel predictive mortality calculator utilizes information from fetal MR imaging and provides prognostic information for health care providers. Creation of similar predictive tools by other institutions, using their distinct populations, might prove useful in family counseling, especially where there are discordant imaging findings.

A mortality model based on a mixture distribution function.

A new mortality model based on a mixture distribution function is proposed. We mix a half-normal distribution with a generalization of the skew-normal distribution. As a result, we get a six-parameter distribution function that has a good fit with a wide variety of mortality patterns. This mixture model is fitted to several mortality data schedules and compared with the Siler (five-parameter) and Heligman-Pollard (eight-parameter) models. Our proposal serves as a convenient compromise between the Heligman-Pollard model (which ensures a good fit with data but is often overparameterized) and the Siler model (which is more compact but fails to capture 'accident humps').

A 3D Individual-Based Model to Study Effects of Chemotaxis, Competition and Diffusion on the Motile-Phytoplankton Aggregation.

In this paper, we develop a 3D-individual-based model (IBM) to understand effect of various small-scale mechanisms in phytoplankton cells, on the cellular aggregation process. These mechanisms are: spatial interactions between cells due to their chemosensory abilities (chemotaxis), a molecular diffusion and a demographical process. The latter is considered as a branching process with a density-dependent death rate to take into account the local competition on resources. We implement the IBM and simulate various scenarios under real parameter values for phytoplankton cells. To quantify the effects of the different processes quoted above on the spatial and temporal distribution of phytoplankton, we used two spatial statistics: the Clark-Evans index and the group belonging percentage. Our simulation study highlights the role of the branching process with a weak-to-medium competition in reinforcing the aggregating structure that forms from attraction mechanisms (under suitable conditions for diffusion and attraction forces), and shows by contrast that aggregations cannot form when competition is high.

Insights into mortality patterns and causes of death through a process point of view model.

Process point of view (POV) models of mortality, such as the Strehler-Mildvan and stochastic vitality models, represent death in terms of the loss of survival capacity through challenges and dissipation. Drawing on hallmarks of aging, we link these concepts to candidate biological mechanisms through a framework that defines death as challenges to vitality where distal factors defined the age-evolution of vitality and proximal factors define the probability distribution of challenges. To illustrate the process POV, we hypothesize that the immune system is a mortality nexus, characterized by two vitality streams: increasing vitality representing immune system development and immunosenescence representing vitality dissipation. Proximal challenges define three mortality partitions: juvenile and adult extrinsic mortalities and intrinsic adult mortality. Model parameters, generated from Swedish mortality data (1751-2010), exhibit biologically meaningful correspondences to economic, health and cause-of-death patterns. The model characterizes the twentieth century epidemiological transition mainly as a reduction in extrinsic mortality resulting from a shift from high magnitude disease challenges on individuals at all vitality levels to low magnitude stress challenges on low vitality individuals. Of secondary importance, intrinsic mortality was described by a gradual reduction in the rate of loss of vitality presumably resulting from reduction in the rate of immunosenescence. Extensions and limitations of a distal/proximal framework for characterizing more explicit causes of death, e.g. the young adult mortality hump or cancer in old age are discussed.

Estimating incidence and prevalence rates of chronic diseases using disease modeling.

BACKGROUND: Morbidity estimates between different GP registration networks show large, unexplained variations. This research explores the potential of modeling differences between networks in distinguishing new (incident) cases from existing (prevalent) cases in obtaining more reliable estimates. METHODS: Data from five Dutch GP registration networks and data on four chronic diseases (chronic obstructive pulmonary disease [COPD], diabetes, heart failure, and osteoarthritis of the knee) were used. A joint model (DisMod model) was fitted using all information on morbidity (incidence and prevalence) and mortality in each network, including a factor for misclassification of prevalent cases as incident cases. RESULTS: The observed estimates vary considerably between networks. Using disease modeling including a misclassification term improved the consistency between prevalence and incidence rates, but did not systematically decrease the variation between networks. Osteoarthritis of the knee showed large modeled misclassifications, especially in episode of care-based registries. CONCLUSION: Registries that code episodes of care rather than disease generally provide lower estimates of the prevalence of chronic diseases requiring low levels of health care such as osteoarthritis. For other diseases, modeling misclassification rates does not systematically decrease the variation between registration networks. Using disease modeling provides insight in the reliability of estimates.

Influence of application sequence and timing of eugenol and lauric arginate (LAE) on survival of spoilage organisms.

The effectiveness of sequential applications of the antimicrobials eugenol and lauric arginate (LAE) was investigated against Staphylococcus carnosus, Listeria innocua, Escherichia coli K12, and Pseudomonas fluorescens. The antimicrobials were applied simultaneously at half of their minimum lethal concentrations (MLC) or sequentially at t = 0 h and t = 3, 4, 6 or 8 h. Bacterial survival was determined by direct plate counts. Survivals kinetic were fitted to a growth and mortality model to obtain characteristic parameters that described time-dependent changes from growth to mortality or vice versa. The most effective was a simultaneous exposure of both antimicrobials to the spoilage organisms at the beginning of the incubation period. Efficiency decreases depending on order and timing of the two antimicrobials were observed upon sequential treatments. These were most effective when antimicrobials where applied within a short time period (3-4 h) and when eugenol was first applied against S. carnosus and P. fluorescens. No sequence effects were observed for L. innocua, and sequential treatments proved to be ineffective against E. coli K12. These results were attributed to cells adapting to the first applied antimicrobial. In some cases, this provided protection against the second antimicrobial rendering the overall treatment less effective.

The Age Pattern of Increases in Mortality Affected by HIV: Bayesian Fit of the Heligman-Pollard Model to Data from the Agincourt HDSS Field Site in Rural Northeast South Africa.

BACKGROUND: We investigate the sex-age-specific changes in the mortality of a prospectively monitored rural population in South Africa. We quantify changes in the age pattern of mortality in a parsimonious way by estimating the eight parameters of the Heligman-Pollard (HP) model of age-specific mortality. In its traditional form this model is difficult to fit and does not account for uncertainty. OBJECTIVE: 1. To quantify changes in the sex-age pattern of mortality experienced by a population with endemic HIV. 2. To develop and demonstrate a robust Bayesian estimation method for the HP model that accounts for uncertainty. METHODS: Bayesian estimation methods are adapted to work with the HP model. Temporal changes in parameter values are related to changes in HIV prevalence. RESULTS: Over the period when the HIV epidemic in South Africa was growing, mortality in the population described by our data increased profoundly with losses of life expectancy of ~15 years for both males and females. The temporal changes in the HP parameters reflect in a parsimonious way the changes in the age pattern of mortality. We develop a robust Bayesian method to estimate the eight parameters of the HP model and thoroughly demonstrate it. CONCLUSIONS: Changes in mortality in South Africa over the past fifteen years have been profound. The HP model can be fit well using Bayesian methods, and the results can be useful in developing a parsimonious description of changes in the age pattern of mortality. COMMENTS: The motivating aim of this work is to develop new methods that can be useful in applying the HP eight-parameter model of age-specific mortality. We have done this and chosen an interesting application to demonstrate the new methods.

Computational and experimental assessment of health risks of fine particulate matter in Nanjing and Yangzhou, China.

Fine particulate matter (PM2.5) is a major air pollutant in most cities of China, and poses great health risks to local residents. In this study, the health effects of PM2.5 in Nanjing and Yangzhou were compared using computational and experimental methods. The global exposure mortality model (GEMM), including the results of a cohort study in China, was used to estimate the disease-related risks. Premature mortality attributable to PM2.5 exposure were markedly higher in Nanjing than that in Yangzhou at comparable levels of PM2.5 (8191 95% CI, 6975-9994 vs. 6548 95% CI, 5599-8049 in 2015). However, the baseline mortality rate was on a country-level and the age distribution was on a province-level, traditional estimation method could not accurately represent the health burdens of PM2.5 on a city-level. We proposed a refined calculation method which based on the actual deaths of each city and the disease death rates. Conversely, similar concentrations of PM2.5 exposure resulted in higher actual deaths per million population in Yangzhou (1466 95% CI, 1266-1746) than that in Nanjing (1271 95% CI, 1098-1514). Health risks of PM2.5 are associated with the generation of reactive oxygen species, among which hydroxyl radial (·OH) is the most reactive one. We then collected these PM2.5 samples and quantified the induced ·OH. Consistently, average ·OH concentration in 2015 was higher in Yangzhou than that in Nanjing, again indicating that PM2.5 in Yangzhou was more toxic. The combination of computational and experimental methods demonstrated the complex relationship between health risks and PM2.5 concentrations. The refined estimation method could help us better estimate and interpret the risks caused by PM2.5 exposure on a city-level.

An evolutionary explanation of female-biased sexual size dimorphism in North Sea plaice, Pleuronectes platessa L.

Sexual size dimorphism (SSD) is caused by differences in selection pressures and life-history trade-offs faced by males and females. Proximate causes of SSD may involve sex-specific mortality, energy acquisition, and energy expenditure for maintenance, reproductive tissues, and reproductive behavior. Using a quantitative, individual-based, eco-genetic model parameterized for North Sea plaice, we explore the importance of these mechanisms for female-biased SSD, under which males are smaller and reach sexual maturity earlier than females (common among fish, but also arising in arthropods and mammals). We consider two mechanisms potentially serving as ultimate causes: (a) Male investments in male reproductive behavior might evolve to detract energy resources that would otherwise be available for somatic growth, and (b) diminishing returns on male reproductive investments might evolve to reduce energy acquisition. In general, both of these can bring about smaller male body sizes. We report the following findings. First, higher investments in male reproductive behavior alone cannot explain the North Sea plaice SSD. This is because such higher reproductive investments require increased energy acquisition, which would cause a delay in maturation, leading to male-biased SSD contrary to observations. When accounting for the observed differential (lower) male mortality, maturation is postponed even further, leading to even larger males. Second, diminishing returns on male reproductive investments alone can qualitatively account for the North Sea plaice SSD, even though the quantitative match is imperfect. Third, both mechanisms can be reconciled with, and thus provide a mechanistic basis for, the previously advanced Ghiselin-Reiss hypothesis, according to which smaller males will evolve if their reproductive success is dominated by scramble competition for fertilizing females, as males would consequently invest more in reproduction than growth, potentially implying lower survival rates, and thus relaxing male-male competition. Fourth, a good quantitative fit with the North Sea plaice SSD is achieved by combining both mechanisms while accounting for sex-specific costs males incur during their spawning season. Fifth, evolution caused by fishing is likely to have modified the North Sea plaice SSD.

Weeks of life lost to COVID-19, the case of the United States.

BACKGROUND: Years of life lost (YLL) is a preferable indicator to assess the mortality impact of COVID-19. This indicator still has limits, however. Therefore, a new approach and its early-death weeks (eDW) index has been recently proposed to alter YLL. This study aims to add a new approach, the moving excess-deficit mortality model, and its method, the weeks of life lost (WLL) index. The new method was then used to measure WLL associated with COVID-19 in the United States (US). METHODS: The natural mortality law and the random pattern of spreading COVID-19 were employed to support calculating WLL. The natural mortality law implied that under the same living conditions and the weaker would die earlier. The random spreading of COVID-19 assumed that COVID-19 causes the weekly number of early deaths in equal proportions from all of those who would have died eventually distributed through the pandemic. RESULTS: From Week 02 of 2020 to Week 44 of 2021, we found that the US population has lost 56,270,300 weeks to COVID-19; the average WLL per COVID-19-related death is 74 or 1.4 in the unit of years. CONCLUSIONS: The results do not depend on the high heterogeneity of deaths (e.g., age, gender, health status) and on whether COVID-19 is the main cause of death. The moving excess-deficit mortality model and WLL index can be applied promptly at any time and anywhere once excess deaths occurred during the pandemic. The index also provides critical insights into COVID-19, which can support making public health policies and decisions.

Assessment of longevity risk: credibility approach.

To correctly measure the effect of mortality rates on the stability of insurance and pension provider's financial risk, longevity risk should be considered. This paper aims to investigate the future mortality and longevity risk with different age structures for different countries. Lee-Carter mortality model is used on the historical census data to forecast future mortality rates. Turkey, Germany, and Japan are chosen concerning their expected life and population distributions. Then, the longevity risk on a hypothetical portfolio is assessed based on static and dynamic mortality table approaches. To determine the impact of longevity risk, which is retrieved using a stochastic mortality model, a pension insurance product is taken into account. The net single premium for an annuity is quantified under the proposed set up for the selected countries. Additionally, the credibility approach is proposed to establish a reliable estimate for the annuity net single premium.

Metarhizium Entomopathogenic Fungi Against the Beetle Brontispa longissima (Coleoptera: Hispidae): Isolation and Species Identification.

Seven entomopathogenic fungi strains (M1-7) were isolated from field-obtained dead coconut hispine beetles Brontispa longissima (Gestro), identified to species, and bioassayed for their pathogenicity. According to ITS sequences, all isolates belong in the genus Metarhizium, mainly M. flavoviride and M. anisopliae. Measured median lethal times (LT50) of 1×107 conidia/ml of M1-7 against fourth-instar B. longissima larvae within 15 d following exposure were, respectively: 5.43, 10.64, 11.26, 10.93, 6.62, 4.73, and 5.95 d. The isolate M6 yielded the highest mortality to fourth-instar larvae, and was thus selected to be tested against other larval instars and adults of B. longissima, after Time-Dose-Mortality (TDM) models. M6 proved more pathogenic against larvae than adults. The obtained bioassays data produced a good fit to the TDM models, yielding estimated LC50 and LT50 for each of the tested developmental stages of B. longissima. Both the obtained dose (ß) and time effect (ri) parameters from TDM models suggest that first-instar larvae are the most susceptible life stage of the pest insect, while adults are more resistant to M6 infection. Calculated LC50 values were, respectively, 1.23×103 and 1.15×106 conidia/ml for first-instar larvae and adults, on the 15th day following M6 inoculation. Estimated LT50 were 3.3 and 5.9 d for first-instar larvae and adults, respectively, at 1×108 conidia/ml. Taken together, these results would suggest Metarhizium M6 as an option for the biological control of B. longissima in the field.

A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality.

Premature mortality is often a neglected component of overall deaths, and the most difficult to identify. However, it is important to estimate its prevalence. Following Pearson's theory about mortality components, a definition of premature deaths and a parametric model to study its transformations are introduced. The model is a mixture of three distributions: a Half Normal for the first part of the death curve and two Skew Normals to fit the remaining pieces. One advantage of the model is the possibility of obtaining an explicit equation to compute life expectancy at birth and to break it down into mortality components. We estimated the mixture model for Sweden, France, East Germany and Czech Republic. In addition, to the well-known reduction in infant deaths, and compression and shifting trend of adult mortality, we were able to study the trend of the central part of the distribution of deaths in detail. In general, a right shift of the modal age at death for young adults is observed; in some cases, it is also accompanied by an increase in the number of deaths at these ages: in particular for France, in the last twenty years, premature mortality increases.

The Protective Effect of Adenosine-Preconditioning on Paraquat-Induced Damage in Caenorhabditis elegans.

Adenosine plays an important role in the physiological and pathological conditions of the body by combining different types of adenosine receptors widely distributed in various tissues in the body. In present study, an acute model for paraquat-poisoning in Caenorhabditis elegans was established for quantitative assessment via a time-dose-mortality (TDM) modeling technique with various paraquat doses over 8 hours. Adenosine was first used to precondition at high, medium, and low concentrations and the survival rate of C. elegans was recorded to evaluate adenosine antistress protection against paraquat damage. The results revealed that the TDM model was good for the quantitative assessment of paraquat-poisoning on C. elegans based on the Hosmer-Lemeshow test for homogeneity of modeling (P = .38). The survival rates of adenosine-preconditioned C. elegans have a dose-dependent association with adenosine concentration. At 3000 µM (high concentration) and 300 µM (medium concentration), adenosine-preconditioned C. elegans still had survival rates of 5.38% ± 1.68% and 5.0% ± 1.19% in the subsequent 8 hours observation period. On the contrary, the survival rates of those receiving 30 µM (low concentration) and the 0 µM (unpreconditioned treatment) were zero. To conclude, adenosine preconditioning had protective effects on C. elegans intoxicated with paraquat by decreasing its mortality rate.

[Assessment of mortality burden and economic loss attributed to long-term PM(2.5) exposure in the Beijing-Tianjin-Hebei area].

Objective: To accurately estimate the health burden and corresponding economic loss attributed to PM(2.5) pollution in the Beijing-Tianjin-Hebei (BTH) area in China in 2015. Method: By using satellite-retrieved PM(2.5) concentration data and population data provided by NASA (the spatial resolution was 1 km×1 km), this study estimated excess mortality attributed to long-term PM(2).5 exposure in BTH area in 2015 based on Global Exposure Mortality Model (GEMM). Besides, Value of Statistic Life (VSL) method was used to evaluate the corresponding health economic loss. Result: In BTH area, the population-weighted average PM(2.5) concentration during 2012-2014 was 46.25 µg/m(3), and 56.6% of total population lived in the area where annual average PM(2.5) concentration exceeded Grade Ⅱ of National Ambient Air Quality Standard in China (35 µg/m(3)); The PM(2.5)-related premature deaths amounted to 193.8 thousand (95%CI: 140.9 thousand-233.3 thousand), Beijing, Tianjin, Baoding, Shijiazhuang, and Handan were the top five cities with high incidences of PM(2.5)-related premature deaths; The corresponding health economic loss was about 35.934 billion (95%CI: 26.099 billion - 43.255 billion) RMB, accounting for 0.70% (95%CI: 0.51%-0.85%) of the area's GDP in 2015, Beijing, Tianjin, Baoding, Shijiazhuang, and Cangzhou were the top five cities with high health economic loss. Conclusions: PM(2.5) pollution has caused severe disease and economic burden in BTH area. Its spatial distribution suggested that it is particularly necessary to develop the air pollution prevention and control policies for key cities.

Models for acute on chronic liver failure development and mortality in a veterans affairs cohort.

BACKGROUND AND PURPOSE: The diagnosis of acute on chronic liver failure (ACLF) carries a high short-term mortality, making early identification of at-risk patients crucial. To date, there are no models that predict which patients with compensated cirrhosis will develop ACLF, and limited models exist to predict ACLF mortality. We sought to create novel risk prediction models using a large North American cohort. METHODS: We performed a retrospective study of 75,922 patients with compensated cirrhosis from the Veterans Outcomes and Costs Associated with Liver Disease (VOCAL) dataset. Using 70% derivation/30% validation sets, we identified ACLF patients using the Asian Pacific Association of Liver (APASL) definition. Multivariable logistic regression was used to derive prediction models (called VOCAL-Penn) for developing ACLF at 3, 6, and 12 months. We then created prediction models for ACLF mortality at 28 and 90 days. RESULTS: The VOCAL-Penn models for ACLF development had very good discrimination [concordance (C) statistics of 0.93, 0.92, and 0.89 at 3, 6, and 12 months, respectively] and calibration. The mortality models also had good discrimination at 28 and 90 days (C statistics 0.89 and 0.88, respectively), outperforming the Model for End-stage Liver Disease (MELD), MELD-sodium, and the APASL ACLF Research Consortium ACLF scores. CONCLUSION: We have developed novel tools for predicting development of ACLF in compensated cirrhosis patients, as well as for ACLF mortality. These tools may be used to proactively guide patient follow-up, prognostication, escalation of care, and transplant evaluation. Receiver operating characteristic (ROC) curves for predicting development of APASL ACLF at 3 months (a), 6 months (b), and 1 year (c).