Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis

BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.METHODS We extracted demographic,etiological,vital sign,laboratory test,comorbidity,complication,treatment,and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV(MIMIC-IV)and electronic ICU(eICU)collaborative research database(eICU-CRD).Predictor selection and model building were based on the MIMIC-IV dataset.The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors.The final predictors were included in the multivariate logistic regression model,which was used to construct a nomogram.Finally,we conducted external validation using the eICU-CRD.The area under the receiver operating characteristic curve(AUC),decision curve,and calibration curve were used to assess the efficacy of the models.RESULTS Risk factors,including the mean respiratory rate,mean systolic blood pressure,mean heart rate,white blood cells,international normalized ratio,total bilirubin,age,invasive ventilation,vasopressor use,maximum stage of acute kidney injury,and sequential organ failure assessment score,were included in the multivariate logistic regression.The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases,respectively.The calibration curve also confirmed the predictive ability of the model,while the decision curve confirmed its clinical value.CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality.Improving the included predictors may help improve the prognosis of patients.

Comparing gastrointestinal dysfunction score and acute gastrointestinal injury grade for predicting short-term mortality in critically ill patients

BACKGROUND The prognosis of critically ill patients is closely linked to their gastrointestinal(GI)function.The acute GI injury(AGI)grading system,established in 2012,is extensively utilized to evaluate GI dysfunction and forecast outcomes in clinical settings.In 2021,the GI dysfunction score(GIDS)was developed,building on the AGI grading system,to enhance the accuracy of GI dysfunction severity assessment,improve prognostic predictions,reduce subjectivity,and increase reproducibility.AIM To compare the predictive capabilities of GIDS and the AGI grading system for 28-day mortality in critically ill patients.METHODS A retrospective study was conducted at the general intensive care unit(ICU)of a regional university hospital.All data were collected during the first week of ICU admission.The primary outcome was 28-day mortality.Multivariable logistic regression analyzed whether GIDS and AGI grade were independent risk factors for 28-day mortality.The predictive abilities of GIDS and AGI grade were compared using the receiver operating characteristic curve,with DeLong’s test assessing differences between the curves’areas.RESULTS The incidence of AGI in the first week of ICU admission was 92.13%.There were 85 deaths(47.75%)within 28 days of ICU admission.There was no initial 24-hour difference in GIDS between the non-survival and survival groups.Both GIDS(OR 2.01,95%CI:1.25-3.24;P=0.004)and AGI grade(OR 1.94,95%CI:1.12-3.38;P=0.019)were independent predictors of 28-day mortality.No significant difference was found between the predictive accuracy of GIDS and AGI grade for 28-day mortality during the first week of ICU admission(Z=-0.26,P=0.794).CONCLUSION GIDS within the first 24 hours was an unreliable predictor of 28-day mortality.The predictive accuracy for 28-day mortality from both systems during the first week was comparable.

A Nomogram Model for Prediction of Mortality Risk of Patients with Dangerous Upper Gastrointestinal Bleeding:A Two-center Retrospective Study

Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Methods:From January 2020 to April 2022,the clinical data of 256 DUGIB patients who received treatments in the intensive care unit(ICU)were retrospectively collected from Renmin Hospital of Wuhan University(n=179)and the Eastern Campus of Renmin Hospital of Wuhan University(n=77).The 179 patients were treated as the training cohort,and 77 patients as the validation cohort.Logistic regression analysis was used to calculate the independent risk factors,and R packages were used to construct the nomogram model.The prediction accuracy and identification ability were evaluated by the receiver operating characteristic(ROC)curve,C index and calibration curve.The nomogram model was also simultaneously externally validated.Decision curve analysis(DCA)was then used to demonstrate the clinical value of the model.Results:Logistic regression analysis showed that hematemesis,urea nitrogen level,emergency endoscopy,AIMS65,Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB.The ROC curve analysis indicated the area under curve(AUC)of the training cohort was 0.980(95%CI:0.962-0.997),while the AUC of the validation cohort was 0.790(95%CI:0.685-0.895).The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts(P=0.778,P=0.516).Conclusion:The developed nomogram is an effective tool for risk stratification,early identification and intervention for DUGIB patients.

Investigation on maternal mortality in Southeast Asia,Europe and Africa using three delays model

Background:Maternal mortality is a prevalent issue in healthcare provision worldwide.It is particularly common in developing and underdeveloped countries,where maternal deaths during childbirth or pregnancy occur frequently.Various internal and external factors contribute to the high maternal mortality rate in specific regions.One model,known as the three delays model approach,examines three distinct causes that contribute to this problem.The first delay is the lack of awareness in seeking timely healthcare,the second delay involves obstacles in reaching healthcare facilities on time,and the third delay relates to poor or inadequate healthcare provision in tertiary care facilities.These delays are responsible for the elevated maternal mortality rates,with the prevalence of each delay varying across regions.Objective:The objective of this literature review is to examine and critically evaluate existing literature on perceptions and investigations regarding maternal mortality in Southeast Asia,Europe and Africa,utilizing the three delays model approach as a categorization framework.Method:This literature review followed BEME guide No.3.A total of 18 articles were included in the sample after conducting a thorough search of various databases and search engines.A Prisma flowchart was created,and the articles were critically appraised.Results:A total of 18 articles focusing on different regions were analyzed.The findings revealed that in countries of Southeast Asia,the primary cause of maternal mortality is the first delay,which refers to the lack of awareness in seeking medical care.On the other hand,in Africa and other European countries,the second and third delays are more prominently associated with maternal mortality.Conclusion:Inadequate care is one of the major causes of maternal mortality in majority of regions acrossthe globe.Multiple factors can hinder access to appropriate healthcare.The three delays model plays a significant role in the higher maternal mortality rate.By raising awareness among women and their families about the importance of seeking healthcare,the risk of fatality can be reduced.Similarly,in developing regions,it is crucial to ensure that healthcare facilities are easily accessible and provide high-quality emergency obstetric care to meet the needs of pregnant women in critical situations.

Individual and Systemic Factors of Under-five Mortality in Nigeria:A Cox Proportional Hazard Model

Despite considerable efforts to reduce under-five mortality nationwide,Nigeria has fallen short of achieving the Millennium Development Goals(MDGs)target of 67 deaths per 1,000 live births by 2015.Of all the documented factors of under-five mortality,little evidence exists on the impact of systemic barriers and individual factors(maternal health-seeking behaviour)on under-five mortality in Nigeria.The study used a nationally representative sample from Nigeria Demographic and Health Survey(NDHS)2013 dataset.The target population was 20,192 women aged 15-59 years who had given birth to 31,480 children five years before the survey.Stata software was used for data analysis.The risk of death was estimated using Cox proportional hazard models and results are presented as hazards ratios(HR)with 95%confidence intervals(CI).Findings from the overall Model I-IV revealed individual factors(maternal health-seeking indicators)as significant factors of under-five deaths(p<0.05).Children whose mothers received antenatal care coverage(ANC)outside health care facilities(HCF)(HR:1.60,CI:1.0-2.4,p<0.05);or delivered outside HCF(HR:1.02,CI:0.7-1.5,p<0.05)had elevated hazard risk of death before age five.Conversely,children who were presented for postnatal check within two weeks of delivery(HR:0.60,CI:0.5-0.8,p<0.05),or delivered within the longer birth interval(HR:0.67,CI:0.6-0.8,p<0.001)had significantly lower hazard risk of death before age five.As part of systemic factors,children whose mothers were covered by health insurance scheme had significantly(HR:0.52,CI:0.2-1.2,p<0.001)lower risk of death when compared with their counterparts without health insurance coverage.The study emphasized the need to revitalize strategies and programs to improve women health seeking behaviour and investment in the health sector through health insurance,infrastructure,and supplies.

Nonlinear mixed-effects height to crown base and crown length dynamic models using the branch mortality technique for a Korean larch( Larix olgensis ) plantations in northeast China

Korean larch(Larix olgensis)is one of the main tree species for aff orestation and timber production in northeast China.However,its timber quality and growth ability are largely infl uenced by crown size,structure and shape.The majority of crown models are static models based on tree size and stand characteristics from temporary sample plots,but crown dynamic models has seldom been constructed.Therefore,this study aimed to develop height to crown base(HCB)and crown length(CL)dynamic models using the branch mortality technique for a Korean larch plantation.The nonlinear mixed-eff ects model with random eff ects,variance functions and correlation structures,was used to build HCB and CL dynamic models.The data were obtained from 95 sample trees of 19 plots in Meng JiaGang forest farm in Northeast China.The results showed that HCB progressively increases as tree age,tree height growth(HT growth)and diameter at breast height growth(DBH growth).The CL was increased with tree age in 20 years ago,and subsequently stabilized.HT growth,DBH growth stand basal area(BAS)and crown competition factor(CCF)signifi cantly infl uenced HCB and CL.The HCB was positively correlated with BAS,HT growth and DBH growth,but negatively correlated with CCF.The CL was positively correlated with BAS and CCF,but negatively correlated with DBH growth.Model fi tting and validation confi rmed that the mixed-eff ects model considering the stand and tree level random eff ects was accurate and reliable for predicting the HCB and CL dynamics.However,the models involving adding variance functions and time series correlation structure could not completely remove heterogeneity and autocorrelation,and the fi tting precision of the models was reduced.Therefore,from the point of view of application,we should take care to avoid setting up over-complex models.The HCB and CL dynamic models in our study may also be incorporated into stand growth and yield model systems in China.

Modelling tree mortality across diameter classes using mixedeffects zero-inflated models

The mortality of trees across diameter class model is a useful tool for predicting changes in stand structure.Mortality data commonly contain a large fraction of zeros and general discrete models thus show more errors.Based on the traditional Poisson model and the negative binomial model,different forms of zero-inflated and hurdle models were applied to spruce-fir mixed forests data to simulate the number of dead trees.By comparing the residuals and Vuong test statistics,the zero-inflated negative binomial model performed best.A random effect was added to improve the model accuracy;however,the mixed-effects zero-inflated model did not show increased advantages.According to the model principle,the zeroinflated negative binomial model was the most suitable,indicating that the"0"events in this study,mainly from the sample"0",i.e.,the zero mortality data,are largely due to the limitations of the experimental design and sample selection.These results also show that the number of dead trees in the diameter class is positively correlated with the number of trees in that class and the mean stand diameter,and inversely related to class size,and slope and aspect of the site.

Individual-tree diameter growth model for Korean pine plantations based on optimized interpolation of meteorological variables

To explore the influence of meteorological variables on the growth of Korean pine(Pinus koraiensis Sieb.et Zucc.) plantations and provide a scientific reference for the production and management of Korean pine,three approaches to interpolate meteorological variables during the growing season(i.e.,May-September) were compared in Heilongjiang Province,China.Optimized meteorological variable interpolation results were then combined with stand and individual tree variables,based on data from 56 sample plots and 2886 sample trees from Korean pine plantations in two regions of the province to develop an individualtree diameter growth model(Model I) and an individualtree diameter growth model with meteorological variables(Model Ⅱ) using a stepwise regression method.Moreover,an individual-tree diameter growth model with regional effects(Model Ⅲ) was developed using dummy variables in the regression,and the significance of introducing these dummy variables was verified with an F-test statistical analysis.The models were validated using an independent data set,and the predictive performance of the three models was assessed via the adjusted coefficient of determination(R_(a)^(2)) and root mean square error(RMSE).The results suggest that the growth increment in tree diameter of Korean pine plantations was significantly correlated with the natural logarithm of initial diameter(ln D),stand basal area(BAS),logarithmic deformation of the stand density index(ln SDI),ratio of basal area of trees larger than the subject tree to their initial diameter at breast height(DBH)(BAL/D),and the maximum growingseason precipitation(Pgmax).The individual-tree diameter growth models of Korean pine plantations developed in this study will provide a good basis for estimating and predicting growth increments of Korean pine forests over larger areas.

Prediction model of in-hospital mortality in elderly patients with acute heart failure based on retrospective study

Objectives The aim of this study was to develop a clinical risk model that is predictive of in-hospital mortality in elderly patients hos- pitalized with acute heart failure （AHF）. Methods 2486 patients who were 60 years and older from intensive care units of Cardiology De- partment in the hospital were analyzed. Independent risk factors for in-hospital mortality were obtained by binary logistic regression and then used to establish the risk prediction score system （RPSS）. The area under the curve （AUC） of receiver operator characteristic and C-statistic test were adopted to assess the performance of RPSS and to compare with previous get with the guidelines-heart failure （GWTG-HF）. Re- sults By binary logistic regression analysis, heart rate （OR： 1.043, 95% CI： 1.030-1.057, P 〈 0.001）, left ventricular ejection fraction （OR： 0.918, 95% CI： 0.833~）.966, P 〈 0.001）, pH value （OR： 0.001, 95% CI： 0.000-0.002, P 〈 0.001）, renal dysfunction （OR： 0.120, 95% CI： 0.066M）.220, P 〈 0.001） and NT-pro BNP （OR： 3.463, 95% CI： 1.870-6.413, P 〈 0.001） were independent risk factors of in-hospital mortal- ity for elderly AHF patients. Additionally, RPSS, which was composed of all the above-mentioned parameters, provided a better risk predic- tion than GWTG-THF （AUC： 0.873 vs. 0.818, P = 0.016）. Conclusions Our risk prediction model, RPSS, provided a good prediction for in-hospital mortality in elderly patients with A/IF.

Can a multistage approach improve individual tree mortality predictions across the complex mixed-species and managed forests of eastern North America?

Tree mortality plays a fundamental role in the dynamics of forest ecosystems,yet it is one of the most difficult phenomena to accurately predict.Various modeling strategies have been developed to improve individual tree mortality predictions.One less explored strategy is the use of a multistage modeling approach.Potential improvements from this approach have remained largely unknown.In this study,we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation.Extensive permanent plot data(n=9442)covering the Acadian Region of North America and over multiple decades(1965–2014)were used in this study.Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach.The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach.In addition,tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations,whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data.The new multistage approach also predictions of zero mortality in individual plots,a result not possible in conventional models.Finally,the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values.Overall,this new multistage approach deserves to be considered and tested in future studies.

Post Millennium Development Goals Prospect on Child Mortality in India: An Analysis Using Autoregressive Integrated Moving Averages (ARIMA) Model

Background & Objectives: Sustainable Development Goals (SDGs) are set up as a part of the Post Millennium Development Goals (MDGs). Then it becomes essential to review the achievement of the MDGs in India and lessons learned to incorporate into the SDGs. The present study reviews and predicts different components of under-five mortality rate beyond 2015 to assess the present situation and to determine the future possibilities of achieving the new targets for SDGs in India. Data and Methods: It uses available time series data on different components of U5MR from the India’s Sample Registration System (SRS). Autoregressive Integrated Moving Averages (ARIMA) model has been taken as the method of time series analysis to forecast the mortality rates beyond 2015. Results: There is a consistent pattern of faster decline in the under-five mortality compared with the neonatal mortality rate across all major states in India although neonatal mortality contributes largest share in under-five mortality. Again, share of neonatal death among under-five death is increasing steadily over the future projected years. This indicates very slow progress of reduction in neonatal mortality. Stimulating efforts with new intervention programmes will be needed to focus more on lowering neonatal mortality particularly in rural India.

Comparison of Models for Excess Mortality of Influenza Applied to Japan

To elucidate the social effects of an influenza outbreak, the World Health Organization recommends a concept for excess mortality attributable to an influenza outbreak. However, because several models exist to estimate excess mortality, we would like to ascertain the most appropriate of three models: the Center for Disease Control and Prevention (CDC) model, the seasonal autoregressive integrated moving average (SARIMA) model, and the National Institution of Infectious Diseases (NIID) model. Excess mortality is defined as the difference between the actual number of deaths and the epidemiological threshold. The epidemiological threshold is defined as upper bound of 95% confidence interval (CI) of the baseline. The actual number of deaths might be less than the baseline, which implies inconsistent with the definition of baseline. Especially, actual deaths fewer than the lower bound of 95% CI of baseline suggest the inappropriateness of a model of excess mortality. Among 123 months during epidemic periods, the NIID model found excess mortality in 56 months, CDC model in 31 months, and SARIMA model in 35 months. Conversely, the NIID model found negative excess mortality in only 2 months, but the CDC model and SARIMA model found it respectively for 10 and 33 months. Negative excess mortality represents the logical inconsistency of the model. Therefore, NIID model might be the best among the three models considered.

Model for Assessment of Child Mortality under Different Parity: A Bayesian Swatch

In this study a model is conceptualized to measure the child mortality under different parity of women such that a better strategy can be formulated to bring down mortality rates. In the estimation of probability of child mortality some socio demographic variables are taken in consideration. The estimates are obtained under Bayesian procedure. Two different models are formulated for it and model fitting is observed by graphical approach along with the chi square test. First model is betabinomial and second is binomial regression model. Second model shows the better fit on the data. The estimate of probability of child mortality at higher parities namely, parity 3, parity 4 and parity 5 were obtained as 0.06, 0.09 and 0.13 respectively on the basis of the second model.

Developing a model for reducing maternal mortality in South Africa

Objective:High maternal mortality ratios(MMRs)remain a concern in many parts of the world,especially in developing countries like South Africa.Different models have been developed,tried,and tested worldwide,in the hope that they will reduce maternal mor tality,but without much success.Methods:A qualitative approach was used to conveniently select a sample of 10 women attending an antenatal clinic in a rural area,in one of the districts of Kwa Zulu-Natal(KZN)Province.Data were collected by means of interviews with the women.Data were analyzed employing Burnard’s content analysis approach.Results:Four themes emerged:(1)age at first pregnancy;(2)birth intervals,risks in pregnancy and hospitalization;(3)the use of contraception;and(4)HIV status.All themes that emerged revealed inattention to reproductive health(RH)needs,resulting in poor RH outcomes as an area of concern.Conclusions:Greater emphasis needs to be placed on meeting the sexual and reproductive health(SRH)needs of South African women,if maternal mor tality rates are to be reduced.An alternative model for reducing maternal mor tality in South Africa is proposed.

Cox Proportional Hazard Model for Survival Time of Neonatal Mortality in Neonatal Intensive Care Unit of Hospitals in River Nile State-Sudan

Cox Proportional Hazard model is a popular statistical technique for exploring the relationship between the survival time of neonates and several explanatory variables. It provides an estimate of the study variables’ effect on survival after adjustment for other explanatory variables, and allows us to estimate the hazard (or risk) of death of newborn in NICU of hospitals in River Nile State-Sudan for the period (2018-2020). Study Data represented (neonate gender, mode of delivery, birth type, neonate weight, resident type, gestational age, and survival time). Kaplan-Meier method is used to estimate survival and hazard function for survival times of newborns that have not completed their first month. Of 700 neonates in the study area, 25% of them died during 2018-2020. Variables of interest that had a significant effect on neonatal death by Cox Proportional Hazard Model analysis were neonate weight, resident type, and gestational age. In Cox Proportional Hazard Model analysis all the variables of interest had an effect on neonatal death, but the variables with a significant effect included, weight of neonate, resident type and gestational age.

Linear Mixed Model Analysis of Worldwide Longitudinal Infant Mortality Rate Data and Association with Human Development Index

A linear mixed model is used to determine the explaining infant mortality rate data of United Nations countries. The HDI （human development index） has a significant negative linear relationship with infant mortality rate. United Nations data shows that the infant mortality rate has a descending trend over the period 1990-2010. This study aims to assess the value of the HDI as a predictor of infant mortality rate. Findings in the paper suggest that significant percentage reductions in infant mortality might be possible for countries for controlling the HDI.

MELD score can predict early mortality in patients with rebleeding after band ligation for variceal bleeding

AIM:To investigate the outcomes,as well as risk factors for 6-wk mortality,in patients with early rebleeding after endoscopic variceal band ligation (EVL) for esophageal variceal hemorrhage (EVH).METHODS:Among 817 EVL procedures performed for EVH between January 2007 and December 2008,128 patients with early rebleeding,defined as rebleeding within 6 wk after EVL,were enrolled for analysis.RESULT:The rate of early rebleeding after EVL for acute EVH was 15.6% (128/817).The 5-d,6-wk,3-mo,and 6-mo mortality rates were 7.8%,38.3%,55.5%,and 58.6%,respectively,in these early rebleeding patients.The use of beta-blockers,occurrence of hypovolemic shock,and higher model for end-stage liver disease (MELD) score at the time of rebleeding were independent predictors for 6-wk mortality.A cut-off value of 21.5 for the MELD score was found with an area under ROC curve of 0.862 (P < 0.001).The sensitivity,specificity,positive predictive value,and negative predictive value were 77.6%,81%,71.7%,and 85.3%,respectively.As for the 6-mo survival rate,patients with a MELD score ≥ 21.5 had a significantly lower survival rate than patients with a MELD score < 21.5 (P < 0.001).CONCLUSION:This study demonstrated that the MELD score is an easy and powerful predictor for 6-wk mortality and outcomes of patients with early rebleeding after EVL for EVH.

Health Inequalities during 20 Years of Rapid Economic Development in China (1980-2000):A Mortality Analysis

Objective To examine the influence of China＇s economic reforms on population health and regional mortality rates.Methods Longitudinal study measuring the mortality trends and their regional variations.Using data from the three most recent national censuses,we used the model life table to adjust the mortality levels within the population for each census,and to calculate life expectancy.We then examined the variation in patterns of mortality and population health by economic status,region and gender from 1980-2000.Results Life expectancy varied with economic status,province,and gender.Results showed that,although life expectancy in China had increased overall since the early 1980s,regional differences became more pronounced.Life expectancy for populations who live in the eastern coastal provinces are greater than those in the western regions.Conclusion Differences in life expectancy are primarily related to differences in regional economic development,which in turn exacerbate regional health inequalities.Therefore,it is necessary to improve economic development in less developed regions and to improve health policies and the public health system that address the needs of everyone.

Characteristics of alcoholic liver disease and predictive factors for mortality of patients with alcoholic cirrhosis

BACKGROUND:Alcoholic liver disease is one of the major chronic liver diseases worldwide.The aim of the study was to describe the clinical characteristics of alcoholic liver disease and to compare the predictive values of biochemical parameters,complications,Child-Turcotte-Pugh score,model for end-stage liver disease(MELD)score and discriminant function score for the mortality of in-hospital or 3-month after discharge of patients with alcoholic cirrhosis(AC).METHODS:A retrospective record review and statistical analysis were performed on 205 consecutive patients with the discharge diagnosis of alcoholic liver disease.Three models were used to predict the mortality of patients with AC.The number of variceal hemorrhage,infection,hepatic encephalopathy and hepatocellular carcinoma was analyzed as"numbers of complications".Model 1 consisted of creatinine,white blood cell count,international normalized ratio and"numbers of complications".Model 2 consisted of MELD score.Model 3included"numbers of complications"and MELD score.RESULTS:The risk of developing AC was significant for patients with alcohol consumption of higher than 80 g/d(OR=2.807,P【0.050)and drinking duration of longer than 10 years(OR=3.429,P【0.028).The area under curve for predicting inhospital mortality of models 1,2 and 3 was 0.950,0.886 and 0.911(all P【0.001),respectively.The area under curve for predicting the 3-month mortality of models 1,2 and 3 was 0.867,0.878 and0.893(all P【0.001),respectively.CONCLUSIONS:There is a dose-dependent relationship between alcohol consumption and the risk of developing AC.MELD score has a better predictive value than Child-TurcottePugh or discriminant function score for patients with AC,and model 1 or 3 is better than model 2.

Natural mortality estimation and rational exploitation of purpleback flying squid Sthenoteuthis oualaniensis in the southern South China Sea

Based on the biological data of purpleback flying squid(Sthenoteuthis oualaniensis)collected by light falling-net in the southern South China Sea(SCS) during September to October 2012 and March to April 2013,growth and mortality of 'Medium' and 'Dwarf' forms of squid are derived using the Powell-Wetherall,ELEFAN methods and length-converted catch curves(FiSAT package).Given a lack of commercial exploitation,we assume total mortality to be due entirely to natural mortality.We estimate these squid have fast growth,with growth coefficients(k) ranging from 1.42 to 2.39,and high natural mortality(M),with estimates ranging from 1.61 to 2.92.To sustainably exploit these squid stocks,yield per recruitment based on growth and natural mortality was determined using the Beverton-Holt dynamic pool model.We demonstrate squid stocks could sustain high fishing mortality and low ages at first capture,with an optimal fishing mortality >3.0,with the optimal age at first capture increased to 0.4-0.6 years when fishing mortality approached optimal levels.On the basis of our analyses and estimates of stock biomass,we believe considerable potential exists to expand the squid fishery into the open SCS,relieving fishing pressure on coastal waters.