Life expectancies of South African adults starting antiretroviral treatment: collaborative analysis of cohort studies

BACKGROUND Few estimates exist of the life expectancy of HIV-positive adults receiving antiretroviral treatment (ART) in low- and middle-income countries. We aimed to estimate the life expectancy of patients starting ART in South Africa and compare it with that of HIV-negative adults. METHODS AND FINDINGS Data were collected from six South African ART cohorts. Analysis was restricted to 37,740 HIV-positive adults starting ART for the first time. Estimates of mortality were obtained by linking patient records to the national population register. Relative survival models were used to estimate the excess mortality attributable to HIV by age, for different baseline CD4 categories and different durations. Non-HIV mortality was estimated using a South African demographic model. The average life expectancy of men starting ART varied between 27.6 y (95% CI: 25.2-30.2) at age 20 y and 10.1 y (95% CI: 9.3-10.8) at age 60 y, while estimates for women at the same ages were substantially higher, at 36.8 y (95% CI: 34.0-39.7) and 14.4 y (95% CI: 13.3-15.3), respectively. The life expectancy of a 20-y-old woman was 43.1 y (95% CI: 40.1-46.0) if her baseline CD4 count was ≥ 200 cells/µl, compared to 29.5 y (95% CI: 26.2-33.0) if her baseline CD4 count was <50 cells/µl. Life expectancies of patients with baseline CD4 counts ≥ 200 cells/µl were between 70% and 86% of those in HIV-negative adults of the same age and sex, and life expectancies were increased by 15%-20% in patients who had survived 2 y after starting ART. However, the analysis was limited by a lack of mortality data at longer durations. CONCLUSIONS South African HIV-positive adults can have a near-normal life expectancy, provided that they start ART before their CD4 count drops below 200 cells/µl. These findings demonstrate that the near-normal life expectancies of HIV-positive individuals receiving ART in high-income countries can apply to low- and middle-income countries as well. Please see later in the article for the Editors' Summary.

Prognosis of Children with HIV-1 Infection Starting Antiretroviral Therapy in Southern Africa: A Collaborative Analysis of Treatment Programs

BACKGROUND: Prognostic models for children starting antiretroviral therapy (ART) in Africa are lacking. We developed models to estimate the probability of death during the first year receiving ART in Southern Africa. METHODS: We analyzed data from children ≤10 years old who started ART in Malawi, South Africa, Zambia or Zimbabwe from 2004-2010. Children lost to follow-up or transferred were excluded. The primary outcome was all-cause mortality in the first year of ART. We used Weibull survival models to construct two prognostic models: one with CD4%, age, WHO clinical stage, weight-for-age z-score (WAZ) and anemia and one without CD4%, because it is not routinely measured in many programs. We used multiple imputation to account for missing data. RESULTS: Among 12655 children, 877 (6.9%) died in the first year of ART. 1780 children were lost to follow-up/transferred and excluded from main analyses; 10875 children were included. With the CD4% model probability of death at 1 year ranged from 1.8% (95% CI: 1.5-2.3) in children 5-10 years with CD4% ≥10%, WHO stage I/II, WAZ ≥-2 and without severe anemia to 46.3% (95% CI: 38.2-55.2) in children <1 year with CD4% <5%, stage III/IV, WAZ< -3 and severe anemia. The corresponding range for the model without CD4% was 2.2% (95% CI: 1.8-2.7) to 33.4% (95% CI: 28.2-39.3). Agreement between predicted and observed mortality was good (C-statistics=0.753 and 0.745 for models with and without CD4% respectively). CONCLUSION: These models may be useful to counsel children/caregivers, for program planning and to assess program outcomes after allowing for differences in patient disease severity characteristics.

Recurrent stroke predictors differ in medically treated patients with pathogenic vs other PFOs

OBJECTIVE: To examine predictors of stroke recurrence in patients with a high vs a low likelihood of having an incidental patent foramen ovale (PFO) as defined by the Risk of Paradoxical Embolism (RoPE) score.METHODS: Patients in the RoPE database with cryptogenic stroke (CS) and PFO were classified as having a probable PFO-related stroke (RoPE score of >6, n = 647) and others (RoPE score of survival models with component database as a stratification factor. An interaction with RoPE score was checked for the variables that were significant.RESULTS: Follow-up was available for 92%, 79%, and 57% at 1, 2, and 3 years. Overall, a higher recurrence risk was associated with an index TIA. For all other predictors, effects were significantly different in the 2 RoPE score categories. For the low RoPE score group, but not the high RoPE score group, older age and antiplatelet (vs warfarin) treatment predicted recurrence. Conversely, echocardiographic features (septal hypermobility and a small shunt) and a prior (clinical) stroke/TIA were significant predictors in the high but not low RoPE score group.CONCLUSION: Predictors of recurrence differ when PFO relatedness is classified by the RoPE score, suggesting that patients with CS and PFO form a heterogeneous group with different stroke mechanisms. Echocardiographic features were only associated with recurrence in the high RoPE score group.

Debris discs around M stars: non-existence versus non-detection

Motivated by the reported dearth of debris discs around M stars, we use survival models to study the occurrence of planetesimal discs around them. These survival models describe a planetesimal disc with a small number of parameters, determine if it may survive a series of dynamical processes and compute the associated infrared excess. For the Wide-field Infrared Survey Explorer (WISE) satellite, we demonstrate that the dearth of debris discs around M stars may be attributed to the small semimajor axes generally probed if either: (1) the dust grains behave like blackbodies emitting at a peak wavelength coincident with the observed one; (2) or the grains are hotter than predicted by their blackbody temperatures and emit at peak wavelengths that are shorter than the observed one. At these small distances from the M star, planetesimals are unlikely to survive or persist for time-scales of 300 Myr or longer if the disc is too massive. Conversely, our survival models allow for the existence of a large population of low-mass debris discs that are too faint to be detected with current instruments. We gain further confidence in our interpretation by demonstrating the ability to compute infrared excesses for Sun-like stars that are broadly consistent with reported values in the literature. However, our interpretation becomes less clear and large infrared excesses are allowed if only one of these scenarios holds: (3) the dust grains are hotter than blackbody and predominantly emit at the observed wavelength; (4) or are blackbody in nature and emit at peak wavelengths longer than the observed one. Both scenarios imply that the parent planetesimals reside at larger distances from the star than inferred if the dust grains behaved like blackbodies. In all scenarios, we show that the infrared excesses detected at 22 μm (via WISE) and 70 μm (via Spitzer) from AU Mic are easily reconciled with its young age (12 Myr). Conversely, the existence of the old debris disc (2–8 Gyr) from GJ 581 is due to the large semimajor axes probed by the Herschel PACS instrument. We elucidate the conditions under which stellar wind drag may be neglected when considering dust populations around M stars. The WISE satellite should be capable of detecting debris discs around young M stars with ages ∼10 Myr.

A novel clinical scoring system for outcome prediction in dogs with acute kidney injury managed by hemodialysis.

BACKGROUND No reliable tool to predict outcome of acute kidney injury (AKI) exists. HYPOTHESIS A statistically derived scoring system can accurately predict outcome in dogs with AKI managed with hemodialysis. ANIMALS One hundred and eighty-two client-owned dogs with AKI. METHODS Logistic regression analyses were performed initially on clinical variables available on the 1st day of hospitalization for relevance to outcome. Variables with P< or = .1 were considered for further analyses. Continuous variables outside the reference range were divided into quartiles to yield quartile-specific odds ratios (ORs) for survival. Models were developed by incorporating weighting factors assigned to each quartile based on the OR, using either the integer value of the OR (Model A) or the exact OR (Models B or C, when the etiology was known). A predictive score for each model was calculated for each dog by summing all weighting factors. In Model D, actual values for continuous variables were used in a logistic regression model. Receiver-operating curve analyses were performed to assess sensitivities, specificities, and optimal cutoff points for all models. RESULTS Higher scores were associated with decreased probability of survival (P < .001). Models A, B, C, and D correctly classified outcomes in 81, 83, 87, and 76% of cases, respectively, and optimal sensitivities/specificities were 77/85, 81/85, 83/90 and 92/61%, respectively. CONCLUSIONS AND CLINICAL RELEVANCE The models allowed outcome prediction that corresponded with actual outcome in our cohort. However, each model should be validated further in independent cohorts. The models may also be useful to assess AKI severity.

Positive affect moderates the effect of negative affect on cardiovascular disease-related hospitalizations and all-cause mortality after cardiac rehabilitation

BACKGROUND Little is known as to whether negative emotions adversely impact the prognosis of patients who undergo cardiac rehabilitation. We prospectively investigated the predictive value of state negative affect (NA) assessed at discharge from cardiac rehabilitation for prognosis and the moderating role of positive affect (PA) on the effect of NA on outcomes. METHODS A total of 564 cardiac patients (62.49 ± 11.51) completed a comprehensive three-month outpatient cardiac rehabilitation program, filling in the Global Mood Scale (GMS) at discharge. The combined endpoint was cardiovascular disease (CVD)-related hospitalizations plus all-cause mortality at follow-up. Cox regression models estimated the predictive value of NA, as well as the moderating influence of PA on outcomes. Survival models were adjusted for sociodemographic factors, traditional cardiovascular risk factors, and severity of disease. RESULTS During a mean follow-up period of 3.4 years, 71 patients were hospitalized for a CVD-related event and 15 patients died. NA score (range 0-20) was a significant and independent predictor (hazard ratio (HR) 1.091, 95% confidence interval (CI) 1.012-1.175; p = 0.023) with a three-point higher level in NA increasing the relative risk by 9.1%. Furthermore, PA interacted significantly with NA (p < 0.001). The relative risk of poor prognosis with NA was increased in patients with low PA (p = 0.012) but remained unchanged in combination with high PA (p = 0.12). CONCLUSION The combination of NA with low PA was particularly predictive of poor prognosis. Whether reduction of NA and increase of PA, particularly in those with high NA, improves outcome needs to be tested.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach.

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.

Anticoagulation Management Practices and Outcomes in Elderly Patients with Acute Venous Thromboembolism: A Clinical Research Study.

Whether anticoagulation management practices are associated with improved outcomes in elderly patients with acute venous thromboembolism (VTE) is uncertain. Thus, we aimed to examine whether practices recommended by the American College of Chest Physicians guidelines are associated with outcomes in elderly patients with VTE. We studied 991 patients aged ≥65 years with acute VTE in a Swiss prospective multicenter cohort study and assessed the adherence to four management practices: parenteral anticoagulation ≥5 days, INR ≥2.0 for ≥24 hours before stopping parenteral anticoagulation, early start with vitamin K antagonists (VKA) ≤24 hours of VTE diagnosis, and the use of low-molecular-weight heparin (LMWH) or fondaparinux. The outcomes were all-cause mortality, VTE recurrence, and major bleeding at 6 months, and the length of hospital stay (LOS). We used Cox regression and lognormal survival models, adjusting for patient characteristics. Overall, 9% of patients died, 3% had VTE recurrence, and 7% major bleeding. Early start with VKA was associated with a lower risk of major bleeding (adjusted hazard ratio 0.37, 95% CI 0.20-0.71). Early start with VKA (adjusted time ratio [TR] 0.77, 95% CI 0.69-0.86) and use of LMWH/fondaparinux (adjusted TR 0.87, 95% CI 0.78-0.97) were associated with a shorter LOS. An INR ≥2.0 for ≥24 hours before stopping parenteral anticoagulants was associated with a longer LOS (adjusted TR 1.2, 95% CI 1.08-1.33). In elderly patients with VTE, the adherence to recommended anticoagulation management practices showed mixed results. In conclusion, only early start with VKA and use of parenteral LMWH/fondaparinux were associated with better outcomes.

Métodos de adequação e diagnóstico em modelos de sobrevivência dinâmicos

A análise de dados de sobrevivência tem sido tradicionalmente baseada no modelo de regressão de Cox (COX, 1972). No entanto, a suposição de taxas de falha proporcionais assumida para esse modelo pode não ser atendida em diversas situações práticas. Essa restrição do modelo de Cox tem gerado interesse em abordagens alternativas, dentre elas os modelos dinâmicos que permitem efeito das covariáveis variando no tempo. Neste trabalho, foram revisados os principais modelos de sobrevivência dinâmicos com estrutura aditiva e multiplicativa nos contextos não paramétrico e semiparamétrico. Métodos gráficos baseados em resíduos foram apresentados com a finalidade de avaliar a qualidade de ajuste desses modelos. Uma versão tempo-dependente da área sob a curva ROC, denotada por AUC(t), foi proposta com a finalidade de avaliar e comparar a qualidade de predição entre modelos de sobrevivência com estruturas aditiva e multiplicativa. O desempenho da AUC(t) foi avaliado por meio de um estudo de simulação. Dados de três estudos descritos na literatura foram também analisados para ilustrar ou complementar os cenários que foram considerados no estudo de simulação. De modo geral, os resultados obtidos indicaram que os métodos gráficos apresentados para avaliar a adequação dos modelos em conjunto com a AUC(t) se constituem em um conjunto de ferramentas estatísticas úteis para o próposito de avaliar modelos de sobrevivência dinâmicos nos contextos não paramétrico e semiparamétrico. Além disso, a aplicação desse conjunto de ferramentas em alguns conjuntos de dados evidenciou que se, por um lado, os modelos dinâmicos são atrativos por permitirem covariáveis tempo-dependentes, por outro lado podem não ser apropriados para todos os conjuntos de dados, tendo em vista que estimação pode apresentar restrições para alguns deles.

Avaliação e correções de testes de hipóteses em modelos de sobrevivência com fração de cura

Survival models deals with the modelling of time to event data. In certain situations, a share of the population can no longer be subjected to the event occurrence. In this context, the cure fraction models emerged. Among the models that incorporate a fraction of cured one of the most known is the promotion time model. In the present study we discuss hypothesis testing in the promotion time model with Weibull distribution for the failure times of susceptible individuals. Hypothesis testing in this model may be performed based on likelihood ratio, gradient, score or Wald statistics. The critical values are obtained from asymptotic approximations, which may result in size distortions in nite sample sizes. This study proposes bootstrap corrections to the aforementioned tests and Bartlett bootstrap to the likelihood ratio statistic in Weibull promotion time model. Using Monte Carlo simulations we compared the nite sample performances of the proposed corrections in contrast with the usual tests. The numerical evidence favors the proposed corrected tests. At the end of the work an empirical application is presented.

Regression models for bivariate survival data

Multivariate lifetime data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated lifetime when an individual is followed for the occurrence of two or more types of events, or when distinct individuals have dependent event times. In most studies there are covariates such as treatments, group indicators, individual characteristics, or environmental conditions, whose relationship to lifetime is of interest. This leads to a consideration of regression models.The well known Cox proportional hazards model and its variations, using the marginal hazard functions employed for the analysis of multivariate survival data in literature are not sufficient to explain the complete dependence structure of pair of lifetimes on the covariate vector. Motivated by this, in Chapter 2, we introduced a bivariate proportional hazards model using vector hazard function of Johnson and Kotz (1975), in which the covariates under study have different effect on two components of the vector hazard function. The proposed model is useful in real life situations to study the dependence structure of pair of lifetimes on the covariate vector . The well known partial likelihood approach is used for the estimation of parameter vectors. We then introduced a bivariate proportional hazards model for gap times of recurrent events in Chapter 3. The model incorporates both marginal and joint dependence of the distribution of gap times on the covariate vector . In many fields of application, mean residual life function is considered superior concept than the hazard function. Motivated by this, in Chapter 4, we considered a new semi-parametric model, bivariate proportional mean residual life time model, to assess the relationship between mean residual life and covariates for gap time of recurrent events. The counting process approach is used for the inference procedures of the gap time of recurrent events. In many survival studies, the distribution of lifetime may depend on the distribution of censoring time. In Chapter 5, we introduced a proportional hazards model for duration times and developed inference procedures under dependent (informative) censoring. In Chapter 6, we introduced a bivariate proportional hazards model for competing risks data under right censoring. The asymptotic properties of the estimators of the parameters of different models developed in previous chapters, were studied. The proposed models were applied to various real life situations.

Parametric accelerated failure time models with random effects and an application to kidney transplant survival

Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney transplantation. Different combinations of the distribution of the random effects and baseline hazard function are considered and the fit of such models to the transplant data is critically assessed. A mixture model that combines short- and long-term components of a hazard function is then developed, which provides a more flexible model for the hazard function. The model can incorporate different explanatory variables and random effects in each component. The model is straightforward to fit using standard statistical software, and is shown to be a good fit to the transplant data. Copyright (C) 2004 John Wiley Sons, Ltd.