Competing risk models of stillbirth inform populations but not individuals.
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ABSTRACT:
SUBMITTER: Smith G
PROVIDER: S-EPMC4982053 | biostudies-literature | 2016 Jun
REPOSITORIES: biostudies-literature
Publications
Competing risk models of stillbirth inform populations but not individuals.
BJOG : an international journal of obstetrics and gynaecology 20160229 7
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Project description:Competing events can preclude the event of interest from occurring in epidemiologic data and can be analyzed by using extensions of survival analysis methods. In this paper, the authors outline 3 regression approaches for estimating 2 key quantities in competing risks analysis: the cause-specific relative hazard ((cs)RH) and the subdistribution relative hazard ((sd)RH). They compare and contrast the structure of the risk sets and the interpretation of parameters obtained with these methods. They also demonstrate the use of these methods with data from the Women's Interagency HIV Study established in 1993, treating time to initiation of highly active antiretroviral therapy or to clinical disease progression as competing events. In our example, women with an injection drug use history were less likely than those without a history of injection drug use to initiate therapy prior to progression to acquired immunodeficiency syndrome or death by both measures of association ((cs)RH = 0.67, 95% confidence interval: 0.57, 0.80 and (sd)RH = 0.60, 95% confidence interval: 0.50, 0.71). Moreover, the relative hazards for disease progression prior to treatment were elevated ((cs)RH = 1.71, 95% confidence interval: 1.37, 2.13 and (sd)RH = 2.01, 95% confidence interval: 1.62, 2.51). Methods for competing risks should be used by epidemiologists, with the choice of method guided by the scientific question.
| S-EPMC2732996 | biostudies-literature
Project description:Risk factor analyses for nosocomial infections (NIs) are complex. First, due to competing events for NI, the association between risk factors of NI as measured using hazard rates may not coincide with the association using cumulative probability (risk). Second, patients from the same intensive care unit (ICU) who share the same environmental exposure are likely to be more similar with regard to risk factors predisposing to a NI than patients from different ICUs. We aimed to develop an analytical approach to account for both features and to use it to evaluate associations between patient- and ICU-level characteristics with both rates of NI and competing risks and with the cumulative probability of infection.We considered a multicenter database of 159 intensive care units containing 109,216 admissions (813,739 admission-days) from the Spanish HELICS-ENVIN ICU network. We analyzed the data using two models: an etiologic model (rate based) and a predictive model (risk based). In both models, random effects (shared frailties) were introduced to assess heterogeneity. Death and discharge without NI are treated as competing events for NI.There was a large heterogeneity across ICUs in NI hazard rates, which remained after accounting for multilevel risk factors, meaning that there are remaining unobserved ICU-specific factors that influence NI occurrence. Heterogeneity across ICUs in terms of cumulative probability of NI was even more pronounced. Several risk factors had markedly different associations in the rate-based and risk-based models. For some, the associations differed in magnitude. For example, high Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were associated with modest increases in the rate of nosocomial bacteremia, but large increases in the risk. Others differed in sign, for example respiratory vs cardiovascular diagnostic categories were associated with a reduced rate of nosocomial bacteremia, but an increased risk.A combination of competing risks and multilevel models is required to understand direct and indirect risk factors for NI and distinguish patient-level from ICU-level factors.
| S-EPMC4056071 | biostudies-literature
Project description:BackgroundDespite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk.ObjectiveThis study aimed to examine the association between timing of SARS-CoV-2 infection during pregnancy and risk of stillbirth.Study designThis retrospective cohort study used multilevel logistic regression analyses of nationwide electronic health records in the United States. Data were from 75 healthcare systems and institutes across 50 states. A total of 191,403 pregnancies of 190,738 individuals of reproductive age (15-49 years) who had childbirth between March 1, 2020 and May 31, 2021 were identified and included. The main outcome was stillbirth at ≥20 weeks of gestation. Exposures were the timing of SARS-CoV-2 infection: early pregnancy (<20 weeks), midpregnancy (21-27 weeks), the third trimester (28-43 weeks), any time before delivery, and never infected (reference).ResultsWe identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. After adjusting for maternal and clinical characteristics, increased odds of stillbirth were observed among pregnant individuals with SARS-CoV-2 infection only in early pregnancy (odds ratio, 1.75, 95% confidence interval, 1.25-2.46) and midpregnancy (odds ratio, 2.09; 95% confidence interval, 1.49-2.93), as opposed to pregnant individuals who were never infected. Older age, Black race, hypertension, acute respiratory distress syndrome or acute respiratory failure, and placental abruption were found to be consistently associated with stillbirth across different trimesters.ConclusionIncreased risk of stillbirth was associated with COVID-19 only when pregnant individuals were infected during early and midpregnancy, and not at any time before the delivery or during the third trimester, suggesting the potential vulnerability of the fetus to SARS-CoV-2 infection in early pregnancy. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals infected with SARS-CoV-2 during early and midpregnancy.
| S-EPMC9970919 | biostudies-literature
Project description:Local perceptions and understanding of the causes of ill health and death can influence health-seeking behaviour and practices in pregnancy. We aimed to understand individual explanatory models for stillbirth in Afghanistan to inform future stillbirth prevention. This was an exploratory qualitative study of 42 semi-structured interviews with women and men whose child was stillborn, community elders, and healthcare providers in Kabul province, Afghanistan between October-November 2017. We used thematic data analysis framing the findings around Kleinman's explanatory framework. Perceived causes of stillbirth were broadly classified into four categories-biomedical, spiritual and supernatural, extrinsic factors, and mental wellbeing. Most respondents attributed stillbirths to multiple categories, and many believed that stillbirths could be prevented. Prevention practices in pregnancy aligned with perceived causes and included engaging self-care, religious rituals, superstitious practices and imposing social restrictions. Symptoms preceding the stillbirth included both physical and non-physical symptoms or no symptoms at all. The impacts of stillbirth concerned psychological effects and grief, the physical effect on women's health, and social implications for women and how their communities perceive them. Our findings show that local explanations for stillbirth vary and need to be taken into consideration when developing health education messages for stillbirth prevention. The overarching belief that stillbirth was preventable is encouraging and offers opportunities for health education. Such messages should emphasise the importance of care-seeking for problems and should be delivered at all levels in the community. Community engagement will be important to dispel misinformation around pregnancy loss and reduce social stigma.
| S-EPMC10284382 | biostudies-literature
Project description:BackgroundDementia prevention can be addressed if the intervention is applied early.ObjectiveThe objective of this study was to develop and validate competing risk models to predict the late risk of dementia based on variables assessed in middle age in a southern European population.MethodsWe conducted a prospective observational study of the EPIC-Spain cohort that included 25,015 participants. Dementia cases were identified from electronic health records and validated by neurologists. Data were gathered on sociodemographic characteristics and cardiovascular risk factors. To stratify dementia risk, Fine and Gray competing risk prediction models were constructed for the entire sample and for over-55-year-olds. Risk scores were calculated for low (the 30% of the sample with the lowest risk), moderate (> 30% -60%), and high (> 60% -100%) risk.ResultsThe 755 cases of dementia identified represented a cumulative incidence of 3.1% throughout the study period. The AUC of the model for over-55-year-olds was much higher (80.8%) than the overall AUC (68.5%) in the first 15 years of follow-up and remained that way in the subsequent follow-up. The weight of the competing risk of death was greater than that of dementia and especially when the entire population was included.ConclusionThis study presents the first dementia risk score calculated in a southern European population in mid-life and followed up for 20 years. The score makes it feasible to achieve the early identification of individuals in a southern European population who could be targeted for the prevention of dementia based on the intensive control of risk factors.
| S-EPMC9108562 | biostudies-literature
Project description:BackgroundDistinguishing between mortality attributed to respiratory causes and other causes among people with asthma, COPD, and asthma-COPD overlap (ACO) is important. This study used electronic health records in England to estimate excess risk of death from respiratory-related causes after accounting for other causes of death.MethodsWe used linked Clinical Practice Research Datalink (CPRD) primary care and Office for National Statistics mortality data to identify adults with asthma and COPD from 2005 to 2015. Causes of death were ascertained using death certificates. Hazard ratios (HR) and excess risk of death were estimated using Fine-Gray competing risk models and adjusting for age, sex, smoking status, body mass index and socioeconomic status.Results65,021 people with asthma and 45,649 with COPD in the CPRD dataset were frequency matched 5:1 with people without the disease on age, sex and general practice. Only 14 in 100,000 people with asthma are predicted to experience a respiratory-related death up to 10 years post-diagnosis, whereas in COPD this is 98 in 100,000. Asthma is associated with an 0.01% excess incidence of respiratory related mortality whereas COPD is associated with an 0.07% excess. Among people with asthma-COPD overlap (N = 22,145) we observed an increased risk of respiratory-related death compared to those with asthma alone (HR = 1.30; 95% CI 1.21-1.40) but not COPD alone (HR = 0.89; 95% CI 0.83-0.94).ConclusionsAsthma and COPD are associated with an increased risk of respiratory-related death after accounting for other causes; however, diagnosis of COPD carries a much higher probability. ACO is associated with a lower risk compared to COPD alone but higher risk compared to asthma alone.
| S-EPMC8742941 | biostudies-literature
Project description:BackgroundAssessing calibration-the agreement between estimated risk and observed proportions-is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention.MethodsWe propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure.ResultsThe simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk outcomes.ConclusionsThe calibration curves and numeric calibration metrics permit a comprehensive comparison of the calibration of different competing risk models.
| S-EPMC8762819 | biostudies-literature
Project description:Rationale & objectiveThere is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI.Study designExternal validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways.Setting & participantsValidation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013.PredictorsKDIGO-based measures of AKI severity and comorbidities specified in the original models.OutcomesDeath or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI.Analytical approachDecision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery).Results26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring.LimitationsBoth original models overstated risks, indicating a need for regular updating.ConclusionsFollow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.
| S-EPMC8234511 | biostudies-literature
Project description:BackgroundIn medical research, one common competing risks situation is the study of different types of events, such as disease recurrence and death. We focused on that situation but considered death under two aspects: "expected death" and "excess death", the latter could be directly or indirectly associated with the disease.MethodsThe excess hazard method allows estimating an excess mortality hazard using the population (expected) mortality hazard. We propose models combining the competing risks approach and the excess hazard method. These models are based on a joint modelling of each event-specific hazard, including the event-free excess death hazard. The proposed models are parsimonious, allow time-dependent hazard ratios, and facilitate comparisons between event-specific hazards and between covariate effects on different events. In a simulation study, we assessed the performance of the estimators and showed their good properties with different drop-out censoring rates and different sample sizes.ResultsWe analyzed a population-based dataset on French colon cancer patients who have undergone curative surgery. Considering three competing events (local recurrence, distant metastasis, and death), we showed that the recurrence-free excess mortality hazard reached zero six months after treatment. Covariates sex, age, and cancer stage had the same effects on local recurrence and distant metastasis but a different effect on excess mortality.ConclusionsThe proposed models consider the excess mortality within the framework of competing risks. Moreover, the joint estimation of the parameters allow (i) direct comparisons between covariate effects, and (ii) fitting models with common parameters to obtain more parsimonious models and more efficient parameter estimators.
| S-EPMC3123657 | biostudies-literature
Project description:BackgroundPrevious predictive models using logistic regression for stillbirth do not leverage the advanced and nuanced techniques involved in sophisticated machine learning methods, such as modeling nonlinear relationships between outcomes.ObjectiveThis study aimed to create and refine machine learning models for predicting stillbirth using data available before viability (22-24 weeks) and throughout pregnancy, as well as demographic, medical, and prenatal visit data, including ultrasound and fetal genetics.Study designThis is a secondary analysis of the Stillbirth Collaborative Research Network, which included data from pregnancies resulting in stillborn and live-born infants delivered at 59 hospitals in 5 diverse regions across the United States from 2006 to 2009. The primary aim was the creation of a model for predicting stillbirth using data available before viability. Secondary aims included refining models with variables available throughout pregnancy and determining variable importance.ResultsAmong 3000 live births and 982 stillbirths, 101 variables of interest were identified. Of the models incorporating data available before viability, the random forests model had 85.1% accuracy (area under the curve) and high sensitivity (88.6%), specificity (85.3%), positive predictive value (85.3%), and negative predictive value (84.8%). A random forests model using data collected throughout pregnancy resulted in accuracy of 85.0%; this model had 92.2% sensitivity, 77.9% specificity, 84.7% positive predictive value, and 88.3% negative predictive value. Important variables in the previability model included previous stillbirth, minority race, gestational age at the earliest prenatal visit and ultrasound, and second-trimester serum screening.ConclusionApplying advanced machine learning techniques to a comprehensive database of stillbirths and live births with unique and clinically relevant variables resulted in an algorithm that could accurately identify 85% of pregnancies that would result in stillbirth, before they reached viability. Once validated in representative databases reflective of the US birthing population and then prospectively, these models may provide effective risk stratification and clinical decision-making support to better identify and monitor those at risk of stillbirth.
| S-EPMC10527568 | biostudies-literature