A Frailty Index to Predict Mortality, Resource Utilization and Costs in Patients Undergoing Coronary Artery Bypass Graft Surgery in Ontario.

Background: People living with frailty are vulnerable to poor outcomes and incur higher health care costs after coronary artery bypass graft (CABG) surgery. Frailty-defining instruments for population-level research in the CABG setting have not been established. The objectives of the study were to develop a preoperative frailty index for CABG (pFI-C) surgery using Ontario administrative data; assess pFI-C suitability in predicting clinical and economic outcomes; and compare pFI-C predictive capabilities with other indices. Methods: A retrospective cohort study was conducted using health administrative data of 50,682 CABG patients. The pFI-C comprised 27 frailty-related health deficits. Associations between index scores and mortality, resource use and health care costs (2022 Canadian dollars [CAD]) were assessed using multivariable regression models. Capabilities of the pFI-C in predicting mortality were evaluated using concordance statistics; goodness of fit of the models was assessed using Akakie Information Criterion. Results: As assessed by the pFI-C, 22% of the cohort lived with frailty. The pFI-C score was strongly associated with mortality per 10% increase (odds ratio [OR], 3.04; 95% confidence interval [CI], [2.83,3.27]), and was significantly associated with resource utilization and costs. The predictive performances of the pFI-C, Charlson, and Elixhauser indices and Johns Hopkins Aggregated Diagnostic Groups were similar, and mortality models containing the pFI-C had a concordance (C)-statistic of 0.784. Cost models containing the pFI-C showed the best fit. Conclusions: The pFI-C is predictive of mortality and associated with resource utilization and costs during the year following CABG. This index could aid in identifying a subgroup of high-risk CABG patients who could benefit from targeted perioperative health care interventions.

Contexte: Les personnes dont l'état de santé est fragilisé sont susceptibles de connaître des issues défavorables et de générer des coûts plus élevés pour le système de santé après un pontage aortocoronarien. Aucun instrument n'a été établi pour définir la fragilité dans la recherche populationnelle en contexte de pontage aortocoronarien. Les objectifs de l'étude étaient les suivants : 1) concevoir un indice de fragilité préopératoire en vue d'un pontage aortocoronarien (preoperative frailty index for CABG surgery, pFI-C) en utilisant des données administratives de l'Ontario; 2) évaluer la capacité de cet indice à prédire les issues cliniques et économiques; et 3) comparer la valeur prédictive de cet indice avec celle d'autres indices. Méthodologie: Une étude de cohorte rétrospective a été menée à partir de données médico-administratives portant sur 50 682 patients ayant subi un pontage aortocoronarien. Le pFI-C comprenait 27 déficits de santé liés à la fragilité. Des liens entre les scores de l'indice et la mortalité, l'utilisation des ressources et les coûts de soins de santé (en $ CA de 2022) ont été évalués à l'aide de modèles de régression multivariable. La capacité du pFI-C à prédire la mortalité a été évaluée à l'aide de la statistique de concordance; la qualité de l'ajustement des modèles a été évaluée en fonction du critère d'information d'Akaike. Résultats: Selon l'évaluation par le pFI-C, 22 % de la cohorte vivait avec une fragilité. Le score de l'indice était fortement corrélé à la mortalité par tranche d'augmentation de 10 % (rapport de cotes de 3,04; intervalle de confiance à 95 % de 2,83 à 3,27) et était corrélé de manière significative à l'utilisation des ressources et aux coûts. La valeur prédictive du pFI-C, des indices de Charlson et Elixhauser, et de Johns Hopkins Aggregated Diagnostic Groups était similaire, et les modèles de mortalité contenant le pFI-C affichaient une valeur statistique C de 0,784. Les modèles de coûts contenant le pFI-C affichaient le meilleur ajustement. Conclusions: Le pFI-C est un facteur prédictif de mortalité et est corrélé à l'utilisation des ressources et aux coûts engagés durant l'année qui suit un pontage aortocoronarien. Cet indice pourrait faciliter la détection d'un sous-groupe de patients subissant un pontage aortocoronarien et présentant un risque élevé qui pourraient bénéficier de soins périopératoires ciblés.

A support vector machine-based cure rate model for interval censored data.

The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic function. This readily implies that the boundary classifying the cured and uncured subjects is linear. In this article, we propose a new mixture cure rate model based on interval censored data that uses the support vector machine to model the effect of covariates on the uncured or the cured probability (i.e. on the incidence part of the model). Our proposed model inherits the features of the support vector machine and provides flexibility to capture classification boundaries that are nonlinear and more complex. The latency part is modeled by a proportional hazards structure with an unspecified baseline hazard function. We develop an estimation procedure based on the expectation maximization algorithm to estimate the cured/uncured probability and the latency model parameters. Our simulation study results show that the proposed model performs better in capturing complex classification boundaries when compared to both logistic regression-based and spline regression-based mixture cure rate models. We also show that our model's ability to capture complex classification boundaries improve the estimation results corresponding to the latency part of the model. For illustrative purpose, we present our analysis by applying the proposed methodology to the NASA's Hypobaric Decompression Sickness Database.

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

Improving marginal hazard ratio estimation using quadratic inference functions.

Clustered and multivariate failure time data are commonly encountered in biomedical studies and a marginal regression approach is often employed to identify the potential risk factors of a failure. We consider a semiparametric marginal Cox proportional hazards model for right-censored survival data with potential correlation. We propose to use a quadratic inference function method based on the generalized method of moments to obtain the optimal hazard ratio estimators. The inverse of the working correlation matrix is represented by the linear combination of basis matrices in the context of the estimating equation. We investigate the asymptotic properties of the regression estimators from the proposed method. The optimality of the hazard ratio estimators is discussed. Our simulation study shows that the estimator from the quadratic inference approach is more efficient than those from existing estimating equation methods whether the working correlation structure is correctly specified or not. Finally, we apply the model and the proposed estimation method to analyze a study of tooth loss and have uncovered new insights that were previously inaccessible using existing methods.

Mean residual life cure models for right-censored data with and without length-biased sampling.

We propose a semiparametric mean residual life mixture cure model for right-censored survival data with a cured fraction. The model employs the proportional mean residual life model to describe the effects of covariates on the mean residual time of uncured subjects and the logistic regression model to describe the effects of covariates on the cure rate. We develop estimating equations to estimate the proposed cure model for the right-censored data with and without length-biased sampling, the latter is often found in prevalent cohort studies. In particular, we propose two estimating equations to estimate the effects of covariates in the cure rate and a method to combine them to improve the estimation efficiency. The consistency and asymptotic normality of the proposed estimates are established. The finite sample performance of the estimates is confirmed with simulations. The proposed estimation methods are applied to a clinical trial study on melanoma and a prevalent cohort study on early-onset type 2 diabetes mellitus.

Measures of explained variation under the mixture cure model for survival data.

Explained variation is well understood under linear regression models and has been extended to models for survival data. In this article, we consider the mixture cure models. We propose two approaches to define explained variation under the mixture cure models, one based on the Kullback-Leibler information gain and the other based on residual sum of squares. We show that the proposed measures have desired properties as measures of explained variation, similar to those under other regression models. A simulation study is conducted to demonstrate the properties of the proposed measures. They are also applied to real data analyses to illustrate the use of explained variation.

Mode of conception and risk of spontaneous vs. provider-initiated preterm birth: population-based cohort study.

OBJECTIVE: To study the association between mode of conception and risk of preterm birth, including, spontaneous and provider-initiated subtypes. DESIGN: Population-based retrospective cohort study. SETTING: Not applicable. PATIENTS: All singleton livebirths and stillbirth in Ontario, Canada, 2006-2014. INTERVENTION: The main exposure was mode of conception, namely unassisted conception, infertility without fertility treatment (i.e., known infertility but conceived without assistance), ovulation induction (OI) or intrauterine insemination (IUI), and in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). Modified Poisson regression generated risk ratios (RRs) and 95% confidence intervals for the association between exposure categories and preterm birth adjusted for clinically relevant covariates using a propensity score. MAIN OUTCOME MEASURE(S): The primary outcome was preterm birth <37 weeks, further categorized as spontaneous or provider-initiated subtypes. The secondary outcome was preterm birth <34 weeks. RESULTS: We included 732,810 singleton births born to 649,918 mothers, of which 646,926 (88.3%) were from an unassisted conception, 68,822 (9.4%) with infertility but no fertility treatment, 9,024 (1.2%) following OI/IUI, and 8,038 (1.1%) following IVF/ICSI. Preterm birth <37 weeks occurred among 6.0% of births by unassisted conception, 7.7% with infertility without fertility treatment, 8.0% with OI/IUI, and 10.8% following IVF/ICSI. Relative to unassisted conception, the unadjusted RR of provider-initiated preterm birth was 1.30 (1.26-1.33) in women with infertility without fertility treatment, 1.36 (1.26-1.45) after OI/IUI, and 1.82 (1.70-1.93) after IVF/ICSI. The corresponding adjusted RRs (aRR) were 1.23 (1.16-1.31), 1.48 (1.29-1.69), and 2.35 (2.09-2.64). The unadjusted RR of spontaneous preterm birth was 1.22 (1.18-1.27) in women with infertility without fertility treatment, 1.22 (1.12-1.34) after OI/IUI, and 1.47 (1.35-1.60) after IVF/ICSI. The corresponding aRR were 1.15 (1.10-1.19), 1.19 (1.09-1.31), and 1.40 (1.27-1.53). For preterm birth <34 weeks, the RRs followed a similar pattern as for preterm birth <37 weeks, with the exception of women with infertility without fertility treatment (aRR 1.08; confidence interval, 0.95-1.23). CONCLUSIONS: Infertility and receipt of fertility treatment are each associated with a higher risk of preterm birth, spontaneous and provider-initiated subtypes, even in singleton pregnancies. Strategies are needed to reduce the risk for preterm birth in these women.

Explaining regional variations in colon cancer survival in Ontario, Canada: a population-based retrospective cohort study.

OBJECTIVES: Regional variation in cancer survival is an important health system performance measurement. We evaluated if regional variation in colon cancer survival may be driven by differences in the patient population, their health and healthcare utilisation, and/or cancer care delivery. DESIGN: Population-based retrospective cohort study using routinely collected linked health administrative data. SETTING: Ontario, Canada. PARTICIPANTS: Patients with colon cancer diagnosed between 1 January 2009 and 31 December 2012. OUTCOME: Cancer-specific survival was compared across the province's 14 health regions. Using accelerated failure time models, we assessed whether regional survival variations were mediated through differences in case mix, including age, sex, comorbidities, stage at diagnosis and colon subsite, potential marginalisation and/or prediagnosis healthcare. RESULTS: The study population included 16 895 patients with colon cancer. There was statistically significant regional variation in cancer-specific survival. Three regions had cancer-specific survival that was between 30% (95% CI 1.03 to 1.65) and 39% (95% CI 1.13 to 1.71) longer and one region had cancer-specific survival that was 26% shorter (95% CI 0.58 to 0.93) than the reference region. For three of these regions, case mix explained between 26% and 56% of the survival variation. Further adjustment for rurality explained 22% of the remaining survival variation in one region. Adjustment for continuity of primary care and the diagnostic interval length explained 10% and 11% of the remaining survival variation in two other regions. Socioeconomic marginalisation, recent immigration and colonoscopy history did not explain colon cancer survival variation. CONCLUSIONS: Case mix accounted for much of the regional variation in colon cancer survival, indicating that efforts to monitor the quality of cancer care through survival metrics should consider case mix when reporting regional survival differences. Future work should repeat this approach in other settings and other cancer sites considering a broad range of potential mediators.

Bystander-initiated cardiopulmonary resuscitation and automated external defibrillator use after out-of-hospital cardiac arrest: Uncovering disparities in care and survival across the urban-rural spectrum.

AIM: To evaluate the association between bystander cardiopulmonary resuscitation (CPR), automated external defibrillator (AED) use, and survival after out-of-hospital cardiac arrest (OHCA) across the urban-rural spectrum. METHODS: This was a retrospective cohort study of 325,477 adult OHCAs within the Cardiac Arrest Registry to Enhance Survival from 2013 to 2019. Bystander interventions were categorized into no bystander intervention, bystander CPR alone, and bystander AED use (with or without CPR). The primary outcome was survival to hospital discharge with good neurological outcome. Multivariable logistic regression was used to evaluate the association between bystander interventions and survival by geographical status (urban, suburban, large rural, small town, or rural). RESULTS: Bystander CPR alone occurred most often in rural areas (50.8%), and least often in urban areas (35.4%). Bystander AED use in public settings was similar across the urban-rural spectrum (10.5-13.1%). Survival with good neurological outcome varied for urban (8.1%), suburban (7.7%), large rural (9.1%), small town (7.1%), and rural areas (6.1%). In comparison to no bystander intervention, the adjusted odds ratios (95% confidence intervals) for bystander AED use and survival were 2.57 (2.37-2.79) in urban areas, 2.58 (1.81-3.67) in suburban areas, 1.99 (1.44-2.76) in large rural areas, 1.90 (1.27-2.86) in small towns, and 3.05 (1.99-4.68) in rural areas. Bystander CPR alone was also associated with survival in all areas (adjusted odds ratio range: 1.29-1.45). There was no strong evidence of interaction between bystander interventions and geographical status on the primary outcome (p = 0.63). CONCLUSION: Bystander CPR and AED use are associated with positive clinical outcomes after OHCA in all areas along the urban-rural spectrum.

Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction.

Left-truncated data are often encountered in epidemiological cohort studies, where individuals are recruited according to a certain cross-sectional sampling criterion. Length-biased data, a special case of left-truncated data, assume that the incidence of the initial event follows a homogeneous Poisson process. In this article, we consider an analysis of length-biased and interval-censored data with a nonsusceptible fraction. We first point out the importance of a well-defined target population, which depends on the prior knowledge for the support of the failure times of susceptible individuals. Given the target population, we proceed with a length-biased sampling and draw valid inferences from a length-biased sample. When there is no covariate, we show that it suffices to consider a discrete version of the survival function for the susceptible individuals with jump points at the left endpoints of the censoring intervals when maximizing the full likelihood function, and propose an EM algorithm to obtain the nonparametric maximum likelihood estimates of nonsusceptible rate and the survival function of the susceptible individuals. We also develop a novel graphical method for assessing the stationarity assumption. When covariates are present, we consider the Cox proportional hazards model for the survival time of the susceptible individuals and the logistic regression model for the probability of being susceptible. We construct the full likelihood function and obtain the nonparametric maximum likelihood estimates of the regression parameters by employing the EM algorithm. The large sample properties of the estimates are established. The performance of the method is assessed by simulations. The proposed model and method are applied to data from an early-onset diabetes mellitus study.

An additive hazards frailty model with semi-varying coefficients.

Proportional hazards frailty models have been extensively investigated and used to analyze clustered and recurrent failure times data. However, the proportional hazards assumption in the models may not always hold in practice. In this paper, we propose an additive hazards frailty model with semi-varying coefficients, which allows some covariate effects to be time-invariant while other covariate effects to be time-varying. The time-varying and time-invariant regression coefficients are estimated by a set of estimating equations, whereas the frailty parameter is estimated by the moment method. The large sample properties of the proposed estimators are established. The finite sample performance of the estimators is examined by simulation studies. The proposed model and estimation are illustrated with an analysis of data from a rehospitalization study of colorectal cancer patients.

Mental Health Resource Use Among Patients Undergoing Curative Intent Treatment for Bladder Cancer.

BACKGROUND: Patients with bladder cancer may experience mental health distress. Mental health-care service (MHS) use can quantify the magnitude of the problem. METHODS: The Ontario Cancer Registry was used to identify all patients with bladder cancer treated with curative-intent cystectomy or radiotherapy in Ontario, Canada (2004-2013). Population-level databases were used to identify MHS use (visits to general practitioner, psychiatrist, emergency department, or hospitalization). Generalized estimating equations were used to compare rates of MHS use. Baseline, peritreatment, and posttreatment MHS use were defined as visits from 2 years to 3 months before, 3 months before to 3 months after, and from 3 months after to 2 years after start of treatment, respectively. RESULTS: From 2004 to 2013, 4296 patients underwent cystectomy (n = 3332) or curative-intent radiotherapy (n = 964). Compared with baseline, the rate of MHS use was higher in the peritreatment (adjusted rate ratio [aRR] = 1.64, 95% confidence interval [CI] = 1.48 to 1.82) and posttreatment periods (aRR = 1.45, 95% CI =1.30 to 1.63). By 2 years posttreatment, 24.6% (95% CI = 23.4% to 25.9%) of all patients had MHS use. Patients with baseline MHS use had substantially higher MHS use in the peritreatment (aRR = 5.77, 95% CI = 4.86 to 6.86) and posttreatment periods (aRR = 4.58, 95% CI = 3.78 to 5.55). Female patients had higher use MHS use overall, but males had a higher incremental increase in the posttreatment period compared with baseline (2-sided Pinteraction = .02). Male patients had a statistically significant increase in MHS use following surgery or radiotherapy, whereas female patients only had an increase following surgery. CONCLUSIONS: MHS use is common among patients undergoing treatment for bladder cancer, particularly in the peritreatment period. Screening for mental health concerns in this population is warranted.

Prescription opioid characteristics at initiation for non-cancer pain and risk of treated opioid use disorder: A population-based study.

BACKGROUND: Long-term prescription opioid use has been associated with adverse health outcomes, including opioid use disorder (OUD). We examined a population of opioid naïve individuals who initiated prescription opioids for non-cancer pain and investigated the associations between opioid prescription characteristics at initiation and time to treated OUD. METHODS: We conducted a retrospective population-based cohort study in Ontario, Canada among opioid naïve individuals aged 15 years and older dispensed an opioid for non-cancer pain between 2013 and 2016. We used the Narcotic Monitoring System to abstract opioid dispensing data. A multivariable Cox regression model was used to examine the association between average daily dose and time to treated OUD. RESULTS: We identified 1,607,659 opioid-naïve individuals who initiated a prescription opioid within the study period. The incidence of treated OUD within the study period was 86 cases per 100,000 person-years. Compared to an average daily dose of <20 morphine milligrams equivalent (MME), higher average daily doses at initiation were associated with greater hazard of treated OUD, 20-50 MME (HR 1.11, 95% CI: 1.02, 1.21), >50-90 MME (HR 1.29, 95% CI: 1.16, 1.44), >90-150 MME (HR 1.29, 95% CI: 1.06, 1.56), >150-200 MME (HR 2.49, 95% CI: 1.54, 4.03) and >200 MME (HR 4.15, 95% CI: 2.89, 5.97). Long-acting formulations and days' supply ≥11 days were also associated with greater hazard of treated OUD. CONCLUSION: Prescription opioid characteristics at initiation are associated with risk of treated OUD, identifying potentially important and modifiable risk factors among people initiating opioids for non-cancer pain.

Long-Term Mental Health Service Utilization Among Survivors of Testicular Cancer: A Population-Based Cohort Study.

PURPOSE: Testicular cancer survivors may experience mental illness as a consequence of their cancer diagnosis and treatment. METHODS: All incident cases of testicular cancer treated with orchiectomy in Ontario, Canada (2000-2010), were identified using the Ontario Cancer Registry. Cases were matched to controls in a 1:5 ratio on age and geography. Population-level databases were used to identify mental health service use episodes; outpatient use included visits to a general practitioner for a mental health concern or any visit to a psychiatrist. Negative binomial regression modeling was used to estimate the rate of mental health service use in the pretreatment (2 years prior until 1 month before orchiectomy), peritreatment (1 month before until 1 month after orchiectomy), and post-treatment periods (1 month after orchiectomy until end of follow-up). Rate ratios (RR) comparing cases with controls in the peri- and post-treatment periods were adjusted for baseline mental health service use. RESULTS: Two thousand six hundred nineteen cases of testicular cancer were matched to 13,095 controls. There was no baseline difference in the rate of mental health service use. Cases were significantly more likely than controls to have an outpatient visit for a mental health concern in the peritreatment (adjusted RR [aRR], 2.45; 95% CI, 2.06 to 2.92) and post-treatment periods (aRR, 1.30; 95% CI, 1.12 to 1.52). The difference in mental health service use persisted over a median follow-up of 12 years. In the postorchiectomy period, cases with baseline mental health service use were those most likely to use mental health services (aRR, 5.64; 95% CI, 4.64 to 6.85). CONCLUSION: Testicular cancer survivors use mental health services more often than healthy controls. Survivorship care plans that address the long-term mental healthcare needs of this population are needed.

Joint analysis of longitudinal measurements and survival times with a cure fraction based on partly linear mixed and semiparametric cure models.

In a joint analysis of longitudinal quality of life (QoL) scores and relapse-free survival (RFS) times from a clinical trial on early breast cancer conducted by the Canadian Cancer Trials Group, we observed a complicated trajectory of QoL scores and existence of long-term survivors. Motivated by this observation, we proposed in this paper a flexible joint model for the longitudinal measurements and survival times. A partly linear mixed effect model is used to capture the complicated but smooth trajectory of longitudinal measurements and approximated by B-splines and a semiparametric mixture cure model with the B-spline baseline hazard to model survival times with a cure fraction. These two models are linked by shared random effects to explore the dependence between longitudinal measurements and survival times. A semiparametric inference procedure with an EM algorithm is proposed to estimate the parameters in the joint model. The performance of proposed procedures are evaluated by simulation studies and through the application to the analysis of data from the clinical trial which motivated this research.

A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate.

Identification of a subset of patients who may be sensitive to a specific treatment is an important problem in clinical trials. In this paper, we consider the case where the treatment effect is measured by longitudinal outcomes, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. A threshold linear mixed model is introduced, and a smoothing maximum likelihood method is proposed to obtain the estimation of the parameters in the model. Broyden-Fletcher-Goldfarb-Shanno algorithm is employed to maximize the proposed smoothing likelihood function. The proposed procedure is evaluated through simulation studies and application to the analysis of data from a randomized clinical trial on patients with advanced colorectal cancer.

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

BACKGROUND: Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application. METHODS: PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN loss was quantified per cancer cell and per tissue microarray core. Thresholds for identifying clinically relevant PTEN loss were determined using log-rank statistics in the training cohort. Univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) analyses on various subpopulations were performed to assess biochemical recurrence-free survival (BRFS) and were independently validated. All statistical tests were two-sided. RESULTS: PTEN loss in more than 65% cancer cells was most clinically relevant and had statistically significant association with reduced BRFS in training (hazard ratio [HR] = 2.48, 95% confidence interval [CI] = 1.59 to 3.87; P < .001) and validation cohorts (HR = 4.22, 95% CI = 2.01 to 8.83; P < .001). The qPTEN scoring method identified patients who recurred within 5.4 years after surgery (P < .001). In men with favorable risk of biochemical recurrence (Cancer of the Prostate Risk Assessment - Postsurgical scores <5 and no adverse pathological features), qPTEN identified a subset of patients with shorter BRFS (HR = 5.52, 95% CI = 2.36 to 12.90; P < .001) who may be considered for intensified monitoring and/or adjuvant therapy. CONCLUSIONS: Compared with previous qualitative approaches, qPTEN improves risk stratification of postradical prostatectomy patients and may be considered as a complementary tool to guide disease management after surgery.

Variable selection via penalized generalized estimating equations for a marginal survival model.

Clustered and multivariate survival times, such as times to recurrent events, commonly arise in biomedical and health research, and marginal survival models are often used to model such data. When a large number of predictors are available, variable selection is always an important issue when modeling such data with a survival model. We consider a Cox's proportional hazards model for a marginal survival model. Under the sparsity assumption, we propose a penalized generalized estimating equation approach to select important variables and to estimate regression coefficients simultaneously in the marginal model. The proposed method explicitly models the correlation structure within clusters or correlated variables by using a prespecified working correlation matrix. The asymptotic properties of the estimators from the penalized generalized estimating equations are established and the number of candidate covariates is allowed to increase in the same order as the number of clusters does. We evaluate the performance of the proposed method through a simulation study and analyze two real datasets for the application.

Investigating the diagnostic capacity of uric acid in the occurrence of preeclampsia.

OBJECTIVES: To compare the distribution of uric acid (UA) concentration in women with normal and preeclamptic pregnancy, to investigate the significance of UA concentration in diagnosis of preeclampsia, and to estimate the UA rate of change over time before delivery. STUDY DESIGN: A case-control study of singleton pregnancies was completed at a tertiary care center in Kingston, Ontario. Patients with preeclampsia were recruited through two prospective cohort studies (n = 218); the Preeclampsia New Emerging Team (September 2003-October 2009) and the Maternal Health Clinic (May 2011-June 2016). Individuals who had an uncomplicated pregnancy and delivered (July 2016-August 2017), were included in the control arm (n = 73). MAIN OUTCOME MEASURES: Longitudinal analysis using a linear mixed-effects model examined the UA rate of change over time. The distribution of the UA level was compared using a t-test. The significance of the UA level in the diagnosis of preeclampsia was examined using multiple logistic regression. RESULTS: The rate of change in UA before delivery had an increasing non-constant logarithmic trend with time. Mean UA level in preeclamptic pregnancies (369.53 ± 75.78 µmol/l) was significantly elevated compared with the normal pregnancies (292.55 ± 54.73 µmol/l). UA had an adjusted odds ratio of 1.39 (95%CI: 1.14-1.69; P = 0.001) associated with the incidence of preeclampsia and UA level >349 µmol/l close to delivery is an accurate measurement for diagnosing preeclampsia. CONCLUSION: The UA concentration in preeclamptic pregnancies is significantly increased compared to normal pregnancies and the level of UA may have diagnostic ability in the occurrence of preeclampsia.

Management of Cancer-Associated Intestinal Obstruction in the Final Year of Life.

BACKGROUND: There is variation in the clinical management of intestinal obstruction (IO) in patients with cancer. We describe the management of cancer-associated IO near the end of life in a population-based cohort with universal health coverage. METHODS: Patients who died of gastric, colorectal, ovarian, and pancreatic cancers from 2002 to 2015 were identified from the Ontario Cancer Registry. Those with ≥1 hospital admission for IO in the final year of life were identified from administrative data. Management of IO at index admission was categorized as surgery, gastrostomy, stent, feeding jejunostomy, and medical management. Trends in management over the study period were assessed by the Cochran-Armitage test. RESULTS: The cohort included 57 378 patients (gastric [n = 7448, 13%], colorectal [n = 30 577 53%], ovarian [n = 6273, 11%], and pancreatic [n = 13 080, 23%] cancers). Of those, 7618 (13%) patients had ≥1 admission for IO in the final year of life. Of these patients, 2657 (35%) patients were managed with a surgical/procedural intervention at index admission (surgery [86%], gastrostomy [8%], stent [6%], and jejunostomy [0.4%]); the remaining patients (n = 4961, 65%) received medical management. Over the study period, there was a small but statistically significant increase in the use of stents (0% in 2002 to 5% in 2015, P < .0001) and gastrostomy tubes (2% in 2002 to 4% in 2015, P = .002) and a large decrease in the use of surgery (41% in 2002 to 28% in 2015, P = .04). CONCLUSIONS: Management of IO has changed over time with the increased use of stents and gastrostomy tubes and decreased use of surgery.