Investigating Causal Genetic Effects on Overall Survival of Glioblastoma Patients using Normalizing Flow and Structural Causal Model

Glioblastoma (GBM) is the most common and aggressive brain tumor with short overall survival (OS) of about 15 months. Understanding the causal factors affecting the patient survival is crucial for disease prognosis and treatment planning. Although previous efforts on survival prediction using multi-omics data has yielded useful predictive models, the causation of the correlated genetic risk factors has not been addressed. Recent advances in causal deep learning models enable the study of causality from complex dataset. In this paper, we leverage the recently proposed structural causal model (SCM) with normalizing flows parameterized by deep networks to perform the counterfactual query to investigate the causal relationship between gene mutation and OS with the presence of other confounders including sex, age and radiomics features. The query amounts to the question that what the survival days will be if the gene mutation status has been changed, i.e., from mutant to non-mutant and vice versa. The trained causal model will infer the counterfactual outcome given the intervention on specific gene mutation. We apply multivariate Cox-PH model to find the genes associated with survival, and investigate the causal genetic effect by comparing the original and counterfactual survival days in a bi-directional fashion. Particularly, the following two scenarios are considered: (1) intervention on a specific gene with non-mutant status to generate the counterfactual survival days as if the gene is mutant, with which the original survival days of the subjects with that mutant gene will be compared; (2) intervention on the gene with mutant status and perform the comparison with survival days of subjects with that non-mutant gene. Our experimental results show that no causation of two correlated genes (NF1, RB1) was revealed in the cohort (n=181), while their genetic effects on OS in terms of prolonging or shortening are generally in accordance with clinical findings.

Glasgow prognostic score as an outcome predictor for patients initiating hemodialysis

INTRODUCTION

This retrospective study examined the relationship between the Glasgow Prognostic Score (GPS) at hemodialysis (HD) initiation and overall/cardiovascular mortality.

METHODS

A total of 264 patients starting HD between 2014 and 2015 at a single center were studied. Follow-up persisted until therapy change, death, or study end (December 31, 2021), with a median of 6.8 years.

RESULTS

Patients with a higher GPS more frequently had emergent HD initiation and showed increased eGFR at initiation. During follow-up, 60% of patients died, with cardiovascular disease being the leading cause. Univariate analysis revealed a significant difference in median survival time across GPS classes. Cox proportional hazard models confirmed a significant association between GPS and mortality.

CONCLUSIONS

We report a significant association between GPS at HD initiation and mortality. GPS may prove useful as a prognostic tool for identifying high-risk patients, underscoring the need for future research to validate these findings and explore the potential of GPS-based interventions.

Advanced considerations in survival analysis

Investigation of survival during the follow-up period is common in cardiovascular research and has intrinsic issues that require precise knowledge, such as survival or censoring. Besides, as the follow-up period lengthens and events other than mortality are studied, the analysis becomes more complex, so Kaplan-Meier analyses or Cox models are not always sufficient. In this primer, we provide the reader with detailed information on the interpretation of the most common survival analyses and delve into methods to analyse competing risks or alternatives to the conventional methods when the proportional hazards assumption is not met.

Regression analysis of multivariate recurrent event data allowing time-varying dependence with application to stroke registry data

In multivariate recurrent event data, each patient may repeatedly experience more than one type of event. Analysis of such data gets further complicated by the time-varying dependence structure among different types of recurrent events. The available literature regarding the joint modeling of multivariate recurrent events assumes a constant dependency over time, which is strict and often violated in practice. To close the knowledge gap, we propose a class of flexible shared random effects models for multivariate recurrent event data that allow for time-varying dependence to adequately capture complex correlation structures among different types of recurrent events. We developed an expectation-maximization algorithm for stable and efficient model fitting. Extensive simulation studies demonstrated that the estimators of the proposed approach have satisfactory finite sample performance. We applied the proposed model and the estimating method to data from a cohort of stroke patients identified in the University of Texas Houston Stroke Registry and evaluated the effects of risk factors and the dependence structure of different types of post-stroke readmission events.

Applying Trial-Derived Treatment Effects to Real-World Populations: Generalizing Cost-Effectiveness Estimates When Modeling Complex Hazards

OBJECTIVES

Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example.

METHODS

Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example.

RESULTS

Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling.

CONCLUSIONS

Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated.

Intradialytic Hypotension and Mortality in Adolescents and Young Adults With Kidney Failure Receiving Maintenance Hemodialysis

Rationale & Objective

Intradialytic hypotension (IDH) is associated with mortality in adults with kidney failure requiring hemodialysis (HD); however, large-scale pediatric studies are lacking. Moreover, there is no evidence-based consensus definition of IDH in pediatric literature. We aimed to examine the association of commonly used definitions of IDH with mortality in adolescents and young adults.

Study Design

This was a retrospective observational cohort study.

Setting & Participants

In total, 1,199 adolescents and young adults (N = 320, aged 10-18 years and N = 879, aged 19-21 years) who initiated HD in a large dialysis organization were included.

Exposures

This study used different definitions of IDH.

Outcome

The study outcome was 2-year all-cause mortality.

Analytical Approach

Several definitions of IDH were selected a priori based on a literature review. Patients were classified as having IDH if it was present in at least 30% of HD treatments during the first 90 days after dialysis initiation. Cox proportional hazards regression was used to test whether IDH associated with 2-year all-cause mortality.

Results

Over a 2-year follow-up period, 54 (4.5%) patients died. Dependent on its definition, IDH was present in 2.9%-61.1% of patients. After the multivariable adjustment for sociodemographic and clinical characteristics, we found no association of IDH with mortality. Results were consistent across subgroups stratified by age (aged <18 and 19-21 years) and predialysis systolic blood pressure (<120, 120-150, and >150 mm Hg). We also examined IDH as occurring in <5%, 5%-29%, 30%-50%, and >50% of baseline treatments, and did not find a dose-response association with mortality (P > 0.05).

Limitations

Owing to low event rates, our current sample size may have been too small to detect a difference in mortality.

Conclusions

Our study found that IDH was not associated with mortality in adolescents and young adults.

Efficacy of airway stenting and nasogastric tube insertion in airway-esophageal fistula patients with airways compromised by advanced malignancy

INTRODUCTION

Whether airway-compromised airway-esophageal fistula (AEF) patients should undergo combined airway and esophageal stenting is controversial. This study was designed to evaluate the survival prognosis and poststent interventions in AEF patients with airways compromised by advanced malignancy with or without airway stents.

METHODS

A retrospective analysis of the medical records, survival times, and poststent interventions of 17 patients with or without airway stents was performed.

RESULTS

The causes of AEF were esophageal cancer (11/17, 64.7%), lung cancer (6/17, 29.4%), and thyroid cancer (1/17, 5.9%). All patients received a nasogastric tube (n = 12) or underwent gastrostomy (n = 5) to resume enteral nutrition. Thirteen patients underwent airway stent insertion (13/17, 76.5%), whereas four patients did not. Four patients with a high risk of stent migration received external stent fixation to the trachea. Three of the patients with stents suffered severe granulation tissue formation and needed repeated bronchoscopy interventions. In the stented group, none of the patients developed stent migration, and the overall median survival time was 9 months, compared with 1.25 months in the nonstented group (P = 0.04). Cox proportional hazards regression revealed that stent insertion, nasogastric tube insertion, and transcatheter bronchial artery chemoembolization were protective factors against death, whereas surgery-related fistula, fistula larger than 2 cm, continued chemotherapy, and age were risk factors for poor survival (P < 0.05).

CONCLUSION

In airway-compromised AEF patients, airway stents and nasogastric tubes are probably the preferred treatments. Airway stenting is tolerable, and routine weekly poststent bronchoscopy is needed in the first month and depending on respiratory symptoms thereafter.

Statistics in the Operating Room: A Cardiovascular Surgeon's Guide to Numbers That Matter

Pediatric cardiac surgery demands meticulous technique, but optimal outcomes hinge on translating data into actionable insights. This editorial bridges the gap between scalpel and statistical jargon, empowering surgeons to decipher common tests. Descriptive statistics paint portraits of patient cohorts, while hypothesis testing discerns real differences from chance. Regression analysis unveils hidden relationships, predicting outcomes based on complex interplays of variables. Survival analysis tracks the delicate dance of time and survival, informing therapeutic strategies. By embracing statistical fluency, surgeons become architects of personalized care, tailoring interventions to mitigate risks and maximize the precious gift of a beating heart.

Prognostic Factors and Nomogram for Choroid Plexus Tumors: A Population-Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis

Background: Choroid plexus tumors (CPTs) are rare neoplasms found in the central nervous system, comprising 1% of all brain tumors. These tumors include choroid plexus papilloma (CPP), atypical choroid plexus papilloma (aCPP), and choroid plexus carcinoma (CPC). Although gross total resection for choroid plexus papillomas (CPPs) is associated with long-term survival, there is a scarcity of prospective data concerning the role and sequence of neoadjuvant therapy in treating aCPP and CPC. Methods: From the years 2000 to 2019, 679 patients with CPT were identified from the Surveillance, Epidemiology, and End Result (SEER) database. Among these patients, 456 patients had CPP, 75 patients had aCPP, and 142 patients had CPC. Univariate and multivariable Cox proportional hazard models were run to identify variables that had a significant impact on the primary endpoint of overall survival (OS). A predictive nomogram was built for patients with CPC to predict 5-year and 10-year survival probability. Results: Histology was a significant predictor of OS, with 5-year OS rates of 90, 79, and 61% for CPP, aCPP, and CPC, respectively. Older age and African American race were prognostic for worse OS for patients with CPP. Older age was also associated with reduced OS for patients with aCPP. American Indian/Alaskan Native race was linked to poorer OS for patients with CPC. Overall, treatment with gross total resection or subtotal resection had no difference in OS in patients with CPP or aCPP. Meanwhile, in patients with CPC, gross total resection (GTR) was associated with significantly better OS than subtotal resection (STR) only. However, there is no difference in OS between patients that receive GTR and patients that receive STR with adjuvant therapy. The nomogram for CPC considers types of treatments received. It demonstrates acceptable accuracy in estimating survival probability at 5-year and 10-year intervals, with a C-index of 0.608 (95% CI of 0.446 to 0.77). Conclusions: This is the largest study on CPT to date and highlights the optimal treatment strategies for these rare tumors. Overall, there is no difference in OS with GTR vs. STR in CPP or aCPP. Furthermore, OS is equivalent for CPC with GTR and STR plus adjuvant therapy.

Principal stratification analysis of noncompliance with time-to-event outcomes

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325 mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.

A Bayesian survival treed hazards model using latent Gaussian processes

Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazard assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian treed hazards partition model that is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the log-hazard function in each partition using a latent Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is compared with some existing methods on simulated data and a liver cirrhosis dataset.

Estimation of the causal effects of time-varying treatments in nested case-control studies using marginal structural Cox models

When estimating the causal effects of time-varying treatments on survival in nested case-control (NCC) studies, marginal structural Cox models (Cox-MSMs) with inverse probability weights (IPWs) are a natural approach. However, calculating IPWs from the cases and controls is difficult because they are not random samples from the full cohort, and the number of subjects may be insufficient for calculation. To overcome these difficulties, we propose a method for calculating IPWs to fit Cox-MSMs to NCC sampling data. We estimate the IPWs using a pseudo-likelihood estimation method with an inverse probability of sampling weight using NCC samples, and additional samples of subjects who experience treatment changes and subjects whose follow-up is censored are required to calculate the weights. Our method only requires covariate histories for the samples. The confidence intervals are calculated from the robust variance estimator for the NCC sampling data. We also derive the asymptotic properties of the estimator of Cox-MSM under NCC sampling. The proposed methods will allow researchers to apply several case-control matching methods to improve statistical efficiency. A simulation study was conducted to evaluate the finite sample performance of the proposed method. We also applied our method to a motivating pharmacoepidemiological study examining the effect of statins on the incidence of coronary heart disease. The proposed method may be useful for estimating the causal effects of time-varying treatments in NCC studies.

Efficacy of bortezomib, cyclophosphamide, and dexamethasone for newly diagnosed POEMS syndrome patients

Background

Due to the rarity of polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes (POEMS) syndrome, the best first-line treatment has not been established, although there are several options in guidelines. The preferred treatments vary according to the preference of the physician and anecdote.

Objectives

First, to analyze the efficacy of a new treatment mode in POEMS syndrome that uses the four-cycle treatment as the induction regimen, followed by sequential transplantation as the consolidation regimen for transplantation-eligible patients, or received another two-cycle treatment for transplantation-ineligible patients. Second, to compare the efficacy and safety of regimens with a proteasome inhibitor (bortezomib-cyclophosphamide-dexamethasone, BCD) or without a proteasome inhibitor (cyclophosphamide-dexamethasone ± thalidomide, CD ± T).

Design

We conducted a retrospective study using real-world data from Capital Medical University, Xuanwu Hospital.

Methods

A total of 34 newly diagnosed POEMS syndrome patients met Dispenzieri's diagnostic criteria, and those who completed at least four cycles of treatment from July 2013 to March 2021 were included.

Results

The overall vascular endothelial growth factor (VEGF) response rate of this new treatment mode was 100%. The cumulative VEGF complete remission (CRV) rate was 67.9%, and the cumulative complete hematological response (CRH) rate was 55.6%. During the median 49-month follow-up, the 5-year-overall survival (OS) rate was 90.7%, the 3-year-progression-free survival (PFS) rate was 78.4%, and the 5-year-PFS rate was 73.8%. The BCD regimen achieved a 75% CRV rate (median time from diagnosis to CRV = 130 days) and 66.7% CRH rate (median time from diagnosis to CRH = 218 days). In addition, the VEGF response was less than the partial remission (PRV) after four-cycle induction treatment, which, together with a decrease on the Overall Neurological Limitation Scale of less than three points 1 year after consolidation treatment, was an independent poor prognostic factor.

Conclusion

Bortezomib was well-tolerated by patients with POEMS syndrome. Compared with CD ± T regimen, BCD as the induction regimen achieved better VEGF response and earlier hematological remission. Autologous stem cell transplantation used as consolidation therapy further improved the neurological and hematological remission rates, resulting in better OS and PFS.

Fractal Analysis of Left Ventricular Trabeculae in Patients with End-Stage Renal Disease: A Random Survival Tree Analysis

BACKGROUND

The complexity of left ventricular (LV) trabeculae is related to the prognosis of several cardiovascular diseases.

PURPOSE

To evaluate the prognostic value of LV trabecular complexity in patients with end-stage renal disease (ESRD).

STUDY TYPE

Prospective outcome study.

POPULATION

207 participants on maintenance dialysis, divided into development (160 patients from 2 centers) and external validation (47 patients from a third center) cohorts, and 72 healthy controls.

FIELD STRENGTH

3.0T, steady-state free precession (SSFP) and modified Look-Locker imaging sequences.

ASSESSMENT

All participants had their trabecular complexity quantified by fractal analysis using cine SSFP images. Patients were followed up every 2 weeks until April 2023, or endpoint events happened. Random Forest (RF) and Cox regression models including age, diabetes, LV mass index, mean basal fractal dimension (FD), and left atrial volume index, were developed to predict major adverse cardiac events (MACE). Patients were divided into low- and high-risk groups based on scores derived from the RF model and survival compared.

STATISTICAL TESTS

Receiver operating characteristic curve analysis; Kaplan-Meier survival analysis with log rank tests; Harrel's C-index to assess model performance. A P value <0.05 was considered statistically significant.

RESULTS

Fifty-five patients (26.57%) experienced MACE during a median follow-up time of 21.83 months. An increased mean basal FD (≥1.324) was associated with a significantly higher risk of MACE. The RF model (C-index: 0.81) had significantly better discrimination than the Cox regression model (C-index: 0.74). Participants of the external validation dataset classified into the high-risk group had a hazard of experiencing MACE increased by 12.29 times compared to those in the low-risk group.

DATA CONCLUSION

LV basal FD was an independent predictor for MACE in patients with ESRD. Reliable risk stratification models could be generated based on LV basal FD and other MRI variables using RF analysis.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Decision Support System Detecting Patients at Risk of Prolonged QT and Associated Mortality

Prolonged QT interval is an independent risk factor for all-cause mortality. However, evaluation of mortality associated to the implementation of a clinical decision support system to increase awareness and provide management recommendations has been challenging. Here we present our attempt to develop a model using only electronic data and different control groups.

Dosiomics for intensity-modulated radiotherapy in patients with prostate cancer: survival analysis stratified by baseline prostate-specific antigen and Gleason grade group in a 2-institutional retrospective study

OBJECTIVE

This study evaluated the prognostic impact of the quality of dose distribution using dosiomics in patients with prostate cancer, stratified by pretreatment prostate-specific antigen (PSA) levels and Gleason grade (GG) group.

METHODS

A total of 721 patients (Japanese Foundation for Cancer Research [JFCR] cohort: N = 489 and Tokyo Radiation Oncology Clinic [TROC] cohort: N = 232) with localized prostate cancer treated by intensity-modulated radiation therapy were enrolled. Two predictive dosiomic features for biochemical recurrence (BCR) were selected and patients were divided into certain groups stratified by pretreatment PSA levels and GG. Freedom from biochemical failure (FFBF) was estimated using the Kaplan-Meier method based on each dosiomic feature and univariate discrimination was evaluated using the log-rank test. As an exploratory analysis, a dosiomics hazard (DH) score was developed and its prognostic power for BCR was examined.

RESULTS

The dosiomic feature extracted from planning target volume (PTV) significantly distinguished the high- and low-risk groups in patients with PSA levels >10 ng/mL (7-year FFBF: 86.7% vs 76.1%, P < .01), GG 4 (92.2% vs 76.9%, P < .01), and GG 5 (83.1% vs 77.8%, P = .04). The DH score showed significant association with BCR (hazard score: 2.04; 95% confidence interval: 1.38-3.01; P < .001).

CONCLUSION

The quality of planned dose distribution on PTV may affect the prognosis of patients with poor prognostic factors, such as PSA levels >10 ng/mL and higher GGs.

ADVANCES IN KNOWLEDGE

The effects of planned dose distribution on prognosis differ depending on the patient's clinical background.

Handling missing covariates in observational studies: an illustration with the assessment of prognostic factors of survival outcomes in soft-tissue or visceral sarcomas in irradiated fields (SIF)

Background

Missing covariates are common in observational research and can lead to bias and loss of statistical power. Limited data regarding prognostic factors of survival outcomes of sarcomas in irradiated fields (SIF) are available. Because of the long lag time between irradiation of first cancer and scarcity of SIF, missing data are a critical issue when analyzing long-term outcomes. We assessed prognostic factors of overall (OS), progression-free (PFS), and metastatic-progression-free (MPFS) survivals in SIF using three methods to account for missing covariates.

Methods

We relied on the NETSARC French Sarcoma Group database, Cox (OS/PFS), and competitive hazards (MPFS) survival models. Covariates investigated were age, sex, histological subtype, tumor size, depth and grade, metastasis, surgery, surgical resection, surgeon's expertise, imaging, and neo-adjuvant treatment. We first applied multiple imputation (MI): observed data were used to estimate the missing covariate. With the missing-data modality approach, a category missing was created for qualitative variables. With the complete-case (CC) approach, analysis was restricted to patients without missing covariates.

Results

CC subjects (N = 167; 33%) presented more often with soft-tissue sarcoma (versus visceral sarcoma) and grade I-II tumors as compared to the 504 eligible cases. With MI (N = 504), factors associated with the worst outcome included metastasis (p = 0.04) and R1/R2 resection (p < 0.001) for OS; higher grade/non-gradable tumors (p = 0.002) and R1/R2 resection (p < 0.001) for PFS; and metastasis (p = 0.01) for M-PFS. The 'missing-data modality' approach (N = 504) led to different associations, including significance reached due to variables with the modality 'missing'. The CC analysis led to different results and reduced precision.

Conclusion

The CC population was not representative of the eligible population, introducing bias, in addition to worst precision. The 'missing-data modality method' results in biased estimates in non-randomized studies, as outcomes may be related to variables with missing values. Appropriate statistical methods for missing covariates, for example, MI, should therefore be considered.

First trimester risk of preeclampsia and rate of spontaneous birth in patients without preeclampsia

BACKGROUND

First-trimester screening for preeclampsia using a combination of maternal risk factors and mean arterial pressure, uterine artery pulsatility index, and placental growth factor, as proposed by the Fetal Medicine Foundation, provides effective prediction of preterm preeclampsia. Placental dysfunction is a potential precursor of spontaneous birth.

OBJECTIVE

The objective of this study was to examine if the estimated risk of preeclampsia is associated with the gestational age at onset of spontaneous delivery in the absence of preeclampsia.

STUDY DESIGN

This was a secondary analysis of the data from the Screening programme for pre-eclampsia trial in which there was a comparison of the performance of first-trimester screening for preterm preeclampsia using the Fetal Medicine Foundation model vs a traditional history-based risk scoring system. A subgroup of women from the trial with spontaneous onset of delivery (labor with intact membranes or preterm prelabor rupture of membranes) was included in this study and was arbitrarily divided into 3 groups according to the risk for preterm preeclampsia as determined by the Fetal Medicine Foundation model at 11 to 13 weeks' gestation as follows: group 1 low risk (˂1/100); group 2 intermediate risk (1/50 to 1/100); and group 3 high risk (˃1/50). A survival analysis was carried out using a Kaplan-Meier estimator and a Cox regression analysis with stratification by the 3 preeclampsia risk groups. Occurrence of spontaneous birth in the study groups was compared using log-rank tests and hazard ratios.

RESULTS

The study population comprised 10,820 cases with delivery after spontaneous onset of labor among the 16,451 cases who participated in the Screening programme for pre-eclampsia trial. There were 9795 cases in group 1, 583 in group 2, and 442 in group 3. The gestational age at delivery was <28, <32, <35, <37, and <40 weeks in 0.29%, 0.64%, 1.68%, 4.52%, and 44.97% of cases, respectively, in group 1; 0.69%, 1.71%, 3.26%, 7.72%, and 55.23% of cases, respectively, in group 2; and 0.45%, 1.81%, 5.66%, 13.80%, and 63.12% of cases, respectively, in group 3. The curve profile of gestational age at spontaneous birth in the 3 study groups was significantly different overall and in pairwise comparisons (P values <.001). The Cox regression analysis showed that risks increased for spontaneous birth by 18% when the intermediate-risk group was compared with the low-risk group (P˂.001) and by 41% when the high-risk group was compared with the low-risk group (P˂.001).

CONCLUSION

In this study that investigated birth after spontaneous onset of labor in women without preeclampsia, there were 2 major findings. First, the duration of pregnancy decreased with increasing first-trimester risk for preeclampsia. Second, in the high-risk group, when compared with the low-risk group, the risk for spontaneous birth was 4 times higher at a gestational age of 24 to 26 weeks, 3 times higher at 28 to 32 weeks, and 2 times higher at 34 to 39 weeks. These differences present major clinical implications for antepartum counselling, monitoring, and interventions in these pregnancies.

Transporting randomized trial results to estimate counterfactual survival functions in target populations

When the distributions of treatment effect modifiers differ between a randomized trial and an external target population, the sample average treatment effect in the trial may be substantially different from the target population average treatment, and accurate estimation of the latter requires adjusting for the differential distribution of effect modifiers. Despite the increasingly rich literature on transportability, little attention has been devoted to methods for transporting trial results to estimate counterfactual survival functions in target populations, when the primary outcome is time to event and subject to right censoring. In this article, we study inverse probability weighting and doubly robust estimators to estimate counterfactual survival functions and the target average survival treatment effect in the target population, and provide their respective approximate variance estimators. We focus on a common scenario where the target population information is observed only through a complex survey, and elucidate how the survey weights can be incorporated into each estimator we considered. Simulation studies are conducted to examine the finite-sample performances of the proposed estimators in terms of bias, efficiency and coverage, under both correct and incorrect model specifications. Finally, we apply the proposed method to assess transportability of the results in the Action to Control Cardiovascular Risk in Diabetes-Blood Pressure (ACCORD-BP) trial to all adults with Diabetes in the United States.

Survival benefit of combined immunotherapy and chemoradiotherapy in locally advanced unresectable esophageal cancer: an analysis based on the SEER database

Background

While simultaneous chemoradiotherapy remains the established therapeutic modality for patients afflicted with locally advanced esophageal cancer, the effectiveness of this radical approach falls short of the desired outcome. Numerous investigations have illuminated the prospect of enhancing therapeutic efficacy through the amalgamation of chemoradiotherapy and immunotherapeutic interventions. Consequently, we embarked on an examination to scrutinize the potential survival advantages conferred by the confluence of chemoradiotherapy and immunotherapy in relation to locally advanced unresectable esophageal carcinoma, drawing upon the extensive SEER database for our analysis.

Methods

We extracted clinicopathological attributes and survival statistics of patients afflicted with locally advanced unresectable esophageal carcinoma, diagnosed within the temporal span encompassing the years 2004-2014 and 2019-2020, from the extensive SEER database. To discern disparities in both overall survival (OS) and cancer-specific survival (CSS) between the cohorts subjected to chemoradiotherapy combined with immunotherapy and chemoradiotherapy alone, we employed analytical tools such as Kaplan-Meier analysis, the Log-rank test, the Cox regression proportional risk model, and propensity-matched score (PSM) methodology.

Results

A total of 7,758 eligible patients were encompassed in this research, with 6,395 individuals having undergone chemoradiotherapy alone, while 1,363 patients received the combined treatment of chemoradiotherapy and immunotherapy. After 1:4 propensity score matching, 6,447 patients were successfully harmonized, yielding a well-balanced cohort. The Kaplan-Meier curves demonstrated a substantial enhancement in OS (P = 0.0091) and CSS (P < 0.001) for the group subjected to chemoradiotherapy combined with immunotherapy as compared to chemoradiotherapy alone. Further multivariable analysis with PSM confirmed that chemoradiotherapy combined with immunotherapy benefits OS(HR=0.89, 95% CI 0.81-0.98) and CSS (HR=0.68, 95% CI 0.61-0.76). In addition, Univariable and multivariable Cox regression analyses of the matched patient groups unveiled several independent prognostic factors for OS and CSS, including sex, age, marital status, tumor location, tumor size, pathologic grade, SEER historic staging, and treatment modality. Among these factors, being female, married, and receiving chemoradiotherapy combined with immunotherapy emerged as independent protective factors, while age exceeding 75 years, non-superior segment tumor location, tumor size greater than 6 cm, Grade 3-4 pathology, and regional SEER historic staging were all found to be independent risk factors. The survival advantage of the chemoradiotherapy combined with the immunotherapy group over the chemoradiotherapy alone group was substantial.

Conclusions

This investigation furnishes compelling evidence that the integration of immunotherapy with chemoradiotherapy confers a noteworthy survival advantage when contrasted with conventional chemoradiotherapy for individuals grappling with locally advanced unresectable esophageal carcinoma.

Bayesian semiparametric model for sequential treatment decisions with informative timing

We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT is not randomized, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semiparametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time. G-computation is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using our approach, we estimate the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.

Effects of Low-Osmolar Contrast Media on Long-Term Renal Impairment After Coronary Angiography: Iohexol Versus Iopromide

To investigate the long-term effects of 2 commonly used low-osmolar contrast media, iohexol and iopromide, on renal function and survival in patients who underwent coronary angiography. A total of 14,141 cardiology patients from 2006 to 2013 were recruited, of whom 1,793 patients (679 patients on iohexol and 1,114 on iopromide) were evaluated for long-term renal impairment and 5,410 patients (1,679 patients on iohexol and 3,731 on iopromide) were admitted for survival analyses spanning as long as 15 years. Univariate and multivariate logistic regression were used to explore the risk factors for long-term renal impairment. Cox proportional hazard regression was used to investigate the risk factors affecting survival. Propensity score matching and inverse probability of treatment weighting were applied to balance the baseline clinical characteristics. Patients receiving iohexol demonstrated a greater occurrence of renal impairment compared with those who received iopromide. Such difference remained consistent both before and after propensity score matching or inverse probability of treatment weighting, with a statistical significance of p <0.05. Among clinical variables, receiving contrast-enhanced contrast tomography/magnetic resonance imaging during follow-up, antihypertensive medication usage, presence of proteinuria, and anemia were identified as risk factors for long-term renal impairment (p = 0.041, 0.049, 0.006, and 0.029, respectively). During survival analyses, the difference was insignificant after propensity score matching and inverse probability of treatment weighting. In conclusion, administration of iohexol was more likely to induce long-term renal impairment than iopromide, particularly among patients diagnosed with anemia and proteinuria and those taking antihypertensive medication and with additional contrast exposure. The all-cause mortality, however, showed no significant difference between iohexol and iopromide administration.

Modeling the multi-state natural history of rare diseases with heterogeneous individual patient data: A simulation study

Multi-state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one-stage and two-stage approaches to meta-analyzing individual patient data (IPD) in a multi-state survival setting when the number and size of studies being meta-analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi-state IPD were simulated from study- and transition-specific hazard functions. One-stage frailty and two-stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population-level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real-world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out-performed by two-stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta-analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay.

Exploring the utility of a latent variable as comprehensive inflammatory prognostic index in critically ill patients with cerebral infarction

Objective

We introduce the comprehensive inflammatory prognostic index (CIPI), a novel prognostic tool for critically ill cerebral infarction patients, designed to meet the urgent need for timely and convenient clinical decision-making in this high-risk patient group.

Methods

Using exploratory factor analysis on selected indices-neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SIII)-we derived CIPI, a latent variable capturing their combined predictive power. Data from 1,022 patients in the Medical Information Mart for Intensive Care (MIMIC)-IV database were used to develop CIPI-based survival models, with the robustness and area under the receiver operating characteristic curve (AUC) performance of CIPI validated against an independent dataset of 326 patients from the MIMIC-III CareVue subset. The CIPI's predictive power for in-hospital and intensive care unit (ICU) mortality was assessed through Kaplan-Meier analysis, univariate and multivariate Cox regression models, and time-dependent AUC analysis. Linearity, subgroup sensitivity analyses and interaction effects with CIPI were also evaluated.

Results

CIPI was an independent prognostic factor, demonstrating a statistically significant association with in-hospital and ICU mortality, when assessed as a continuous and a categorical variable. It showed a linear relationship with mortality rates and demonstrated stability across most subgroups, with no significant interactions observed. Its predictive capabilities for in-hospital and ICU mortality among critically ill cerebral infarction patients matched those of established prognostic indices in the MIMIC database.

Conclusion

Our study indicates that CIPI is a reliable and effective prognostic tool for critically ill cerebral infarction patients in predicting in-hospital and ICU mortality. Its straightforward calculation, rooted in routine blood tests, enhances its practicality, promising significant utility in clinical settings.

Short-term clinical outcomes and five-year survival analysis of laparoscopic-assisted transanal natural orifice specimen extraction versus conventional laparoscopic surgery for sigmoid and rectal cancer: a single-center retrospective study

Background

The cosmetic benefits of natural orifice specimen extraction (NOSE) are easily noticeable, but its principles of aseptic and tumor-free procedure have caused controversy.

Methods

We conducted a retrospective analysis of the clinical data of patients who underwent laparoscopic-assisted transanal NOSE or conventional laparoscopic surgery (CLS) for sigmoid and rectal cancer at our hospital between January 2018 and December 2018. The study aimed to compare the general characteristics, perioperative indicators, postoperative complications, and five-year follow-up results between the two groups.

Results

A total of 121 eligible patients were enrolled, with 52 underwent laparoscopic-assisted transanal NOSE and 69 underwent CLS. There were no significant differences observed between the two groups in terms of gender, age, body mass index (BMI), TNM stage, etc. (P > 0.05). However, the NOSE group exhibited significantly shorter total incision length and longer operation time compared to the CLS group (P < 0.05). There were no statistically significant differences observed between the two groups in terms of positive rate of bacterial culture, incidence rates of intraabdominal infections or anastomotic leakage (P > 0.05). Furthermore, during follow-up period there was no statistically significant difference observed between these two groups concerning overall survival rate and disease-free survival outcomes (P > 0.05).

Conclusions

The management of surgical complications in CLS is exemplary, with NOSE presenting a sole advantage in terms of incision length albeit at the cost of prolonged operative time. Therefore, NOSE may be deemed appropriate for patients who place high emphasis on postoperative cosmetic outcomes.