Difference In Mean Survival Time Research Articles

Extended pleurectomy decortication and chemotherapy versus chemotherapy alone for pleural mesothelioma (MARS 2): a phase 3 randomised controlled trial.

Extended pleurectomy decortication for complete macroscopic resection for pleural mesothelioma has never been evaluated in a randomised trial. The aim of this study was to compare outcomes after extended pleurectomy decortication plus chemotherapy versus chemotherapy alone. MARS 2 was a phase 3, national, multicentre, open-label, parallel two-group, pragmatic, superiority randomised controlled trial conducted in the UK. The trial took place across 26 hospitals (21 recruiting only, one surgical only, and four recruiting and surgical). Following two cycles of chemotherapy, eligible participants with pleural mesothelioma were randomly assigned (1:1) to surgery and chemotherapy or chemotherapy alone using a secure web-based system. Individuals aged 16 years or older with resectable pleural mesothelioma and adequate organ and lung function were eligible for inclusion. Participants in the chemotherapy only group received two to four further cycles of chemotherapy, and participants in the surgery and chemotherapy group received pleurectomy decortication or extended pleurectomy decortication, followed by two to four further cycles of chemotherapy. It was not possible to mask allocation because the intervention was a major surgical procedure. The primary outcome was overall survival, defined as time from randomisation to death from any cause. Analyses were done on the intention-to-treat population for all outcomes, unless specified. This study is registered with ClinicalTrials.gov, NCT02040272, and is closed to new participants. Between June 19, 2015, and Jan 21, 2021, of 1030 assessed for eligibility, 335 participants were randomly assigned (169 to surgery and chemotherapy, and 166 to chemotherapy alone). 291 (87%) participants were men and 44 (13%) women, and 288 (86%) were diagnosed with epithelioid mesothelioma. At a median follow-up of 22·4 months (IQR 11·3-30·8), median survival was shorter in the surgery and chemotherapy group (19·3 months [IQR 10·0-33·7]) than in the chemotherapy alone group (24·8 months [IQR 12·6-37·4]), and the difference in restricted mean survival time at 2 years was -1·9 months (95% CI -3·4 to -0·3, p=0·019). There were 318 serious adverse events (grade ≥3) in the surgery group and 169 in the chemotherapy group (incidence rate ratio 3·6 [95% CI 2·3 to 5·5], p<0·0001), with increased incidence of cardiac (30 vs 12; 3·01 [1·13 to 8·02]) and respiratory (84 vs 34; 2·62 [1·58 to 4·33]) disorders, infection (124 vs 53; 2·13 [1·36 to 3·33]), and additional surgical or medical procedures (15 vs eight; 2·41 [1·04 to 5·57]) in the surgery group. Extended pleurectomy decortication was associated with worse survival to 2 years, and more serious adverse events for individuals with resectable pleural mesothelioma, compared with chemotherapy alone. National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (15/188/31), Cancer Research UK Feasibility Studies Project Grant (A15895).

Reverse total shoulder replacement versus anatomical total shoulder replacement for osteoarthritis: population based cohort study using data from the National Joint Registry and Hospital Episode Statistics for England

ObjectivesTo answer a national research priority by comparing the risk-benefit and costs associated with reverse total shoulder replacement (RTSR) and anatomical total shoulder replacement (TSR) in patients having elective primary...

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients. To discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL). Six models were developed to offer individualized treatment suggestions. Outcomes for patients whose actual treatments aligned with model recommendations were compared to those whose did not. The influence of certain baseline features of patients on NST selection was visualized and quantified by multivariate logistic regression and Poisson regression analyses. Our study included 94,487 female breast cancer patients. The Balanced Individual Treatment Effect for Survival data (BITES) model outperformed other models in performance, showing a statistically significant protective effect with inverse probability treatment weighting (IPTW)-adjusted baseline features [IPTW-adjusted hazard ratio: 0.51, 95% confidence interval (CI), 0.41-0.64; IPTW-adjusted risk difference: 21.46, 95% CI 18.90-24.01; IPTW-adjusted difference in restricted mean survival time: 21.51, 95% CI 19.37-23.80]. Adherence to BITES recommendations is associated with reduced breast cancer mortality and fewer adverse effects. BITES suggests that patients with TNM stage IIB, IIIB, triple-negative subtype, a higher number of positive axillary lymph nodes, and larger tumors are most likely to benefit from NST. Our results demonstrated the potential of BITES to aid in clinical treatment decisions and offer quantitative treatment insights. In our further research, these models should be validated in clinical settings and additional patient features as well as outcome measures should be studied in depth.

Long-Term Impact of Severe Postoperative Complications after Esophagectomy for Cancer: Individual Patient Data Meta-Analysis.

Severe postoperative complications (SPCs) may occur after curative esophagectomy for cancer and are associated with prolonged hospital stay, augmented costs, and increased in-hospital mortality. However, the effect of SPCs on survival after esophagectomy is uncertain. To assess the impact of severe postoperative complications (SPCs) on long-term survival following curative esophagectomy for cancer, we conducted a systematic search of PubMed, MEDLINE, Scopus, and Web of Science databases up to December 2023. The included studies examined the relationship between SPCs and survival outcomes, defining SPCs as Clavien-Dindo grade > 3. The primary outcome measure was long-term overall survival (OS). We used restricted mean survival time difference (RMSTD) and 95% confidence intervals (CIs) to calculate pooled effect sizes. Additionally, we applied the GRADE methodology to evaluate the certainty of the evidence. Ten studies (2181 patients) were included. SPCs were reported in 651 (29.8%) patients. The RMSTD overall survival analysis shows that at 60-month follow-up, patients experiencing SPCs lived for 8.6 months (95% Cis -12.5, -4.7; p < 0.001) less, on average, compared with no-SPC patients. No differences were found for 60-month follow-up disease-free survival (-4.6 months, 95% CIs -11.9, 1.9; p = 0.17) and cancer-specific survival (-6.8 months, 95% CIs -11.9, 1.7; p = 0.21). The GRADE certainty of this evidence ranged from low to very low. This study suggests a statistically significant detrimental effect of SPCs on OS in patients undergoing curative esophagectomy for cancer. Also, a clinical trend toward reduced CSS and DFS was perceived.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Individualized Treatment Effect Prediction with Machine Learning — Salient Considerations

BackgroundMachine learning–based approaches that seek to accomplish individualized treatment effect prediction have gained traction; however, some salient challenges lack wider recognition.MethodsWe describe key methodologic considerations for individualized treatment effect prediction models using data from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial for spironolactone in heart failure with preserved ejection fraction. The causal survival forest algorithm was used for model development. Calibration and discrimination were evaluated using a bootstrapping-based internal validation procedure. Observed benefits were described for predicted benefit quartiles and quartiles of a known effect modifier: ejection fraction. A negative control analysis with noncardiovascular death as the outcome was implemented to detect confounding.ResultsAmong 3445 participants, 671 events occurred over a median of 3.3 years of follow-up. In internal validation, a higher average observed benefit was noted among patients in the highest quartile of predicted benefit. The median (interquartile range) of the observed restricted mean survival time difference at 3.3 years at the highest quartile of model-predicted benefit was 62 days (32 to 83) and was 47 days (26 to 67) at the lowest quartile of ejection fraction. Body-mass index had higher contribution to prediction of benefit relative to other included measures (33.7% vs. glomerular filtration rate [27.3%], ejection fraction [15.1%], and younger age [12.8%]) No benefit was observed for noncardiovascular death at higher model-predicted benefit quartiles, although benefit for noncardiovascular death was observed at lower quartiles.ConclusionsCarefully applied and validated predictive models hold promise in identifying heterogeneous treatment effects and are useful for hypothesis generation regarding the role of phenotypic characteristics in modifying the benefit of experimental interventions in clinical trials. (Funded by the National Heart, Lung, and Blood Institute; ClinicalTrials.gov number, NCT00094302.)

Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions.

Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.

Invasive Treatment Strategy in Adults With Frailty and Non–ST-Segment Elevation Myocardial Infarction

The MOSCA-FRAIL randomized clinical trial compared invasive and conservative treatment strategies in patients with frailty with non-ST-segment elevation myocardial infarction (NSTEMI). It showed no differences in the number of days alive and out of the hospital at 1 year. To assess the outcomes of the MOSCA-FRAIL trial during extended follow-up. The MOSCA-FRAIL randomized clinical trial was conducted at 13 hospitals in Spain between July 7, 2017, and January 9, 2021, and included 167 adults (aged ≥70 years) with frailty (Clinical Frailty Scale score ≥4) and NSTEMI. In this preplanned secondary analysis, follow-up was extended to January 31, 2023. Data analysis was performed from April 5 to 29, 2023, using the intention-to-treat principle. Patients were randomized to a routine invasive (coronary angiography and revascularization if feasible [n = 84]) or a conservative (medical treatment with coronary angiography only if recurrent ischemia [n = 83]) strategy. The primary end point was the difference in restricted mean survival time (RMST). Secondary end points included readmissions for any cause, considering recurrent readmissions. Among the 167 patients included in the analysis, the mean (SD) age was 86 (5) years; 79 (47.3%) were men and 88 (52.7%) were women. A total of 93 deaths and 367 readmissions accrued. The RMST for all-cause death over the entire follow-up was 3.13 (95% CI, 2.72-3.60) years in the invasive and 3.06 (95% CI, 2.84-3.32) years in the conservative treatment groups. The RMST analysis showed inconclusive differences in survival time (invasive minus conservative difference, 28 [95% CI, -188 to 230] days). Patients under invasive treatment tended to have shorter survival in the first year (-28 [95% CI, -63 to 7] days), which improved after the first year (192 [95% CI, 90-230] days). Kaplan-Meier mortality curves intersected, displaying higher mortality to 1 year in the invasive group that shifted to a late benefit (landmark analysis hazard ratio, 0.58 [95% CI, 0.33-0.99]; P = .045). Early harm was more evident in the subgroup with a Clinical Frailty Scale score greater than 4. No differences were found for the secondary end points. In this extended follow-up of a randomized clinical trial of patients with frailty and NSTEMI, an invasive treatment strategy did not improve outcomes at a median follow-up of 1113 (IQR, 443-1441) days. However, a differential distribution of deaths was observed, with early harm followed by later benefit. The phenomenon of depletion of susceptible patients may be responsible for this behavior. ClinicalTrials.gov Identifier: NCT03208153.

Effect of complete mesocolic excision (cme) on long-term survival after right colectomy for cancer: multivariate meta-analysis and restricted mean survival time estimation.

Debate exists concerning the impact of complete mesocolic excision (CME) on long-term oncological outcomes. The aim of this review was to condense the updated literature and assess the effect of CME on long-term survival after right colectomy for cancer. PubMed, MEDLINE, Scopus, and Web of Science were searched through July 2023. The included studies evaluated the effect of CME on survival. The primary outcome was long-term overall survival. Restricted mean survival time difference (RMSTD), hazard ratio (HR), and 95% confidence intervals (CI) were used as pooled effect size measures. GRADE methodology was used to summarize the certainty of evidence. Ten studies (3665 patients) were included. Overall, 1443 (39.4%) underwent CME. The RMSTD analysis shows that at 60-month follow-up, stage I-III CME patients lived 2.5months (95% CI 1.1-4.1) more on average compared with noCME patients. Similarly, stage III patients that underwent CME lived longer compared to noCME patients at 55-month follow-up (6.1months; 95% CI 3.4-8.5). The time-dependent HRs analysis for CME vs. noCME (stage I-III disease) shows a higher mortality hazard in patients with noCME at 6months (HR 0.46, 95% CI 0.29-0.71), 12months (HR 0.57, 95% CI 0.43-0.73), and 24months (HR 0.73, 95% CI 0.57-0.92) up to 27months. This study suggests that CME is associated with unclear OS benefit in stage I-III disease. Caution is recommended to avoid overestimation of the effect of CME in stage III disease since the marginal benefit of a more extended resection may have been influenced by tumor biology/molecular profile and multimodal adjuvant treatments.

Survival outcomes of surgical resection in perihilar cholangiocarcinoma in endemic area of O. Viverrini, Northeast Thailand

BackgroundPerihilar cholangiocarcinoma (pCCA) is an intractable malignancy and remains the most challenge for surgeon. This study aims to investigate survival outcomes and prognostic factors in pCCA patient. MethodsFrom October 2013 to December 2018, 240 consecutive patients with pCCA underwent surgical exploration were retrospectively reviewed. The clinicopathological parameters and surgical outcomes were extracted. Patients were divided into two groups: unresectable and resectable group. The restricted mean survival time between two groups were analyzed. Factors associated with overall survival in resectable group were explored with multivariable Cox regression analysis. ResultsOf the 240 patients, 201 (83.75%) were received surgical resection. The survival outcomes of resectable group were better than unresectable group significantly. The restricted mean survival time difference were 0.5 (95%CI 0.22–0.82) months, 1.8 (95%CI 1.15–2.49) months, 4.7 (95%CI 3.58–5.87) months, and 9.1 (95%CI 7.40–10.78) months at four landmark time points of 3, 6, 12 and 24 months, respectively. The incidence of major complications and 90-day mortality in resectable group were 35.82% and 11.44%, respectively. Multivariable analysis revealed that Bismuth type IV (HR:4.43, 95%CI 1.85–10.59), positive resection margin (HR:4.24, 95%CI 1.74–10.34), and lymph node metastasis (HR:2.29, 95%CI 1.04–4.99) were all independent predictors of long-term survival. For pM0, R0 and pN0 patients, the median survival time was better than pM0, R1 or pN1/2 patients and pM0, R1 and pN1/2 patients (32.4, 10.4 and 4.9 months, respectively; p < 0.001) ConclusionSurgical resection increased survival in pCCA. Bismuth type IV, positive resection margin and lymph node metastasis were independent factors for long-term survival.

Adjustment for Renal Function Improves the Prognostic Performance of Urinary Thromboxane Metabolites.

Systemic thromboxane A2 generation, assessed by quantifying the concentration of stable thromboxane B2 metabolites (TXB2-M) in the urine adjusted for urinary creatinine, is strongly associated with mortality risk. We sought to define optimal TXB2-M cutpoints for aspirin users and nonusers and determine if adjusting TXB2-M for estimated glomerular filtration rate (eGFR) in addition to urinary creatinine improved mortality risk assessment. Urinary TXB2-M were measured by competitive ELISA in 1363 aspirin users and 1681 nonusers participating in the Framingham Heart Study. Cutpoints were determined for TXB2-M and TXB2-M/eGFR using log-rank statistics and used to assess mortality risk by Cox proportional hazard modeling and restricted mean survival time. Multivariable models were compared using the Akaike Information Criterion (AIC). A cohort of 105 aspirin users with heart failure was used for external validation. Optimized cutpoints of TXB2-M were 1291 and 5609 pg/mg creatinine and of TXB2-M/eGFR were 16.6 and 62.1 filtered prostanoid units (defined as pg·min/creatinine·mL·1.73 m2), for aspirin users and nonusers, respectively. TXB2-M/eGFR cutpoints provided more robust all-cause mortality risk discrimination than TXB2-M cutpoints, with a larger unadjusted hazard ratio (2.88 vs 2.16, AIC P < 0.0001) and greater differences in restricted mean survival time between exposure groups (1.46 vs 1.10 years), findings that were confirmed in the external validation cohort of aspirin users. TXB2-M/eGFR cutpoints also provided better cardiovascular/stroke mortality risk discrimination than TXB2-M cutpoints (unadjusted hazard ratio 3.31 vs 2.13, AIC P < 0.0001). Adjustment for eGFR strengthens the association of urinary TXB2-M with long-term mortality risk irrespective of aspirin use.

Optimizing adjuvant treatment options for patients with glioblastoma.

This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method. We trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT). The Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48-0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55-0.78; dRMST: 7.92, 95% CI, 7.81-8.15; NNT: 1.67, 95% CI, 1.24-2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT. Our study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.

Abatacept in individuals at high risk of rheumatoid arthritis (APIPPRA): a randomised, double-blind, multicentre, parallel, placebo-controlled, phase 2b clinical trial

Individuals with serum antibodies to citrullinated protein antigens (ACPA), rheumatoid factor, and symptoms, such as inflammatory joint pain, are at high risk of developing rheumatoid arthritis. In the arthritis prevention in the pre-clinical phase of rheumatoid arthritis with abatacept (APIPPRA) trial, we aimed to evaluate the feasibility, efficacy, and acceptability of treating high risk individuals with the T-cell co-stimulation modulator abatacept. The APIPPRA study was a randomised, double-blind, multicentre, parallel, placebo-controlled, phase 2b clinical trial done in 28 hospital-based early arthritis clinics in the UK and three in the Netherlands. Participants (aged ≥18 years) at risk of rheumatoid arthritis positive for ACPA and rheumatoid factor with inflammatory joint pain were recruited. Exclusion criteria included previous episodes of clinical synovitis and previous use of corticosteroids or disease-modifying antirheumatic drugs. Participants were randomly assigned (1:1) using a computer-generated permuted block randomisation (block sizes of 2 and 4) stratified by sex, smoking, and country, to 125 mg abatacept subcutaneous injections weekly or placebo for 12 months, and then followed up for 12 months. Masking was achieved by providing four kits (identical in appearance and packaging) with pre-filled syringes with coded labels of abatacept or placebo every 3 months. The primary endpoint was the time to development of clinical synovitis in three or more joints or rheumatoid arthritis according to American College of Rheumatology and European Alliance of Associations for Rheumatology 2010 criteria, whichever was met first. Synovitis was confirmed by ultrasonography. Follow-up was completed on Jan 13, 2021. All participants meeting the intention-to-treat principle were included in the analysis. This trial was registered with EudraCT (2013-003413-18). Between Dec 22, 2014, and Jan 14, 2019, 280 individuals were evaluated for eligibility and, of 213 participants, 110 were randomly assigned to abatacept and 103 to placebo. During the treatment period, seven (6%) of 110 participants in the abatacept group and 30 (29%) of 103 participants in the placebo group met the primary endpoint. At 24 months, 27 (25%) of 110 participants in the abatacept group had progressed to rheumatoid arthritis, compared with 38 (37%) of 103 in the placebo group. The estimated proportion of participants remaining arthritis-free at 12 months was 92·8% (SE 2·6) in the abatacept group and 69·2% (4·7) in the placebo group. Kaplan-Meier arthritis-free survival plots over 24 months favoured abatacept (log-rank test p=0·044). The difference in restricted mean survival time between groups was 53 days (95% CI 28-78; p<0·0001) at 12 months and 99 days (95% CI 38-161; p=0·0016) at 24 months in favour of abatacept. During treatment, abatacept was associated with improvements in pain scores, functional wellbeing, and quality-of-life measurements, as well as low scores of subclinical synovitis by ultrasonography, compared with placebo. However, the effects were not sustained at 24 months. Seven serious adverse events occurred in the abatacept group and 11 in the placebo group, including one death in each group deemed unrelated to treatment. Therapeutic intervention during the at-risk phase of rheumatoid arthritis is feasible, with acceptable safety profiles. T-cell co-stimulation modulation with abatacept for 12 months reduces progression to rheumatoid arthritis, with evidence of sustained efficacy beyond the treatment period, and with no new safety signals. Bristol Myers Squibb.

Impact of pulmonary complications following esophagectomy on long-term survival: multivariate meta-analysis and restricted mean survival time assessment.

Pulmonary complications (PC) are common after esophagectomy and their impact on long-term survival is not defined yet. The present study aimed to assess the effect of postoperative PCs on long-term survival after esophagectomy for cancer. Systematic review of the literature through February 1, 2023, was performed. The included studies evaluated the effect of PC on long-term survival. Primary outcome was long-term overall survival (OS). Cancer-specific survival (CSS) and disease-free survival (DFS) were secondary outcomes. Restricted mean survival time difference (RMSTD), hazard ratio (HR), and 95% confidence intervals (CI) were used as pooled effect size measures. Eleven studies were included (3423 patients). Overall, 674 (19.7%) patients developed PC. The RMSTD analysis shows that at 60-month follow-up, patients not experiencing PC live an average of 8.5 (95% CI 6.2-10.8; p < 0.001) months longer compared with those with PC. Similarly, patients not experiencing postoperative PC seem to have significantly longer CSS (8months; 95% CI 3.7-12.3; p < 0.001) and DFS (5.4months; 95% CI 1.6-9.1; p = 0.005). The time-dependent HRs analysis shows a reduced mortality hazard in patients without PC at 12 (HR 0.6, 95% CI 0.51-0.69), 24 (HR 0.64, 95% CI 0.55-0.73), 36 (HR 0.67, 95% CI 0.55-0.79), and 60months (HR 0.69, 95% CI 0.51-0.89). This study suggests a moderate clinical impact of PC on long-term OS, CSS, and DFS after esophagectomy. Patients not experiencing PC seem to have a significantly reduced mortality hazard up to 5years of follow-up.

Survival outcomes for rural patients with advanced prostate cancer: A SEER investigation.

22 Background: It is unknown if the impact of rural versus urban settings may impact outcomes in men with metastatic prostate cancer, and in particular survival. This study aimed to examine differences in survival for patients in the United States with de novo metastatic prostate cancer, according to urban-rural status. Methods: This retrospective cohort study utilised data from the publicly available Surveillance, Epidemiology, and End Results database. Data on demographics, urban-rural status, histopathology, and survival were extracted for men aged 18-75 diagnosed with metastatic prostate cancer between 2009-2018. Rurality was re-classified from the rural-urban continuum code which ranged from 1 (most urban) to 9 (most rural). Individuals missing rurality status or survival-related data were excluded. Overall and cancer-specific survival between individuals living in urban and rural settings were analysed using multivariable Cox regression and adjusted restricted mean survival time modelling. Subgroup analyses were performed for histological subtypes of prostate cancer. Sensitivity analyses were performed using varying definitions of rurality. Results: Altogether, 21,291 individuals were included, with 2,510 living in rural settings, defined as rural-urban continuum code ≥ 4. The rural and urban participants differed in age, race, U.S. region, and marital status. There was no association between urban-rural status and overall survival (adjusted hazard ratio [HR] = 1.03, 95% confidence interval [CI]: 0.97-1.09, 11,333 events) or cancer-specific survival (1.03, 0.97-1.10, 9,380 events) using Cox regression models. Adjusted restricted mean survival time modelling demonstrated that urban patients lived 2.29 months longer than rural patients (95% CI: 0.61-3.97). Sub-analyses of neuroendocrine, intraductal, and other histological subtypes did not show an association between urban-rural status and overall survival. A more selective definition of rurality (rural-urban continuum code ≥ 5) resulted in a persisting difference in overall mean survival time (2.12 months, 0.24-4.01) using restricted mean survival time modelling. Similarly, a broader definition of rurality (rural-urban continuum code ≥ 3) also resulted in a difference in overall mean survival time (1.98 months, 0.59-3.36). Conclusions: This retrospective analysis demonstrated that a U.S. individual with metastatic prostate cancer who resided rurally died sooner compared to those from urban areas, suggesting greater disease impact in rural settings.

Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning

BackgroundThere are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important.PurposeThis study aims to establish a novel causal inference deep learning (DL) model to discern whether a patient can benefit more from RP and to identify heterogeneity in treatment responses among PCa patients.MethodsWe introduce the Self-Normalizing Balanced individual treatment effect for survival data (SNB). Six models were trained to make individualized treatment recommendations for PCa patients. Inverse probability treatment weighting (IPTW) was used to avoid treatment selection bias.Results35,236 patients were included. Patients whose actual treatment was consistent with SNB recommendations had better survival outcomes than those who were inconsistent (multivariate hazard ratio (HR): 0.76, 95% confidence interval (CI), 0.64–0.92; IPTW-adjusted HR: 0.77, 95% CI, 0.61–0.95; risk difference (RD): 3.80, 95% CI, 2.48–5.11; IPTW-adjusted RD: 2.17, 95% CI, 0.92–3.35; the difference in restricted mean survival time (dRMST): 3.81, 95% CI, 2.66–4.85; IPTW-adjusted dRMST: 3.23, 95% CI, 2.06–4.45). Keeping other covariates unchanged, patients with 1 ng/mL increase in PSA levels received RP caused 1.77 months increase in the time to 90% mortality, and the similar results could be found in age, Gleason score, tumor size, TNM stages, and metastasis status.ConclusionsOur highly interpretable and reliable DL model (SNB) may identify patients with PCa who could benefit from RP, outperforming other models and clinical guidelines. Additionally, the DL-based treatment guidelines obtained can provide priori evidence for subsequent studies.

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL). Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model. A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type. Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

Efficacy of immune checkpoint inhibitors in patients with HIV-associated unresectable HCC (uHCC): Propensity-score matched analyses from two international consortia.

442 Background: HIV-associated HCC is an incompletely characterized disease with poor prognosis. People living with HIV (PWH) are commonly excluded from clinical trials, leading to a paucity of high-level evidence for optimal management. Whether ICIs are tolerated and effective in HIV-associated uHCC remains unclear. Methods: Using data from the CATCH-IT consortium and from a global dataset recruiting patients with HCC from 14 centers in 3 continents, we selected patients with HIV-associated uHCC treated with ICIs and compared their outcomes with a matched cohort of HIV negative (HIV-) patients. Primary endpoints were overall (OS) and progression-free survival (PFS). Propensity score matching (PSM) between the two groups was performed for the following variables: age, sex, HCC aetiology, Child-Pugh class (CP-C), ECOG-PS, BCLC stage, alpha-fetoprotein (AFP) levels, treatment line, portal vein tumor thrombosis (PVTT), and extrahepatic spread (EHS). Restricted mean survival time (RMST) difference analyses stratified by HIV status were performed for OS and PFS at 3, 6 and 9 months. Results: We accrued 46 PWH and 400 HIV-, treated mostly in the first-line setting (58.7% vs. 43.7%) with either atezolizumab plus bevacizumab (28.3% vs 17.5%) or anti-PD-1 monotherapy. Median age at ICI start was 62 (38-76) and 64 (18-87); viral hepatitis (HCV: 58.7%; HBV: 34.8%) were the prevalent aetiologies in PWH, non-viral (46%) was the most common in HIV-; most patients had BCLC-C HCC (84.8% and 82.3%). In PWH, median CD4 count was 345 cells/mm3 (IQR: 200-465); viral load was undetectable in 45 patients, all were established on combined anti-retroviral therapy. Following PSM, 44 PWH and 117 HIV- were included. Median OS was 7.5 months (95%CI: 5.2-NR) in PWH and 10.8 months (95%CI: 7.3-19.5) in HIV- (HR: 0.89; 95%CI: 0.56-1.42; p=0.58). Presence of PVTT, AFP&gt;400 ng/mL, and ECOG-PS=1 but not HIV status were associated with worse OS in uni- and multivariable models. Median PFS was 2.8 months (95%CI: 2.6-4.8) for PWH vs 3.0 months (95%CI: 2.4-5.3) for HIV- (HR: 0.83-95%CI:0.56-1.24 ;p=0.38); HIV status was not associated with worse PFS. RMST confirmed no OS or PFS differences based on HIV status. In matched cohorts, neither ORR (21% vs. 19.1%, p=0.97) nor DCR (44.7% vs. 55.4%, p=0.19) were associated with HIV status. All grade immune-related adverse events (irAEs) were more frequent in HIV (44.0% vs 21.7%, p=0.003), with similar incidence of grade ≥3 irAEs (22.2% vs 13.0%). PWH had lower incidence of dermatological toxicities (4.3% vs 22%, p=0.003) and a trend towards lower incidence of hepatotoxicity (6.5% vs 18%, p=0.059). Conclusions: This study provides practice-informing evidence to support the use of ICIs in PWH and uHCC. Our findings encourage the inclusion of patients with well-controlled HIV in prospective clinical trials.

Long-Term Impact of D2 Lymphadenectomy during Gastrectomy for Cancer: Individual Patient Data Meta-Analysis and Restricted Mean Survival Time Estimation.

Debate exists concerning the impact of D2 vs. D1 lymphadenectomy on long-term oncological outcomes after gastrectomy for cancer. PubMed, MEDLINE, Scopus, and Web of Science were searched and randomized controlled trials (RCTs) analyzing the effect of D2 vs. D1 on survival were included. Overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS) were assessed. Restricted mean survival time difference (RMSTD) and 95% confidence intervals (CI) were used as effect size measures. Five RCTs (1653 patients) were included. Overall, 805 (48.7%) underwent D2 lymphadenectomy. The RMSTD OS analysis shows that at 60-month follow-up, D2 patients lived 1.8 months (95% CI -4.2, 0.7; p = 0.14) longer on average compared to D1 patients. Similarly, 60-month CSS (1.2 months, 95% CI -3.9, 5.7; p = 0.72) and DFS (0.8 months, 95% CI -1.7, 3.4; p = 0.53) tended to be improved for D2 vs. D1 lymphadenectomy. Compared to D1, D2 lymphadenectomy is associated with a clinical trend toward improved OS, CSS, and DFS at 60-month follow-up.

Clinician's Approach to Advanced Statistical Methods: Win Ratios, Restricted Mean Survival Time, Responder Analyses, and Standardized Mean Differences.

Novel statistical methods have emerged in recent medical literature, which clinicians must understand to properly appraise and integrate evidence into their practice. Some of these key concepts include win ratios, restricted mean survival time, responder analyses, and standardized mean difference. This article offers guidance to busy clinicians on the comprehension and practical applicability of the results to patients. Win ratios provide an alternative method to analyze composite outcomes by prioritizing individual components of the composite; prioritization of the outcomes should be evidence-based, pre-specified, and patient-centered. Restricted mean survival time presents a method to analyze Kaplan-Meier curves when assumptions required for Cox proportional hazards analysis are not met. As it only considers outcomes that occur within a specific timeframe, the duration of follow-up must be appropriately defined and based on prior epidemiologic and mechanistic evidence. Researchers can analyze continuous outcomes with responder analyses, in which participants are dichotomized into "responders" or "non-responders." While clinicians and patients may more easily grasp outcomes analyzed in this way, they should be aware of the loss of information and resulting imprecision, as well as potential to manipulate data presentation. When meta-analyzing continuous outcomes, point estimates can be converted to standardized mean differences to facilitate the combination of data utilizing various outcome measures. However, clinicians may find it challenging to grasp the clinical meaningfulness of a standardized mean difference, and may benefit from converting it to well-known outcomes. By providing the background knowledge of these statistical methods, along with practical applicability, benefits, and inevitable limitations, this article aims to provide clinicians with an approach to appraise the literature and apply the results in clinical practice.